diff --git a/.github/workflows/test_common.yml b/.github/workflows/test_common.yml index 6b79060f07..674b38a776 100644 --- a/.github/workflows/test_common.yml +++ b/.github/workflows/test_common.yml @@ -15,6 +15,10 @@ env: RUNTIME__LOG_LEVEL: ERROR RUNTIME__DLTHUB_TELEMETRY_ENDPOINT: ${{ secrets.RUNTIME__DLTHUB_TELEMETRY_ENDPOINT }} + # we need the secrets only for the rest_api_pipeline tests which are in tests/sources + # so we inject them only at the end + SOURCES__GITHUB__ACCESS_TOKEN: ${{ secrets.GITHUB_TOKEN }} + jobs: get_docs_changes: name: docs changes @@ -87,11 +91,11 @@ jobs: run: poetry install --no-interaction --with sentry-sdk - run: | - poetry run pytest tests/common tests/normalize tests/reflection tests/sources tests/load/test_dummy_client.py tests/extract/test_extract.py tests/extract/test_sources.py tests/pipeline/test_pipeline_state.py + poetry run pytest tests/common tests/normalize tests/reflection tests/load/test_dummy_client.py tests/extract/test_extract.py tests/extract/test_sources.py tests/pipeline/test_pipeline_state.py if: runner.os != 'Windows' name: Run common tests with minimum dependencies Linux/MAC - run: | - poetry run pytest tests/common tests/normalize tests/reflection tests/sources tests/load/test_dummy_client.py tests/extract/test_extract.py tests/extract/test_sources.py tests/pipeline/test_pipeline_state.py -m "not forked" + poetry run pytest tests/common tests/normalize tests/reflection tests/load/test_dummy_client.py tests/extract/test_extract.py tests/extract/test_sources.py tests/pipeline/test_pipeline_state.py -m "not forked" if: runner.os == 'Windows' name: Run common tests with minimum dependencies Windows shell: cmd @@ -122,15 +126,29 @@ jobs: name: Run pipeline tests with pyarrow but no pandas installed Windows shell: cmd - - name: Install pipeline dependencies - run: poetry install --no-interaction -E duckdb -E cli -E parquet --with sentry-sdk --with pipeline -E deltalake + - name: Install pipeline and sources dependencies + run: poetry install --no-interaction -E duckdb -E cli -E parquet -E deltalake -E sql_database --with sentry-sdk,pipeline,sources + + - run: | + poetry run pytest tests/extract tests/pipeline tests/libs tests/cli/common tests/destinations tests/sources + if: runner.os != 'Windows' + name: Run extract and pipeline tests Linux/MAC + - run: | + poetry run pytest tests/extract tests/pipeline tests/libs tests/cli/common tests/destinations tests/sources -m "not forked" + if: runner.os == 'Windows' + name: Run extract tests Windows + shell: cmd + + # here we upgrade sql alchemy to 2 an run the sql_database tests again + - name: Upgrade sql alchemy + run: poetry run pip install sqlalchemy==2.0.32 - run: | - poetry run pytest tests/extract tests/pipeline tests/libs tests/cli/common tests/destinations + poetry run pytest tests/sources/sql_database if: runner.os != 'Windows' name: Run extract and pipeline tests Linux/MAC - run: | - poetry run pytest tests/extract tests/pipeline tests/libs tests/cli/common tests/destinations -m "not forked" + poetry run pytest tests/sources/sql_database if: runner.os == 'Windows' name: Run extract tests Windows shell: cmd diff --git a/.github/workflows/test_destination_athena.yml b/.github/workflows/test_destination_athena.yml index c7aed6f70e..a03c17d342 100644 --- a/.github/workflows/test_destination_athena.yml +++ b/.github/workflows/test_destination_athena.yml @@ -22,7 +22,7 @@ env: RUNTIME__DLTHUB_TELEMETRY_ENDPOINT: ${{ secrets.RUNTIME__DLTHUB_TELEMETRY_ENDPOINT }} ACTIVE_DESTINATIONS: "[\"athena\"]" ALL_FILESYSTEM_DRIVERS: "[\"memory\"]" - EXCLUDED_DESTINATION_CONFIGURATIONS: "[\"athena-parquet-staging-iceberg\", \"athena-parquet-no-staging-iceberg\"]" + EXCLUDED_DESTINATION_CONFIGURATIONS: "[\"athena-parquet-iceberg-no-staging-iceberg\", \"athena-parquet-iceberg-staging-iceberg\"]" jobs: get_docs_changes: @@ -73,11 +73,11 @@ jobs: run: pwd && echo "$DLT_SECRETS_TOML" > tests/.dlt/secrets.toml - run: | - poetry run pytest tests/load -m "essential" + poetry run pytest tests/load --ignore tests/load/sources -m "essential" name: Run essential tests Linux if: ${{ ! (contains(github.event.pull_request.labels.*.name, 'ci full') || !github.event_name == 'schedule')}} - run: | - poetry run pytest tests/load + poetry run pytest tests/load --ignore tests/load/sources name: Run all tests Linux if: ${{ contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule'}} diff --git a/.github/workflows/test_destination_athena_iceberg.yml b/.github/workflows/test_destination_athena_iceberg.yml index 40514ce58e..2c35a99393 100644 --- a/.github/workflows/test_destination_athena_iceberg.yml +++ b/.github/workflows/test_destination_athena_iceberg.yml @@ -73,11 +73,11 @@ jobs: run: pwd && echo "$DLT_SECRETS_TOML" > tests/.dlt/secrets.toml - run: | - poetry run pytest tests/load -m "essential" + poetry run pytest tests/load --ignore tests/load/sources -m "essential" name: Run essential tests Linux if: ${{ ! (contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule')}} - run: | - poetry run pytest tests/load + poetry run pytest tests/load --ignore tests/load/sources name: Run all tests Linux if: ${{ contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule'}} diff --git a/.github/workflows/test_destination_bigquery.yml b/.github/workflows/test_destination_bigquery.yml index b3926fb18c..e0908892b3 100644 --- a/.github/workflows/test_destination_bigquery.yml +++ b/.github/workflows/test_destination_bigquery.yml @@ -72,5 +72,5 @@ jobs: run: pwd && echo "$DLT_SECRETS_TOML" > tests/.dlt/secrets.toml - run: | - poetry run pytest tests/load + poetry run pytest tests/load --ignore tests/load/sources name: Run all tests Linux diff --git a/.github/workflows/test_destination_clickhouse.yml b/.github/workflows/test_destination_clickhouse.yml index 5b6848f2fe..89e189974c 100644 --- a/.github/workflows/test_destination_clickhouse.yml +++ b/.github/workflows/test_destination_clickhouse.yml @@ -75,7 +75,7 @@ jobs: name: Start ClickHouse OSS - - run: poetry run pytest tests/load -m "essential" + - run: poetry run pytest tests/load --ignore tests/load/sources -m "essential" name: Run essential tests Linux (ClickHouse OSS) if: ${{ ! (contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule')}} env: @@ -87,7 +87,7 @@ jobs: DESTINATION__CLICKHOUSE__CREDENTIALS__HTTP_PORT: 8123 DESTINATION__CLICKHOUSE__CREDENTIALS__SECURE: 0 - - run: poetry run pytest tests/load + - run: poetry run pytest tests/load --ignore tests/load/sources name: Run all tests Linux (ClickHouse OSS) if: ${{ contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule'}} env: @@ -105,12 +105,12 @@ jobs: # ClickHouse Cloud - run: | - poetry run pytest tests/load -m "essential" + poetry run pytest tests/load --ignore tests/load/sources -m "essential" name: Run essential tests Linux (ClickHouse Cloud) if: ${{ ! (contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule')}} - run: | - poetry run pytest tests/load + poetry run pytest tests/load --ignore tests/load/sources name: Run all tests Linux (ClickHouse Cloud) if: ${{ contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule'}} diff --git a/.github/workflows/test_destination_databricks.yml b/.github/workflows/test_destination_databricks.yml index 81ec575145..b3d30bcefc 100644 --- a/.github/workflows/test_destination_databricks.yml +++ b/.github/workflows/test_destination_databricks.yml @@ -70,11 +70,11 @@ jobs: run: pwd && echo "$DLT_SECRETS_TOML" > tests/.dlt/secrets.toml - run: | - poetry run pytest tests/load -m "essential" + poetry run pytest tests/load --ignore tests/load/sources -m "essential" name: Run essential tests Linux if: ${{ ! (contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule')}} - run: | - poetry run pytest tests/load + poetry run pytest tests/load --ignore tests/load/sources name: Run all tests Linux if: ${{ contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule'}} diff --git a/.github/workflows/test_destination_dremio.yml b/.github/workflows/test_destination_dremio.yml index 7ec6c4f697..b78e67dc5c 100644 --- a/.github/workflows/test_destination_dremio.yml +++ b/.github/workflows/test_destination_dremio.yml @@ -68,7 +68,7 @@ jobs: run: poetry install --no-interaction -E s3 -E gs -E az -E parquet --with sentry-sdk --with pipeline - run: | - poetry run pytest tests/load + poetry run pytest tests/load --ignore tests/load/sources if: runner.os != 'Windows' name: Run tests Linux/MAC env: @@ -80,7 +80,7 @@ jobs: DESTINATION__MINIO__CREDENTIALS__ENDPOINT_URL: http://127.0.0.1:9010 - run: | - poetry run pytest tests/load + poetry run pytest tests/load --ignore tests/load/sources if: runner.os == 'Windows' name: Run tests Windows shell: cmd diff --git a/.github/workflows/test_destination_lancedb.yml b/.github/workflows/test_destination_lancedb.yml index 02b5ef66eb..b191f79465 100644 --- a/.github/workflows/test_destination_lancedb.yml +++ b/.github/workflows/test_destination_lancedb.yml @@ -71,11 +71,11 @@ jobs: run: poetry run pip install openai - run: | - poetry run pytest tests/load -m "essential" + poetry run pytest tests/load --ignore tests/load/sources -m "essential" name: Run essential tests Linux if: ${{ ! (contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule')}} - run: | - poetry run pytest tests/load + poetry run pytest tests/load --ignore tests/load/sources name: Run all tests Linux if: ${{ contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule'}} diff --git a/.github/workflows/test_destination_motherduck.yml b/.github/workflows/test_destination_motherduck.yml index a51fb3cc8f..6c81dd28f7 100644 --- a/.github/workflows/test_destination_motherduck.yml +++ b/.github/workflows/test_destination_motherduck.yml @@ -70,11 +70,11 @@ jobs: run: pwd && echo "$DLT_SECRETS_TOML" > tests/.dlt/secrets.toml - run: | - poetry run pytest tests/load -m "essential" + poetry run pytest tests/load --ignore tests/load/sources -m "essential" name: Run essential tests Linux if: ${{ ! (contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule')}} - run: | - poetry run pytest tests/load + poetry run pytest tests/load --ignore tests/load/sources name: Run all tests Linux if: ${{ contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule'}} diff --git a/.github/workflows/test_destination_mssql.yml b/.github/workflows/test_destination_mssql.yml index 3b5bfd8d42..2065568a5e 100644 --- a/.github/workflows/test_destination_mssql.yml +++ b/.github/workflows/test_destination_mssql.yml @@ -75,5 +75,5 @@ jobs: run: pwd && echo "$DLT_SECRETS_TOML" > tests/.dlt/secrets.toml # always run full suite, also on branches - - run: poetry run pytest tests/load + - run: poetry run pytest tests/load --ignore tests/load/sources name: Run tests Linux diff --git a/.github/workflows/test_destination_qdrant.yml b/.github/workflows/test_destination_qdrant.yml index 168fe315ce..e231f4dbbb 100644 --- a/.github/workflows/test_destination_qdrant.yml +++ b/.github/workflows/test_destination_qdrant.yml @@ -69,11 +69,11 @@ jobs: run: poetry install --no-interaction -E qdrant -E parquet --with sentry-sdk --with pipeline - run: | - poetry run pytest tests/load -m "essential" + poetry run pytest tests/load --ignore tests/load/sources -m "essential" name: Run essential tests Linux if: ${{ ! (contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule')}} - run: | - poetry run pytest tests/load + poetry run pytest tests/load --ignore tests/load/sources name: Run all tests Linux if: ${{ contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule'}} diff --git a/.github/workflows/test_destination_snowflake.yml b/.github/workflows/test_destination_snowflake.yml index 0c9a2b08d1..a2716fb597 100644 --- a/.github/workflows/test_destination_snowflake.yml +++ b/.github/workflows/test_destination_snowflake.yml @@ -70,11 +70,11 @@ jobs: run: pwd && echo "$DLT_SECRETS_TOML" > tests/.dlt/secrets.toml - run: | - poetry run pytest tests/load -m "essential" + poetry run pytest tests/load --ignore tests/load/sources -m "essential" name: Run essential tests Linux if: ${{ ! (contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule')}} - run: | - poetry run pytest tests/load + poetry run pytest tests/load --ignore tests/load/sources name: Run all tests Linux if: ${{ contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule'}} diff --git a/.github/workflows/test_destination_synapse.yml b/.github/workflows/test_destination_synapse.yml index 4d3049853c..be1b493916 100644 --- a/.github/workflows/test_destination_synapse.yml +++ b/.github/workflows/test_destination_synapse.yml @@ -73,11 +73,11 @@ jobs: run: pwd && echo "$DLT_SECRETS_TOML" > tests/.dlt/secrets.toml - run: | - poetry run pytest tests/load -m "essential" + poetry run pytest tests/load --ignore tests/load/sources -m "essential" name: Run essential tests Linux if: ${{ ! (contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule')}} - run: | - poetry run pytest tests/load + poetry run pytest tests/load --ignore tests/load/sources name: Run all tests Linux if: ${{ contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule'}} diff --git a/.github/workflows/test_destinations.yml b/.github/workflows/test_destinations.yml index 7fae69ff9e..ada73b85d9 100644 --- a/.github/workflows/test_destinations.yml +++ b/.github/workflows/test_destinations.yml @@ -30,6 +30,7 @@ env: # postgres runs again here so we can test on mac/windows ACTIVE_DESTINATIONS: "[\"redshift\", \"postgres\", \"duckdb\", \"filesystem\", \"dummy\"]" # note that all buckets are enabled for testing + ALL_FILESYSTEM_DRIVERS: "[\"memory\", \"file\", \"r2\", \"s3\", \"gs\", \"az\", \"abfss\", \"gdrive\"]" #excludes sftp jobs: get_docs_changes: @@ -82,11 +83,11 @@ jobs: run: pwd && echo "$DLT_SECRETS_TOML" > tests/.dlt/secrets.toml - run: | - poetry run pytest tests/load -m "essential" + poetry run pytest tests/load --ignore tests/load/sources -m "essential" name: Run essential tests Linux if: ${{ ! (contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule')}} - run: | - poetry run pytest tests/load + poetry run pytest tests/load --ignore tests/load/sources name: Run all tests Linux if: ${{ contains(github.event.pull_request.labels.*.name, 'ci full') || github.event_name == 'schedule'}} diff --git a/.github/workflows/test_local_destinations.yml b/.github/workflows/test_local_destinations.yml index 78ea23ec1c..8911e05ecc 100644 --- a/.github/workflows/test_local_destinations.yml +++ b/.github/workflows/test_local_destinations.yml @@ -22,7 +22,7 @@ env: RUNTIME__LOG_LEVEL: ERROR RUNTIME__DLTHUB_TELEMETRY_ENDPOINT: ${{ secrets.RUNTIME__DLTHUB_TELEMETRY_ENDPOINT }} ACTIVE_DESTINATIONS: "[\"duckdb\", \"postgres\", \"filesystem\", \"weaviate\", \"qdrant\"]" - ALL_FILESYSTEM_DRIVERS: "[\"memory\", \"file\"]" + ALL_FILESYSTEM_DRIVERS: "[\"memory\", \"file\", \"sftp\"]" DESTINATION__WEAVIATE__VECTORIZER: text2vec-contextionary DESTINATION__WEAVIATE__MODULE_CONFIG: "{\"text2vec-contextionary\": {\"vectorizeClassName\": false, \"vectorizePropertyName\": true}}" @@ -95,18 +95,35 @@ jobs: key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }}-local-destinations - name: Install dependencies - run: poetry install --no-interaction -E postgres -E duckdb -E parquet -E filesystem -E cli -E weaviate -E qdrant --with sentry-sdk --with pipeline -E deltalake + run: poetry install --no-interaction -E postgres -E duckdb -E parquet -E filesystem -E cli -E weaviate -E qdrant -E sftp --with sentry-sdk --with pipeline -E deltalake - - name: create secrets.toml - run: pwd && echo "$DLT_SECRETS_TOML" > tests/.dlt/secrets.toml + - name: Start SFTP server + run: docker compose -f "tests/load/filesystem_sftp/docker-compose.yml" up -d + + - name: Configure SSH Agent for sftp tests + run: | + mkdir -p /home/runner/.ssh + cp tests/load/filesystem_sftp/bootstrap/bobby_rsa /home/runner/.ssh/id_rsa + cp tests/load/filesystem_sftp/bootstrap/bobby_rsa.pub /home/runner/.ssh/id_rsa.pub # always run full suite, also on branches - - run: poetry run pytest tests/load && poetry run pytest tests/cli - name: Run tests Linux + - name: Run tests Linux + run: | + eval "$(ssh-agent -s)" + poetry run pytest tests/load --ignore tests/load/sources + poetry run pytest tests/cli env: DESTINATION__POSTGRES__CREDENTIALS: postgresql://loader:loader@localhost:5432/dlt_data DESTINATION__QDRANT__CREDENTIALS__location: http://localhost:6333 + DESTINATION__FILESYSTEM__CREDENTIALS__SFTP_PORT: 2222 + DESTINATION__FILESYSTEM__CREDENTIALS__SFTP_USERNAME: foo + DESTINATION__FILESYSTEM__CREDENTIALS__SFTP_PASSWORD: pass + - name: Stop weaviate if: always() run: docker compose -f ".github/weaviate-compose.yml" down -v + + - name: Stop SFTP server + if: always() + run: docker compose -f "tests/load/filesystem_sftp/docker-compose.yml" down -v diff --git a/.github/workflows/test_local_sources.yml b/.github/workflows/test_local_sources.yml new file mode 100644 index 0000000000..3d9e7b29a5 --- /dev/null +++ b/.github/workflows/test_local_sources.yml @@ -0,0 +1,101 @@ +# Tests sources against a couple of local destinations + +name: src | rest_api, sql_database, filesystem + +on: + pull_request: + branches: + - master + - devel + workflow_dispatch: + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +env: + + RUNTIME__SENTRY_DSN: https://6f6f7b6f8e0f458a89be4187603b55fe@o1061158.ingest.sentry.io/4504819859914752 + RUNTIME__LOG_LEVEL: ERROR + RUNTIME__DLTHUB_TELEMETRY_ENDPOINT: ${{ secrets.RUNTIME__DLTHUB_TELEMETRY_ENDPOINT }} + + ACTIVE_DESTINATIONS: "[\"duckdb\", \"postgres\", \"filesystem\"]" + ALL_FILESYSTEM_DRIVERS: "[\"file\"]" + +jobs: + get_docs_changes: + name: docs changes + uses: ./.github/workflows/get_docs_changes.yml + + run_loader: + name: src | rest_api, sql_database, filesystem + needs: get_docs_changes + if: needs.get_docs_changes.outputs.changes_outside_docs == 'true' + strategy: + fail-fast: false + defaults: + run: + shell: bash + runs-on: "ubuntu-latest" + + # Service containers to run with `container-job` + services: + # Label used to access the service container + postgres: + # Docker Hub image + image: postgres + # Provide the password for postgres + env: + POSTGRES_DB: dlt_data + POSTGRES_USER: loader + POSTGRES_PASSWORD: loader + ports: + - 5432:5432 + # Set health checks to wait until postgres has started + options: >- + --health-cmd pg_isready + --health-interval 10s + --health-timeout 5s + --health-retries 5 + + steps: + - name: Check out + uses: actions/checkout@master + + - name: Setup Python + uses: actions/setup-python@v4 + with: + python-version: "3.10.x" + + - name: Install Poetry + uses: snok/install-poetry@v1.3.2 + with: + virtualenvs-create: true + virtualenvs-in-project: true + installer-parallel: true + + - name: Load cached venv + id: cached-poetry-dependencies + uses: actions/cache@v3 + with: + path: .venv + key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }}-local-sources + + # TODO: which deps should we enable? + - name: Install dependencies + run: poetry install --no-interaction -E postgres -E duckdb -E parquet -E filesystem -E cli -E sql_database --with sentry-sdk,pipeline,sources + + # run sources tests in load against configured destinations + - run: poetry run pytest tests/load/sources + name: Run tests Linux + env: + DESTINATION__POSTGRES__CREDENTIALS: postgresql://loader:loader@localhost:5432/dlt_data + + # here we upgrade sql alchemy to 2 an run the sql_database tests again + - name: Upgrade sql alchemy + run: poetry run pip install sqlalchemy==2.0.32 + + - run: poetry run pytest tests/load/sources/sql_database + name: Run tests Linux + env: + DESTINATION__POSTGRES__CREDENTIALS: postgresql://loader:loader@localhost:5432/dlt_data \ No newline at end of file diff --git a/.github/workflows/test_pyarrow17.yml b/.github/workflows/test_pyarrow17.yml index dd48c2af9d..78d6742ac1 100644 --- a/.github/workflows/test_pyarrow17.yml +++ b/.github/workflows/test_pyarrow17.yml @@ -23,6 +23,7 @@ env: RUNTIME__DLTHUB_TELEMETRY_ENDPOINT: ${{ secrets.RUNTIME__DLTHUB_TELEMETRY_ENDPOINT }} ACTIVE_DESTINATIONS: "[\"filesystem\"]" + ALL_FILESYSTEM_DRIVERS: "[\"memory\", \"file\", \"r2\", \"s3\", \"gs\", \"az\", \"abfss\", \"gdrive\"]" #excludes sftp jobs: get_docs_changes: @@ -72,6 +73,7 @@ jobs: - name: create secrets.toml run: pwd && echo "$DLT_SECRETS_TOML" > tests/.dlt/secrets.toml - - run: | - poetry run pytest tests/libs tests/load -m needspyarrow17 - name: Run needspyarrow17 tests Linux + - name: Run needspyarrow17 tests Linux + run: | + poetry run pytest tests/libs -m "needspyarrow17" + poetry run pytest tests/load -m "needspyarrow17" diff --git a/.github/workflows/test_sqlalchemy_destinations.yml b/.github/workflows/test_sqlalchemy_destinations.yml new file mode 100644 index 0000000000..5da2dac04b --- /dev/null +++ b/.github/workflows/test_sqlalchemy_destinations.yml @@ -0,0 +1,99 @@ +# Tests destinations that can run without credentials. +# i.e. local postgres, duckdb, filesystem (with local fs/memory bucket) + +name: dest | sqlalchemy mysql and sqlite + +on: + pull_request: + branches: + - master + - devel + workflow_dispatch: + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +env: + # NOTE: this workflow can't use github secrets! + # DLT_SECRETS_TOML: ${{ secrets.DLT_SECRETS_TOML }} + + RUNTIME__SENTRY_DSN: https://6f6f7b6f8e0f458a89be4187603b55fe@o1061158.ingest.sentry.io/4504819859914752 + RUNTIME__LOG_LEVEL: ERROR + RUNTIME__DLTHUB_TELEMETRY_ENDPOINT: ${{ secrets.RUNTIME__DLTHUB_TELEMETRY_ENDPOINT }} + ACTIVE_DESTINATIONS: "[\"sqlalchemy\"]" + ALL_FILESYSTEM_DRIVERS: "[\"memory\", \"file\"]" + +jobs: + get_docs_changes: + name: docs changes + uses: ./.github/workflows/get_docs_changes.yml + + run_loader: + name: dest | sqlalchemy mysql and sqlite + needs: get_docs_changes + if: needs.get_docs_changes.outputs.changes_outside_docs == 'true' + strategy: + fail-fast: false + # Run on sqlalchemy 1.4 and 2.0 + matrix: + sqlalchemy: [1.4, 2] + defaults: + run: + shell: bash + runs-on: "ubuntu-latest" + + # Service containers to run with `container-job` + services: + # Label used to access the service container + mysql: + image: mysql:8 + env: + MYSQL_ROOT_PASSWORD: root + MYSQL_DATABASE: dlt_data + MYSQL_USER: loader + MYSQL_PASSWORD: loader + ports: + - 3306:3306 + # Wait for the service to be ready before completing the job + options: >- + --health-cmd="mysqladmin ping -h localhost -u root -proot" + --health-interval=10s + --health-timeout=5s + --health-retries=5 + + steps: + - name: Check out + uses: actions/checkout@master + + - name: Setup Python + uses: actions/setup-python@v4 + with: + python-version: "3.10.x" + + - name: Install Poetry + uses: snok/install-poetry@v1.3.2 + with: + virtualenvs-create: true + virtualenvs-in-project: true + installer-parallel: true + + - name: Load cached venv + id: cached-poetry-dependencies + uses: actions/cache@v3 + with: + path: .venv + key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }}-local-destinations + + - name: Install dependencies + run: poetry install --no-interaction -E parquet -E filesystem -E sqlalchemy -E cli --with sentry-sdk --with pipeline && poetry run pip install mysqlclient && poetry run pip install "sqlalchemy==${{ matrix.sqlalchemy }}" + + - name: create secrets.toml + run: pwd && echo "$DLT_SECRETS_TOML" > tests/.dlt/secrets.toml + + # always run full suite, also on branches + - run: poetry run pytest tests/load -x --ignore tests/load/sources + name: Run tests Linux + env: + DESTINATION__SQLALCHEMY_MYSQL__CREDENTIALS: mysql://root:root@127.0.0.1:3306/dlt_data # Use root cause we need to create databases + DESTINATION__SQLALCHEMY_SQLITE__CREDENTIALS: sqlite:///_storage/dl_data.sqlite diff --git a/Makefile b/Makefile index f47047a3fe..3878dddd15 100644 --- a/Makefile +++ b/Makefile @@ -44,7 +44,7 @@ has-poetry: poetry --version dev: has-poetry - poetry install --all-extras --with airflow,docs,providers,pipeline,sentry-sdk,dbt + poetry install --all-extras --with docs,providers,pipeline,sources,sentry-sdk lint: ./tools/check-package.sh diff --git a/README.md b/README.md index bc0f40b62f..ee2b2e1fdc 100644 --- a/README.md +++ b/README.md @@ -94,15 +94,16 @@ You can find examples for various use cases in the [examples](docs/examples) fol ## Adding as dependency -`dlt` follows the semantic versioning with the [`MAJOR.MINOR.PATCH`](https://peps.python.org/pep-0440/#semantic-versioning) pattern. Currently, we are using **pre-release versioning** with the major version being 0. +`dlt` follows the semantic versioning with the [`MAJOR.MINOR.PATCH`](https://peps.python.org/pep-0440/#semantic-versioning) pattern. -- `minor` version change means breaking changes -- `patch` version change means new features that should be backward compatible -- any suffix change, e.g., `post10` -> `post11`, is considered a patch +* `major` means breaking changes and removed deprecations +* `minor` new features, sometimes automatic migrations +* `patch` bug fixes We suggest that you allow only `patch` level updates automatically: -* Using the [Compatible Release Specifier](https://packaging.python.org/en/latest/specifications/version-specifiers/#compatible-release). For example **dlt~=0.3.10** allows only versions **>=0.3.10** and less than **<0.4** -* Poetry [caret requirements](https://python-poetry.org/docs/dependency-specification/). For example **^0.3.10** allows only versions **>=0.3.10** to **<0.4** +* Using the [Compatible Release Specifier](https://packaging.python.org/en/latest/specifications/version-specifiers/#compatible-release). For example **dlt~=1.0** allows only versions **>=1.0** and less than **<1.1** +* Poetry [caret requirements](https://python-poetry.org/docs/dependency-specification/). For example **^1.0** allows only versions **>=1.0** to **<1.0** + ## Get Involved The dlt project is quickly growing, and we're excited to have you join our community! Here's how you can get involved: diff --git a/dlt/cli/_dlt.py b/dlt/cli/_dlt.py index 7c6526c0a2..0a4a86b9de 100644 --- a/dlt/cli/_dlt.py +++ b/dlt/cli/_dlt.py @@ -16,7 +16,7 @@ from dlt.cli.init_command import ( init_command, - list_verified_sources_command, + list_sources_command, DLT_INIT_DOCS_URL, DEFAULT_VERIFIED_SOURCES_REPO, ) @@ -54,12 +54,18 @@ def on_exception(ex: Exception, info: str) -> None: def init_command_wrapper( source_name: str, destination_type: str, - use_generic_template: bool, repo_location: str, branch: str, + omit_core_sources: bool = False, ) -> int: try: - init_command(source_name, destination_type, use_generic_template, repo_location, branch) + init_command( + source_name, + destination_type, + repo_location, + branch, + omit_core_sources, + ) except Exception as ex: on_exception(ex, DLT_INIT_DOCS_URL) return -1 @@ -67,9 +73,9 @@ def init_command_wrapper( @utils.track_command("list_sources", False) -def list_verified_sources_command_wrapper(repo_location: str, branch: str) -> int: +def list_sources_command_wrapper(repo_location: str, branch: str) -> int: try: - list_verified_sources_command(repo_location, branch) + list_sources_command(repo_location, branch) except Exception as ex: on_exception(ex, DLT_INIT_DOCS_URL) return -1 @@ -306,11 +312,11 @@ def main() -> int: ), ) init_cmd.add_argument( - "--list-verified-sources", + "--list-sources", "-l", default=False, action="store_true", - help="List available verified sources", + help="List available sources", ) init_cmd.add_argument( "source", @@ -334,14 +340,14 @@ def main() -> int: default=None, help="Advanced. Uses specific branch of the init repository to fetch the template.", ) + init_cmd.add_argument( - "--generic", + "--omit-core-sources", default=False, action="store_true", help=( - "When present uses a generic template with all the dlt loading code present will be" - " used. Otherwise a debug template is used that can be immediately run to get familiar" - " with the dlt sources." + "When present, will not create the new pipeline with a core source of the given name" + " but will take a source of this name from the default or provided location." ), ) @@ -434,7 +440,10 @@ def main() -> int: "--format", choices=["json", "yaml"], default="yaml", help="Display schema in this format" ) schema.add_argument( - "--remove-defaults", action="store_true", help="Does not show default hint values" + "--remove-defaults", + action="store_true", + help="Does not show default hint values", + default=True, ) pipe_cmd = subparsers.add_parser( @@ -510,7 +519,10 @@ def main() -> int: help="Display schema in this format", ) pipe_cmd_schema.add_argument( - "--remove-defaults", action="store_true", help="Does not show default hint values" + "--remove-defaults", + action="store_true", + help="Does not show default hint values", + default=True, ) pipe_cmd_drop = pipeline_subparsers.add_parser( @@ -588,26 +600,34 @@ def main() -> int: del command_kwargs["list_pipelines"] return pipeline_command_wrapper(**command_kwargs) elif args.command == "init": - if args.list_verified_sources: - return list_verified_sources_command_wrapper(args.location, args.branch) + if args.list_sources: + return list_sources_command_wrapper(args.location, args.branch) else: if not args.source or not args.destination: init_cmd.print_usage() return -1 else: return init_command_wrapper( - args.source, args.destination, args.generic, args.location, args.branch + args.source, + args.destination, + args.location, + args.branch, + args.omit_core_sources, ) elif args.command == "deploy": try: deploy_args = vars(args) - return deploy_command_wrapper( - pipeline_script_path=deploy_args.pop("pipeline_script_path"), - deployment_method=deploy_args.pop("deployment_method"), - repo_location=deploy_args.pop("location"), - branch=deploy_args.pop("branch"), - **deploy_args, - ) + if deploy_args.get("deployment_method") is None: + print_help(deploy_cmd) + return -1 + else: + return deploy_command_wrapper( + pipeline_script_path=deploy_args.pop("pipeline_script_path"), + deployment_method=deploy_args.pop("deployment_method"), + repo_location=deploy_args.pop("location"), + branch=deploy_args.pop("branch"), + **deploy_args, + ) except (NameError, KeyError): fmt.warning( "Please install additional command line dependencies to use deploy command:" diff --git a/dlt/cli/config_toml_writer.py b/dlt/cli/config_toml_writer.py index 0f8984842f..1b39653a55 100644 --- a/dlt/cli/config_toml_writer.py +++ b/dlt/cli/config_toml_writer.py @@ -34,7 +34,7 @@ def generate_typed_example(name: str, hint: AnyType) -> Any: return 1.0 if sc_type == "bool": return True - if sc_type == "complex": + if sc_type == "json": if is_subclass(inner_hint, C_Sequence): return ["a", "b", "c"] else: diff --git a/dlt/cli/deploy_command.py b/dlt/cli/deploy_command.py index 5a25752a6d..b48dffa881 100644 --- a/dlt/cli/deploy_command.py +++ b/dlt/cli/deploy_command.py @@ -4,7 +4,7 @@ from enum import Enum from importlib.metadata import version as pkg_version -from dlt.common.configuration.providers import SECRETS_TOML, SECRETS_TOML_KEY, StringTomlProvider +from dlt.common.configuration.providers import SECRETS_TOML, SECRETS_TOML_KEY from dlt.common.configuration.paths import make_dlt_settings_path from dlt.common.configuration.utils import serialize_value from dlt.common.git import is_dirty @@ -393,12 +393,7 @@ def _echo_instructions(self, *args: Optional[Any]) -> None: f" {SECRETS_TOML_KEY} variable." ) fmt.echo() - toml_provider = StringTomlProvider("") - for s_v in self.secret_envs: - toml_provider.set_value(s_v.key, s_v.value, None, *s_v.sections) - for s_v in self.envs: - toml_provider.set_value(s_v.key, s_v.value, None, *s_v.sections) - fmt.echo(toml_provider.dumps()) + self._echo_secrets_toml() else: raise ValueError(self.secrets_format) diff --git a/dlt/cli/deploy_command_helpers.py b/dlt/cli/deploy_command_helpers.py index 5fe46415dd..2afbfbf46e 100644 --- a/dlt/cli/deploy_command_helpers.py +++ b/dlt/cli/deploy_command_helpers.py @@ -14,8 +14,9 @@ import dlt from dlt.common import git -from dlt.common.configuration.exceptions import LookupTrace +from dlt.common.configuration.exceptions import LookupTrace, ConfigFieldMissingException from dlt.common.configuration.providers import ConfigTomlProvider, EnvironProvider +from dlt.common.configuration.providers.toml import BaseDocProvider, StringTomlProvider from dlt.common.git import get_origin, get_repo, Repo from dlt.common.configuration.specs.run_configuration import get_default_pipeline_name from dlt.common.typing import StrAny @@ -198,12 +199,50 @@ def _update_envs(self, trace: PipelineTrace) -> None: # fmt.echo(f"{resolved_value.key} in {resolved_value.sections} moved to CONFIG") def _echo_secrets(self) -> None: + display_info = False for s_v in self.secret_envs: fmt.secho("Name:", fg="green") fmt.echo(fmt.bold(self.env_prov.get_key_name(s_v.key, *s_v.sections))) - fmt.secho("Secret:", fg="green") - fmt.echo(s_v.value) + try: + fmt.secho("Secret:", fg="green") + fmt.echo(self._lookup_secret_value(s_v)) + except ConfigFieldMissingException: + fmt.secho("please set me up!", fg="red") + display_info = True fmt.echo() + if display_info: + self._display_missing_secret_info() + fmt.echo() + + def _echo_secrets_toml(self) -> None: + display_info = False + toml_provider = StringTomlProvider("") + for s_v in self.secret_envs: + try: + secret_value = self._lookup_secret_value(s_v) + except ConfigFieldMissingException: + secret_value = "please set me up!" + display_info = True + toml_provider.set_value(s_v.key, secret_value, None, *s_v.sections) + for s_v in self.envs: + toml_provider.set_value(s_v.key, s_v.value, None, *s_v.sections) + fmt.echo(toml_provider.dumps()) + if display_info: + self._display_missing_secret_info() + fmt.echo() + + def _display_missing_secret_info(self) -> None: + fmt.warning( + "We could not read and display some secrets. Starting from 1.0 version of dlt," + " those are not stored in the traces. Instead we are trying to read them from the" + " available configuration ie. secrets.toml file. Please run the deploy command from" + " the same working directory you ran your pipeline script. If you pass the" + " credentials in code we will not be able to display them here. See" + " https://dlthub.com/docs/general-usage/credentials" + ) + + def _lookup_secret_value(self, trace: LookupTrace) -> Any: + return dlt.secrets[BaseDocProvider.get_key_name(trace.key, *trace.sections)] def _echo_envs(self) -> None: for v in self.envs: diff --git a/dlt/cli/echo.py b/dlt/cli/echo.py index bd9cf24f64..302b74b076 100644 --- a/dlt/cli/echo.py +++ b/dlt/cli/echo.py @@ -15,9 +15,11 @@ def always_choose(always_choose_default: bool, always_choose_value: Any) -> Iter _always_choose_value = ALWAYS_CHOOSE_VALUE ALWAYS_CHOOSE_DEFAULT = always_choose_default ALWAYS_CHOOSE_VALUE = always_choose_value - yield - ALWAYS_CHOOSE_DEFAULT = _always_choose_default - ALWAYS_CHOOSE_VALUE = _always_choose_value + try: + yield + finally: + ALWAYS_CHOOSE_DEFAULT = _always_choose_default + ALWAYS_CHOOSE_VALUE = _always_choose_value echo = click.echo diff --git a/dlt/cli/init_command.py b/dlt/cli/init_command.py index a1434133f0..797917a165 100644 --- a/dlt/cli/init_command.py +++ b/dlt/cli/init_command.py @@ -5,6 +5,8 @@ from types import ModuleType from typing import Dict, List, Sequence, Tuple from importlib.metadata import version as pkg_version +from pathlib import Path +from importlib import import_module from dlt.common import git from dlt.common.configuration.paths import get_dlt_settings_dir, make_dlt_settings_path @@ -23,6 +25,7 @@ from dlt.common.schema.utils import is_valid_schema_name from dlt.common.schema.exceptions import InvalidSchemaName from dlt.common.storages.file_storage import FileStorage +from dlt.sources import pipeline_templates as init_module import dlt.reflection.names as n from dlt.reflection.script_inspector import inspect_pipeline_script, load_script_module @@ -31,28 +34,44 @@ from dlt.cli import utils from dlt.cli.config_toml_writer import WritableConfigValue, write_values from dlt.cli.pipeline_files import ( - VerifiedSourceFiles, + SourceConfiguration, TVerifiedSourceFileEntry, TVerifiedSourceFileIndex, ) from dlt.cli.exceptions import CliCommandException from dlt.cli.requirements import SourceRequirements + DLT_INIT_DOCS_URL = "https://dlthub.com/docs/reference/command-line-interface#dlt-init" DEFAULT_VERIFIED_SOURCES_REPO = "https://github.com/dlt-hub/verified-sources.git" -INIT_MODULE_NAME = "init" +TEMPLATES_MODULE_NAME = "pipeline_templates" SOURCES_MODULE_NAME = "sources" -def _get_template_files( - command_module: ModuleType, use_generic_template: bool -) -> Tuple[str, List[str]]: - template_files: List[str] = command_module.TEMPLATE_FILES - pipeline_script: str = command_module.PIPELINE_SCRIPT - if use_generic_template: - pipeline_script, py = os.path.splitext(pipeline_script) - pipeline_script = f"{pipeline_script}_generic{py}" - return pipeline_script, template_files +def _get_core_sources_storage() -> FileStorage: + """Get FileStorage for core sources""" + local_path = Path(os.path.dirname(os.path.realpath(__file__))).parent / SOURCES_MODULE_NAME + return FileStorage(str(local_path)) + + +def _get_templates_storage() -> FileStorage: + """Get FileStorage for single file templates""" + # look up init storage in core + init_path = ( + Path(os.path.dirname(os.path.realpath(__file__))).parent + / SOURCES_MODULE_NAME + / TEMPLATES_MODULE_NAME + ) + return FileStorage(str(init_path)) + + +def _clone_and_get_verified_sources_storage(repo_location: str, branch: str = None) -> FileStorage: + """Clone and get FileStorage for verified sources templates""" + + fmt.echo("Looking up verified sources at %s..." % fmt.bold(repo_location)) + clone_storage = git.get_fresh_repo_files(repo_location, get_dlt_repos_dir(), branch=branch) + # copy dlt source files from here + return FileStorage(clone_storage.make_full_path(SOURCES_MODULE_NAME)) def _select_source_files( @@ -127,16 +146,38 @@ def _get_dependency_system(dest_storage: FileStorage) -> str: return None +def _list_template_sources() -> Dict[str, SourceConfiguration]: + template_storage = _get_templates_storage() + sources: Dict[str, SourceConfiguration] = {} + for source_name in files_ops.get_sources_names(template_storage, source_type="template"): + sources[source_name] = files_ops.get_template_configuration(template_storage, source_name) + return sources + + +def _list_core_sources() -> Dict[str, SourceConfiguration]: + core_sources_storage = _get_core_sources_storage() + + sources: Dict[str, SourceConfiguration] = {} + for source_name in files_ops.get_sources_names(core_sources_storage, source_type="core"): + sources[source_name] = files_ops.get_core_source_configuration( + core_sources_storage, source_name + ) + return sources + + def _list_verified_sources( repo_location: str, branch: str = None -) -> Dict[str, VerifiedSourceFiles]: - clone_storage = git.get_fresh_repo_files(repo_location, get_dlt_repos_dir(), branch=branch) - sources_storage = FileStorage(clone_storage.make_full_path(SOURCES_MODULE_NAME)) +) -> Dict[str, SourceConfiguration]: + verified_sources_storage = _clone_and_get_verified_sources_storage(repo_location, branch) - sources: Dict[str, VerifiedSourceFiles] = {} - for source_name in files_ops.get_verified_source_names(sources_storage): + sources: Dict[str, SourceConfiguration] = {} + for source_name in files_ops.get_sources_names( + verified_sources_storage, source_type="verified" + ): try: - sources[source_name] = files_ops.get_verified_source_files(sources_storage, source_name) + sources[source_name] = files_ops.get_verified_source_configuration( + verified_sources_storage, source_name + ) except Exception as ex: fmt.warning(f"Verified source {source_name} not available: {ex}") @@ -146,23 +187,23 @@ def _list_verified_sources( def _welcome_message( source_name: str, destination_type: str, - source_files: VerifiedSourceFiles, + source_configuration: SourceConfiguration, dependency_system: str, is_new_source: bool, ) -> None: fmt.echo() - if source_files.is_template: + if source_configuration.source_type in ["template", "core"]: fmt.echo("Your new pipeline %s is ready to be customized!" % fmt.bold(source_name)) fmt.echo( "* Review and change how dlt loads your data in %s" - % fmt.bold(source_files.dest_pipeline_script) + % fmt.bold(source_configuration.dest_pipeline_script) ) else: if is_new_source: fmt.echo("Verified source %s was added to your project!" % fmt.bold(source_name)) fmt.echo( "* See the usage examples and code snippets to copy from %s" - % fmt.bold(source_files.dest_pipeline_script) + % fmt.bold(source_configuration.dest_pipeline_script) ) else: fmt.echo( @@ -175,9 +216,16 @@ def _welcome_message( % (fmt.bold(destination_type), fmt.bold(make_dlt_settings_path(SECRETS_TOML))) ) + if destination_type == "destination": + fmt.echo( + "* You have selected the custom destination as your pipelines destination. Please refer" + " to our docs at https://dlthub.com/docs/dlt-ecosystem/destinations/destination on how" + " to add a destination function that will consume your data." + ) + if dependency_system: fmt.echo("* Add the required dependencies to %s:" % fmt.bold(dependency_system)) - compiled_requirements = source_files.requirements.compiled() + compiled_requirements = source_configuration.requirements.compiled() for dep in compiled_requirements: fmt.echo(" " + fmt.bold(dep)) fmt.echo( @@ -212,37 +260,69 @@ def _welcome_message( ) -def list_verified_sources_command(repo_location: str, branch: str = None) -> None: - fmt.echo("Looking up for verified sources in %s..." % fmt.bold(repo_location)) - for source_name, source_files in _list_verified_sources(repo_location, branch).items(): - reqs = source_files.requirements +def list_sources_command(repo_location: str, branch: str = None) -> None: + fmt.echo("---") + fmt.echo("Available dlt core sources:") + fmt.echo("---") + core_sources = _list_core_sources() + for source_name, source_configuration in core_sources.items(): + msg = "%s: %s" % (fmt.bold(source_name), source_configuration.doc) + fmt.echo(msg) + + fmt.echo("---") + fmt.echo("Available dlt single file templates:") + fmt.echo("---") + template_sources = _list_template_sources() + for source_name, source_configuration in template_sources.items(): + msg = "%s: %s" % (fmt.bold(source_name), source_configuration.doc) + fmt.echo(msg) + + fmt.echo("---") + fmt.echo("Available verified sources:") + fmt.echo("---") + for source_name, source_configuration in _list_verified_sources(repo_location, branch).items(): + reqs = source_configuration.requirements dlt_req_string = str(reqs.dlt_requirement_base) - msg = "%s: %s" % (fmt.bold(source_name), source_files.doc) + msg = "%s: " % (fmt.bold(source_name)) + if source_name in core_sources.keys(): + msg += "(Deprecated since dlt 1.0.0 in favor of core source of the same name) " + msg += source_configuration.doc if not reqs.is_installed_dlt_compatible(): msg += fmt.warning_style(" [needs update: %s]" % (dlt_req_string)) + fmt.echo(msg) def init_command( source_name: str, destination_type: str, - use_generic_template: bool, repo_location: str, branch: str = None, + omit_core_sources: bool = False, ) -> None: # try to import the destination and get config spec destination_reference = Destination.from_reference(destination_type) destination_spec = destination_reference.spec - fmt.echo("Looking up the init scripts in %s..." % fmt.bold(repo_location)) - clone_storage = git.get_fresh_repo_files(repo_location, get_dlt_repos_dir(), branch=branch) - # copy init files from here - init_storage = FileStorage(clone_storage.make_full_path(INIT_MODULE_NAME)) - # copy dlt source files from here - sources_storage = FileStorage(clone_storage.make_full_path(SOURCES_MODULE_NAME)) - # load init module and get init files and script - init_module = load_script_module(clone_storage.storage_path, INIT_MODULE_NAME) - pipeline_script, template_files = _get_template_files(init_module, use_generic_template) + # lookup core storages + core_sources_storage = _get_core_sources_storage() + templates_storage = _get_templates_storage() + + # discover type of source + source_type: files_ops.TSourceType = "template" + if ( + source_name in files_ops.get_sources_names(core_sources_storage, source_type="core") + ) and not omit_core_sources: + source_type = "core" + else: + if omit_core_sources: + fmt.echo("Omitting dlt core sources.") + verified_sources_storage = _clone_and_get_verified_sources_storage(repo_location, branch) + if source_name in files_ops.get_sources_names( + verified_sources_storage, source_type="verified" + ): + source_type = "verified" + # prepare destination storage dest_storage = FileStorage(os.path.abspath(".")) if not dest_storage.has_folder(get_dlt_settings_dir()): @@ -256,16 +336,21 @@ def init_command( is_new_source = len(local_index["files"]) == 0 # look for existing source - source_files: VerifiedSourceFiles = None + source_configuration: SourceConfiguration = None remote_index: TVerifiedSourceFileIndex = None - if sources_storage.has_folder(source_name): + remote_modified: Dict[str, TVerifiedSourceFileEntry] = {} + remote_deleted: Dict[str, TVerifiedSourceFileEntry] = {} + + if source_type == "verified": # get pipeline files - source_files = files_ops.get_verified_source_files(sources_storage, source_name) + source_configuration = files_ops.get_verified_source_configuration( + verified_sources_storage, source_name + ) # get file index from remote verified source files being copied remote_index = files_ops.get_remote_source_index( - source_files.storage.storage_path, - source_files.files, - source_files.requirements.dlt_version_constraint(), + source_configuration.storage.storage_path, + source_configuration.files, + source_configuration.requirements.dlt_version_constraint(), ) # diff local and remote index to get modified and deleted files remote_new, remote_modified, remote_deleted = files_ops.gen_index_diff( @@ -292,39 +377,41 @@ def init_command( " update correctly in the future." ) # add template files - source_files.files.extend(template_files) + source_configuration.files.extend(files_ops.TEMPLATE_FILES) else: - if not is_valid_schema_name(source_name): - raise InvalidSchemaName(source_name) - dest_pipeline_script = source_name + ".py" - source_files = VerifiedSourceFiles( - True, - init_storage, - pipeline_script, - dest_pipeline_script, - template_files, - SourceRequirements([]), - "", - ) - if dest_storage.has_file(dest_pipeline_script): - fmt.warning("Pipeline script %s already exist, exiting" % dest_pipeline_script) + if source_type == "core": + source_configuration = files_ops.get_core_source_configuration( + core_sources_storage, source_name + ) + else: + if not is_valid_schema_name(source_name): + raise InvalidSchemaName(source_name) + source_configuration = files_ops.get_template_configuration( + templates_storage, source_name + ) + + if dest_storage.has_file(source_configuration.dest_pipeline_script): + fmt.warning( + "Pipeline script %s already exists, exiting" + % source_configuration.dest_pipeline_script + ) return # add .dlt/*.toml files to be copied - source_files.files.extend( + source_configuration.files.extend( [make_dlt_settings_path(CONFIG_TOML), make_dlt_settings_path(SECRETS_TOML)] ) # add dlt extras line to requirements - source_files.requirements.update_dlt_extras(destination_type) + source_configuration.requirements.update_dlt_extras(destination_type) # Check compatibility with installed dlt - if not source_files.requirements.is_installed_dlt_compatible(): + if not source_configuration.requirements.is_installed_dlt_compatible(): msg = ( "This pipeline requires a newer version of dlt than your installed version" - f" ({source_files.requirements.current_dlt_version()}). Pipeline requires" - f" '{source_files.requirements.dlt_requirement_base}'" + f" ({source_configuration.requirements.current_dlt_version()}). Pipeline requires" + f" '{source_configuration.requirements.dlt_requirement_base}'" ) fmt.warning(msg) if not fmt.confirm( @@ -332,28 +419,29 @@ def init_command( ): fmt.echo( "You can update dlt with: pip3 install -U" - f' "{source_files.requirements.dlt_requirement_base}"' + f' "{source_configuration.requirements.dlt_requirement_base}"' ) return # read module source and parse it visitor = utils.parse_init_script( "init", - source_files.storage.load(source_files.pipeline_script), - source_files.pipeline_script, + source_configuration.storage.load(source_configuration.src_pipeline_script), + source_configuration.src_pipeline_script, ) if visitor.is_destination_imported: raise CliCommandException( "init", - f"The pipeline script {source_files.pipeline_script} import a destination from" - " dlt.destinations. You should specify destinations by name when calling dlt.pipeline" - " or dlt.run in init scripts.", + f"The pipeline script {source_configuration.src_pipeline_script} imports a destination" + " from dlt.destinations. You should specify destinations by name when calling" + " dlt.pipeline or dlt.run in init scripts.", ) if n.PIPELINE not in visitor.known_calls: raise CliCommandException( "init", - f"The pipeline script {source_files.pipeline_script} does not seem to initialize" - " pipeline with dlt.pipeline. Please initialize pipeline explicitly in init scripts.", + f"The pipeline script {source_configuration.src_pipeline_script} does not seem to" + " initialize a pipeline with dlt.pipeline. Please initialize pipeline explicitly in" + " your init scripts.", ) # find all arguments in all calls to replace @@ -364,18 +452,18 @@ def init_command( ("pipeline_name", source_name), ("dataset_name", source_name + "_data"), ], - source_files.pipeline_script, + source_configuration.src_pipeline_script, ) # inspect the script inspect_pipeline_script( - source_files.storage.storage_path, - source_files.storage.to_relative_path(source_files.pipeline_script), + source_configuration.storage.storage_path, + source_configuration.storage.to_relative_path(source_configuration.src_pipeline_script), ignore_missing_imports=True, ) # detect all the required secrets and configs that should go into tomls files - if source_files.is_template: + if source_configuration.source_type == "template": # replace destination, pipeline_name and dataset_name in templates transformed_nodes = source_detection.find_call_arguments_to_replace( visitor, @@ -384,21 +472,22 @@ def init_command( ("pipeline_name", source_name), ("dataset_name", source_name + "_data"), ], - source_files.pipeline_script, + source_configuration.src_pipeline_script, ) # template sources are always in module starting with "pipeline" # for templates, place config and secrets into top level section required_secrets, required_config, checked_sources = source_detection.detect_source_configs( - _SOURCES, "pipeline", () + _SOURCES, source_configuration.source_module_prefix, () ) # template has a strict rules where sources are placed for source_q_name, source_config in checked_sources.items(): if source_q_name not in visitor.known_sources_resources: raise CliCommandException( "init", - f"The pipeline script {source_files.pipeline_script} imports a source/resource" - f" {source_config.f.__name__} from module {source_config.module.__name__}. In" - " init scripts you must declare all sources and resources in single file.", + f"The pipeline script {source_configuration.src_pipeline_script} imports a" + f" source/resource {source_config.f.__name__} from module" + f" {source_config.module.__name__}. In init scripts you must declare all" + " sources and resources in single file.", ) # rename sources and resources transformed_nodes.extend( @@ -407,19 +496,22 @@ def init_command( else: # replace only destination for existing pipelines transformed_nodes = source_detection.find_call_arguments_to_replace( - visitor, [("destination", destination_type)], source_files.pipeline_script + visitor, [("destination", destination_type)], source_configuration.src_pipeline_script ) # pipeline sources are in module with name starting from {pipeline_name} # for verified pipelines place in the specific source section required_secrets, required_config, checked_sources = source_detection.detect_source_configs( - _SOURCES, source_name, (known_sections.SOURCES, source_name) + _SOURCES, + source_configuration.source_module_prefix, + (known_sections.SOURCES, source_name), ) - if len(checked_sources) == 0: + # the intro template does not use sources, for now allow it to pass here + if len(checked_sources) == 0 and source_name != "intro": raise CliCommandException( "init", - f"The pipeline script {source_files.pipeline_script} is not creating or importing any" - " sources or resources", + f"The pipeline script {source_configuration.src_pipeline_script} is not creating or" + " importing any sources or resources. Exiting...", ) # add destination spec to required secrets @@ -439,37 +531,57 @@ def init_command( # ask for confirmation if is_new_source: - if source_files.is_template: + if source_configuration.source_type == "core": + fmt.echo( + "Creating a new pipeline with the dlt core source %s (%s)" + % (fmt.bold(source_name), source_configuration.doc) + ) fmt.echo( - "A verified source %s was not found. Using a template to create a new source and" - " pipeline with name %s." % (fmt.bold(source_name), fmt.bold(source_name)) + "NOTE: Beginning with dlt 1.0.0, the source %s will no longer be copied from the" + " verified sources repo but imported from dlt.sources. You can provide the" + " --omit-core-sources flag to revert to the old behavior." % (fmt.bold(source_name)) + ) + elif source_configuration.source_type == "verified": + fmt.echo( + "Creating and configuring a new pipeline with the verified source %s (%s)" + % (fmt.bold(source_name), source_configuration.doc) ) else: + if source_configuration.is_default_template: + fmt.echo( + "NOTE: Could not find a dlt source or template wih the name %s. Selecting the" + " default template." % (fmt.bold(source_name)) + ) + fmt.echo( + "NOTE: In case you did not want to use the default template, run 'dlt init -l'" + " to see all available sources and templates." + ) fmt.echo( - "Cloning and configuring a verified source %s (%s)" - % (fmt.bold(source_name), source_files.doc) + "Creating and configuring a new pipeline with the dlt core template %s (%s)" + % (fmt.bold(source_configuration.src_pipeline_script), source_configuration.doc) ) - if use_generic_template: - fmt.warning("--generic parameter is meaningless if verified source is found") + if not fmt.confirm("Do you want to proceed?", default=True): raise CliCommandException("init", "Aborted") dependency_system = _get_dependency_system(dest_storage) - _welcome_message(source_name, destination_type, source_files, dependency_system, is_new_source) + _welcome_message( + source_name, destination_type, source_configuration, dependency_system, is_new_source + ) # copy files at the very end - for file_name in source_files.files: + for file_name in source_configuration.files: dest_path = dest_storage.make_full_path(file_name) # get files from init section first - if init_storage.has_file(file_name): + if templates_storage.has_file(file_name): if dest_storage.has_file(dest_path): # do not overwrite any init files continue - src_path = init_storage.make_full_path(file_name) + src_path = templates_storage.make_full_path(file_name) else: # only those that were modified should be copied from verified sources if file_name in remote_modified: - src_path = source_files.storage.make_full_path(file_name) + src_path = source_configuration.storage.make_full_path(file_name) else: continue os.makedirs(os.path.dirname(dest_path), exist_ok=True) @@ -484,8 +596,8 @@ def init_command( source_name, remote_index, remote_modified, remote_deleted ) # create script - if not dest_storage.has_file(source_files.dest_pipeline_script): - dest_storage.save(source_files.dest_pipeline_script, dest_script_source) + if not dest_storage.has_file(source_configuration.dest_pipeline_script): + dest_storage.save(source_configuration.dest_pipeline_script, dest_script_source) # generate tomls with comments secrets_prov = SecretsTomlProvider() @@ -504,5 +616,5 @@ def init_command( # if there's no dependency system write the requirements file if dependency_system is None: - requirements_txt = "\n".join(source_files.requirements.compiled()) + requirements_txt = "\n".join(source_configuration.requirements.compiled()) dest_storage.save(utils.REQUIREMENTS_TXT, requirements_txt) diff --git a/dlt/cli/pipeline_files.py b/dlt/cli/pipeline_files.py index 49c0f71b21..6ca39e0195 100644 --- a/dlt/cli/pipeline_files.py +++ b/dlt/cli/pipeline_files.py @@ -4,7 +4,7 @@ import yaml import posixpath from pathlib import Path -from typing import Dict, NamedTuple, Sequence, Tuple, TypedDict, List +from typing import Dict, NamedTuple, Sequence, Tuple, TypedDict, List, Literal from dlt.cli.exceptions import VerifiedSourceRepoError from dlt.common import git @@ -16,21 +16,35 @@ from dlt.cli import utils from dlt.cli.requirements import SourceRequirements +TSourceType = Literal["core", "verified", "template"] SOURCES_INIT_INFO_ENGINE_VERSION = 1 SOURCES_INIT_INFO_FILE = ".sources" IGNORE_FILES = ["*.py[cod]", "*$py.class", "__pycache__", "py.typed", "requirements.txt"] -IGNORE_SOURCES = [".*", "_*"] - - -class VerifiedSourceFiles(NamedTuple): - is_template: bool +IGNORE_VERIFIED_SOURCES = [".*", "_*"] +IGNORE_CORE_SOURCES = [ + ".*", + "_*", + "helpers", + "pipeline_templates", +] +PIPELINE_FILE_SUFFIX = "_pipeline.py" + +# hardcode default template files here +TEMPLATE_FILES = [".gitignore", ".dlt/config.toml", ".dlt/secrets.toml"] +DEFAULT_PIPELINE_TEMPLATE = "default_pipeline.py" + + +class SourceConfiguration(NamedTuple): + source_type: TSourceType + source_module_prefix: str storage: FileStorage - pipeline_script: str + src_pipeline_script: str dest_pipeline_script: str files: List[str] requirements: SourceRequirements doc: str + is_default_template: bool class TVerifiedSourceFileEntry(TypedDict): @@ -147,22 +161,88 @@ def get_remote_source_index( } -def get_verified_source_names(sources_storage: FileStorage) -> List[str]: +def get_sources_names(sources_storage: FileStorage, source_type: TSourceType) -> List[str]: candidates: List[str] = [] - for name in [ - n - for n in sources_storage.list_folder_dirs(".", to_root=False) - if not any(fnmatch.fnmatch(n, ignore) for ignore in IGNORE_SOURCES) - ]: - # must contain at least one valid python script - if any(f.endswith(".py") for f in sources_storage.list_folder_files(name, to_root=False)): - candidates.append(name) + + # for the templates we just find all the filenames + if source_type == "template": + for name in sources_storage.list_folder_files(".", to_root=False): + if name.endswith(PIPELINE_FILE_SUFFIX): + candidates.append(name.replace(PIPELINE_FILE_SUFFIX, "")) + else: + ignore_cases = IGNORE_VERIFIED_SOURCES if source_type == "verified" else IGNORE_CORE_SOURCES + for name in [ + n + for n in sources_storage.list_folder_dirs(".", to_root=False) + if not any(fnmatch.fnmatch(n, ignore) for ignore in ignore_cases) + ]: + # must contain at least one valid python script + if any( + f.endswith(".py") for f in sources_storage.list_folder_files(name, to_root=False) + ): + candidates.append(name) + + candidates.sort() return candidates -def get_verified_source_files( +def _get_docstring_for_module(sources_storage: FileStorage, source_name: str) -> str: + # read the docs + init_py = os.path.join(source_name, utils.MODULE_INIT) + docstring: str = "" + if sources_storage.has_file(init_py): + docstring = get_module_docstring(sources_storage.load(init_py)) + if docstring: + docstring = docstring.splitlines()[0] + return docstring + + +def get_template_configuration( sources_storage: FileStorage, source_name: str -) -> VerifiedSourceFiles: +) -> SourceConfiguration: + destination_pipeline_file_name = source_name + PIPELINE_FILE_SUFFIX + source_pipeline_file_name = destination_pipeline_file_name + + if not sources_storage.has_file(source_pipeline_file_name): + source_pipeline_file_name = DEFAULT_PIPELINE_TEMPLATE + + docstring = get_module_docstring(sources_storage.load(source_pipeline_file_name)) + if docstring: + docstring = docstring.splitlines()[0] + return SourceConfiguration( + "template", + source_pipeline_file_name.replace("pipeline.py", ""), + sources_storage, + source_pipeline_file_name, + destination_pipeline_file_name, + TEMPLATE_FILES, + SourceRequirements([]), + docstring, + source_pipeline_file_name == DEFAULT_PIPELINE_TEMPLATE, + ) + + +def get_core_source_configuration( + sources_storage: FileStorage, source_name: str +) -> SourceConfiguration: + pipeline_file = source_name + "_pipeline.py" + + return SourceConfiguration( + "core", + "dlt.sources." + source_name, + sources_storage, + pipeline_file, + pipeline_file, + [".gitignore"], + SourceRequirements([]), + _get_docstring_for_module(sources_storage, source_name), + False, + ) + + +def get_verified_source_configuration( + sources_storage: FileStorage, source_name: str +) -> SourceConfiguration: if not sources_storage.has_folder(source_name): raise VerifiedSourceRepoError( f"Verified source {source_name} could not be found in the repository", source_name @@ -189,13 +269,6 @@ def get_verified_source_files( if all(not fnmatch.fnmatch(file, ignore) for ignore in IGNORE_FILES) ] ) - # read the docs - init_py = os.path.join(source_name, utils.MODULE_INIT) - docstring: str = "" - if sources_storage.has_file(init_py): - docstring = get_module_docstring(sources_storage.load(init_py)) - if docstring: - docstring = docstring.splitlines()[0] # read requirements requirements_path = os.path.join(source_name, utils.REQUIREMENTS_TXT) if sources_storage.has_file(requirements_path): @@ -203,8 +276,16 @@ def get_verified_source_files( else: requirements = SourceRequirements([]) # find requirements - return VerifiedSourceFiles( - False, sources_storage, example_script, example_script, files, requirements, docstring + return SourceConfiguration( + "verified", + source_name, + sources_storage, + example_script, + example_script, + files, + requirements, + _get_docstring_for_module(sources_storage, source_name), + False, ) diff --git a/dlt/common/configuration/container.py b/dlt/common/configuration/container.py index 84d6194966..d6b67b6e62 100644 --- a/dlt/common/configuration/container.py +++ b/dlt/common/configuration/container.py @@ -1,7 +1,7 @@ from contextlib import contextmanager, nullcontext, AbstractContextManager import re import threading -from typing import ClassVar, Dict, Iterator, Tuple, Type, TypeVar, Any +from typing import ClassVar, Dict, Iterator, Optional, Tuple, Type, TypeVar, Any from dlt.common.configuration.specs.base_configuration import ContainerInjectableContext from dlt.common.configuration.exceptions import ( @@ -171,6 +171,12 @@ def injectable_context( # value was modified in the meantime and not restored raise ContainerInjectableContextMangled(spec, context[spec], config) + def get(self, spec: Type[TConfiguration]) -> Optional[TConfiguration]: + try: + return self[spec] + except KeyError: + return None + @staticmethod def thread_pool_prefix() -> str: """Creates a container friendly pool prefix that contains starting thread id. Container implementation will automatically use it diff --git a/dlt/common/configuration/specs/__init__.py b/dlt/common/configuration/specs/__init__.py index f1d7d819ff..179445dde3 100644 --- a/dlt/common/configuration/specs/__init__.py +++ b/dlt/common/configuration/specs/__init__.py @@ -28,6 +28,7 @@ AnyAzureCredentials, ) +from .sftp_crendentials import SFTPCredentials # backward compatibility for service account credentials from .gcp_credentials import ( @@ -62,4 +63,5 @@ "AnyAzureCredentials", "GcpClientCredentials", "GcpClientCredentialsWithDefault", + "SFTPCredentials", ] diff --git a/dlt/common/configuration/specs/connection_string_credentials.py b/dlt/common/configuration/specs/connection_string_credentials.py index 5b9a4587c7..5d3ec689c4 100644 --- a/dlt/common/configuration/specs/connection_string_credentials.py +++ b/dlt/common/configuration/specs/connection_string_credentials.py @@ -1,7 +1,7 @@ import dataclasses from typing import Any, ClassVar, Dict, List, Optional, Union -from dlt.common.libs.sql_alchemy import URL, make_url +from dlt.common.libs.sql_alchemy_shims import URL, make_url from dlt.common.configuration.specs.exceptions import InvalidConnectionString from dlt.common.typing import TSecretValue from dlt.common.configuration.specs.base_configuration import CredentialsConfiguration, configspec diff --git a/dlt/common/configuration/specs/sftp_crendentials.py b/dlt/common/configuration/specs/sftp_crendentials.py new file mode 100644 index 0000000000..92f83cc438 --- /dev/null +++ b/dlt/common/configuration/specs/sftp_crendentials.py @@ -0,0 +1,69 @@ +from typing import Any, Dict, Optional + +from dlt.common.typing import TSecretStrValue, DictStrAny +from dlt.common.configuration.specs.base_configuration import CredentialsConfiguration, configspec + + +@configspec +class SFTPCredentials(CredentialsConfiguration): + """Credentials for SFTP filesystem, compatible with fsspec SFTP protocol. + + Authentication is attempted in the following order of priority: + + - `key_filename` may contain OpenSSH public certificate paths + as well as regular private-key paths; when files ending in `-cert.pub` are found, they are assumed to match + a private key, and both components will be loaded. + + - Any key found through an SSH agent: any “id_rsa”, “id_dsa”, or “id_ecdsa” key discoverable in ~/.ssh/. + + - Plain username/password authentication, if a password was provided. + + - If a private key requires a password to unlock it, and a password is provided, that password will be used to + attempt to unlock the key. + + For more information about parameters: + https://docs.paramiko.org/en/3.3/api/client.html#paramiko.client.SSHClient.connect + """ + + sftp_port: Optional[int] = 22 + sftp_username: Optional[str] = None + sftp_password: Optional[TSecretStrValue] = None + sftp_key_filename: Optional[str] = None + sftp_key_passphrase: Optional[TSecretStrValue] = None + sftp_timeout: Optional[float] = None + sftp_banner_timeout: Optional[float] = None + sftp_auth_timeout: Optional[float] = None + sftp_channel_timeout: Optional[float] = None + sftp_allow_agent: Optional[bool] = True + sftp_look_for_keys: Optional[bool] = True + sftp_compress: Optional[bool] = False + sftp_gss_auth: Optional[bool] = False + sftp_gss_kex: Optional[bool] = False + sftp_gss_deleg_creds: Optional[bool] = True + sftp_gss_host: Optional[str] = None + sftp_gss_trust_dns: Optional[bool] = True + + def to_fsspec_credentials(self) -> Dict[str, Any]: + """Return a dict that can be passed to fsspec SFTP/SSHClient.connect method.""" + + credentials: Dict[str, Any] = { + "port": self.sftp_port, + "username": self.sftp_username, + "password": self.sftp_password, + "key_filename": self.sftp_key_filename, + "passphrase": self.sftp_key_passphrase, + "timeout": self.sftp_timeout, + "banner_timeout": self.sftp_banner_timeout, + "auth_timeout": self.sftp_auth_timeout, + "channel_timeout": self.sftp_channel_timeout, + "allow_agent": self.sftp_allow_agent, + "look_for_keys": self.sftp_look_for_keys, + "compress": self.sftp_compress, + "gss_auth": self.sftp_gss_auth, + "gss_kex": self.sftp_gss_kex, + "gss_deleg_creds": self.sftp_gss_deleg_creds, + "gss_host": self.sftp_gss_host, + "gss_trust_dns": self.sftp_gss_trust_dns, + } + + return credentials diff --git a/dlt/common/configuration/utils.py b/dlt/common/configuration/utils.py index bc52241a26..450dde29df 100644 --- a/dlt/common/configuration/utils.py +++ b/dlt/common/configuration/utils.py @@ -77,8 +77,8 @@ def deserialize_value(key: str, value: Any, hint: Type[TAny]) -> TAny: hint_dt = py_type_to_sc_type(hint_origin) value_dt = py_type_to_sc_type(type(value)) - # eval only if value is string and hint is "complex" - if value_dt == "text" and hint_dt == "complex": + # eval only if value is string and hint is "json" + if value_dt == "text" and hint_dt == "json": if hint_origin is tuple: # use literal eval for tuples value = ast.literal_eval(value) @@ -89,7 +89,7 @@ def deserialize_value(key: str, value: Any, hint: Type[TAny]) -> TAny: if not isinstance(value, hint_origin): raise ValueError(value) else: - # for types that are not complex, reuse schema coercion rules + # for types that are not nested, reuse schema coercion rules if value_dt != hint_dt: value = coerce_value(hint_dt, value_dt, value) if literal_values and value not in literal_values: @@ -119,7 +119,7 @@ def serialize_value(value: Any) -> str: def auto_cast(value: str) -> Any: - """Parse and cast str `value` to bool, int, float and complex (via JSON) + """Parse and cast str `value` to bool, int, float and json F[f]alse and T[t]rue strings are cast to bool values """ diff --git a/dlt/common/data_types/type_helpers.py b/dlt/common/data_types/type_helpers.py index d8ab9eb118..61886563de 100644 --- a/dlt/common/data_types/type_helpers.py +++ b/dlt/common/data_types/type_helpers.py @@ -31,7 +31,7 @@ def py_type_to_sc_type(t: Type[Any]) -> TDataType: if t is int: return "bigint" if issubclass(t, (dict, list)): - return "complex" + return "json" # those are special types that will not be present in json loaded dict # wei is subclass of decimal and must be checked first @@ -56,7 +56,7 @@ def py_type_to_sc_type(t: Type[Any]) -> TDataType: if issubclass(t, bytes): return "binary" if dataclasses.is_dataclass(t) or issubclass(t, (C_Mapping, C_Sequence)): - return "complex" + return "json" # Enum is coerced to str or int respectively if issubclass(t, Enum): if issubclass(t, int): @@ -68,7 +68,7 @@ def py_type_to_sc_type(t: Type[Any]) -> TDataType: raise TypeError(t) -def complex_to_str(value: Any) -> str: +def json_to_str(value: Any) -> str: return json.dumps(map_nested_in_place(custom_pua_remove, value)) @@ -93,8 +93,8 @@ def coerce_from_date_types( def coerce_value(to_type: TDataType, from_type: TDataType, value: Any) -> Any: if to_type == from_type: - if to_type == "complex": - # complex types need custom encoding to be removed + if to_type == "json": + # nested types need custom encoding to be removed return map_nested_in_place(custom_pua_remove, value) # Make sure we use enum value instead of the object itself # This check is faster than `isinstance(value, Enum)` for non-enum types @@ -105,7 +105,7 @@ def coerce_value(to_type: TDataType, from_type: TDataType, value: Any) -> Any: return int(value.value) return value - if to_type == "complex": + if to_type == "json": # try to coerce from text if from_type == "text": try: @@ -114,8 +114,8 @@ def coerce_value(to_type: TDataType, from_type: TDataType, value: Any) -> Any: raise ValueError(value) if to_type == "text": - if from_type == "complex": - return complex_to_str(value) + if from_type == "json": + return json_to_str(value) else: # use the same string encoding as in json try: @@ -194,7 +194,7 @@ def coerce_value(to_type: TDataType, from_type: TDataType, value: Any) -> Any: if to_type == "bool": if from_type == "text": return str2bool(value) - if from_type not in ["complex", "binary", "timestamp"]: + if from_type not in ["json", "binary", "timestamp"]: # all the numeric types will convert to bool on 0 - False, 1 - True return bool(value) diff --git a/dlt/common/data_types/typing.py b/dlt/common/data_types/typing.py index d061b28df0..3d56c6131a 100644 --- a/dlt/common/data_types/typing.py +++ b/dlt/common/data_types/typing.py @@ -8,7 +8,7 @@ "timestamp", "bigint", "binary", - "complex", + "json", "decimal", "wei", "date", diff --git a/dlt/common/data_writers/buffered.py b/dlt/common/data_writers/buffered.py index 945fca6580..e2b6c9a442 100644 --- a/dlt/common/data_writers/buffered.py +++ b/dlt/common/data_writers/buffered.py @@ -45,7 +45,7 @@ def __init__( file_max_items: int = None, file_max_bytes: int = None, disable_compression: bool = False, - _caps: DestinationCapabilitiesContext = None + _caps: DestinationCapabilitiesContext = None, ): self.writer_spec = writer_spec if self.writer_spec.requires_destination_capabilities and not _caps: @@ -99,29 +99,17 @@ def write_data_item(self, item: TDataItems, columns: TTableSchemaColumns) -> int # until the first chunk is written we can change the columns schema freely if columns is not None: self._current_columns = dict(columns) - - new_rows_count: int - if isinstance(item, List): - # items coming in single list will be written together, not matter how many are there - self._buffered_items.extend(item) - # update row count, if item supports "num_rows" it will be used to count items - if len(item) > 0 and hasattr(item[0], "num_rows"): - new_rows_count = sum(tbl.num_rows for tbl in item) - else: - new_rows_count = len(item) - else: - self._buffered_items.append(item) - # update row count, if item supports "num_rows" it will be used to count items - if hasattr(item, "num_rows"): - new_rows_count = item.num_rows - else: - new_rows_count = 1 + # add item to buffer and count new rows + new_rows_count = self._buffer_items_with_row_count(item) self._buffered_items_count += new_rows_count - # flush if max buffer exceeded - if self._buffered_items_count >= self.buffer_max_items: - self._flush_items() # set last modification date self._last_modified = time.time() + # flush if max buffer exceeded, the second path of the expression prevents empty data frames to pile up in the buffer + if ( + self._buffered_items_count >= self.buffer_max_items + or len(self._buffered_items) >= self.buffer_max_items + ): + self._flush_items() # rotate the file if max_bytes exceeded if self._file: # rotate on max file size @@ -218,6 +206,26 @@ def __exit__(self, exc_type: Type[BaseException], exc_val: BaseException, exc_tb if not in_exception: raise + def _buffer_items_with_row_count(self, item: TDataItems) -> int: + """Adds `item` to in-memory buffer and counts new rows, depending in item type""" + new_rows_count: int + if isinstance(item, List): + # update row count, if item supports "num_rows" it will be used to count items + if len(item) > 0 and hasattr(item[0], "num_rows"): + new_rows_count = sum(tbl.num_rows for tbl in item) + else: + new_rows_count = len(item) + # items coming in a single list will be written together, no matter how many there are + self._buffered_items.extend(item) + else: + self._buffered_items.append(item) + # update row count, if item supports "num_rows" it will be used to count items + if hasattr(item, "num_rows"): + new_rows_count = item.num_rows + else: + new_rows_count = 1 + return new_rows_count + def _rotate_file(self, allow_empty_file: bool = False) -> DataWriterMetrics: metrics = self._flush_and_close_file(allow_empty_file) self._file_name = ( diff --git a/dlt/common/data_writers/exceptions.py b/dlt/common/data_writers/exceptions.py index 3b11ed70fc..cc63fdf9a8 100644 --- a/dlt/common/data_writers/exceptions.py +++ b/dlt/common/data_writers/exceptions.py @@ -1,4 +1,5 @@ from typing import NamedTuple, Sequence + from dlt.common.destination import TLoaderFileFormat from dlt.common.exceptions import DltException diff --git a/dlt/common/data_writers/writers.py b/dlt/common/data_writers/writers.py index abd3343ea1..d6be15abdd 100644 --- a/dlt/common/data_writers/writers.py +++ b/dlt/common/data_writers/writers.py @@ -32,11 +32,12 @@ from dlt.common.destination import ( DestinationCapabilitiesContext, TLoaderFileFormat, - ALL_SUPPORTED_FILE_FORMATS, + LOADER_FILE_FORMATS, ) from dlt.common.metrics import DataWriterMetrics from dlt.common.schema.typing import TTableSchemaColumns -from dlt.common.typing import StrAny +from dlt.common.schema.utils import is_nullable_column +from dlt.common.typing import StrAny, TDataItem if TYPE_CHECKING: @@ -72,8 +73,8 @@ def __init__(self, f: IO[Any], caps: DestinationCapabilitiesContext = None) -> N def write_header(self, columns_schema: TTableSchemaColumns) -> None: # noqa pass - def write_data(self, rows: Sequence[Any]) -> None: - self.items_count += len(rows) + def write_data(self, items: Sequence[TDataItem]) -> None: + self.items_count += len(items) def write_footer(self) -> None: # noqa pass @@ -81,9 +82,9 @@ def write_footer(self) -> None: # noqa def close(self) -> None: # noqa pass - def write_all(self, columns_schema: TTableSchemaColumns, rows: Sequence[Any]) -> None: + def write_all(self, columns_schema: TTableSchemaColumns, items: Sequence[TDataItem]) -> None: self.write_header(columns_schema) - self.write_data(rows) + self.write_data(items) self.write_footer() @classmethod @@ -115,7 +116,7 @@ def item_format_from_file_extension(cls, extension: str) -> TDataItemFormat: elif extension == "parquet": return "arrow" # those files may be imported by normalizer as is - elif extension in ALL_SUPPORTED_FILE_FORMATS: + elif extension in LOADER_FILE_FORMATS: return "file" else: raise ValueError(f"Cannot figure out data item format for extension {extension}") @@ -156,9 +157,9 @@ def writer_spec(cls) -> FileWriterSpec: class JsonlWriter(DataWriter): - def write_data(self, rows: Sequence[Any]) -> None: - super().write_data(rows) - for row in rows: + def write_data(self, items: Sequence[TDataItem]) -> None: + super().write_data(items) + for row in items: json.dump(row, self._f) self._f.write(b"\n") @@ -175,12 +176,12 @@ def writer_spec(cls) -> FileWriterSpec: class TypedJsonlListWriter(JsonlWriter): - def write_data(self, rows: Sequence[Any]) -> None: + def write_data(self, items: Sequence[TDataItem]) -> None: # skip JsonlWriter when calling super - super(JsonlWriter, self).write_data(rows) + super(JsonlWriter, self).write_data(items) # write all rows as one list which will require to write just one line # encode types with PUA characters - json.typed_dump(rows, self._f) + json.typed_dump(items, self._f) self._f.write(b"\n") @classmethod @@ -222,11 +223,11 @@ def write_header(self, columns_schema: TTableSchemaColumns) -> None: if self.writer_type == "default": self._f.write("VALUES\n") - def write_data(self, rows: Sequence[Any]) -> None: - super().write_data(rows) + def write_data(self, items: Sequence[TDataItem]) -> None: + super().write_data(items) # do not write empty rows, such things may be produced by Arrow adapters - if len(rows) == 0: + if len(items) == 0: return def write_row(row: StrAny, last_row: bool = False) -> None: @@ -244,11 +245,11 @@ def write_row(row: StrAny, last_row: bool = False) -> None: self._f.write(self.sep) # write rows - for row in rows[:-1]: + for row in items[:-1]: write_row(row) # write last row without separator so we can write footer eventually - write_row(rows[-1], last_row=True) + write_row(items[-1], last_row=True) self._chunks_written += 1 def write_footer(self) -> None: @@ -288,7 +289,7 @@ def __init__( self.writer: Optional[pyarrow.parquet.ParquetWriter] = None self.schema: Optional[pyarrow.Schema] = None - self.complex_indices: List[str] = None + self.nested_indices: List[str] = None self.parquet_flavor = flavor self.parquet_version = version self.parquet_data_page_size = data_page_size @@ -331,30 +332,30 @@ def write_header(self, columns_schema: TTableSchemaColumns) -> None: self._caps, self.timestamp_timezone, ), - nullable=schema_item.get("nullable", True), + nullable=is_nullable_column(schema_item), ) for name, schema_item in columns_schema.items() ] ) - # find row items that are of the complex type (could be abstracted out for use in other writers?) - self.complex_indices = [ - i for i, field in columns_schema.items() if field["data_type"] == "complex" + # find row items that are of the json type (could be abstracted out for use in other writers?) + self.nested_indices = [ + i for i, field in columns_schema.items() if field["data_type"] == "json" ] self.writer = self._create_writer(self.schema) - def write_data(self, rows: Sequence[Any]) -> None: - super().write_data(rows) + def write_data(self, items: Sequence[TDataItem]) -> None: + super().write_data(items) from dlt.common.libs.pyarrow import pyarrow - # replace complex types with json - for key in self.complex_indices: - for row in rows: + # serialize json types and replace with strings + for key in self.nested_indices: + for row in items: if (value := row.get(key)) is not None: # TODO: make this configurable if value is not None and not isinstance(value, str): row[key] = json.dumps(value) - table = pyarrow.Table.from_pylist(rows, schema=self.schema) + table = pyarrow.Table.from_pylist(items, schema=self.schema) # Write self.writer.write_table(table, row_group_size=self.parquet_row_group_size) @@ -414,20 +415,20 @@ def write_header(self, columns_schema: TTableSchemaColumns) -> None: ) if self.include_header: self.writer.writeheader() - # find row items that are of the complex type (could be abstracted out for use in other writers?) - self.complex_indices = [ - i for i, field in columns_schema.items() if field["data_type"] == "complex" + # find row items that are of the json type + self.nested_indices = [ + i for i, field in columns_schema.items() if field["data_type"] == "json" ] - # find row items that are of the complex type (could be abstracted out for use in other writers?) + # find row items that are of the binary type self.bytes_indices = [ i for i, field in columns_schema.items() if field["data_type"] == "binary" ] - def write_data(self, rows: Sequence[Any]) -> None: + def write_data(self, items: Sequence[TDataItem]) -> None: # convert bytes and json - if self.complex_indices or self.bytes_indices: - for row in rows: - for key in self.complex_indices: + if self.nested_indices or self.bytes_indices: + for row in items: + for key in self.nested_indices: if (value := row.get(key)) is not None: row[key] = json.dumps(value) for key in self.bytes_indices: @@ -445,9 +446,9 @@ def write_data(self, rows: Sequence[Any]) -> None: " type as binary.", ) - self.writer.writerows(rows) + self.writer.writerows(items) # count rows that got written - self.items_count += sum(len(row) for row in rows) + self.items_count += sum(len(row) for row in items) def close(self) -> None: self.writer = None @@ -471,20 +472,21 @@ def write_header(self, columns_schema: TTableSchemaColumns) -> None: # Schema will be written as-is from the arrow table self._column_schema = columns_schema - def write_data(self, rows: Sequence[Any]) -> None: - from dlt.common.libs.pyarrow import pyarrow + def write_data(self, items: Sequence[TDataItem]) -> None: + from dlt.common.libs.pyarrow import concat_batches_and_tables_in_order - for row in rows: - if not self.writer: - self.writer = self._create_writer(row.schema) - if isinstance(row, pyarrow.Table): - self.writer.write_table(row, row_group_size=self.parquet_row_group_size) - elif isinstance(row, pyarrow.RecordBatch): - self.writer.write_batch(row, row_group_size=self.parquet_row_group_size) - else: - raise ValueError(f"Unsupported type {type(row)}") - # count rows that got written - self.items_count += row.num_rows + if not items: + return + # concat batches and tables into a single one, preserving order + # pyarrow writer starts a row group for each item it writes (even with 0 rows) + # it also converts batches into tables internally. by creating a single table + # we allow the user rudimentary control over row group size via max buffered items + table = concat_batches_and_tables_in_order(items) + self.items_count += table.num_rows + if not self.writer: + self.writer = self._create_writer(table.schema) + # write concatenated tables + self.writer.write_table(table, row_group_size=self.parquet_row_group_size) def write_footer(self) -> None: if not self.writer: @@ -528,12 +530,12 @@ def __init__( def write_header(self, columns_schema: TTableSchemaColumns) -> None: self._columns_schema = columns_schema - def write_data(self, rows: Sequence[Any]) -> None: + def write_data(self, items: Sequence[TDataItem]) -> None: from dlt.common.libs.pyarrow import pyarrow import pyarrow.csv - for row in rows: - if isinstance(row, (pyarrow.Table, pyarrow.RecordBatch)): + for item in items: + if isinstance(item, (pyarrow.Table, pyarrow.RecordBatch)): if not self.writer: if self.quoting == "quote_needed": quoting = "needed" @@ -544,14 +546,14 @@ def write_data(self, rows: Sequence[Any]) -> None: try: self.writer = pyarrow.csv.CSVWriter( self._f, - row.schema, + item.schema, write_options=pyarrow.csv.WriteOptions( include_header=self.include_header, delimiter=self._delimiter_b, quoting_style=quoting, ), ) - self._first_schema = row.schema + self._first_schema = item.schema except pyarrow.ArrowInvalid as inv_ex: if "Unsupported Type" in str(inv_ex): raise InvalidDataItem( @@ -563,18 +565,18 @@ def write_data(self, rows: Sequence[Any]) -> None: ) raise # make sure that Schema stays the same - if not row.schema.equals(self._first_schema): + if not item.schema.equals(self._first_schema): raise InvalidDataItem( "csv", "arrow", "Arrow schema changed without rotating the file. This may be internal" " error or misuse of the writer.\nFirst" - f" schema:\n{self._first_schema}\n\nCurrent schema:\n{row.schema}", + f" schema:\n{self._first_schema}\n\nCurrent schema:\n{item.schema}", ) # write headers only on the first write try: - self.writer.write(row) + self.writer.write(item) except pyarrow.ArrowInvalid as inv_ex: if "Invalid UTF8 payload" in str(inv_ex): raise InvalidDataItem( @@ -595,9 +597,9 @@ def write_data(self, rows: Sequence[Any]) -> None: ) raise else: - raise ValueError(f"Unsupported type {type(row)}") + raise ValueError(f"Unsupported type {type(item)}") # count rows that got written - self.items_count += row.num_rows + self.items_count += item.num_rows def write_footer(self) -> None: if self.writer is None and self.include_header: @@ -633,8 +635,8 @@ def writer_spec(cls) -> FileWriterSpec: class ArrowToObjectAdapter: """A mixin that will convert object writer into arrow writer.""" - def write_data(self, rows: Sequence[Any]) -> None: - for batch in rows: + def write_data(self, items: Sequence[TDataItem]) -> None: + for batch in items: # convert to object data item format super().write_data(batch.to_pylist()) # type: ignore[misc] diff --git a/dlt/common/destination/__init__.py b/dlt/common/destination/__init__.py index b7b98416a6..2f50b3e3d2 100644 --- a/dlt/common/destination/__init__.py +++ b/dlt/common/destination/__init__.py @@ -2,15 +2,17 @@ DestinationCapabilitiesContext, merge_caps_file_formats, TLoaderFileFormat, - ALL_SUPPORTED_FILE_FORMATS, + LOADER_FILE_FORMATS, ) from dlt.common.destination.reference import TDestinationReferenceArg, Destination, TDestination +from dlt.common.destination.typing import PreparedTableSchema __all__ = [ "DestinationCapabilitiesContext", "merge_caps_file_formats", "TLoaderFileFormat", - "ALL_SUPPORTED_FILE_FORMATS", + "LOADER_FILE_FORMATS", + "PreparedTableSchema", "TDestinationReferenceArg", "Destination", "TDestination", diff --git a/dlt/common/destination/capabilities.py b/dlt/common/destination/capabilities.py index 52e7d74833..8f0dce79ce 100644 --- a/dlt/common/destination/capabilities.py +++ b/dlt/common/destination/capabilities.py @@ -1,15 +1,20 @@ +from abc import ABC, abstractmethod from typing import ( Any, Callable, ClassVar, + Iterable, Literal, Optional, Sequence, Tuple, Set, Protocol, + Type, get_args, ) +from dlt.common.data_types import TDataType +from dlt.common.exceptions import TerminalValueError from dlt.common.normalizers.typing import TNamingConventionReferenceArg from dlt.common.typing import TLoaderFileFormat from dlt.common.configuration.utils import serialize_value @@ -20,36 +25,109 @@ DestinationLoadingViaStagingNotSupported, DestinationLoadingWithoutStagingNotSupported, ) -from dlt.common.normalizers.naming import NamingConvention +from dlt.common.destination.typing import PreparedTableSchema from dlt.common.arithmetics import DEFAULT_NUMERIC_PRECISION, DEFAULT_NUMERIC_SCALE +from dlt.common.schema.typing import ( + TColumnSchema, + TColumnType, + TTableSchema, + TLoaderMergeStrategy, + TTableFormat, +) from dlt.common.wei import EVM_DECIMAL_PRECISION TLoaderParallelismStrategy = Literal["parallel", "table-sequential", "sequential"] -ALL_SUPPORTED_FILE_FORMATS: Set[TLoaderFileFormat] = set(get_args(TLoaderFileFormat)) +LOADER_FILE_FORMATS: Set[TLoaderFileFormat] = set(get_args(TLoaderFileFormat)) -class LoaderFileFormatAdapter(Protocol): - """Callback protocol for `loader_file_format_adapter` capability.""" +class LoaderFileFormatSelector(Protocol): + """Selects preferred and supported file formats for a given table schema""" + @staticmethod def __call__( - self, preferred_loader_file_format: TLoaderFileFormat, supported_loader_file_formats: Sequence[TLoaderFileFormat], /, *, - table_schema: "TTableSchema", # type: ignore[name-defined] # noqa: F821 + table_schema: TTableSchema, ) -> Tuple[TLoaderFileFormat, Sequence[TLoaderFileFormat]]: ... +class MergeStrategySelector(Protocol): + """Selects right set of merge strategies for a given table schema""" + + @staticmethod + def __call__( + supported_merge_strategies: Sequence[TLoaderMergeStrategy], + /, + *, + table_schema: TTableSchema, + ) -> Sequence["TLoaderMergeStrategy"]: ... + + +class DataTypeMapper(ABC): + def __init__(self, capabilities: "DestinationCapabilitiesContext") -> None: + """Maps dlt data types into destination data types""" + self.capabilities = capabilities + + @abstractmethod + def to_destination_type(self, column: TColumnSchema, table: PreparedTableSchema) -> str: + """Gets destination data type for a particular `column` in prepared `table`""" + pass + + @abstractmethod + def from_destination_type( + self, db_type: str, precision: Optional[int], scale: Optional[int] + ) -> TColumnType: + """Gets column type from db type""" + pass + + @abstractmethod + def ensure_supported_type( + self, + column: TColumnSchema, + table: PreparedTableSchema, + loader_file_format: TLoaderFileFormat, + ) -> None: + """Makes sure that dlt type in `column` in prepared `table` is supported by the destination for a given file format""" + pass + + +class UnsupportedTypeMapper(DataTypeMapper): + """Type Mapper that can't map any type""" + + def to_destination_type(self, column: TColumnSchema, table: PreparedTableSchema) -> str: + raise NotImplementedError("No types are supported, use real type mapper") + + def from_destination_type( + self, db_type: str, precision: Optional[int], scale: Optional[int] + ) -> TColumnType: + raise NotImplementedError("No types are supported, use real type mapper") + + def ensure_supported_type( + self, + column: TColumnSchema, + table: PreparedTableSchema, + loader_file_format: TLoaderFileFormat, + ) -> None: + raise TerminalValueError( + "No types are supported, use real type mapper", column["data_type"] + ) + + @configspec class DestinationCapabilitiesContext(ContainerInjectableContext): """Injectable destination capabilities required for many Pipeline stages ie. normalize""" + # do not allow to create default value, destination caps must be always explicitly inserted into container + can_create_default: ClassVar[bool] = False + preferred_loader_file_format: TLoaderFileFormat = None supported_loader_file_formats: Sequence[TLoaderFileFormat] = None - loader_file_format_adapter: LoaderFileFormatAdapter = None + loader_file_format_selector: LoaderFileFormatSelector = None """Callable that adapts `preferred_loader_file_format` and `supported_loader_file_formats` at runtime.""" - supported_table_formats: Sequence["TTableFormat"] = None # type: ignore[name-defined] # noqa: F821 + supported_table_formats: Sequence[TTableFormat] = None + type_mapper: Optional[Type[DataTypeMapper]] = None recommended_file_size: Optional[int] = None """Recommended file size in bytes when writing extract/load files""" preferred_staging_file_format: Optional[TLoaderFileFormat] = None @@ -89,17 +167,21 @@ class DestinationCapabilitiesContext(ContainerInjectableContext): max_table_nesting: Optional[int] = None """Allows a destination to overwrite max_table_nesting from source""" - supported_merge_strategies: Sequence["TLoaderMergeStrategy"] = None # type: ignore[name-defined] # noqa: F821 + supported_merge_strategies: Sequence[TLoaderMergeStrategy] = None + merge_strategies_selector: MergeStrategySelector = None # TODO: also add `supported_replace_strategies` capability - # do not allow to create default value, destination caps must be always explicitly inserted into container - can_create_default: ClassVar[bool] = False - max_parallel_load_jobs: Optional[int] = None - """The destination can set the maxium amount of parallel load jobs being executed""" + """The destination can set the maximum amount of parallel load jobs being executed""" loader_parallelism_strategy: Optional[TLoaderParallelismStrategy] = None """The destination can override the parallelism strategy""" + max_query_parameters: Optional[int] = None + """The maximum number of parameters that can be supplied in a single parametrized query""" + + supports_native_boolean: bool = True + """The destination supports a native boolean type, otherwise bool columns are usually stored as integers""" + def generates_case_sensitive_identifiers(self) -> bool: """Tells if capabilities as currently adjusted, will generate case sensitive identifiers""" # must have case sensitive support and folding function must preserve casing @@ -109,16 +191,17 @@ def generates_case_sensitive_identifiers(self) -> bool: def generic_capabilities( preferred_loader_file_format: TLoaderFileFormat = None, naming_convention: TNamingConventionReferenceArg = None, - loader_file_format_adapter: LoaderFileFormatAdapter = None, - supported_table_formats: Sequence["TTableFormat"] = None, # type: ignore[name-defined] # noqa: F821 - supported_merge_strategies: Sequence["TLoaderMergeStrategy"] = None, # type: ignore[name-defined] # noqa: F821 + loader_file_format_selector: LoaderFileFormatSelector = None, + supported_table_formats: Sequence[TTableFormat] = None, + supported_merge_strategies: Sequence[TLoaderMergeStrategy] = None, + merge_strategies_selector: MergeStrategySelector = None, ) -> "DestinationCapabilitiesContext": from dlt.common.data_writers.escape import format_datetime_literal caps = DestinationCapabilitiesContext() caps.preferred_loader_file_format = preferred_loader_file_format caps.supported_loader_file_formats = ["jsonl", "insert_values", "parquet", "csv"] - caps.loader_file_format_adapter = loader_file_format_adapter + caps.loader_file_format_selector = loader_file_format_selector caps.preferred_staging_file_format = None caps.supported_staging_file_formats = [] caps.naming_convention = naming_convention or caps.naming_convention @@ -140,8 +223,12 @@ def generic_capabilities( caps.supports_transactions = True caps.supports_multiple_statements = True caps.supported_merge_strategies = supported_merge_strategies or [] + caps.merge_strategies_selector = merge_strategies_selector return caps + def get_type_mapper(self, *args: Any, **kwargs: Any) -> DataTypeMapper: + return self.type_mapper(self, *args, **kwargs) + def merge_caps_file_formats( destination: str, diff --git a/dlt/common/destination/exceptions.py b/dlt/common/destination/exceptions.py index 49c9b822e3..50796998ad 100644 --- a/dlt/common/destination/exceptions.py +++ b/dlt/common/destination/exceptions.py @@ -1,4 +1,4 @@ -from typing import Any, Iterable, List +from typing import Any, Iterable, List, Sequence from dlt.common.exceptions import DltException, TerminalException, TransientException @@ -102,6 +102,37 @@ def __init__( ) +class UnsupportedDataType(DestinationTerminalException): + def __init__( + self, + destination_type: str, + table_name: str, + column: str, + data_type: str, + file_format: str, + available_in_formats: Sequence[str], + more_info: str, + ) -> None: + self.destination_type = destination_type + self.table_name = table_name + self.column = column + self.data_type = data_type + self.file_format = file_format + self.available_in_formats = available_in_formats + self.more_info = more_info + msg = ( + f"Destination {destination_type} cannot load data type '{data_type}' from" + f" '{file_format}' files. The affected table is '{table_name}' column '{column}'." + ) + if available_in_formats: + msg += f" Note: '{data_type}' can be loaded from {available_in_formats} formats(s)." + else: + msg += f" None of available file formats support '{data_type}' for this destination." + if more_info: + msg += " More info: " + more_info + super().__init__(msg) + + class DestinationHasFailedJobs(DestinationTerminalException): def __init__(self, destination_name: str, load_id: str, failed_jobs: List[Any]) -> None: self.destination_name = destination_name diff --git a/dlt/common/destination/reference.py b/dlt/common/destination/reference.py index e7bba266df..9e27b66335 100644 --- a/dlt/common/destination/reference.py +++ b/dlt/common/destination/reference.py @@ -21,22 +21,18 @@ ) from typing_extensions import Annotated import datetime # noqa: 251 -from copy import deepcopy import inspect from dlt.common import logger, pendulum from dlt.common.configuration.specs.base_configuration import extract_inner_hint -from dlt.common.destination.utils import verify_schema_capabilities +from dlt.common.destination.typing import PreparedTableSchema +from dlt.common.destination.utils import verify_schema_capabilities, verify_supported_data_types from dlt.common.exceptions import TerminalValueError from dlt.common.metrics import LoadJobMetrics from dlt.common.normalizers.naming import NamingConvention -from dlt.common.schema import Schema, TTableSchema, TSchemaTables -from dlt.common.schema.utils import ( - get_file_format, - get_write_disposition, - get_table_format, - get_merge_strategy, -) +from dlt.common.schema import Schema, TSchemaTables +from dlt.common.schema.typing import C_DLT_LOAD_ID, _TTableSchemaBase, TWriteDisposition +from dlt.common.schema.utils import fill_hints_from_parent_and_clone_table from dlt.common.configuration import configspec, resolve_configuration, known_sections, NotResolved from dlt.common.configuration.specs import BaseConfiguration, CredentialsConfiguration from dlt.common.destination.capabilities import DestinationCapabilitiesContext @@ -90,6 +86,20 @@ def from_normalized_mapping( schema=normalized_doc[naming_convention.normalize_identifier("schema")], ) + def to_normalized_mapping(self, naming_convention: NamingConvention) -> Dict[str, Any]: + """Convert this instance to mapping where keys are normalized according to given naming convention + + Args: + naming_convention: Naming convention that should be used to normalize keys + + Returns: + Dict[str, Any]: Mapping with normalized keys (e.g. {Version: ..., SchemaName: ...}) + """ + return { + naming_convention.normalize_identifier(key): value + for key, value in self._asdict().items() + } + @dataclasses.dataclass class StateInfo: @@ -104,7 +114,7 @@ class StateInfo: def as_doc(self) -> TPipelineStateDoc: doc: TPipelineStateDoc = dataclasses.asdict(self) # type: ignore[assignment] if self._dlt_load_id is None: - doc.pop("_dlt_load_id") + doc.pop(C_DLT_LOAD_ID) # type: ignore[misc] if self.version_hash is None: doc.pop("version_hash") return doc @@ -129,7 +139,7 @@ def from_normalized_mapping( state=normalized_doc[naming_convention.normalize_identifier("state")], created_at=normalized_doc[naming_convention.normalize_identifier("created_at")], version_hash=normalized_doc.get(naming_convention.normalize_identifier("version_hash")), - _dlt_load_id=normalized_doc.get(naming_convention.normalize_identifier("_dlt_load_id")), + _dlt_load_id=normalized_doc.get(naming_convention.normalize_identifier(C_DLT_LOAD_ID)), ) @@ -257,7 +267,7 @@ class DestinationClientStagingConfiguration(DestinationClientDwhConfiguration): Also supports datasets and can act as standalone destination. """ - as_staging: bool = False + as_staging_destination: bool = False bucket_url: str = None # layout of the destination files layout: str = DEFAULT_FILE_LAYOUT @@ -347,11 +357,11 @@ def __init__(self, file_path: str) -> None: # variables needed by most jobs, set by the loader in set_run_vars self._schema: Schema = None - self._load_table: TTableSchema = None + self._load_table: PreparedTableSchema = None self._load_id: str = None self._job_client: "JobClientBase" = None - def set_run_vars(self, load_id: str, schema: Schema, load_table: TTableSchema) -> None: + def set_run_vars(self, load_id: str, schema: Schema, load_table: PreparedTableSchema) -> None: """ called by the loader right before the job is run """ @@ -443,7 +453,7 @@ def __init__( self.capabilities = capabilities @abstractmethod - def initialize_storage(self, truncate_tables: Iterable[str] = None) -> None: + def initialize_storage(self, truncate_tables: Optional[Iterable[str]] = None) -> None: """Prepares storage to be used ie. creates database schema or file system folder. Truncates requested tables.""" pass @@ -457,6 +467,38 @@ def drop_storage(self) -> None: """Brings storage back into not initialized state. Typically data in storage is destroyed.""" pass + def verify_schema( + self, only_tables: Iterable[str] = None, new_jobs: Iterable[ParsedLoadJobFileName] = None + ) -> List[PreparedTableSchema]: + """Verifies schema before loading, returns a list of verified loaded tables.""" + if exceptions := verify_schema_capabilities( + self.schema, + self.capabilities, + self.config.destination_type, + warnings=False, + ): + for exception in exceptions: + logger.error(str(exception)) + raise exceptions[0] + + prepared_tables = [ + self.prepare_load_table(table_name) + for table_name in set( + list(only_tables or []) + self.schema.data_table_names(seen_data_only=True) + ) + ] + if exceptions := verify_supported_data_types( + prepared_tables, + new_jobs, + self.capabilities, + self.config.destination_type, + warnings=False, + ): + for exception in exceptions: + logger.error(str(exception)) + raise exceptions[0] + return prepared_tables + def update_stored_schema( self, only_tables: Iterable[str] = None, @@ -473,7 +515,6 @@ def update_stored_schema( Returns: Optional[TSchemaTables]: Returns an update that was applied at the destination. """ - self._verify_schema() # make sure that schema being saved was not modified from the moment it was loaded from storage version_hash = self.schema.version_hash if self.schema.is_modified: @@ -482,11 +523,19 @@ def update_stored_schema( ) return expected_update + def prepare_load_table(self, table_name: str) -> PreparedTableSchema: + """Prepares a table schema to be loaded by filling missing hints and doing other modifications requires by given destination.""" + try: + return fill_hints_from_parent_and_clone_table(self.schema.tables, self.schema.tables[table_name]) # type: ignore[return-value] + + except KeyError: + raise UnknownTableException(self.schema.name, table_name) + @abstractmethod def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: - """Creates a load job for a particular `table` with content in `file_path`""" + """Creates a load job for a particular `table` with content in `file_path`. Table is already prepared to be loaded.""" pass def prepare_load_job_execution( # noqa: B027, optional override @@ -495,15 +544,15 @@ def prepare_load_job_execution( # noqa: B027, optional override """Prepare the connected job client for the execution of a load job (used for query tags in sql clients)""" pass - def should_truncate_table_before_load(self, table: TTableSchema) -> bool: - return table["write_disposition"] == "replace" + def should_truncate_table_before_load(self, table_name: str) -> bool: + return self.prepare_load_table(table_name)["write_disposition"] == "replace" def create_table_chain_completed_followup_jobs( self, - table_chain: Sequence[TTableSchema], + table_chain: Sequence[PreparedTableSchema], completed_table_chain_jobs: Optional[Sequence[LoadJobInfo]] = None, ) -> List[FollowupJobRequest]: - """Creates a list of followup jobs that should be executed after a table chain is completed""" + """Creates a list of followup jobs that should be executed after a table chain is completed. Tables are already prepared to be loaded.""" return [] @abstractmethod @@ -521,34 +570,6 @@ def __exit__( ) -> None: pass - def _verify_schema(self) -> None: - """Verifies schema before loading""" - if exceptions := verify_schema_capabilities( - self.schema, self.capabilities, self.config.destination_type, warnings=False - ): - for exception in exceptions: - logger.error(str(exception)) - raise exceptions[0] - - def prepare_load_table( - self, table_name: str, prepare_for_staging: bool = False - ) -> TTableSchema: - try: - # make a copy of the schema so modifications do not affect the original document - table = deepcopy(self.schema.tables[table_name]) - # add write disposition if not specified - in child tables - if "write_disposition" not in table: - table["write_disposition"] = get_write_disposition(self.schema.tables, table_name) - if "x-merge-strategy" not in table: - table["x-merge-strategy"] = get_merge_strategy(self.schema.tables, table_name) # type: ignore[typeddict-unknown-key] - if "table_format" not in table: - table["table_format"] = get_table_format(self.schema.tables, table_name) - if "file_format" not in table: - table["file_format"] = get_file_format(self.schema.tables, table_name) - return table - except KeyError: - raise UnknownTableException(self.schema.name, table_name) - class WithStateSync(ABC): @abstractmethod @@ -571,7 +592,7 @@ class WithStagingDataset(ABC): """Adds capability to use staging dataset and request it from the loader""" @abstractmethod - def should_load_data_to_staging_dataset(self, table: TTableSchema) -> bool: + def should_load_data_to_staging_dataset(self, table_name: str) -> bool: return False @abstractmethod @@ -583,9 +604,7 @@ def with_staging_dataset(self) -> ContextManager["JobClientBase"]: class SupportsStagingDestination(ABC): """Adds capability to support a staging destination for the load""" - def should_load_data_to_staging_dataset_on_staging_destination( - self, table: TTableSchema - ) -> bool: + def should_load_data_to_staging_dataset_on_staging_destination(self, table_name: str) -> bool: """If set to True, and staging destination is configured, the data will be loaded to staging dataset on staging destination instead of a regular dataset on staging destination. Currently it is used by Athena Iceberg which uses staging dataset on staging destination to copy data to iceberg tables stored on regular dataset on staging destination. @@ -595,7 +614,7 @@ def should_load_data_to_staging_dataset_on_staging_destination( return False @abstractmethod - def should_truncate_table_before_load_on_staging_destination(self, table: TTableSchema) -> bool: + def should_truncate_table_before_load_on_staging_destination(self, table_name: str) -> bool: """If set to True, data in `table` will be truncated on staging destination (regular dataset). This is the default behavior which can be changed with a config flag. For Athena + Iceberg this setting is always False - Athena uses regular dataset to store Iceberg tables and we avoid touching it. diff --git a/dlt/common/destination/typing.py b/dlt/common/destination/typing.py new file mode 100644 index 0000000000..bdfbddaa8c --- /dev/null +++ b/dlt/common/destination/typing.py @@ -0,0 +1,8 @@ +from dlt.common.schema.typing import _TTableSchemaBase, TWriteDisposition + + +class PreparedTableSchema(_TTableSchemaBase, total=False): + """Table schema with all hints prepared to be loaded""" + + write_disposition: TWriteDisposition + _x_prepared: bool # needed for the type checker diff --git a/dlt/common/destination/utils.py b/dlt/common/destination/utils.py index 931413126c..0bad5b152e 100644 --- a/dlt/common/destination/utils.py +++ b/dlt/common/destination/utils.py @@ -1,14 +1,23 @@ -from typing import List +import contextlib +from typing import Dict, Iterable, List, Optional, Set from dlt.common import logger -from dlt.common.destination.exceptions import IdentifierTooLongException +from dlt.common.configuration.inject import with_config +from dlt.common.destination.exceptions import ( + DestinationCapabilitiesException, + IdentifierTooLongException, +) +from dlt.common.destination.typing import PreparedTableSchema +from dlt.common.destination.exceptions import UnsupportedDataType +from dlt.common.destination.capabilities import DestinationCapabilitiesContext, LOADER_FILE_FORMATS from dlt.common.schema import Schema from dlt.common.schema.exceptions import ( SchemaIdentifierNormalizationCollision, ) -from dlt.common.typing import DictStrStr - -from .capabilities import DestinationCapabilitiesContext +from dlt.common.schema.typing import TColumnType, TLoaderMergeStrategy, TSchemaTables, TTableSchema +from dlt.common.schema.utils import get_merge_strategy, is_complete_column +from dlt.common.storages import ParsedLoadJobFileName +from dlt.common.typing import ConfigValue, DictStrStr, TLoaderFileFormat def verify_schema_capabilities( @@ -17,7 +26,8 @@ def verify_schema_capabilities( destination_type: str, warnings: bool = True, ) -> List[Exception]: - """Verifies schema tables before loading against capabilities. Returns a list of exceptions representing critical problems with the schema. + """Verifies `load_tables` that have all hints filled by job client before loading against capabilities. + Returns a list of exceptions representing critical problems with the schema. It will log warnings by default. It is up to the caller to eventually raise exception * Checks all table and column name lengths against destination capabilities and raises on too long identifiers @@ -104,3 +114,126 @@ def verify_schema_capabilities( ) ) return exception_log + + +def column_type_to_str(column: TColumnType) -> str: + """Converts column type to db-like type string""" + data_type: str = column["data_type"] + precision = column.get("precision") + scale = column.get("scale") + if precision is not None and scale is not None: + data_type += f"({precision},{scale})" + elif precision is not None: + data_type += f"({precision})" + return data_type + + +def verify_supported_data_types( + prepared_tables: Iterable[PreparedTableSchema], + new_jobs: Iterable[ParsedLoadJobFileName], + capabilities: DestinationCapabilitiesContext, + destination_type: str, + warnings: bool = True, +) -> List[Exception]: + exception_log: List[Exception] = [] + # can't check types without type mapper + if capabilities.type_mapper is None or not new_jobs: + return exception_log + + type_mapper = capabilities.get_type_mapper() + + # index available file formats + table_file_formats: Dict[str, Set[TLoaderFileFormat]] = {} + for parsed_file in new_jobs: + formats = table_file_formats.setdefault(parsed_file.table_name, set()) + if parsed_file.file_format in LOADER_FILE_FORMATS: + formats.add(parsed_file.file_format) # type: ignore[arg-type] + # all file formats + all_file_formats = set(capabilities.supported_loader_file_formats or []) | set( + capabilities.supported_staging_file_formats or [] + ) + + for table in prepared_tables: + # map types + for column in table["columns"].values(): + # do not verify incomplete columns, those won't be created + if not is_complete_column(column): + continue + try: + type_mapper.to_destination_type(column, table) + except Exception as ex: + # collect mapping exceptions + exception_log.append(ex) + # ensure if types can be loaded from file formats present in jobs + for format_ in table_file_formats.get(table["name"], []): + try: + type_mapper.ensure_supported_type(column, table, format_) + except ValueError as err: + # figure out where data type is supported + available_in_formats: List[TLoaderFileFormat] = [] + for candidate_format in all_file_formats - set([format_]): + with contextlib.suppress(Exception): + type_mapper.ensure_supported_type(column, table, candidate_format) + available_in_formats.append(candidate_format) + exception_log.append( + UnsupportedDataType( + destination_type, + table["name"], + column["name"], + column_type_to_str(column), + format_, + available_in_formats, + err.args[0], + ) + ) + + return exception_log + + +@with_config +def resolve_merge_strategy( + tables: TSchemaTables, + table: TTableSchema, + destination_capabilities: Optional[DestinationCapabilitiesContext] = ConfigValue, +) -> Optional[TLoaderMergeStrategy]: + """Resolve merge strategy for a table, possibly resolving the 'x-merge-strategy from a table chain. strategies selector in `destination_capabilities` + is used if present. If `table` does not contain strategy hint, a default value will be used which is the first. + + `destination_capabilities` are injected from context if not explicitly passed. + + Returns None if table write disposition is not merge + """ + if table.get("write_disposition") == "merge": + destination_capabilities = ( + destination_capabilities or DestinationCapabilitiesContext.generic_capabilities() + ) + supported_strategies = destination_capabilities.supported_merge_strategies + table_name = table["name"] + if destination_capabilities.merge_strategies_selector: + supported_strategies = destination_capabilities.merge_strategies_selector( + supported_strategies, table_schema=table + ) + if not supported_strategies: + table_format_info = "" + if destination_capabilities.supported_table_formats: + table_format_info = ( + " or try different table format which may offer `merge`:" + f" {destination_capabilities.supported_table_formats}" + ) + logger.warning( + "Destination does not support any merge strategies and `merge` write disposition " + f" for table `{table_name}` cannot be met and will fall back to `append`. Change" + f" write disposition{table_format_info}." + ) + return None + merge_strategy = get_merge_strategy(tables, table_name) + # use first merge strategy as default + if merge_strategy is None and supported_strategies: + merge_strategy = supported_strategies[0] + if merge_strategy not in supported_strategies: + raise DestinationCapabilitiesException( + f"`{merge_strategy}` merge strategy not supported" + f" for table `{table_name}`. Available strategies: {supported_strategies}" + ) + return merge_strategy + return None diff --git a/dlt/common/json/__init__.py b/dlt/common/json/__init__.py index 72ab453cbf..fe762cdf11 100644 --- a/dlt/common/json/__init__.py +++ b/dlt/common/json/__init__.py @@ -19,35 +19,7 @@ from dlt.common.utils import map_nested_in_place -class SupportsJson(Protocol): - """Minimum adapter for different json parser implementations""" - - _impl_name: str - """Implementation name""" - - def dump( - self, obj: Any, fp: IO[bytes], sort_keys: bool = False, pretty: bool = False - ) -> None: ... - - def typed_dump(self, obj: Any, fp: IO[bytes], pretty: bool = False) -> None: ... - - def typed_dumps(self, obj: Any, sort_keys: bool = False, pretty: bool = False) -> str: ... - - def typed_loads(self, s: str) -> Any: ... - - def typed_dumpb(self, obj: Any, sort_keys: bool = False, pretty: bool = False) -> bytes: ... - - def typed_loadb(self, s: Union[bytes, bytearray, memoryview]) -> Any: ... - - def dumps(self, obj: Any, sort_keys: bool = False, pretty: bool = False) -> str: ... - - def dumpb(self, obj: Any, sort_keys: bool = False, pretty: bool = False) -> bytes: ... - - def load(self, fp: Union[IO[bytes], IO[str]]) -> Any: ... - - def loads(self, s: str) -> Any: ... - - def loadb(self, s: Union[bytes, bytearray, memoryview]) -> Any: ... +TPuaDecoders = List[Callable[[Any], Any]] def custom_encode(obj: Any) -> str: @@ -104,7 +76,7 @@ def _datetime_decoder(obj: str) -> datetime: # define decoder for each prefix -DECODERS: List[Callable[[Any], Any]] = [ +DECODERS: TPuaDecoders = [ Decimal, _datetime_decoder, pendulum.Date.fromisoformat, @@ -114,6 +86,11 @@ def _datetime_decoder(obj: str) -> datetime: Wei, pendulum.Time.fromisoformat, ] +# Alternate decoders that decode date/time/datetime to stdlib types instead of pendulum +PY_DATETIME_DECODERS = list(DECODERS) +PY_DATETIME_DECODERS[1] = datetime.fromisoformat +PY_DATETIME_DECODERS[2] = date.fromisoformat +PY_DATETIME_DECODERS[7] = time.fromisoformat # how many decoders? PUA_CHARACTER_MAX = len(DECODERS) @@ -151,13 +128,13 @@ def custom_pua_encode(obj: Any) -> str: raise TypeError(repr(obj) + " is not JSON serializable") -def custom_pua_decode(obj: Any) -> Any: +def custom_pua_decode(obj: Any, decoders: TPuaDecoders = DECODERS) -> Any: if isinstance(obj, str) and len(obj) > 1: c = ord(obj[0]) - PUA_START # decode only the PUA space defined in DECODERS if c >= 0 and c <= PUA_CHARACTER_MAX: try: - return DECODERS[c](obj[1:]) + return decoders[c](obj[1:]) except Exception: # return strings that cannot be parsed # this may be due @@ -167,11 +144,11 @@ def custom_pua_decode(obj: Any) -> Any: return obj -def custom_pua_decode_nested(obj: Any) -> Any: +def custom_pua_decode_nested(obj: Any, decoders: TPuaDecoders = DECODERS) -> Any: if isinstance(obj, str): - return custom_pua_decode(obj) + return custom_pua_decode(obj, decoders) elif isinstance(obj, (list, dict)): - return map_nested_in_place(custom_pua_decode, obj) + return map_nested_in_place(custom_pua_decode, obj, decoders=decoders) return obj @@ -190,6 +167,39 @@ def may_have_pua(line: bytes) -> bool: return PUA_START_UTF8_MAGIC in line +class SupportsJson(Protocol): + """Minimum adapter for different json parser implementations""" + + _impl_name: str + """Implementation name""" + + def dump( + self, obj: Any, fp: IO[bytes], sort_keys: bool = False, pretty: bool = False + ) -> None: ... + + def typed_dump(self, obj: Any, fp: IO[bytes], pretty: bool = False) -> None: ... + + def typed_dumps(self, obj: Any, sort_keys: bool = False, pretty: bool = False) -> str: ... + + def typed_loads(self, s: str) -> Any: ... + + def typed_dumpb(self, obj: Any, sort_keys: bool = False, pretty: bool = False) -> bytes: ... + + def typed_loadb( + self, s: Union[bytes, bytearray, memoryview], decoders: TPuaDecoders = DECODERS + ) -> Any: ... + + def dumps(self, obj: Any, sort_keys: bool = False, pretty: bool = False) -> str: ... + + def dumpb(self, obj: Any, sort_keys: bool = False, pretty: bool = False) -> bytes: ... + + def load(self, fp: Union[IO[bytes], IO[str]]) -> Any: ... + + def loads(self, s: str) -> Any: ... + + def loadb(self, s: Union[bytes, bytearray, memoryview]) -> Any: ... + + # pick the right impl json: SupportsJson = None if os.environ.get(known_env.DLT_USE_JSON) == "simplejson": @@ -216,4 +226,7 @@ def may_have_pua(line: bytes) -> bool: "custom_pua_remove", "SupportsJson", "may_have_pua", + "TPuaDecoders", + "DECODERS", + "PY_DATETIME_DECODERS", ] diff --git a/dlt/common/json/_orjson.py b/dlt/common/json/_orjson.py index d2d960e6ce..d066ffe875 100644 --- a/dlt/common/json/_orjson.py +++ b/dlt/common/json/_orjson.py @@ -1,7 +1,13 @@ from typing import IO, Any, Union import orjson -from dlt.common.json import custom_pua_encode, custom_pua_decode_nested, custom_encode +from dlt.common.json import ( + custom_pua_encode, + custom_pua_decode_nested, + custom_encode, + TPuaDecoders, + DECODERS, +) from dlt.common.typing import AnyFun _impl_name = "orjson" @@ -38,8 +44,8 @@ def typed_loads(s: str) -> Any: return custom_pua_decode_nested(loads(s)) -def typed_loadb(s: Union[bytes, bytearray, memoryview]) -> Any: - return custom_pua_decode_nested(loadb(s)) +def typed_loadb(s: Union[bytes, bytearray, memoryview], decoders: TPuaDecoders = DECODERS) -> Any: + return custom_pua_decode_nested(loadb(s), decoders) def dumps(obj: Any, sort_keys: bool = False, pretty: bool = False) -> str: diff --git a/dlt/common/json/_simplejson.py b/dlt/common/json/_simplejson.py index 10ee17e2f6..e5adcc7120 100644 --- a/dlt/common/json/_simplejson.py +++ b/dlt/common/json/_simplejson.py @@ -4,7 +4,13 @@ import simplejson import platform -from dlt.common.json import custom_pua_encode, custom_pua_decode_nested, custom_encode +from dlt.common.json import ( + custom_pua_encode, + custom_pua_decode_nested, + custom_encode, + TPuaDecoders, + DECODERS, +) if platform.python_implementation() == "PyPy": # disable speedups on PyPy, it can be actually faster than Python C @@ -73,8 +79,8 @@ def typed_dumpb(obj: Any, sort_keys: bool = False, pretty: bool = False) -> byte return typed_dumps(obj, sort_keys, pretty).encode("utf-8") -def typed_loadb(s: Union[bytes, bytearray, memoryview]) -> Any: - return custom_pua_decode_nested(loadb(s)) +def typed_loadb(s: Union[bytes, bytearray, memoryview], decoders: TPuaDecoders = DECODERS) -> Any: + return custom_pua_decode_nested(loadb(s), decoders) def dumps(obj: Any, sort_keys: bool = False, pretty: bool = False) -> str: diff --git a/dlt/common/libs/deltalake.py b/dlt/common/libs/deltalake.py index 38b23ea27a..9caba55183 100644 --- a/dlt/common/libs/deltalake.py +++ b/dlt/common/libs/deltalake.py @@ -35,6 +35,7 @@ def ensure_delta_compatible_arrow_schema(schema: pa.Schema) -> pa.Schema: pa.types.is_time: pa.string(), pa.types.is_decimal256: pa.string(), # pyarrow does not allow downcasting to decimal128 } + # NOTE: also consider calling _convert_pa_schema_to_delta() from delta.schema which casts unsigned types return cast_arrow_schema_types(schema, ARROW_TO_DELTA_COMPATIBLE_ARROW_TYPE_MAP) @@ -174,7 +175,7 @@ def get_delta_tables( def _deltalake_storage_options(config: FilesystemConfiguration) -> Dict[str, str]: """Returns dict that can be passed as `storage_options` in `deltalake` library.""" - creds = {} + creds = {} # type: ignore extra_options = {} # TODO: create a mixin with to_object_store_rs_credentials for a proper discovery if hasattr(config.credentials, "to_object_store_rs_credentials"): @@ -192,14 +193,20 @@ def _deltalake_storage_options(config: FilesystemConfiguration) -> Dict[str, str return {**creds, **extra_options} -def _evolve_delta_table_schema(delta_table: DeltaTable, arrow_schema: pa.Schema) -> None: +def _evolve_delta_table_schema(delta_table: DeltaTable, arrow_schema: pa.Schema) -> DeltaTable: """Evolves `delta_table` schema if different from `arrow_schema`. + We compare fields via names. Actual types and nullability are ignored. This is + how schemas are evolved for other destinations. Existing columns are never modified. + Variant columns are created. + Adds column(s) to `delta_table` present in `arrow_schema` but not in `delta_table`. """ new_fields = [ deltalake.Field.from_pyarrow(field) for field in ensure_delta_compatible_arrow_schema(arrow_schema) - if field not in delta_table.to_pyarrow_dataset().schema + if field.name not in delta_table.schema().to_pyarrow().names ] - delta_table.alter.add_columns(new_fields) + if new_fields: + delta_table.alter.add_columns(new_fields) + return delta_table diff --git a/dlt/common/libs/pyarrow.py b/dlt/common/libs/pyarrow.py index 9d3e97421c..adba832c43 100644 --- a/dlt/common/libs/pyarrow.py +++ b/dlt/common/libs/pyarrow.py @@ -17,10 +17,11 @@ from dlt import version from dlt.common.pendulum import pendulum from dlt.common.exceptions import MissingDependencyException -from dlt.common.schema.typing import DLT_NAME_PREFIX, TTableSchemaColumns +from dlt.common.schema.typing import C_DLT_ID, C_DLT_LOAD_ID, TTableSchemaColumns from dlt.common.destination.capabilities import DestinationCapabilitiesContext from dlt.common.schema.typing import TColumnType +from dlt.common.schema.utils import is_nullable_column from dlt.common.typing import StrStr, TFileOrPath from dlt.common.normalizers.naming import NamingConvention @@ -29,6 +30,7 @@ import pyarrow.parquet import pyarrow.compute import pyarrow.dataset + from pyarrow.parquet import ParquetFile except ModuleNotFoundError: raise MissingDependencyException( "dlt pyarrow helpers", @@ -54,12 +56,17 @@ def get_py_arrow_datatype( elif column_type == "bool": return pyarrow.bool_() elif column_type == "timestamp": - return get_py_arrow_timestamp(column.get("precision") or caps.timestamp_precision, tz) + # sets timezone to None when timezone hint is false + timezone = tz if column.get("timezone", True) else None + precision = column.get("precision") + if precision is None: + precision = caps.timestamp_precision + return get_py_arrow_timestamp(precision, timezone) elif column_type == "bigint": return get_pyarrow_int(column.get("precision")) elif column_type == "binary": return pyarrow.binary(column.get("precision") or -1) - elif column_type == "complex": + elif column_type == "json": # return pyarrow.struct([pyarrow.field('json', pyarrow.string())]) return pyarrow.string() elif column_type == "decimal": @@ -75,7 +82,10 @@ def get_py_arrow_datatype( elif column_type == "date": return pyarrow.date32() elif column_type == "time": - return get_py_arrow_time(column.get("precision") or caps.timestamp_precision) + precision = column.get("precision") + if precision is None: + precision = caps.timestamp_precision + return get_py_arrow_time(precision) else: raise ValueError(column_type) @@ -139,6 +149,10 @@ def get_column_type_from_py_arrow(dtype: pyarrow.DataType) -> TColumnType: precision = 6 else: precision = 9 + + if dtype.tz is None: + return dict(data_type="timestamp", precision=precision, timezone=False) + return dict(data_type="timestamp", precision=precision) elif pyarrow.types.is_date(dtype): return dict(data_type="date") @@ -165,7 +179,7 @@ def get_column_type_from_py_arrow(dtype: pyarrow.DataType) -> TColumnType: elif pyarrow.types.is_decimal(dtype): return dict(data_type="decimal", precision=dtype.precision, scale=dtype.scale) elif pyarrow.types.is_nested(dtype): - return dict(data_type="complex") + return dict(data_type="json") else: raise ValueError(dtype) @@ -230,7 +244,7 @@ def should_normalize_arrow_schema( ) -> Tuple[bool, Mapping[str, str], Dict[str, str], Dict[str, bool], bool, TTableSchemaColumns]: rename_mapping = get_normalized_arrow_fields_mapping(schema, naming) rev_mapping = {v: k for k, v in rename_mapping.items()} - nullable_mapping = {k: v.get("nullable", True) for k, v in columns.items()} + nullable_mapping = {k: is_nullable_column(v) for k, v in columns.items()} # All fields from arrow schema that have nullable set to different value than in columns # Key is the renamed column name nullable_updates: Dict[str, bool] = {} @@ -239,8 +253,8 @@ def should_normalize_arrow_schema( if norm_name in nullable_mapping and field.nullable != nullable_mapping[norm_name]: nullable_updates[norm_name] = nullable_mapping[norm_name] - dlt_load_id_col = naming.normalize_table_identifier("_dlt_load_id") - dlt_id_col = naming.normalize_table_identifier("_dlt_id") + dlt_load_id_col = naming.normalize_identifier(C_DLT_LOAD_ID) + dlt_id_col = naming.normalize_identifier(C_DLT_ID) dlt_columns = {dlt_load_id_col, dlt_id_col} # Do we need to add a load id column? @@ -319,7 +333,7 @@ def normalize_py_arrow_item( new_field = pyarrow.field( column_name, get_py_arrow_datatype(column, caps, "UTC"), - nullable=column.get("nullable", True), + nullable=is_nullable_column(column), ) new_fields.append(new_field) new_columns.append(pyarrow.nulls(item.num_rows, type=new_field.type)) @@ -336,7 +350,7 @@ def normalize_py_arrow_item( load_id_type = pyarrow.dictionary(pyarrow.int8(), pyarrow.string()) new_fields.append( pyarrow.field( - naming.normalize_table_identifier("_dlt_load_id"), + naming.normalize_identifier(C_DLT_LOAD_ID), load_id_type, nullable=False, ) @@ -493,6 +507,30 @@ def cast_arrow_schema_types( return schema +def concat_batches_and_tables_in_order( + tables_or_batches: Iterable[Union[pyarrow.Table, pyarrow.RecordBatch]] +) -> pyarrow.Table: + """Concatenate iterable of tables and batches into a single table, preserving row order. Zero copy is used during + concatenation so schemas must be identical. + """ + batches = [] + tables = [] + for item in tables_or_batches: + if isinstance(item, pyarrow.RecordBatch): + batches.append(item) + elif isinstance(item, pyarrow.Table): + if batches: + tables.append(pyarrow.Table.from_batches(batches)) + batches = [] + tables.append(item) + else: + raise ValueError(f"Unsupported type {type(item)}") + if batches: + tables.append(pyarrow.Table.from_batches(batches)) + # "none" option ensures 0 copy concat + return pyarrow.concat_tables(tables, promote_options="none") + + class NameNormalizationCollision(ValueError): def __init__(self, reason: str) -> None: msg = f"Arrow column name collision after input data normalization. {reason}" diff --git a/dlt/common/libs/pydantic.py b/dlt/common/libs/pydantic.py index 15e3e53409..df3554ff21 100644 --- a/dlt/common/libs/pydantic.py +++ b/dlt/common/libs/pydantic.py @@ -32,6 +32,7 @@ is_subclass, is_union_type, ) +from dlt.common.warnings import Dlt100DeprecationWarning try: from pydantic import BaseModel, ValidationError, Json, create_model @@ -69,11 +70,12 @@ class DltConfig(TypedDict, total=False): >>> class ItemModel(BaseModel): >>> b: bool >>> nested: Dict[str, Any] - >>> dlt_config: ClassVar[DltConfig] = {"skip_complex_types": True} + >>> dlt_config: ClassVar[DltConfig] = {"skip_nested_types": True} """ - skip_complex_types: bool + skip_nested_types: bool """If True, columns of complex types (`dict`, `list`, `BaseModel`) will be excluded from dlt schema generated from the model""" + skip_complex_types: bool # deprecated def pydantic_to_table_schema_columns( @@ -90,9 +92,17 @@ def pydantic_to_table_schema_columns( Returns: TTableSchemaColumns: table schema columns dict """ - skip_complex_types = False + skip_nested_types = False if hasattr(model, "dlt_config"): - skip_complex_types = model.dlt_config.get("skip_complex_types", False) + if "skip_complex_types" in model.dlt_config: + warnings.warn( + "`skip_complex_types` is deprecated, use `skip_nested_types` instead.", + Dlt100DeprecationWarning, + stacklevel=2, + ) + skip_nested_types = model.dlt_config["skip_complex_types"] + else: + skip_nested_types = model.dlt_config.get("skip_nested_types", False) result: TTableSchemaColumns = {} @@ -130,16 +140,16 @@ def pydantic_to_table_schema_columns( data_type = py_type_to_sc_type(inner_type) except TypeError: if is_subclass(inner_type, BaseModel): - data_type = "complex" + data_type = "json" is_inner_type_pydantic_model = True else: # try to coerce unknown type to text data_type = "text" - if is_inner_type_pydantic_model and not skip_complex_types: + if is_inner_type_pydantic_model and not skip_nested_types: result[name] = { "name": name, - "data_type": "complex", + "data_type": "json", "nullable": nullable, } elif is_inner_type_pydantic_model: @@ -154,7 +164,7 @@ def pydantic_to_table_schema_columns( **hints, "name": snake_case_naming_convention.make_path(name, hints["name"]), } - elif data_type == "complex" and skip_complex_types: + elif data_type == "json" and skip_nested_types: continue else: result[name] = { diff --git a/dlt/common/libs/sql_alchemy.py b/dlt/common/libs/sql_alchemy.py index 2f3b51ec0d..19ebbbc78a 100644 --- a/dlt/common/libs/sql_alchemy.py +++ b/dlt/common/libs/sql_alchemy.py @@ -1,446 +1,20 @@ -""" -Ports fragments of URL class from Sql Alchemy to use them when dependency is not available. -""" - -from typing import cast - +from dlt.common.exceptions import MissingDependencyException +from dlt import version try: - import sqlalchemy -except ImportError: - # port basic functionality without the whole Sql Alchemy - - import re - from typing import ( - Any, - Dict, - Iterable, - List, - Mapping, - NamedTuple, - Optional, - Sequence, - Tuple, - TypeVar, - Union, - overload, + from sqlalchemy import MetaData, Table, Column, create_engine + from sqlalchemy.engine import Engine, URL, make_url, Row + from sqlalchemy.sql import sqltypes, Select + from sqlalchemy.sql.sqltypes import TypeEngine + from sqlalchemy.exc import CompileError + import sqlalchemy as sa +except ModuleNotFoundError: + raise MissingDependencyException( + "dlt sql_database helpers ", + [f"{version.DLT_PKG_NAME}[sql_database]"], + "Install the sql_database helpers for loading from sql_database sources. Note that you may" + " need to install additional SQLAlchemy dialects for your source database.", ) - import collections.abc as collections_abc - from urllib.parse import ( - quote_plus, - parse_qsl, - quote, - unquote, - ) - - _KT = TypeVar("_KT", bound=Any) - _VT = TypeVar("_VT", bound=Any) - - class ImmutableDict(Dict[_KT, _VT]): - """Not a real immutable dict""" - - def __setitem__(self, __key: _KT, __value: _VT) -> None: - raise NotImplementedError("Cannot modify immutable dict") - - def __delitem__(self, _KT: Any) -> None: - raise NotImplementedError("Cannot modify immutable dict") - - def update(self, *arg: Any, **kw: Any) -> None: - raise NotImplementedError("Cannot modify immutable dict") - - EMPTY_DICT: ImmutableDict[Any, Any] = ImmutableDict() - - def to_list(value: Any, default: Optional[List[Any]] = None) -> List[Any]: - if value is None: - return default - if not isinstance(value, collections_abc.Iterable) or isinstance(value, str): - return [value] - elif isinstance(value, list): - return value - else: - return list(value) - - class URL(NamedTuple): - """ - Represent the components of a URL used to connect to a database. - - Based on SqlAlchemy URL class with copyright as below: - - # engine/url.py - # Copyright (C) 2005-2023 the SQLAlchemy authors and contributors - # - # This module is part of SQLAlchemy and is released under - # the MIT License: https://www.opensource.org/licenses/mit-license.php - """ - - drivername: str - """database backend and driver name, such as `postgresql+psycopg2`""" - username: Optional[str] - "username string" - password: Optional[str] - """password, which is normally a string but may also be any object that has a `__str__()` method.""" - host: Optional[str] - """hostname or IP number. May also be a data source name for some drivers.""" - port: Optional[int] - """integer port number""" - database: Optional[str] - """database name""" - query: ImmutableDict[str, Union[Tuple[str, ...], str]] - """an immutable mapping representing the query string. contains strings - for keys and either strings or tuples of strings for values""" - - @classmethod - def create( - cls, - drivername: str, - username: Optional[str] = None, - password: Optional[str] = None, - host: Optional[str] = None, - port: Optional[int] = None, - database: Optional[str] = None, - query: Mapping[str, Union[Sequence[str], str]] = None, - ) -> "URL": - """Create a new `URL` object.""" - return cls( - cls._assert_str(drivername, "drivername"), - cls._assert_none_str(username, "username"), - password, - cls._assert_none_str(host, "host"), - cls._assert_port(port), - cls._assert_none_str(database, "database"), - cls._str_dict(query or EMPTY_DICT), - ) - - @classmethod - def _assert_port(cls, port: Optional[int]) -> Optional[int]: - if port is None: - return None - try: - return int(port) - except TypeError: - raise TypeError("Port argument must be an integer or None") - - @classmethod - def _assert_str(cls, v: str, paramname: str) -> str: - if not isinstance(v, str): - raise TypeError("%s must be a string" % paramname) - return v - - @classmethod - def _assert_none_str(cls, v: Optional[str], paramname: str) -> Optional[str]: - if v is None: - return v - - return cls._assert_str(v, paramname) - - @classmethod - def _str_dict( - cls, - dict_: Optional[ - Union[ - Sequence[Tuple[str, Union[Sequence[str], str]]], - Mapping[str, Union[Sequence[str], str]], - ] - ], - ) -> ImmutableDict[str, Union[Tuple[str, ...], str]]: - if dict_ is None: - return EMPTY_DICT - - @overload - def _assert_value( - val: str, - ) -> str: ... - - @overload - def _assert_value( - val: Sequence[str], - ) -> Union[str, Tuple[str, ...]]: ... - - def _assert_value( - val: Union[str, Sequence[str]], - ) -> Union[str, Tuple[str, ...]]: - if isinstance(val, str): - return val - elif isinstance(val, collections_abc.Sequence): - return tuple(_assert_value(elem) for elem in val) - else: - raise TypeError( - "Query dictionary values must be strings or sequences of strings" - ) - - def _assert_str(v: str) -> str: - if not isinstance(v, str): - raise TypeError("Query dictionary keys must be strings") - return v - - dict_items: Iterable[Tuple[str, Union[Sequence[str], str]]] - if isinstance(dict_, collections_abc.Sequence): - dict_items = dict_ - else: - dict_items = dict_.items() - - return ImmutableDict( - { - _assert_str(key): _assert_value( - value, - ) - for key, value in dict_items - } - ) - - def set( # noqa - self, - drivername: Optional[str] = None, - username: Optional[str] = None, - password: Optional[str] = None, - host: Optional[str] = None, - port: Optional[int] = None, - database: Optional[str] = None, - query: Optional[Mapping[str, Union[Sequence[str], str]]] = None, - ) -> "URL": - """return a new `URL` object with modifications.""" - - kw: Dict[str, Any] = {} - if drivername is not None: - kw["drivername"] = drivername - if username is not None: - kw["username"] = username - if password is not None: - kw["password"] = password - if host is not None: - kw["host"] = host - if port is not None: - kw["port"] = port - if database is not None: - kw["database"] = database - if query is not None: - kw["query"] = query - - return self._assert_replace(**kw) - - def _assert_replace(self, **kw: Any) -> "URL": - """argument checks before calling _replace()""" - - if "drivername" in kw: - self._assert_str(kw["drivername"], "drivername") - for name in "username", "host", "database": - if name in kw: - self._assert_none_str(kw[name], name) - if "port" in kw: - self._assert_port(kw["port"]) - if "query" in kw: - kw["query"] = self._str_dict(kw["query"]) - - return self._replace(**kw) - - def update_query_string(self, query_string: str, append: bool = False) -> "URL": - return self.update_query_pairs(parse_qsl(query_string), append=append) - - def update_query_pairs( - self, - key_value_pairs: Iterable[Tuple[str, Union[str, List[str]]]], - append: bool = False, - ) -> "URL": - """Return a new `URL` object with the `query` parameter dictionary updated by the given sequence of key/value pairs""" - existing_query = self.query - new_keys: Dict[str, Union[str, List[str]]] = {} - - for key, value in key_value_pairs: - if key in new_keys: - new_keys[key] = to_list(new_keys[key]) - cast("List[str]", new_keys[key]).append(cast(str, value)) - else: - new_keys[key] = to_list(value) if isinstance(value, (list, tuple)) else value - - new_query: Mapping[str, Union[str, Sequence[str]]] - if append: - new_query = {} - - for k in new_keys: - if k in existing_query: - new_query[k] = tuple(to_list(existing_query[k]) + to_list(new_keys[k])) - else: - new_query[k] = new_keys[k] - - new_query.update( - {k: existing_query[k] for k in set(existing_query).difference(new_keys)} - ) - else: - new_query = ImmutableDict( - { - **self.query, - **{k: tuple(v) if isinstance(v, list) else v for k, v in new_keys.items()}, - } - ) - return self.set(query=new_query) - - def update_query_dict( - self, - query_parameters: Mapping[str, Union[str, List[str]]], - append: bool = False, - ) -> "URL": - return self.update_query_pairs(query_parameters.items(), append=append) - - def render_as_string(self, hide_password: bool = True) -> str: - """Render this `URL` object as a string.""" - s = self.drivername + "://" - if self.username is not None: - s += quote(self.username, safe=" +") - if self.password is not None: - s += ":" + ("***" if hide_password else quote(str(self.password), safe=" +")) - s += "@" - if self.host is not None: - if ":" in self.host: - s += f"[{self.host}]" - else: - s += self.host - if self.port is not None: - s += ":" + str(self.port) - if self.database is not None: - s += "/" + self.database - if self.query: - keys = to_list(self.query) - keys.sort() - s += "?" + "&".join( - f"{quote_plus(k)}={quote_plus(element)}" - for k in keys - for element in to_list(self.query[k]) - ) - return s - - def __repr__(self) -> str: - return self.render_as_string() - - def __copy__(self) -> "URL": - return self.__class__.create( - self.drivername, - self.username, - self.password, - self.host, - self.port, - self.database, - self.query.copy(), - ) - - def __deepcopy__(self, memo: Any) -> "URL": - return self.__copy__() - - def __hash__(self) -> int: - return hash(str(self)) - - def __eq__(self, other: Any) -> bool: - return ( - isinstance(other, URL) - and self.drivername == other.drivername - and self.username == other.username - and self.password == other.password - and self.host == other.host - and self.database == other.database - and self.query == other.query - and self.port == other.port - ) - - def __ne__(self, other: Any) -> bool: - return not self == other - - def get_backend_name(self) -> str: - """Return the backend name. - - This is the name that corresponds to the database backend in - use, and is the portion of the `drivername` - that is to the left of the plus sign. - - """ - if "+" not in self.drivername: - return self.drivername - else: - return self.drivername.split("+")[0] - - def get_driver_name(self) -> str: - """Return the backend name. - - This is the name that corresponds to the DBAPI driver in - use, and is the portion of the `drivername` - that is to the right of the plus sign. - """ - - if "+" not in self.drivername: - return self.drivername - else: - return self.drivername.split("+")[1] - - def make_url(name_or_url: Union[str, URL]) -> URL: - """Given a string, produce a new URL instance. - - The format of the URL generally follows `RFC-1738`, with some exceptions, including - that underscores, and not dashes or periods, are accepted within the - "scheme" portion. - - If a `URL` object is passed, it is returned as is.""" - - if isinstance(name_or_url, str): - return _parse_url(name_or_url) - elif not isinstance(name_or_url, URL): - raise ValueError(f"Expected string or URL object, got {name_or_url!r}") - else: - return name_or_url - - def _parse_url(name: str) -> URL: - pattern = re.compile( - r""" - (?P[\w\+]+):// - (?: - (?P[^:/]*) - (?::(?P[^@]*))? - @)? - (?: - (?: - \[(?P[^/\?]+)\] | - (?P[^/:\?]+) - )? - (?::(?P[^/\?]*))? - )? - (?:/(?P[^\?]*))? - (?:\?(?P.*))? - """, - re.X, - ) - - m = pattern.match(name) - if m is not None: - components = m.groupdict() - query: Optional[Dict[str, Union[str, List[str]]]] - if components["query"] is not None: - query = {} - - for key, value in parse_qsl(components["query"]): - if key in query: - query[key] = to_list(query[key]) - cast("List[str]", query[key]).append(value) - else: - query[key] = value - else: - query = None - - components["query"] = query - if components["username"] is not None: - components["username"] = unquote(components["username"]) - - if components["password"] is not None: - components["password"] = unquote(components["password"]) - - ipv4host = components.pop("ipv4host") - ipv6host = components.pop("ipv6host") - components["host"] = ipv4host or ipv6host - name = components.pop("name") - - if components["port"]: - components["port"] = int(components["port"]) - - return URL.create(name, **components) # type: ignore - - else: - raise ValueError("Could not parse SQLAlchemy URL from string '%s'" % name) -else: - from sqlalchemy.engine import URL, make_url # type: ignore[assignment] +# TODO: maybe use sa.__version__? +IS_SQL_ALCHEMY_20 = hasattr(sa, "Double") diff --git a/dlt/common/libs/sql_alchemy_shims.py b/dlt/common/libs/sql_alchemy_shims.py new file mode 100644 index 0000000000..2f3b51ec0d --- /dev/null +++ b/dlt/common/libs/sql_alchemy_shims.py @@ -0,0 +1,446 @@ +""" +Ports fragments of URL class from Sql Alchemy to use them when dependency is not available. +""" + +from typing import cast + + +try: + import sqlalchemy +except ImportError: + # port basic functionality without the whole Sql Alchemy + + import re + from typing import ( + Any, + Dict, + Iterable, + List, + Mapping, + NamedTuple, + Optional, + Sequence, + Tuple, + TypeVar, + Union, + overload, + ) + import collections.abc as collections_abc + from urllib.parse import ( + quote_plus, + parse_qsl, + quote, + unquote, + ) + + _KT = TypeVar("_KT", bound=Any) + _VT = TypeVar("_VT", bound=Any) + + class ImmutableDict(Dict[_KT, _VT]): + """Not a real immutable dict""" + + def __setitem__(self, __key: _KT, __value: _VT) -> None: + raise NotImplementedError("Cannot modify immutable dict") + + def __delitem__(self, _KT: Any) -> None: + raise NotImplementedError("Cannot modify immutable dict") + + def update(self, *arg: Any, **kw: Any) -> None: + raise NotImplementedError("Cannot modify immutable dict") + + EMPTY_DICT: ImmutableDict[Any, Any] = ImmutableDict() + + def to_list(value: Any, default: Optional[List[Any]] = None) -> List[Any]: + if value is None: + return default + if not isinstance(value, collections_abc.Iterable) or isinstance(value, str): + return [value] + elif isinstance(value, list): + return value + else: + return list(value) + + class URL(NamedTuple): + """ + Represent the components of a URL used to connect to a database. + + Based on SqlAlchemy URL class with copyright as below: + + # engine/url.py + # Copyright (C) 2005-2023 the SQLAlchemy authors and contributors + # + # This module is part of SQLAlchemy and is released under + # the MIT License: https://www.opensource.org/licenses/mit-license.php + """ + + drivername: str + """database backend and driver name, such as `postgresql+psycopg2`""" + username: Optional[str] + "username string" + password: Optional[str] + """password, which is normally a string but may also be any object that has a `__str__()` method.""" + host: Optional[str] + """hostname or IP number. May also be a data source name for some drivers.""" + port: Optional[int] + """integer port number""" + database: Optional[str] + """database name""" + query: ImmutableDict[str, Union[Tuple[str, ...], str]] + """an immutable mapping representing the query string. contains strings + for keys and either strings or tuples of strings for values""" + + @classmethod + def create( + cls, + drivername: str, + username: Optional[str] = None, + password: Optional[str] = None, + host: Optional[str] = None, + port: Optional[int] = None, + database: Optional[str] = None, + query: Mapping[str, Union[Sequence[str], str]] = None, + ) -> "URL": + """Create a new `URL` object.""" + return cls( + cls._assert_str(drivername, "drivername"), + cls._assert_none_str(username, "username"), + password, + cls._assert_none_str(host, "host"), + cls._assert_port(port), + cls._assert_none_str(database, "database"), + cls._str_dict(query or EMPTY_DICT), + ) + + @classmethod + def _assert_port(cls, port: Optional[int]) -> Optional[int]: + if port is None: + return None + try: + return int(port) + except TypeError: + raise TypeError("Port argument must be an integer or None") + + @classmethod + def _assert_str(cls, v: str, paramname: str) -> str: + if not isinstance(v, str): + raise TypeError("%s must be a string" % paramname) + return v + + @classmethod + def _assert_none_str(cls, v: Optional[str], paramname: str) -> Optional[str]: + if v is None: + return v + + return cls._assert_str(v, paramname) + + @classmethod + def _str_dict( + cls, + dict_: Optional[ + Union[ + Sequence[Tuple[str, Union[Sequence[str], str]]], + Mapping[str, Union[Sequence[str], str]], + ] + ], + ) -> ImmutableDict[str, Union[Tuple[str, ...], str]]: + if dict_ is None: + return EMPTY_DICT + + @overload + def _assert_value( + val: str, + ) -> str: ... + + @overload + def _assert_value( + val: Sequence[str], + ) -> Union[str, Tuple[str, ...]]: ... + + def _assert_value( + val: Union[str, Sequence[str]], + ) -> Union[str, Tuple[str, ...]]: + if isinstance(val, str): + return val + elif isinstance(val, collections_abc.Sequence): + return tuple(_assert_value(elem) for elem in val) + else: + raise TypeError( + "Query dictionary values must be strings or sequences of strings" + ) + + def _assert_str(v: str) -> str: + if not isinstance(v, str): + raise TypeError("Query dictionary keys must be strings") + return v + + dict_items: Iterable[Tuple[str, Union[Sequence[str], str]]] + if isinstance(dict_, collections_abc.Sequence): + dict_items = dict_ + else: + dict_items = dict_.items() + + return ImmutableDict( + { + _assert_str(key): _assert_value( + value, + ) + for key, value in dict_items + } + ) + + def set( # noqa + self, + drivername: Optional[str] = None, + username: Optional[str] = None, + password: Optional[str] = None, + host: Optional[str] = None, + port: Optional[int] = None, + database: Optional[str] = None, + query: Optional[Mapping[str, Union[Sequence[str], str]]] = None, + ) -> "URL": + """return a new `URL` object with modifications.""" + + kw: Dict[str, Any] = {} + if drivername is not None: + kw["drivername"] = drivername + if username is not None: + kw["username"] = username + if password is not None: + kw["password"] = password + if host is not None: + kw["host"] = host + if port is not None: + kw["port"] = port + if database is not None: + kw["database"] = database + if query is not None: + kw["query"] = query + + return self._assert_replace(**kw) + + def _assert_replace(self, **kw: Any) -> "URL": + """argument checks before calling _replace()""" + + if "drivername" in kw: + self._assert_str(kw["drivername"], "drivername") + for name in "username", "host", "database": + if name in kw: + self._assert_none_str(kw[name], name) + if "port" in kw: + self._assert_port(kw["port"]) + if "query" in kw: + kw["query"] = self._str_dict(kw["query"]) + + return self._replace(**kw) + + def update_query_string(self, query_string: str, append: bool = False) -> "URL": + return self.update_query_pairs(parse_qsl(query_string), append=append) + + def update_query_pairs( + self, + key_value_pairs: Iterable[Tuple[str, Union[str, List[str]]]], + append: bool = False, + ) -> "URL": + """Return a new `URL` object with the `query` parameter dictionary updated by the given sequence of key/value pairs""" + existing_query = self.query + new_keys: Dict[str, Union[str, List[str]]] = {} + + for key, value in key_value_pairs: + if key in new_keys: + new_keys[key] = to_list(new_keys[key]) + cast("List[str]", new_keys[key]).append(cast(str, value)) + else: + new_keys[key] = to_list(value) if isinstance(value, (list, tuple)) else value + + new_query: Mapping[str, Union[str, Sequence[str]]] + if append: + new_query = {} + + for k in new_keys: + if k in existing_query: + new_query[k] = tuple(to_list(existing_query[k]) + to_list(new_keys[k])) + else: + new_query[k] = new_keys[k] + + new_query.update( + {k: existing_query[k] for k in set(existing_query).difference(new_keys)} + ) + else: + new_query = ImmutableDict( + { + **self.query, + **{k: tuple(v) if isinstance(v, list) else v for k, v in new_keys.items()}, + } + ) + return self.set(query=new_query) + + def update_query_dict( + self, + query_parameters: Mapping[str, Union[str, List[str]]], + append: bool = False, + ) -> "URL": + return self.update_query_pairs(query_parameters.items(), append=append) + + def render_as_string(self, hide_password: bool = True) -> str: + """Render this `URL` object as a string.""" + s = self.drivername + "://" + if self.username is not None: + s += quote(self.username, safe=" +") + if self.password is not None: + s += ":" + ("***" if hide_password else quote(str(self.password), safe=" +")) + s += "@" + if self.host is not None: + if ":" in self.host: + s += f"[{self.host}]" + else: + s += self.host + if self.port is not None: + s += ":" + str(self.port) + if self.database is not None: + s += "/" + self.database + if self.query: + keys = to_list(self.query) + keys.sort() + s += "?" + "&".join( + f"{quote_plus(k)}={quote_plus(element)}" + for k in keys + for element in to_list(self.query[k]) + ) + return s + + def __repr__(self) -> str: + return self.render_as_string() + + def __copy__(self) -> "URL": + return self.__class__.create( + self.drivername, + self.username, + self.password, + self.host, + self.port, + self.database, + self.query.copy(), + ) + + def __deepcopy__(self, memo: Any) -> "URL": + return self.__copy__() + + def __hash__(self) -> int: + return hash(str(self)) + + def __eq__(self, other: Any) -> bool: + return ( + isinstance(other, URL) + and self.drivername == other.drivername + and self.username == other.username + and self.password == other.password + and self.host == other.host + and self.database == other.database + and self.query == other.query + and self.port == other.port + ) + + def __ne__(self, other: Any) -> bool: + return not self == other + + def get_backend_name(self) -> str: + """Return the backend name. + + This is the name that corresponds to the database backend in + use, and is the portion of the `drivername` + that is to the left of the plus sign. + + """ + if "+" not in self.drivername: + return self.drivername + else: + return self.drivername.split("+")[0] + + def get_driver_name(self) -> str: + """Return the backend name. + + This is the name that corresponds to the DBAPI driver in + use, and is the portion of the `drivername` + that is to the right of the plus sign. + """ + + if "+" not in self.drivername: + return self.drivername + else: + return self.drivername.split("+")[1] + + def make_url(name_or_url: Union[str, URL]) -> URL: + """Given a string, produce a new URL instance. + + The format of the URL generally follows `RFC-1738`, with some exceptions, including + that underscores, and not dashes or periods, are accepted within the + "scheme" portion. + + If a `URL` object is passed, it is returned as is.""" + + if isinstance(name_or_url, str): + return _parse_url(name_or_url) + elif not isinstance(name_or_url, URL): + raise ValueError(f"Expected string or URL object, got {name_or_url!r}") + else: + return name_or_url + + def _parse_url(name: str) -> URL: + pattern = re.compile( + r""" + (?P[\w\+]+):// + (?: + (?P[^:/]*) + (?::(?P[^@]*))? + @)? + (?: + (?: + \[(?P[^/\?]+)\] | + (?P[^/:\?]+) + )? + (?::(?P[^/\?]*))? + )? + (?:/(?P[^\?]*))? + (?:\?(?P.*))? + """, + re.X, + ) + + m = pattern.match(name) + if m is not None: + components = m.groupdict() + query: Optional[Dict[str, Union[str, List[str]]]] + if components["query"] is not None: + query = {} + + for key, value in parse_qsl(components["query"]): + if key in query: + query[key] = to_list(query[key]) + cast("List[str]", query[key]).append(value) + else: + query[key] = value + else: + query = None + + components["query"] = query + if components["username"] is not None: + components["username"] = unquote(components["username"]) + + if components["password"] is not None: + components["password"] = unquote(components["password"]) + + ipv4host = components.pop("ipv4host") + ipv6host = components.pop("ipv6host") + components["host"] = ipv4host or ipv6host + name = components.pop("name") + + if components["port"]: + components["port"] = int(components["port"]) + + return URL.create(name, **components) # type: ignore + + else: + raise ValueError("Could not parse SQLAlchemy URL from string '%s'" % name) + +else: + from sqlalchemy.engine import URL, make_url # type: ignore[assignment] diff --git a/dlt/common/normalizers/json/relational.py b/dlt/common/normalizers/json/relational.py index 33184640f0..ad4b017336 100644 --- a/dlt/common/normalizers/json/relational.py +++ b/dlt/common/normalizers/json/relational.py @@ -1,5 +1,6 @@ from functools import lru_cache from typing import Dict, List, Mapping, Optional, Sequence, Tuple, cast, TypedDict, Any +from dlt.common.destination.utils import resolve_merge_strategy from dlt.common.json import json from dlt.common.normalizers.exceptions import InvalidJsonNormalizer from dlt.common.normalizers.typing import TJSONNormalizer, TRowIdType @@ -8,20 +9,21 @@ from dlt.common.typing import DictStrAny, TDataItem, StrAny from dlt.common.schema import Schema from dlt.common.schema.typing import ( - TLoaderMergeStrategy, + C_DLT_ID, + C_DLT_LOAD_ID, TColumnSchema, TColumnName, TSimpleRegex, DLT_NAME_PREFIX, + TTableSchema, ) from dlt.common.schema.utils import ( column_name_validator, - get_validity_column_names, get_columns_names_with_prop, get_first_column_name_with_prop, - get_merge_strategy, + has_column_with_prop, + is_nested_table, ) -from dlt.common.schema.exceptions import ColumnNameConflictException from dlt.common.utils import digest128, update_dict_nested from dlt.common.normalizers.json import ( TNormalizedRowIterator, @@ -37,17 +39,12 @@ class RelationalNormalizerConfigPropagation(TypedDict, total=False): class RelationalNormalizerConfig(TypedDict, total=False): - generate_dlt_id: Optional[bool] max_nesting: Optional[int] propagation: Optional[RelationalNormalizerConfigPropagation] class DataItemNormalizer(DataItemNormalizerBase[RelationalNormalizerConfig]): # known normalizer props - C_DLT_ID = "_dlt_id" - """unique id of current row""" - C_DLT_LOAD_ID = "_dlt_load_id" - """load id to identify records loaded together that ie. need to be processed""" C_DLT_ROOT_ID = "_dlt_root_id" """unique id of top level parent""" C_DLT_PARENT_ID = "_dlt_parent_id" @@ -74,9 +71,9 @@ def __init__(self, schema: Schema) -> None: def _reset(self) -> None: # normalize known normalizer column identifiers - self.c_dlt_id: TColumnName = TColumnName(self.naming.normalize_identifier(self.C_DLT_ID)) + self.c_dlt_id: TColumnName = TColumnName(self.naming.normalize_identifier(C_DLT_ID)) self.c_dlt_load_id: TColumnName = TColumnName( - self.naming.normalize_identifier(self.C_DLT_LOAD_ID) + self.naming.normalize_identifier(C_DLT_LOAD_ID) ) self.c_dlt_root_id: TColumnName = TColumnName( self.naming.normalize_identifier(self.C_DLT_ROOT_ID) @@ -98,33 +95,6 @@ def _reset(self) -> None: # self.known_types: Dict[str, TDataType] = {} # self.primary_keys = Dict[str, ] - # for those paths the complex nested objects should be left in place - def _is_complex_type(self, table_name: str, field_name: str, _r_lvl: int) -> bool: - # turn everything at the recursion level into complex type - max_nesting = self.max_nesting - schema = self.schema - max_table_nesting = self._get_table_nesting_level(schema, table_name) - if max_table_nesting is not None: - max_nesting = max_table_nesting - - assert _r_lvl <= max_nesting - if _r_lvl == max_nesting: - return True - - # use cached value - # path = f"{table_name}▶{field_name}" - # or use definition in the schema - column: TColumnSchema = None - table = schema.tables.get(table_name) - if table: - column = table["columns"].get(field_name) - if column is None or "data_type" not in column: - data_type = schema.get_preferred_type(field_name) - else: - data_type = column["data_type"] - - return data_type == "complex" - def _flatten( self, table: str, dict_row: DictStrAny, _r_lvl: int ) -> Tuple[DictStrAny, Dict[Tuple[str, ...], Sequence[Any]]]: @@ -141,13 +111,15 @@ def norm_row_dicts(dict_row: StrAny, __r_lvl: int, path: Tuple[str, ...] = ()) - norm_k = self.EMPTY_KEY_IDENTIFIER # if norm_k != k: # print(f"{k} -> {norm_k}") - child_name = ( + nested_name = ( norm_k if path == () else schema_naming.shorten_fragments(*path, norm_k) ) - # for lists and dicts we must check if type is possibly complex + # for lists and dicts we must check if type is possibly nested if isinstance(v, (dict, list)): - if not self._is_complex_type(table, child_name, __r_lvl): - # TODO: if schema contains table {table}__{child_name} then convert v into single element list + if not self._is_nested_type( + self.schema, table, nested_name, self.max_nesting, __r_lvl + ): + # TODO: if schema contains table {table}__{nested_name} then convert v into single element list if isinstance(v, dict): # flatten the dict more norm_row_dicts(v, __r_lvl + 1, path + (norm_k,)) @@ -156,10 +128,10 @@ def norm_row_dicts(dict_row: StrAny, __r_lvl: int, path: Tuple[str, ...] = ()) - out_rec_list[path + (schema_naming.normalize_table_identifier(k),)] = v continue else: - # pass the complex value to out_rec_row + # pass the nested value to out_rec_row pass - out_rec_row[child_name] = v + out_rec_row[nested_name] = v norm_row_dicts(dict_row, _r_lvl) return out_rec_row, out_rec_list @@ -179,10 +151,10 @@ def get_row_hash(row: Dict[str, Any], subset: Optional[List[str]] = None) -> str return digest128(row_str, DLT_ID_LENGTH_BYTES) @staticmethod - def _get_child_row_hash(parent_row_id: str, child_table: str, list_idx: int) -> str: - # create deterministic unique id of the child row taking into account that all lists are ordered - # and all child tables must be lists - return digest128(f"{parent_row_id}_{child_table}_{list_idx}", DLT_ID_LENGTH_BYTES) + def _get_nested_row_hash(parent_row_id: str, nested_table: str, list_idx: int) -> str: + # create deterministic unique id of the nested row taking into account that all lists are ordered + # and all nested tables must be lists + return digest128(f"{parent_row_id}_{nested_table}_{list_idx}", DLT_ID_LENGTH_BYTES) def _link_row(self, row: DictStrAny, parent_row_id: str, list_idx: int) -> DictStrAny: assert parent_row_id @@ -204,29 +176,27 @@ def _add_row_id( pos: int, _r_lvl: int, ) -> str: - primary_key = False - if _r_lvl > 0: # child table - primary_key = bool( - self.schema.filter_row_with_hint(table, "primary_key", flattened_row) - ) - row_id_type = self._get_row_id_type(self.schema, table, primary_key, _r_lvl) - - if row_id_type == "random": - row_id = generate_dlt_id() - else: - if _r_lvl == 0: # root table - if row_id_type in ("key_hash", "row_hash"): - subset = None - if row_id_type == "key_hash": - subset = self._get_primary_key(self.schema, table) - # base hash on `dict_row` instead of `flattened_row` - # so changes in child tables lead to new row id - row_id = self.get_row_hash(dict_row, subset=subset) - elif _r_lvl > 0: # child table - if row_id_type == "row_hash": - row_id = DataItemNormalizer._get_child_row_hash(parent_row_id, table, pos) - # link to parent table + if _r_lvl == 0: # root table + row_id_type = self._get_root_row_id_type(self.schema, table) + if row_id_type in ("key_hash", "row_hash"): + subset = None + if row_id_type == "key_hash": + subset = self._get_primary_key(self.schema, table) + # base hash on `dict_row` instead of `flattened_row` + # so changes in nested tables lead to new row id + row_id = self.get_row_hash(dict_row, subset=subset) + else: + row_id = generate_dlt_id() + else: # nested table + row_id_type, is_nested = self._get_nested_row_id_type(self.schema, table) + if row_id_type == "row_hash": + row_id = DataItemNormalizer._get_nested_row_hash(parent_row_id, table, pos) + # link to parent table + if is_nested: self._link_row(flattened_row, parent_row_id, pos) + else: + # do not create link if primary key was found for nested table + row_id = generate_dlt_id() flattened_row[self.c_dlt_id] = row_id return row_id @@ -236,7 +206,7 @@ def _get_propagated_values(self, table: str, row: DictStrAny, _r_lvl: int) -> St config = self.propagation_config if config: - # mapping(k:v): propagate property with name "k" as property with name "v" in child table + # mapping(k:v): propagate property with name "k" as property with name "v" in nested table mappings: Dict[TColumnName, TColumnName] = {} if _r_lvl == 0: mappings.update(config.get("root") or {}) @@ -249,7 +219,7 @@ def _get_propagated_values(self, table: str, row: DictStrAny, _r_lvl: int) -> St return extend - # generate child tables only for lists + # generate nested tables only for lists def _normalize_list( self, seq: Sequence[Any], @@ -262,8 +232,8 @@ def _normalize_list( table = self.schema.naming.shorten_fragments(*parent_path, *ident_path) for idx, v in enumerate(seq): - # yield child table row if isinstance(v, dict): + # found dict element in seq yield from self._normalize_row( v, extend, ident_path, parent_path, parent_row_id, idx, _r_lvl ) @@ -279,13 +249,11 @@ def _normalize_list( _r_lvl + 1, ) else: - # list of simple types - child_row_hash = DataItemNormalizer._get_child_row_hash(parent_row_id, table, idx) + # found non-dict in seq, so wrap it wrap_v = wrap_in_dict(self.c_value, v) - wrap_v[self.c_dlt_id] = child_row_hash - e = self._link_row(wrap_v, parent_row_id, idx) - DataItemNormalizer._extend_row(extend, e) - yield (table, self.schema.naming.shorten_fragments(*parent_path)), e + DataItemNormalizer._extend_row(extend, wrap_v) + self._add_row_id(table, wrap_v, wrap_v, parent_row_id, idx, _r_lvl) + yield (table, self.schema.naming.shorten_fragments(*parent_path)), wrap_v def _normalize_row( self, @@ -308,7 +276,7 @@ def _normalize_row( if not row_id: row_id = self._add_row_id(table, dict_row, flattened_row, parent_row_id, pos, _r_lvl) - # find fields to propagate to child tables in config + # find fields to propagate to nested tables in config extend.update(self._get_propagated_values(table, flattened_row, _r_lvl)) # yield parent table first @@ -351,9 +319,10 @@ def extend_schema(self) -> None: TSimpleRegex(self.c_dlt_list_idx), TSimpleRegex(self.c_dlt_load_id), ], - "foreign_key": [TSimpleRegex(self.c_dlt_parent_id)], + "parent_key": [TSimpleRegex(self.c_dlt_parent_id)], "root_key": [TSimpleRegex(self.c_dlt_root_id)], "unique": [TSimpleRegex(self.c_dlt_id)], + "row_key": [TSimpleRegex(self.c_dlt_id)], }, normalize_identifiers=False, # already normalized ) @@ -368,7 +337,7 @@ def extend_table(self, table_name: str) -> None: Table name should be normalized. """ table = self.schema.tables.get(table_name) - if not table.get("parent") and table.get("write_disposition") == "merge": + if not is_nested_table(table) and table.get("write_disposition") == "merge": DataItemNormalizer.update_normalizer_config( self.schema, { @@ -392,11 +361,6 @@ def normalize_data_item( row = cast(DictStrAny, item) # identify load id if loaded data must be processed after loading incrementally row[self.c_dlt_load_id] = load_id - if self._get_merge_strategy(self.schema, table_name) == "scd2": - self._validate_validity_column_names( - self.schema.name, self._get_validity_column_names(self.schema, table_name), item - ) - yield from self._normalize_row( row, {}, @@ -459,18 +423,12 @@ def _normalize_prop( ) @staticmethod - @lru_cache(maxsize=None) def _get_table_nesting_level(schema: Schema, table_name: str) -> Optional[int]: table = schema.tables.get(table_name) if table: return table.get("x-normalizer", {}).get("max_nesting") # type: ignore return None - @staticmethod - @lru_cache(maxsize=None) - def _get_merge_strategy(schema: Schema, table_name: str) -> Optional[TLoaderMergeStrategy]: - return get_merge_strategy(schema.tables, table_name) - @staticmethod @lru_cache(maxsize=None) def _get_primary_key(schema: Schema, table_name: str) -> List[str]: @@ -481,16 +439,50 @@ def _get_primary_key(schema: Schema, table_name: str) -> List[str]: @staticmethod @lru_cache(maxsize=None) - def _get_validity_column_names(schema: Schema, table_name: str) -> List[Optional[str]]: - return get_validity_column_names(schema.get_table(table_name)) + def _is_nested_type( + schema: Schema, table_name: str, field_name: str, max_nesting: int, _r_lvl: int + ) -> bool: + """For those paths the nested objects should be left in place. + Cache perf: max_nesting < _r_lvl: ~2x faster, full check 10x faster + """ + # turn everything at the recursion level into nested type + max_table_nesting = DataItemNormalizer._get_table_nesting_level(schema, table_name) + if max_table_nesting is not None: + max_nesting = max_table_nesting + + assert _r_lvl <= max_nesting + if _r_lvl == max_nesting: + return True + + column: TColumnSchema = None + table = schema.tables.get(table_name) + if table: + column = table["columns"].get(field_name) + if column is None or "data_type" not in column: + data_type = schema.get_preferred_type(field_name) + else: + data_type = column["data_type"] + + return data_type == "json" @staticmethod @lru_cache(maxsize=None) - def _get_row_id_type( - schema: Schema, table_name: str, primary_key: bool, _r_lvl: int - ) -> TRowIdType: - if _r_lvl == 0: # root table - merge_strategy = DataItemNormalizer._get_merge_strategy(schema, table_name) + def _get_nested_row_id_type(schema: Schema, table_name: str) -> Tuple[TRowIdType, bool]: + """Gets type of row id to be added to nested table and if linking information should be added""" + if table := schema.tables.get(table_name): + merge_strategy = resolve_merge_strategy(schema.tables, table) + if merge_strategy not in ("upsert", "scd2") and not is_nested_table(table): + return "random", False + else: + # table will be created, use standard linking + pass + return "row_hash", True + + @staticmethod + @lru_cache(maxsize=None) + def _get_root_row_id_type(schema: Schema, table_name: str) -> TRowIdType: + if table := schema.tables.get(table_name): + merge_strategy = resolve_merge_strategy(schema.tables, table) if merge_strategy == "upsert": return "key_hash" elif merge_strategy == "scd2": @@ -499,26 +491,6 @@ def _get_row_id_type( "x-row-version", include_incomplete=True, ) - if x_row_version_col == DataItemNormalizer.C_DLT_ID: + if x_row_version_col == schema.naming.normalize_identifier(C_DLT_ID): return "row_hash" - elif _r_lvl > 0: # child table - merge_strategy = DataItemNormalizer._get_merge_strategy(schema, table_name) - if merge_strategy in ("upsert", "scd2"): - # these merge strategies rely on deterministic child row hash - return "row_hash" - if not primary_key: - return "row_hash" return "random" - - @staticmethod - def _validate_validity_column_names( - schema_name: str, validity_column_names: List[Optional[str]], item: TDataItem - ) -> None: - """Raises exception if configured validity column name appears in data item.""" - for validity_column_name in validity_column_names: - if validity_column_name in item.keys(): - raise ColumnNameConflictException( - schema_name, - "Found column in data item with same name as validity column" - f' "{validity_column_name}".', - ) diff --git a/dlt/common/normalizers/naming/snake_case.py b/dlt/common/normalizers/naming/snake_case.py index d38841a238..819fe7fc57 100644 --- a/dlt/common/normalizers/naming/snake_case.py +++ b/dlt/common/normalizers/naming/snake_case.py @@ -21,7 +21,7 @@ class NamingConvention(BaseNamingConvention): - Replaces all trailing `_` with `x` - Replaces `+` and `*` with `x`, `-` with `_`, `@` with `a` and `|` with `l` - Uses __ as patent-child separator for tables and flattened column names. + Uses __ as parent-child separator for tables and flattened column names. """ RE_UNDERSCORES: ClassVar[REPattern] = RE_UNDERSCORES diff --git a/dlt/common/normalizers/utils.py b/dlt/common/normalizers/utils.py index d852cfb7d9..c090aa1bde 100644 --- a/dlt/common/normalizers/utils.py +++ b/dlt/common/normalizers/utils.py @@ -1,188 +1,11 @@ import os -from importlib import import_module -from types import ModuleType -from typing import Any, Dict, Optional, Type, Tuple, cast, List +from typing import List -import dlt -from dlt.common import logger from dlt.common import known_env -from dlt.common.configuration.inject import with_config -from dlt.common.configuration.specs import known_sections -from dlt.common.destination import DestinationCapabilitiesContext -from dlt.common.normalizers.configuration import NormalizersConfiguration -from dlt.common.normalizers.exceptions import InvalidJsonNormalizer -from dlt.common.normalizers.json import SupportsDataItemNormalizer, DataItemNormalizer -from dlt.common.normalizers.naming import NamingConvention -from dlt.common.normalizers.naming.exceptions import ( - NamingTypeNotFound, - UnknownNamingModule, - InvalidNamingType, -) -from dlt.common.normalizers.typing import ( - TJSONNormalizer, - TNormalizersConfig, - TNamingConventionReferenceArg, -) -from dlt.common.typing import is_subclass -from dlt.common.utils import get_full_class_name, uniq_id_base64, many_uniq_ids_base64 +from dlt.common.utils import uniq_id_base64, many_uniq_ids_base64 -DEFAULT_NAMING_NAMESPACE = os.environ.get( - known_env.DLT_DEFAULT_NAMING_NAMESPACE, "dlt.common.normalizers.naming" -) -DEFAULT_NAMING_MODULE = os.environ.get(known_env.DLT_DEFAULT_NAMING_MODULE, "snake_case") -DLT_ID_LENGTH_BYTES = int(os.environ.get(known_env.DLT_DLT_ID_LENGTH_BYTES, 10)) - - -def _section_for_schema(kwargs: Dict[str, Any]) -> Tuple[str, ...]: - """Uses the schema name to generate dynamic section normalizer settings""" - if schema_name := kwargs.get("schema_name"): - return (known_sections.SOURCES, schema_name) - else: - return (known_sections.SOURCES,) - - -@with_config(spec=NormalizersConfiguration, sections=_section_for_schema) # type: ignore[call-overload] -def explicit_normalizers( - naming: TNamingConventionReferenceArg = dlt.config.value, - json_normalizer: TJSONNormalizer = dlt.config.value, - allow_identifier_change_on_table_with_data: bool = None, - schema_name: Optional[str] = None, -) -> TNormalizersConfig: - """Gets explicitly configured normalizers without any defaults or capabilities injection. If `naming` - is a module or a type it will get converted into string form via import. - - If `schema_name` is present, a section ("sources", schema_name, "schema") is used to inject the config - """ - - norm_conf: TNormalizersConfig = {"names": serialize_reference(naming), "json": json_normalizer} - if allow_identifier_change_on_table_with_data is not None: - norm_conf["allow_identifier_change_on_table_with_data"] = ( - allow_identifier_change_on_table_with_data - ) - return norm_conf - - -@with_config -def import_normalizers( - explicit_normalizers: TNormalizersConfig, - default_normalizers: TNormalizersConfig = None, - destination_capabilities: DestinationCapabilitiesContext = None, -) -> Tuple[TNormalizersConfig, NamingConvention, Type[DataItemNormalizer[Any]]]: - """Imports the normalizers specified in `normalizers_config` or taken from defaults. Returns the updated config and imported modules. - - `destination_capabilities` are used to get naming convention, max length of the identifier and max nesting level. - """ - if default_normalizers is None: - default_normalizers = {} - # add defaults to normalizer_config - naming: TNamingConventionReferenceArg = explicit_normalizers.get("names") - if naming is None: - if destination_capabilities: - naming = destination_capabilities.naming_convention - if naming is None: - naming = default_normalizers.get("names") or DEFAULT_NAMING_MODULE - naming_convention = naming_from_reference(naming, destination_capabilities) - explicit_normalizers["names"] = serialize_reference(naming) - - item_normalizer = explicit_normalizers.get("json") or default_normalizers.get("json") or {} - item_normalizer.setdefault("module", "dlt.common.normalizers.json.relational") - # if max_table_nesting is set, we need to set the max_table_nesting in the json_normalizer - if destination_capabilities and destination_capabilities.max_table_nesting is not None: - # TODO: this is a hack, we need a better method to do this - from dlt.common.normalizers.json.relational import DataItemNormalizer - try: - DataItemNormalizer.ensure_this_normalizer(item_normalizer) - item_normalizer.setdefault("config", {}) - item_normalizer["config"]["max_nesting"] = destination_capabilities.max_table_nesting # type: ignore[index] - except InvalidJsonNormalizer: - # not a right normalizer - logger.warning(f"JSON Normalizer {item_normalizer} does not support max_nesting") - pass - json_module = cast(SupportsDataItemNormalizer, import_module(item_normalizer["module"])) - explicit_normalizers["json"] = item_normalizer - return ( - explicit_normalizers, - naming_convention, - json_module.DataItemNormalizer, - ) - - -def naming_from_reference( - names: TNamingConventionReferenceArg, - destination_capabilities: DestinationCapabilitiesContext = None, -) -> NamingConvention: - """Resolves naming convention from reference in `names` and applies max length from `destination_capabilities` - - Reference may be: (1) shorthand name pointing to `dlt.common.normalizers.naming` namespace - (2) a type name which is a module containing `NamingConvention` attribute (3) a type of class deriving from NamingConvention - """ - - def _import_naming(module: str) -> ModuleType: - if "." in module: - # TODO: bump schema engine version and migrate schema. also change the name in TNormalizersConfig from names to naming - if module == "dlt.common.normalizers.names.snake_case": - module = f"{DEFAULT_NAMING_NAMESPACE}.{DEFAULT_NAMING_MODULE}" - # this is full module name - naming_module = import_module(module) - else: - # from known location - try: - naming_module = import_module(f"{DEFAULT_NAMING_NAMESPACE}.{module}") - except ImportError: - # also import local module - naming_module = import_module(module) - return naming_module - - def _get_type(naming_module: ModuleType, cls: str) -> Type[NamingConvention]: - class_: Type[NamingConvention] = getattr(naming_module, cls, None) - if class_ is None: - raise NamingTypeNotFound(naming_module.__name__, cls) - if is_subclass(class_, NamingConvention): - return class_ - raise InvalidNamingType(naming_module.__name__, cls) - - if is_subclass(names, NamingConvention): - class_: Type[NamingConvention] = names # type: ignore[assignment] - elif isinstance(names, ModuleType): - class_ = _get_type(names, "NamingConvention") - elif isinstance(names, str): - try: - class_ = _get_type(_import_naming(names), "NamingConvention") - except ImportError: - parts = names.rsplit(".", 1) - # we have no more options to try - if len(parts) <= 1: - raise UnknownNamingModule(names) - try: - class_ = _get_type(_import_naming(parts[0]), parts[1]) - except UnknownNamingModule: - raise - except ImportError: - raise UnknownNamingModule(names) - else: - raise ValueError(names) - - # get max identifier length - if destination_capabilities: - max_length = min( - destination_capabilities.max_identifier_length, - destination_capabilities.max_column_identifier_length, - ) - else: - max_length = None - - return class_(max_length) - - -def serialize_reference(naming: Optional[TNamingConventionReferenceArg]) -> Optional[str]: - """Serializes generic `naming` reference to importable string.""" - if naming is None: - return naming - if isinstance(naming, str): - return naming - # import reference and use naming to get valid path to type - return get_full_class_name(naming_from_reference(naming)) +DLT_ID_LENGTH_BYTES = int(os.environ.get(known_env.DLT_DLT_ID_LENGTH_BYTES, 10)) def generate_dlt_ids(n_ids: int) -> List[str]: diff --git a/dlt/common/normalizers/configuration.py b/dlt/common/schema/configuration.py similarity index 91% rename from dlt/common/normalizers/configuration.py rename to dlt/common/schema/configuration.py index 6011ba4774..e64dd57494 100644 --- a/dlt/common/normalizers/configuration.py +++ b/dlt/common/schema/configuration.py @@ -7,7 +7,7 @@ @configspec -class NormalizersConfiguration(BaseConfiguration): +class SchemaConfiguration(BaseConfiguration): # always in section __section__: ClassVar[str] = known_sections.SCHEMA diff --git a/dlt/common/schema/exceptions.py b/dlt/common/schema/exceptions.py index 2e75b4b3a1..2b9a2d8cd1 100644 --- a/dlt/common/schema/exceptions.py +++ b/dlt/common/schema/exceptions.py @@ -73,8 +73,8 @@ def __init__(self, schema_name: str, table_name: str, prop_name: str, val1: str, self.val2 = val2 super().__init__( schema_name, - f"Cannot merge partial tables for {table_name} due to property {prop_name}: {val1} !=" - f" {val2}", + f"Cannot merge partial tables into table `{table_name}` due to property `{prop_name}`" + f' with different values: "{val1}" != "{val2}"', ) diff --git a/dlt/common/schema/migrations.py b/dlt/common/schema/migrations.py index b64714ba19..d9e758f204 100644 --- a/dlt/common/schema/migrations.py +++ b/dlt/common/schema/migrations.py @@ -1,7 +1,6 @@ from typing import Dict, List, cast from dlt.common.data_types import TDataType -from dlt.common.normalizers.utils import explicit_normalizers from dlt.common.typing import DictStrAny from dlt.common.schema.typing import ( LOADS_TABLE_NAME, @@ -9,12 +8,16 @@ TSimpleRegex, TStoredSchema, TTableSchemaColumns, - TColumnHint, + TColumnDefaultHint, ) from dlt.common.schema.exceptions import SchemaEngineNoUpgradePathException - -from dlt.common.normalizers.utils import import_normalizers -from dlt.common.schema.utils import new_table, version_table, loads_table +from dlt.common.schema.utils import ( + get_columns_names_with_prop, + new_table, + version_table, + loads_table, + migrate_complex_types, +) def migrate_schema(schema_dict: DictStrAny, from_engine: int, to_engine: int) -> TStoredSchema: @@ -26,16 +29,19 @@ def migrate_schema(schema_dict: DictStrAny, from_engine: int, to_engine: int) -> schema_dict["excludes"] = [] from_engine = 2 if from_engine == 2 and to_engine > 2: + from dlt.common.schema.normalizers import import_normalizers, explicit_normalizers + # current version of the schema current = cast(TStoredSchema, schema_dict) # add default normalizers and root hash propagation - normalizers = explicit_normalizers() + # use explicit None to get default settings. ignore any naming conventions + normalizers = explicit_normalizers(naming=None, json_normalizer=None) current["normalizers"], _, _ = import_normalizers(normalizers, normalizers) current["normalizers"]["json"]["config"] = { "propagation": {"root": {"_dlt_id": "_dlt_root_id"}} } # move settings, convert strings to simple regexes - d_h: Dict[TColumnHint, List[TSimpleRegex]] = schema_dict.pop("hints", {}) + d_h: Dict[TColumnDefaultHint, List[TSimpleRegex]] = schema_dict.pop("hints", {}) for h_k, h_l in d_h.items(): d_h[h_k] = list(map(lambda r: TSimpleRegex("re:" + r), h_l)) p_t: Dict[TSimpleRegex, TDataType] = schema_dict.pop("preferred_types", {}) @@ -119,6 +125,50 @@ def migrate_filters(group: str, filters: List[str]) -> None: x_normalizer = table.setdefault("x-normalizer", {}) x_normalizer["seen-data"] = True from_engine = 9 + if from_engine == 9 and to_engine > 9: + from dlt.common.schema.normalizers import import_normalizers + + # current = cast(TStoredSchema, schema_dict) + + normalizers = schema_dict["normalizers"] + _, naming, _ = import_normalizers(normalizers) + c_dlt_id = naming.normalize_identifier("_dlt_id") + c_dlt_parent_id = naming.normalize_identifier("_dlt_parent_id") + + for table in schema_dict["tables"].values(): + # migrate complex -> json + migrate_complex_types(table) + # modify hints + if dlt_id_col := table["columns"].get(c_dlt_id): + # add row key only if unique is set + dlt_id_col["row_key"] = dlt_id_col.get("unique", False) + if parent_dlt_id_col := table["columns"].get(c_dlt_parent_id): + # add parent key + parent_dlt_id_col["parent_key"] = parent_dlt_id_col.get("foreign_key", False) + # drop all foreign keys + for column in table["columns"].values(): + column.pop("foreign_key", None) + + # migrate preferred types + if settings := schema_dict.get("settings"): + if p_t := settings.get("preferred_types"): + for re_ in list(p_t.keys()): + if p_t[re_] == "complex": + p_t[re_] = "json" + # migrate default hints + if default_hints := schema_dict["settings"].get("default_hints"): + # drop foreign key + default_hints.pop("foreign_key", None) + # add row and parent key + default_hints["row_key"] = [TSimpleRegex(c_dlt_id)] + default_hints["parent_key"] = [TSimpleRegex(c_dlt_parent_id)] + + # remove `generate_dlt_id` from normalizer + if json_norm := normalizers.get("json"): + if json_config := json_norm.get("config"): + json_config.pop("generate_dlt_id", None) + + from_engine = 10 schema_dict["engine_version"] = from_engine if from_engine != to_engine: diff --git a/dlt/common/schema/normalizers.py b/dlt/common/schema/normalizers.py new file mode 100644 index 0000000000..9b2a37e708 --- /dev/null +++ b/dlt/common/schema/normalizers.py @@ -0,0 +1,186 @@ +import os +from importlib import import_module +from types import ModuleType +from typing import Any, Dict, Optional, Type, Tuple, cast + +import dlt +from dlt.common import logger +from dlt.common import known_env +from dlt.common.configuration.inject import with_config +from dlt.common.configuration.specs import known_sections +from dlt.common.schema.configuration import SchemaConfiguration +from dlt.common.normalizers.exceptions import InvalidJsonNormalizer +from dlt.common.normalizers.json import SupportsDataItemNormalizer, DataItemNormalizer +from dlt.common.normalizers.naming import NamingConvention +from dlt.common.normalizers.naming.exceptions import ( + NamingTypeNotFound, + UnknownNamingModule, + InvalidNamingType, +) +from dlt.common.normalizers.typing import ( + TJSONNormalizer, + TNormalizersConfig, + TNamingConventionReferenceArg, +) +from dlt.common.typing import is_subclass +from dlt.common.utils import get_full_class_name + +DEFAULT_NAMING_NAMESPACE = os.environ.get( + known_env.DLT_DEFAULT_NAMING_NAMESPACE, "dlt.common.normalizers.naming" +) +DEFAULT_NAMING_MODULE = os.environ.get(known_env.DLT_DEFAULT_NAMING_MODULE, "snake_case") + + +def _section_for_schema(kwargs: Dict[str, Any]) -> Tuple[str, ...]: + """Uses the schema name to generate dynamic section normalizer settings""" + if schema_name := kwargs.get("schema_name"): + return (known_sections.SOURCES, schema_name) + else: + return (known_sections.SOURCES,) + + +@with_config(spec=SchemaConfiguration, sections=_section_for_schema) # type: ignore[call-overload] +def explicit_normalizers( + naming: TNamingConventionReferenceArg = dlt.config.value, + json_normalizer: TJSONNormalizer = dlt.config.value, + allow_identifier_change_on_table_with_data: bool = None, + schema_name: Optional[str] = None, +) -> TNormalizersConfig: + """Gets explicitly configured normalizers without any defaults or capabilities injection. If `naming` + is a module or a type it will get converted into string form via import. + + If `schema_name` is present, a section ("sources", schema_name, "schema") is used to inject the config + """ + + norm_conf: TNormalizersConfig = {"names": serialize_reference(naming), "json": json_normalizer} + if allow_identifier_change_on_table_with_data is not None: + norm_conf["allow_identifier_change_on_table_with_data"] = ( + allow_identifier_change_on_table_with_data + ) + return norm_conf + + +@with_config +def import_normalizers( + explicit_normalizers: TNormalizersConfig, + default_normalizers: TNormalizersConfig = None, +) -> Tuple[TNormalizersConfig, NamingConvention, Type[DataItemNormalizer[Any]]]: + """Imports the normalizers specified in `normalizers_config` or taken from defaults. Returns the updated config and imported modules. + + `destination_capabilities` are used to get naming convention, max length of the identifier and max nesting level. + """ + # use container to get destination capabilities, do not use config injection to resolve circular dependencies + from dlt.common.destination.capabilities import DestinationCapabilitiesContext + from dlt.common.configuration.container import Container + + destination_capabilities = Container().get(DestinationCapabilitiesContext) + if default_normalizers is None: + default_normalizers = {} + # add defaults to normalizer_config + naming: Optional[TNamingConventionReferenceArg] = explicit_normalizers.get("names") + if naming is None: + if destination_capabilities: + naming = destination_capabilities.naming_convention + if naming is None: + naming = default_normalizers.get("names") or DEFAULT_NAMING_MODULE + # get max identifier length + if destination_capabilities: + max_length = min( + destination_capabilities.max_identifier_length, + destination_capabilities.max_column_identifier_length, + ) + else: + max_length = None + naming_convention = naming_from_reference(naming, max_length) + explicit_normalizers["names"] = serialize_reference(naming) + + item_normalizer = explicit_normalizers.get("json") or default_normalizers.get("json") or {} + item_normalizer.setdefault("module", "dlt.common.normalizers.json.relational") + # if max_table_nesting is set, we need to set the max_table_nesting in the json_normalizer + if destination_capabilities and destination_capabilities.max_table_nesting is not None: + # TODO: this is a hack, we need a better method to do this + from dlt.common.normalizers.json.relational import DataItemNormalizer + + try: + DataItemNormalizer.ensure_this_normalizer(item_normalizer) + item_normalizer.setdefault("config", {}) + item_normalizer["config"]["max_nesting"] = destination_capabilities.max_table_nesting # type: ignore[index] + except InvalidJsonNormalizer: + # not a right normalizer + logger.warning(f"JSON Normalizer {item_normalizer} does not support max_nesting") + pass + json_module = cast(SupportsDataItemNormalizer, import_module(item_normalizer["module"])) + explicit_normalizers["json"] = item_normalizer + return ( + explicit_normalizers, + naming_convention, + json_module.DataItemNormalizer, + ) + + +def naming_from_reference( + names: TNamingConventionReferenceArg, + max_length: Optional[int] = None, +) -> NamingConvention: + """Resolves naming convention from reference in `names` and applies max length if specified + + Reference may be: (1) shorthand name pointing to `dlt.common.normalizers.naming` namespace + (2) a type name which is a module containing `NamingConvention` attribute (3) a type of class deriving from NamingConvention + """ + + def _import_naming(module: str) -> ModuleType: + if "." in module: + # TODO: bump schema engine version and migrate schema. also change the name in TNormalizersConfig from names to naming + if module == "dlt.common.normalizers.names.snake_case": + module = f"{DEFAULT_NAMING_NAMESPACE}.{DEFAULT_NAMING_MODULE}" + # this is full module name + naming_module = import_module(module) + else: + # from known location + try: + naming_module = import_module(f"{DEFAULT_NAMING_NAMESPACE}.{module}") + except ImportError: + # also import local module + naming_module = import_module(module) + return naming_module + + def _get_type(naming_module: ModuleType, cls: str) -> Type[NamingConvention]: + class_: Type[NamingConvention] = getattr(naming_module, cls, None) + if class_ is None: + raise NamingTypeNotFound(naming_module.__name__, cls) + if is_subclass(class_, NamingConvention): + return class_ + raise InvalidNamingType(naming_module.__name__, cls) + + if is_subclass(names, NamingConvention): + class_: Type[NamingConvention] = names # type: ignore[assignment] + elif isinstance(names, ModuleType): + class_ = _get_type(names, "NamingConvention") + elif isinstance(names, str): + try: + class_ = _get_type(_import_naming(names), "NamingConvention") + except ImportError: + parts = names.rsplit(".", 1) + # we have no more options to try + if len(parts) <= 1: + raise UnknownNamingModule(names) + try: + class_ = _get_type(_import_naming(parts[0]), parts[1]) + except UnknownNamingModule: + raise + except ImportError: + raise UnknownNamingModule(names) + else: + raise ValueError(names) + + return class_(max_length) + + +def serialize_reference(naming: Optional[TNamingConventionReferenceArg]) -> Optional[str]: + """Serializes generic `naming` reference to importable string.""" + if naming is None: + return naming + if isinstance(naming, str): + return naming + # import reference and use naming to get valid path to type + return get_full_class_name(naming_from_reference(naming)) diff --git a/dlt/common/schema/schema.py b/dlt/common/schema/schema.py index da9e581637..0dbeda93cf 100644 --- a/dlt/common/schema/schema.py +++ b/dlt/common/schema/schema.py @@ -23,12 +23,10 @@ TDataItem, ) from dlt.common.normalizers import TNormalizersConfig, NamingConvention -from dlt.common.normalizers.utils import explicit_normalizers, import_normalizers from dlt.common.normalizers.json import DataItemNormalizer, TNormalizedRowIterator from dlt.common.schema import utils from dlt.common.data_types import py_type_to_sc_type, coerce_value, TDataType from dlt.common.schema.typing import ( - COLUMN_HINTS, DLT_NAME_PREFIX, SCHEMA_ENGINE_VERSION, LOADS_TABLE_NAME, @@ -46,6 +44,7 @@ TColumnSchema, TColumnProp, TColumnHint, + TColumnDefaultHint, TTypeDetections, TSchemaContractDict, TSchemaContract, @@ -58,8 +57,9 @@ SchemaCorruptedException, TableIdentifiersFrozen, ) -from dlt.common.validation import validate_dict +from dlt.common.schema.normalizers import import_normalizers, explicit_normalizers from dlt.common.schema.exceptions import DataValidationError +from dlt.common.validation import validate_dict DEFAULT_SCHEMA_CONTRACT_MODE: TSchemaContractDict = { @@ -99,7 +99,7 @@ class Schema: # list of preferred types: map regex on columns into types _compiled_preferred_types: List[Tuple[REPattern, TDataType]] # compiled default hints - _compiled_hints: Dict[TColumnHint, Sequence[REPattern]] + _compiled_hints: Dict[TColumnDefaultHint, Sequence[REPattern]] # compiled exclude filters per table _compiled_excludes: Dict[str, Sequence[REPattern]] # compiled include filters per table @@ -387,7 +387,7 @@ def resolve_contract_settings_for_table( tables = self._schema_tables # find root table try: - table = utils.get_top_level_table(tables, table_name) + table = utils.get_root_table(tables, table_name) settings = table["schema_contract"] except KeyError: settings = self._settings.get("schema_contract", {}) @@ -396,14 +396,19 @@ def resolve_contract_settings_for_table( return Schema.expand_schema_contract_settings(settings) def update_table( - self, partial_table: TPartialTableSchema, normalize_identifiers: bool = True + self, + partial_table: TPartialTableSchema, + normalize_identifiers: bool = True, + from_diff: bool = False, ) -> TPartialTableSchema: - """Adds or merges `partial_table` into the schema. Identifiers are normalized by default""" + """Adds or merges `partial_table` into the schema. Identifiers are normalized by default. + `from_diff` + """ + parent_table_name = partial_table.get("parent") if normalize_identifiers: partial_table = utils.normalize_table_identifiers(partial_table, self.naming) table_name = partial_table["name"] - parent_table_name = partial_table.get("parent") # check if parent table present if parent_table_name is not None: if self._schema_tables.get(parent_table_name) is None: @@ -418,10 +423,14 @@ def update_table( table = self._schema_tables.get(table_name) if table is None: # add the whole new table to SchemaTables + assert not from_diff, "Cannot update the whole table from diff" self._schema_tables[table_name] = partial_table else: - # merge tables performing additional checks - partial_table = utils.merge_table(self.name, table, partial_table) + if from_diff: + partial_table = utils.merge_diff(table, partial_table) + else: + # merge tables performing additional checks + partial_table = utils.merge_table(self.name, table, partial_table) self.data_item_normalizer.extend_table(table_name) return partial_table @@ -447,7 +456,9 @@ def drop_tables( result.append(self._schema_tables.pop(table_name)) return result - def filter_row_with_hint(self, table_name: str, hint_type: TColumnHint, row: StrAny) -> StrAny: + def filter_row_with_hint( + self, table_name: str, hint_type: TColumnDefaultHint, row: StrAny + ) -> StrAny: rv_row: DictStrAny = {} column_prop: TColumnProp = utils.hint_to_column_prop(hint_type) try: @@ -459,7 +470,7 @@ def filter_row_with_hint(self, table_name: str, hint_type: TColumnHint, row: Str rv_row[column_name] = row[column_name] except KeyError: for k, v in row.items(): - if self._infer_hint(hint_type, v, k): + if self._infer_hint(hint_type, k): rv_row[k] = v # dicts are ordered and we will return the rows with hints in the same order as they appear in the columns @@ -467,7 +478,7 @@ def filter_row_with_hint(self, table_name: str, hint_type: TColumnHint, row: Str def merge_hints( self, - new_hints: Mapping[TColumnHint, Sequence[TSimpleRegex]], + new_hints: Mapping[TColumnDefaultHint, Sequence[TSimpleRegex]], normalize_identifiers: bool = True, ) -> None: """Merges existing default hints with `new_hints`. Normalizes names in column regexes if possible. Compiles setting at the end @@ -747,6 +758,7 @@ def update_normalizers(self) -> None: def will_update_normalizers(self) -> bool: """Checks if schema has any pending normalizer updates due to configuration or destination capabilities""" + # import desired modules _, to_naming, _ = import_normalizers( explicit_normalizers(schema_name=self._schema_name), self._normalizers_config @@ -765,11 +777,16 @@ def _infer_column( column_schema = TColumnSchema( name=k, data_type=data_type or self._infer_column_type(v, k), - nullable=not self._infer_hint("not_null", v, k), + nullable=not self._infer_hint("not_null", k), ) - for hint in COLUMN_HINTS: + # check other preferred hints that are available + for hint in self._compiled_hints: + # already processed + if hint == "not_null": + continue column_prop = utils.hint_to_column_prop(hint) - hint_value = self._infer_hint(hint, v, k) + hint_value = self._infer_hint(hint, k) + # set only non-default values if not utils.has_default_column_prop_value(column_prop, hint_value): column_schema[column_prop] = hint_value @@ -783,7 +800,7 @@ def _coerce_null_value( """Raises when column is explicitly not nullable""" if col_name in table_columns: existing_column = table_columns[col_name] - if not existing_column.get("nullable", True): + if not utils.is_nullable_column(existing_column): raise CannotCoerceNullException(self.name, table_name, col_name) def _coerce_non_null_value( @@ -824,11 +841,18 @@ def _coerce_non_null_value( v, ) # otherwise we must create variant extension to the table - # pass final=True so no more auto-variants can be created recursively - # TODO: generate callback so dlt user can decide what to do + # backward compatibility for complex types: if such column exists then use it variant_col_name = self.naming.shorten_fragments( col_name, VARIANT_FIELD_FORMAT % py_type ) + if py_type == "json": + old_complex_col_name = self.naming.shorten_fragments( + col_name, VARIANT_FIELD_FORMAT % "complex" + ) + if old_column := table_columns.get(old_complex_col_name): + if old_column.get("variant"): + variant_col_name = old_complex_col_name + # pass final=True so no more auto-variants can be created recursively return self._coerce_non_null_value( table_columns, table_name, variant_col_name, v, is_variant=True ) @@ -872,7 +896,7 @@ def _infer_column_type(self, v: Any, col_name: str, skip_preferred: bool = False preferred_type = self.get_preferred_type(col_name) return preferred_type or mapped_type - def _infer_hint(self, hint_type: TColumnHint, _: Any, col_name: str) -> bool: + def _infer_hint(self, hint_type: TColumnDefaultHint, col_name: str) -> bool: if hint_type in self._compiled_hints: return any(h.search(col_name) for h in self._compiled_hints[hint_type]) else: @@ -880,7 +904,7 @@ def _infer_hint(self, hint_type: TColumnHint, _: Any, col_name: str) -> bool: def _merge_hints( self, - new_hints: Mapping[TColumnHint, Sequence[TSimpleRegex]], + new_hints: Mapping[TColumnDefaultHint, Sequence[TSimpleRegex]], normalize_identifiers: bool = True, ) -> None: """Used by `merge_hints method, does not compile settings at the end""" @@ -968,8 +992,8 @@ def _add_standard_hints(self) -> None: self._settings["detections"] = type_detections def _normalize_default_hints( - self, default_hints: Mapping[TColumnHint, Sequence[TSimpleRegex]] - ) -> Dict[TColumnHint, List[TSimpleRegex]]: + self, default_hints: Mapping[TColumnDefaultHint, Sequence[TSimpleRegex]] + ) -> Dict[TColumnDefaultHint, List[TSimpleRegex]]: """Normalizes the column names in default hints. In case of column names that are regexes, normalization is skipped""" return { hint: [utils.normalize_simple_regex_column(self.naming, regex) for regex in regexes] @@ -1116,7 +1140,6 @@ def _renormalize_schema_identifiers( def _configure_normalizers(self, explicit_normalizers: TNormalizersConfig) -> None: """Gets naming and item normalizer from schema yaml, config providers and destination capabilities and applies them to schema.""" - # import desired modules normalizers_config, to_naming, item_normalizer_class = import_normalizers( explicit_normalizers, self._normalizers_config ) @@ -1136,7 +1159,7 @@ def _reset_schema(self, name: str, normalizers: TNormalizersConfig = None) -> No self._settings: TSchemaSettings = {} self._compiled_preferred_types: List[Tuple[REPattern, TDataType]] = [] - self._compiled_hints: Dict[TColumnHint, Sequence[REPattern]] = {} + self._compiled_hints: Dict[TColumnDefaultHint, Sequence[REPattern]] = {} self._compiled_excludes: Dict[str, Sequence[REPattern]] = {} self._compiled_includes: Dict[str, Sequence[REPattern]] = {} self._type_detections: Sequence[TTypeDetections] = None diff --git a/dlt/common/schema/typing.py b/dlt/common/schema/typing.py index 284c55caac..9221cca7ff 100644 --- a/dlt/common/schema/typing.py +++ b/dlt/common/schema/typing.py @@ -4,9 +4,11 @@ Dict, List, Literal, + NamedTuple, Optional, Sequence, Set, + Tuple, Type, TypedDict, NewType, @@ -26,7 +28,7 @@ # current version of schema engine -SCHEMA_ENGINE_VERSION = 9 +SCHEMA_ENGINE_VERSION = 10 # dlt tables VERSION_TABLE_NAME = "_dlt_version" @@ -34,39 +36,97 @@ PIPELINE_STATE_TABLE_NAME = "_dlt_pipeline_state" DLT_NAME_PREFIX = "_dlt" +# default dlt columns +C_DLT_ID = "_dlt_id" +"""unique id of current row""" +C_DLT_LOAD_ID = "_dlt_load_id" +"""load id to identify records loaded in a single load package""" + TColumnProp = Literal[ "name", + # data type "data_type", + "precision", + "scale", + "timezone", "nullable", + "variant", + # hints "partition", "cluster", "primary_key", - "foreign_key", "sort", "unique", "merge_key", + "row_key", + "parent_key", "root_key", "hard_delete", "dedup_sort", ] -"""Known properties and hints of the column""" -# TODO: merge TColumnHint with TColumnProp +"""All known properties of the column, including name, data type info and hints""" +COLUMN_PROPS: Set[TColumnProp] = set(get_args(TColumnProp)) + TColumnHint = Literal[ - "not_null", + "nullable", "partition", "cluster", "primary_key", - "foreign_key", "sort", "unique", "merge_key", + "row_key", + "parent_key", "root_key", "hard_delete", "dedup_sort", ] -"""Known hints of a column used to declare hint regexes.""" +"""Known hints of a column""" +COLUMN_HINTS: Set[TColumnHint] = set(get_args(TColumnHint)) + + +class TColumnPropInfo(NamedTuple): + name: Union[TColumnProp, str] + defaults: Tuple[Any, ...] = (None,) + is_hint: bool = False + + +_ColumnPropInfos = [ + TColumnPropInfo("name"), + TColumnPropInfo("data_type"), + TColumnPropInfo("precision"), + TColumnPropInfo("scale"), + TColumnPropInfo("timezone", (True, None)), + TColumnPropInfo("nullable", (True, None)), + TColumnPropInfo("variant", (False, None)), + TColumnPropInfo("partition", (False, None)), + TColumnPropInfo("cluster", (False, None)), + TColumnPropInfo("primary_key", (False, None)), + TColumnPropInfo("sort", (False, None)), + TColumnPropInfo("unique", (False, None)), + TColumnPropInfo("merge_key", (False, None)), + TColumnPropInfo("row_key", (False, None)), + TColumnPropInfo("parent_key", (False, None)), + TColumnPropInfo("root_key", (False, None)), + TColumnPropInfo("hard_delete", (False, None)), + TColumnPropInfo("dedup_sort", (False, None)), + # any x- hint with special settings ie. defaults + TColumnPropInfo("x-active-record-timestamp", (), is_hint=True), # no default values +] + +ColumnPropInfos: Dict[Union[TColumnProp, str], TColumnPropInfo] = { + info.name: info for info in _ColumnPropInfos +} +# verify column props and column hints infos +for hint in COLUMN_HINTS: + assert hint in COLUMN_PROPS, f"Hint {hint} must be a column prop" -TTableFormat = Literal["iceberg", "delta"] +for prop in COLUMN_PROPS: + assert prop in ColumnPropInfos, f"Column {prop} has no info, please define" + if prop in COLUMN_HINTS: + ColumnPropInfos[prop] = ColumnPropInfos[prop]._replace(is_hint=True) + +TTableFormat = Literal["iceberg", "delta", "hive"] TFileFormat = Literal[Literal["preferred"], TLoaderFileFormat] TTypeDetections = Literal[ "timestamp", "iso_timestamp", "iso_date", "large_integer", "hexbytes_to_text", "wei_to_double" @@ -75,32 +135,19 @@ TColumnNames = Union[str, Sequence[str]] """A string representing a column name or a list of""" -# COLUMN_PROPS: Set[TColumnProp] = set(get_args(TColumnProp)) -COLUMN_HINTS: Set[TColumnHint] = set( - [ - "partition", - "cluster", - "primary_key", - "foreign_key", - "sort", - "unique", - "merge_key", - "root_key", - ] -) - class TColumnType(TypedDict, total=False): data_type: Optional[TDataType] + nullable: Optional[bool] precision: Optional[int] scale: Optional[int] + timezone: Optional[bool] class TColumnSchemaBase(TColumnType, total=False): """TypedDict that defines basic properties of a column: name, data type and nullable""" name: Optional[str] - nullable: Optional[bool] class TColumnSchema(TColumnSchemaBase, total=False): @@ -112,7 +159,8 @@ class TColumnSchema(TColumnSchemaBase, total=False): unique: Optional[bool] sort: Optional[bool] primary_key: Optional[bool] - foreign_key: Optional[bool] + row_key: Optional[bool] + parent_key: Optional[bool] root_key: Optional[bool] merge_key: Optional[bool] variant: Optional[bool] @@ -194,13 +242,9 @@ class TMergeDispositionDict(TWriteDispositionDict, total=False): TWriteDispositionConfig = Union[TWriteDisposition, TWriteDispositionDict, TMergeDispositionDict] -# TypedDict that defines properties of a table -class TTableSchema(TTableProcessingHints, total=False): - """TypedDict that defines properties of a table""" - +class _TTableSchemaBase(TTableProcessingHints, total=False): name: Optional[str] description: Optional[str] - write_disposition: Optional[TWriteDisposition] schema_contract: Optional[TSchemaContract] table_sealed: Optional[bool] parent: Optional[str] @@ -211,18 +255,26 @@ class TTableSchema(TTableProcessingHints, total=False): file_format: Optional[TFileFormat] +class TTableSchema(_TTableSchemaBase, total=False): + """TypedDict that defines properties of a table""" + + write_disposition: Optional[TWriteDisposition] + + class TPartialTableSchema(TTableSchema): pass TSchemaTables = Dict[str, TTableSchema] TSchemaUpdate = Dict[str, List[TPartialTableSchema]] +TColumnDefaultHint = Literal["not_null", TColumnHint] +"""Allows using not_null in default hints setting section""" class TSchemaSettings(TypedDict, total=False): schema_contract: Optional[TSchemaContract] detections: Optional[List[TTypeDetections]] - default_hints: Optional[Dict[TColumnHint, List[TSimpleRegex]]] + default_hints: Optional[Dict[TColumnDefaultHint, List[TSimpleRegex]]] preferred_types: Optional[Dict[TSimpleRegex, TDataType]] diff --git a/dlt/common/schema/utils.py b/dlt/common/schema/utils.py index 8b87a7e5fe..4c458e52a6 100644 --- a/dlt/common/schema/utils.py +++ b/dlt/common/schema/utils.py @@ -1,6 +1,7 @@ import re import base64 import hashlib +import warnings import yaml from copy import deepcopy, copy from typing import Dict, List, Sequence, Tuple, Type, Any, cast, Iterable, Optional, Union @@ -17,12 +18,13 @@ from dlt.common.validation import TCustomValidator, validate_dict_ignoring_xkeys from dlt.common.schema import detections from dlt.common.schema.typing import ( - COLUMN_HINTS, + C_DLT_ID, SCHEMA_ENGINE_VERSION, LOADS_TABLE_NAME, SIMPLE_REGEX_PREFIX, VERSION_TABLE_NAME, PIPELINE_STATE_TABLE_NAME, + ColumnPropInfos, TColumnName, TFileFormat, TPartialTableSchema, @@ -36,7 +38,7 @@ TColumnSchema, TColumnProp, TTableFormat, - TColumnHint, + TColumnDefaultHint, TTableSchemaColumns, TTypeDetectionFunc, TTypeDetections, @@ -51,11 +53,11 @@ TablePropertiesConflictException, InvalidSchemaName, ) +from dlt.common.warnings import Dlt100DeprecationWarning RE_NON_ALPHANUMERIC_UNDERSCORE = re.compile(r"[^a-zA-Z\d_]") DEFAULT_WRITE_DISPOSITION: TWriteDisposition = "append" -DEFAULT_MERGE_STRATEGY: TLoaderMergeStrategy = "delete-insert" def is_valid_schema_name(name: str) -> bool: @@ -67,6 +69,12 @@ def is_valid_schema_name(name: str) -> bool: ) +def is_nested_table(table: TTableSchema) -> bool: + """Checks if table is a dlt nested table: connected to parent table via row_key - parent_key reference""" + # "parent" table hint indicates NESTED table. + return bool(table.get("parent")) + + def normalize_schema_name(name: str) -> str: """Normalizes schema name by using snake case naming convention. The maximum length is 64 characters""" snake_case = SnakeCase(InvalidSchemaName.MAXIMUM_SCHEMA_NAME_LENGTH) @@ -81,12 +89,6 @@ def apply_defaults(stored_schema: TStoredSchema) -> TStoredSchema: for table_name, table in stored_schema["tables"].items(): # overwrite name table["name"] = table_name - # add default write disposition to root tables - if table.get("parent") is None: - if table.get("write_disposition") is None: - table["write_disposition"] = DEFAULT_WRITE_DISPOSITION - if table.get("resource") is None: - table["resource"] = table_name for column_name in table["columns"]: # add default hints to tables column = table["columns"][column_name] @@ -94,6 +96,12 @@ def apply_defaults(stored_schema: TStoredSchema) -> TStoredSchema: column["name"] = column_name # set column with default # table["columns"][column_name] = column + # add default write disposition to root tables + if not is_nested_table(table): + if table.get("write_disposition") is None: + table["write_disposition"] = DEFAULT_WRITE_DISPOSITION + if table.get("resource") is None: + table["resource"] = table_name return stored_schema @@ -104,11 +112,11 @@ def remove_defaults(stored_schema: TStoredSchema) -> TStoredSchema: * removed resource name if same as table name """ clean_tables = deepcopy(stored_schema["tables"]) - for table_name, t in clean_tables.items(): - del t["name"] - if t.get("resource") == table_name: - del t["resource"] - for c in t["columns"].values(): + for table in clean_tables.values(): + del table["name"] + # if t.get("resource") == table_name: + # del t["resource"] + for c in table["columns"].values(): # remove defaults only on complete columns # if is_complete_column(c): # remove_column_defaults(c) @@ -124,15 +132,9 @@ def remove_defaults(stored_schema: TStoredSchema) -> TStoredSchema: def has_default_column_prop_value(prop: str, value: Any) -> bool: """Checks if `value` is a default for `prop`.""" # remove all boolean hints that are False, except "nullable" which is removed when it is True - # TODO: merge column props and hints - if prop in COLUMN_HINTS: - return value in (False, None) - # TODO: type all the hints including default value so those exceptions may be removed - if prop == "nullable": - return value in (True, None) - if prop == "x-active-record-timestamp": - # None is a valid value so it is not a default - return False + if prop in ColumnPropInfos: + return value in ColumnPropInfos[prop].defaults + # for any unknown hint ie. "x-" the defaults are return value in (None, False) @@ -357,14 +359,11 @@ def is_nullable_column(col: TColumnSchemaBase) -> bool: return col.get("nullable", True) -def find_incomplete_columns( - tables: List[TTableSchema], -) -> Iterable[Tuple[str, TColumnSchemaBase, bool]]: - """Yields (table_name, column, nullable) for all incomplete columns in `tables`""" - for table in tables: - for col in table["columns"].values(): - if not is_complete_column(col): - yield table["name"], col, is_nullable_column(col) +def find_incomplete_columns(table: TTableSchema) -> Iterable[Tuple[TColumnSchemaBase, bool]]: + """Yields (column, nullable) for all incomplete columns in `table`""" + for col in table["columns"].values(): + if not is_complete_column(col): + yield col, is_nullable_column(col) def compare_complete_columns(a: TColumnSchema, b: TColumnSchema) -> bool: @@ -431,6 +430,10 @@ def diff_table( * when columns with the same name have different data types * when table links to different parent tables """ + if tab_a["name"] != tab_b["name"]: + raise TablePropertiesConflictException( + schema_name, tab_a["name"], "name", tab_a["name"], tab_b["name"] + ) table_name = tab_a["name"] # check if table properties can be merged if tab_a.get("parent") != tab_b.get("parent"): @@ -476,7 +479,7 @@ def diff_table( partial_table[k] = v # type: ignore # this should not really happen - if tab_a.get("parent") is not None and (resource := tab_b.get("resource")): + if is_nested_table(tab_a) and (resource := tab_b.get("resource")): raise TablePropertiesConflictException( schema_name, table_name, "resource", resource, tab_a.get("parent") ) @@ -500,25 +503,24 @@ def merge_table( schema_name: str, table: TTableSchema, partial_table: TPartialTableSchema ) -> TPartialTableSchema: """Merges "partial_table" into "table". `table` is merged in place. Returns the diff partial table. + `table` and `partial_table` names must be identical. A table diff is generated and applied to `table` + """ + return merge_diff(table, diff_table(schema_name, table, partial_table)) + - `table` and `partial_table` names must be identical. A table diff is generated and applied to `table`: +def merge_diff(table: TTableSchema, table_diff: TPartialTableSchema) -> TPartialTableSchema: + """Merges a table diff `table_diff` into `table`. `table` is merged in place. Returns the diff. * new columns are added, updated columns are replaced from diff * incomplete columns in `table` that got completed in `partial_table` are removed to preserve order * table hints are added or replaced from diff * nothing gets deleted """ - - if table["name"] != partial_table["name"]: - raise TablePropertiesConflictException( - schema_name, table["name"], "name", table["name"], partial_table["name"] - ) - diff = diff_table(schema_name, table, partial_table) # add new columns when all checks passed - updated_columns = merge_columns(table["columns"], diff["columns"]) - table.update(diff) + updated_columns = merge_columns(table["columns"], table_diff["columns"]) + table.update(table_diff) table["columns"] = updated_columns - return diff + return table_diff def normalize_table_identifiers(table: TTableSchema, naming: NamingConvention) -> TTableSchema: @@ -584,7 +586,7 @@ def get_processing_hints(tables: TSchemaTables) -> Dict[str, List[str]]: return hints -def hint_to_column_prop(h: TColumnHint) -> TColumnProp: +def hint_to_column_prop(h: TColumnDefaultHint) -> TColumnProp: if h == "not_null": return "nullable" return h @@ -594,8 +596,8 @@ def get_columns_names_with_prop( table: TTableSchema, column_prop: Union[TColumnProp, str], include_incomplete: bool = False ) -> List[str]: return [ - c["name"] - for c in table["columns"].values() + c_n + for c_n, c in table["columns"].items() if column_prop in c and not has_default_column_prop_value(column_prop, c[column_prop]) # type: ignore[literal-required] and (include_incomplete or is_complete_column(c)) @@ -668,9 +670,8 @@ def get_inherited_table_hint( if hint: return hint - parent = table.get("parent") - if parent: - return get_inherited_table_hint(tables, parent, table_hint_name, allow_none) + if is_nested_table(table): + return get_inherited_table_hint(tables, table.get("parent"), table_hint_name, allow_none) if allow_none: return None @@ -713,13 +714,18 @@ def fill_hints_from_parent_and_clone_table( """Takes write disposition and table format from parent tables if not present""" # make a copy of the schema so modifications do not affect the original document table = deepcopy(table) - # add write disposition if not specified - in child tables + table_name = table["name"] if "write_disposition" not in table: - table["write_disposition"] = get_write_disposition(tables, table["name"]) + table["write_disposition"] = get_write_disposition(tables, table_name) if "table_format" not in table: - table["table_format"] = get_table_format(tables, table["name"]) + if table_format := get_table_format(tables, table_name): + table["table_format"] = table_format if "file_format" not in table: - table["file_format"] = get_file_format(tables, table["name"]) + if file_format := get_file_format(tables, table_name): + table["file_format"] = file_format + if "x-merge-strategy" not in table: + if strategy := get_merge_strategy(tables, table_name): + table["x-merge-strategy"] = strategy # type: ignore[typeddict-unknown-key] return table @@ -736,24 +742,26 @@ def table_schema_has_type_with_precision(table: TTableSchema, _typ: TDataType) - ) -def get_top_level_table(tables: TSchemaTables, table_name: str) -> TTableSchema: - """Finds top level (without parent) of a `table_name` following the ancestry hierarchy.""" +def get_root_table(tables: TSchemaTables, table_name: str) -> TTableSchema: + """Finds root (without parent) of a `table_name` following the nested references (row_key - parent_key).""" table = tables[table_name] - parent = table.get("parent") - if parent: - return get_top_level_table(tables, parent) + if is_nested_table(table): + return get_root_table(tables, table.get("parent")) return table -def get_child_tables(tables: TSchemaTables, table_name: str) -> List[TTableSchema]: - """Get child tables for table name and return a list of tables ordered by ancestry so the child tables are always after their parents""" +def get_nested_tables(tables: TSchemaTables, table_name: str) -> List[TTableSchema]: + """Get nested tables for table name and return a list of tables ordered by ancestry so the nested tables are always after their parents + + Note that this function follows only NESTED TABLE reference typically expressed on _dlt_parent_id (PARENT_KEY) to _dlt_id (ROW_KEY). + """ chain: List[TTableSchema] = [] def _child(t: TTableSchema) -> None: name = t["name"] chain.append(t) for candidate in tables.values(): - if candidate.get("parent") == name: + if is_nested_table(candidate) and candidate.get("parent") == name: _child(candidate) _child(tables[table_name]) @@ -771,10 +779,27 @@ def group_tables_by_resource( resource = table.get("resource") if resource and (pattern is None or pattern.match(resource)): resource_tables = result.setdefault(resource, []) - resource_tables.extend(get_child_tables(tables, table["name"])) + resource_tables.extend(get_nested_tables(tables, table["name"])) return result +def migrate_complex_types(table: TTableSchema, warn: bool = False) -> None: + if "columns" not in table: + return + table_name = table.get("name") + for col_name, column in table["columns"].items(): + if data_type := column.get("data_type"): + if data_type == "complex": + if warn: + warnings.warn( + f"`complex` data type found on column {col_name} table {table_name} is" + " deprecated. Please use `json` type instead.", + Dlt100DeprecationWarning, + stacklevel=3, + ) + column["data_type"] = "json" + + def version_table() -> TTableSchema: # NOTE: always add new columns at the end of the table so we have identical layout # after an update of existing tables (always at the end) @@ -824,6 +849,22 @@ def loads_table() -> TTableSchema: return table +def dlt_id_column() -> TColumnSchema: + """Definition of dlt id column""" + return { + "name": C_DLT_ID, + "data_type": "text", + "nullable": False, + "unique": True, + "row_key": True, + } + + +def dlt_load_id_column() -> TColumnSchema: + """Definition of dlt load id column""" + return {"name": "_dlt_load_id", "data_type": "text", "nullable": False} + + def pipeline_state_table(add_dlt_id: bool = False) -> TTableSchema: # NOTE: always add new columns at the end of the table so we have identical layout # after an update of existing tables (always at the end) @@ -836,10 +877,10 @@ def pipeline_state_table(add_dlt_id: bool = False) -> TTableSchema: {"name": "state", "data_type": "text", "nullable": False}, {"name": "created_at", "data_type": "timestamp", "nullable": False}, {"name": "version_hash", "data_type": "text", "nullable": True}, - {"name": "_dlt_load_id", "data_type": "text", "nullable": False}, + dlt_load_id_column(), ] if add_dlt_id: - columns.append({"name": "_dlt_id", "data_type": "text", "nullable": False, "unique": True}) + columns.append(dlt_id_column()) table = new_table( PIPELINE_STATE_TABLE_NAME, write_disposition="append", @@ -866,28 +907,36 @@ def new_table( "name": table_name, "columns": {} if columns is None else {c["name"]: c for c in columns}, } + + if write_disposition: + table["write_disposition"] = write_disposition + if resource: + table["resource"] = resource + if schema_contract is not None: + table["schema_contract"] = schema_contract + if table_format: + table["table_format"] = table_format + if file_format: + table["file_format"] = file_format if parent_table_name: table["parent"] = parent_table_name - assert write_disposition is None - assert resource is None - assert schema_contract is None else: - # set write disposition only for root tables - table["write_disposition"] = write_disposition or DEFAULT_WRITE_DISPOSITION - table["resource"] = resource or table_name - if schema_contract is not None: - table["schema_contract"] = schema_contract - if table_format: - table["table_format"] = table_format - if file_format: - table["file_format"] = file_format + # set only for root tables + if not write_disposition: + # set write disposition only for root tables + table["write_disposition"] = DEFAULT_WRITE_DISPOSITION + if not resource: + table["resource"] = table_name + + # migrate complex types to json + migrate_complex_types(table, warn=True) + if validate_schema: validate_dict_ignoring_xkeys( spec=TColumnSchema, doc=table["columns"], path=f"new_table/{table_name}", ) - return table @@ -916,7 +965,7 @@ def new_column( return column -def default_hints() -> Dict[TColumnHint, List[TSimpleRegex]]: +def default_hints() -> Dict[TColumnDefaultHint, List[TSimpleRegex]]: return None diff --git a/dlt/common/storages/configuration.py b/dlt/common/storages/configuration.py index 04780528c4..4da44bceee 100644 --- a/dlt/common/storages/configuration.py +++ b/dlt/common/storages/configuration.py @@ -12,6 +12,7 @@ GcpOAuthCredentials, AnyAzureCredentials, BaseConfiguration, + SFTPCredentials, ) from dlt.common.typing import DictStrAny from dlt.common.utils import digest128 @@ -48,10 +49,19 @@ class LoadStorageConfiguration(BaseConfiguration): FileSystemCredentials = Union[ - AwsCredentials, GcpServiceAccountCredentials, AnyAzureCredentials, GcpOAuthCredentials + AwsCredentials, + GcpServiceAccountCredentials, + AnyAzureCredentials, + GcpOAuthCredentials, + SFTPCredentials, ] +def _make_sftp_url(scheme: str, fs_path: str, bucket_url: str) -> str: + parsed_bucket_url = urlparse(bucket_url) + return f"{scheme}://{parsed_bucket_url.hostname}{fs_path}" + + def _make_az_url(scheme: str, fs_path: str, bucket_url: str) -> str: parsed_bucket_url = urlparse(bucket_url) if parsed_bucket_url.username: @@ -76,7 +86,7 @@ def _make_file_url(scheme: str, fs_path: str, bucket_url: str) -> str: return p_.as_uri() -MAKE_URI_DISPATCH = {"az": _make_az_url, "file": _make_file_url} +MAKE_URI_DISPATCH = {"az": _make_az_url, "file": _make_file_url, "sftp": _make_sftp_url} MAKE_URI_DISPATCH["adl"] = MAKE_URI_DISPATCH["az"] MAKE_URI_DISPATCH["abfs"] = MAKE_URI_DISPATCH["az"] @@ -109,6 +119,7 @@ class FilesystemConfiguration(BaseConfiguration): * az, abfs, adl, abfss, azure * file, memory * gdrive + * sftp """ PROTOCOL_CREDENTIALS: ClassVar[Dict[str, Any]] = { @@ -121,6 +132,7 @@ class FilesystemConfiguration(BaseConfiguration): "adl": AnyAzureCredentials, "abfss": AnyAzureCredentials, "azure": AnyAzureCredentials, + "sftp": SFTPCredentials, } bucket_url: str = None diff --git a/dlt/common/storages/fsspec_filesystem.py b/dlt/common/storages/fsspec_filesystem.py index 7da5ebabef..6ac5f31007 100644 --- a/dlt/common/storages/fsspec_filesystem.py +++ b/dlt/common/storages/fsspec_filesystem.py @@ -30,6 +30,7 @@ GcpCredentials, AwsCredentials, AzureCredentials, + SFTPCredentials, ) from dlt.common.exceptions import MissingDependencyException from dlt.common.storages.configuration import ( @@ -64,6 +65,7 @@ class FileItem(TypedDict, total=False): "file": lambda f: ensure_pendulum_datetime(f["mtime"]), "memory": lambda f: ensure_pendulum_datetime(f["created"]), "gdrive": lambda f: ensure_pendulum_datetime(f["modifiedTime"]), + "sftp": lambda f: ensure_pendulum_datetime(f["mtime"]), } # Support aliases MTIME_DISPATCH["gs"] = MTIME_DISPATCH["gcs"] @@ -77,6 +79,7 @@ class FileItem(TypedDict, total=False): "az": lambda config: cast(AzureCredentials, config.credentials).to_adlfs_credentials(), "gs": lambda config: cast(GcpCredentials, config.credentials).to_gcs_credentials(), "gdrive": lambda config: {"credentials": cast(GcpCredentials, config.credentials)}, + "sftp": lambda config: cast(SFTPCredentials, config.credentials).to_fsspec_credentials(), } CREDENTIALS_DISPATCH["adl"] = CREDENTIALS_DISPATCH["az"] CREDENTIALS_DISPATCH["abfs"] = CREDENTIALS_DISPATCH["az"] @@ -84,6 +87,16 @@ class FileItem(TypedDict, total=False): CREDENTIALS_DISPATCH["abfss"] = CREDENTIALS_DISPATCH["az"] CREDENTIALS_DISPATCH["gcs"] = CREDENTIALS_DISPATCH["gs"] +# Default kwargs for protocol +DEFAULT_KWARGS = { + # disable concurrent + "az": {"max_concurrency": 1} +} +DEFAULT_KWARGS["adl"] = DEFAULT_KWARGS["az"] +DEFAULT_KWARGS["abfs"] = DEFAULT_KWARGS["az"] +DEFAULT_KWARGS["azure"] = DEFAULT_KWARGS["az"] +DEFAULT_KWARGS["abfss"] = DEFAULT_KWARGS["az"] + def fsspec_filesystem( protocol: str, @@ -125,6 +138,11 @@ def prepare_fsspec_args(config: FilesystemConfiguration) -> DictStrAny: register_implementation("gdrive", GoogleDriveFileSystem, "GoogleDriveFileSystem") + fs_kwargs.update(DEFAULT_KWARGS.get(protocol, {})) + + if protocol == "sftp": + fs_kwargs.clear() + if config.kwargs is not None: fs_kwargs.update(config.kwargs) if config.client_kwargs is not None: @@ -144,6 +162,7 @@ def fsspec_from_config(config: FilesystemConfiguration) -> Tuple[AbstractFileSys * s3 * az, abfs, abfss, adl, azure * gcs, gs + * sftp All other filesystems are not authenticated diff --git a/dlt/common/storages/load_storage.py b/dlt/common/storages/load_storage.py index 8ac1d74e9a..076615fa5b 100644 --- a/dlt/common/storages/load_storage.py +++ b/dlt/common/storages/load_storage.py @@ -5,7 +5,7 @@ from dlt.common.json import json from dlt.common.configuration import known_sections from dlt.common.configuration.inject import with_config -from dlt.common.destination import ALL_SUPPORTED_FILE_FORMATS, TLoaderFileFormat +from dlt.common.destination import LOADER_FILE_FORMATS, TLoaderFileFormat from dlt.common.configuration.accessors import config from dlt.common.schema import TSchemaTables from dlt.common.storages.file_storage import FileStorage @@ -46,7 +46,7 @@ class LoadStorage(VersionedStorage): LOADED_FOLDER = "loaded" # folder to keep the loads that were completely processed NEW_PACKAGES_FOLDER = "new" # folder where new packages are created - ALL_SUPPORTED_FILE_FORMATS = ALL_SUPPORTED_FILE_FORMATS + ALL_SUPPORTED_FILE_FORMATS = LOADER_FILE_FORMATS @with_config(spec=LoadStorageConfiguration, sections=(known_sections.LOAD,)) def __init__( diff --git a/dlt/common/time.py b/dlt/common/time.py index 8532f566b8..26de0b5645 100644 --- a/dlt/common/time.py +++ b/dlt/common/time.py @@ -143,6 +143,14 @@ def ensure_pendulum_time(value: Union[str, datetime.time]) -> pendulum.Time: return result else: raise ValueError(f"{value} is not a valid ISO time string.") + elif isinstance(value, timedelta): + # Assume timedelta is seconds passed since midnight. Some drivers (mysqlclient) return time in this format + return pendulum.time( + value.seconds // 3600, + (value.seconds // 60) % 60, + value.seconds % 60, + value.microseconds, + ) raise TypeError(f"Cannot coerce {value} to a pendulum.Time object.") diff --git a/dlt/common/typing.py b/dlt/common/typing.py index ee11a77965..8d18d84400 100644 --- a/dlt/common/typing.py +++ b/dlt/common/typing.py @@ -42,6 +42,8 @@ get_original_bases, ) +from typing_extensions import is_typeddict as _is_typeddict + try: from types import UnionType # type: ignore[attr-defined] except ImportError: @@ -293,7 +295,7 @@ def is_newtype_type(t: Type[Any]) -> bool: def is_typeddict(t: Type[Any]) -> bool: - if isinstance(t, _TypedDict): + if _is_typeddict(t): return True if inner_t := extract_type_if_modifier(t): return is_typeddict(inner_t) @@ -425,3 +427,23 @@ def decorator(func: Callable[..., TReturnVal]) -> Callable[TInputArgs, TReturnVa return func return decorator + + +def copy_sig_any( + wrapper: Callable[Concatenate[TDataItem, TInputArgs], Any], +) -> Callable[ + [Callable[..., TReturnVal]], Callable[Concatenate[TDataItem, TInputArgs], TReturnVal] +]: + """Copies docstring and signature from wrapper to func but keeps the func return value type + + It converts the type of first argument of the wrapper to Any which allows to type transformers in DltSources. + See filesystem source readers as example + """ + + def decorator( + func: Callable[..., TReturnVal] + ) -> Callable[Concatenate[Any, TInputArgs], TReturnVal]: + func.__doc__ = wrapper.__doc__ + return func + + return decorator diff --git a/dlt/common/utils.py b/dlt/common/utils.py index c1d130e477..436e5504f7 100644 --- a/dlt/common/utils.py +++ b/dlt/common/utils.py @@ -282,29 +282,31 @@ def clone_dict_nested(src: TDict) -> TDict: return update_dict_nested({}, src, copy_src_dicts=True) # type: ignore[return-value] -def map_nested_in_place(func: AnyFun, _complex: TAny) -> TAny: - """Applies `func` to all elements in `_dict` recursively, replacing elements in nested dictionaries and lists in place.""" - if isinstance(_complex, tuple): - if hasattr(_complex, "_asdict"): - _complex = _complex._asdict() +def map_nested_in_place(func: AnyFun, _nested: TAny, *args: Any, **kwargs: Any) -> TAny: + """Applies `func` to all elements in `_dict` recursively, replacing elements in nested dictionaries and lists in place. + Additional `*args` and `**kwargs` are passed to `func`. + """ + if isinstance(_nested, tuple): + if hasattr(_nested, "_asdict"): + _nested = _nested._asdict() else: - _complex = list(_complex) # type: ignore + _nested = list(_nested) # type: ignore - if isinstance(_complex, dict): - for k, v in _complex.items(): + if isinstance(_nested, dict): + for k, v in _nested.items(): if isinstance(v, (dict, list, tuple)): - _complex[k] = map_nested_in_place(func, v) + _nested[k] = map_nested_in_place(func, v, *args, **kwargs) else: - _complex[k] = func(v) - elif isinstance(_complex, list): - for idx, _l in enumerate(_complex): + _nested[k] = func(v, *args, **kwargs) + elif isinstance(_nested, list): + for idx, _l in enumerate(_nested): if isinstance(_l, (dict, list, tuple)): - _complex[idx] = map_nested_in_place(func, _l) + _nested[idx] = map_nested_in_place(func, _l, *args, **kwargs) else: - _complex[idx] = func(_l) + _nested[idx] = func(_l, *args, **kwargs) else: - raise ValueError(_complex, "Not a complex type") - return _complex + raise ValueError(_nested, "Not a nested type") + return _nested def is_interactive() -> bool: @@ -566,6 +568,27 @@ def get_exception_trace_chain( return traces +def group_dict_of_lists(input_dict: Dict[str, List[Any]]) -> List[Dict[str, Any]]: + """Decomposes a dictionary with list values into a list of dictionaries with unique keys. + + This function takes an input dictionary where each key maps to a list of objects. + It returns a list of dictionaries, each containing at most one object per key. + The goal is to ensure that no two objects with the same key appear in the same dictionary. + + Parameters: + input_dict (Dict[str, List[Any]]): A dictionary with string keys and list of objects as values. + + Returns: + List[Dict[str, Any]]: A list of dictionaries, each with unique keys and single objects. + """ + max_length = max(len(v) for v in input_dict.values()) + list_of_dicts: List[Dict[str, Any]] = [{} for _ in range(max_length)] + for name, value_list in input_dict.items(): + for idx, obj in enumerate(value_list): + list_of_dicts[idx][name] = obj + return list_of_dicts + + def order_deduped(lst: List[Any]) -> List[Any]: """Returns deduplicated list preserving order of input elements. diff --git a/dlt/common/warnings.py b/dlt/common/warnings.py index 9c62c69bf8..95d5a19f08 100644 --- a/dlt/common/warnings.py +++ b/dlt/common/warnings.py @@ -39,7 +39,8 @@ def __init__( if isinstance(expected_due, semver.VersionInfo) else semver.parse_version_info(expected_due) ) - self.expected_due = expected_due if expected_due is not None else self.since.bump_minor() + # we deprecate across major version since 1.0.0 + self.expected_due = expected_due if expected_due is not None else self.since.bump_major() def __str__(self) -> str: message = ( @@ -57,6 +58,15 @@ def __init__(self, message: str, *args: typing.Any, expected_due: VersionString ) +class Dlt100DeprecationWarning(DltDeprecationWarning): + V100 = semver.parse_version_info("1.0.0") + + def __init__(self, message: str, *args: typing.Any, expected_due: VersionString = None) -> None: + super().__init__( + message, *args, since=Dlt100DeprecationWarning.V100, expected_due=expected_due + ) + + # show dlt deprecations once warnings.simplefilter("once", DltDeprecationWarning) diff --git a/dlt/destinations/__init__.py b/dlt/destinations/__init__.py index 0546d16bcd..a856f574d8 100644 --- a/dlt/destinations/__init__.py +++ b/dlt/destinations/__init__.py @@ -16,6 +16,7 @@ from dlt.destinations.impl.databricks.factory import databricks from dlt.destinations.impl.dremio.factory import dremio from dlt.destinations.impl.clickhouse.factory import clickhouse +from dlt.destinations.impl.sqlalchemy.factory import sqlalchemy __all__ = [ @@ -37,4 +38,5 @@ "dremio", "clickhouse", "destination", + "sqlalchemy", ] diff --git a/dlt/destinations/decorators.py b/dlt/destinations/decorators.py index c202a37e28..c398086fc0 100644 --- a/dlt/destinations/decorators.py +++ b/dlt/destinations/decorators.py @@ -55,7 +55,7 @@ def destination( loader_file_format: defines in which format files are stored in the load package before being sent to the destination function, this can be puae-jsonl or parquet. name: defines the name of the destination that get's created by the destination decorator, defaults to the name of the function naming_convention: defines the name of the destination that gets created by the destination decorator. This controls how table and column names are normalized. The default is direct which will keep all names the same. - max_nesting_level: defines how deep the normalizer will go to normalize complex fields on your data to create subtables. This overwrites any settings on your source and is set to zero to not create any nested tables by default. + max_nesting_level: defines how deep the normalizer will go to normalize nested fields on your data to create subtables. This overwrites any settings on your source and is set to zero to not create any nested tables by default. skip_dlt_columns_and_tables: defines wether internal tables and columns will be fed into the custom destination function. This is set to True by default. spec: defines a configuration spec that will be used to to inject arguments into the decorated functions. Argument not in spec will not be injected max_parallel_load_jobs: how many load jobs at most will be running during the load diff --git a/dlt/destinations/impl/athena/athena.py b/dlt/destinations/impl/athena/athena.py index a5a8ae2562..04078dd510 100644 --- a/dlt/destinations/impl/athena/athena.py +++ b/dlt/destinations/impl/athena/athena.py @@ -17,7 +17,6 @@ import re from contextlib import contextmanager -from fsspec import AbstractFileSystem from pendulum.datetime import DateTime, Date from datetime import datetime # noqa: I251 @@ -33,19 +32,15 @@ ) from dlt.common import logger -from dlt.common.exceptions import TerminalValueError -from dlt.common.utils import uniq_id, without_none -from dlt.common.schema import TColumnSchema, Schema, TTableSchema +from dlt.common.utils import uniq_id +from dlt.common.schema import TColumnSchema, Schema from dlt.common.schema.typing import ( - TTableSchema, TColumnType, TTableFormat, TSortOrder, ) -from dlt.common.schema.utils import table_schema_has_type -from dlt.common.destination import DestinationCapabilitiesContext -from dlt.common.destination.reference import LoadJob -from dlt.common.destination.reference import FollowupJobRequest, SupportsStagingDestination +from dlt.common.destination import DestinationCapabilitiesContext, PreparedTableSchema +from dlt.common.destination.reference import FollowupJobRequest, SupportsStagingDestination, LoadJob from dlt.common.data_writers.escape import escape_hive_identifier from dlt.destinations.sql_jobs import SqlStagingCopyFollowupJob, SqlMergeFollowupJob @@ -54,7 +49,6 @@ DatabaseTerminalException, DatabaseTransientException, DatabaseUndefinedRelation, - LoadJobTerminalException, ) from dlt.destinations.sql_client import ( SqlClientBase, @@ -63,73 +57,13 @@ raise_open_connection_error, ) from dlt.destinations.typing import DBApiCursor -from dlt.destinations.job_client_impl import SqlJobClientWithStaging +from dlt.destinations.job_client_impl import SqlJobClientWithStagingDataset from dlt.destinations.job_impl import FinalizedLoadJobWithFollowupJobs, FinalizedLoadJob from dlt.destinations.impl.athena.configuration import AthenaClientConfiguration -from dlt.destinations.type_mapping import TypeMapper from dlt.destinations import path_utils from dlt.destinations.impl.athena.athena_adapter import PARTITION_HINT -class AthenaTypeMapper(TypeMapper): - sct_to_unbound_dbt = { - "complex": "string", - "text": "string", - "double": "double", - "bool": "boolean", - "date": "date", - "timestamp": "timestamp", - "bigint": "bigint", - "binary": "binary", - "time": "string", - } - - sct_to_dbt = {"decimal": "decimal(%i,%i)", "wei": "decimal(%i,%i)"} - - dbt_to_sct = { - "varchar": "text", - "double": "double", - "boolean": "bool", - "date": "date", - "timestamp": "timestamp", - "bigint": "bigint", - "binary": "binary", - "varbinary": "binary", - "decimal": "decimal", - "tinyint": "bigint", - "smallint": "bigint", - "int": "bigint", - } - - def __init__(self, capabilities: DestinationCapabilitiesContext): - super().__init__(capabilities) - - def to_db_integer_type( - self, precision: Optional[int], table_format: TTableFormat = None - ) -> str: - if precision is None: - return "bigint" - if precision <= 8: - return "int" if table_format == "iceberg" else "tinyint" - elif precision <= 16: - return "int" if table_format == "iceberg" else "smallint" - elif precision <= 32: - return "int" - elif precision <= 64: - return "bigint" - raise TerminalValueError( - f"bigint with {precision} bits precision cannot be mapped into athena integer type" - ) - - def from_db_type( - self, db_type: str, precision: Optional[int], scale: Optional[int] - ) -> TColumnType: - for key, val in self.dbt_to_sct.items(): - if db_type.startswith(key): - return without_none(dict(data_type=val, precision=precision, scale=scale)) # type: ignore[return-value] - return dict(data_type=None) - - # add a formatter for pendulum to be used by pyathen dbapi def _format_pendulum_datetime(formatter: Formatter, escaper: Callable[[str], str], val: Any) -> Any: # copied from https://github.com/laughingman7743/PyAthena/blob/f4b21a0b0f501f5c3504698e25081f491a541d4e/pyathena/formatter.py#L114 @@ -165,7 +99,9 @@ class AthenaMergeJob(SqlMergeFollowupJob): def _new_temp_table_name(cls, name_prefix: str, sql_client: SqlClientBase[Any]) -> str: # reproducible name so we know which table to drop with sql_client.with_staging_dataset(): - return sql_client.make_qualified_table_name(name_prefix) + return sql_client.make_qualified_table_name( + cls._shorten_table_name(name_prefix, sql_client) + ) @classmethod def _to_temp_table(cls, select_sql: str, temp_table_name: str) -> str: @@ -366,7 +302,7 @@ def execute_query(self, query: AnyStr, *args: Any, **kwargs: Any) -> Iterator[DB yield DBApiCursorImpl(cursor) # type: ignore -class AthenaClient(SqlJobClientWithStaging, SupportsStagingDestination): +class AthenaClient(SqlJobClientWithStagingDataset, SupportsStagingDestination): def __init__( self, schema: Schema, @@ -391,7 +327,7 @@ def __init__( super().__init__(schema, config, sql_client) self.sql_client: AthenaSQLClient = sql_client # type: ignore self.config: AthenaClientConfiguration = config - self.type_mapper = AthenaTypeMapper(self.capabilities) + self.type_mapper = self.capabilities.get_type_mapper() def initialize_storage(self, truncate_tables: Iterable[str] = None) -> None: # only truncate tables in iceberg mode @@ -401,11 +337,11 @@ def initialize_storage(self, truncate_tables: Iterable[str] = None) -> None: def _from_db_type( self, hive_t: str, precision: Optional[int], scale: Optional[int] ) -> TColumnType: - return self.type_mapper.from_db_type(hive_t, precision, scale) + return self.type_mapper.from_destination_type(hive_t, precision, scale) - def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = None) -> str: + def _get_column_def_sql(self, c: TColumnSchema, table: PreparedTableSchema = None) -> str: return ( - f"{self.sql_client.escape_ddl_identifier(c['name'])} {self.type_mapper.to_db_type(c, table_format)}" + f"{self.sql_client.escape_ddl_identifier(c['name'])} {self.type_mapper.to_destination_type(c, table)}" ) def _iceberg_partition_clause(self, partition_hints: Optional[Dict[str, str]]) -> str: @@ -428,15 +364,15 @@ def _get_table_update_sql( # for the system tables we need to create empty iceberg tables to be able to run, DELETE and UPDATE queries # or if we are in iceberg mode, we create iceberg tables for all tables - table = self.prepare_load_table(table_name, self.in_staging_mode) - table_format = table.get("table_format") - is_iceberg = self._is_iceberg_table(table) or table.get("write_disposition", None) == "skip" - columns = ", ".join([self._get_column_def_sql(c, table_format) for c in new_columns]) + table = self.prepare_load_table(table_name) + # do not create iceberg tables on staging dataset + create_iceberg = self._is_iceberg_table(table, self.in_staging_dataset_mode) + columns = ", ".join([self._get_column_def_sql(c, table) for c in new_columns]) # create unique tag for iceberg table so it is never recreated in the same folder # athena requires some kind of special cleaning (or that is a bug) so we cannot refresh # iceberg tables without it - location_tag = uniq_id(6) if is_iceberg else "" + location_tag = uniq_id(6) if create_iceberg else "" # this will fail if the table prefix is not properly defined table_prefix = self.table_prefix_layout.format(table_name=table_name + location_tag) location = f"{bucket}/{dataset}/{table_prefix}" @@ -447,7 +383,7 @@ def _get_table_update_sql( # alter table to add new columns at the end sql.append(f"""ALTER TABLE {qualified_table_name} ADD COLUMNS ({columns});""") else: - if is_iceberg: + if create_iceberg: partition_clause = self._iceberg_partition_clause( cast(Optional[Dict[str, str]], table.get(PARTITION_HINT)) ) @@ -469,28 +405,22 @@ def _get_table_update_sql( return sql def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: """Starts SqlLoadJob for files ending with .sql or returns None to let derived classes to handle their specific jobs""" - if table_schema_has_type(table, "time"): - raise LoadJobTerminalException( - file_path, - "Athena cannot load TIME columns from parquet tables. Please convert" - " `datetime.time` objects in your data to `str` or `datetime.datetime`.", - ) job = super().create_load_job(table, file_path, load_id, restore) if not job: job = ( FinalizedLoadJobWithFollowupJobs(file_path) - if self._is_iceberg_table(self.prepare_load_table(table["name"])) + if self._is_iceberg_table(table) else FinalizedLoadJob(file_path) ) return job def _create_append_followup_jobs( - self, table_chain: Sequence[TTableSchema] + self, table_chain: Sequence[PreparedTableSchema] ) -> List[FollowupJobRequest]: - if self._is_iceberg_table(self.prepare_load_table(table_chain[0]["name"])): + if self._is_iceberg_table(table_chain[0]): return [ SqlStagingCopyFollowupJob.from_table_chain( table_chain, self.sql_client, {"replace": False} @@ -499,9 +429,9 @@ def _create_append_followup_jobs( return super()._create_append_followup_jobs(table_chain) def _create_replace_followup_jobs( - self, table_chain: Sequence[TTableSchema] + self, table_chain: Sequence[PreparedTableSchema] ) -> List[FollowupJobRequest]: - if self._is_iceberg_table(self.prepare_load_table(table_chain[0]["name"])): + if self._is_iceberg_table(table_chain[0]): return [ SqlStagingCopyFollowupJob.from_table_chain( table_chain, self.sql_client, {"replace": True} @@ -510,46 +440,43 @@ def _create_replace_followup_jobs( return super()._create_replace_followup_jobs(table_chain) def _create_merge_followup_jobs( - self, table_chain: Sequence[TTableSchema] + self, table_chain: Sequence[PreparedTableSchema] ) -> List[FollowupJobRequest]: return [AthenaMergeJob.from_table_chain(table_chain, self.sql_client)] - def _is_iceberg_table(self, table: TTableSchema) -> bool: + def _is_iceberg_table( + self, table: PreparedTableSchema, is_staging_dataset: bool = False + ) -> bool: table_format = table.get("table_format") - return table_format == "iceberg" + # all dlt tables that are not loaded via files are iceberg tables, no matter if they are on staging or regular dataset + # all other iceberg tables are HIVE (external) tables on staging dataset + table_format_iceberg = table_format == "iceberg" or ( + self.config.force_iceberg and table_format is None + ) + return (table_format_iceberg and not is_staging_dataset) or table[ + "write_disposition" + ] == "skip" - def should_load_data_to_staging_dataset(self, table: TTableSchema) -> bool: + def should_load_data_to_staging_dataset(self, table_name: str) -> bool: # all iceberg tables need staging - if self._is_iceberg_table(self.prepare_load_table(table["name"])): + table = self.prepare_load_table(table_name) + if self._is_iceberg_table(table): return True - return super().should_load_data_to_staging_dataset(table) + return super().should_load_data_to_staging_dataset(table_name) - def should_truncate_table_before_load_on_staging_destination(self, table: TTableSchema) -> bool: + def should_truncate_table_before_load_on_staging_destination(self, table_name: str) -> bool: # on athena we only truncate replace tables that are not iceberg - table = self.prepare_load_table(table["name"]) - if table["write_disposition"] == "replace" and not self._is_iceberg_table( - self.prepare_load_table(table["name"]) - ): + table = self.prepare_load_table(table_name) + if table["write_disposition"] == "replace" and not self._is_iceberg_table(table): return True return False - def should_load_data_to_staging_dataset_on_staging_destination( - self, table: TTableSchema - ) -> bool: + def should_load_data_to_staging_dataset_on_staging_destination(self, table_name: str) -> bool: """iceberg table data goes into staging on staging destination""" - if self._is_iceberg_table(self.prepare_load_table(table["name"])): + table = self.prepare_load_table(table_name) + if self._is_iceberg_table(table): return True - return super().should_load_data_to_staging_dataset_on_staging_destination(table) - - def prepare_load_table( - self, table_name: str, prepare_for_staging: bool = False - ) -> TTableSchema: - table = super().prepare_load_table(table_name, prepare_for_staging) - if self.config.force_iceberg: - table["table_format"] = "iceberg" - if prepare_for_staging and table.get("table_format", None) == "iceberg": - table.pop("table_format") - return table + return super().should_load_data_to_staging_dataset_on_staging_destination(table_name) @staticmethod def is_dbapi_exception(ex: Exception) -> bool: diff --git a/dlt/destinations/impl/athena/athena_adapter.py b/dlt/destinations/impl/athena/athena_adapter.py index 50f7abc54a..426c2ca1b8 100644 --- a/dlt/destinations/impl/athena/athena_adapter.py +++ b/dlt/destinations/impl/athena/athena_adapter.py @@ -1,9 +1,5 @@ -from typing import Any, Optional, Dict, Protocol, Sequence, Union, Final +from typing import Any, Dict, Sequence, Union, Final -from dateutil import parser - -from dlt.common.pendulum import timezone -from dlt.common.schema.typing import TColumnNames, TTableSchemaColumns, TColumnSchema from dlt.destinations.utils import get_resource_for_adapter from dlt.extract import DltResource from dlt.extract.items import TTableHintTemplate diff --git a/dlt/destinations/impl/athena/configuration.py b/dlt/destinations/impl/athena/configuration.py index 59dfeee4ec..8a0f14b4cc 100644 --- a/dlt/destinations/impl/athena/configuration.py +++ b/dlt/destinations/impl/athena/configuration.py @@ -1,9 +1,12 @@ import dataclasses from typing import ClassVar, Final, List, Optional +import warnings +from dlt.common import logger from dlt.common.configuration import configspec from dlt.common.destination.reference import DestinationClientDwhWithStagingConfiguration from dlt.common.configuration.specs import AwsCredentials +from dlt.common.warnings import Dlt100DeprecationWarning @configspec @@ -13,14 +16,24 @@ class AthenaClientConfiguration(DestinationClientDwhWithStagingConfiguration): credentials: AwsCredentials = None athena_work_group: Optional[str] = None aws_data_catalog: Optional[str] = "awsdatacatalog" - supports_truncate_command: bool = False - force_iceberg: Optional[bool] = False + force_iceberg: Optional[bool] = None __config_gen_annotations__: ClassVar[List[str]] = ["athena_work_group"] + def on_resolved(self) -> None: + if self.force_iceberg is not None: + warnings.warn( + "The `force_iceberg` is deprecated.If you upgraded dlt on existing pipeline and you" + " have data already loaded, please keep this flag to make sure your data is" + " consistent.If you are creating a new dataset and no data was loaded, please set" + " `table_format='iceberg`` on your resources explicitly.", + Dlt100DeprecationWarning, + stacklevel=1, + ) + def __str__(self) -> str: """Return displayable destination location""" if self.staging_config: - return str(self.staging_config.credentials) + return f"{self.staging_config} on {self.aws_data_catalog}" else: return "[no staging set]" diff --git a/dlt/destinations/impl/athena/factory.py b/dlt/destinations/impl/athena/factory.py index 07d784ed49..5a7ae1ba8c 100644 --- a/dlt/destinations/impl/athena/factory.py +++ b/dlt/destinations/impl/athena/factory.py @@ -1,5 +1,6 @@ import typing as t +from dlt.common.data_types.typing import TDataType from dlt.common.destination import Destination, DestinationCapabilitiesContext from dlt.common.configuration.specs import AwsCredentials from dlt.common.data_writers.escape import ( @@ -7,13 +8,101 @@ format_bigquery_datetime_literal, ) from dlt.common.arithmetics import DEFAULT_NUMERIC_PRECISION, DEFAULT_NUMERIC_SCALE +from dlt.common.destination.typing import PreparedTableSchema +from dlt.common.exceptions import TerminalValueError +from dlt.common.schema.typing import TColumnSchema, TColumnType, TLoaderMergeStrategy, TTableSchema +from dlt.common.typing import TLoaderFileFormat +from dlt.common.utils import without_none +from dlt.destinations.type_mapping import TypeMapperImpl from dlt.destinations.impl.athena.configuration import AthenaClientConfiguration if t.TYPE_CHECKING: from dlt.destinations.impl.athena.athena import AthenaClient +def athena_merge_strategies_selector( + supported_merge_strategies: t.Sequence[TLoaderMergeStrategy], + /, + *, + table_schema: TTableSchema, +) -> t.Sequence[TLoaderMergeStrategy]: + if table_schema.get("table_format") == "iceberg": + return supported_merge_strategies + else: + return [] + + +class AthenaTypeMapper(TypeMapperImpl): + sct_to_unbound_dbt = { + "json": "string", + "text": "string", + "double": "double", + "bool": "boolean", + "date": "date", + "timestamp": "timestamp", + "bigint": "bigint", + "binary": "binary", + "time": "string", + } + + sct_to_dbt = {"decimal": "decimal(%i,%i)", "wei": "decimal(%i,%i)"} + + dbt_to_sct = { + "varchar": "text", + "double": "double", + "boolean": "bool", + "date": "date", + "timestamp": "timestamp", + "bigint": "bigint", + "binary": "binary", + "varbinary": "binary", + "decimal": "decimal", + "tinyint": "bigint", + "smallint": "bigint", + "int": "bigint", + } + + def ensure_supported_type( + self, + column: TColumnSchema, + table: PreparedTableSchema, + loader_file_format: TLoaderFileFormat, + ) -> None: + # TIME is not supported for parquet on Athena + if loader_file_format == "parquet" and column["data_type"] == "time": + raise TerminalValueError( + "Please convert `datetime.time` objects in your data to `str` or" + " `datetime.datetime`.", + "time", + ) + + def to_db_integer_type(self, column: TColumnSchema, table: PreparedTableSchema = None) -> str: + precision = column.get("precision") + table_format = table.get("table_format") + if precision is None: + return "bigint" + if precision <= 8: + return "int" if table_format == "iceberg" else "tinyint" + elif precision <= 16: + return "int" if table_format == "iceberg" else "smallint" + elif precision <= 32: + return "int" + elif precision <= 64: + return "bigint" + raise TerminalValueError( + f"bigint with {precision} bits precision cannot be mapped into athena integer type" + ) + + def from_destination_type( + self, db_type: str, precision: t.Optional[int], scale: t.Optional[int] + ) -> TColumnType: + for key, val in self.dbt_to_sct.items(): + if db_type.startswith(key): + return without_none(dict(data_type=val, precision=precision, scale=scale)) # type: ignore[return-value] + return dict(data_type=None) + + class athena(Destination[AthenaClientConfiguration, "AthenaClient"]): spec = AthenaClientConfiguration @@ -22,9 +111,11 @@ def _raw_capabilities(self) -> DestinationCapabilitiesContext: # athena only supports loading from staged files on s3 for now caps.preferred_loader_file_format = None caps.supported_loader_file_formats = [] - caps.supported_table_formats = ["iceberg"] + caps.supported_table_formats = ["iceberg", "hive"] caps.preferred_staging_file_format = "parquet" - caps.supported_staging_file_formats = ["parquet", "jsonl"] + caps.supported_staging_file_formats = ["parquet"] + caps.type_mapper = AthenaTypeMapper + # athena is storing all identifiers in lower case and is case insensitive # it also uses lower case in all the queries # https://docs.aws.amazon.com/athena/latest/ug/tables-databases-columns-names.html @@ -47,6 +138,7 @@ def _raw_capabilities(self) -> DestinationCapabilitiesContext: caps.timestamp_precision = 3 caps.supports_truncate_command = False caps.supported_merge_strategies = ["delete-insert", "upsert", "scd2"] + caps.merge_strategies_selector = athena_merge_strategies_selector return caps @property @@ -61,7 +153,6 @@ def __init__( credentials: t.Union[AwsCredentials, t.Dict[str, t.Any], t.Any] = None, athena_work_group: t.Optional[str] = None, aws_data_catalog: t.Optional[str] = "awsdatacatalog", - force_iceberg: bool = False, destination_name: t.Optional[str] = None, environment: t.Optional[str] = None, **kwargs: t.Any, @@ -75,7 +166,6 @@ def __init__( credentials: AWS credentials to connect to the Athena database. athena_work_group: Athena work group to use aws_data_catalog: Athena data catalog to use - force_iceberg: Force iceberg tables **kwargs: Additional arguments passed to the destination config """ super().__init__( @@ -83,7 +173,6 @@ def __init__( credentials=credentials, athena_work_group=athena_work_group, aws_data_catalog=aws_data_catalog, - force_iceberg=force_iceberg, destination_name=destination_name, environment=environment, **kwargs, diff --git a/dlt/destinations/impl/bigquery/bigquery.py b/dlt/destinations/impl/bigquery/bigquery.py index 1dd4c727be..b4b9e01dfa 100644 --- a/dlt/destinations/impl/bigquery/bigquery.py +++ b/dlt/destinations/impl/bigquery/bigquery.py @@ -1,36 +1,29 @@ -import functools import os from pathlib import Path -import time from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, cast import google.cloud.bigquery as bigquery # noqa: I250 from google.api_core import exceptions as api_core_exceptions -from google.cloud import exceptions as gcp_exceptions from google.api_core import retry +from google.cloud import exceptions as gcp_exceptions from google.cloud.bigquery.retry import _RETRYABLE_REASONS from dlt.common import logger -from dlt.common.runtime.signals import sleep -from dlt.common.json import json -from dlt.common.destination import DestinationCapabilitiesContext +from dlt.common.destination import DestinationCapabilitiesContext, PreparedTableSchema from dlt.common.destination.reference import ( HasFollowupJobs, FollowupJobRequest, - TLoadJobState, RunnableLoadJob, SupportsStagingDestination, LoadJob, ) +from dlt.common.json import json +from dlt.common.runtime.signals import sleep from dlt.common.schema import TColumnSchema, Schema, TTableSchemaColumns -from dlt.common.schema.typing import TTableSchema, TColumnType, TTableFormat -from dlt.common.schema.utils import get_inherited_table_hint -from dlt.common.schema.utils import table_schema_has_type -from dlt.common.storages.file_storage import FileStorage +from dlt.common.schema.typing import TColumnType +from dlt.common.schema.utils import get_inherited_table_hint, get_columns_names_with_prop from dlt.common.storages.load_package import destination_state from dlt.common.typing import DictStrAny -from dlt.destinations.job_impl import DestinationJsonlLoadJob, DestinationParquetLoadJob -from dlt.destinations.sql_client import SqlClientBase from dlt.destinations.exceptions import ( DatabaseTransientException, DatabaseUndefinedRelation, @@ -47,64 +40,14 @@ ROUND_HALF_EVEN_HINT, ROUND_HALF_AWAY_FROM_ZERO_HINT, TABLE_EXPIRATION_HINT, + should_autodetect_schema, ) from dlt.destinations.impl.bigquery.configuration import BigQueryClientConfiguration from dlt.destinations.impl.bigquery.sql_client import BigQuerySqlClient, BQ_TERMINAL_REASONS -from dlt.destinations.job_client_impl import SqlJobClientWithStaging +from dlt.destinations.job_client_impl import SqlJobClientWithStagingDataset +from dlt.destinations.job_impl import DestinationJsonlLoadJob, DestinationParquetLoadJob from dlt.destinations.job_impl import ReferenceFollowupJobRequest from dlt.destinations.sql_jobs import SqlMergeFollowupJob -from dlt.destinations.type_mapping import TypeMapper -from dlt.destinations.utils import parse_db_data_type_str_with_precision - - -class BigQueryTypeMapper(TypeMapper): - sct_to_unbound_dbt = { - "complex": "JSON", - "text": "STRING", - "double": "FLOAT64", - "bool": "BOOL", - "date": "DATE", - "timestamp": "TIMESTAMP", - "bigint": "INT64", - "binary": "BYTES", - "wei": "BIGNUMERIC", # non-parametrized should hold wei values - "time": "TIME", - } - - sct_to_dbt = { - "text": "STRING(%i)", - "binary": "BYTES(%i)", - } - - dbt_to_sct = { - "STRING": "text", - "FLOAT64": "double", - "BOOL": "bool", - "DATE": "date", - "TIMESTAMP": "timestamp", - "INT64": "bigint", - "BYTES": "binary", - "NUMERIC": "decimal", - "BIGNUMERIC": "decimal", - "JSON": "complex", - "TIME": "time", - } - - def to_db_decimal_type(self, precision: Optional[int], scale: Optional[int]) -> str: - # Use BigQuery's BIGNUMERIC for large precision decimals - precision, scale = self.decimal_precision(precision, scale) - if precision > 38 or scale > 9: - return "BIGNUMERIC(%i,%i)" % (precision, scale) - return "NUMERIC(%i,%i)" % (precision, scale) - - # noinspection PyTypeChecker,PydanticTypeChecker - def from_db_type( - self, db_type: str, precision: Optional[int], scale: Optional[int] - ) -> TColumnType: - # precision is present in the type name - if db_type == "BIGNUMERIC": - return dict(data_type="wei") - return super().from_db_type(*parse_db_data_type_str_with_precision(db_type)) class BigQueryLoadJob(RunnableLoadJob, HasFollowupJobs): @@ -212,7 +155,7 @@ def gen_key_table_clauses( return sql -class BigQueryClient(SqlJobClientWithStaging, SupportsStagingDestination): +class BigQueryClient(SqlJobClientWithStagingDataset, SupportsStagingDestination): def __init__( self, schema: Schema, @@ -232,15 +175,15 @@ def __init__( super().__init__(schema, config, sql_client) self.config: BigQueryClientConfiguration = config self.sql_client: BigQuerySqlClient = sql_client # type: ignore - self.type_mapper = BigQueryTypeMapper(self.capabilities) + self.type_mapper = self.capabilities.get_type_mapper() def _create_merge_followup_jobs( - self, table_chain: Sequence[TTableSchema] + self, table_chain: Sequence[PreparedTableSchema] ) -> List[FollowupJobRequest]: return [BigQueryMergeJob.from_table_chain(table_chain, self.sql_client)] def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: job = super().create_load_job(table, file_path, load_id) @@ -281,12 +224,12 @@ def create_load_job( def _get_table_update_sql( self, table_name: str, new_columns: Sequence[TColumnSchema], generate_alter: bool ) -> List[str]: - # return empty columns which will skip table CREATE or ALTER - # to let BigQuery autodetect table from data - if self._should_autodetect_schema(table_name): + # Return empty columns which will skip table CREATE or ALTER to let BigQuery + # auto-detect table from data. + table = self.prepare_load_table(table_name) + if should_autodetect_schema(table): return [] - table: Optional[TTableSchema] = self.prepare_load_table(table_name) sql = super()._get_table_update_sql(table_name, new_columns, generate_alter) canonical_name = self.sql_client.make_qualified_table_name(table_name) @@ -354,17 +297,23 @@ def _get_table_update_sql( return sql - def prepare_load_table( - self, table_name: str, prepare_for_staging: bool = False - ) -> Optional[TTableSchema]: - table = super().prepare_load_table(table_name, prepare_for_staging) - if table_name in self.schema.data_table_names(): + def prepare_load_table(self, table_name: str) -> Optional[PreparedTableSchema]: + table = super().prepare_load_table(table_name) + if table_name not in self.schema.dlt_table_names(): if TABLE_DESCRIPTION_HINT not in table: table[TABLE_DESCRIPTION_HINT] = ( # type: ignore[name-defined, typeddict-unknown-key, unused-ignore] get_inherited_table_hint( self.schema.tables, table_name, TABLE_DESCRIPTION_HINT, allow_none=True ) ) + if AUTODETECT_SCHEMA_HINT not in table: + table[AUTODETECT_SCHEMA_HINT] = ( # type: ignore[typeddict-unknown-key] + get_inherited_table_hint( + self.schema.tables, table_name, AUTODETECT_SCHEMA_HINT, allow_none=True + ) + or self.config.autodetect_schema + ) + return table def get_storage_tables( @@ -417,10 +366,10 @@ def _get_info_schema_columns_query( return query, folded_table_names - def _get_column_def_sql(self, column: TColumnSchema, table_format: TTableFormat = None) -> str: + def _get_column_def_sql(self, column: TColumnSchema, table: PreparedTableSchema = None) -> str: name = self.sql_client.escape_column_name(column["name"]) column_def_sql = ( - f"{name} {self.type_mapper.to_db_type(column, table_format)} {self._gen_not_null(column.get('nullable', True))}" + f"{name} {self.type_mapper.to_destination_type(column, table)} {self._gen_not_null(column.get('nullable', True))}" ) if column.get(ROUND_HALF_EVEN_HINT, False): column_def_sql += " OPTIONS (rounding_mode='ROUND_HALF_EVEN')" @@ -428,7 +377,7 @@ def _get_column_def_sql(self, column: TColumnSchema, table_format: TTableFormat column_def_sql += " OPTIONS (rounding_mode='ROUND_HALF_AWAY_FROM_ZERO')" return column_def_sql - def _create_load_job(self, table: TTableSchema, file_path: str) -> bigquery.LoadJob: + def _create_load_job(self, table: PreparedTableSchema, file_path: str) -> bigquery.LoadJob: # append to table for merge loads (append to stage) and regular appends. table_name = table["name"] @@ -457,19 +406,9 @@ def _create_load_job(self, table: TTableSchema, file_path: str) -> bigquery.Load ignore_unknown_values=False, max_bad_records=0, ) - if self._should_autodetect_schema(table_name): - # allow BigQuery to infer and evolve the schema, note that dlt is not - # creating such tables at all - job_config.autodetect = True - job_config.schema_update_options = bigquery.SchemaUpdateOption.ALLOW_FIELD_ADDITION - job_config.create_disposition = bigquery.CreateDisposition.CREATE_IF_NEEDED - elif ext == "parquet" and table_schema_has_type(table, "complex"): - # if table contains complex types, we cannot load with parquet - raise LoadJobTerminalException( - file_path, - "Bigquery cannot load into JSON data type from parquet. Enable autodetect_schema in" - " config or via BigQuery adapter or use jsonl format instead.", - ) + if should_autodetect_schema(table): + # Allow BigQuery to infer and evolve the schema, note that dlt is not creating such tables at all. + job_config = self._set_user_hints_with_schema_autodetection(table, job_config) if bucket_path: return self.sql_client.native_connection.load_table_from_uri( @@ -489,6 +428,37 @@ def _create_load_job(self, table: TTableSchema, file_path: str) -> bigquery.Load timeout=self.config.file_upload_timeout, ) + def _set_user_hints_with_schema_autodetection( + self, table: PreparedTableSchema, job_config: bigquery.LoadJobConfig + ) -> bigquery.LoadJobConfig: + job_config.autodetect = True + job_config.schema_update_options = bigquery.SchemaUpdateOption.ALLOW_FIELD_ADDITION + job_config.create_disposition = bigquery.CreateDisposition.CREATE_IF_NEEDED + if partition_column_ := get_columns_names_with_prop(table, PARTITION_HINT): + partition_column = partition_column_[0] + col_dtype = table["columns"][partition_column]["data_type"] + if col_dtype == "date": + job_config.time_partitioning = bigquery.TimePartitioning(field=partition_column) + elif col_dtype == "timestamp": + job_config.time_partitioning = bigquery.TimePartitioning( + type_=bigquery.TimePartitioningType.DAY, field=partition_column + ) + elif col_dtype == "bigint": + job_config.range_partitioning = bigquery.RangePartitioning( + field=partition_column, + range_=bigquery.PartitionRange(start=-172800000, end=691200000, interval=86400), + ) + if clustering_columns := get_columns_names_with_prop(table, CLUSTER_HINT): + job_config.clustering_fields = clustering_columns + if table_description := table.get(TABLE_DESCRIPTION_HINT, False): + job_config.destination_table_description = table_description + if table_expiration := table.get(TABLE_EXPIRATION_HINT, False): + raise ValueError( + f"Table expiration time ({table_expiration}) can't be set with BigQuery type" + " auto-detection enabled!" + ) + return job_config + def _retrieve_load_job(self, file_path: str) -> bigquery.LoadJob: job_id = BigQueryLoadJob.get_job_id_from_file_path(file_path) return cast(bigquery.LoadJob, self.sql_client.native_connection.get_job(job_id)) @@ -496,14 +466,9 @@ def _retrieve_load_job(self, file_path: str) -> bigquery.LoadJob: def _from_db_type( self, bq_t: str, precision: Optional[int], scale: Optional[int] ) -> TColumnType: - return self.type_mapper.from_db_type(bq_t, precision, scale) - - def _should_autodetect_schema(self, table_name: str) -> bool: - return get_inherited_table_hint( - self.schema._schema_tables, table_name, AUTODETECT_SCHEMA_HINT, allow_none=True - ) or (self.config.autodetect_schema and table_name not in self.schema.dlt_table_names()) + return self.type_mapper.from_destination_type(bq_t, precision, scale) - def should_truncate_table_before_load_on_staging_destination(self, table: TTableSchema) -> bool: + def should_truncate_table_before_load_on_staging_destination(self, table_name: str) -> bool: return self.config.truncate_tables_on_staging_destination_before_load diff --git a/dlt/destinations/impl/bigquery/bigquery_adapter.py b/dlt/destinations/impl/bigquery/bigquery_adapter.py index 55fe1b6b74..ce4a455da0 100644 --- a/dlt/destinations/impl/bigquery/bigquery_adapter.py +++ b/dlt/destinations/impl/bigquery/bigquery_adapter.py @@ -2,6 +2,7 @@ from dateutil import parser +from dlt.common.destination import PreparedTableSchema from dlt.common.pendulum import timezone from dlt.common.schema.typing import ( TColumnNames, @@ -174,3 +175,8 @@ def bigquery_adapter( " specified." ) return resource + + +def should_autodetect_schema(table: PreparedTableSchema) -> bool: + """Tells if schema should be auto detected for a given prepared `table`""" + return table.get(AUTODETECT_SCHEMA_HINT, False) # type: ignore[return-value] diff --git a/dlt/destinations/impl/bigquery/factory.py b/dlt/destinations/impl/bigquery/factory.py index 34dd1790ae..7f4fd74825 100644 --- a/dlt/destinations/impl/bigquery/factory.py +++ b/dlt/destinations/impl/bigquery/factory.py @@ -1,17 +1,91 @@ import typing as t +from dlt.common.destination.typing import PreparedTableSchema +from dlt.common.exceptions import TerminalValueError from dlt.common.normalizers.naming import NamingConvention from dlt.common.configuration.specs import GcpServiceAccountCredentials from dlt.common.arithmetics import DEFAULT_NUMERIC_PRECISION, DEFAULT_NUMERIC_SCALE from dlt.common.data_writers.escape import escape_hive_identifier, format_bigquery_datetime_literal from dlt.common.destination import Destination, DestinationCapabilitiesContext +from dlt.common.schema.typing import TColumnSchema, TColumnType +from dlt.common.typing import TLoaderFileFormat +from dlt.destinations.type_mapping import TypeMapperImpl +from dlt.destinations.impl.bigquery.bigquery_adapter import should_autodetect_schema from dlt.destinations.impl.bigquery.configuration import BigQueryClientConfiguration +from dlt.destinations.utils import parse_db_data_type_str_with_precision + if t.TYPE_CHECKING: from dlt.destinations.impl.bigquery.bigquery import BigQueryClient +class BigQueryTypeMapper(TypeMapperImpl): + sct_to_unbound_dbt = { + "json": "JSON", + "text": "STRING", + "double": "FLOAT64", + "bool": "BOOL", + "date": "DATE", + "timestamp": "TIMESTAMP", + "bigint": "INT64", + "binary": "BYTES", + "wei": "BIGNUMERIC", # non-parametrized should hold wei values + "time": "TIME", + } + + sct_to_dbt = { + "text": "STRING(%i)", + "binary": "BYTES(%i)", + } + + dbt_to_sct = { + "STRING": "text", + "FLOAT64": "double", + "BOOL": "bool", + "DATE": "date", + "TIMESTAMP": "timestamp", + "INT64": "bigint", + "BYTES": "binary", + "NUMERIC": "decimal", + "BIGNUMERIC": "decimal", + "JSON": "json", + "TIME": "time", + } + + def ensure_supported_type( + self, + column: TColumnSchema, + table: PreparedTableSchema, + loader_file_format: TLoaderFileFormat, + ) -> None: + # if table contains json types, we cannot load with parquet + if ( + loader_file_format == "parquet" + and column["data_type"] == "json" + and not should_autodetect_schema(table) + ): + raise TerminalValueError( + "Enable autodetect_schema in config or via BigQuery adapter", column["data_type"] + ) + + def to_db_decimal_type(self, column: TColumnSchema) -> str: + # Use BigQuery's BIGNUMERIC for large precision decimals + precision, scale = self.decimal_precision(column.get("precision"), column.get("scale")) + if precision > 38 or scale > 9: + return "BIGNUMERIC(%i,%i)" % (precision, scale) + return "NUMERIC(%i,%i)" % (precision, scale) + + # noinspection PyTypeChecker,PydanticTypeChecker + def from_destination_type( + self, db_type: str, precision: t.Optional[int], scale: t.Optional[int] + ) -> TColumnType: + # precision is present in the type name + if db_type == "BIGNUMERIC": + return dict(data_type="wei") + return super().from_destination_type(*parse_db_data_type_str_with_precision(db_type)) + + # noinspection PyPep8Naming class bigquery(Destination[BigQueryClientConfiguration, "BigQueryClient"]): spec = BigQueryClientConfiguration @@ -22,6 +96,7 @@ def _raw_capabilities(self) -> DestinationCapabilitiesContext: caps.supported_loader_file_formats = ["jsonl", "parquet"] caps.preferred_staging_file_format = "parquet" caps.supported_staging_file_formats = ["parquet", "jsonl"] + caps.type_mapper = BigQueryTypeMapper # BigQuery is by default case sensitive but that cannot be turned off for a dataset # https://cloud.google.com/bigquery/docs/reference/standard-sql/lexical#case_sensitivity caps.escape_identifier = escape_hive_identifier diff --git a/dlt/destinations/impl/clickhouse/clickhouse.py b/dlt/destinations/impl/clickhouse/clickhouse.py index 282fbaf338..b6f23ee221 100644 --- a/dlt/destinations/impl/clickhouse/clickhouse.py +++ b/dlt/destinations/impl/clickhouse/clickhouse.py @@ -16,6 +16,7 @@ ) from dlt.common.destination import DestinationCapabilitiesContext from dlt.common.destination.reference import ( + PreparedTableSchema, SupportsStagingDestination, TLoadJobState, HasFollowupJobs, @@ -26,9 +27,9 @@ from dlt.common.schema import Schema, TColumnSchema from dlt.common.schema.typing import ( TTableFormat, - TTableSchema, TColumnType, ) +from dlt.common.schema.utils import is_nullable_column from dlt.common.storages import FileStorage from dlt.destinations.exceptions import LoadJobTerminalException from dlt.destinations.impl.clickhouse.configuration import ( @@ -50,78 +51,10 @@ ) from dlt.destinations.job_client_impl import ( SqlJobClientBase, - SqlJobClientWithStaging, + SqlJobClientWithStagingDataset, ) from dlt.destinations.job_impl import ReferenceFollowupJobRequest, FinalizedLoadJobWithFollowupJobs from dlt.destinations.sql_jobs import SqlMergeFollowupJob -from dlt.destinations.type_mapping import TypeMapper - - -class ClickHouseTypeMapper(TypeMapper): - sct_to_unbound_dbt = { - "complex": "String", - "text": "String", - "double": "Float64", - "bool": "Boolean", - "date": "Date", - "timestamp": "DateTime64(6,'UTC')", - "time": "String", - "bigint": "Int64", - "binary": "String", - "wei": "Decimal", - } - - sct_to_dbt = { - "decimal": "Decimal(%i,%i)", - "wei": "Decimal(%i,%i)", - "timestamp": "DateTime64(%i,'UTC')", - } - - dbt_to_sct = { - "String": "text", - "Float64": "double", - "Bool": "bool", - "Date": "date", - "DateTime": "timestamp", - "DateTime64": "timestamp", - "Time": "timestamp", - "Int64": "bigint", - "Object('json')": "complex", - "Decimal": "decimal", - } - - def from_db_type( - self, db_type: str, precision: Optional[int] = None, scale: Optional[int] = None - ) -> TColumnType: - # Remove "Nullable" wrapper. - db_type = re.sub(r"^Nullable\((?P.+)\)$", r"\g", db_type) - - # Remove timezone details. - if db_type == "DateTime('UTC')": - db_type = "DateTime" - if datetime_match := re.match( - r"DateTime64(?:\((?P\d+)(?:,?\s*'(?PUTC)')?\))?", - db_type, - ): - if datetime_match["precision"]: - precision = int(datetime_match["precision"]) - else: - precision = None - db_type = "DateTime64" - - # Extract precision and scale, parameters and remove from string. - if decimal_match := re.match( - r"Decimal\((?P\d+)\s*(?:,\s*(?P\d+))?\)", db_type - ): - precision, scale = decimal_match.groups() # type: ignore[assignment] - precision = int(precision) - scale = int(scale) if scale else 0 - db_type = "Decimal" - - if db_type == "Decimal" and (precision, scale) == self.capabilities.wei_precision: - return cast(TColumnType, dict(data_type="wei")) - - return super().from_db_type(db_type, precision, scale) class ClickHouseLoadJob(RunnableLoadJob, HasFollowupJobs): @@ -255,7 +188,7 @@ def gen_key_table_clauses( key_clauses: Sequence[str], for_delete: bool, ) -> List[str]: - join_conditions = " AND ".join([c.format(d="d", s="s") for c in key_clauses]) + join_conditions = " OR ".join([c.format(d="d", s="s") for c in key_clauses]) return [ f"FROM {root_table_name} AS d JOIN {staging_root_table_name} AS s ON {join_conditions}" ] @@ -269,7 +202,7 @@ def requires_temp_table_for_delete(cls) -> bool: return True -class ClickHouseClient(SqlJobClientWithStaging, SupportsStagingDestination): +class ClickHouseClient(SqlJobClientWithStagingDataset, SupportsStagingDestination): def __init__( self, schema: Schema, @@ -286,14 +219,14 @@ def __init__( super().__init__(schema, config, self.sql_client) self.config: ClickHouseClientConfiguration = config self.active_hints = deepcopy(HINT_TO_CLICKHOUSE_ATTR) - self.type_mapper = ClickHouseTypeMapper(self.capabilities) + self.type_mapper = self.capabilities.get_type_mapper() def _create_merge_followup_jobs( - self, table_chain: Sequence[TTableSchema] + self, table_chain: Sequence[PreparedTableSchema] ) -> List[FollowupJobRequest]: return [ClickHouseMergeJob.from_table_chain(table_chain, self.sql_client)] - def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = None) -> str: + def _get_column_def_sql(self, c: TColumnSchema, table: PreparedTableSchema = None) -> str: # Build column definition. # The primary key and sort order definition is defined outside column specification. hints_ = " ".join( @@ -307,9 +240,9 @@ def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = Non # Alter table statements only accept `Nullable` modifiers. # JSON type isn't nullable in ClickHouse. type_with_nullability_modifier = ( - f"Nullable({self.type_mapper.to_db_type(c)})" - if c.get("nullable", True) - else self.type_mapper.to_db_type(c) + f"Nullable({self.type_mapper.to_destination_type(c,table)})" + if is_nullable_column(c) + else self.type_mapper.to_destination_type(c, table) ) return ( @@ -318,7 +251,7 @@ def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = Non ) def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: return super().create_load_job(table, file_path, load_id, restore) or ClickHouseLoadJob( file_path, @@ -333,7 +266,7 @@ def _get_table_update_sql( new_columns: Sequence[TColumnSchema], generate_alter: bool, ) -> List[str]: - table: TTableSchema = self.prepare_load_table(table_name, self.in_staging_mode) + table = self.prepare_load_table(table_name) sql = SqlJobClientBase._get_table_update_sql(self, table_name, new_columns, generate_alter) if generate_alter: @@ -371,7 +304,7 @@ def _gen_not_null(v: bool) -> str: def _from_db_type( self, ch_t: str, precision: Optional[int], scale: Optional[int] ) -> TColumnType: - return self.type_mapper.from_db_type(ch_t, precision, scale) + return self.type_mapper.from_destination_type(ch_t, precision, scale) - def should_truncate_table_before_load_on_staging_destination(self, table: TTableSchema) -> bool: + def should_truncate_table_before_load_on_staging_destination(self, table_name: str) -> bool: return self.config.truncate_tables_on_staging_destination_before_load diff --git a/dlt/destinations/impl/clickhouse/factory.py b/dlt/destinations/impl/clickhouse/factory.py index 93da6c866a..696c2783ca 100644 --- a/dlt/destinations/impl/clickhouse/factory.py +++ b/dlt/destinations/impl/clickhouse/factory.py @@ -1,3 +1,4 @@ +import re import sys import typing as t @@ -8,6 +9,9 @@ format_clickhouse_datetime_literal, ) from dlt.common.destination import Destination, DestinationCapabilitiesContext + +from dlt.common.schema.typing import TColumnType +from dlt.destinations.type_mapping import TypeMapperImpl from dlt.destinations.impl.clickhouse.configuration import ( ClickHouseClientConfiguration, ClickHouseCredentials, @@ -19,6 +23,73 @@ from clickhouse_driver.dbapi import Connection # type: ignore[import-untyped] +class ClickHouseTypeMapper(TypeMapperImpl): + sct_to_unbound_dbt = { + "json": "String", + "text": "String", + "double": "Float64", + "bool": "Boolean", + "date": "Date", + "timestamp": "DateTime64(6,'UTC')", + "time": "String", + "bigint": "Int64", + "binary": "String", + "wei": "Decimal", + } + + sct_to_dbt = { + "decimal": "Decimal(%i,%i)", + "wei": "Decimal(%i,%i)", + "timestamp": "DateTime64(%i,'UTC')", + } + + dbt_to_sct = { + "String": "text", + "Float64": "double", + "Bool": "bool", + "Date": "date", + "DateTime": "timestamp", + "DateTime64": "timestamp", + "Time": "timestamp", + "Int64": "bigint", + "Object('json')": "json", + "Decimal": "decimal", + } + + def from_destination_type( + self, db_type: str, precision: t.Optional[int] = None, scale: t.Optional[int] = None + ) -> TColumnType: + # Remove "Nullable" wrapper. + db_type = re.sub(r"^Nullable\((?P.+)\)$", r"\g", db_type) + + # Remove timezone details. + if db_type == "DateTime('UTC')": + db_type = "DateTime" + if datetime_match := re.match( + r"DateTime64(?:\((?P\d+)(?:,?\s*'(?PUTC)')?\))?", + db_type, + ): + if datetime_match["precision"]: + precision = int(datetime_match["precision"]) + else: + precision = None + db_type = "DateTime64" + + # Extract precision and scale, parameters and remove from string. + if decimal_match := re.match( + r"Decimal\((?P\d+)\s*(?:,\s*(?P\d+))?\)", db_type + ): + precision, scale = decimal_match.groups() # type: ignore[assignment] + precision = int(precision) + scale = int(scale) if scale else 0 + db_type = "Decimal" + + if db_type == "Decimal" and (precision, scale) == self.capabilities.wei_precision: + return t.cast(TColumnType, dict(data_type="wei")) + + return super().from_destination_type(db_type, precision, scale) + + class clickhouse(Destination[ClickHouseClientConfiguration, "ClickHouseClient"]): spec = ClickHouseClientConfiguration @@ -28,6 +99,7 @@ def _raw_capabilities(self) -> DestinationCapabilitiesContext: caps.supported_loader_file_formats = ["parquet", "jsonl"] caps.preferred_staging_file_format = "jsonl" caps.supported_staging_file_formats = ["parquet", "jsonl"] + caps.type_mapper = ClickHouseTypeMapper caps.format_datetime_literal = format_clickhouse_datetime_literal caps.escape_identifier = escape_clickhouse_identifier diff --git a/dlt/destinations/impl/clickhouse/typing.py b/dlt/destinations/impl/clickhouse/typing.py index 658822149c..3e710eeca2 100644 --- a/dlt/destinations/impl/clickhouse/typing.py +++ b/dlt/destinations/impl/clickhouse/typing.py @@ -12,7 +12,6 @@ HINT_TO_CLICKHOUSE_ATTR: Dict[TColumnHint, str] = { "primary_key": "PRIMARY KEY", "unique": "", # No unique constraints available in ClickHouse. - "foreign_key": "", # No foreign key constraints support in ClickHouse. } TABLE_ENGINE_TYPE_TO_CLICKHOUSE_ATTR: Dict[TTableEngineType, str] = { diff --git a/dlt/destinations/impl/databricks/databricks.py b/dlt/destinations/impl/databricks/databricks.py index 614e6e97c5..2cdff8a82c 100644 --- a/dlt/destinations/impl/databricks/databricks.py +++ b/dlt/destinations/impl/databricks/databricks.py @@ -6,6 +6,7 @@ from dlt.common.destination.reference import ( HasFollowupJobs, FollowupJobRequest, + PreparedTableSchema, RunnableLoadJob, SupportsStagingDestination, LoadJob, @@ -17,93 +18,20 @@ from dlt.common.exceptions import TerminalValueError from dlt.common.storages.file_storage import FileStorage from dlt.common.schema import TColumnSchema, Schema -from dlt.common.schema.typing import TTableSchema, TColumnType, TSchemaTables, TTableFormat -from dlt.common.schema.utils import table_schema_has_type +from dlt.common.schema.typing import TColumnType from dlt.common.storages import FilesystemConfiguration, fsspec_from_config from dlt.destinations.insert_job_client import InsertValuesJobClient -from dlt.destinations.job_impl import FinalizedLoadJobWithFollowupJobs from dlt.destinations.exceptions import LoadJobTerminalException from dlt.destinations.impl.databricks.configuration import DatabricksClientConfiguration from dlt.destinations.impl.databricks.sql_client import DatabricksSqlClient from dlt.destinations.sql_jobs import SqlMergeFollowupJob from dlt.destinations.job_impl import ReferenceFollowupJobRequest -from dlt.destinations.type_mapping import TypeMapper - AZURE_BLOB_STORAGE_PROTOCOLS = ["az", "abfss", "abfs"] -class DatabricksTypeMapper(TypeMapper): - sct_to_unbound_dbt = { - "complex": "STRING", # Databricks supports complex types like ARRAY - "text": "STRING", - "double": "DOUBLE", - "bool": "BOOLEAN", - "date": "DATE", - "timestamp": "TIMESTAMP", # TIMESTAMP for local timezone - "bigint": "BIGINT", - "binary": "BINARY", - "decimal": "DECIMAL", # DECIMAL(p,s) format - "time": "STRING", - } - - dbt_to_sct = { - "STRING": "text", - "DOUBLE": "double", - "BOOLEAN": "bool", - "DATE": "date", - "TIMESTAMP": "timestamp", - "BIGINT": "bigint", - "INT": "bigint", - "SMALLINT": "bigint", - "TINYINT": "bigint", - "BINARY": "binary", - "DECIMAL": "decimal", - } - - sct_to_dbt = { - "decimal": "DECIMAL(%i,%i)", - "wei": "DECIMAL(%i,%i)", - } - - def to_db_integer_type( - self, precision: Optional[int], table_format: TTableFormat = None - ) -> str: - if precision is None: - return "BIGINT" - if precision <= 8: - return "TINYINT" - if precision <= 16: - return "SMALLINT" - if precision <= 32: - return "INT" - if precision <= 64: - return "BIGINT" - raise TerminalValueError( - f"bigint with {precision} bits precision cannot be mapped into databricks integer type" - ) - - def from_db_type( - self, db_type: str, precision: Optional[int] = None, scale: Optional[int] = None - ) -> TColumnType: - # precision and scale arguments here are meaningless as they're not included separately in information schema - # We use full_data_type from databricks which is either in form "typename" or "typename(precision, scale)" - type_parts = db_type.split("(") - if len(type_parts) > 1: - db_type = type_parts[0] - scale_str = type_parts[1].strip(")") - precision, scale = [int(val) for val in scale_str.split(",")] - else: - scale = precision = None - db_type = db_type.upper() - if db_type == "DECIMAL": - if (precision, scale) == self.wei_precision(): - return dict(data_type="wei", precision=precision, scale=scale) - return super().from_db_type(db_type, precision, scale) - - class DatabricksLoadJob(RunnableLoadJob, HasFollowupJobs): def __init__( self, @@ -200,31 +128,6 @@ def run(self) -> None: " compression in the data writer configuration:" " https://dlthub.com/docs/reference/performance#disabling-and-enabling-file-compression", ) - if table_schema_has_type(self._load_table, "decimal"): - raise LoadJobTerminalException( - self._file_path, - "Databricks loader cannot load DECIMAL type columns from json files. Switch to" - " parquet format to load decimals.", - ) - if table_schema_has_type(self._load_table, "binary"): - raise LoadJobTerminalException( - self._file_path, - "Databricks loader cannot load BINARY type columns from json files. Switch to" - " parquet format to load byte values.", - ) - if table_schema_has_type(self._load_table, "complex"): - raise LoadJobTerminalException( - self._file_path, - "Databricks loader cannot load complex columns (lists and dicts) from json" - " files. Switch to parquet format to load complex types.", - ) - if table_schema_has_type(self._load_table, "date"): - raise LoadJobTerminalException( - self._file_path, - "Databricks loader cannot load DATE type columns from json files. Switch to" - " parquet format to load dates.", - ) - source_format = "JSON" format_options_clause = "FORMAT_OPTIONS('inferTimestamp'='true')" # Databricks fails when trying to load empty json files, so we have to check the file size @@ -303,10 +206,10 @@ def __init__( super().__init__(schema, config, sql_client) self.config: DatabricksClientConfiguration = config self.sql_client: DatabricksSqlClient = sql_client # type: ignore[assignment] - self.type_mapper = DatabricksTypeMapper(self.capabilities) + self.type_mapper = self.capabilities.get_type_mapper() def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: job = super().create_load_job(table, file_path, load_id, restore) @@ -318,15 +221,17 @@ def create_load_job( return job def _create_merge_followup_jobs( - self, table_chain: Sequence[TTableSchema] + self, table_chain: Sequence[PreparedTableSchema] ) -> List[FollowupJobRequest]: return [DatabricksMergeJob.from_table_chain(table_chain, self.sql_client)] def _make_add_column_sql( - self, new_columns: Sequence[TColumnSchema], table_format: TTableFormat = None + self, new_columns: Sequence[TColumnSchema], table: PreparedTableSchema = None ) -> List[str]: # Override because databricks requires multiple columns in a single ADD COLUMN clause - return ["ADD COLUMN\n" + ",\n".join(self._get_column_def_sql(c) for c in new_columns)] + return [ + "ADD COLUMN\n" + ",\n".join(self._get_column_def_sql(c, table) for c in new_columns) + ] def _get_table_update_sql( self, @@ -349,12 +254,12 @@ def _get_table_update_sql( def _from_db_type( self, bq_t: str, precision: Optional[int], scale: Optional[int] ) -> TColumnType: - return self.type_mapper.from_db_type(bq_t, precision, scale) + return self.type_mapper.from_destination_type(bq_t, precision, scale) - def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = None) -> str: + def _get_column_def_sql(self, c: TColumnSchema, table: PreparedTableSchema = None) -> str: name = self.sql_client.escape_column_name(c["name"]) return ( - f"{name} {self.type_mapper.to_db_type(c)} {self._gen_not_null(c.get('nullable', True))}" + f"{name} {self.type_mapper.to_destination_type(c,table)} {self._gen_not_null(c.get('nullable', True))}" ) def _get_storage_table_query_columns(self) -> List[str]: @@ -364,5 +269,5 @@ def _get_storage_table_query_columns(self) -> List[str]: ) return fields - def should_truncate_table_before_load_on_staging_destination(self, table: TTableSchema) -> bool: + def should_truncate_table_before_load_on_staging_destination(self, table_name: str) -> bool: return self.config.truncate_tables_on_staging_destination_before_load diff --git a/dlt/destinations/impl/databricks/factory.py b/dlt/destinations/impl/databricks/factory.py index 6108b69da9..b02f191423 100644 --- a/dlt/destinations/impl/databricks/factory.py +++ b/dlt/destinations/impl/databricks/factory.py @@ -1,9 +1,15 @@ import typing as t +from dlt.common.data_types.typing import TDataType from dlt.common.destination import Destination, DestinationCapabilitiesContext from dlt.common.data_writers.escape import escape_databricks_identifier, escape_databricks_literal from dlt.common.arithmetics import DEFAULT_NUMERIC_PRECISION, DEFAULT_NUMERIC_SCALE +from dlt.common.destination.typing import PreparedTableSchema +from dlt.common.exceptions import TerminalValueError +from dlt.common.schema.typing import TColumnSchema, TColumnType, TTableSchema +from dlt.common.typing import TLoaderFileFormat +from dlt.destinations.type_mapping import TypeMapperImpl from dlt.destinations.impl.databricks.configuration import ( DatabricksCredentials, DatabricksClientConfiguration, @@ -13,6 +19,89 @@ from dlt.destinations.impl.databricks.databricks import DatabricksClient +class DatabricksTypeMapper(TypeMapperImpl): + sct_to_unbound_dbt = { + "json": "STRING", # Json type stored as string + "text": "STRING", + "double": "DOUBLE", + "bool": "BOOLEAN", + "date": "DATE", + "timestamp": "TIMESTAMP", # TIMESTAMP for local timezone + "bigint": "BIGINT", + "binary": "BINARY", + "decimal": "DECIMAL", # DECIMAL(p,s) format + "time": "STRING", + } + + dbt_to_sct = { + "STRING": "text", + "DOUBLE": "double", + "BOOLEAN": "bool", + "DATE": "date", + "TIMESTAMP": "timestamp", + "BIGINT": "bigint", + "INT": "bigint", + "SMALLINT": "bigint", + "TINYINT": "bigint", + "BINARY": "binary", + "DECIMAL": "decimal", + } + + sct_to_dbt = { + "decimal": "DECIMAL(%i,%i)", + "wei": "DECIMAL(%i,%i)", + } + + def ensure_supported_type( + self, + column: TColumnSchema, + table: PreparedTableSchema, + loader_file_format: TLoaderFileFormat, + ) -> None: + if loader_file_format == "jsonl" and column["data_type"] in { + "decimal", + "wei", + "binary", + "json", + "date", + }: + raise TerminalValueError("", column["data_type"]) + + def to_db_integer_type(self, column: TColumnSchema, table: PreparedTableSchema = None) -> str: + precision = column.get("precision") + if precision is None: + return "BIGINT" + if precision <= 8: + return "TINYINT" + if precision <= 16: + return "SMALLINT" + if precision <= 32: + return "INT" + if precision <= 64: + return "BIGINT" + raise TerminalValueError( + f"bigint with {precision} bits precision cannot be mapped into databricks integer type" + ) + + def from_destination_type( + self, db_type: str, precision: t.Optional[int] = None, scale: t.Optional[int] = None + ) -> TColumnType: + # precision and scale arguments here are meaningless as they're not included separately in information schema + # We use full_data_type from databricks which is either in form "typename" or "typename(precision, scale)" + type_parts = db_type.split("(") + if len(type_parts) > 1: + db_type = type_parts[0] + scale_str = type_parts[1].strip(")") + precision, scale = [int(val) for val in scale_str.split(",")] + else: + scale = precision = None + db_type = db_type.upper() + if db_type == "DECIMAL": + if (precision, scale) == self.wei_precision(): + return dict(data_type="wei", precision=precision, scale=scale) + return super().from_destination_type(db_type, precision, scale) + + class databricks(Destination[DatabricksClientConfiguration, "DatabricksClient"]): spec = DatabricksClientConfiguration @@ -22,6 +111,8 @@ def _raw_capabilities(self) -> DestinationCapabilitiesContext: caps.supported_loader_file_formats = [] caps.preferred_staging_file_format = "parquet" caps.supported_staging_file_formats = ["jsonl", "parquet"] + caps.supported_table_formats = ["delta"] + caps.type_mapper = DatabricksTypeMapper caps.escape_identifier = escape_databricks_identifier # databricks identifiers are case insensitive and stored in lower case # https://docs.databricks.com/en/sql/language-manual/sql-ref-identifiers.html diff --git a/dlt/destinations/impl/databricks/sql_client.py b/dlt/destinations/impl/databricks/sql_client.py index 4c06ef1cf3..8228fa06a4 100644 --- a/dlt/destinations/impl/databricks/sql_client.py +++ b/dlt/destinations/impl/databricks/sql_client.py @@ -148,7 +148,7 @@ def _make_database_exception(ex: Exception) -> Exception: return DatabaseTransientException(ex) return DatabaseTerminalException(ex) elif isinstance(ex, databricks_lib.OperationalError): - return DatabaseTerminalException(ex) + return DatabaseTransientException(ex) elif isinstance(ex, (databricks_lib.ProgrammingError, databricks_lib.IntegrityError)): return DatabaseTerminalException(ex) elif isinstance(ex, databricks_lib.DatabaseError): diff --git a/dlt/destinations/impl/destination/destination.py b/dlt/destinations/impl/destination/destination.py index 0c4da81471..253fb8722f 100644 --- a/dlt/destinations/impl/destination/destination.py +++ b/dlt/destinations/impl/destination/destination.py @@ -1,14 +1,13 @@ -from copy import deepcopy from types import TracebackType from typing import ClassVar, Optional, Type, Iterable, cast, List -from dlt.destinations.job_impl import FinalizedLoadJobWithFollowupJobs, FinalizedLoadJob -from dlt.common.destination.reference import LoadJob +from dlt.destinations.job_impl import FinalizedLoadJob +from dlt.common.destination.reference import LoadJob, PreparedTableSchema from dlt.common.typing import AnyFun from dlt.common.storages.load_package import destination_state from dlt.common.configuration import create_resolved_partial -from dlt.common.schema import Schema, TTableSchema, TSchemaTables +from dlt.common.schema import Schema, TSchemaTables from dlt.common.destination import DestinationCapabilitiesContext from dlt.common.destination.reference import ( JobClientBase, @@ -56,7 +55,7 @@ def update_stored_schema( return super().update_stored_schema(only_tables, expected_update) def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: # skip internal tables and remove columns from schema if so configured if self.config.skip_dlt_columns_and_tables: @@ -89,10 +88,8 @@ def create_load_job( ) return None - def prepare_load_table( - self, table_name: str, prepare_for_staging: bool = False - ) -> TTableSchema: - table = super().prepare_load_table(table_name, prepare_for_staging) + def prepare_load_table(self, table_name: str) -> PreparedTableSchema: + table = super().prepare_load_table(table_name) if self.config.skip_dlt_columns_and_tables: for column in list(table["columns"].keys()): if column.startswith(self.schema._dlt_tables_prefix): diff --git a/dlt/destinations/impl/dremio/configuration.py b/dlt/destinations/impl/dremio/configuration.py index 9b1e52f292..d1893e76b7 100644 --- a/dlt/destinations/impl/dremio/configuration.py +++ b/dlt/destinations/impl/dremio/configuration.py @@ -4,7 +4,7 @@ from dlt.common.configuration import configspec from dlt.common.configuration.specs import ConnectionStringCredentials from dlt.common.destination.reference import DestinationClientDwhWithStagingConfiguration -from dlt.common.libs.sql_alchemy import URL +from dlt.common.libs.sql_alchemy_shims import URL from dlt.common.typing import TSecretStrValue from dlt.common.utils import digest128 diff --git a/dlt/destinations/impl/dremio/dremio.py b/dlt/destinations/impl/dremio/dremio.py index 149d106dcd..ab23f58ab4 100644 --- a/dlt/destinations/impl/dremio/dremio.py +++ b/dlt/destinations/impl/dremio/dremio.py @@ -4,76 +4,32 @@ from dlt.common.destination import DestinationCapabilitiesContext from dlt.common.destination.reference import ( HasFollowupJobs, + PreparedTableSchema, TLoadJobState, RunnableLoadJob, SupportsStagingDestination, FollowupJobRequest, LoadJob, ) -from dlt.common.schema import TColumnSchema, Schema, TTableSchemaColumns -from dlt.common.schema.typing import TTableSchema, TColumnType, TTableFormat, TColumnSchemaBase +from dlt.common.schema import TColumnSchema, Schema +from dlt.common.schema.typing import TColumnType, TTableFormat from dlt.common.storages.file_storage import FileStorage from dlt.common.utils import uniq_id from dlt.destinations.exceptions import LoadJobTerminalException from dlt.destinations.impl.dremio.configuration import DremioClientConfiguration from dlt.destinations.impl.dremio.sql_client import DremioSqlClient -from dlt.destinations.job_client_impl import SqlJobClientWithStaging -from dlt.destinations.job_impl import FinalizedLoadJobWithFollowupJobs +from dlt.destinations.job_client_impl import SqlJobClientWithStagingDataset from dlt.destinations.job_impl import ReferenceFollowupJobRequest from dlt.destinations.sql_jobs import SqlMergeFollowupJob -from dlt.destinations.type_mapping import TypeMapper from dlt.destinations.sql_client import SqlClientBase -class DremioTypeMapper(TypeMapper): - BIGINT_PRECISION = 19 - sct_to_unbound_dbt = { - "complex": "VARCHAR", - "text": "VARCHAR", - "double": "DOUBLE", - "bool": "BOOLEAN", - "date": "DATE", - "timestamp": "TIMESTAMP", - "bigint": "BIGINT", - "binary": "VARBINARY", - "time": "TIME", - } - - sct_to_dbt = { - "decimal": "DECIMAL(%i,%i)", - "wei": "DECIMAL(%i,%i)", - } - - dbt_to_sct = { - "VARCHAR": "text", - "DOUBLE": "double", - "FLOAT": "double", - "BOOLEAN": "bool", - "DATE": "date", - "TIMESTAMP": "timestamp", - "VARBINARY": "binary", - "BINARY": "binary", - "BINARY VARYING": "binary", - "VARIANT": "complex", - "TIME": "time", - "BIGINT": "bigint", - "DECIMAL": "decimal", - } - - def from_db_type( - self, db_type: str, precision: Optional[int] = None, scale: Optional[int] = None - ) -> TColumnType: - if db_type == "DECIMAL": - if (precision, scale) == self.capabilities.wei_precision: - return dict(data_type="wei") - return dict(data_type="decimal", precision=precision, scale=scale) - return super().from_db_type(db_type, precision, scale) - - class DremioMergeJob(SqlMergeFollowupJob): @classmethod def _new_temp_table_name(cls, name_prefix: str, sql_client: SqlClientBase[Any]) -> str: - return sql_client.make_qualified_table_name(f"_temp_{name_prefix}_{uniq_id()}") + return sql_client.make_qualified_table_name( + cls._shorten_table_name(f"_temp_{name_prefix}_{uniq_id()}", sql_client) + ) @classmethod def _to_temp_table(cls, select_sql: str, temp_table_name: str) -> str: @@ -134,7 +90,7 @@ def run(self) -> None: """) -class DremioClient(SqlJobClientWithStaging, SupportsStagingDestination): +class DremioClient(SqlJobClientWithStagingDataset, SupportsStagingDestination): def __init__( self, schema: Schema, @@ -150,10 +106,10 @@ def __init__( super().__init__(schema, config, sql_client) self.config: DremioClientConfiguration = config self.sql_client: DremioSqlClient = sql_client # type: ignore - self.type_mapper = DremioTypeMapper(self.capabilities) + self.type_mapper = self.capabilities.get_type_mapper() def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: job = super().create_load_job(table, file_path, load_id, restore) @@ -193,23 +149,27 @@ def _get_table_update_sql( def _from_db_type( self, bq_t: str, precision: Optional[int], scale: Optional[int] ) -> TColumnType: - return self.type_mapper.from_db_type(bq_t, precision, scale) + return self.type_mapper.from_destination_type(bq_t, precision, scale) - def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = None) -> str: + def _get_column_def_sql(self, c: TColumnSchema, table: PreparedTableSchema = None) -> str: name = self.sql_client.escape_column_name(c["name"]) return ( - f"{name} {self.type_mapper.to_db_type(c)} {self._gen_not_null(c.get('nullable', True))}" + f"{name} {self.type_mapper.to_destination_type(c,table)} {self._gen_not_null(c.get('nullable', True))}" ) def _create_merge_followup_jobs( - self, table_chain: Sequence[TTableSchema] + self, table_chain: Sequence[PreparedTableSchema] ) -> List[FollowupJobRequest]: return [DremioMergeJob.from_table_chain(table_chain, self.sql_client)] def _make_add_column_sql( - self, new_columns: Sequence[TColumnSchema], table_format: TTableFormat = None + self, new_columns: Sequence[TColumnSchema], table: PreparedTableSchema = None ) -> List[str]: - return ["ADD COLUMNS (" + ", ".join(self._get_column_def_sql(c) for c in new_columns) + ")"] + return [ + "ADD COLUMNS (" + + ", ".join(self._get_column_def_sql(c, table) for c in new_columns) + + ")" + ] - def should_truncate_table_before_load_on_staging_destination(self, table: TTableSchema) -> bool: + def should_truncate_table_before_load_on_staging_destination(self, table_name: str) -> bool: return self.config.truncate_tables_on_staging_destination_before_load diff --git a/dlt/destinations/impl/dremio/factory.py b/dlt/destinations/impl/dremio/factory.py index b8c7e1b746..29ec6257e6 100644 --- a/dlt/destinations/impl/dremio/factory.py +++ b/dlt/destinations/impl/dremio/factory.py @@ -4,6 +4,11 @@ from dlt.common.arithmetics import DEFAULT_NUMERIC_PRECISION, DEFAULT_NUMERIC_SCALE from dlt.common.data_writers.escape import escape_dremio_identifier +from dlt.common.destination.typing import PreparedTableSchema +from dlt.common.exceptions import TerminalValueError +from dlt.common.schema.typing import TColumnSchema, TColumnType +from dlt.common.typing import TLoaderFileFormat +from dlt.destinations.type_mapping import TypeMapperImpl from dlt.destinations.impl.dremio.configuration import ( DremioCredentials, DremioClientConfiguration, @@ -13,6 +18,68 @@ from dlt.destinations.impl.dremio.dremio import DremioClient +class DremioTypeMapper(TypeMapperImpl): + BIGINT_PRECISION = 19 + sct_to_unbound_dbt = { + "json": "VARCHAR", + "text": "VARCHAR", + "double": "DOUBLE", + "bool": "BOOLEAN", + "date": "DATE", + "timestamp": "TIMESTAMP", + "bigint": "BIGINT", + "binary": "VARBINARY", + "time": "TIME", + } + + sct_to_dbt = { + "decimal": "DECIMAL(%i,%i)", + "wei": "DECIMAL(%i,%i)", + } + + dbt_to_sct = { + "VARCHAR": "text", + "DOUBLE": "double", + "FLOAT": "double", + "BOOLEAN": "bool", + "DATE": "date", + "TIMESTAMP": "timestamp", + "VARBINARY": "binary", + "BINARY": "binary", + "BINARY VARYING": "binary", + "VARIANT": "json", + "TIME": "time", + "BIGINT": "bigint", + "DECIMAL": "decimal", + } + + def ensure_supported_type( + self, + column: TColumnSchema, + table: PreparedTableSchema, + loader_file_format: TLoaderFileFormat, + ) -> None: + if loader_file_format == "insert_values": + return + if loader_file_format == "parquet": + # binary not supported on parquet if precision is set + if column.get("precision") is not None and column["data_type"] == "binary": + raise TerminalValueError( + "Dremio cannot load fixed width 'binary' columns from parquet files. Switch to" + " other file format or use binary columns without precision.", + "binary", + ) + + def from_destination_type( + self, db_type: str, precision: t.Optional[int] = None, scale: t.Optional[int] = None + ) -> TColumnType: + if db_type == "DECIMAL": + if (precision, scale) == self.capabilities.wei_precision: + return dict(data_type="wei") + return dict(data_type="decimal", precision=precision, scale=scale) + return super().from_destination_type(db_type, precision, scale) + + class dremio(Destination[DremioClientConfiguration, "DremioClient"]): spec = DremioClientConfiguration @@ -23,6 +90,7 @@ def _raw_capabilities(self) -> DestinationCapabilitiesContext: caps.preferred_staging_file_format = "parquet" caps.supported_staging_file_formats = ["jsonl", "parquet"] caps.escape_identifier = escape_dremio_identifier + caps.type_mapper = DremioTypeMapper # all identifiers are case insensitive but are stored as is # https://docs.dremio.com/current/sonar/data-sources caps.has_case_sensitive_identifiers = False diff --git a/dlt/destinations/impl/duckdb/duck.py b/dlt/destinations/impl/duckdb/duck.py index 3d5905ff40..3bd4c83e1f 100644 --- a/dlt/destinations/impl/duckdb/duck.py +++ b/dlt/destinations/impl/duckdb/duck.py @@ -1,114 +1,26 @@ -import threading -from typing import ClassVar, Dict, Optional +from typing import Dict, Optional from dlt.common.destination import DestinationCapabilitiesContext -from dlt.common.data_types import TDataType from dlt.common.exceptions import TerminalValueError from dlt.common.schema import TColumnSchema, TColumnHint, Schema -from dlt.common.destination.reference import RunnableLoadJob, HasFollowupJobs, LoadJob -from dlt.common.schema.typing import TTableSchema, TColumnType, TTableFormat +from dlt.common.destination.reference import ( + PreparedTableSchema, + RunnableLoadJob, + HasFollowupJobs, + LoadJob, +) +from dlt.common.schema.typing import TColumnType, TTableFormat from dlt.common.storages.file_storage import FileStorage -from dlt.common.utils import maybe_context from dlt.destinations.insert_job_client import InsertValuesJobClient from dlt.destinations.impl.duckdb.sql_client import DuckDbSqlClient from dlt.destinations.impl.duckdb.configuration import DuckDbClientConfiguration -from dlt.destinations.type_mapping import TypeMapper HINT_TO_POSTGRES_ATTR: Dict[TColumnHint, str] = {"unique": "UNIQUE"} -class DuckDbTypeMapper(TypeMapper): - sct_to_unbound_dbt = { - "complex": "JSON", - "text": "VARCHAR", - "double": "DOUBLE", - "bool": "BOOLEAN", - "date": "DATE", - # Duck does not allow specifying precision on timestamp with tz - "timestamp": "TIMESTAMP WITH TIME ZONE", - "bigint": "BIGINT", - "binary": "BLOB", - "time": "TIME", - } - - sct_to_dbt = { - # VARCHAR(n) is alias for VARCHAR in duckdb - # "text": "VARCHAR(%i)", - "decimal": "DECIMAL(%i,%i)", - "wei": "DECIMAL(%i,%i)", - } - - dbt_to_sct = { - "VARCHAR": "text", - "JSON": "complex", - "DOUBLE": "double", - "BOOLEAN": "bool", - "DATE": "date", - "TIMESTAMP WITH TIME ZONE": "timestamp", - "BLOB": "binary", - "DECIMAL": "decimal", - "TIME": "time", - # Int types - "TINYINT": "bigint", - "SMALLINT": "bigint", - "INTEGER": "bigint", - "BIGINT": "bigint", - "HUGEINT": "bigint", - "TIMESTAMP_S": "timestamp", - "TIMESTAMP_MS": "timestamp", - "TIMESTAMP_NS": "timestamp", - } - - def to_db_integer_type( - self, precision: Optional[int], table_format: TTableFormat = None - ) -> str: - if precision is None: - return "BIGINT" - # Precision is number of bits - if precision <= 8: - return "TINYINT" - elif precision <= 16: - return "SMALLINT" - elif precision <= 32: - return "INTEGER" - elif precision <= 64: - return "BIGINT" - elif precision <= 128: - return "HUGEINT" - raise TerminalValueError( - f"bigint with {precision} bits precision cannot be mapped into duckdb integer type" - ) - - def to_db_datetime_type( - self, precision: Optional[int], table_format: TTableFormat = None - ) -> str: - if precision is None or precision == 6: - return super().to_db_datetime_type(precision, table_format) - if precision == 0: - return "TIMESTAMP_S" - if precision == 3: - return "TIMESTAMP_MS" - if precision == 9: - return "TIMESTAMP_NS" - raise TerminalValueError( - f"timestamp with {precision} decimals after seconds cannot be mapped into duckdb" - " TIMESTAMP type" - ) - - def from_db_type( - self, db_type: str, precision: Optional[int], scale: Optional[int] - ) -> TColumnType: - # duckdb provides the types with scale and precision - db_type = db_type.split("(")[0].upper() - if db_type == "DECIMAL": - if precision == 38 and scale == 0: - return dict(data_type="wei", precision=precision, scale=scale) - return super().from_db_type(db_type, precision, scale) - - class DuckDbCopyJob(RunnableLoadJob, HasFollowupJobs): def __init__(self, file_path: str) -> None: super().__init__(file_path) @@ -152,17 +64,17 @@ def __init__( self.config: DuckDbClientConfiguration = config self.sql_client: DuckDbSqlClient = sql_client # type: ignore self.active_hints = HINT_TO_POSTGRES_ATTR if self.config.create_indexes else {} - self.type_mapper = DuckDbTypeMapper(self.capabilities) + self.type_mapper = self.capabilities.get_type_mapper() def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: job = super().create_load_job(table, file_path, load_id, restore) if not job: job = DuckDbCopyJob(file_path) return job - def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = None) -> str: + def _get_column_def_sql(self, c: TColumnSchema, table: PreparedTableSchema = None) -> str: hints_str = " ".join( self.active_hints.get(h, "") for h in self.active_hints.keys() @@ -170,10 +82,10 @@ def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = Non ) column_name = self.sql_client.escape_column_name(c["name"]) return ( - f"{column_name} {self.type_mapper.to_db_type(c)} {hints_str} {self._gen_not_null(c.get('nullable', True))}" + f"{column_name} {self.type_mapper.to_destination_type(c,table)} {hints_str} {self._gen_not_null(c.get('nullable', True))}" ) def _from_db_type( self, pq_t: str, precision: Optional[int], scale: Optional[int] ) -> TColumnType: - return self.type_mapper.from_db_type(pq_t, precision, scale) + return self.type_mapper.from_destination_type(pq_t, precision, scale) diff --git a/dlt/destinations/impl/duckdb/factory.py b/dlt/destinations/impl/duckdb/factory.py index 2c4df2cb58..6c2011c549 100644 --- a/dlt/destinations/impl/duckdb/factory.py +++ b/dlt/destinations/impl/duckdb/factory.py @@ -1,9 +1,13 @@ import typing as t +from dlt.common import logger from dlt.common.destination import Destination, DestinationCapabilitiesContext from dlt.common.data_writers.escape import escape_postgres_identifier, escape_duckdb_literal from dlt.common.arithmetics import DEFAULT_NUMERIC_PRECISION, DEFAULT_NUMERIC_SCALE - +from dlt.common.destination.typing import PreparedTableSchema +from dlt.common.exceptions import TerminalValueError +from dlt.common.schema.typing import TColumnSchema, TColumnType +from dlt.destinations.type_mapping import TypeMapperImpl from dlt.destinations.impl.duckdb.configuration import DuckDbCredentials, DuckDbClientConfiguration if t.TYPE_CHECKING: @@ -11,6 +15,113 @@ from dlt.destinations.impl.duckdb.duck import DuckDbClient +class DuckDbTypeMapper(TypeMapperImpl): + sct_to_unbound_dbt = { + "json": "JSON", + "text": "VARCHAR", + "double": "DOUBLE", + "bool": "BOOLEAN", + "date": "DATE", + # Duck does not allow specifying precision on timestamp with tz + "timestamp": "TIMESTAMP WITH TIME ZONE", + "bigint": "BIGINT", + "binary": "BLOB", + "time": "TIME", + } + + sct_to_dbt = { + # VARCHAR(n) is alias for VARCHAR in duckdb + # "text": "VARCHAR(%i)", + "decimal": "DECIMAL(%i,%i)", + "wei": "DECIMAL(%i,%i)", + } + + dbt_to_sct = { + "VARCHAR": "text", + "JSON": "json", + "DOUBLE": "double", + "BOOLEAN": "bool", + "DATE": "date", + "TIMESTAMP WITH TIME ZONE": "timestamp", + "BLOB": "binary", + "DECIMAL": "decimal", + "TIME": "time", + # Int types + "TINYINT": "bigint", + "SMALLINT": "bigint", + "INTEGER": "bigint", + "BIGINT": "bigint", + "HUGEINT": "bigint", + "TIMESTAMP_S": "timestamp", + "TIMESTAMP_MS": "timestamp", + "TIMESTAMP_NS": "timestamp", + } + + def to_db_integer_type(self, column: TColumnSchema, table: PreparedTableSchema = None) -> str: + precision = column.get("precision") + if precision is None: + return "BIGINT" + # Precision is number of bits + if precision <= 8: + return "TINYINT" + elif precision <= 16: + return "SMALLINT" + elif precision <= 32: + return "INTEGER" + elif precision <= 64: + return "BIGINT" + elif precision <= 128: + return "HUGEINT" + raise TerminalValueError( + f"bigint with {precision} bits precision cannot be mapped into duckdb integer type" + ) + + def to_db_datetime_type( + self, + column: TColumnSchema, + table: PreparedTableSchema = None, + ) -> str: + column_name = column["name"] + table_name = table["name"] + timezone = column.get("timezone", True) + precision = column.get("precision") + + if timezone and precision is not None: + logger.warn( + f"DuckDB does not support both timezone and precision for column '{column_name}' in" + f" table '{table_name}'. Will default to timezone. Please set timezone to False to" + " use precision types." + ) + + if timezone: + # default timestamp mapping for timezone + return None + + if precision is None or precision == 6: + return "TIMESTAMP" + elif precision == 0: + return "TIMESTAMP_S" + elif precision == 3: + return "TIMESTAMP_MS" + elif precision == 9: + return "TIMESTAMP_NS" + + raise TerminalValueError( + f"DuckDB does not support precision '{precision}' for '{column_name}' in table" + f" '{table_name}'" + ) + + def from_destination_type( + self, db_type: str, precision: t.Optional[int], scale: t.Optional[int] + ) -> TColumnType: + # duckdb provides the types with scale and precision + db_type = db_type.split("(")[0].upper() + if db_type == "DECIMAL": + if precision == 38 and scale == 0: + return dict(data_type="wei", precision=precision, scale=scale) + return super().from_destination_type(db_type, precision, scale) + + class duckdb(Destination[DuckDbClientConfiguration, "DuckDbClient"]): spec = DuckDbClientConfiguration @@ -20,6 +131,7 @@ def _raw_capabilities(self) -> DestinationCapabilitiesContext: caps.supported_loader_file_formats = ["insert_values", "parquet", "jsonl"] caps.preferred_staging_file_format = None caps.supported_staging_file_formats = [] + caps.type_mapper = DuckDbTypeMapper caps.escape_identifier = escape_postgres_identifier # all identifiers are case insensitive but are stored as is caps.escape_literal = escape_duckdb_literal diff --git a/dlt/destinations/impl/dummy/dummy.py b/dlt/destinations/impl/dummy/dummy.py index fc87faaf5a..72563e903d 100644 --- a/dlt/destinations/impl/dummy/dummy.py +++ b/dlt/destinations/impl/dummy/dummy.py @@ -16,7 +16,7 @@ import time from dlt.common.metrics import LoadJobMetrics from dlt.common.pendulum import pendulum -from dlt.common.schema import Schema, TTableSchema, TSchemaTables +from dlt.common.schema import Schema, TSchemaTables from dlt.common.storages import FileStorage from dlt.common.storages.load_package import LoadJobInfo from dlt.common.destination import DestinationCapabilitiesContext @@ -27,6 +27,7 @@ from dlt.common.destination.reference import ( HasFollowupJobs, FollowupJobRequest, + PreparedTableSchema, SupportsStagingDestination, TLoadJobState, RunnableLoadJob, @@ -160,7 +161,7 @@ def update_stored_schema( return applied_update def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: job_id = FileStorage.get_file_name_from_file_path(file_path) if restore and job_id not in JOBS: @@ -178,7 +179,7 @@ def create_load_job( def create_table_chain_completed_followup_jobs( self, - table_chain: Sequence[TTableSchema], + table_chain: Sequence[PreparedTableSchema], completed_table_chain_jobs: Optional[Sequence[LoadJobInfo]] = None, ) -> List[FollowupJobRequest]: """Creates a list of followup jobs that should be executed after a table chain is completed""" @@ -199,10 +200,10 @@ def create_table_chain_completed_followup_jobs( def complete_load(self, load_id: str) -> None: pass - def should_load_data_to_staging_dataset(self, table: TTableSchema) -> bool: - return super().should_load_data_to_staging_dataset(table) + def should_load_data_to_staging_dataset(self, table_name: str) -> bool: + return super().should_load_data_to_staging_dataset(table_name) - def should_truncate_table_before_load_on_staging_destination(self, table: TTableSchema) -> bool: + def should_truncate_table_before_load_on_staging_destination(self, table_name: str) -> bool: return self.config.truncate_tables_on_staging_destination_before_load @contextmanager diff --git a/dlt/destinations/impl/filesystem/factory.py b/dlt/destinations/impl/filesystem/factory.py index ff3c8a59e1..c5218f14a3 100644 --- a/dlt/destinations/impl/filesystem/factory.py +++ b/dlt/destinations/impl/filesystem/factory.py @@ -2,18 +2,17 @@ from dlt.common.destination import Destination, DestinationCapabilitiesContext, TLoaderFileFormat from dlt.common.destination.reference import DEFAULT_FILE_LAYOUT -from dlt.common.schema.typing import TTableSchema +from dlt.common.schema.typing import TLoaderMergeStrategy, TTableSchema from dlt.common.storages.configuration import FileSystemCredentials from dlt.destinations.impl.filesystem.configuration import FilesystemDestinationClientConfiguration from dlt.destinations.impl.filesystem.typing import TCurrentDateTime, TExtraPlaceholders -from dlt.common.normalizers.naming.naming import NamingConvention if t.TYPE_CHECKING: from dlt.destinations.impl.filesystem.filesystem import FilesystemClient -def loader_file_format_adapter( +def filesystem_loader_file_format_selector( preferred_loader_file_format: TLoaderFileFormat, supported_loader_file_formats: t.Sequence[TLoaderFileFormat], /, @@ -25,22 +24,33 @@ def loader_file_format_adapter( return (preferred_loader_file_format, supported_loader_file_formats) +def filesystem_merge_strategies_selector( + supported_merge_strategies: t.Sequence[TLoaderMergeStrategy], + /, + *, + table_schema: TTableSchema, +) -> t.Sequence[TLoaderMergeStrategy]: + if table_schema.get("table_format") == "delta": + return supported_merge_strategies + else: + return [] + + class filesystem(Destination[FilesystemDestinationClientConfiguration, "FilesystemClient"]): spec = FilesystemDestinationClientConfiguration def _raw_capabilities(self) -> DestinationCapabilitiesContext: caps = DestinationCapabilitiesContext.generic_capabilities( preferred_loader_file_format="jsonl", - loader_file_format_adapter=loader_file_format_adapter, + loader_file_format_selector=filesystem_loader_file_format_selector, supported_table_formats=["delta"], - # TODO: make `supported_merge_strategies` depend on configured - # `table_format` (perhaps with adapter similar to how we handle - # loader file format) supported_merge_strategies=["upsert"], + merge_strategies_selector=filesystem_merge_strategies_selector, ) caps.supported_loader_file_formats = list(caps.supported_loader_file_formats) + [ "reference", ] + caps.has_case_sensitive_identifiers = True return caps @property diff --git a/dlt/destinations/impl/filesystem/filesystem.py b/dlt/destinations/impl/filesystem/filesystem.py index ac5ffb9ef3..c9f9797785 100644 --- a/dlt/destinations/impl/filesystem/filesystem.py +++ b/dlt/destinations/impl/filesystem/filesystem.py @@ -9,20 +9,24 @@ import dlt from dlt.common import logger, time, json, pendulum +from dlt.common.destination.utils import resolve_merge_strategy from dlt.common.metrics import LoadJobMetrics +from dlt.common.schema.typing import C_DLT_LOAD_ID, TTableSchemaColumns from dlt.common.storages.fsspec_filesystem import glob_files from dlt.common.typing import DictStrAny -from dlt.common.schema import Schema, TSchemaTables, TTableSchema +from dlt.common.schema import Schema, TSchemaTables from dlt.common.schema.utils import get_columns_names_with_prop from dlt.common.storages import FileStorage, fsspec_from_config from dlt.common.storages.load_package import ( LoadJobInfo, + ParsedLoadJobFileName, TPipelineStateDoc, load_package as current_load_package, ) from dlt.common.destination import DestinationCapabilitiesContext from dlt.common.destination.reference import ( FollowupJobRequest, + PreparedTableSchema, TLoadJobState, RunnableLoadJob, JobClientBase, @@ -42,6 +46,7 @@ from dlt.destinations.impl.filesystem.configuration import FilesystemDestinationClientConfiguration from dlt.destinations import path_utils from dlt.destinations.fs_client import FSClientBase +from dlt.destinations.utils import verify_schema_merge_disposition INIT_FILE_NAME = "init" FILENAME_SEPARATOR = "__" @@ -101,45 +106,56 @@ class DeltaLoadFilesystemJob(FilesystemLoadJob): def __init__(self, file_path: str) -> None: super().__init__(file_path=file_path) - # create Arrow dataset from Parquet files - from dlt.common.libs.pyarrow import pyarrow as pa - self.file_paths = ReferenceFollowupJobRequest.resolve_references(self._file_path) - self.arrow_ds = pa.dataset.dataset(self.file_paths) def make_remote_path(self) -> str: # remote path is table dir - delta will create its file structure inside it return self._job_client.get_table_dir(self.load_table_name) def run(self) -> None: - logger.info(f"Will copy file(s) {self.file_paths} to delta table {self.make_remote_url()}") - + # create Arrow dataset from Parquet files + from dlt.common.libs.pyarrow import pyarrow as pa from dlt.common.libs.deltalake import write_delta_table, merge_delta_table + logger.info( + f"Will copy file(s) {self.file_paths} to delta table {self.make_remote_url()} [arrow" + f" buffer: {pa.total_allocated_bytes()}]" + ) + source_ds = pa.dataset.dataset(self.file_paths) + delta_table = self._delta_table() + # explicitly check if there is data # (https://github.com/delta-io/delta-rs/issues/2686) - if self.arrow_ds.head(1).num_rows == 0: - self._create_or_evolve_delta_table() - return - - with self.arrow_ds.scanner().to_reader() as arrow_rbr: # RecordBatchReader - if self._load_table["write_disposition"] == "merge" and self._delta_table is not None: - assert self._load_table["x-merge-strategy"] in self._job_client.capabilities.supported_merge_strategies # type: ignore[typeddict-item] - merge_delta_table( - table=self._delta_table, - data=arrow_rbr, - schema=self._load_table, - ) - else: - write_delta_table( - table_or_uri=( - self.make_remote_url() if self._delta_table is None else self._delta_table - ), - data=arrow_rbr, - write_disposition=self._load_table["write_disposition"], - partition_by=self._partition_columns, - storage_options=self._storage_options, - ) + if source_ds.head(1).num_rows == 0: + delta_table = self._create_or_evolve_delta_table(source_ds, delta_table) + else: + with source_ds.scanner().to_reader() as arrow_rbr: # RecordBatchReader + if self._load_table["write_disposition"] == "merge" and delta_table is not None: + self._load_table["x-merge-strategy"] = resolve_merge_strategy( # type: ignore[typeddict-unknown-key] + self._schema.tables, self._load_table, self._job_client.capabilities + ) + merge_delta_table( + table=delta_table, + data=arrow_rbr, + schema=self._load_table, + ) + else: + write_delta_table( + table_or_uri=( + self.make_remote_url() if delta_table is None else delta_table + ), + data=arrow_rbr, + write_disposition=self._load_table["write_disposition"], + partition_by=self._partition_columns, + storage_options=self._storage_options, + ) + # release memory ASAP by deleting objects explicitly + del source_ds + del delta_table + logger.info( + f"Copied {self.file_paths} to delta table {self.make_remote_url()} [arrow buffer:" + f" {pa.total_allocated_bytes()}]" + ) @property def _storage_options(self) -> Dict[str, str]: @@ -147,7 +163,6 @@ def _storage_options(self) -> Dict[str, str]: return _deltalake_storage_options(self._job_client.config) - @property def _delta_table(self) -> Optional["DeltaTable"]: # type: ignore[name-defined] # noqa: F821 from dlt.common.libs.deltalake import try_get_deltatable @@ -157,23 +172,23 @@ def _delta_table(self) -> Optional["DeltaTable"]: # type: ignore[name-defined] def _partition_columns(self) -> List[str]: return get_columns_names_with_prop(self._load_table, "partition") - def _create_or_evolve_delta_table(self) -> None: + def _create_or_evolve_delta_table(self, arrow_ds: "Dataset", delta_table: "DeltaTable") -> "DeltaTable": # type: ignore[name-defined] # noqa: F821 from dlt.common.libs.deltalake import ( DeltaTable, ensure_delta_compatible_arrow_schema, _evolve_delta_table_schema, ) - if self._delta_table is None: - DeltaTable.create( + if delta_table is None: + return DeltaTable.create( table_uri=self.make_remote_url(), - schema=ensure_delta_compatible_arrow_schema(self.arrow_ds.schema), + schema=ensure_delta_compatible_arrow_schema(arrow_ds.schema), mode="overwrite", partition_by=self._partition_columns, storage_options=self._storage_options, ) else: - _evolve_delta_table_schema(self._delta_table, self.arrow_ds.schema) + return _evolve_delta_table_schema(delta_table, arrow_ds.schema) class FilesystemLoadJobWithFollowup(HasFollowupJobs, FilesystemLoadJob): @@ -236,9 +251,7 @@ def dataset_path(self) -> str: def with_staging_dataset(self) -> Iterator["FilesystemClient"]: current_dataset_name = self.dataset_name try: - self.dataset_name = self.schema.naming.normalize_table_identifier( - current_dataset_name + "_staging" - ) + self.dataset_name = self.config.normalize_staging_dataset_name(self.schema) yield self finally: # restore previous dataset name @@ -265,6 +278,17 @@ def drop_tables(self, *tables: str, delete_schema: bool = True) -> None: if fileparts[0] == self.schema.name: self._delete_file(filename) + def get_storage_tables( + self, table_names: Iterable[str] + ) -> Iterable[Tuple[str, TTableSchemaColumns]]: + """Yields tables that have files in storage, does not return column schemas""" + for table_name in table_names: + if len(self.list_table_files(table_name)) > 0: + yield (table_name, {"_column": {}}) + else: + # if no columns we assume that table does not exist + yield (table_name, {}) + def truncate_tables(self, table_names: List[str]) -> None: """Truncate a set of regular tables with given `table_names`""" table_dirs = set(self.get_table_dirs(table_names)) @@ -291,6 +315,19 @@ def _delete_file(self, file_path: str) -> None: if self.fs_client.exists(file_path): raise FileExistsError(file_path) + def verify_schema( + self, only_tables: Iterable[str] = None, new_jobs: Iterable[ParsedLoadJobFileName] = None + ) -> List[PreparedTableSchema]: + loaded_tables = super().verify_schema(only_tables, new_jobs) + # TODO: finetune verify_schema_merge_disposition ie. hard deletes are not supported + if exceptions := verify_schema_merge_disposition( + self.schema, loaded_tables, self.capabilities, warnings=True + ): + for exception in exceptions: + logger.error(str(exception)) + raise exceptions[0] + return loaded_tables + def update_stored_schema( self, only_tables: Iterable[str] = None, @@ -307,13 +344,21 @@ def update_stored_schema( self.fs_client.touch(self.pathlib.join(directory, INIT_FILE_NAME)) # don't store schema when used as staging - if not self.config.as_staging: + if not self.config.as_staging_destination: self._store_current_schema() # we assume that expected_update == applied_update so table schemas in dest were not # externally changed return applied_update + def prepare_load_table(self, table_name: str) -> PreparedTableSchema: + table = super().prepare_load_table(table_name) + if self.config.as_staging_destination: + if table["write_disposition"] == "merge": + table["write_disposition"] = "append" + table.pop("table_format", None) + return table + def get_table_dir(self, table_name: str, remote: bool = False) -> str: # dlt tables do not respect layout (for now) table_prefix = self.get_table_prefix(table_name) @@ -369,12 +414,12 @@ def is_storage_initialized(self) -> bool: return self.fs_client.exists(self.pathlib.join(self.dataset_path, INIT_FILE_NAME)) # type: ignore[no-any-return] def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: # skip the state table, we create a jsonl file in the complete_load step # this does not apply to scenarios where we are using filesystem as staging # where we want to load the state the regular way - if table["name"] == self.schema.state_table_name and not self.config.as_staging: + if table["name"] == self.schema.state_table_name and not self.config.as_staging_destination: return FinalizedLoadJob(file_path) if table.get("table_format") == "delta": import dlt.common.libs.deltalake # assert dependencies are installed @@ -385,7 +430,11 @@ def create_load_job( # otherwise just continue return FinalizedLoadJobWithFollowupJobs(file_path) - cls = FilesystemLoadJobWithFollowup if self.config.as_staging else FilesystemLoadJob + cls = ( + FilesystemLoadJobWithFollowup + if self.config.as_staging_destination + else FilesystemLoadJob + ) return cls(file_path) def make_remote_url(self, remote_path: str) -> str: @@ -403,10 +452,11 @@ def __exit__( ) -> None: pass - def should_load_data_to_staging_dataset(self, table: TTableSchema) -> bool: + def should_load_data_to_staging_dataset(self, table_name: str) -> bool: return False - def should_truncate_table_before_load(self, table: TTableSchema) -> bool: + def should_truncate_table_before_load(self, table_name: str) -> bool: + table = self.prepare_load_table(table_name) return ( table["write_disposition"] == "replace" and not table.get("table_format") == "delta" # Delta can do a logical replace @@ -472,7 +522,7 @@ def _get_state_file_name(self, pipeline_name: str, version_hash: str, load_id: s def _store_current_state(self, load_id: str) -> None: # don't save the state this way when used as staging - if self.config.as_staging: + if self.config.as_staging_destination: return # get state doc from current pipeline @@ -506,7 +556,7 @@ def get_stored_state(self, pipeline_name: str) -> Optional[StateInfo]: # we had dlt_load_id stored until version 0.5 and since we do not have any version control # we always migrate if load_id := state_json.pop("dlt_load_id", None): # type: ignore[typeddict-item] - state_json["_dlt_load_id"] = load_id + state_json[C_DLT_LOAD_ID] = load_id # type: ignore[literal-required] return StateInfo(**state_json) return None @@ -587,7 +637,7 @@ def get_stored_schema_by_hash(self, version_hash: str) -> Optional[StorageSchema def create_table_chain_completed_followup_jobs( self, - table_chain: Sequence[TTableSchema], + table_chain: Sequence[PreparedTableSchema], completed_table_chain_jobs: Optional[Sequence[LoadJobInfo]] = None, ) -> List[FollowupJobRequest]: assert completed_table_chain_jobs is not None @@ -601,6 +651,13 @@ def create_table_chain_completed_followup_jobs( for job in completed_table_chain_jobs if job.job_file_info.table_name == table["name"] ] - file_name = FileStorage.get_file_name_from_file_path(table_job_paths[0]) - jobs.append(ReferenceFollowupJobRequest(file_name, table_job_paths)) + if table_job_paths: + file_name = FileStorage.get_file_name_from_file_path(table_job_paths[0]) + jobs.append(ReferenceFollowupJobRequest(file_name, table_job_paths)) + else: + # file_name = ParsedLoadJobFileName(table["name"], "empty", 0, "reference").file_name() + # TODO: if we implement removal od orphaned rows, we may need to propagate such job without files + # to the delta load job + pass + return jobs diff --git a/dlt/destinations/impl/lancedb/configuration.py b/dlt/destinations/impl/lancedb/configuration.py index ba3a8b49d9..329132f495 100644 --- a/dlt/destinations/impl/lancedb/configuration.py +++ b/dlt/destinations/impl/lancedb/configuration.py @@ -90,7 +90,7 @@ class LanceDBClientConfiguration(DestinationClientDwhConfiguration): but it is configurable in rare cases. Make sure it corresponds with the associated embedding model's dimensionality.""" - vector_field_name: str = "vector__" + vector_field_name: str = "vector" """Name of the special field to store the vector embeddings.""" id_field_name: str = "id__" """Name of the special field to manage deduplication.""" diff --git a/dlt/destinations/impl/lancedb/factory.py b/dlt/destinations/impl/lancedb/factory.py index f2e17168b9..339453133f 100644 --- a/dlt/destinations/impl/lancedb/factory.py +++ b/dlt/destinations/impl/lancedb/factory.py @@ -1,11 +1,21 @@ import typing as t from dlt.common.destination import Destination, DestinationCapabilitiesContext +from dlt.common.destination.capabilities import DataTypeMapper +from dlt.common.exceptions import MissingDependencyException from dlt.destinations.impl.lancedb.configuration import ( LanceDBCredentials, LanceDBClientConfiguration, ) +LanceDBTypeMapper: t.Type[DataTypeMapper] +try: + # lancedb type mapper cannot be used without pyarrow installed + from dlt.destinations.impl.lancedb.type_mapper import LanceDBTypeMapper +except MissingDependencyException: + # assign mock type mapper if no arrow + from dlt.common.destination.capabilities import UnsupportedTypeMapper as LanceDBTypeMapper + if t.TYPE_CHECKING: from dlt.destinations.impl.lancedb.lancedb_client import LanceDBClient @@ -18,6 +28,7 @@ def _raw_capabilities(self) -> DestinationCapabilitiesContext: caps = DestinationCapabilitiesContext() caps.preferred_loader_file_format = "jsonl" caps.supported_loader_file_formats = ["jsonl"] + caps.type_mapper = LanceDBTypeMapper caps.max_identifier_length = 200 caps.max_column_identifier_length = 1024 diff --git a/dlt/destinations/impl/lancedb/lancedb_client.py b/dlt/destinations/impl/lancedb/lancedb_client.py index 78a37952b9..ffa556797e 100644 --- a/dlt/destinations/impl/lancedb/lancedb_client.py +++ b/dlt/destinations/impl/lancedb/lancedb_client.py @@ -1,7 +1,6 @@ import uuid from types import TracebackType from typing import ( - ClassVar, List, Any, cast, @@ -15,6 +14,7 @@ TYPE_CHECKING, ) +from dlt.common.destination.capabilities import DataTypeMapper import lancedb # type: ignore import pyarrow as pa from lancedb import DBConnection @@ -33,18 +33,17 @@ ) from dlt.common.destination.reference import ( JobClientBase, + PreparedTableSchema, WithStateSync, RunnableLoadJob, StorageSchemaInfo, StateInfo, - TLoadJobState, LoadJob, ) from dlt.common.pendulum import timedelta -from dlt.common.schema import Schema, TTableSchema, TSchemaTables +from dlt.common.schema import Schema, TSchemaTables from dlt.common.schema.typing import ( - TColumnType, - TTableFormat, + C_DLT_LOAD_ID, TTableSchemaColumns, TWriteDisposition, ) @@ -70,83 +69,13 @@ generate_uuid, set_non_standard_providers_environment_variables, ) -from dlt.destinations.job_impl import FinalizedLoadJobWithFollowupJobs -from dlt.destinations.type_mapping import TypeMapper if TYPE_CHECKING: NDArray = ndarray[Any, Any] else: NDArray = ndarray - -TIMESTAMP_PRECISION_TO_UNIT: Dict[int, str] = {0: "s", 3: "ms", 6: "us", 9: "ns"} -UNIT_TO_TIMESTAMP_PRECISION: Dict[str, int] = {v: k for k, v in TIMESTAMP_PRECISION_TO_UNIT.items()} - - -class LanceDBTypeMapper(TypeMapper): - sct_to_unbound_dbt = { - "text": pa.string(), - "double": pa.float64(), - "bool": pa.bool_(), - "bigint": pa.int64(), - "binary": pa.binary(), - "date": pa.date32(), - "complex": pa.string(), - } - - sct_to_dbt = {} - - dbt_to_sct = { - pa.string(): "text", - pa.float64(): "double", - pa.bool_(): "bool", - pa.int64(): "bigint", - pa.binary(): "binary", - pa.date32(): "date", - } - - def to_db_decimal_type( - self, precision: Optional[int], scale: Optional[int] - ) -> pa.Decimal128Type: - precision, scale = self.decimal_precision(precision, scale) - return pa.decimal128(precision, scale) - - def to_db_datetime_type( - self, precision: Optional[int], table_format: TTableFormat = None - ) -> pa.TimestampType: - unit: str = TIMESTAMP_PRECISION_TO_UNIT[self.capabilities.timestamp_precision] - return pa.timestamp(unit, "UTC") - - def to_db_time_type( - self, precision: Optional[int], table_format: TTableFormat = None - ) -> pa.Time64Type: - unit: str = TIMESTAMP_PRECISION_TO_UNIT[self.capabilities.timestamp_precision] - return pa.time64(unit) - - def from_db_type( - self, - db_type: pa.DataType, - precision: Optional[int] = None, - scale: Optional[int] = None, - ) -> TColumnType: - if isinstance(db_type, pa.TimestampType): - return dict( - data_type="timestamp", - precision=UNIT_TO_TIMESTAMP_PRECISION[db_type.unit], - scale=scale, - ) - if isinstance(db_type, pa.Time64Type): - return dict( - data_type="time", - precision=UNIT_TO_TIMESTAMP_PRECISION[db_type.unit], - scale=scale, - ) - if isinstance(db_type, pa.Decimal128Type): - precision, scale = db_type.precision, db_type.scale - if (precision, scale) == self.capabilities.wei_precision: - return cast(TColumnType, dict(data_type="wei")) - return dict(data_type="decimal", precision=precision, scale=scale) - return super().from_db_type(db_type, precision, scale) +EMPTY_STRING_PLACEHOLDER = "0uEoDNBpQUBwsxKbmxxB" def upload_batch( @@ -221,7 +150,7 @@ def __init__( read_consistency_interval=timedelta(0), ) self.registry = EmbeddingFunctionRegistry.get_instance() - self.type_mapper = LanceDBTypeMapper(self.capabilities) + self.type_mapper = self.capabilities.get_type_mapper() self.sentinel_table_name = config.sentinel_table_name embedding_model_provider = self.config.embedding_model_provider @@ -233,20 +162,11 @@ def __init__( embedding_model_provider, self.config.credentials.embedding_model_provider_api_key, ) - # Use the monkey-patched implementation if openai was chosen. - if embedding_model_provider == "openai": - from dlt.destinations.impl.lancedb.models import PatchedOpenAIEmbeddings - - self.model_func = PatchedOpenAIEmbeddings( - max_retries=self.config.options.max_retries, - api_key=self.config.credentials.api_key, - ) - else: - self.model_func = self.registry.get(embedding_model_provider).create( - name=self.config.embedding_model, - max_retries=self.config.options.max_retries, - api_key=self.config.credentials.api_key, - ) + self.model_func = self.registry.get(embedding_model_provider).create( + name=self.config.embedding_model, + max_retries=self.config.options.max_retries, + api_key=self.config.credentials.api_key, + ) self.vector_field_name = self.config.vector_field_name self.id_field_name = self.config.id_field_name @@ -376,9 +296,7 @@ def update_stored_schema( only_tables: Iterable[str] = None, expected_update: TSchemaTables = None, ) -> Optional[TSchemaTables]: - super().update_stored_schema(only_tables, expected_update) - applied_update: TSchemaTables = {} - + applied_update = super().update_stored_schema(only_tables, expected_update) try: schema_info = self.get_stored_schema_by_hash(self.schema.stored_version_hash) except DestinationUndefinedEntity: @@ -389,6 +307,7 @@ def update_stored_schema( f"Schema with hash {self.schema.stored_version_hash} " "not found in the storage. upgrading" ) + # TODO: return a real updated table schema (like in SQL job client) self._execute_schema_update(only_tables) else: logger.info( @@ -396,6 +315,8 @@ def update_stored_schema( f"inserted at {schema_info.inserted_at} found " "in storage, no upgrade required" ) + # we assume that expected_update == applied_update so table schemas in dest were not + # externally changed return applied_update def get_storage_table(self, table_name: str) -> Tuple[bool, TTableSchemaColumns]: @@ -415,7 +336,7 @@ def get_storage_table(self, table_name: str) -> Tuple[bool, TTableSchemaColumns] name = self.schema.naming.normalize_identifier(field.name) table_schema[name] = { "name": name, - **self.type_mapper.from_db_type(field.type), + **self.type_mapper.from_destination_type(field.type, None, None), } return True, table_schema @@ -546,7 +467,7 @@ def get_stored_state(self, pipeline_name: str) -> Optional[StateInfo]: # normalize property names p_load_id = self.schema.naming.normalize_identifier("load_id") - p_dlt_load_id = self.schema.naming.normalize_identifier("_dlt_load_id") + p_dlt_load_id = self.schema.naming.normalize_identifier(C_DLT_LOAD_ID) p_pipeline_name = self.schema.naming.normalize_identifier("pipeline_name") p_status = self.schema.naming.normalize_identifier("status") p_version = self.schema.naming.normalize_identifier("version") @@ -688,7 +609,7 @@ def complete_load(self, load_id: str) -> None: ) def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: return LanceDBLoadJob( file_path=file_path, @@ -707,12 +628,12 @@ class LanceDBLoadJob(RunnableLoadJob): def __init__( self, file_path: str, - type_mapper: LanceDBTypeMapper, + type_mapper: DataTypeMapper, model_func: TextEmbeddingFunction, fq_table_name: str, ) -> None: super().__init__(file_path) - self._type_mapper: TypeMapper = type_mapper + self._type_mapper = type_mapper self._fq_table_name: str = fq_table_name self._model_func = model_func self._job_client: "LanceDBClient" = None @@ -731,6 +652,19 @@ def run(self) -> None: with FileStorage.open_zipsafe_ro(self._file_path) as f: records: List[DictStrAny] = [json.loads(line) for line in f] + # Replace empty strings with placeholder string if OpenAI is used. + # https://github.com/lancedb/lancedb/issues/1577#issuecomment-2318104218. + if (self._job_client.config.embedding_model_provider == "openai") and ( + source_columns := get_columns_names_with_prop(self._load_table, VECTORIZE_HINT) + ): + records = [ + { + k: EMPTY_STRING_PLACEHOLDER if k in source_columns and v in ("", None) else v + for k, v in record.items() + } + for record in records + ] + if self._load_table not in self._schema.dlt_tables(): for record in records: # Add reserved ID fields. diff --git a/dlt/destinations/impl/lancedb/models.py b/dlt/destinations/impl/lancedb/models.py deleted file mode 100644 index d90adb62bd..0000000000 --- a/dlt/destinations/impl/lancedb/models.py +++ /dev/null @@ -1,34 +0,0 @@ -from typing import Union, List - -import numpy as np -from lancedb.embeddings import OpenAIEmbeddings # type: ignore -from lancedb.embeddings.registry import register # type: ignore -from lancedb.embeddings.utils import TEXT # type: ignore - - -@register("openai_patched") -class PatchedOpenAIEmbeddings(OpenAIEmbeddings): - EMPTY_STRING_PLACEHOLDER: str = "___EMPTY___" - - def sanitize_input(self, texts: TEXT) -> Union[List[str], np.ndarray]: # type: ignore[type-arg] - """ - Replace empty strings with a placeholder value. - """ - - sanitized_texts = super().sanitize_input(texts) - return [self.EMPTY_STRING_PLACEHOLDER if item == "" else item for item in sanitized_texts] - - def generate_embeddings( - self, - texts: Union[List[str], np.ndarray], # type: ignore[type-arg] - ) -> List[np.array]: # type: ignore[valid-type] - """ - Generate embeddings, treating the placeholder as an empty result. - """ - embeddings: List[np.array] = super().generate_embeddings(texts) # type: ignore[valid-type] - - for i, text in enumerate(texts): - if text == self.EMPTY_STRING_PLACEHOLDER: - embeddings[i] = np.zeros(self.ndims()) - - return embeddings diff --git a/dlt/destinations/impl/lancedb/schema.py b/dlt/destinations/impl/lancedb/schema.py index c7cceec274..27c6fb33a1 100644 --- a/dlt/destinations/impl/lancedb/schema.py +++ b/dlt/destinations/impl/lancedb/schema.py @@ -13,7 +13,8 @@ from dlt.common.schema import Schema, TColumnSchema from dlt.common.typing import DictStrAny -from dlt.destinations.type_mapping import TypeMapper + +from dlt.common.destination.capabilities import DataTypeMapper TArrowSchema: TypeAlias = pa.Schema @@ -30,17 +31,17 @@ def arrow_schema_to_dict(schema: TArrowSchema) -> DictStrAny: def make_arrow_field_schema( column_name: str, column: TColumnSchema, - type_mapper: TypeMapper, + type_mapper: DataTypeMapper, ) -> TArrowField: """Creates a PyArrow field from a dlt column schema.""" - dtype = cast(TArrowDataType, type_mapper.to_db_type(column)) + dtype = cast(TArrowDataType, type_mapper.to_destination_type(column, None)) return pa.field(column_name, dtype) def make_arrow_table_schema( table_name: str, schema: Schema, - type_mapper: TypeMapper, + type_mapper: DataTypeMapper, id_field_name: Optional[str] = None, vector_field_name: Optional[str] = None, embedding_fields: Optional[List[str]] = None, diff --git a/dlt/destinations/impl/lancedb/type_mapper.py b/dlt/destinations/impl/lancedb/type_mapper.py new file mode 100644 index 0000000000..46ea7a9809 --- /dev/null +++ b/dlt/destinations/impl/lancedb/type_mapper.py @@ -0,0 +1,85 @@ +from typing import Dict, Optional, cast +from dlt.common import logger +from dlt.common.destination.typing import PreparedTableSchema +from dlt.common.libs.pyarrow import pyarrow as pa + +from dlt.common.schema.typing import TColumnSchema, TColumnType +from dlt.destinations.type_mapping import TypeMapperImpl + +TIMESTAMP_PRECISION_TO_UNIT: Dict[int, str] = {0: "s", 3: "ms", 6: "us", 9: "ns"} +UNIT_TO_TIMESTAMP_PRECISION: Dict[str, int] = {v: k for k, v in TIMESTAMP_PRECISION_TO_UNIT.items()} + + +# TODO: TypeMapperImpl must be a Generic where pa.DataType will be a concrete class +class LanceDBTypeMapper(TypeMapperImpl): + sct_to_unbound_dbt = { + "text": pa.string(), + "double": pa.float64(), + "bool": pa.bool_(), + "bigint": pa.int64(), + "binary": pa.binary(), + "date": pa.date32(), + "json": pa.string(), + } + + sct_to_dbt = {} + + dbt_to_sct = { + pa.string(): "text", + pa.float64(): "double", + pa.bool_(): "bool", + pa.int64(): "bigint", + pa.binary(): "binary", + pa.date32(): "date", + } + + def to_db_decimal_type(self, column: TColumnSchema) -> pa.Decimal128Type: + precision, scale = self.decimal_precision(column.get("precision"), column.get("scale")) + return pa.decimal128(precision, scale) + + def to_db_datetime_type( + self, + column: TColumnSchema, + table: PreparedTableSchema = None, + ) -> pa.TimestampType: + column_name = column.get("name") + timezone = column.get("timezone") + precision = column.get("precision") + if timezone is not None or precision is not None: + logger.warning( + "LanceDB does not currently support column flags for timezone or precision." + f" These flags were used in column '{column_name}'." + ) + unit: str = TIMESTAMP_PRECISION_TO_UNIT[self.capabilities.timestamp_precision] + return pa.timestamp(unit, "UTC") + + def to_db_time_type( + self, column: TColumnSchema, table: PreparedTableSchema = None + ) -> pa.Time64Type: + unit: str = TIMESTAMP_PRECISION_TO_UNIT[self.capabilities.timestamp_precision] + return pa.time64(unit) + + def from_destination_type( + self, + db_type: pa.DataType, + precision: Optional[int] = None, + scale: Optional[int] = None, + ) -> TColumnType: + if isinstance(db_type, pa.TimestampType): + return dict( + data_type="timestamp", + precision=UNIT_TO_TIMESTAMP_PRECISION[db_type.unit], + scale=scale, + ) + if isinstance(db_type, pa.Time64Type): + return dict( + data_type="time", + precision=UNIT_TO_TIMESTAMP_PRECISION[db_type.unit], + scale=scale, + ) + if isinstance(db_type, pa.Decimal128Type): + precision, scale = db_type.precision, db_type.scale + if (precision, scale) == self.capabilities.wei_precision: + return cast(TColumnType, dict(data_type="wei")) + return dict(data_type="decimal", precision=precision, scale=scale) + return super().from_destination_type(db_type, precision, scale) diff --git a/dlt/destinations/impl/motherduck/factory.py b/dlt/destinations/impl/motherduck/factory.py index 0f4218f7cb..ac5dc70b57 100644 --- a/dlt/destinations/impl/motherduck/factory.py +++ b/dlt/destinations/impl/motherduck/factory.py @@ -4,6 +4,7 @@ from dlt.common.data_writers.escape import escape_postgres_identifier, escape_duckdb_literal from dlt.common.arithmetics import DEFAULT_NUMERIC_PRECISION, DEFAULT_NUMERIC_SCALE +from dlt.destinations.impl.duckdb.factory import DuckDbTypeMapper from dlt.destinations.impl.motherduck.configuration import ( MotherDuckCredentials, MotherDuckClientConfiguration, @@ -21,6 +22,7 @@ def _raw_capabilities(self) -> DestinationCapabilitiesContext: caps = DestinationCapabilitiesContext() caps.preferred_loader_file_format = "parquet" caps.supported_loader_file_formats = ["parquet", "insert_values", "jsonl"] + caps.type_mapper = DuckDbTypeMapper caps.escape_identifier = escape_postgres_identifier # all identifiers are case insensitive but are stored as is caps.escape_literal = escape_duckdb_literal diff --git a/dlt/destinations/impl/mssql/configuration.py b/dlt/destinations/impl/mssql/configuration.py index 64d87065f3..5b08546f73 100644 --- a/dlt/destinations/impl/mssql/configuration.py +++ b/dlt/destinations/impl/mssql/configuration.py @@ -1,6 +1,6 @@ import dataclasses from typing import Final, ClassVar, Any, List, Dict -from dlt.common.libs.sql_alchemy import URL +from dlt.common.libs.sql_alchemy_shims import URL from dlt.common.configuration import configspec from dlt.common.configuration.specs import ConnectionStringCredentials diff --git a/dlt/destinations/impl/mssql/factory.py b/dlt/destinations/impl/mssql/factory.py index f1a8bb136a..2fd668bdb6 100644 --- a/dlt/destinations/impl/mssql/factory.py +++ b/dlt/destinations/impl/mssql/factory.py @@ -1,16 +1,83 @@ import typing as t from dlt.common.destination import Destination, DestinationCapabilitiesContext +from dlt.common.destination.typing import PreparedTableSchema +from dlt.common.exceptions import TerminalValueError from dlt.common.normalizers.naming.naming import NamingConvention from dlt.common.data_writers.escape import escape_postgres_identifier, escape_mssql_literal from dlt.common.arithmetics import DEFAULT_NUMERIC_PRECISION, DEFAULT_NUMERIC_SCALE +from dlt.common.schema.typing import TColumnSchema, TColumnType +from dlt.destinations.type_mapping import TypeMapperImpl from dlt.destinations.impl.mssql.configuration import MsSqlCredentials, MsSqlClientConfiguration if t.TYPE_CHECKING: from dlt.destinations.impl.mssql.mssql import MsSqlJobClient +class MsSqlTypeMapper(TypeMapperImpl): + sct_to_unbound_dbt = { + "json": "nvarchar(max)", + "text": "nvarchar(max)", + "double": "float", + "bool": "bit", + "bigint": "bigint", + "binary": "varbinary(max)", + "date": "date", + "timestamp": "datetimeoffset", + "time": "time", + } + + sct_to_dbt = { + "json": "nvarchar(%i)", + "text": "nvarchar(%i)", + "timestamp": "datetimeoffset(%i)", + "binary": "varbinary(%i)", + "decimal": "decimal(%i,%i)", + "time": "time(%i)", + "wei": "decimal(%i,%i)", + } + + dbt_to_sct = { + "nvarchar": "text", + "float": "double", + "bit": "bool", + "datetimeoffset": "timestamp", + "date": "date", + "bigint": "bigint", + "varbinary": "binary", + "decimal": "decimal", + "time": "time", + "tinyint": "bigint", + "smallint": "bigint", + "int": "bigint", + } + + def to_db_integer_type(self, column: TColumnSchema, table: PreparedTableSchema = None) -> str: + precision = column.get("precision") + if precision is None: + return "bigint" + if precision <= 8: + return "tinyint" + if precision <= 16: + return "smallint" + if precision <= 32: + return "int" + elif precision <= 64: + return "bigint" + raise TerminalValueError( + f"bigint with {precision} bits precision cannot be mapped into mssql integer type" + ) + + def from_destination_type( + self, db_type: str, precision: t.Optional[int], scale: t.Optional[int] + ) -> TColumnType: + if db_type == "decimal": + if (precision, scale) == self.capabilities.wei_precision: + return dict(data_type="wei") + return super().from_destination_type(db_type, precision, scale) + + class mssql(Destination[MsSqlClientConfiguration, "MsSqlJobClient"]): spec = MsSqlClientConfiguration @@ -20,6 +87,7 @@ def _raw_capabilities(self) -> DestinationCapabilitiesContext: caps.supported_loader_file_formats = ["insert_values"] caps.preferred_staging_file_format = None caps.supported_staging_file_formats = [] + caps.type_mapper = MsSqlTypeMapper # mssql is by default case insensitive and stores identifiers as is # case sensitivity can be changed by database collation so we allow to reconfigure # capabilities in the mssql factory diff --git a/dlt/destinations/impl/mssql/mssql.py b/dlt/destinations/impl/mssql/mssql.py index 750dc93a10..9eabfcf392 100644 --- a/dlt/destinations/impl/mssql/mssql.py +++ b/dlt/destinations/impl/mssql/mssql.py @@ -1,10 +1,9 @@ from typing import Dict, Optional, Sequence, List, Any -from dlt.common.exceptions import TerminalValueError -from dlt.common.destination.reference import FollowupJobRequest +from dlt.common.destination.reference import FollowupJobRequest, PreparedTableSchema from dlt.common.destination import DestinationCapabilitiesContext from dlt.common.schema import TColumnSchema, TColumnHint, Schema -from dlt.common.schema.typing import TTableSchema, TColumnType, TTableFormat +from dlt.common.schema.typing import TColumnType from dlt.destinations.sql_jobs import SqlStagingCopyFollowupJob, SqlMergeFollowupJob, SqlJobParams @@ -13,7 +12,6 @@ from dlt.destinations.impl.mssql.sql_client import PyOdbcMsSqlClient from dlt.destinations.impl.mssql.configuration import MsSqlClientConfiguration from dlt.destinations.sql_client import SqlClientBase -from dlt.destinations.type_mapping import TypeMapper HINT_TO_MSSQL_ATTR: Dict[TColumnHint, str] = {"unique": "UNIQUE"} @@ -21,75 +19,11 @@ VARBINARY_MAX_N: int = 8000 -class MsSqlTypeMapper(TypeMapper): - sct_to_unbound_dbt = { - "complex": "nvarchar(max)", - "text": "nvarchar(max)", - "double": "float", - "bool": "bit", - "bigint": "bigint", - "binary": "varbinary(max)", - "date": "date", - "timestamp": "datetimeoffset", - "time": "time", - } - - sct_to_dbt = { - "complex": "nvarchar(%i)", - "text": "nvarchar(%i)", - "timestamp": "datetimeoffset(%i)", - "binary": "varbinary(%i)", - "decimal": "decimal(%i,%i)", - "time": "time(%i)", - "wei": "decimal(%i,%i)", - } - - dbt_to_sct = { - "nvarchar": "text", - "float": "double", - "bit": "bool", - "datetimeoffset": "timestamp", - "date": "date", - "bigint": "bigint", - "varbinary": "binary", - "decimal": "decimal", - "time": "time", - "tinyint": "bigint", - "smallint": "bigint", - "int": "bigint", - } - - def to_db_integer_type( - self, precision: Optional[int], table_format: TTableFormat = None - ) -> str: - if precision is None: - return "bigint" - if precision <= 8: - return "tinyint" - if precision <= 16: - return "smallint" - if precision <= 32: - return "int" - elif precision <= 64: - return "bigint" - raise TerminalValueError( - f"bigint with {precision} bits precision cannot be mapped into mssql integer type" - ) - - def from_db_type( - self, db_type: str, precision: Optional[int], scale: Optional[int] - ) -> TColumnType: - if db_type == "decimal": - if (precision, scale) == self.capabilities.wei_precision: - return dict(data_type="wei") - return super().from_db_type(db_type, precision, scale) - - class MsSqlStagingCopyJob(SqlStagingCopyFollowupJob): @classmethod def generate_sql( cls, - table_chain: Sequence[TTableSchema], + table_chain: Sequence[PreparedTableSchema], sql_client: SqlClientBase[Any], params: Optional[SqlJobParams] = None, ) -> List[str]: @@ -137,8 +71,7 @@ def _to_temp_table(cls, select_sql: str, temp_table_name: str) -> str: @classmethod def _new_temp_table_name(cls, name_prefix: str, sql_client: SqlClientBase[Any]) -> str: - name = SqlMergeFollowupJob._new_temp_table_name(name_prefix, sql_client) - return "#" + name + return SqlMergeFollowupJob._new_temp_table_name("#" + name_prefix, sql_client) class MsSqlJobClient(InsertValuesJobClient): @@ -158,28 +91,26 @@ def __init__( self.config: MsSqlClientConfiguration = config self.sql_client = sql_client self.active_hints = HINT_TO_MSSQL_ATTR if self.config.create_indexes else {} - self.type_mapper = MsSqlTypeMapper(self.capabilities) + self.type_mapper = capabilities.get_type_mapper() def _create_merge_followup_jobs( - self, table_chain: Sequence[TTableSchema] + self, table_chain: Sequence[PreparedTableSchema] ) -> List[FollowupJobRequest]: return [MsSqlMergeJob.from_table_chain(table_chain, self.sql_client)] def _make_add_column_sql( - self, new_columns: Sequence[TColumnSchema], table_format: TTableFormat = None + self, new_columns: Sequence[TColumnSchema], table: PreparedTableSchema = None ) -> List[str]: # Override because mssql requires multiple columns in a single ADD COLUMN clause - return [ - "ADD \n" + ",\n".join(self._get_column_def_sql(c, table_format) for c in new_columns) - ] + return ["ADD \n" + ",\n".join(self._get_column_def_sql(c, table) for c in new_columns)] - def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = None) -> str: + def _get_column_def_sql(self, c: TColumnSchema, table: PreparedTableSchema = None) -> str: sc_type = c["data_type"] if sc_type == "text" and c.get("unique"): # MSSQL does not allow index on large TEXT columns db_type = "nvarchar(%i)" % (c.get("precision") or 900) else: - db_type = self.type_mapper.to_db_type(c) + db_type = self.type_mapper.to_destination_type(c, table) hints_str = " ".join( self.active_hints.get(h, "") @@ -190,7 +121,7 @@ def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = Non return f"{column_name} {db_type} {hints_str} {self._gen_not_null(c.get('nullable', True))}" def _create_replace_followup_jobs( - self, table_chain: Sequence[TTableSchema] + self, table_chain: Sequence[PreparedTableSchema] ) -> List[FollowupJobRequest]: if self.config.replace_strategy == "staging-optimized": return [MsSqlStagingCopyJob.from_table_chain(table_chain, self.sql_client)] @@ -199,4 +130,4 @@ def _create_replace_followup_jobs( def _from_db_type( self, pq_t: str, precision: Optional[int], scale: Optional[int] ) -> TColumnType: - return self.type_mapper.from_db_type(pq_t, precision, scale) + return self.type_mapper.from_destination_type(pq_t, precision, scale) diff --git a/dlt/destinations/impl/postgres/configuration.py b/dlt/destinations/impl/postgres/configuration.py index 13bdc7f6b2..fab398fc21 100644 --- a/dlt/destinations/impl/postgres/configuration.py +++ b/dlt/destinations/impl/postgres/configuration.py @@ -2,7 +2,7 @@ from typing import Dict, Final, ClassVar, Any, List, Optional from dlt.common.data_writers.configuration import CsvFormatConfiguration -from dlt.common.libs.sql_alchemy import URL +from dlt.common.libs.sql_alchemy_shims import URL from dlt.common.configuration import configspec from dlt.common.configuration.specs import ConnectionStringCredentials from dlt.common.utils import digest128 diff --git a/dlt/destinations/impl/postgres/factory.py b/dlt/destinations/impl/postgres/factory.py index e14aa61465..1a33d44577 100644 --- a/dlt/destinations/impl/postgres/factory.py +++ b/dlt/destinations/impl/postgres/factory.py @@ -4,8 +4,12 @@ from dlt.common.destination import Destination, DestinationCapabilitiesContext from dlt.common.data_writers.escape import escape_postgres_identifier, escape_postgres_literal from dlt.common.arithmetics import DEFAULT_NUMERIC_PRECISION, DEFAULT_NUMERIC_SCALE +from dlt.common.destination.typing import PreparedTableSchema +from dlt.common.exceptions import TerminalValueError +from dlt.common.schema.typing import TColumnSchema, TColumnType from dlt.common.wei import EVM_DECIMAL_PRECISION +from dlt.destinations.type_mapping import TypeMapperImpl from dlt.destinations.impl.postgres.configuration import ( PostgresCredentials, PostgresClientConfiguration, @@ -15,6 +19,101 @@ from dlt.destinations.impl.postgres.postgres import PostgresClient +class PostgresTypeMapper(TypeMapperImpl): + sct_to_unbound_dbt = { + "json": "jsonb", + "text": "varchar", + "double": "double precision", + "bool": "boolean", + "date": "date", + "bigint": "bigint", + "binary": "bytea", + "timestamp": "timestamp with time zone", + "time": "time without time zone", + } + + sct_to_dbt = { + "text": "varchar(%i)", + "timestamp": "timestamp (%i) with time zone", + "decimal": "numeric(%i,%i)", + "time": "time (%i) without time zone", + "wei": "numeric(%i,%i)", + } + + dbt_to_sct = { + "varchar": "text", + "jsonb": "json", + "double precision": "double", + "boolean": "bool", + "timestamp with time zone": "timestamp", + "timestamp without time zone": "timestamp", + "date": "date", + "bigint": "bigint", + "bytea": "binary", + "numeric": "decimal", + "time without time zone": "time", + "character varying": "text", + "smallint": "bigint", + "integer": "bigint", + } + + def to_db_integer_type(self, column: TColumnSchema, table: PreparedTableSchema = None) -> str: + precision = column.get("precision") + if precision is None: + return "bigint" + # Precision is number of bits + if precision <= 16: + return "smallint" + elif precision <= 32: + return "integer" + elif precision <= 64: + return "bigint" + raise TerminalValueError( + f"bigint with {precision} bits precision cannot be mapped into postgres integer type" + ) + + def to_db_datetime_type( + self, + column: TColumnSchema, + table: PreparedTableSchema = None, + ) -> str: + column_name = column.get("name") + table_name = table.get("name") + timezone = column.get("timezone") + precision = column.get("precision") + + if timezone is None and precision is None: + return None + + timestamp = "timestamp" + + # append precision if specified and valid + if precision is not None: + if 0 <= precision <= 6: + timestamp += f" ({precision})" + else: + raise TerminalValueError( + f"Postgres does not support precision '{precision}' for '{column_name}' in" + f" table '{table_name}'" + ) + + # append timezone part + if timezone is None or timezone: # timezone True and None + timestamp += " with time zone" + else: # timezone is explicitly False + timestamp += " without time zone" + + return timestamp + + def from_destination_type( + self, db_type: str, precision: t.Optional[int] = None, scale: t.Optional[int] = None + ) -> TColumnType: + if db_type == "numeric": + if (precision, scale) == self.capabilities.wei_precision: + return dict(data_type="wei") + return super().from_destination_type(db_type, precision, scale) + + class postgres(Destination[PostgresClientConfiguration, "PostgresClient"]): spec = PostgresClientConfiguration @@ -25,6 +124,7 @@ def _raw_capabilities(self) -> DestinationCapabilitiesContext: caps.supported_loader_file_formats = ["insert_values", "csv"] caps.preferred_staging_file_format = None caps.supported_staging_file_formats = [] + caps.type_mapper = PostgresTypeMapper caps.escape_identifier = escape_postgres_identifier # postgres has case sensitive identifiers but by default # it folds them to lower case which makes them case insensitive diff --git a/dlt/destinations/impl/postgres/postgres.py b/dlt/destinations/impl/postgres/postgres.py index a832bfe07f..682f70da04 100644 --- a/dlt/destinations/impl/postgres/postgres.py +++ b/dlt/destinations/impl/postgres/postgres.py @@ -8,6 +8,7 @@ ) from dlt.common.destination.reference import ( HasFollowupJobs, + PreparedTableSchema, RunnableLoadJob, FollowupJobRequest, LoadJob, @@ -16,7 +17,8 @@ from dlt.common.destination import DestinationCapabilitiesContext from dlt.common.exceptions import TerminalValueError from dlt.common.schema import TColumnSchema, TColumnHint, Schema -from dlt.common.schema.typing import TTableSchema, TColumnType, TTableFormat +from dlt.common.schema.typing import TColumnType, TTableFormat +from dlt.common.schema.utils import is_nullable_column from dlt.common.storages.file_storage import FileStorage from dlt.destinations.sql_jobs import SqlStagingCopyFollowupJob, SqlJobParams @@ -24,78 +26,15 @@ from dlt.destinations.impl.postgres.sql_client import Psycopg2SqlClient from dlt.destinations.impl.postgres.configuration import PostgresClientConfiguration from dlt.destinations.sql_client import SqlClientBase -from dlt.destinations.type_mapping import TypeMapper HINT_TO_POSTGRES_ATTR: Dict[TColumnHint, str] = {"unique": "UNIQUE"} -class PostgresTypeMapper(TypeMapper): - sct_to_unbound_dbt = { - "complex": "jsonb", - "text": "varchar", - "double": "double precision", - "bool": "boolean", - "date": "date", - "bigint": "bigint", - "binary": "bytea", - "timestamp": "timestamp with time zone", - "time": "time without time zone", - } - - sct_to_dbt = { - "text": "varchar(%i)", - "timestamp": "timestamp (%i) with time zone", - "decimal": "numeric(%i,%i)", - "time": "time (%i) without time zone", - "wei": "numeric(%i,%i)", - } - - dbt_to_sct = { - "varchar": "text", - "jsonb": "complex", - "double precision": "double", - "boolean": "bool", - "timestamp with time zone": "timestamp", - "date": "date", - "bigint": "bigint", - "bytea": "binary", - "numeric": "decimal", - "time without time zone": "time", - "character varying": "text", - "smallint": "bigint", - "integer": "bigint", - } - - def to_db_integer_type( - self, precision: Optional[int], table_format: TTableFormat = None - ) -> str: - if precision is None: - return "bigint" - # Precision is number of bits - if precision <= 16: - return "smallint" - elif precision <= 32: - return "integer" - elif precision <= 64: - return "bigint" - raise TerminalValueError( - f"bigint with {precision} bits precision cannot be mapped into postgres integer type" - ) - - def from_db_type( - self, db_type: str, precision: Optional[int] = None, scale: Optional[int] = None - ) -> TColumnType: - if db_type == "numeric": - if (precision, scale) == self.capabilities.wei_precision: - return dict(data_type="wei") - return super().from_db_type(db_type, precision, scale) - - class PostgresStagingCopyJob(SqlStagingCopyFollowupJob): @classmethod def generate_sql( cls, - table_chain: Sequence[TTableSchema], + table_chain: Sequence[PreparedTableSchema], sql_client: SqlClientBase[Any], params: Optional[SqlJobParams] = None, ) -> List[str]: @@ -170,7 +109,7 @@ def run(self) -> None: for col in self._job_client.schema.get_table_columns(table_name).values(): norm_col = sql_client.escape_column_name(col["name"], escape=True) split_columns.append(norm_col) - if norm_col in split_headers and col.get("nullable", True): + if norm_col in split_headers and is_nullable_column(col): split_null_headers.append(norm_col) split_unknown_headers = set(split_headers).difference(split_columns) if split_unknown_headers: @@ -223,17 +162,17 @@ def __init__( self.config: PostgresClientConfiguration = config self.sql_client: Psycopg2SqlClient = sql_client self.active_hints = HINT_TO_POSTGRES_ATTR if self.config.create_indexes else {} - self.type_mapper = PostgresTypeMapper(self.capabilities) + self.type_mapper = self.capabilities.get_type_mapper() def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: job = super().create_load_job(table, file_path, load_id, restore) if not job and file_path.endswith("csv"): job = PostgresCsvCopyJob(file_path) return job - def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = None) -> str: + def _get_column_def_sql(self, c: TColumnSchema, table: PreparedTableSchema = None) -> str: hints_str = " ".join( self.active_hints.get(h, "") for h in self.active_hints.keys() @@ -241,11 +180,11 @@ def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = Non ) column_name = self.sql_client.escape_column_name(c["name"]) return ( - f"{column_name} {self.type_mapper.to_db_type(c)} {hints_str} {self._gen_not_null(c.get('nullable', True))}" + f"{column_name} {self.type_mapper.to_destination_type(c,table)} {hints_str} {self._gen_not_null(c.get('nullable', True))}" ) def _create_replace_followup_jobs( - self, table_chain: Sequence[TTableSchema] + self, table_chain: Sequence[PreparedTableSchema] ) -> List[FollowupJobRequest]: if self.config.replace_strategy == "staging-optimized": return [PostgresStagingCopyJob.from_table_chain(table_chain, self.sql_client)] @@ -254,4 +193,4 @@ def _create_replace_followup_jobs( def _from_db_type( self, pq_t: str, precision: Optional[int], scale: Optional[int] ) -> TColumnType: - return self.type_mapper.from_db_type(pq_t, precision, scale) + return self.type_mapper.from_destination_type(pq_t, precision, scale) diff --git a/dlt/destinations/impl/qdrant/qdrant_adapter.py b/dlt/destinations/impl/qdrant/qdrant_adapter.py index e39d3e3644..abe301fff0 100644 --- a/dlt/destinations/impl/qdrant/qdrant_adapter.py +++ b/dlt/destinations/impl/qdrant/qdrant_adapter.py @@ -1,7 +1,7 @@ from typing import Any from dlt.common.schema.typing import TColumnNames, TTableSchemaColumns -from dlt.extract import DltResource, resource as make_resource +from dlt.extract import DltResource from dlt.destinations.utils import get_resource_for_adapter VECTORIZE_HINT = "x-qdrant-embed" diff --git a/dlt/destinations/impl/qdrant/qdrant_job_client.py b/dlt/destinations/impl/qdrant/qdrant_job_client.py index 65019c6626..2536bd369d 100644 --- a/dlt/destinations/impl/qdrant/qdrant_job_client.py +++ b/dlt/destinations/impl/qdrant/qdrant_job_client.py @@ -5,7 +5,8 @@ from dlt.common import logger from dlt.common.json import json from dlt.common.pendulum import pendulum -from dlt.common.schema import Schema, TTableSchema, TSchemaTables +from dlt.common.schema import Schema, TSchemaTables +from dlt.common.schema.typing import C_DLT_LOAD_ID from dlt.common.schema.utils import ( get_columns_names_with_prop, loads_table, @@ -14,6 +15,7 @@ ) from dlt.common.destination import DestinationCapabilitiesContext from dlt.common.destination.reference import ( + PreparedTableSchema, TLoadJobState, RunnableLoadJob, JobClientBase, @@ -99,11 +101,11 @@ def _get_embedding_doc(self, data: Dict[str, Any], embedding_fields: List[str]) doc = "\n".join(str(data[key]) for key in embedding_fields) return doc - def _list_unique_identifiers(self, table_schema: TTableSchema) -> Sequence[str]: + def _list_unique_identifiers(self, table_schema: PreparedTableSchema) -> Sequence[str]: """Returns a list of unique identifiers for a table. Args: - table_schema (TTableSchema): a dlt table schema. + table_schema (PreparedTableSchema): a dlt table schema. Returns: Sequence[str]: A list of unique column identifiers. @@ -291,8 +293,7 @@ def update_stored_schema( only_tables: Iterable[str] = None, expected_update: TSchemaTables = None, ) -> Optional[TSchemaTables]: - super().update_stored_schema(only_tables, expected_update) - applied_update: TSchemaTables = {} + applied_update = super().update_stored_schema(only_tables, expected_update) schema_info = self.get_stored_schema_by_hash(self.schema.stored_version_hash) if schema_info is None: logger.info( @@ -306,6 +307,8 @@ def update_stored_schema( f"inserted at {schema_info.inserted_at} found " "in storage, no upgrade required" ) + # we assume that expected_update == applied_update so table schemas in dest were not + # externally changed return applied_update def get_stored_state(self, pipeline_name: str) -> Optional[StateInfo]: @@ -314,7 +317,7 @@ def get_stored_state(self, pipeline_name: str) -> Optional[StateInfo]: """ # normalize property names p_load_id = self.schema.naming.normalize_identifier("load_id") - p_dlt_load_id = self.schema.naming.normalize_identifier("_dlt_load_id") + p_dlt_load_id = self.schema.naming.normalize_identifier(C_DLT_LOAD_ID) p_pipeline_name = self.schema.naming.normalize_identifier("pipeline_name") p_created_at = self.schema.naming.normalize_identifier("created_at") @@ -440,7 +443,7 @@ def get_stored_schema_by_hash(self, schema_hash: str) -> Optional[StorageSchemaI raise def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: return QDrantLoadJob( file_path, diff --git a/dlt/destinations/impl/redshift/factory.py b/dlt/destinations/impl/redshift/factory.py index ef1ee6b754..20b7df859f 100644 --- a/dlt/destinations/impl/redshift/factory.py +++ b/dlt/destinations/impl/redshift/factory.py @@ -1,10 +1,17 @@ import typing as t +from dlt.common.data_types.typing import TDataType from dlt.common.destination import Destination, DestinationCapabilitiesContext from dlt.common.data_writers.escape import escape_redshift_identifier, escape_redshift_literal from dlt.common.arithmetics import DEFAULT_NUMERIC_PRECISION, DEFAULT_NUMERIC_SCALE +from dlt.common.destination.typing import PreparedTableSchema +from dlt.common.exceptions import TerminalValueError from dlt.common.normalizers.naming import NamingConvention +from dlt.common.schema.typing import TColumnSchema, TColumnType +from dlt.common.typing import TLoaderFileFormat + +from dlt.destinations.type_mapping import TypeMapperImpl from dlt.destinations.impl.redshift.configuration import ( RedshiftCredentials, RedshiftClientConfiguration, @@ -14,6 +21,92 @@ from dlt.destinations.impl.redshift.redshift import RedshiftClient +class RedshiftTypeMapper(TypeMapperImpl): + sct_to_unbound_dbt = { + "json": "super", + "text": "varchar(max)", + "double": "double precision", + "bool": "boolean", + "date": "date", + "timestamp": "timestamp with time zone", + "bigint": "bigint", + "binary": "varbinary", + "time": "time without time zone", + } + + sct_to_dbt = { + "decimal": "numeric(%i,%i)", + "wei": "numeric(%i,%i)", + "text": "varchar(%i)", + "binary": "varbinary(%i)", + } + + dbt_to_sct = { + "super": "json", + "varchar(max)": "text", + "double precision": "double", + "boolean": "bool", + "date": "date", + "timestamp with time zone": "timestamp", + "bigint": "bigint", + "binary varying": "binary", + "numeric": "decimal", + "time without time zone": "time", + "varchar": "text", + "smallint": "bigint", + "integer": "bigint", + } + + def ensure_supported_type( + self, + column: TColumnSchema, + table: PreparedTableSchema, + loader_file_format: TLoaderFileFormat, + ) -> None: + if loader_file_format == "insert_values": + return + # time not supported on staging file formats + if column["data_type"] == "time": + raise TerminalValueError( + "Please convert `datetime.time` objects in your data to `str` or" + " `datetime.datetime`.", + "time", + ) + if loader_file_format == "jsonl": + if column["data_type"] == "binary": + raise TerminalValueError("", "binary") + if loader_file_format == "parquet": + # binary not supported on parquet if precision is set + if column.get("precision") and column["data_type"] == "binary": + raise TerminalValueError( + "Redshift cannot load fixed width VARBYTE columns from parquet files. Switch" + " to other file format or use binary columns without precision.", + "binary", + ) + + def to_db_integer_type(self, column: TColumnSchema, table: PreparedTableSchema = None) -> str: + precision = column.get("precision") + if precision is None: + return "bigint" + if precision <= 16: + return "smallint" + elif precision <= 32: + return "integer" + elif precision <= 64: + return "bigint" + raise TerminalValueError( + f"bigint with {precision} bits precision cannot be mapped into postgres integer type" + ) + + def from_destination_type( + self, db_type: str, precision: t.Optional[int], scale: t.Optional[int] + ) -> TColumnType: + if db_type == "numeric": + if (precision, scale) == self.capabilities.wei_precision: + return dict(data_type="wei") + return super().from_destination_type(db_type, precision, scale) + + class redshift(Destination[RedshiftClientConfiguration, "RedshiftClient"]): spec = RedshiftClientConfiguration @@ -23,6 +116,7 @@ def _raw_capabilities(self) -> DestinationCapabilitiesContext: caps.supported_loader_file_formats = ["insert_values"] caps.preferred_staging_file_format = "jsonl" caps.supported_staging_file_formats = ["jsonl", "parquet"] + caps.type_mapper = RedshiftTypeMapper # redshift is case insensitive and will lower case identifiers when stored # you can enable case sensitivity https://docs.aws.amazon.com/redshift/latest/dg/r_enable_case_sensitive_identifier.html # then redshift behaves like postgres diff --git a/dlt/destinations/impl/redshift/redshift.py b/dlt/destinations/impl/redshift/redshift.py index 0e201dc4e0..6581889296 100644 --- a/dlt/destinations/impl/redshift/redshift.py +++ b/dlt/destinations/impl/redshift/redshift.py @@ -16,26 +16,23 @@ from dlt.common.destination.reference import ( FollowupJobRequest, CredentialsConfiguration, + PreparedTableSchema, SupportsStagingDestination, LoadJob, ) -from dlt.common.data_types import TDataType from dlt.common.destination.capabilities import DestinationCapabilitiesContext from dlt.common.schema import TColumnSchema, TColumnHint, Schema -from dlt.common.exceptions import TerminalValueError -from dlt.common.schema.utils import table_schema_has_type, table_schema_has_type_with_precision -from dlt.common.schema.typing import TTableSchema, TColumnType, TTableFormat, TTableSchemaColumns +from dlt.common.schema.utils import table_schema_has_type +from dlt.common.schema.typing import TColumnType from dlt.common.configuration.specs import AwsCredentialsWithoutDefaults from dlt.destinations.insert_job_client import InsertValuesJobClient from dlt.destinations.sql_jobs import SqlMergeFollowupJob -from dlt.destinations.exceptions import DatabaseTerminalException, LoadJobTerminalException +from dlt.destinations.exceptions import DatabaseTerminalException from dlt.destinations.job_client_impl import CopyRemoteFileLoadJob from dlt.destinations.impl.postgres.sql_client import Psycopg2SqlClient from dlt.destinations.impl.redshift.configuration import RedshiftClientConfiguration from dlt.destinations.job_impl import ReferenceFollowupJobRequest -from dlt.destinations.sql_client import SqlClientBase -from dlt.destinations.type_mapping import TypeMapper HINT_TO_REDSHIFT_ATTR: Dict[TColumnHint, str] = { @@ -46,66 +43,6 @@ } -class RedshiftTypeMapper(TypeMapper): - sct_to_unbound_dbt = { - "complex": "super", - "text": "varchar(max)", - "double": "double precision", - "bool": "boolean", - "date": "date", - "timestamp": "timestamp with time zone", - "bigint": "bigint", - "binary": "varbinary", - "time": "time without time zone", - } - - sct_to_dbt = { - "decimal": "numeric(%i,%i)", - "wei": "numeric(%i,%i)", - "text": "varchar(%i)", - "binary": "varbinary(%i)", - } - - dbt_to_sct = { - "super": "complex", - "varchar(max)": "text", - "double precision": "double", - "boolean": "bool", - "date": "date", - "timestamp with time zone": "timestamp", - "bigint": "bigint", - "binary varying": "binary", - "numeric": "decimal", - "time without time zone": "time", - "varchar": "text", - "smallint": "bigint", - "integer": "bigint", - } - - def to_db_integer_type( - self, precision: Optional[int], table_format: TTableFormat = None - ) -> str: - if precision is None: - return "bigint" - if precision <= 16: - return "smallint" - elif precision <= 32: - return "integer" - elif precision <= 64: - return "bigint" - raise TerminalValueError( - f"bigint with {precision} bits precision cannot be mapped into postgres integer type" - ) - - def from_db_type( - self, db_type: str, precision: Optional[int], scale: Optional[int] - ) -> TColumnType: - if db_type == "numeric": - if (precision, scale) == self.capabilities.wei_precision: - return dict(data_type="wei") - return super().from_db_type(db_type, precision, scale) - - class RedshiftSqlClient(Psycopg2SqlClient): @staticmethod def _maybe_make_terminal_exception_from_data_error( @@ -153,34 +90,15 @@ def run(self) -> None: file_type = "" dateformat = "" compression = "" - if table_schema_has_type(self._load_table, "time"): - raise LoadJobTerminalException( - self.file_name(), - f"Redshift cannot load TIME columns from {ext} files. Switch to direct INSERT file" - " format or convert `datetime.time` objects in your data to `str` or" - " `datetime.datetime`", - ) if ext == "jsonl": - if table_schema_has_type(self._load_table, "binary"): - raise LoadJobTerminalException( - self.file_name(), - "Redshift cannot load VARBYTE columns from json files. Switch to parquet to" - " load binaries.", - ) file_type = "FORMAT AS JSON 'auto'" dateformat = "dateformat 'auto' timeformat 'auto'" compression = "GZIP" elif ext == "parquet": - if table_schema_has_type_with_precision(self._load_table, "binary"): - raise LoadJobTerminalException( - self.file_name(), - f"Redshift cannot load fixed width VARBYTE columns from {ext} files. Switch to" - " direct INSERT file format or use binary columns without precision.", - ) file_type = "PARQUET" - # if table contains complex types then SUPER field will be used. + # if table contains json types then SUPER field will be used. # https://docs.aws.amazon.com/redshift/latest/dg/ingest-super.html - if table_schema_has_type(self._load_table, "complex"): + if table_schema_has_type(self._load_table, "json"): file_type += " SERIALIZETOJSON" else: raise ValueError(f"Unsupported file type {ext} for Redshift.") @@ -236,14 +154,14 @@ def __init__( super().__init__(schema, config, sql_client) self.sql_client = sql_client self.config: RedshiftClientConfiguration = config - self.type_mapper = RedshiftTypeMapper(self.capabilities) + self.type_mapper = self.capabilities.get_type_mapper() def _create_merge_followup_jobs( - self, table_chain: Sequence[TTableSchema] + self, table_chain: Sequence[PreparedTableSchema] ) -> List[FollowupJobRequest]: return [RedshiftMergeJob.from_table_chain(table_chain, self.sql_client)] - def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = None) -> str: + def _get_column_def_sql(self, c: TColumnSchema, table: PreparedTableSchema = None) -> str: hints_str = " ".join( HINT_TO_REDSHIFT_ATTR.get(h, "") for h in HINT_TO_REDSHIFT_ATTR.keys() @@ -251,11 +169,11 @@ def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = Non ) column_name = self.sql_client.escape_column_name(c["name"]) return ( - f"{column_name} {self.type_mapper.to_db_type(c)} {hints_str} {self._gen_not_null(c.get('nullable', True))}" + f"{column_name} {self.type_mapper.to_destination_type(c,table)} {hints_str} {self._gen_not_null(c.get('nullable', True))}" ) def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: """Starts SqlLoadJob for files ending with .sql or returns None to let derived classes to handle their specific jobs""" job = super().create_load_job(table, file_path, load_id, restore) @@ -273,7 +191,7 @@ def create_load_job( def _from_db_type( self, pq_t: str, precision: Optional[int], scale: Optional[int] ) -> TColumnType: - return self.type_mapper.from_db_type(pq_t, precision, scale) + return self.type_mapper.from_destination_type(pq_t, precision, scale) - def should_truncate_table_before_load_on_staging_destination(self, table: TTableSchema) -> bool: + def should_truncate_table_before_load_on_staging_destination(self, table_name: str) -> bool: return self.config.truncate_tables_on_staging_destination_before_load diff --git a/dlt/destinations/impl/snowflake/configuration.py b/dlt/destinations/impl/snowflake/configuration.py index 08fc132fc3..3fc479f237 100644 --- a/dlt/destinations/impl/snowflake/configuration.py +++ b/dlt/destinations/impl/snowflake/configuration.py @@ -4,7 +4,7 @@ from dlt import version from dlt.common.data_writers.configuration import CsvFormatConfiguration -from dlt.common.libs.sql_alchemy import URL +from dlt.common.libs.sql_alchemy_shims import URL from dlt.common.exceptions import MissingDependencyException from dlt.common.typing import TSecretStrValue from dlt.common.configuration.specs import ConnectionStringCredentials diff --git a/dlt/destinations/impl/snowflake/factory.py b/dlt/destinations/impl/snowflake/factory.py index c5fbd8600b..6c2369a5aa 100644 --- a/dlt/destinations/impl/snowflake/factory.py +++ b/dlt/destinations/impl/snowflake/factory.py @@ -4,7 +4,11 @@ from dlt.common.destination import Destination, DestinationCapabilitiesContext from dlt.common.data_writers.escape import escape_snowflake_identifier from dlt.common.arithmetics import DEFAULT_NUMERIC_PRECISION, DEFAULT_NUMERIC_SCALE +from dlt.common.destination.typing import PreparedTableSchema +from dlt.common.exceptions import TerminalValueError +from dlt.common.schema.typing import TColumnSchema, TColumnType +from dlt.destinations.type_mapping import TypeMapperImpl from dlt.destinations.impl.snowflake.configuration import ( SnowflakeCredentials, SnowflakeClientConfiguration, @@ -14,6 +18,80 @@ from dlt.destinations.impl.snowflake.snowflake import SnowflakeClient +class SnowflakeTypeMapper(TypeMapperImpl): + BIGINT_PRECISION = 19 + sct_to_unbound_dbt = { + "json": "VARIANT", + "text": "VARCHAR", + "double": "FLOAT", + "bool": "BOOLEAN", + "date": "DATE", + "timestamp": "TIMESTAMP_TZ", + "bigint": f"NUMBER({BIGINT_PRECISION},0)", # Snowflake has no integer types + "binary": "BINARY", + "time": "TIME", + } + + sct_to_dbt = { + "text": "VARCHAR(%i)", + "timestamp": "TIMESTAMP_TZ(%i)", + "decimal": "NUMBER(%i,%i)", + "time": "TIME(%i)", + "wei": "NUMBER(%i,%i)", + } + + dbt_to_sct = { + "VARCHAR": "text", + "FLOAT": "double", + "BOOLEAN": "bool", + "DATE": "date", + "TIMESTAMP_TZ": "timestamp", + "BINARY": "binary", + "VARIANT": "json", + "TIME": "time", + } + + def from_destination_type( + self, db_type: str, precision: t.Optional[int] = None, scale: t.Optional[int] = None + ) -> TColumnType: + if db_type == "NUMBER": + if precision == self.BIGINT_PRECISION and scale == 0: + return dict(data_type="bigint") + elif (precision, scale) == self.capabilities.wei_precision: + return dict(data_type="wei") + return dict(data_type="decimal", precision=precision, scale=scale) + if db_type == "TIMESTAMP_NTZ": + return dict(data_type="timestamp", precision=precision, scale=scale, timezone=False) + return super().from_destination_type(db_type, precision, scale) + + def to_db_datetime_type( + self, + column: TColumnSchema, + table: PreparedTableSchema = None, + ) -> str: + timezone = column.get("timezone", True) + precision = column.get("precision") + + if timezone and precision is None: + return None + + timestamp = "TIMESTAMP_TZ" if timezone else "TIMESTAMP_NTZ" + + # append precision if specified and valid + if precision is not None: + if 0 <= precision <= 9: + timestamp += f"({precision})" + else: + column_name = column["name"] + table_name = table["name"] + raise TerminalValueError( + f"Snowflake does not support precision '{precision}' for '{column_name}' in" + f" table '{table_name}'" + ) + + return timestamp + + class snowflake(Destination[SnowflakeClientConfiguration, "SnowflakeClient"]): spec = SnowflakeClientConfiguration @@ -23,6 +101,7 @@ def _raw_capabilities(self) -> DestinationCapabilitiesContext: caps.supported_loader_file_formats = ["jsonl", "parquet", "csv"] caps.preferred_staging_file_format = "jsonl" caps.supported_staging_file_formats = ["jsonl", "parquet", "csv"] + caps.type_mapper = SnowflakeTypeMapper # snowflake is case sensitive but all unquoted identifiers are upper cased # so upper case identifiers are considered case insensitive caps.escape_identifier = escape_snowflake_identifier diff --git a/dlt/destinations/impl/snowflake/snowflake.py b/dlt/destinations/impl/snowflake/snowflake.py index 6688b5bc17..41a8384754 100644 --- a/dlt/destinations/impl/snowflake/snowflake.py +++ b/dlt/destinations/impl/snowflake/snowflake.py @@ -6,6 +6,7 @@ from dlt.common.destination.reference import ( HasFollowupJobs, LoadJob, + PreparedTableSchema, RunnableLoadJob, CredentialsConfiguration, SupportsStagingDestination, @@ -16,66 +17,18 @@ ) from dlt.common.storages.configuration import FilesystemConfiguration from dlt.common.storages.file_storage import FileStorage -from dlt.common.schema import TColumnSchema, Schema, TTableSchemaColumns -from dlt.common.schema.typing import TTableSchema, TColumnType, TTableFormat +from dlt.common.schema import TColumnSchema, Schema +from dlt.common.schema.typing import TColumnType +from dlt.common.exceptions import TerminalValueError - -from dlt.common.storages.load_package import ParsedLoadJobFileName from dlt.common.typing import TLoaderFileFormat -from dlt.destinations.job_client_impl import SqlJobClientWithStaging -from dlt.destinations.job_impl import FinalizedLoadJobWithFollowupJobs +from dlt.destinations.job_client_impl import SqlJobClientWithStagingDataset from dlt.destinations.exceptions import LoadJobTerminalException from dlt.destinations.impl.snowflake.configuration import SnowflakeClientConfiguration from dlt.destinations.impl.snowflake.sql_client import SnowflakeSqlClient from dlt.destinations.impl.snowflake.sql_client import SnowflakeSqlClient from dlt.destinations.job_impl import ReferenceFollowupJobRequest -from dlt.destinations.type_mapping import TypeMapper - - -class SnowflakeTypeMapper(TypeMapper): - BIGINT_PRECISION = 19 - sct_to_unbound_dbt = { - "complex": "VARIANT", - "text": "VARCHAR", - "double": "FLOAT", - "bool": "BOOLEAN", - "date": "DATE", - "timestamp": "TIMESTAMP_TZ", - "bigint": f"NUMBER({BIGINT_PRECISION},0)", # Snowflake has no integer types - "binary": "BINARY", - "time": "TIME", - } - - sct_to_dbt = { - "text": "VARCHAR(%i)", - "timestamp": "TIMESTAMP_TZ(%i)", - "decimal": "NUMBER(%i,%i)", - "time": "TIME(%i)", - "wei": "NUMBER(%i,%i)", - } - - dbt_to_sct = { - "VARCHAR": "text", - "FLOAT": "double", - "BOOLEAN": "bool", - "DATE": "date", - "TIMESTAMP_TZ": "timestamp", - "BINARY": "binary", - "VARIANT": "complex", - "TIME": "time", - } - - def from_db_type( - self, db_type: str, precision: Optional[int] = None, scale: Optional[int] = None - ) -> TColumnType: - if db_type == "NUMBER": - if precision == self.BIGINT_PRECISION and scale == 0: - return dict(data_type="bigint") - elif (precision, scale) == self.capabilities.wei_precision: - return dict(data_type="wei") - return dict(data_type="decimal", precision=precision, scale=scale) - return super().from_db_type(db_type, precision, scale) class SnowflakeLoadJob(RunnableLoadJob, HasFollowupJobs): @@ -252,7 +205,7 @@ def gen_copy_sql( """ -class SnowflakeClient(SqlJobClientWithStaging, SupportsStagingDestination): +class SnowflakeClient(SqlJobClientWithStagingDataset, SupportsStagingDestination): def __init__( self, schema: Schema, @@ -269,10 +222,10 @@ def __init__( super().__init__(schema, config, sql_client) self.config: SnowflakeClientConfiguration = config self.sql_client: SnowflakeSqlClient = sql_client # type: ignore - self.type_mapper = SnowflakeTypeMapper(self.capabilities) + self.type_mapper = self.capabilities.get_type_mapper() def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: job = super().create_load_job(table, file_path, load_id, restore) @@ -289,12 +242,11 @@ def create_load_job( return job def _make_add_column_sql( - self, new_columns: Sequence[TColumnSchema], table_format: TTableFormat = None + self, new_columns: Sequence[TColumnSchema], table: PreparedTableSchema = None ) -> List[str]: # Override because snowflake requires multiple columns in a single ADD COLUMN clause return [ - "ADD COLUMN\n" - + ",\n".join(self._get_column_def_sql(c, table_format) for c in new_columns) + "ADD COLUMN\n" + ",\n".join(self._get_column_def_sql(c, table) for c in new_columns) ] def _get_table_update_sql( @@ -318,13 +270,13 @@ def _get_table_update_sql( def _from_db_type( self, bq_t: str, precision: Optional[int], scale: Optional[int] ) -> TColumnType: - return self.type_mapper.from_db_type(bq_t, precision, scale) + return self.type_mapper.from_destination_type(bq_t, precision, scale) - def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = None) -> str: + def _get_column_def_sql(self, c: TColumnSchema, table: PreparedTableSchema = None) -> str: name = self.sql_client.escape_column_name(c["name"]) return ( - f"{name} {self.type_mapper.to_db_type(c)} {self._gen_not_null(c.get('nullable', True))}" + f"{name} {self.type_mapper.to_destination_type(c,table)} {self._gen_not_null(c.get('nullable', True))}" ) - def should_truncate_table_before_load_on_staging_destination(self, table: TTableSchema) -> bool: + def should_truncate_table_before_load_on_staging_destination(self, table_name: str) -> bool: return self.config.truncate_tables_on_staging_destination_before_load diff --git a/docs/examples/parent_child_relationship/__init__.py b/dlt/destinations/impl/sqlalchemy/__init__.py similarity index 100% rename from docs/examples/parent_child_relationship/__init__.py rename to dlt/destinations/impl/sqlalchemy/__init__.py diff --git a/dlt/destinations/impl/sqlalchemy/alter_table.py b/dlt/destinations/impl/sqlalchemy/alter_table.py new file mode 100644 index 0000000000..f85101a740 --- /dev/null +++ b/dlt/destinations/impl/sqlalchemy/alter_table.py @@ -0,0 +1,38 @@ +from typing import List + +import sqlalchemy as sa +from alembic.runtime.migration import MigrationContext +from alembic.operations import Operations + + +class ListBuffer: + """A partial implementation of string IO to use with alembic. + SQL statements are stored in a list instead of file/stdio + """ + + def __init__(self) -> None: + self._buf = "" + self.sql_lines: List[str] = [] + + def write(self, data: str) -> None: + self._buf += data + + def flush(self) -> None: + if self._buf: + self.sql_lines.append(self._buf) + self._buf = "" + + +class MigrationMaker: + def __init__(self, dialect: sa.engine.Dialect) -> None: + self._buf = ListBuffer() + self.ctx = MigrationContext(dialect, None, {"as_sql": True, "output_buffer": self._buf}) + self.ops = Operations(self.ctx) + + def add_column(self, table_name: str, column: sa.Column, schema: str) -> None: + self.ops.add_column(table_name, column, schema=schema) + + def consume_statements(self) -> List[str]: + lines = self._buf.sql_lines[:] + self._buf.sql_lines.clear() + return lines diff --git a/dlt/destinations/impl/sqlalchemy/configuration.py b/dlt/destinations/impl/sqlalchemy/configuration.py new file mode 100644 index 0000000000..f99b06a27b --- /dev/null +++ b/dlt/destinations/impl/sqlalchemy/configuration.py @@ -0,0 +1,63 @@ +from typing import TYPE_CHECKING, Optional, Any, Final, Type, Dict, Union +import dataclasses + +from dlt.common.configuration import configspec +from dlt.common.configuration.specs import ConnectionStringCredentials +from dlt.common.destination.reference import DestinationClientDwhConfiguration + +if TYPE_CHECKING: + from sqlalchemy.engine import Engine, Dialect + + +@configspec(init=False) +class SqlalchemyCredentials(ConnectionStringCredentials): + if TYPE_CHECKING: + _engine: Optional["Engine"] = None + + username: Optional[str] = None # e.g. sqlite doesn't need username + + def __init__( + self, connection_string: Optional[Union[str, Dict[str, Any], "Engine"]] = None + ) -> None: + super().__init__(connection_string) # type: ignore[arg-type] + + def parse_native_representation(self, native_value: Any) -> None: + from sqlalchemy.engine import Engine + + if isinstance(native_value, Engine): + self.engine = native_value + super().parse_native_representation( + native_value.url.render_as_string(hide_password=False) + ) + else: + super().parse_native_representation(native_value) + + @property + def engine(self) -> Optional["Engine"]: + return getattr(self, "_engine", None) # type: ignore[no-any-return] + + @engine.setter + def engine(self, value: "Engine") -> None: + self._engine = value + + def get_dialect(self) -> Optional[Type["Dialect"]]: + if not self.drivername: + return None + # Type-ignore because of ported URL class has no get_dialect method, + # but here sqlalchemy should be available + if engine := self.engine: + return type(engine.dialect) + return self.to_url().get_dialect() # type: ignore[attr-defined,no-any-return] + + +@configspec +class SqlalchemyClientConfiguration(DestinationClientDwhConfiguration): + destination_type: Final[str] = dataclasses.field(default="sqlalchemy", init=False, repr=False, compare=False) # type: ignore + credentials: SqlalchemyCredentials = None + """SQLAlchemy connection string""" + + engine_args: Dict[str, Any] = dataclasses.field(default_factory=dict) + """Additional arguments passed to `sqlalchemy.create_engine`""" + + def get_dialect(self) -> Type["Dialect"]: + return self.credentials.get_dialect() diff --git a/dlt/destinations/impl/sqlalchemy/db_api_client.py b/dlt/destinations/impl/sqlalchemy/db_api_client.py new file mode 100644 index 0000000000..c6c8ba53d6 --- /dev/null +++ b/dlt/destinations/impl/sqlalchemy/db_api_client.py @@ -0,0 +1,432 @@ +from typing import ( + Optional, + Iterator, + Any, + Sequence, + ContextManager, + AnyStr, + Union, + Tuple, + List, + Dict, +) +from contextlib import contextmanager +from functools import wraps +import inspect +from pathlib import Path + +import sqlalchemy as sa +from sqlalchemy.engine import Connection + +from dlt.common.destination import DestinationCapabilitiesContext +from dlt.destinations.exceptions import ( + DatabaseUndefinedRelation, + DatabaseTerminalException, + DatabaseTransientException, + LoadClientNotConnected, + DatabaseException, +) +from dlt.destinations.typing import DBTransaction, DBApiCursor +from dlt.destinations.sql_client import SqlClientBase, DBApiCursorImpl +from dlt.destinations.impl.sqlalchemy.configuration import SqlalchemyCredentials +from dlt.destinations.impl.sqlalchemy.alter_table import MigrationMaker +from dlt.common.typing import TFun + + +class SqlaTransactionWrapper(DBTransaction): + def __init__(self, sqla_transaction: sa.engine.Transaction) -> None: + self.sqla_transaction = sqla_transaction + + def commit_transaction(self) -> None: + if self.sqla_transaction.is_active: + self.sqla_transaction.commit() + + def rollback_transaction(self) -> None: + if self.sqla_transaction.is_active: + self.sqla_transaction.rollback() + + +def raise_database_error(f: TFun) -> TFun: + @wraps(f) + def _wrap_gen(self: "SqlalchemyClient", *args: Any, **kwargs: Any) -> Any: + try: + return (yield from f(self, *args, **kwargs)) + except Exception as e: + raise self._make_database_exception(e) from e + + @wraps(f) + def _wrap(self: "SqlalchemyClient", *args: Any, **kwargs: Any) -> Any: + try: + return f(self, *args, **kwargs) + except Exception as e: + raise self._make_database_exception(e) from e + + if inspect.isgeneratorfunction(f): + return _wrap_gen # type: ignore[return-value] + return _wrap # type: ignore[return-value] + + +class SqlaDbApiCursor(DBApiCursorImpl): + def __init__(self, curr: sa.engine.CursorResult) -> None: + # Sqlalchemy CursorResult is *mostly* compatible with DB-API cursor + self.native_cursor = curr # type: ignore[assignment] + curr.columns + + self.fetchall = curr.fetchall # type: ignore[assignment] + self.fetchone = curr.fetchone # type: ignore[assignment] + self.fetchmany = curr.fetchmany # type: ignore[assignment] + + def _get_columns(self) -> List[str]: + return list(self.native_cursor.keys()) # type: ignore[attr-defined] + + # @property + # def description(self) -> Any: + # # Get the underlying driver's cursor description, this is mostly used in tests + # return self.native_cursor.cursor.description # type: ignore[attr-defined] + + def execute(self, query: AnyStr, *args: Any, **kwargs: Any) -> None: + raise NotImplementedError("execute not implemented") + + +class DbApiProps: + # Only needed for some tests + paramstyle = "named" + + +class SqlalchemyClient(SqlClientBase[Connection]): + external_engine: bool = False + dbapi = DbApiProps # type: ignore[assignment] + migrations: Optional[MigrationMaker] = None # lazy init as needed + _engine: Optional[sa.engine.Engine] = None + + def __init__( + self, + dataset_name: str, + staging_dataset_name: str, + credentials: SqlalchemyCredentials, + capabilities: DestinationCapabilitiesContext, + engine_args: Optional[Dict[str, Any]] = None, + ) -> None: + super().__init__(credentials.database, dataset_name, staging_dataset_name, capabilities) + self.credentials = credentials + + self.engine_args = engine_args or {} + + if credentials.engine: + self._engine = credentials.engine + self.external_engine = True + else: + # Default to nullpool because we don't use connection pooling + self.engine_args.setdefault("poolclass", sa.pool.NullPool) + + self._current_connection: Optional[Connection] = None + self._current_transaction: Optional[SqlaTransactionWrapper] = None + self.metadata = sa.MetaData() + + @property + def engine(self) -> sa.engine.Engine: + # Create engine lazily + if self._engine is not None: + return self._engine + self._engine = sa.create_engine( + self.credentials.to_url().render_as_string(hide_password=False), **self.engine_args + ) + return self._engine + + @property + def dialect(self) -> sa.engine.interfaces.Dialect: + return self.engine.dialect + + @property + def dialect_name(self) -> str: + return self.dialect.name + + def open_connection(self) -> Connection: + if self._current_connection is None: + self._current_connection = self.engine.connect() + if self.dialect_name == "sqlite": + self._sqlite_reattach_dataset_if_exists(self.dataset_name) + return self._current_connection + + def close_connection(self) -> None: + if not self.external_engine: + try: + if self._current_connection is not None: + self._current_connection.close() + self.engine.dispose() + finally: + self._current_connection = None + self._current_transaction = None + + @property + def native_connection(self) -> Connection: + if not self._current_connection: + raise LoadClientNotConnected(type(self).__name__, self.dataset_name) + return self._current_connection + + def _in_transaction(self) -> bool: + return ( + self._current_transaction is not None + and self._current_transaction.sqla_transaction.is_active + ) + + @contextmanager + @raise_database_error + def begin_transaction(self) -> Iterator[DBTransaction]: + trans = self._current_transaction = SqlaTransactionWrapper(self._current_connection.begin()) + try: + yield trans + except Exception: + if self._in_transaction(): + self.rollback_transaction() + raise + else: + if self._in_transaction(): # Transaction could be committed/rolled back before __exit__ + self.commit_transaction() + finally: + self._current_transaction = None + + def commit_transaction(self) -> None: + """Commits the current transaction.""" + self._current_transaction.commit_transaction() + + def rollback_transaction(self) -> None: + """Rolls back the current transaction.""" + self._current_transaction.rollback_transaction() + + @contextmanager + def _transaction(self) -> Iterator[DBTransaction]: + """Context manager yielding either a new or the currently open transaction. + New transaction will be committed/rolled back on exit. + If the transaction is already open, finalization is handled by the top level context manager. + """ + if self._in_transaction(): + yield self._current_transaction + return + with self.begin_transaction() as tx: + yield tx + + def has_dataset(self) -> bool: + with self._transaction(): + schema_names = self.engine.dialect.get_schema_names(self._current_connection) # type: ignore[attr-defined] + return self.dataset_name in schema_names + + def _sqlite_dataset_filename(self, dataset_name: str) -> str: + db_name = self.engine.url.database + current_file_path = Path(db_name) + return str( + current_file_path.parent + / f"{current_file_path.stem}__{dataset_name}{current_file_path.suffix}" + ) + + def _sqlite_is_memory_db(self) -> bool: + return self.engine.url.database == ":memory:" + + def _sqlite_reattach_dataset_if_exists(self, dataset_name: str) -> None: + """Re-attach previously created databases for a new sqlite connection""" + if self._sqlite_is_memory_db(): + return + new_db_fn = self._sqlite_dataset_filename(dataset_name) + if Path(new_db_fn).exists(): + self._sqlite_create_dataset(dataset_name) + + def _sqlite_create_dataset(self, dataset_name: str) -> None: + """Mimic multiple schemas in sqlite using ATTACH DATABASE to + attach a new database file to the current connection. + """ + if self._sqlite_is_memory_db(): + new_db_fn = ":memory:" + else: + new_db_fn = self._sqlite_dataset_filename(dataset_name) + + statement = "ATTACH DATABASE :fn AS :name" + self.execute_sql(statement, fn=new_db_fn, name=dataset_name) + + def _sqlite_drop_dataset(self, dataset_name: str) -> None: + """Drop a dataset in sqlite by detaching the database file + attached to the current connection. + """ + # Get a list of attached databases and filenames + rows = self.execute_sql("PRAGMA database_list") + dbs = {row[1]: row[2] for row in rows} # db_name: filename + if dataset_name != "main": # main is the default database, it cannot be detached + statement = "DETACH DATABASE :name" + self.execute_sql(statement, name=dataset_name) + + fn = dbs[dataset_name] + if not fn: # It's a memory database, nothing to do + return + # Delete the database file + Path(fn).unlink() + + def create_dataset(self) -> None: + if self.dialect_name == "sqlite": + return self._sqlite_create_dataset(self.dataset_name) + self.execute_sql(sa.schema.CreateSchema(self.dataset_name)) + + def drop_dataset(self) -> None: + if self.dialect_name == "sqlite": + return self._sqlite_drop_dataset(self.dataset_name) + try: + self.execute_sql(sa.schema.DropSchema(self.dataset_name, cascade=True)) + except DatabaseException: # Try again in case cascade is not supported + self.execute_sql(sa.schema.DropSchema(self.dataset_name)) + + def truncate_tables(self, *tables: str) -> None: + # TODO: alchemy doesn't have a construct for TRUNCATE TABLE + for table in tables: + tbl = sa.Table(table, self.metadata, schema=self.dataset_name, keep_existing=True) + self.execute_sql(tbl.delete()) + + def drop_tables(self, *tables: str) -> None: + for table in tables: + tbl = sa.Table(table, self.metadata, schema=self.dataset_name, keep_existing=True) + self.execute_sql(sa.schema.DropTable(tbl, if_exists=True)) + + def execute_sql( + self, sql: Union[AnyStr, sa.sql.Executable], *args: Any, **kwargs: Any + ) -> Optional[Sequence[Sequence[Any]]]: + with self.execute_query(sql, *args, **kwargs) as cursor: + if cursor.returns_rows: # type: ignore[attr-defined] + return cursor.fetchall() + return None + + @contextmanager + def execute_query( + self, query: Union[AnyStr, sa.sql.Executable], *args: Any, **kwargs: Any + ) -> Iterator[DBApiCursor]: + if args and kwargs: + raise ValueError("Cannot use both positional and keyword arguments") + if isinstance(query, str): + if args: + # Sqlalchemy text supports :named paramstyle for all dialects + query, kwargs = self._to_named_paramstyle(query, args) # type: ignore[assignment] + args = (kwargs,) + query = sa.text(query) + if kwargs: + # sqla2 takes either a dict or list of dicts + args = (kwargs,) + with self._transaction(): + yield SqlaDbApiCursor(self._current_connection.execute(query, *args)) # type: ignore[call-overload, abstract] + + def get_existing_table(self, table_name: str) -> Optional[sa.Table]: + """Get a table object from metadata if it exists""" + key = self.dataset_name + "." + table_name + return self.metadata.tables.get(key) # type: ignore[no-any-return] + + def create_table(self, table_obj: sa.Table) -> None: + with self._transaction(): + table_obj.create(self._current_connection) + + def _make_qualified_table_name(self, table: sa.Table, escape: bool = True) -> str: + if escape: + return self.dialect.identifier_preparer.format_table(table) # type: ignore[attr-defined,no-any-return] + return table.fullname # type: ignore[no-any-return] + + def make_qualified_table_name(self, table_name: str, escape: bool = True) -> str: + tbl = self.get_existing_table(table_name) + if tbl is None: + tmp_metadata = sa.MetaData() + tbl = sa.Table(table_name, tmp_metadata, schema=self.dataset_name) + return self._make_qualified_table_name(tbl, escape) + + def fully_qualified_dataset_name(self, escape: bool = True, staging: bool = False) -> str: + if staging: + raise NotImplementedError("Staging not supported") + return self.dialect.identifier_preparer.format_schema(self.dataset_name) # type: ignore[attr-defined, no-any-return] + + def alter_table_add_columns(self, columns: Sequence[sa.Column]) -> None: + if not columns: + return + if self.migrations is None: + self.migrations = MigrationMaker(self.dialect) + for column in columns: + self.migrations.add_column(column.table.name, column, self.dataset_name) + statements = self.migrations.consume_statements() + for statement in statements: + self.execute_sql(statement) + + def escape_column_name(self, column_name: str, escape: bool = True) -> str: + if self.dialect.requires_name_normalize: # type: ignore[attr-defined] + column_name = self.dialect.normalize_name(column_name) # type: ignore[func-returns-value] + if escape: + return self.dialect.identifier_preparer.format_column(sa.Column(column_name)) # type: ignore[attr-defined,no-any-return] + return column_name + + def compile_column_def(self, column: sa.Column) -> str: + """Compile a column definition including type for ADD COLUMN clause""" + return str(sa.schema.CreateColumn(column).compile(self.engine)) + + def reflect_table( + self, + table_name: str, + metadata: Optional[sa.MetaData] = None, + include_columns: Optional[Sequence[str]] = None, + ) -> Optional[sa.Table]: + """Reflect a table from the database and return the Table object""" + if metadata is None: + metadata = self.metadata + try: + with self._transaction(): + return sa.Table( + table_name, + metadata, + autoload_with=self._current_connection, + schema=self.dataset_name, + include_columns=include_columns, + extend_existing=True, + ) + except DatabaseUndefinedRelation: + return None + + def compare_storage_table(self, table_name: str) -> Tuple[sa.Table, List[sa.Column], bool]: + """Reflect the table from database and compare it with the version already in metadata. + Returns a 3 part tuple: + - The current version of the table in metadata + - List of columns that are missing from the storage table (all columns if it doesn't exist in storage) + - boolean indicating whether the table exists in storage + """ + existing = self.get_existing_table(table_name) + assert existing is not None, "Table must be present in metadata" + all_columns = list(existing.columns) + all_column_names = [c.name for c in all_columns] + tmp_metadata = sa.MetaData() + reflected = self.reflect_table( + table_name, include_columns=all_column_names, metadata=tmp_metadata + ) + if reflected is None: + missing_columns = all_columns + else: + missing_columns = [c for c in all_columns if c.name not in reflected.columns] + return existing, missing_columns, reflected is not None + + @staticmethod + def _make_database_exception(e: Exception) -> Exception: + if isinstance(e, sa.exc.NoSuchTableError): + return DatabaseUndefinedRelation(e) + msg = str(e).lower() + if isinstance(e, (sa.exc.ProgrammingError, sa.exc.OperationalError)): + if "exist" in msg: # TODO: Hack + return DatabaseUndefinedRelation(e) + elif "unknown table" in msg: + return DatabaseUndefinedRelation(e) + elif "unknown database" in msg: + return DatabaseUndefinedRelation(e) + elif "no such table" in msg: # sqlite # TODO: Hack + return DatabaseUndefinedRelation(e) + elif "no such database" in msg: # sqlite # TODO: Hack + return DatabaseUndefinedRelation(e) + elif "syntax" in msg: + return DatabaseTransientException(e) + elif isinstance(e, (sa.exc.OperationalError, sa.exc.IntegrityError)): + return DatabaseTerminalException(e) + return DatabaseTransientException(e) + elif isinstance(e, sa.exc.SQLAlchemyError): + return DatabaseTransientException(e) + else: + return e + # return DatabaseTerminalException(e) + + def _ensure_native_conn(self) -> None: + if not self.native_connection: + raise LoadClientNotConnected(type(self).__name__, self.dataset_name) diff --git a/dlt/destinations/impl/sqlalchemy/factory.py b/dlt/destinations/impl/sqlalchemy/factory.py new file mode 100644 index 0000000000..10372cda34 --- /dev/null +++ b/dlt/destinations/impl/sqlalchemy/factory.py @@ -0,0 +1,99 @@ +import typing as t + +from dlt.common.destination import Destination, DestinationCapabilitiesContext +from dlt.common.destination.capabilities import DataTypeMapper +from dlt.common.arithmetics import DEFAULT_NUMERIC_PRECISION, DEFAULT_NUMERIC_SCALE +from dlt.common.normalizers import NamingConvention + +from dlt.destinations.impl.sqlalchemy.configuration import ( + SqlalchemyCredentials, + SqlalchemyClientConfiguration, +) + +SqlalchemyTypeMapper: t.Type[DataTypeMapper] + +try: + from dlt.destinations.impl.sqlalchemy.type_mapper import SqlalchemyTypeMapper +except ModuleNotFoundError: + # assign mock type mapper if no sqlalchemy + from dlt.common.destination.capabilities import UnsupportedTypeMapper as SqlalchemyTypeMapper + +if t.TYPE_CHECKING: + # from dlt.destinations.impl.sqlalchemy.sqlalchemy_client import SqlalchemyJobClient + from dlt.destinations.impl.sqlalchemy.sqlalchemy_job_client import SqlalchemyJobClient + + +class sqlalchemy(Destination[SqlalchemyClientConfiguration, "SqlalchemyJobClient"]): + spec = SqlalchemyClientConfiguration + + def _raw_capabilities(self) -> DestinationCapabilitiesContext: + # https://www.sqlalchemyql.org/docs/current/limits.html + caps = DestinationCapabilitiesContext.generic_capabilities() + caps.preferred_loader_file_format = "typed-jsonl" + caps.supported_loader_file_formats = ["typed-jsonl", "parquet"] + caps.preferred_staging_file_format = None + caps.supported_staging_file_formats = [] + caps.has_case_sensitive_identifiers = True + caps.decimal_precision = (DEFAULT_NUMERIC_PRECISION, DEFAULT_NUMERIC_SCALE) + caps.wei_precision = (DEFAULT_NUMERIC_PRECISION, 0) + caps.max_identifier_length = 63 + caps.max_column_identifier_length = 63 + caps.max_query_length = 32 * 1024 * 1024 + caps.is_max_query_length_in_bytes = True + caps.max_text_data_type_length = 1024 * 1024 * 1024 + caps.is_max_text_data_type_length_in_bytes = True + caps.supports_ddl_transactions = True + caps.max_query_parameters = 20_0000 + caps.max_rows_per_insert = 10_000 # Set a default to avoid OOM on large datasets + caps.type_mapper = SqlalchemyTypeMapper + + return caps + + @classmethod + def adjust_capabilities( + cls, + caps: DestinationCapabilitiesContext, + config: SqlalchemyClientConfiguration, + naming: t.Optional[NamingConvention], + ) -> DestinationCapabilitiesContext: + caps = super(sqlalchemy, cls).adjust_capabilities(caps, config, naming) + dialect = config.get_dialect() + if dialect is None: + return caps + caps.max_identifier_length = dialect.max_identifier_length + caps.max_column_identifier_length = dialect.max_identifier_length + caps.supports_native_boolean = dialect.supports_native_boolean + + return caps + + @property + def client_class(self) -> t.Type["SqlalchemyJobClient"]: + from dlt.destinations.impl.sqlalchemy.sqlalchemy_job_client import SqlalchemyJobClient + + return SqlalchemyJobClient + + def __init__( + self, + credentials: t.Union[SqlalchemyCredentials, t.Dict[str, t.Any], str] = None, + destination_name: t.Optional[str] = None, + environment: t.Optional[str] = None, + engine_args: t.Optional[t.Dict[str, t.Any]] = None, + **kwargs: t.Any, + ) -> None: + """Configure the Sqlalchemy destination to use in a pipeline. + + All arguments provided here supersede other configuration sources such as environment variables and dlt config files. + + Args: + credentials: Credentials to connect to the sqlalchemy database. Can be an instance of `SqlalchemyCredentials` or + a connection string in the format `mysql://user:password@host:port/database` + destination_name: The name of the destination + environment: The environment to use + **kwargs: Additional arguments passed to the destination + """ + super().__init__( + credentials=credentials, + destination_name=destination_name, + environment=environment, + **kwargs, + ) diff --git a/dlt/destinations/impl/sqlalchemy/sqlalchemy_job_client.py b/dlt/destinations/impl/sqlalchemy/sqlalchemy_job_client.py new file mode 100644 index 0000000000..c51d3cbe3a --- /dev/null +++ b/dlt/destinations/impl/sqlalchemy/sqlalchemy_job_client.py @@ -0,0 +1,359 @@ +from typing import Iterable, Optional, Dict, Any, Iterator, Sequence, List, Tuple, IO +from contextlib import suppress +import math + +import sqlalchemy as sa + +from dlt.common import logger +from dlt.common import pendulum +from dlt.common.destination.reference import ( + JobClientBase, + LoadJob, + RunnableLoadJob, + StorageSchemaInfo, + StateInfo, + PreparedTableSchema, +) +from dlt.destinations.job_client_impl import SqlJobClientBase +from dlt.common.destination.capabilities import DestinationCapabilitiesContext +from dlt.common.schema import Schema, TTableSchema, TColumnSchema, TSchemaTables +from dlt.common.schema.typing import TColumnType, TTableSchemaColumns +from dlt.common.schema.utils import pipeline_state_table, normalize_table_identifiers +from dlt.common.storages import FileStorage +from dlt.common.json import json, PY_DATETIME_DECODERS +from dlt.destinations.exceptions import DatabaseUndefinedRelation + + +# from dlt.destinations.impl.sqlalchemy.sql_client import SqlalchemyClient +from dlt.destinations.impl.sqlalchemy.db_api_client import SqlalchemyClient +from dlt.destinations.impl.sqlalchemy.configuration import SqlalchemyClientConfiguration + + +class SqlalchemyJsonLInsertJob(RunnableLoadJob): + def __init__(self, file_path: str, table: sa.Table) -> None: + super().__init__(file_path) + self._job_client: "SqlalchemyJobClient" = None + self.table = table + + def _open_load_file(self) -> IO[bytes]: + return FileStorage.open_zipsafe_ro(self._file_path, "rb") + + def _iter_data_items(self) -> Iterator[Dict[str, Any]]: + all_cols = {col.name: None for col in self.table.columns} + with FileStorage.open_zipsafe_ro(self._file_path, "rb") as f: + for line in f: + # Decode date/time to py datetime objects. Some drivers have issues with pendulum objects + for item in json.typed_loadb(line, decoders=PY_DATETIME_DECODERS): + # Fill any missing columns in item with None. Bulk insert fails when items have different keys + if item.keys() != all_cols.keys(): + yield {**all_cols, **item} + else: + yield item + + def _iter_data_item_chunks(self) -> Iterator[Sequence[Dict[str, Any]]]: + max_rows = self._job_client.capabilities.max_rows_per_insert or math.inf + # Limit by max query length should not be needed, + # bulk insert generates an INSERT template with a single VALUES tuple of placeholders + # If any dialects don't do that we need to check the str length of the query + # TODO: Max params may not be needed. Limits only apply to placeholders in sql string (mysql/sqlite) + max_params = self._job_client.capabilities.max_query_parameters or math.inf + chunk: List[Dict[str, Any]] = [] + params_count = 0 + for item in self._iter_data_items(): + if len(chunk) + 1 == max_rows or params_count + len(item) > max_params: + # Rotate chunk + yield chunk + chunk = [] + params_count = 0 + params_count += len(item) + chunk.append(item) + + if chunk: + yield chunk + + def run(self) -> None: + _sql_client = self._job_client.sql_client + + with _sql_client.begin_transaction(): + for chunk in self._iter_data_item_chunks(): + _sql_client.execute_sql(self.table.insert(), chunk) + + +class SqlalchemyParquetInsertJob(SqlalchemyJsonLInsertJob): + def _iter_data_item_chunks(self) -> Iterator[Sequence[Dict[str, Any]]]: + from dlt.common.libs.pyarrow import ParquetFile + + num_cols = len(self.table.columns) + max_rows = self._job_client.capabilities.max_rows_per_insert or None + max_params = self._job_client.capabilities.max_query_parameters or None + read_limit = None + + with ParquetFile(self._file_path) as reader: + if max_params is not None: + read_limit = math.floor(max_params / num_cols) + + if max_rows is not None: + if read_limit is None: + read_limit = max_rows + else: + read_limit = min(read_limit, max_rows) + + if read_limit is None: + yield reader.read().to_pylist() + return + + for chunk in reader.iter_batches(batch_size=read_limit): + yield chunk.to_pylist() + + +class SqlalchemyJobClient(SqlJobClientBase): + sql_client: SqlalchemyClient # type: ignore[assignment] + + def __init__( + self, + schema: Schema, + config: SqlalchemyClientConfiguration, + capabilities: DestinationCapabilitiesContext, + ) -> None: + self.sql_client = SqlalchemyClient( + config.normalize_dataset_name(schema), + None, + config.credentials, + capabilities, + engine_args=config.engine_args, + ) + + self.schema = schema + self.capabilities = capabilities + self.config = config + self.type_mapper = self.capabilities.get_type_mapper(self.sql_client.dialect) + + def _to_table_object(self, schema_table: PreparedTableSchema) -> sa.Table: + existing = self.sql_client.get_existing_table(schema_table["name"]) + if existing is not None: + existing_col_names = set(col.name for col in existing.columns) + new_col_names = set(schema_table["columns"]) + # Re-generate the table if columns have changed + if existing_col_names == new_col_names: + return existing + return sa.Table( + schema_table["name"], + self.sql_client.metadata, + *[ + self._to_column_object(col, schema_table) + for col in schema_table["columns"].values() + ], + extend_existing=True, + schema=self.sql_client.dataset_name, + ) + + def _to_column_object( + self, schema_column: TColumnSchema, table: PreparedTableSchema + ) -> sa.Column: + return sa.Column( + schema_column["name"], + self.type_mapper.to_destination_type(schema_column, table), + nullable=schema_column.get("nullable", True), + unique=schema_column.get("unique", False), + ) + + def create_load_job( + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False + ) -> LoadJob: + if file_path.endswith(".typed-jsonl"): + table_obj = self._to_table_object(table) + return SqlalchemyJsonLInsertJob(file_path, table_obj) + elif file_path.endswith(".parquet"): + table_obj = self._to_table_object(table) + return SqlalchemyParquetInsertJob(file_path, table_obj) + return None + + def complete_load(self, load_id: str) -> None: + loads_table = self._to_table_object(self.schema.tables[self.schema.loads_table_name]) # type: ignore[arg-type] + now_ts = pendulum.now() + self.sql_client.execute_sql( + loads_table.insert().values( + ( + load_id, + self.schema.name, + 0, + now_ts, + self.schema.version_hash, + ) + ) + ) + + def _get_table_key(self, name: str, schema: Optional[str]) -> str: + if schema is None: + return name + else: + return schema + "." + name + + def get_storage_tables( + self, table_names: Iterable[str] + ) -> Iterable[Tuple[str, TTableSchemaColumns]]: + metadata = sa.MetaData() + for table_name in table_names: + table_obj = self.sql_client.reflect_table(table_name, metadata) + if table_obj is None: + yield table_name, {} + continue + yield table_name, { + col.name: { + "name": col.name, + "nullable": col.nullable, + **self.type_mapper.from_destination_type(col.type, None, None), + } + for col in table_obj.columns + } + + def update_stored_schema( + self, only_tables: Iterable[str] = None, expected_update: TSchemaTables = None + ) -> Optional[TSchemaTables]: + # super().update_stored_schema(only_tables, expected_update) + JobClientBase.update_stored_schema(self, only_tables, expected_update) + + schema_info = self.get_stored_schema_by_hash(self.schema.stored_version_hash) + if schema_info is not None: + logger.info( + "Schema with hash %s inserted at %s found in storage, no upgrade required", + self.schema.stored_version_hash, + schema_info.inserted_at, + ) + return {} + else: + logger.info( + "Schema with hash %s not found in storage, upgrading", + self.schema.stored_version_hash, + ) + + # Create all schema tables in metadata + for table_name in only_tables or self.schema.tables: + self._to_table_object(self.schema.tables[table_name]) # type: ignore[arg-type] + + schema_update: TSchemaTables = {} + tables_to_create: List[sa.Table] = [] + columns_to_add: List[sa.Column] = [] + + for table_name in only_tables or self.schema.tables: + table = self.schema.tables[table_name] + table_obj, new_columns, exists = self.sql_client.compare_storage_table(table["name"]) + if not new_columns: # Nothing to do, don't create table without columns + continue + if not exists: + tables_to_create.append(table_obj) + else: + columns_to_add.extend(new_columns) + partial_table = self.prepare_load_table(table_name) + new_column_names = set(col.name for col in new_columns) + partial_table["columns"] = { + col_name: col_def + for col_name, col_def in partial_table["columns"].items() + if col_name in new_column_names + } + schema_update[table_name] = partial_table + + with self.sql_client.begin_transaction(): + for table_obj in tables_to_create: + self.sql_client.create_table(table_obj) + self.sql_client.alter_table_add_columns(columns_to_add) + self._update_schema_in_storage(self.schema) + + return schema_update + + def _delete_schema_in_storage(self, schema: Schema) -> None: + version_table = schema.tables[schema.version_table_name] + table_obj = self._to_table_object(version_table) # type: ignore[arg-type] + schema_name_col = schema.naming.normalize_identifier("schema_name") + self.sql_client.execute_sql( + table_obj.delete().where(table_obj.c[schema_name_col] == schema.name) + ) + + def _update_schema_in_storage(self, schema: Schema) -> None: + version_table = schema.tables[schema.version_table_name] + table_obj = self._to_table_object(version_table) # type: ignore[arg-type] + schema_str = json.dumps(schema.to_dict()) + + schema_mapping = StorageSchemaInfo( + version=schema.version, + engine_version=str(schema.ENGINE_VERSION), + schema_name=schema.name, + version_hash=schema.stored_version_hash, + schema=schema_str, + inserted_at=pendulum.now(), + ).to_normalized_mapping(schema.naming) + + self.sql_client.execute_sql(table_obj.insert().values(schema_mapping)) + + def _get_stored_schema( + self, version_hash: Optional[str] = None, schema_name: Optional[str] = None + ) -> Optional[StorageSchemaInfo]: + version_table = self.schema.tables[self.schema.version_table_name] + table_obj = self._to_table_object(version_table) # type: ignore[arg-type] + with suppress(DatabaseUndefinedRelation): + q = sa.select(table_obj) + if version_hash is not None: + version_hash_col = self.schema.naming.normalize_identifier("version_hash") + q = q.where(table_obj.c[version_hash_col] == version_hash) + if schema_name is not None: + schema_name_col = self.schema.naming.normalize_identifier("schema_name") + q = q.where(table_obj.c[schema_name_col] == schema_name) + inserted_at_col = self.schema.naming.normalize_identifier("inserted_at") + q = q.order_by(table_obj.c[inserted_at_col].desc()) + with self.sql_client.execute_query(q) as cur: + row = cur.fetchone() + if row is None: + return None + + # TODO: Decode compressed schema str if needed + return StorageSchemaInfo.from_normalized_mapping( + row._mapping, self.schema.naming # type: ignore[attr-defined] + ) + + def get_stored_schema_by_hash(self, version_hash: str) -> Optional[StorageSchemaInfo]: + return self._get_stored_schema(version_hash) + + def get_stored_schema(self) -> Optional[StorageSchemaInfo]: + """Get the latest stored schema""" + return self._get_stored_schema(schema_name=self.schema.name) + + def get_stored_state(self, pipeline_name: str) -> StateInfo: + state_table = self.schema.tables.get( + self.schema.state_table_name + ) or normalize_table_identifiers(pipeline_state_table(), self.schema.naming) + state_table_obj = self._to_table_object(state_table) # type: ignore[arg-type] + loads_table = self.schema.tables[self.schema.loads_table_name] + loads_table_obj = self._to_table_object(loads_table) # type: ignore[arg-type] + + c_load_id, c_dlt_load_id, c_pipeline_name, c_status = map( + self.schema.naming.normalize_identifier, + ("load_id", "_dlt_load_id", "pipeline_name", "status"), + ) + + query = ( + sa.select(state_table_obj) + .join(loads_table_obj, loads_table_obj.c[c_load_id] == state_table_obj.c[c_dlt_load_id]) + .where( + sa.and_( + state_table_obj.c[c_pipeline_name] == pipeline_name, + loads_table_obj.c[c_status] == 0, + ) + ) + .order_by(loads_table_obj.c[c_load_id].desc()) + ) + + with self.sql_client.execute_query(query) as cur: + row = cur.fetchone() + if not row: + return None + mapping = dict(row._mapping) # type: ignore[attr-defined] + + return StateInfo.from_normalized_mapping(mapping, self.schema.naming) + + def _from_db_type( + self, db_type: str, precision: Optional[int], scale: Optional[int] + ) -> TColumnType: + raise NotImplementedError() + + def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableSchema = None) -> str: + raise NotImplementedError() diff --git a/dlt/destinations/impl/sqlalchemy/type_mapper.py b/dlt/destinations/impl/sqlalchemy/type_mapper.py new file mode 100644 index 0000000000..767d2115d4 --- /dev/null +++ b/dlt/destinations/impl/sqlalchemy/type_mapper.py @@ -0,0 +1,174 @@ +from typing import Optional, Dict, Any +import inspect + +import sqlalchemy as sa +from sqlalchemy.sql import sqltypes + +from dlt.common.exceptions import TerminalValueError +from dlt.common.typing import TLoaderFileFormat +from dlt.common.destination.capabilities import DataTypeMapper, DestinationCapabilitiesContext +from dlt.common.destination.typing import PreparedTableSchema +from dlt.common.schema.typing import TColumnSchema + + +# TODO: base type mapper should be a generic class to support TypeEngine instead of str types +class SqlalchemyTypeMapper(DataTypeMapper): + def __init__( + self, + capabilities: DestinationCapabilitiesContext, + dialect: Optional[sa.engine.Dialect] = None, + ): + super().__init__(capabilities) + # Mapper is used to verify supported types without client, dialect is not important for this + self.dialect = dialect or sa.engine.default.DefaultDialect() + + def _db_integer_type(self, precision: Optional[int]) -> sa.types.TypeEngine: + if precision is None: + return sa.BigInteger() + elif precision <= 16: + return sa.SmallInteger() + elif precision <= 32: + return sa.Integer() + elif precision <= 64: + return sa.BigInteger() + raise TerminalValueError(f"Unsupported precision for integer type: {precision}") + + def _create_date_time_type( + self, sc_t: str, precision: Optional[int], timezone: Optional[bool] + ) -> sa.types.TypeEngine: + """Use the dialect specific datetime/time type if possible since the generic type doesn't accept precision argument""" + precision = precision if precision is not None else self.capabilities.timestamp_precision + base_type: sa.types.TypeEngine + timezone = timezone is None or bool(timezone) + if sc_t == "timestamp": + base_type = sa.DateTime() + if self.dialect.name == "mysql": + # Special case, type_descriptor does not return the specifc datetime type + from sqlalchemy.dialects.mysql import DATETIME + + return DATETIME(fsp=precision) + elif sc_t == "time": + base_type = sa.Time() + + dialect_type = type( + self.dialect.type_descriptor(base_type) + ) # Get the dialect specific subtype + precision = precision if precision is not None else self.capabilities.timestamp_precision + + # Find out whether the dialect type accepts precision or fsp argument + params = inspect.signature(dialect_type).parameters + kwargs: Dict[str, Any] = dict(timezone=timezone) + if "fsp" in params: + kwargs["fsp"] = precision # MySQL uses fsp for fractional seconds + elif "precision" in params: + kwargs["precision"] = precision + return dialect_type(**kwargs) # type: ignore[no-any-return,misc] + + def _create_double_type(self) -> sa.types.TypeEngine: + if dbl := getattr(sa, "Double", None): + # Sqlalchemy 2 has generic double type + return dbl() # type: ignore[no-any-return] + elif self.dialect.name == "mysql": + # MySQL has a specific double type + from sqlalchemy.dialects.mysql import DOUBLE + + return DOUBLE() + return sa.Float(precision=53) # Otherwise use float + + def _to_db_decimal_type(self, column: TColumnSchema) -> sa.types.TypeEngine: + precision, scale = column.get("precision"), column.get("scale") + if precision is None and scale is None: + precision, scale = self.capabilities.decimal_precision + return sa.Numeric(precision, scale) + + def to_destination_type( # type: ignore[override] + self, column: TColumnSchema, table: PreparedTableSchema = None + ) -> sqltypes.TypeEngine: + sc_t = column["data_type"] + precision = column.get("precision") + # TODO: Precision and scale for supported types + if sc_t == "text": + length = precision + if length is None and column.get("unique"): + length = 128 + if length is None: + return sa.Text() + return sa.String(length=length) + elif sc_t == "double": + return self._create_double_type() + elif sc_t == "bool": + return sa.Boolean() + elif sc_t == "timestamp": + return self._create_date_time_type(sc_t, precision, column.get("timezone")) + elif sc_t == "bigint": + return self._db_integer_type(precision) + elif sc_t == "binary": + return sa.LargeBinary(length=precision) + elif sc_t == "json": + return sa.JSON(none_as_null=True) + elif sc_t == "decimal": + return self._to_db_decimal_type(column) + elif sc_t == "wei": + wei_precision, wei_scale = self.capabilities.wei_precision + return sa.Numeric(precision=wei_precision, scale=wei_scale) + elif sc_t == "date": + return sa.Date() + elif sc_t == "time": + return self._create_date_time_type(sc_t, precision, column.get("timezone")) + raise TerminalValueError(f"Unsupported data type: {sc_t}") + + def _from_db_integer_type(self, db_type: sa.Integer) -> TColumnSchema: + if isinstance(db_type, sa.SmallInteger): + return dict(data_type="bigint", precision=16) + elif isinstance(db_type, sa.Integer): + return dict(data_type="bigint", precision=32) + elif isinstance(db_type, sa.BigInteger): + return dict(data_type="bigint") + return dict(data_type="bigint") + + def _from_db_decimal_type(self, db_type: sa.Numeric) -> TColumnSchema: + precision, scale = db_type.precision, db_type.scale + if (precision, scale) == self.capabilities.wei_precision: + return dict(data_type="wei") + + return dict(data_type="decimal", precision=precision, scale=scale) + + def from_destination_type( # type: ignore[override] + self, + db_type: sqltypes.TypeEngine, + precision: Optional[int] = None, + scale: Optional[int] = None, + ) -> TColumnSchema: + # TODO: pass the sqla type through dialect.type_descriptor before instance check + # Possibly need to check both dialect specific and generic types + if isinstance(db_type, sa.String): + return dict(data_type="text") + elif isinstance(db_type, sa.Float): + return dict(data_type="double") + elif isinstance(db_type, sa.Boolean): + return dict(data_type="bool") + elif isinstance(db_type, sa.DateTime): + return dict(data_type="timestamp", timezone=db_type.timezone) + elif isinstance(db_type, sa.Integer): + return self._from_db_integer_type(db_type) + elif isinstance(db_type, sqltypes._Binary): + return dict(data_type="binary", precision=db_type.length) + elif isinstance(db_type, sa.JSON): + return dict(data_type="json") + elif isinstance(db_type, sa.Numeric): + return self._from_db_decimal_type(db_type) + elif isinstance(db_type, sa.Date): + return dict(data_type="date") + elif isinstance(db_type, sa.Time): + return dict(data_type="time") + raise TerminalValueError(f"Unsupported db type: {db_type}") + + pass + + def ensure_supported_type( + self, + column: TColumnSchema, + table: PreparedTableSchema, + loader_file_format: TLoaderFileFormat, + ) -> None: + pass diff --git a/dlt/destinations/impl/synapse/factory.py b/dlt/destinations/impl/synapse/factory.py index d5a0281bec..f035f2f713 100644 --- a/dlt/destinations/impl/synapse/factory.py +++ b/dlt/destinations/impl/synapse/factory.py @@ -1,10 +1,15 @@ import typing as t -from dlt.common.destination import Destination, DestinationCapabilitiesContext +from dlt.common.data_types.typing import TDataType +from dlt.common.destination import Destination, DestinationCapabilitiesContext, PreparedTableSchema +from dlt.common.exceptions import TerminalValueError from dlt.common.normalizers.naming import NamingConvention from dlt.common.data_writers.escape import escape_postgres_identifier, escape_mssql_literal from dlt.common.arithmetics import DEFAULT_NUMERIC_PRECISION, DEFAULT_NUMERIC_SCALE +from dlt.common.schema.typing import TColumnSchema +from dlt.common.typing import TLoaderFileFormat +from dlt.destinations.impl.mssql.factory import MsSqlTypeMapper from dlt.destinations.impl.synapse.configuration import ( SynapseCredentials, SynapseClientConfiguration, @@ -15,6 +20,22 @@ from dlt.destinations.impl.synapse.synapse import SynapseClient +class SynapseTypeMapper(MsSqlTypeMapper): + def ensure_supported_type( + self, + column: TColumnSchema, + table: PreparedTableSchema, + loader_file_format: TLoaderFileFormat, + ) -> None: + # TIME is not supported for parquet + if loader_file_format == "parquet" and column["data_type"] == "time": + raise TerminalValueError( + "Please convert `datetime.time` objects in your data to `str` or" + " `datetime.datetime`.", + "time", + ) + + class synapse(Destination[SynapseClientConfiguration, "SynapseClient"]): spec = SynapseClientConfiguration @@ -30,6 +51,7 @@ def _raw_capabilities(self) -> DestinationCapabilitiesContext: caps.supported_loader_file_formats = ["insert_values"] caps.preferred_staging_file_format = "parquet" caps.supported_staging_file_formats = ["parquet"] + caps.type_mapper = SynapseTypeMapper caps.insert_values_writer_type = "select_union" # https://stackoverflow.com/a/77014299 diff --git a/dlt/destinations/impl/synapse/synapse.py b/dlt/destinations/impl/synapse/synapse.py index 750a4895f0..15c979bafa 100644 --- a/dlt/destinations/impl/synapse/synapse.py +++ b/dlt/destinations/impl/synapse/synapse.py @@ -5,9 +5,14 @@ from urllib.parse import urlparse, urlunparse from dlt.common.destination import DestinationCapabilitiesContext -from dlt.common.destination.reference import SupportsStagingDestination, FollowupJobRequest, LoadJob +from dlt.common.destination.reference import ( + PreparedTableSchema, + SupportsStagingDestination, + FollowupJobRequest, + LoadJob, +) -from dlt.common.schema import TTableSchema, TColumnSchema, Schema, TColumnHint +from dlt.common.schema import TColumnSchema, Schema, TColumnHint from dlt.common.schema.utils import ( table_schema_has_type, get_inherited_table_hint, @@ -19,16 +24,15 @@ AzureServicePrincipalCredentialsWithoutDefaults, ) +from dlt.destinations.impl.mssql.factory import MsSqlTypeMapper from dlt.destinations.job_impl import ReferenceFollowupJobRequest from dlt.destinations.sql_client import SqlClientBase from dlt.destinations.job_client_impl import ( SqlJobClientBase, CopyRemoteFileLoadJob, ) -from dlt.destinations.exceptions import LoadJobTerminalException from dlt.destinations.impl.mssql.mssql import ( - MsSqlTypeMapper, MsSqlJobClient, VARCHAR_MAX_N, VARBINARY_MAX_N, @@ -76,10 +80,16 @@ def __init__( def _get_table_update_sql( self, table_name: str, new_columns: Sequence[TColumnSchema], generate_alter: bool ) -> List[str]: - table = self.prepare_load_table(table_name, staging=self.in_staging_mode) + table = self.prepare_load_table(table_name) + if self.in_staging_dataset_mode and self.config.replace_strategy == "insert-from-staging": + # Staging tables should always be heap tables, because "when you are + # temporarily landing data in dedicated SQL pool, you may find that + # using a heap table makes the overall process faster." + table[TABLE_INDEX_TYPE_HINT] = "heap" # type: ignore[typeddict-unknown-key] + table_index_type = cast(TTableIndexType, table.get(TABLE_INDEX_TYPE_HINT)) - if self.in_staging_mode: - final_table = self.prepare_load_table(table_name, staging=False) + if self.in_staging_dataset_mode: + final_table = self.prepare_load_table(table_name) final_table_index_type = cast(TTableIndexType, final_table.get(TABLE_INDEX_TYPE_HINT)) else: final_table_index_type = table_index_type @@ -130,18 +140,13 @@ def _get_columstore_valid_column(self, c: TColumnSchema) -> TColumnSchema: return c def _create_replace_followup_jobs( - self, table_chain: Sequence[TTableSchema] + self, table_chain: Sequence[PreparedTableSchema] ) -> List[FollowupJobRequest]: return SqlJobClientBase._create_replace_followup_jobs(self, table_chain) - def prepare_load_table(self, table_name: str, staging: bool = False) -> TTableSchema: - table = super().prepare_load_table(table_name, staging) - if staging and self.config.replace_strategy == "insert-from-staging": - # Staging tables should always be heap tables, because "when you are - # temporarily landing data in dedicated SQL pool, you may find that - # using a heap table makes the overall process faster." - table[TABLE_INDEX_TYPE_HINT] = "heap" # type: ignore[typeddict-unknown-key] - elif table_name in self.schema.dlt_table_names(): + def prepare_load_table(self, table_name: str) -> PreparedTableSchema: + table = super().prepare_load_table(table_name) + if table_name in self.schema.dlt_table_names(): # dlt tables should always be heap tables, because "for small lookup # tables, less than 60 million rows, consider using HEAP or clustered # index for faster query performance." @@ -159,7 +164,7 @@ def prepare_load_table(self, table_name: str, staging: bool = False) -> TTableSc return table def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: job = super().create_load_job(table, file_path, load_id, restore) if not job: @@ -173,7 +178,7 @@ def create_load_job( ) return job - def should_truncate_table_before_load_on_staging_destination(self, table: TTableSchema) -> bool: + def should_truncate_table_before_load_on_staging_destination(self, table_name: str) -> bool: return self.config.truncate_tables_on_staging_destination_before_load @@ -194,15 +199,6 @@ def run(self) -> None: # get format ext = os.path.splitext(self._bucket_path)[1][1:] if ext == "parquet": - if table_schema_has_type(self._load_table, "time"): - # Synapse interprets Parquet TIME columns as bigint, resulting in - # an incompatibility error. - raise LoadJobTerminalException( - self.file_name(), - "Synapse cannot load TIME columns from Parquet files. Switch to direct INSERT" - " file format or convert `datetime.time` objects in your data to `str` or" - " `datetime.datetime`", - ) file_type = "PARQUET" # dlt-generated DDL statements will still create the table, but diff --git a/dlt/destinations/impl/weaviate/factory.py b/dlt/destinations/impl/weaviate/factory.py index 3d78c9582a..a5c1e9f2a1 100644 --- a/dlt/destinations/impl/weaviate/factory.py +++ b/dlt/destinations/impl/weaviate/factory.py @@ -2,6 +2,7 @@ from dlt.common.destination import Destination, DestinationCapabilitiesContext +from dlt.destinations.type_mapping import TypeMapperImpl from dlt.destinations.impl.weaviate.configuration import ( WeaviateCredentials, WeaviateClientConfiguration, @@ -11,6 +12,33 @@ from dlt.destinations.impl.weaviate.weaviate_client import WeaviateClient +class WeaviateTypeMapper(TypeMapperImpl): + sct_to_unbound_dbt = { + "text": "text", + "double": "number", + "bool": "boolean", + "timestamp": "date", + "date": "date", + "time": "text", + "bigint": "int", + "binary": "blob", + "decimal": "text", + "wei": "number", + "json": "text", + } + + sct_to_dbt = {} + + dbt_to_sct = { + "text": "text", + "number": "double", + "boolean": "bool", + "date": "timestamp", + "int": "bigint", + "blob": "binary", + } + + class weaviate(Destination[WeaviateClientConfiguration, "WeaviateClient"]): spec = WeaviateClientConfiguration @@ -18,6 +46,7 @@ def _raw_capabilities(self) -> DestinationCapabilitiesContext: caps = DestinationCapabilitiesContext() caps.preferred_loader_file_format = "jsonl" caps.supported_loader_file_formats = ["jsonl"] + caps.type_mapper = WeaviateTypeMapper # weaviate names are case sensitive following GraphQL naming convention # https://weaviate.io/developers/weaviate/config-refs/schema caps.has_case_sensitive_identifiers = False diff --git a/dlt/destinations/impl/weaviate/weaviate_client.py b/dlt/destinations/impl/weaviate/weaviate_client.py index b8bf3d62c6..76e5fd8b1e 100644 --- a/dlt/destinations/impl/weaviate/weaviate_client.py +++ b/dlt/destinations/impl/weaviate/weaviate_client.py @@ -29,8 +29,8 @@ from dlt.common.pendulum import pendulum from dlt.common.typing import StrAny, TFun from dlt.common.time import ensure_pendulum_datetime -from dlt.common.schema import Schema, TTableSchema, TSchemaTables, TTableSchemaColumns -from dlt.common.schema.typing import TColumnSchema, TColumnType +from dlt.common.schema import Schema, TSchemaTables, TTableSchemaColumns +from dlt.common.schema.typing import C_DLT_LOAD_ID, TColumnSchema, TColumnType from dlt.common.schema.utils import ( get_columns_names_with_prop, loads_table, @@ -39,6 +39,7 @@ ) from dlt.common.destination import DestinationCapabilitiesContext from dlt.common.destination.reference import ( + PreparedTableSchema, TLoadJobState, RunnableLoadJob, JobClientBase, @@ -48,11 +49,9 @@ from dlt.common.storages import FileStorage from dlt.destinations.impl.weaviate.weaviate_adapter import VECTORIZE_HINT, TOKENIZATION_HINT -from dlt.destinations.job_impl import FinalizedLoadJobWithFollowupJobs from dlt.destinations.job_client_impl import StorageSchemaInfo, StateInfo from dlt.destinations.impl.weaviate.configuration import WeaviateClientConfiguration from dlt.destinations.impl.weaviate.exceptions import PropertyNameConflict, WeaviateGrpcError -from dlt.destinations.type_mapping import TypeMapper from dlt.destinations.utils import get_pipeline_state_query_columns @@ -64,33 +63,6 @@ } -class WeaviateTypeMapper(TypeMapper): - sct_to_unbound_dbt = { - "text": "text", - "double": "number", - "bool": "boolean", - "timestamp": "date", - "date": "date", - "time": "text", - "bigint": "int", - "binary": "blob", - "decimal": "text", - "wei": "number", - "complex": "text", - } - - sct_to_dbt = {} - - dbt_to_sct = { - "text": "text", - "number": "double", - "boolean": "bool", - "date": "timestamp", - "int": "bigint", - "blob": "binary", - } - - def wrap_weaviate_error(f: TFun) -> TFun: @wraps(f) def _wrap(self: JobClientBase, *args: Any, **kwargs: Any) -> Any: @@ -163,10 +135,10 @@ def run(self) -> None: self._db_client = self._job_client.db_client self._client_config = self._job_client.config self.unique_identifiers = self.list_unique_identifiers(self._load_table) - self.complex_indices = [ + self.nested_indices = [ i for i, field in self._schema.get_table_columns(self.load_table_name).items() - if field["data_type"] == "complex" + if field["data_type"] == "json" ] self.date_indices = [ i @@ -204,8 +176,8 @@ def check_batch_result(results: List[StrAny]) -> None: ) as batch: for line in f: data = json.loads(line) - # make complex to strings - for key in self.complex_indices: + # serialize json types + for key in self.nested_indices: if key in data: data[key] = json.dumps(data[key]) for key in self.date_indices: @@ -218,7 +190,7 @@ def check_batch_result(results: List[StrAny]) -> None: batch.add_data_object(data, self._class_name, uuid=uuid) - def list_unique_identifiers(self, table_schema: TTableSchema) -> Sequence[str]: + def list_unique_identifiers(self, table_schema: PreparedTableSchema) -> Sequence[str]: if table_schema.get("write_disposition") == "merge": primary_keys = get_columns_names_with_prop(table_schema, "primary_key") if primary_keys: @@ -259,7 +231,7 @@ def __init__( "vectorizer": config.vectorizer, "moduleConfig": config.module_config, } - self.type_mapper = WeaviateTypeMapper(self.capabilities) + self.type_mapper = self.capabilities.get_type_mapper() @property def dataset_name(self) -> str: @@ -435,9 +407,8 @@ def update_stored_schema( only_tables: Iterable[str] = None, expected_update: TSchemaTables = None, ) -> Optional[TSchemaTables]: - super().update_stored_schema(only_tables, expected_update) + applied_update = super().update_stored_schema(only_tables, expected_update) # Retrieve the schema from Weaviate - applied_update: TSchemaTables = {} try: schema_info = self.get_stored_schema_by_hash(self.schema.stored_version_hash) except DestinationUndefinedEntity: @@ -447,6 +418,7 @@ def update_stored_schema( f"Schema with hash {self.schema.stored_version_hash} " "not found in the storage. upgrading" ) + # TODO: return a real updated table schema (like in SQL job client) self._execute_schema_update(only_tables) else: logger.info( @@ -503,7 +475,7 @@ def get_stored_state(self, pipeline_name: str) -> Optional[StateInfo]: """Loads compressed state from destination storage""" # normalize properties p_load_id = self.schema.naming.normalize_identifier("load_id") - p_dlt_load_id = self.schema.naming.normalize_identifier("_dlt_load_id") + p_dlt_load_id = self.schema.naming.normalize_identifier(C_DLT_LOAD_ID) p_pipeline_name = self.schema.naming.normalize_identifier("pipeline_name") p_status = self.schema.naming.normalize_identifier("status") @@ -670,12 +642,12 @@ def _make_property_schema( return { "name": column_name, - "dataType": [self.type_mapper.to_db_type(column)], + "dataType": [self.type_mapper.to_destination_type(column, None)], **extra_kv, } def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: return LoadWeaviateJob( file_path, @@ -727,4 +699,4 @@ def _update_schema_in_storage(self, schema: Schema) -> None: def _from_db_type( self, wt_t: str, precision: Optional[int], scale: Optional[int] ) -> TColumnType: - return self.type_mapper.from_db_type(wt_t, precision, scale) + return self.type_mapper.from_destination_type(wt_t, precision, scale) diff --git a/dlt/destinations/insert_job_client.py b/dlt/destinations/insert_job_client.py index 6ccc65705b..aa608ca2ad 100644 --- a/dlt/destinations/insert_job_client.py +++ b/dlt/destinations/insert_job_client.py @@ -1,15 +1,15 @@ -import os -import abc from typing import Any, Iterator, List -from dlt.common.destination.reference import RunnableLoadJob, HasFollowupJobs, LoadJob -from dlt.common.schema.typing import TTableSchema +from dlt.common.destination.reference import ( + PreparedTableSchema, + RunnableLoadJob, + HasFollowupJobs, + LoadJob, +) from dlt.common.storages import FileStorage from dlt.common.utils import chunks -from dlt.destinations.sql_client import SqlClientBase -from dlt.destinations.job_impl import FinalizedLoadJobWithFollowupJobs -from dlt.destinations.job_client_impl import SqlJobClientWithStaging, SqlJobClientBase +from dlt.destinations.job_client_impl import SqlJobClientWithStagingDataset, SqlJobClientBase class InsertValuesLoadJob(RunnableLoadJob, HasFollowupJobs): @@ -96,9 +96,9 @@ def _insert(self, qualified_table_name: str, file_path: str) -> Iterator[List[st yield insert_sql -class InsertValuesJobClient(SqlJobClientWithStaging): +class InsertValuesJobClient(SqlJobClientWithStagingDataset): def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: job = super().create_load_job(table, file_path, load_id, restore) if not job: diff --git a/dlt/destinations/job_client_impl.py b/dlt/destinations/job_client_impl.py index 1d6403a2c8..0ddded98b6 100644 --- a/dlt/destinations/job_client_impl.py +++ b/dlt/destinations/job_client_impl.py @@ -21,22 +21,24 @@ from dlt.common import pendulum, logger from dlt.common.json import json from dlt.common.schema.typing import ( + C_DLT_LOAD_ID, COLUMN_HINTS, TColumnType, TColumnSchemaBase, - TTableSchema, TTableFormat, ) from dlt.common.schema.utils import ( get_inherited_table_hint, + has_default_column_prop_value, loads_table, normalize_table_identifiers, version_table, ) from dlt.common.storages import FileStorage -from dlt.common.storages.load_package import LoadJobInfo +from dlt.common.storages.load_package import LoadJobInfo, ParsedLoadJobFileName from dlt.common.schema import TColumnSchema, Schema, TTableSchemaColumns, TSchemaTables from dlt.common.destination.reference import ( + PreparedTableSchema, StateInfo, StorageSchemaInfo, WithStateSync, @@ -61,7 +63,7 @@ from dlt.destinations.utils import ( get_pipeline_state_query_columns, info_schema_null_to_bool, - verify_sql_job_client_schema, + verify_schema_merge_disposition, ) # this should suffice for now @@ -208,24 +210,25 @@ def maybe_ddl_transaction(self) -> Iterator[None]: else: yield - def should_truncate_table_before_load(self, table: TTableSchema) -> bool: + def should_truncate_table_before_load(self, table_name: str) -> bool: + table = self.prepare_load_table(table_name) return ( table["write_disposition"] == "replace" and self.config.replace_strategy == "truncate-and-insert" ) def _create_append_followup_jobs( - self, table_chain: Sequence[TTableSchema] + self, table_chain: Sequence[PreparedTableSchema] ) -> List[FollowupJobRequest]: return [] def _create_merge_followup_jobs( - self, table_chain: Sequence[TTableSchema] + self, table_chain: Sequence[PreparedTableSchema] ) -> List[FollowupJobRequest]: return [SqlMergeFollowupJob.from_table_chain(table_chain, self.sql_client)] def _create_replace_followup_jobs( - self, table_chain: Sequence[TTableSchema] + self, table_chain: Sequence[PreparedTableSchema] ) -> List[FollowupJobRequest]: jobs: List[FollowupJobRequest] = [] if self.config.replace_strategy in ["insert-from-staging", "staging-optimized"]: @@ -238,7 +241,7 @@ def _create_replace_followup_jobs( def create_table_chain_completed_followup_jobs( self, - table_chain: Sequence[TTableSchema], + table_chain: Sequence[PreparedTableSchema], completed_table_chain_jobs: Optional[Sequence[LoadJobInfo]] = None, ) -> List[FollowupJobRequest]: """Creates a list of followup jobs for merge write disposition and staging replace strategies""" @@ -255,7 +258,7 @@ def create_table_chain_completed_followup_jobs( return jobs def create_load_job( - self, table: TTableSchema, file_path: str, load_id: str, restore: bool = False + self, table: PreparedTableSchema, file_path: str, load_id: str, restore: bool = False ) -> LoadJob: """Starts SqlLoadJob for files ending with .sql or returns None to let derived classes to handle their specific jobs""" if SqlLoadJob.is_sql_job(file_path): @@ -394,7 +397,7 @@ def get_stored_state(self, pipeline_name: str) -> StateInfo: state_table = self.sql_client.make_qualified_table_name(self.schema.state_table_name) loads_table = self.sql_client.make_qualified_table_name(self.schema.loads_table_name) c_load_id, c_dlt_load_id, c_pipeline_name, c_status = self._norm_and_escape_columns( - "load_id", "_dlt_load_id", "pipeline_name", "status" + "load_id", C_DLT_LOAD_ID, "pipeline_name", "status" ) query = ( f"SELECT {self.state_table_columns} FROM {state_table} AS s JOIN {loads_table} AS l ON" @@ -499,11 +502,11 @@ def _build_schema_update_sql( ): # this will skip incomplete columns new_columns = self._create_table_update(table_name, storage_columns) + generate_alter = len(storage_columns) > 0 if len(new_columns) > 0: # build and add sql to execute - sql_statements = self._get_table_update_sql( - table_name, new_columns, len(storage_columns) > 0 - ) + self._check_table_update_hints(table_name, new_columns, generate_alter) + sql_statements = self._get_table_update_sql(table_name, new_columns, generate_alter) for sql in sql_statements: if not sql.endswith(";"): sql += ";" @@ -517,12 +520,12 @@ def _build_schema_update_sql( return sql_updates, schema_update def _make_add_column_sql( - self, new_columns: Sequence[TColumnSchema], table_format: TTableFormat = None + self, new_columns: Sequence[TColumnSchema], table: PreparedTableSchema = None ) -> List[str]: """Make one or more ADD COLUMN sql clauses to be joined in ALTER TABLE statement(s)""" - return [f"ADD COLUMN {self._get_column_def_sql(c, table_format)}" for c in new_columns] + return [f"ADD COLUMN {self._get_column_def_sql(c, table)}" for c in new_columns] - def _make_create_table(self, qualified_name: str, table: TTableSchema) -> str: + def _make_create_table(self, qualified_name: str, table: PreparedTableSchema) -> str: not_exists_clause = " " if ( table["name"] in self.schema.dlt_table_names() @@ -537,17 +540,16 @@ def _get_table_update_sql( # build sql qualified_name = self.sql_client.make_qualified_table_name(table_name) table = self.prepare_load_table(table_name) - table_format = table.get("table_format") sql_result: List[str] = [] if not generate_alter: # build CREATE sql = self._make_create_table(qualified_name, table) + " (\n" - sql += ",\n".join([self._get_column_def_sql(c, table_format) for c in new_columns]) + sql += ",\n".join([self._get_column_def_sql(c, table) for c in new_columns]) sql += ")" sql_result.append(sql) else: sql_base = f"ALTER TABLE {qualified_name}\n" - add_column_statements = self._make_add_column_sql(new_columns, table_format) + add_column_statements = self._make_add_column_sql(new_columns, table) if self.capabilities.alter_add_multi_column: column_sql = ",\n" sql_result.append(sql_base + column_sql.join(add_column_statements)) @@ -556,38 +558,41 @@ def _get_table_update_sql( sql_result.extend( [sql_base + col_statement for col_statement in add_column_statements] ) + return sql_result + def _check_table_update_hints( + self, table_name: str, new_columns: Sequence[TColumnSchema], generate_alter: bool + ) -> None: # scan columns to get hints if generate_alter: # no hints may be specified on added columns for hint in COLUMN_HINTS: - if any(c.get(hint, False) is True for c in new_columns): + if any(not has_default_column_prop_value(hint, c.get(hint)) for c in new_columns): hint_columns = [ self.sql_client.escape_column_name(c["name"]) for c in new_columns if c.get(hint, False) ] - if hint == "not_null": + if hint == "null": logger.warning( f"Column(s) {hint_columns} with NOT NULL are being added to existing" - f" table {qualified_name}. If there's data in the table the operation" + f" table {table_name}. If there's data in the table the operation" " will fail." ) else: logger.warning( f"Column(s) {hint_columns} with hint {hint} are being added to existing" - f" table {qualified_name}. Several hint types may not be added to" + f" table {table_name}. Several hint types may not be added to" " existing tables." ) - return sql_result @abstractmethod - def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = None) -> str: + def _get_column_def_sql(self, c: TColumnSchema, table: PreparedTableSchema = None) -> str: pass @staticmethod - def _gen_not_null(v: bool) -> str: - return "NOT NULL" if not v else "" + def _gen_not_null(nullable: bool) -> str: + return "NOT NULL" if not nullable else "" def _create_table_update( self, table_name: str, storage_columns: TTableSchemaColumns @@ -658,17 +663,22 @@ def _commit_schema_update(self, schema: Schema, schema_str: str) -> None: schema_str, ) - def _verify_schema(self) -> None: - super()._verify_schema() - if exceptions := verify_sql_job_client_schema(self.schema, warnings=True): + def verify_schema( + self, only_tables: Iterable[str] = None, new_jobs: Iterable[ParsedLoadJobFileName] = None + ) -> List[PreparedTableSchema]: + loaded_tables = super().verify_schema(only_tables, new_jobs) + if exceptions := verify_schema_merge_disposition( + self.schema, loaded_tables, self.capabilities, warnings=True + ): for exception in exceptions: logger.error(str(exception)) raise exceptions[0] + return loaded_tables def prepare_load_job_execution(self, job: RunnableLoadJob) -> None: self._set_query_tags_for_job(load_id=job._load_id, table=job._load_table) - def _set_query_tags_for_job(self, load_id: str, table: TTableSchema) -> None: + def _set_query_tags_for_job(self, load_id: str, table: PreparedTableSchema) -> None: """Sets query tags in sql_client for a job in package `load_id`, starting for a particular `table`""" from dlt.common.pipeline import current_pipeline @@ -679,7 +689,7 @@ def _set_query_tags_for_job(self, load_id: str, table: TTableSchema) -> None: "source": self.schema.name, "resource": ( get_inherited_table_hint( - self.schema._schema_tables, table["name"], "resource", allow_none=True + self.schema.tables, table["name"], "resource", allow_none=True ) or "" ), @@ -690,19 +700,20 @@ def _set_query_tags_for_job(self, load_id: str, table: TTableSchema) -> None: ) -class SqlJobClientWithStaging(SqlJobClientBase, WithStagingDataset): - in_staging_mode: bool = False +class SqlJobClientWithStagingDataset(SqlJobClientBase, WithStagingDataset): + in_staging_dataset_mode: bool = False @contextlib.contextmanager def with_staging_dataset(self) -> Iterator["SqlJobClientBase"]: try: with self.sql_client.with_staging_dataset(): - self.in_staging_mode = True + self.in_staging_dataset_mode = True yield self finally: - self.in_staging_mode = False + self.in_staging_dataset_mode = False - def should_load_data_to_staging_dataset(self, table: TTableSchema) -> bool: + def should_load_data_to_staging_dataset(self, table_name: str) -> bool: + table = self.prepare_load_table(table_name) if table["write_disposition"] == "merge": return True elif table["write_disposition"] == "replace" and ( diff --git a/dlt/destinations/job_impl.py b/dlt/destinations/job_impl.py index 1f54913064..3f261bafed 100644 --- a/dlt/destinations/job_impl.py +++ b/dlt/destinations/job_impl.py @@ -8,13 +8,10 @@ HasFollowupJobs, TLoadJobState, RunnableLoadJob, - JobClientBase, FollowupJobRequest, LoadJob, ) -from dlt.common.metrics import LoadJobMetrics from dlt.common.storages.load_package import commit_load_package_state -from dlt.common.schema import Schema, TTableSchema from dlt.common.storages import FileStorage from dlt.common.typing import TDataItems from dlt.common.storages.load_storage import ParsedLoadJobFileName diff --git a/dlt/destinations/sql_client.py b/dlt/destinations/sql_client.py index 27d1bc7ce5..96f18cea3d 100644 --- a/dlt/destinations/sql_client.py +++ b/dlt/destinations/sql_client.py @@ -133,6 +133,23 @@ def drop_tables(self, *tables: str) -> None: ] self.execute_many(statements) + def _to_named_paramstyle(self, query: str, args: Sequence[Any]) -> Tuple[str, Dict[str, Any]]: + """Convert a query from "format" ( %s ) paramstyle to "named" ( :param_name ) paramstyle. + The %s are replaced with :arg0, :arg1, ... and the arguments are returned as a dictionary. + + Args: + query: SQL query with %s placeholders + args: arguments to be passed to the query + + Returns: + Tuple of the new query and a dictionary of named arguments + """ + keys = [f"arg{i}" for i in range(len(args))] + # Replace position arguments (%s) with named arguments (:arg0, :arg1, ...) + query = query % tuple(f":{key}" for key in keys) + db_args = {key: db_arg for key, db_arg in zip(keys, args)} + return query, db_args + @abstractmethod def execute_sql( self, sql: AnyStr, *args: Any, **kwargs: Any diff --git a/dlt/destinations/sql_jobs.py b/dlt/destinations/sql_jobs.py index d5f005ee9a..2407d2db62 100644 --- a/dlt/destinations/sql_jobs.py +++ b/dlt/destinations/sql_jobs.py @@ -2,9 +2,10 @@ import yaml from dlt.common.time import ensure_pendulum_datetime +from dlt.common.destination.reference import PreparedTableSchema +from dlt.common.destination.utils import resolve_merge_strategy from dlt.common.schema.typing import ( - TTableSchema, TSortOrder, TColumnProp, ) @@ -14,7 +15,7 @@ get_dedup_sort_tuple, get_validity_column_names, get_active_record_timestamp, - DEFAULT_MERGE_STRATEGY, + is_nested_table, ) from dlt.common.storages.load_storage import ParsedLoadJobFileName from dlt.common.storages.load_package import load_package as current_load_package @@ -35,7 +36,9 @@ class SqlJobParams(TypedDict, total=False): class SqlJobCreationException(DestinationTransientException): - def __init__(self, original_exception: Exception, table_chain: Sequence[TTableSchema]) -> None: + def __init__( + self, original_exception: Exception, table_chain: Sequence[PreparedTableSchema] + ) -> None: tables_str = yaml.dump( table_chain, allow_unicode=True, default_flow_style=False, sort_keys=False ) @@ -51,18 +54,18 @@ class SqlFollowupJob(FollowupJobRequestImpl): @classmethod def from_table_chain( cls, - table_chain: Sequence[TTableSchema], + table_chain: Sequence[PreparedTableSchema], sql_client: SqlClientBase[Any], params: Optional[SqlJobParams] = None, ) -> FollowupJobRequestImpl: """Generates a list of sql statements, that will be executed by the sql client when the job is executed in the loader. - The `table_chain` contains a list schemas of a tables with parent-child relationship, ordered by the ancestry (the root of the tree is first on the list). + The `table_chain` contains a list of schemas of nested tables, ordered by the ancestry (the root of the tree is first on the list). """ params = cast(SqlJobParams, {**DEFAULTS, **(params or {})}) - top_table = table_chain[0] + root_table = table_chain[0] file_info = ParsedLoadJobFileName( - top_table["name"], ParsedLoadJobFileName.new_file_id(), 0, "sql" + root_table["name"], ParsedLoadJobFileName.new_file_id(), 0, "sql" ) try: @@ -83,7 +86,7 @@ def from_table_chain( @classmethod def generate_sql( cls, - table_chain: Sequence[TTableSchema], + table_chain: Sequence[PreparedTableSchema], sql_client: SqlClientBase[Any], params: Optional[SqlJobParams] = None, ) -> List[str]: @@ -96,7 +99,7 @@ class SqlStagingCopyFollowupJob(SqlFollowupJob): @classmethod def _generate_clone_sql( cls, - table_chain: Sequence[TTableSchema], + table_chain: Sequence[PreparedTableSchema], sql_client: SqlClientBase[Any], ) -> List[str]: """Drop and clone the table for supported destinations""" @@ -113,7 +116,7 @@ def _generate_clone_sql( @classmethod def _generate_insert_sql( cls, - table_chain: Sequence[TTableSchema], + table_chain: Sequence[PreparedTableSchema], sql_client: SqlClientBase[Any], params: SqlJobParams = None, ) -> List[str]: @@ -138,7 +141,7 @@ def _generate_insert_sql( @classmethod def generate_sql( cls, - table_chain: Sequence[TTableSchema], + table_chain: Sequence[PreparedTableSchema], sql_client: SqlClientBase[Any], params: SqlJobParams = None, ) -> List[str]: @@ -154,13 +157,17 @@ class SqlMergeFollowupJob(SqlFollowupJob): """ @classmethod - def generate_sql( # type: ignore[return] + def generate_sql( cls, - table_chain: Sequence[TTableSchema], + table_chain: Sequence[PreparedTableSchema], sql_client: SqlClientBase[Any], params: Optional[SqlJobParams] = None, ) -> List[str]: - merge_strategy = table_chain[0].get("x-merge-strategy", DEFAULT_MERGE_STRATEGY) + # resolve only root table + root_table = table_chain[0] + merge_strategy = resolve_merge_strategy( + {root_table["name"]: root_table}, root_table, sql_client.capabilities + ) if merge_strategy == "delete-insert": return cls.gen_merge_sql(table_chain, sql_client) elif merge_strategy == "upsert": @@ -332,9 +339,18 @@ def gen_delete_from_sql( ); """ + @classmethod + def _shorten_table_name(cls, ident: str, sql_client: SqlClientBase[Any]) -> str: + """Trims identifier to max length supported by sql_client. Used for dynamically constructed table names""" + from dlt.common.normalizers.naming import NamingConvention + + return NamingConvention.shorten_identifier( + ident, ident, sql_client.capabilities.max_identifier_length + ) + @classmethod def _new_temp_table_name(cls, name_prefix: str, sql_client: SqlClientBase[Any]) -> str: - return f"{name_prefix}_{uniq_id()}" + return cls._shorten_table_name(f"{name_prefix}_{uniq_id()}", sql_client) @classmethod def _to_temp_table(cls, select_sql: str, temp_table_name: str) -> str: @@ -368,7 +384,7 @@ def _escape_list(cls, list_: List[str], escape_id: Callable[[str], str]) -> List @classmethod def _get_hard_delete_col_and_cond( cls, - table: TTableSchema, + table: PreparedTableSchema, escape_id: Callable[[str], str], escape_lit: Callable[[Any], Any], invert: bool = False, @@ -394,33 +410,36 @@ def _get_hard_delete_col_and_cond( return (col, cond) @classmethod - def _get_unique_col( + def _get_row_key_col( cls, - table_chain: Sequence[TTableSchema], + table_chain: Sequence[PreparedTableSchema], sql_client: SqlClientBase[Any], - table: TTableSchema, + table: PreparedTableSchema, ) -> str: - """Returns name of first column in `table` with `unique` property. + """Returns name of first column in `table` with `row_key` property. If not found first `unique` hint will be used Raises `MergeDispositionException` if no such column exists. """ - return cls._get_prop_col_or_raise( - table, - "unique", - MergeDispositionException( - sql_client.fully_qualified_dataset_name(), - sql_client.fully_qualified_dataset_name(staging=True), - [t["name"] for t in table_chain], - f"No `unique` column (e.g. `_dlt_id`) in table `{table['name']}`.", - ), - ) + col = get_first_column_name_with_prop(table, "row_key") + if col is None: + col = cls._get_prop_col_or_raise( + table, + "unique", + MergeDispositionException( + sql_client.fully_qualified_dataset_name(), + sql_client.fully_qualified_dataset_name(staging=True), + [t["name"] for t in table_chain], + f"No `row_key` or `unique` column (e.g. `_dlt_id`) in table `{table['name']}`.", + ), + ) + return col @classmethod def _get_root_key_col( cls, - table_chain: Sequence[TTableSchema], + table_chain: Sequence[PreparedTableSchema], sql_client: SqlClientBase[Any], - table: TTableSchema, + table: PreparedTableSchema, ) -> str: """Returns name of first column in `table` with `root_key` property. @@ -439,7 +458,7 @@ def _get_root_key_col( @classmethod def _get_prop_col_or_raise( - cls, table: TTableSchema, prop: Union[TColumnProp, str], exception: Exception + cls, table: PreparedTableSchema, prop: Union[TColumnProp, str], exception: Exception ) -> str: """Returns name of first column in `table` with `prop` property. @@ -452,15 +471,15 @@ def _get_prop_col_or_raise( @classmethod def gen_merge_sql( - cls, table_chain: Sequence[TTableSchema], sql_client: SqlClientBase[Any] + cls, table_chain: Sequence[PreparedTableSchema], sql_client: SqlClientBase[Any] ) -> List[str]: """Generates a list of sql statements that merge the data in staging dataset with the data in destination dataset. - The `table_chain` contains a list schemas of a tables with parent-child relationship, ordered by the ancestry (the root of the tree is first on the list). + The `table_chain` contains a list schemas of a tables with row_key - parent_key nested reference, ordered by the ancestry (the root of the tree is first on the list). The root table is merged using primary_key and merge_key hints which can be compound and be both specified. In that case the OR clause is generated. - The child tables are merged based on propagated `root_key` which is a type of foreign key but always leading to a root table. + The nested tables are merged based on propagated `root_key` which is a type of foreign key but always leading to a root table. - First we store the root_keys of root table elements to be deleted in the temp table. Then we use the temp table to delete records from root and all child tables in the destination dataset. + First we store the root_keys of root table elements to be deleted in the temp table. Then we use the temp table to delete records from root and all netsed tables in the destination dataset. At the end we copy the data from the staging dataset into destination dataset. If a hard_delete column is specified, records flagged as deleted will be excluded from the copy into the destination dataset. @@ -496,32 +515,32 @@ def gen_merge_sql( if not append_fallback: key_clauses = cls._gen_key_table_clauses(primary_keys, merge_keys) - unique_column: str = None + row_key_column: str = None root_key_column: str = None if len(table_chain) == 1 and not cls.requires_temp_table_for_delete(): key_table_clauses = cls.gen_key_table_clauses( root_table_name, staging_root_table_name, key_clauses, for_delete=True ) - # if no child tables, just delete data from top table + # if no nested tables, just delete data from root table for clause in key_table_clauses: sql.append(f"DELETE {clause};") else: key_table_clauses = cls.gen_key_table_clauses( root_table_name, staging_root_table_name, key_clauses, for_delete=False ) - # use unique hint to create temp table with all identifiers to delete - unique_column = escape_column_id( - cls._get_unique_col(table_chain, sql_client, root_table) + # use row_key or unique hint to create temp table with all identifiers to delete + row_key_column = escape_column_id( + cls._get_row_key_col(table_chain, sql_client, root_table) ) create_delete_temp_table_sql, delete_temp_table_name = ( cls.gen_delete_temp_table_sql( - root_table["name"], unique_column, key_table_clauses, sql_client + root_table["name"], row_key_column, key_table_clauses, sql_client ) ) sql.extend(create_delete_temp_table_sql) - # delete from child tables first. This is important for databricks which does not support temporary tables, + # delete from nested tables first. This is important for databricks which does not support temporary tables, # but uses temporary views instead for table in table_chain[1:]: table_name = sql_client.make_qualified_table_name(table["name"]) @@ -530,14 +549,14 @@ def gen_merge_sql( ) sql.append( cls.gen_delete_from_sql( - table_name, root_key_column, delete_temp_table_name, unique_column + table_name, root_key_column, delete_temp_table_name, row_key_column ) ) - # delete from top table now that child tables have been processed + # delete from root table now that nested tables have been processed sql.append( cls.gen_delete_from_sql( - root_table_name, unique_column, delete_temp_table_name, unique_column + root_table_name, row_key_column, delete_temp_table_name, row_key_column ) ) @@ -565,7 +584,7 @@ def gen_merge_sql( staging_root_table_name, sql_client, primary_keys, - unique_column, + row_key_column, dedup_sort, not_deleted_cond, condition_columns, @@ -579,17 +598,17 @@ def gen_merge_sql( insert_cond = not_deleted_cond if hard_delete_col is not None else "1 = 1" if (len(primary_keys) > 0 and len(table_chain) > 1) or ( len(primary_keys) == 0 - and table.get("parent") is not None # child table + and is_nested_table(table) # nested table and hard_delete_col is not None ): - uniq_column = unique_column if table.get("parent") is None else root_key_column + uniq_column = root_key_column if is_nested_table(table) else row_key_column insert_cond = f"{uniq_column} IN (SELECT * FROM {insert_temp_table_name})" columns = list(map(escape_column_id, get_columns_names_with_prop(table, "name"))) col_str = ", ".join(columns) select_sql = f"SELECT {col_str} FROM {staging_table_name} WHERE {insert_cond}" if len(primary_keys) > 0 and len(table_chain) == 1: - # without child tables we deduplicate inside the query instead of using a temp table + # without nested tables we deduplicate inside the query instead of using a temp table select_sql = cls.gen_select_from_dedup_sql( staging_table_name, primary_keys, columns, dedup_sort, insert_cond ) @@ -599,7 +618,7 @@ def gen_merge_sql( @classmethod def gen_upsert_sql( - cls, table_chain: Sequence[TTableSchema], sql_client: SqlClientBase[Any] + cls, table_chain: Sequence[PreparedTableSchema], sql_client: SqlClientBase[Any] ) -> List[str]: sql: List[str] = [] root_table = table_chain[0] @@ -641,15 +660,15 @@ def gen_upsert_sql( THEN INSERT ({col_str.format(alias="")}) VALUES ({col_str.format(alias="s.")}); """) - # generate statements for child tables if they exist - child_tables = table_chain[1:] - if child_tables: - root_unique_column = escape_column_id( - cls._get_unique_col(table_chain, sql_client, root_table) + # generate statements for nested tables if they exist + nested_tables = table_chain[1:] + if nested_tables: + root_row_key_column = escape_column_id( + cls._get_row_key_col(table_chain, sql_client, root_table) ) - for table in child_tables: - unique_column = escape_column_id( - cls._get_unique_col(table_chain, sql_client, table) + for table in nested_tables: + nested_row_key_column = escape_column_id( + cls._get_row_key_col(table_chain, sql_client, table) ) root_key_column = escape_column_id( cls._get_root_key_col(table_chain, sql_client, table) @@ -659,8 +678,8 @@ def gen_upsert_sql( # delete records for elements no longer in the list sql.append(f""" DELETE FROM {table_name} - WHERE {root_key_column} IN (SELECT {root_unique_column} FROM {staging_root_table_name}) - AND {unique_column} NOT IN (SELECT {unique_column} FROM {staging_table_name}); + WHERE {root_key_column} IN (SELECT {root_row_key_column} FROM {staging_root_table_name}) + AND {nested_row_key_column} NOT IN (SELECT {nested_row_key_column} FROM {staging_table_name}); """) # insert records for new elements in the list @@ -669,7 +688,7 @@ def gen_upsert_sql( col_str = ", ".join(["{alias}" + c for c in table_column_names]) sql.append(f""" MERGE INTO {table_name} d USING {staging_table_name} s - ON d.{unique_column} = s.{unique_column} + ON d.{nested_row_key_column} = s.{nested_row_key_column} WHEN MATCHED THEN UPDATE SET {update_str} WHEN NOT MATCHED @@ -681,7 +700,7 @@ def gen_upsert_sql( sql.append(f""" DELETE FROM {table_name} WHERE {root_key_column} IN ( - SELECT {root_unique_column} + SELECT {root_row_key_column} FROM {staging_root_table_name} WHERE {deleted_cond} ); @@ -690,14 +709,14 @@ def gen_upsert_sql( @classmethod def gen_scd2_sql( - cls, table_chain: Sequence[TTableSchema], sql_client: SqlClientBase[Any] + cls, table_chain: Sequence[PreparedTableSchema], sql_client: SqlClientBase[Any] ) -> List[str]: """Generates SQL statements for the `scd2` merge strategy. The root table can be inserted into and updated. Updates only take place when a record retires (because there is a new version or it is deleted) and only affect the "valid to" column. - Child tables are insert-only. + Nested tables are insert-only. """ sql: List[str] = [] root_table = table_chain[0] @@ -760,23 +779,23 @@ def gen_scd2_sql( WHERE {hash_} NOT IN (SELECT {hash_} FROM {root_table_name} WHERE {is_active_clause}); """) - # insert list elements for new active records in child tables - child_tables = table_chain[1:] - if child_tables: - # TODO: - based on deterministic child hashes (OK) + # insert list elements for new active records in nested tables + nested_tables = table_chain[1:] + if nested_tables: + # TODO: - based on deterministic nested hashes (OK) # - if row hash changes all is right # - if it does not we only capture new records, while we should replace existing with those in stage # - this write disposition is way more similar to regular merge (how root tables are handled is different, other tables handled same) - for table in child_tables: - unique_column = escape_column_id( - cls._get_unique_col(table_chain, sql_client, table) + for table in nested_tables: + row_key_column = escape_column_id( + cls._get_row_key_col(table_chain, sql_client, table) ) table_name, staging_table_name = sql_client.get_qualified_table_names(table["name"]) sql.append(f""" INSERT INTO {table_name} SELECT * FROM {staging_table_name} - WHERE {unique_column} NOT IN (SELECT {unique_column} FROM {table_name}); + WHERE {row_key_column} NOT IN (SELECT {row_key_column} FROM {table_name}); """) return sql diff --git a/dlt/destinations/type_mapping.py b/dlt/destinations/type_mapping.py index dcd938b33c..d615675fa6 100644 --- a/dlt/destinations/type_mapping.py +++ b/dlt/destinations/type_mapping.py @@ -1,13 +1,18 @@ -from typing import Tuple, ClassVar, Dict, Optional - -from dlt.common.schema.typing import TColumnSchema, TDataType, TColumnType, TTableFormat -from dlt.common.destination.capabilities import DestinationCapabilitiesContext +from typing import Tuple, Dict, Optional + +from dlt.common import logger +from dlt.common.destination.reference import PreparedTableSchema +from dlt.common.schema.typing import ( + TColumnSchema, + TDataType, + TColumnType, +) +from dlt.common.destination.capabilities import DataTypeMapper +from dlt.common.typing import TLoaderFileFormat from dlt.common.utils import without_none -class TypeMapper: - capabilities: DestinationCapabilitiesContext - +class TypeMapperImpl(DataTypeMapper): sct_to_unbound_dbt: Dict[TDataType, str] """Data types without precision or scale specified (e.g. `"text": "varchar"` in postgres)""" sct_to_dbt: Dict[TDataType, str] @@ -17,42 +22,62 @@ class TypeMapper: dbt_to_sct: Dict[str, TDataType] - def __init__(self, capabilities: DestinationCapabilitiesContext) -> None: - self.capabilities = capabilities + def ensure_supported_type( + self, + column: TColumnSchema, + table: PreparedTableSchema, + loader_file_format: TLoaderFileFormat, + ) -> None: + pass - def to_db_integer_type( - self, precision: Optional[int], table_format: TTableFormat = None - ) -> str: + def to_db_integer_type(self, column: TColumnSchema, table: PreparedTableSchema = None) -> str: # Override in subclass if db supports other integer types (e.g. smallint, integer, tinyint, etc.) return self.sct_to_unbound_dbt["bigint"] def to_db_datetime_type( - self, precision: Optional[int], table_format: TTableFormat = None + self, + column: TColumnSchema, + table: PreparedTableSchema = None, ) -> str: # Override in subclass if db supports other timestamp types (e.g. with different time resolutions) + timezone = column.get("timezone") + precision = column.get("precision") + + if timezone is not None or precision is not None: + message = ( + "Column flags for timezone or precision are not yet supported in this" + " destination. One or both of these flags were used in column" + f" '{column.get('name')}'." + ) + # TODO: refactor lancedb and wevavite to make table object required + if table: + message += f" in table '{table.get('name')}'." + + logger.warning(message) + return None - def to_db_time_type(self, precision: Optional[int], table_format: TTableFormat = None) -> str: + def to_db_time_type(self, column: TColumnSchema, table: PreparedTableSchema = None) -> str: # Override in subclass if db supports other time types (e.g. with different time resolutions) return None - def to_db_decimal_type(self, precision: Optional[int], scale: Optional[int]) -> str: - precision_tup = self.decimal_precision(precision, scale) + def to_db_decimal_type(self, column: TColumnSchema) -> str: + precision_tup = self.decimal_precision(column.get("precision"), column.get("scale")) if not precision_tup or "decimal" not in self.sct_to_dbt: return self.sct_to_unbound_dbt["decimal"] return self.sct_to_dbt["decimal"] % (precision_tup[0], precision_tup[1]) - def to_db_type(self, column: TColumnSchema, table_format: TTableFormat = None) -> str: - precision, scale = column.get("precision"), column.get("scale") + # TODO: refactor lancedb and weaviate to make table object required + def to_destination_type(self, column: TColumnSchema, table: PreparedTableSchema) -> str: sc_t = column["data_type"] if sc_t == "bigint": - db_t = self.to_db_integer_type(precision, table_format) + db_t = self.to_db_integer_type(column, table) elif sc_t == "timestamp": - db_t = self.to_db_datetime_type(precision, table_format) + db_t = self.to_db_datetime_type(column, table) elif sc_t == "time": - db_t = self.to_db_time_type(precision, table_format) + db_t = self.to_db_time_type(column, table) elif sc_t == "decimal": - db_t = self.to_db_decimal_type(precision, scale) + db_t = self.to_db_decimal_type(column) else: db_t = None if db_t: @@ -61,14 +86,16 @@ def to_db_type(self, column: TColumnSchema, table_format: TTableFormat = None) - bounded_template = self.sct_to_dbt.get(sc_t) if not bounded_template: return self.sct_to_unbound_dbt[sc_t] - precision_tuple = self.precision_tuple_or_default(sc_t, precision, scale) + precision_tuple = self.precision_tuple_or_default(sc_t, column) if not precision_tuple: return self.sct_to_unbound_dbt[sc_t] return self.sct_to_dbt[sc_t] % precision_tuple def precision_tuple_or_default( - self, data_type: TDataType, precision: Optional[int], scale: Optional[int] + self, data_type: TDataType, column: TColumnSchema ) -> Optional[Tuple[int, ...]]: + precision = column.get("precision") + scale = column.get("scale") if data_type in ("timestamp", "time"): if precision is None: return None # Use default which is usually the max @@ -107,7 +134,7 @@ def wei_precision( scale if scale is not None else default_scale, ) - def from_db_type( + def from_destination_type( self, db_type: str, precision: Optional[int], scale: Optional[int] ) -> TColumnType: return without_none( diff --git a/dlt/destinations/utils.py b/dlt/destinations/utils.py index fcc2c4fd16..cd3ee6a54d 100644 --- a/dlt/destinations/utils.py +++ b/dlt/destinations/utils.py @@ -1,16 +1,18 @@ import re -import inspect -from typing import Any, List, Optional, Tuple +from typing import Any, List, Optional, Sequence, Tuple from dlt.common import logger +from dlt.common.destination.capabilities import DestinationCapabilitiesContext +from dlt.common.destination.utils import resolve_merge_strategy from dlt.common.schema import Schema from dlt.common.schema.exceptions import SchemaCorruptedException -from dlt.common.schema.typing import MERGE_STRATEGIES, TTableSchema +from dlt.common.schema.typing import MERGE_STRATEGIES, TColumnType, TTableSchema from dlt.common.schema.utils import ( get_columns_names_with_prop, get_first_column_name_with_prop, has_column_with_prop, + is_nested_table, pipeline_state_table, ) from typing import Any, cast, Tuple, Dict, Type @@ -81,13 +83,22 @@ def get_pipeline_state_query_columns() -> TTableSchema: return state_table -def verify_sql_job_client_schema(schema: Schema, warnings: bool = True) -> List[Exception]: +def verify_schema_merge_disposition( + schema: Schema, + load_tables: Sequence[TTableSchema], + capabilities: DestinationCapabilitiesContext, + warnings: bool = True, +) -> List[Exception]: log = logger.warning if warnings else logger.info # collect all exceptions to show all problems in the schema exception_log: List[Exception] = [] # verifies schema settings specific to sql job client - for table in schema.data_tables(): + for table in load_tables: + # from now on validate only top level tables + if is_nested_table(table): + continue + table_name = table["name"] if table.get("write_disposition") == "merge": if "x-merge-strategy" in table and table["x-merge-strategy"] not in MERGE_STRATEGIES: # type: ignore[typeddict-item] @@ -98,7 +109,9 @@ def verify_sql_job_client_schema(schema: Schema, warnings: bool = True) -> List[ f"""Allowed values: {', '.join(['"' + s + '"' for s in MERGE_STRATEGIES])}.""", ) ) - if table.get("x-merge-strategy") == "delete-insert": + + merge_strategy = resolve_merge_strategy(schema.tables, table, capabilities) + if merge_strategy == "delete-insert": if not has_column_with_prop(table, "primary_key") and not has_column_with_prop( table, "merge_key" ): @@ -108,7 +121,7 @@ def verify_sql_job_client_schema(schema: Schema, warnings: bool = True) -> List[ " merge keys defined." " dlt will fall back to `append` for this table." ) - elif table.get("x-merge-strategy") == "upsert": + elif merge_strategy == "upsert": if not has_column_with_prop(table, "primary_key"): exception_log.append( SchemaCorruptedException( diff --git a/dlt/extract/decorators.py b/dlt/extract/decorators.py index 1eccd86aad..5df165adb7 100644 --- a/dlt/extract/decorators.py +++ b/dlt/extract/decorators.py @@ -160,7 +160,7 @@ def source( max_table_nesting (int, optional): A schema hint that sets the maximum depth of nested table above which the remaining nodes are loaded as structs or JSON. - root_key (bool): Enables merging on all resources by propagating root foreign key to child tables. This option is most useful if you plan to change write disposition of a resource to disable/enable merge. Defaults to False. + root_key (bool): Enables merging on all resources by propagating row key from root to all nested tables. This option is most useful if you plan to change write disposition of a resource to disable/enable merge. Defaults to False. schema (Schema, optional): An explicit `Schema` instance to be associated with the source. If not present, `dlt` creates a new `Schema` object with provided `name`. If such `Schema` already exists in the same folder as the module containing the decorated function, such schema will be loaded from file. diff --git a/dlt/extract/extract.py b/dlt/extract/extract.py index 485a01eb99..e65f6cf0d0 100644 --- a/dlt/extract/extract.py +++ b/dlt/extract/extract.py @@ -25,6 +25,7 @@ TAnySchemaColumns, TColumnNames, TSchemaContract, + TTableFormat, TWriteDispositionConfig, ) from dlt.common.storages import NormalizeStorageConfiguration, LoadPackageInfo, SchemaStorage @@ -34,7 +35,7 @@ TLoadPackageState, commit_load_package_state, ) -from dlt.common.utils import get_callable_name, get_full_class_name +from dlt.common.utils import get_callable_name, get_full_class_name, group_dict_of_lists from dlt.extract.decorators import SourceInjectableContext, SourceSchemaInjectableContext from dlt.extract.exceptions import DataItemRequiredForDynamicTableHints @@ -50,12 +51,14 @@ def data_to_sources( data: Any, pipeline: SupportsPipeline, + *, schema: Schema = None, table_name: str = None, parent_table_name: str = None, write_disposition: TWriteDispositionConfig = None, columns: TAnySchemaColumns = None, primary_key: TColumnNames = None, + table_format: TTableFormat = None, schema_contract: TSchemaContract = None, ) -> List[DltSource]: """Creates a list of sources for data items present in `data` and applies specified hints to all resources. @@ -65,12 +68,13 @@ def data_to_sources( def apply_hint_args(resource: DltResource) -> None: resource.apply_hints( - table_name, - parent_table_name, - write_disposition, - columns, - primary_key, + table_name=table_name, + parent_table_name=parent_table_name, + write_disposition=write_disposition, + columns=columns, + primary_key=primary_key, schema_contract=schema_contract, + table_format=table_format, ) def apply_settings(source_: DltSource) -> None: @@ -93,7 +97,8 @@ def choose_schema() -> Schema: # a list of sources or a list of resources may be passed as data sources: List[DltSource] = [] - resources: List[DltResource] = [] + resources: Dict[str, List[DltResource]] = {} + data_resources: List[DltResource] = [] def append_data(data_item: Any) -> None: if isinstance(data_item, DltSource): @@ -102,13 +107,13 @@ def append_data(data_item: Any) -> None: data_item.schema = schema sources.append(data_item) elif isinstance(data_item, DltResource): - # do not set section to prevent source that represent a standalone resource - # to overwrite other standalone resources (ie. parents) in that source - sources.append(DltSource(effective_schema, "", [data_item])) + # many resources with the same name may be present + r_ = resources.setdefault(data_item.name, []) + r_.append(data_item) else: # iterator/iterable/generator # create resource first without table template - resources.append( + data_resources.append( DltResource.from_data(data_item, name=table_name, section=pipeline.pipeline_name) ) @@ -122,9 +127,17 @@ def append_data(data_item: Any) -> None: else: append_data(data) - # add all the appended resources in one source + # add all appended resource instances in one source if resources: - sources.append(DltSource(effective_schema, pipeline.pipeline_name, resources)) + # decompose into groups so at most single resource with a given name belongs to a group + for r_ in group_dict_of_lists(resources): + # do not set section to prevent source that represent a standalone resource + # to overwrite other standalone resources (ie. parents) in that source + sources.append(DltSource(effective_schema, "", list(r_.values()))) + + # add all the appended data-like items in one source + if data_resources: + sources.append(DltSource(effective_schema, pipeline.pipeline_name, data_resources)) # apply hints and settings for source in sources: @@ -269,8 +282,8 @@ def _write_empty_files( if resource.name not in tables_by_resources: continue for table in tables_by_resources[resource.name]: - # we only need to write empty files for the top tables - if not table.get("parent", None): + # we only need to write empty files for the root tables + if not utils.is_nested_table(table): json_extractor.write_empty_items_file(table["name"]) # collect resources that received empty materialized lists and had no items @@ -287,8 +300,8 @@ def _write_empty_files( if tables := tables_by_resources.get("resource_name"): # write empty tables for table in tables: - # we only need to write empty files for the top tables - if not table.get("parent", None): + # we only need to write empty files for the root tables + if not utils.is_nested_table(table): json_extractor.write_empty_items_file(table["name"]) else: table_name = json_extractor._get_static_table_name(resource, None) diff --git a/dlt/extract/extractors.py b/dlt/extract/extractors.py index 8a91dd7477..41d3035a9f 100644 --- a/dlt/extract/extractors.py +++ b/dlt/extract/extractors.py @@ -11,6 +11,7 @@ from dlt.common.typing import TDataItems, TDataItem, TLoaderFileFormat from dlt.common.schema import Schema, utils from dlt.common.schema.typing import ( + C_DLT_LOAD_ID, TSchemaContractDict, TSchemaEvolutionMode, TTableSchema, @@ -243,7 +244,7 @@ def _compute_and_update_table( # this is a new table so allow evolve once if schema_contract["columns"] != "evolve" and self.schema.is_new_table(table_name): computed_table["x-normalizer"] = {"evolve-columns-once": True} - existing_table = self.schema._schema_tables.get(table_name, None) + existing_table = self.schema.tables.get(table_name, None) if existing_table: # TODO: revise this. computed table should overwrite certain hints (ie. primary and merge keys) completely diff_table = utils.diff_table(self.schema.name, existing_table, computed_table) @@ -257,7 +258,10 @@ def _compute_and_update_table( # merge with schema table if diff_table: - self.schema.update_table(diff_table) + # diff table identifiers already normalized + self.schema.update_table( + diff_table, normalize_identifiers=False, from_diff=bool(existing_table) + ) # process filters if filters: @@ -410,16 +414,10 @@ def _compute_table( arrow_table["columns"] = pyarrow.py_arrow_to_table_schema_columns(item.schema) # Add load_id column if needed - dlt_load_id_col = self.naming.normalize_table_identifier("_dlt_load_id") - if ( - self._normalize_config.add_dlt_load_id - and dlt_load_id_col not in arrow_table["columns"] - ): - arrow_table["columns"][dlt_load_id_col] = { - "name": dlt_load_id_col, - "data_type": "text", - "nullable": False, - } + dlt_load_id = self.naming.normalize_identifier(C_DLT_LOAD_ID) + if self._normalize_config.add_dlt_load_id and dlt_load_id not in arrow_table["columns"]: + # will be normalized line below + arrow_table["columns"][C_DLT_LOAD_ID] = utils.dlt_load_id_column() # normalize arrow table before merging arrow_table = utils.normalize_table_identifiers(arrow_table, self.schema.naming) diff --git a/dlt/extract/hints.py b/dlt/extract/hints.py index 67a6b3e83a..037ebbddf9 100644 --- a/dlt/extract/hints.py +++ b/dlt/extract/hints.py @@ -1,8 +1,9 @@ -from copy import copy, deepcopy from typing import TypedDict, cast, Any, Optional, Dict +from typing_extensions import Self from dlt.common import logger from dlt.common.schema.typing import ( + C_DLT_ID, TColumnNames, TColumnProp, TFileFormat, @@ -19,9 +20,9 @@ ) from dlt.common.schema.utils import ( DEFAULT_WRITE_DISPOSITION, - DEFAULT_MERGE_STRATEGY, merge_column, merge_columns, + migrate_complex_types, new_column, new_table, ) @@ -40,18 +41,21 @@ from dlt.extract.validation import create_item_validator -class TResourceHints(TypedDict, total=False): +class TResourceHintsBase(TypedDict, total=False): + write_disposition: Optional[TTableHintTemplate[TWriteDispositionConfig]] + parent: Optional[TTableHintTemplate[str]] + primary_key: Optional[TTableHintTemplate[TColumnNames]] + schema_contract: Optional[TTableHintTemplate[TSchemaContract]] + table_format: Optional[TTableHintTemplate[TTableFormat]] + merge_key: Optional[TTableHintTemplate[TColumnNames]] + + +class TResourceHints(TResourceHintsBase, total=False): name: TTableHintTemplate[str] # description: TTableHintTemplate[str] - write_disposition: TTableHintTemplate[TWriteDispositionConfig] # table_sealed: Optional[bool] - parent: TTableHintTemplate[str] columns: TTableHintTemplate[TTableSchemaColumns] - primary_key: TTableHintTemplate[TColumnNames] - merge_key: TTableHintTemplate[TColumnNames] incremental: Incremental[Any] - schema_contract: TTableHintTemplate[TSchemaContract] - table_format: TTableHintTemplate[TTableFormat] file_format: TTableHintTemplate[TFileFormat] validator: ValidateItem original_columns: TTableHintTemplate[TAnySchemaColumns] @@ -84,17 +88,11 @@ def make_hints( This method accepts the same table hints arguments as `dlt.resource` decorator. """ validator, schema_contract = create_item_validator(columns, schema_contract) - clean_columns = columns - if columns is not None: - clean_columns = ensure_table_schema_columns_hint(columns) - if not callable(clean_columns): - clean_columns = clean_columns.values() # type: ignore # create a table schema template where hints can be functions taking TDataItem new_template: TResourceHints = new_table( table_name, # type: ignore parent_table_name, # type: ignore write_disposition=write_disposition, # type: ignore - columns=clean_columns, # type: ignore schema_contract=schema_contract, # type: ignore table_format=table_format, # type: ignore file_format=file_format, # type: ignore @@ -103,9 +101,10 @@ def make_hints( new_template.pop("name") if not write_disposition and "write_disposition" in new_template: new_template.pop("write_disposition") - # remember original columns + # remember original columns and set template columns if columns is not None: new_template["original_columns"] = columns + new_template["columns"] = ensure_table_schema_columns_hint(columns) # always remove resource new_template.pop("resource", None) # type: ignore if primary_key is not None: @@ -163,12 +162,16 @@ def columns(self) -> TTableHintTemplate[TTableSchemaColumns]: @property def schema_contract(self) -> TTableHintTemplate[TSchemaContract]: - return self._hints.get("schema_contract") + return None if self._hints is None else self._hints.get("schema_contract") @property def table_format(self) -> TTableHintTemplate[TTableFormat]: return None if self._hints is None else self._hints.get("table_format") + @property + def parent_table_name(self) -> TTableHintTemplate[str]: + return None if self._hints is None else self._hints.get("parent") + def compute_table_schema(self, item: TDataItem = None, meta: Any = None) -> TTableSchema: """Computes the table schema based on hints and column definitions passed during resource creation. `item` parameter is used to resolve table hints based on data. @@ -197,6 +200,7 @@ def compute_table_schema(self, item: TDataItem = None, meta: Any = None) -> TTab if k not in NATURAL_CALLABLES } # type: ignore table_schema = self._create_table_schema(resolved_template, self.name) + migrate_complex_types(table_schema, warn=True) validate_dict_ignoring_xkeys( spec=TTableSchema, doc=table_schema, @@ -218,7 +222,7 @@ def apply_hints( table_format: TTableHintTemplate[TTableFormat] = None, file_format: TTableHintTemplate[TFileFormat] = None, create_table_variant: bool = False, - ) -> None: + ) -> Self: """Creates or modifies existing table schema by setting provided hints. Accepts both static and dynamic hints based on data. If `create_table_variant` is specified, the `table_name` must be a string and hints will be used to create a separate set of hints @@ -234,6 +238,8 @@ def apply_hints( Please note that for efficient incremental loading, the resource must be aware of the Incremental by accepting it as one if its arguments and then using are to skip already loaded data. In non-aware resources, `dlt` will filter out the loaded values, however, the resource will yield all the values again. + + Returns: self for chaining """ if create_table_variant: if not isinstance(table_name, str): @@ -340,6 +346,7 @@ def apply_hints( t["incremental"] = incremental self._set_hints(t, create_table_variant) + return self def _set_hints( self, hints_template: TResourceHints, create_table_variant: bool = False @@ -399,7 +406,6 @@ def _clone_hints(hints_template: TResourceHints) -> TResourceHints: if hints_template is None: return None # creates a deep copy of dict structure without actually copying the objects - # deepcopy(hints_template) # return clone_dict_nested(hints_template) # type: ignore[type-var] @staticmethod @@ -421,7 +427,7 @@ def _merge_key(hint: TColumnProp, keys: TColumnNames, partial: TPartialTableSche partial["columns"][key][hint] = True @staticmethod - def _merge_keys(dict_: Dict[str, Any]) -> None: + def _merge_keys(dict_: TResourceHints) -> None: """Merges primary and merge keys into columns in place.""" if "primary_key" in dict_: @@ -433,67 +439,68 @@ def _merge_keys(dict_: Dict[str, Any]) -> None: def _merge_write_disposition_dict(dict_: Dict[str, Any]) -> None: """Merges write disposition dictionary into write disposition shorthand and x-hints in place.""" - if dict_["write_disposition"]["disposition"] == "merge": + write_disposition = dict_["write_disposition"]["disposition"] + if write_disposition == "merge": DltResourceHints._merge_merge_disposition_dict(dict_) # reduce merge disposition from dict to shorthand - dict_["write_disposition"] = dict_["write_disposition"]["disposition"] + dict_["write_disposition"] = write_disposition @staticmethod def _merge_merge_disposition_dict(dict_: Dict[str, Any]) -> None: """Merges merge disposition dict into x-hints in place.""" - mddict: TMergeDispositionDict = deepcopy(dict_["write_disposition"]) - if mddict is not None: - dict_["x-merge-strategy"] = mddict.get("strategy", DEFAULT_MERGE_STRATEGY) - if "boundary_timestamp" in mddict: - dict_["x-boundary-timestamp"] = mddict["boundary_timestamp"] - # add columns for `scd2` merge strategy - if dict_.get("x-merge-strategy") == "scd2": - if mddict.get("validity_column_names") is None: - from_, to = DEFAULT_VALIDITY_COLUMN_NAMES - else: - from_, to = mddict["validity_column_names"] - dict_["columns"][from_] = { - "name": from_, - "data_type": "timestamp", - "nullable": ( - True - ), # validity columns are empty when first loaded into staging table - "x-valid-from": True, - } - dict_["columns"][to] = { - "name": to, - "data_type": "timestamp", - "nullable": True, - "x-valid-to": True, - "x-active-record-timestamp": mddict.get("active_record_timestamp"), - } - # unique constraint is dropped for C_DLT_ID when used to store - # SCD2 row hash (only applies to root table) - hash_ = mddict.get("row_version_column_name", DataItemNormalizer.C_DLT_ID) - dict_["columns"][hash_] = { - "name": hash_, - "nullable": False, - "x-row-version": True, - # duplicate value in row hash column is possible in case - # of insert-delete-reinsert pattern - "unique": False, - } + md_dict: TMergeDispositionDict = dict_.pop("write_disposition") + if merge_strategy := md_dict.get("strategy"): + dict_["x-merge-strategy"] = merge_strategy + if "boundary_timestamp" in md_dict: + dict_["x-boundary-timestamp"] = md_dict["boundary_timestamp"] + # add columns for `scd2` merge strategy + if merge_strategy == "scd2": + if md_dict.get("validity_column_names") is None: + from_, to = DEFAULT_VALIDITY_COLUMN_NAMES + else: + from_, to = md_dict["validity_column_names"] + dict_["columns"][from_] = { + "name": from_, + "data_type": "timestamp", + "nullable": True, # validity columns are empty when first loaded into staging table + "x-valid-from": True, + } + dict_["columns"][to] = { + "name": to, + "data_type": "timestamp", + "nullable": True, + "x-valid-to": True, + "x-active-record-timestamp": md_dict.get("active_record_timestamp"), + } + # unique constraint is dropped for C_DLT_ID when used to store + # SCD2 row hash (only applies to root table) + hash_ = md_dict.get("row_version_column_name", C_DLT_ID) + dict_["columns"][hash_] = { + "name": hash_, + "nullable": False, + "x-row-version": True, + # duplicate value in row hash column is possible in case + # of insert-delete-reinsert pattern + "unique": False, + "row_key": False, + } @staticmethod def _create_table_schema(resource_hints: TResourceHints, resource_name: str) -> TTableSchema: - """Creates table schema from resource hints and resource name.""" - - dict_ = cast(Dict[str, Any], resource_hints) - DltResourceHints._merge_keys(dict_) - dict_["resource"] = resource_name - if "write_disposition" in dict_: - if isinstance(dict_["write_disposition"], str): - dict_["write_disposition"] = { - "disposition": dict_["write_disposition"] + """Creates table schema from resource hints and resource name. Resource hints are resolved + (do not contain callables) and will be modified in place + """ + DltResourceHints._merge_keys(resource_hints) + if "write_disposition" in resource_hints: + if isinstance(resource_hints["write_disposition"], str): + resource_hints["write_disposition"] = { + "disposition": resource_hints["write_disposition"] } # wrap in dict - DltResourceHints._merge_write_disposition_dict(dict_) - return cast(TTableSchema, dict_) + DltResourceHints._merge_write_disposition_dict(resource_hints) # type: ignore[arg-type] + dict_ = cast(TTableSchema, resource_hints) + dict_["resource"] = resource_name + return dict_ @staticmethod def validate_dynamic_hints(template: TResourceHints) -> None: diff --git a/dlt/extract/incremental/__init__.py b/dlt/extract/incremental/__init__.py index c1117370b5..343a737c07 100644 --- a/dlt/extract/incremental/__init__.py +++ b/dlt/extract/incremental/__init__.py @@ -35,7 +35,12 @@ IncrementalCursorPathMissing, IncrementalPrimaryKeyMissing, ) -from dlt.extract.incremental.typing import IncrementalColumnState, TCursorValue, LastValueFunc +from dlt.extract.incremental.typing import ( + IncrementalColumnState, + TCursorValue, + LastValueFunc, + OnCursorValueMissing, +) from dlt.extract.pipe import Pipe from dlt.extract.items import SupportsPipe, TTableHintTemplate, ItemTransform from dlt.extract.incremental.transform import ( @@ -81,7 +86,7 @@ class Incremental(ItemTransform[TDataItem], BaseConfiguration, Generic[TCursorVa >>> info = p.run(r, destination="duckdb") Args: - cursor_path: The name or a JSON path to an cursor field. Uses the same names of fields as in your JSON document, before they are normalized to store in the database. + cursor_path: The name or a JSON path to a cursor field. Uses the same names of fields as in your JSON document, before they are normalized to store in the database. initial_value: Optional value used for `last_value` when no state is available, e.g. on the first run of the pipeline. If not provided `last_value` will be `None` on the first run. last_value_func: Callable used to determine which cursor value to save in state. It is called with a list of the stored state value and all cursor vals from currently processing items. Default is `max` primary_key: Optional primary key used to deduplicate data. If not provided, a primary key defined by the resource will be used. Pass a tuple to define a compound key. Pass empty tuple to disable unique checks @@ -95,6 +100,7 @@ class Incremental(ItemTransform[TDataItem], BaseConfiguration, Generic[TCursorVa specified range of data. Currently Airflow scheduler is detected: "data_interval_start" and "data_interval_end" are taken from the context and passed Incremental class. The values passed explicitly to Incremental will be ignored. Note that if logical "end date" is present then also "end_value" will be set which means that resource state is not used and exactly this range of date will be loaded + on_cursor_value_missing: Specify what happens when the cursor_path does not exist in a record or a record has `None` at the cursor_path: raise, include, exclude """ # this is config/dataclass so declare members @@ -104,6 +110,7 @@ class Incremental(ItemTransform[TDataItem], BaseConfiguration, Generic[TCursorVa end_value: Optional[Any] = None row_order: Optional[TSortOrder] = None allow_external_schedulers: bool = False + on_cursor_value_missing: OnCursorValueMissing = "raise" # incremental acting as empty EMPTY: ClassVar["Incremental[Any]"] = None @@ -118,6 +125,7 @@ def __init__( end_value: Optional[TCursorValue] = None, row_order: Optional[TSortOrder] = None, allow_external_schedulers: bool = False, + on_cursor_value_missing: OnCursorValueMissing = "raise", ) -> None: # make sure that path is valid if cursor_path: @@ -133,6 +141,11 @@ def __init__( self._primary_key: Optional[TTableHintTemplate[TColumnNames]] = primary_key self.row_order = row_order self.allow_external_schedulers = allow_external_schedulers + if on_cursor_value_missing not in ["raise", "include", "exclude"]: + raise ValueError( + f"Unexpected argument for on_cursor_value_missing. Got {on_cursor_value_missing}" + ) + self.on_cursor_value_missing = on_cursor_value_missing self._cached_state: IncrementalColumnState = None """State dictionary cached on first access""" @@ -171,6 +184,7 @@ def _make_transforms(self) -> None: self.last_value_func, self._primary_key, set(self._cached_state["unique_hashes"]), + self.on_cursor_value_missing, ) @classmethod diff --git a/dlt/extract/incremental/exceptions.py b/dlt/extract/incremental/exceptions.py index a5f94c2974..973d3b6585 100644 --- a/dlt/extract/incremental/exceptions.py +++ b/dlt/extract/incremental/exceptions.py @@ -5,12 +5,27 @@ class IncrementalCursorPathMissing(PipeException): - def __init__(self, pipe_name: str, json_path: str, item: TDataItem, msg: str = None) -> None: + def __init__( + self, pipe_name: str, json_path: str, item: TDataItem = None, msg: str = None + ) -> None: + self.json_path = json_path + self.item = item + msg = ( + msg + or f"Cursor element with JSON path `{json_path}` was not found in extracted data item. All data items must contain this path. Use the same names of fields as in your JSON document because they can be different from the names you see in database." + ) + super().__init__(pipe_name, msg) + + +class IncrementalCursorPathHasValueNone(PipeException): + def __init__( + self, pipe_name: str, json_path: str, item: TDataItem = None, msg: str = None + ) -> None: self.json_path = json_path self.item = item msg = ( msg - or f"Cursor element with JSON path {json_path} was not found in extracted data item. All data items must contain this path. Use the same names of fields as in your JSON document - if those are different from the names you see in database." + or f"Cursor element with JSON path `{json_path}` has the value `None` in extracted data item. All data items must contain a value != None. Construct the incremental with on_cursor_value_none='include' if you want to include such rows" ) super().__init__(pipe_name, msg) diff --git a/dlt/extract/incremental/transform.py b/dlt/extract/incremental/transform.py index 0ac9fdf520..209caabc17 100644 --- a/dlt/extract/incremental/transform.py +++ b/dlt/extract/incremental/transform.py @@ -1,5 +1,5 @@ -from datetime import datetime, date # noqa: I251 -from typing import Any, Optional, Set, Tuple, List +from datetime import datetime # noqa: I251 +from typing import Any, Optional, Set, Tuple, List, Type from dlt.common.exceptions import MissingDependencyException from dlt.common.utils import digest128 @@ -11,8 +11,9 @@ IncrementalCursorInvalidCoercion, IncrementalCursorPathMissing, IncrementalPrimaryKeyMissing, + IncrementalCursorPathHasValueNone, ) -from dlt.extract.incremental.typing import TCursorValue, LastValueFunc +from dlt.extract.incremental.typing import TCursorValue, LastValueFunc, OnCursorValueMissing from dlt.extract.utils import resolve_column_value from dlt.extract.items import TTableHintTemplate from dlt.common.schema.typing import TColumnNames @@ -55,6 +56,7 @@ def __init__( last_value_func: LastValueFunc[TCursorValue], primary_key: Optional[TTableHintTemplate[TColumnNames]], unique_hashes: Set[str], + on_cursor_value_missing: OnCursorValueMissing = "raise", ) -> None: self.resource_name = resource_name self.cursor_path = cursor_path @@ -67,6 +69,7 @@ def __init__( self.primary_key = primary_key self.unique_hashes = unique_hashes self.start_unique_hashes = set(unique_hashes) + self.on_cursor_value_missing = on_cursor_value_missing # compile jsonpath self._compiled_cursor_path = compile_path(cursor_path) @@ -116,21 +119,39 @@ class JsonIncremental(IncrementalTransform): def find_cursor_value(self, row: TDataItem) -> Any: """Finds value in row at cursor defined by self.cursor_path. - Will use compiled JSONPath if present, otherwise it reverts to column search if row is dict + Will use compiled JSONPath if present. + Otherwise, reverts to field access if row is dict, Pydantic model, or of other class. """ - row_value: Any = None + key_exc: Type[Exception] = IncrementalCursorPathHasValueNone if self._compiled_cursor_path: - row_values = find_values(self._compiled_cursor_path, row) - if row_values: - row_value = row_values[0] + # ignores the other found values, e.g. when the path is $data.items[*].created_at + try: + row_value = find_values(self._compiled_cursor_path, row)[0] + except IndexError: + # empty list so raise a proper exception + row_value = None + key_exc = IncrementalCursorPathMissing else: try: - row_value = row[self.cursor_path] - except Exception: - pass - if row_value is None: - raise IncrementalCursorPathMissing(self.resource_name, self.cursor_path, row) - return row_value + try: + row_value = row[self.cursor_path] + except TypeError: + # supports Pydantic models and other classes + row_value = getattr(row, self.cursor_path) + except (KeyError, AttributeError): + # attr not found so raise a proper exception + row_value = None + key_exc = IncrementalCursorPathMissing + + # if we have a value - return it + if row_value is not None: + return row_value + + if self.on_cursor_value_missing == "raise": + # raise missing path or None value exception + raise key_exc(self.resource_name, self.cursor_path, row) + elif self.on_cursor_value_missing == "exclude": + return None def __call__( self, @@ -144,6 +165,12 @@ def __call__( return row, False, False row_value = self.find_cursor_value(row) + if row_value is None: + if self.on_cursor_value_missing == "exclude": + return None, False, False + else: + return row, False, False + last_value = self.last_value last_value_func = self.last_value_func @@ -299,6 +326,7 @@ def __call__( # TODO: Json path support. For now assume the cursor_path is a column name cursor_path = self.cursor_path + # The new max/min value try: # NOTE: datetimes are always pendulum in UTC @@ -310,11 +338,15 @@ def __call__( self.resource_name, cursor_path, tbl, - f"Column name {cursor_path} was not found in the arrow table. Not nested JSON paths" + f"Column name `{cursor_path}` was not found in the arrow table. Nested JSON paths" " are not supported for arrow tables and dataframes, the incremental cursor_path" " must be a column name.", ) from e + if tbl.schema.field(cursor_path).nullable: + tbl_without_null, tbl_with_null = self._process_null_at_cursor_path(tbl) + tbl = tbl_without_null + # If end_value is provided, filter to include table rows that are "less" than end_value if self.end_value is not None: try: @@ -396,12 +428,29 @@ def __call__( ) ) + # drop the temp unique index before concat and returning + if "_dlt_index" in tbl.schema.names: + tbl = pyarrow.remove_columns(tbl, ["_dlt_index"]) + + if self.on_cursor_value_missing == "include": + if tbl.schema.field(cursor_path).nullable: + if isinstance(tbl, pa.RecordBatch): + assert isinstance(tbl_with_null, pa.RecordBatch) + tbl = pa.Table.from_batches([tbl, tbl_with_null]) + else: + tbl = pa.concat_tables([tbl, tbl_with_null]) + if len(tbl) == 0: return None, start_out_of_range, end_out_of_range - try: - tbl = pyarrow.remove_columns(tbl, ["_dlt_index"]) - except KeyError: - pass if is_pandas: - return tbl.to_pandas(), start_out_of_range, end_out_of_range + tbl = tbl.to_pandas() return tbl, start_out_of_range, end_out_of_range + + def _process_null_at_cursor_path(self, tbl: "pa.Table") -> Tuple["pa.Table", "pa.Table"]: + mask = pa.compute.is_valid(tbl[self.cursor_path]) + rows_without_null = tbl.filter(mask) + rows_with_null = tbl.filter(pa.compute.invert(mask)) + if self.on_cursor_value_missing == "raise": + if rows_with_null.num_rows > 0: + raise IncrementalCursorPathHasValueNone(self.resource_name, self.cursor_path) + return rows_without_null, rows_with_null diff --git a/dlt/extract/incremental/typing.py b/dlt/extract/incremental/typing.py index 9cec97d34d..6829e6b370 100644 --- a/dlt/extract/incremental/typing.py +++ b/dlt/extract/incremental/typing.py @@ -1,11 +1,27 @@ -from typing import TypedDict, Optional, Any, List, TypeVar, Callable, Sequence +from typing_extensions import TypedDict +from typing import Any, Callable, List, Literal, Optional, Sequence, TypeVar + +from dlt.common.schema.typing import TColumnNames +from dlt.common.typing import TSortOrder +from dlt.extract.items import TTableHintTemplate TCursorValue = TypeVar("TCursorValue", bound=Any) LastValueFunc = Callable[[Sequence[TCursorValue]], Any] +OnCursorValueMissing = Literal["raise", "include", "exclude"] class IncrementalColumnState(TypedDict): initial_value: Optional[Any] last_value: Optional[Any] unique_hashes: List[str] + + +class IncrementalArgs(TypedDict, total=False): + cursor_path: str + initial_value: Optional[str] + last_value_func: Optional[LastValueFunc[str]] + primary_key: Optional[TTableHintTemplate[TColumnNames]] + end_value: Optional[str] + row_order: Optional[TSortOrder] + allow_external_schedulers: Optional[bool] diff --git a/dlt/extract/source.py b/dlt/extract/source.py index 88a98e14f3..6e5d30b62f 100644 --- a/dlt/extract/source.py +++ b/dlt/extract/source.py @@ -232,7 +232,7 @@ def max_table_nesting(self, value: int) -> None: @property def root_key(self) -> bool: - """Enables merging on all resources by propagating root foreign key to child tables. This option is most useful if you plan to change write disposition of a resource to disable/enable merge""" + """Enables merging on all resources by propagating root foreign key to nested tables. This option is most useful if you plan to change write disposition of a resource to disable/enable merge""" # this also check the normalizer type config = RelationalNormalizer.get_normalizer_config(self._schema).get("propagation") data_normalizer = self._schema.data_item_normalizer diff --git a/dlt/helpers/airflow_helper.py b/dlt/helpers/airflow_helper.py index 8494d3bba3..9623e65850 100644 --- a/dlt/helpers/airflow_helper.py +++ b/dlt/helpers/airflow_helper.py @@ -66,8 +66,7 @@ def __init__( buffer_max_items: int = 1000, retry_policy: Retrying = DEFAULT_RETRY_NO_RETRY, retry_pipeline_steps: Sequence[TPipelineStep] = ("load",), - fail_task_if_any_job_failed: bool = True, - abort_task_if_any_job_failed: bool = False, + abort_task_if_any_job_failed: bool = True, wipe_local_data: bool = True, save_load_info: bool = False, save_trace_info: bool = False, @@ -82,11 +81,7 @@ def __init__( The `data_folder` is available in certain Airflow deployments. In case of Composer, it is a location on the gcs bucket. `use_data_folder` is disabled and should be enabled only when needed. The operations on bucket are non-atomic and way slower than on local storage and should be avoided. - `fail_task_if_any_job_failed` will raise an exception if any of the loading jobs failed permanently and thus fail the current Airflow task. - This happens **after all dlt loading jobs executed**. See more here: https://dlthub.com/docs/running-in-production/running#failed-jobs - - `abort_task_if_any_job_failed` will abort the other dlt loading jobs and fail the Airflow task in any of the jobs failed. This may put your warehouse in - inconsistent state so the option is disabled by default. + `abort_task_if_any_job_failed` will abort the other dlt loading jobs and fail the Airflow task in any of the jobs failed. See https://dlthub.com/docs/running-in-production/running#handle-exceptions-failed-jobs-and-retry-the-pipeline. The load info and trace info can be optionally saved to the destination. See https://dlthub.com/docs/running-in-production/running#inspect-and-save-the-load-info-and-trace @@ -99,7 +94,6 @@ def __init__( buffer_max_items (int, optional): Maximum number of buffered items. Use 0 to keep dlt built-in limit. Defaults to 1000. retry_policy (_type_, optional): Tenacity retry policy. Defaults to no retry. retry_pipeline_steps (Sequence[TPipelineStep], optional): Which pipeline steps are eligible for retry. Defaults to ("load", ). - fail_task_if_any_job_failed (bool, optional): Will fail a task if any of the dlt load jobs failed. Defaults to True. wipe_local_data (bool, optional): Will wipe all the data created by pipeline, also in case of exception. Defaults to False. save_load_info (bool, optional): Will save extensive load info to the destination. Defaults to False. save_trace_info (bool, optional): Will save trace info to the destination. Defaults to False. @@ -112,7 +106,6 @@ def __init__( self.buffer_max_items = buffer_max_items self.retry_policy = retry_policy self.retry_pipeline_steps = retry_pipeline_steps - self.fail_task_if_any_job_failed = fail_task_if_any_job_failed self.abort_task_if_any_job_failed = abort_task_if_any_job_failed self.wipe_local_data = wipe_local_data self.save_load_info = save_load_info @@ -270,10 +263,11 @@ def _run( dlt.config["data_writer.buffer_max_items"] = self.buffer_max_items logger.info(f"Set data_writer.buffer_max_items to {self.buffer_max_items}") - # enable abort package if job failed - if self.abort_task_if_any_job_failed: - dlt.config["load.raise_on_failed_jobs"] = True - logger.info("Set load.abort_task_if_any_job_failed to True") + if self.abort_task_if_any_job_failed is not None: + dlt.config["load.raise_on_failed_jobs"] = self.abort_task_if_any_job_failed + logger.info( + "Set load.abort_task_if_any_job_failed to {self.abort_task_if_any_job_failed}" + ) if self.log_progress_period > 0 and task_pipeline.collector == NULL_COLLECTOR: task_pipeline.collector = log(log_period=self.log_progress_period, logger=logger.LOGGER) @@ -329,9 +323,7 @@ def log_after_attempt(retry_state: RetryCallState) -> None: table_name="_trace", loader_file_format=loader_file_format, ) - # raise on failed jobs if requested - if self.fail_task_if_any_job_failed: - load_info.raise_on_failed_jobs() + finally: # always completely wipe out pipeline folder, in case of success and failure if self.wipe_local_data: diff --git a/dlt/load/configuration.py b/dlt/load/configuration.py index 836da516e9..54b7ad798f 100644 --- a/dlt/load/configuration.py +++ b/dlt/load/configuration.py @@ -13,7 +13,7 @@ class LoaderConfiguration(PoolRunnerConfiguration): parallelism_strategy: Optional[TLoaderParallelismStrategy] = None """Which parallelism strategy to use at load time""" pool_type: TPoolType = "thread" # mostly i/o (upload) so may be thread pool - raise_on_failed_jobs: bool = False + raise_on_failed_jobs: bool = True """when True, raises on terminally failed jobs immediately""" raise_on_max_retries: int = 5 """When gt 0 will raise when job reaches raise_on_max_retries""" diff --git a/dlt/load/load.py b/dlt/load/load.py index f084c9d3d9..3b231f8fa9 100644 --- a/dlt/load/load.py +++ b/dlt/load/load.py @@ -10,7 +10,7 @@ from dlt.common.configuration import with_config, known_sections from dlt.common.configuration.accessors import config from dlt.common.pipeline import LoadInfo, LoadMetrics, SupportsPipeline, WithStepInfo -from dlt.common.schema.utils import get_top_level_table +from dlt.common.schema.utils import get_root_table from dlt.common.storages.load_storage import ( LoadPackageInfo, ParsedLoadJobFileName, @@ -167,20 +167,30 @@ def submit_job( ) logger.info(f"Will load file {file_path} with table name {job_info.table_name}") - # check write disposition + # determine which dataset to use + if is_staging_destination_job: + use_staging_dataset = isinstance( + job_client, SupportsStagingDestination + ) and job_client.should_load_data_to_staging_dataset_on_staging_destination( + job_info.table_name + ) + else: + use_staging_dataset = isinstance( + job_client, WithStagingDataset + ) and job_client.should_load_data_to_staging_dataset(job_info.table_name) + + # prepare table to be loaded load_table = active_job_client.prepare_load_table(job_info.table_name) if load_table["write_disposition"] not in ["append", "replace", "merge"]: raise LoadClientUnsupportedWriteDisposition( job_info.table_name, load_table["write_disposition"], file_path ) - job = active_job_client.create_load_job( load_table, self.load_storage.normalized_packages.storage.make_full_path(file_path), load_id, restore=restore, ) - if job is None: raise DestinationTerminalException( f"Destination could not create a job for file {file_path}. Typically the file" @@ -204,21 +214,8 @@ def submit_job( # only start a thread if this job is runnable if isinstance(job, RunnableLoadJob): - # determine which dataset to use - if is_staging_destination_job: - use_staging_dataset = isinstance( - job_client, SupportsStagingDestination - ) and job_client.should_load_data_to_staging_dataset_on_staging_destination( - load_table - ) - else: - use_staging_dataset = isinstance( - job_client, WithStagingDataset - ) and job_client.should_load_data_to_staging_dataset(load_table) - # set job vars job.set_run_vars(load_id=load_id, schema=schema, load_table=load_table) - # submit to pool self.pool.submit(Load.w_run_job, *(id(self), job, is_staging_destination_job, use_staging_dataset, schema)) # type: ignore @@ -321,7 +318,7 @@ def create_followup_jobs( starting_job_file_name = starting_job.file_name() if state == "completed" and not self.is_staging_destination_job(starting_job_file_name): client = self.destination.client(schema, self.initial_client_config) - top_job_table = get_top_level_table( + root_job_table = get_root_table( schema.tables, starting_job.job_file_info().table_name ) # if all tables of chain completed, create follow up jobs @@ -329,9 +326,13 @@ def create_followup_jobs( load_id ) if table_chain := get_completed_table_chain( - schema, all_jobs_states, top_job_table, starting_job.job_file_info().job_id() + schema, all_jobs_states, root_job_table, starting_job.job_file_info().job_id() ): table_chain_names = [table["name"] for table in table_chain] + # all tables will be prepared for main dataset + prep_table_chain = [ + client.prepare_load_table(table_name) for table_name in table_chain_names + ] table_chain_jobs = [ # we mark all jobs as completed, as by the time the followup job runs the starting job will be in this # folder too @@ -345,12 +346,12 @@ def create_followup_jobs( ] try: if follow_up_jobs := client.create_table_chain_completed_followup_jobs( - table_chain, table_chain_jobs + prep_table_chain, table_chain_jobs ): jobs = jobs + follow_up_jobs except Exception as e: raise TableChainFollowupJobCreationFailedException( - root_table_name=table_chain[0]["name"] + root_table_name=prep_table_chain[0]["name"] ) from e try: diff --git a/dlt/load/utils.py b/dlt/load/utils.py index e3a2ebcd79..6ccd32ec6a 100644 --- a/dlt/load/utils.py +++ b/dlt/load/utils.py @@ -5,8 +5,8 @@ from dlt.common.storages.load_package import LoadJobInfo, PackageStorage, TPackageJobState from dlt.common.schema.utils import ( fill_hints_from_parent_and_clone_table, - get_child_tables, - get_top_level_table, + get_nested_tables, + get_root_table, has_table_seen_data, ) from dlt.common.storages.load_storage import ParsedLoadJobFileName @@ -27,7 +27,7 @@ def get_completed_table_chain( For append and merge write disposition, tables without jobs will be included, providing they have seen data (and were created in the destination) Optionally `being_completed_job_id` can be passed that is considered to be completed before job itself moves in storage """ - # returns ordered list of tables from parent to child leaf tables + # returns ordered list of tables from parent to nested leaf tables table_chain: List[TTableSchema] = [] # allow for jobless tables for those write disposition skip_jobless_table = top_merged_table["write_disposition"] not in ( @@ -38,7 +38,7 @@ def get_completed_table_chain( # make sure all the jobs for the table chain is completed for table in map( lambda t: fill_hints_from_parent_and_clone_table(schema.tables, t), - get_child_tables(schema.tables, top_merged_table["name"]), + get_nested_tables(schema.tables, top_merged_table["name"]), ): table_jobs = PackageStorage.filter_jobs_for_table(all_jobs, table["name"]) # skip tables that never seen data @@ -67,8 +67,8 @@ def init_client( schema: Schema, new_jobs: Iterable[ParsedLoadJobFileName], expected_update: TSchemaTables, - truncate_filter: Callable[[TTableSchema], bool], - load_staging_filter: Callable[[TTableSchema], bool], + truncate_filter: Callable[[str], bool], + load_staging_filter: Callable[[str], bool], drop_tables: Optional[List[TTableSchema]] = None, truncate_tables: Optional[List[TTableSchema]] = None, ) -> TSchemaTables: @@ -81,8 +81,8 @@ def init_client( schema (Schema): The schema as in load package new_jobs (Iterable[LoadJobInfo]): List of new jobs expected_update (TSchemaTables): Schema update as in load package. Always present even if empty - truncate_filter (Callable[[TTableSchema], bool]): A filter that tells which table in destination dataset should be truncated - load_staging_filter (Callable[[TTableSchema], bool]): A filter which tell which table in the staging dataset may be loaded into + truncate_filter (Callable[[str], bool]): A filter that tells which table in destination dataset should be truncated + load_staging_filter (Callable[[str], bool]): A filter which tell which table in the staging dataset may be loaded into drop_tables (Optional[List[TTableSchema]]): List of tables to drop before initializing storage truncate_tables (Optional[List[TTableSchema]]): List of tables to truncate before initializing storage @@ -99,20 +99,21 @@ def init_client( # get all tables that actually have load jobs with data tables_with_jobs = set(job.table_name for job in new_jobs) - tables_no_data - # get tables to truncate by extending tables with jobs with all their child tables + # get tables to truncate by extending tables with jobs with all their nested tables initial_truncate_names = set(t["name"] for t in truncate_tables) if truncate_tables else set() truncate_table_names = set( _extend_tables_with_table_chain( schema, tables_with_jobs, tables_with_jobs, - lambda t: truncate_filter(t) or t["name"] in initial_truncate_names, + lambda table_name: truncate_filter(table_name) + or (table_name in initial_truncate_names), ) ) # get tables to drop drop_table_names = {table["name"] for table in drop_tables} if drop_tables else set() - + job_client.verify_schema(only_tables=tables_with_jobs | dlt_tables, new_jobs=new_jobs) applied_update = _init_dataset_and_update_schema( job_client, expected_update, @@ -175,7 +176,6 @@ def _init_dataset_and_update_schema( f"Client for {job_client.config.destination_type} will update schema to package schema" f" {staging_text}" ) - applied_update = job_client.update_stored_schema( only_tables=update_tables, expected_update=expected_update ) @@ -192,19 +192,19 @@ def _extend_tables_with_table_chain( schema: Schema, tables: Iterable[str], tables_with_jobs: Iterable[str], - include_table_filter: Callable[[TTableSchema], bool] = lambda t: True, + include_table_filter: Callable[[str], bool] = lambda t: True, ) -> Iterable[str]: """Extend 'tables` with all their children and filter out tables that do not have jobs (in `tables_with_jobs`), haven't seen data or are not included by `include_table_filter`. - Note that for top tables with replace and merge, the filter for tables that do not have jobs + Note that for root tables with replace and merge, the filter for tables that do not have jobs - Returns an unordered set of table names and their child tables + Returns an unordered set of table names and their nested tables """ result: Set[str] = set() for table_name in tables: - top_job_table = get_top_level_table(schema.tables, table_name) + top_job_table = get_root_table(schema.tables, table_name) # for replace and merge write dispositions we should include tables - # without jobs in the table chain, because child tables may need + # without jobs in the table chain, because nested tables may need # processing due to changes in the root table skip_jobless_table = top_job_table["write_disposition"] not in ( "replace", @@ -212,14 +212,14 @@ def _extend_tables_with_table_chain( ) for table in map( lambda t: fill_hints_from_parent_and_clone_table(schema.tables, t), - get_child_tables(schema.tables, top_job_table["name"]), + get_nested_tables(schema.tables, top_job_table["name"]), ): chain_table_name = table["name"] table_has_job = chain_table_name in tables_with_jobs # table that never seen data are skipped as they will not be created # also filter out tables # NOTE: this will ie. eliminate all non iceberg tables on ATHENA destination from staging (only iceberg needs that) - if not has_table_seen_data(table) or not include_table_filter(table): + if not has_table_seen_data(table) or not include_table_filter(chain_table_name): continue # if there's no job for the table and we are in append then skip if not table_has_job and skip_jobless_table: diff --git a/dlt/normalize/items_normalizers.py b/dlt/normalize/items_normalizers.py index 650d10c268..fa9a665984 100644 --- a/dlt/normalize/items_normalizers.py +++ b/dlt/normalize/items_normalizers.py @@ -8,8 +8,13 @@ from dlt.common.metrics import DataWriterMetrics from dlt.common.normalizers.json.relational import DataItemNormalizer as RelationalNormalizer from dlt.common.runtime import signals -from dlt.common.schema.typing import TSchemaEvolutionMode, TTableSchemaColumns, TSchemaContractDict -from dlt.common.schema.utils import has_table_seen_data +from dlt.common.schema.typing import ( + C_DLT_ID, + TSchemaEvolutionMode, + TTableSchemaColumns, + TSchemaContractDict, +) +from dlt.common.schema.utils import dlt_id_column, has_table_seen_data from dlt.common.storages import NormalizeStorage from dlt.common.storages.data_item_storage import DataItemStorage from dlt.common.storages.load_package import ParsedLoadJobFileName @@ -242,10 +247,9 @@ def _write_with_dlt_columns( table_update = schema.update_table( { "name": root_table_name, - "columns": { - "_dlt_id": {"name": "_dlt_id", "data_type": "text", "nullable": False} - }, - } + "columns": {C_DLT_ID: dlt_id_column()}, + }, + normalize_identifiers=True, ) table_updates = schema_update.setdefault(root_table_name, []) table_updates.append(table_update) @@ -316,7 +320,8 @@ def _fix_schema_precisions( new_cols: TTableSchemaColumns = {} for key, column in table["columns"].items(): if column.get("data_type") in ("timestamp", "time"): - if prec := column.get("precision"): + prec = column.get("precision") + if prec is not None: # apply the arrow schema precision to dlt column schema data_type = pyarrow.get_column_type_from_py_arrow(arrow_schema.field(key).type) if data_type["data_type"] in ("timestamp", "time"): diff --git a/dlt/normalize/normalize.py b/dlt/normalize/normalize.py index 3df060b141..32db5034b4 100644 --- a/dlt/normalize/normalize.py +++ b/dlt/normalize/normalize.py @@ -34,7 +34,7 @@ from dlt.normalize.configuration import NormalizeConfiguration from dlt.normalize.exceptions import NormalizeJobFailed from dlt.normalize.worker import w_normalize_files, group_worker_files, TWorkerRV -from dlt.normalize.schema import verify_normalized_schema +from dlt.normalize.validate import verify_normalized_table # normalize worker wrapping function signature @@ -185,6 +185,7 @@ def spool_files( # update normalizer specific info for table_name in table_metrics: table = schema.tables[table_name] + verify_normalized_table(schema, table, self.config.destination_capabilities) x_normalizer = table.setdefault("x-normalizer", {}) # drop evolve once for all tables that seen data x_normalizer.pop("evolve-columns-once", None) @@ -196,7 +197,6 @@ def spool_files( x_normalizer["seen-data"] = True # schema is updated, save it to schema volume if schema.is_modified: - verify_normalized_schema(schema) logger.info( f"Saving schema {schema.name} with version {schema.stored_version}:{schema.version}" ) @@ -297,12 +297,18 @@ def run(self, pool: Optional[Executor]) -> TRunMetrics: with self.collector(f"Normalize {schema.name} in {load_id}"): self.collector.update("Files", 0, len(schema_files)) self.collector.update("Items", 0) + # self.verify_package(load_id, schema, schema_files) self._step_info_start_load_id(load_id) self.spool_schema_files(load_id, schema, schema_files) # return info on still pending packages (if extractor saved something in the meantime) return TRunMetrics(False, len(self.normalize_storage.extracted_packages.list_packages())) + # def verify_package(self, load_id, schema: Schema, schema_files: Sequence[str]) -> None: + # """Verifies package schema and jobs against destination capabilities""" + # # get all tables in schema files + # table_names = set(ParsedLoadJobFileName.parse(job).table_name for job in schema_files) + def get_load_package_info(self, load_id: str) -> LoadPackageInfo: """Returns information on extracted/normalized/completed package with given load_id, all jobs and their statuses.""" try: diff --git a/dlt/normalize/schema.py b/dlt/normalize/schema.py deleted file mode 100644 index c01d184c92..0000000000 --- a/dlt/normalize/schema.py +++ /dev/null @@ -1,20 +0,0 @@ -from dlt.common.schema import Schema -from dlt.common.schema.utils import find_incomplete_columns -from dlt.common.schema.exceptions import UnboundColumnException -from dlt.common import logger - - -def verify_normalized_schema(schema: Schema) -> None: - """Verify the schema is valid for next stage after normalization. - - 1. Log warning if any incomplete nullable columns are in any data tables - 2. Raise `UnboundColumnException` on incomplete non-nullable columns (e.g. missing merge/primary key) - """ - for table_name, column, nullable in find_incomplete_columns( - schema.data_tables(seen_data_only=True) - ): - exc = UnboundColumnException(schema.name, table_name, column) - if nullable: - logger.warning(str(exc)) - else: - raise exc diff --git a/dlt/normalize/validate.py b/dlt/normalize/validate.py new file mode 100644 index 0000000000..d680b5bddd --- /dev/null +++ b/dlt/normalize/validate.py @@ -0,0 +1,43 @@ +from dlt.common.destination.capabilities import DestinationCapabilitiesContext +from dlt.common.schema import Schema +from dlt.common.schema.typing import TTableSchema +from dlt.common.schema.utils import find_incomplete_columns +from dlt.common.schema.exceptions import UnboundColumnException +from dlt.common import logger + + +def verify_normalized_table( + schema: Schema, table: TTableSchema, capabilities: DestinationCapabilitiesContext +) -> None: + """Verify `table` schema is valid for next stage after normalization. Only tables that have seen data are verified. + Verification happens before seen-data flag is set so new tables can be detected. + + 1. Log warning if any incomplete nullable columns are in any data tables + 2. Raise `UnboundColumnException` on incomplete non-nullable columns (e.g. missing merge/primary key) + 3. Log warning if table format is not supported by destination capabilities + """ + for column, nullable in find_incomplete_columns(table): + exc = UnboundColumnException(schema.name, table["name"], column) + if nullable: + logger.warning(str(exc)) + else: + raise exc + + # TODO: 3. raise if we detect name conflict for SCD2 columns + # until we track data per column we won't be able to implement this + # if resolve_merge_strategy(schema.tables, table, capabilities) == "scd2": + # for validity_column_name in get_validity_column_names(table): + # if validity_column_name in item.keys(): + # raise ColumnNameConflictException( + # schema_name, + # "Found column in data item with same name as validity column" + # f' "{validity_column_name}".', + # ) + + supported_table_formats = capabilities.supported_table_formats or [] + if "table_format" in table and table["table_format"] not in supported_table_formats: + logger.warning( + "Destination does not support the configured `table_format` value " + f"`{table['table_format']}` for table `{table['name']}`. " + "The setting will probably be ignored." + ) diff --git a/dlt/normalize/worker.py b/dlt/normalize/worker.py index b8969f64a3..53a856f7d0 100644 --- a/dlt/normalize/worker.py +++ b/dlt/normalize/worker.py @@ -73,7 +73,6 @@ def w_normalize_files( ) # TODO: capabilities.supported_*_formats can be None, it should have defaults supported_file_formats = destination_caps.supported_loader_file_formats or [] - supported_table_formats = destination_caps.supported_table_formats or [] # process all files with data items and write to buffered item storage with Container().injectable_context(destination_caps): @@ -90,21 +89,11 @@ def _get_items_normalizer( if table_name in item_normalizers: return item_normalizers[table_name] - if ( - "table_format" in table_schema - and table_schema["table_format"] not in supported_table_formats - ): - logger.warning( - "Destination does not support the configured `table_format` value " - f"`{table_schema['table_format']}` for table `{table_schema['name']}`. " - "The setting will probably be ignored." - ) - items_preferred_file_format = preferred_file_format items_supported_file_formats = supported_file_formats - if destination_caps.loader_file_format_adapter is not None: + if destination_caps.loader_file_format_selector is not None: items_preferred_file_format, items_supported_file_formats = ( - destination_caps.loader_file_format_adapter( + destination_caps.loader_file_format_selector( preferred_file_format, ( supported_file_formats.copy() @@ -233,9 +222,10 @@ def _gather_metrics_and_close( parsed_file_name.table_name ) root_tables.add(root_table_name) + root_table = stored_schema["tables"].get(root_table_name, {"name": root_table_name}) normalizer = _get_items_normalizer( parsed_file_name, - stored_schema["tables"].get(root_table_name, {"name": root_table_name}), + root_table, ) logger.debug( f"Processing extracted items in {extracted_items_file} in load_id" diff --git a/dlt/pipeline/__init__.py b/dlt/pipeline/__init__.py index 8041ca72e0..7af965e989 100644 --- a/dlt/pipeline/__init__.py +++ b/dlt/pipeline/__init__.py @@ -2,7 +2,12 @@ from typing_extensions import TypeVar from dlt.common.schema import Schema -from dlt.common.schema.typing import TColumnSchema, TWriteDispositionConfig, TSchemaContract +from dlt.common.schema.typing import ( + TColumnSchema, + TTableFormat, + TWriteDispositionConfig, + TSchemaContract, +) from dlt.common.typing import TSecretValue, Any from dlt.common.configuration import with_config @@ -219,7 +224,9 @@ def run( columns: Sequence[TColumnSchema] = None, schema: Schema = None, loader_file_format: TLoaderFileFormat = None, + table_format: TTableFormat = None, schema_contract: TSchemaContract = None, + refresh: Optional[TRefreshMode] = None, ) -> LoadInfo: """Loads the data in `data` argument into the destination specified in `destination` and dataset specified in `dataset_name`. @@ -263,6 +270,17 @@ def run( schema (Schema, optional): An explicit `Schema` object in which all table schemas will be grouped. By default `dlt` takes the schema from the source (if passed in `data` argument) or creates a default one itself. + loader_file_format (Literal["jsonl", "insert_values", "parquet"], optional). The file format the loader will use to create the load package. Not all file_formats are compatible with all destinations. Defaults to the preferred file format of the selected destination. + + table_format (Literal["delta", "iceberg"], optional). The table format used by the destination to store tables. Currently you can select table format on filesystem and Athena destinations. + + schema_contract (TSchemaContract, optional): On override for the schema contract settings, this will replace the schema contract settings for all tables in the schema. Defaults to None. + + refresh (str | TRefreshMode): Fully or partially reset sources before loading new data in this run. The following refresh modes are supported: + * `drop_sources`: Drop tables and source and resource state for all sources currently being processed in `run` or `extract` methods of the pipeline. (Note: schema history is erased) + * `drop_resources`: Drop tables and resource state for all resources being processed. Source level state is not modified. (Note: schema history is erased) + * `drop_data`: Wipe all data and resource state for all resources being processed. Schema is not modified. + Raises: PipelineStepFailed when a problem happened during `extract`, `normalize` or `load` steps. Returns: @@ -279,7 +297,9 @@ def run( columns=columns, schema=schema, loader_file_format=loader_file_format, + table_format=table_format, schema_contract=schema_contract, + refresh=refresh, ) diff --git a/dlt/pipeline/helpers.py b/dlt/pipeline/helpers.py index ce81b81433..83e1e66b29 100644 --- a/dlt/pipeline/helpers.py +++ b/dlt/pipeline/helpers.py @@ -4,15 +4,12 @@ Sequence, Iterable, Optional, - Any, - Dict, Union, TYPE_CHECKING, ) from dlt.common.jsonpath import TAnyJsonPath from dlt.common.exceptions import TerminalException -from dlt.common.schema.schema import Schema from dlt.common.schema.typing import TSimpleRegex from dlt.common.pipeline import pipeline_state as current_pipeline_state, TRefreshMode from dlt.common.storages.load_package import TLoadPackageDropTablesState @@ -139,7 +136,7 @@ def __call__(self) -> None: self.pipeline.normalize() try: - self.pipeline.load(raise_on_failed_jobs=True) + self.pipeline.load() except Exception: # Clear extracted state on failure so command can run again self.pipeline.drop_pending_packages() diff --git a/dlt/pipeline/pipeline.py b/dlt/pipeline/pipeline.py index 4f29ca4c87..fa10f5ac89 100644 --- a/dlt/pipeline/pipeline.py +++ b/dlt/pipeline/pipeline.py @@ -42,6 +42,7 @@ from dlt.common.schema.typing import ( TColumnNames, TSchemaTables, + TTableFormat, TWriteDispositionConfig, TAnySchemaColumns, TSchemaContract, @@ -68,7 +69,7 @@ DestinationCapabilitiesContext, merge_caps_file_formats, TDestination, - ALL_SUPPORTED_FILE_FORMATS, + LOADER_FILE_FORMATS, TLoaderFileFormat, ) from dlt.common.destination.reference import ( @@ -401,6 +402,7 @@ def extract( schema: Schema = None, max_parallel_items: int = ConfigValue, workers: int = ConfigValue, + table_format: TTableFormat = None, schema_contract: TSchemaContract = None, refresh: Optional[TRefreshMode] = None, ) -> ExtractInfo: @@ -419,13 +421,14 @@ def extract( for source in data_to_sources( data, self, - schema, - table_name, - parent_table_name, - write_disposition, - columns, - primary_key, - schema_contract, + schema=schema, + table_name=table_name, + parent_table_name=parent_table_name, + write_disposition=write_disposition, + columns=columns, + primary_key=primary_key, + schema_contract=schema_contract, + table_format=table_format, ): if source.exhausted: raise SourceExhausted(source.name) @@ -472,23 +475,6 @@ def _verify_destination_capabilities( set(caps.supported_loader_file_formats), ) - # verify merge strategy - for table in self.default_schema.data_tables(include_incomplete=True): - if ( - "x-merge-strategy" in table - and caps.supported_merge_strategies - and table["x-merge-strategy"] not in caps.supported_merge_strategies # type: ignore[typeddict-item] - ): - if self.destination.destination_name == "filesystem" and table["x-merge-strategy"] == "delete-insert": # type: ignore[typeddict-item] - # `filesystem` does not support `delete-insert`, but no - # error should be raised because it falls back to `append` - pass - else: - raise DestinationCapabilitiesException( - f"`{table.get('x-merge-strategy')}` merge strategy not supported" - f" for `{self.destination.destination_name}` destination." - ) - @with_runtime_trace() @with_schemas_sync @with_config_section((known_sections.NORMALIZE,)) @@ -499,7 +485,7 @@ def normalize( if is_interactive(): workers = 1 - if loader_file_format and loader_file_format not in ALL_SUPPORTED_FILE_FORMATS: + if loader_file_format and loader_file_format not in LOADER_FILE_FORMATS: raise ValueError(f"{loader_file_format} is unknown.") # check if any schema is present, if not then no data was extracted if not self.default_schema_name: @@ -550,7 +536,7 @@ def load( credentials: Any = None, *, workers: int = 20, - raise_on_failed_jobs: bool = False, + raise_on_failed_jobs: bool = ConfigValue, ) -> LoadInfo: """Loads the packages prepared by `normalize` method into the `dataset_name` at `destination`, optionally using provided `credentials`""" # set destination and default dataset if provided (this is the reason we have state sync here) @@ -610,6 +596,7 @@ def run( primary_key: TColumnNames = None, schema: Schema = None, loader_file_format: TLoaderFileFormat = None, + table_format: TTableFormat = None, schema_contract: TSchemaContract = None, refresh: Optional[TRefreshMode] = None, ) -> LoadInfo: @@ -662,6 +649,8 @@ def run( loader_file_format (Literal["jsonl", "insert_values", "parquet"], optional). The file format the loader will use to create the load package. Not all file_formats are compatible with all destinations. Defaults to the preferred file format of the selected destination. + table_format (Literal["delta", "iceberg"], optional). The table format used by the destination to store tables. Currently you can select table format on filesystem and Athena destinations. + schema_contract (TSchemaContract, optional): On override for the schema contract settings, this will replace the schema contract settings for all tables in the schema. Defaults to None. refresh (str | TRefreshMode): Fully or partially reset sources before loading new data in this run. The following refresh modes are supported: @@ -714,6 +703,7 @@ def run( columns=columns, primary_key=primary_key, schema=schema, + table_format=table_format, schema_contract=schema_contract, refresh=refresh or self.refresh, ) @@ -1213,7 +1203,9 @@ def _get_destination_client_initial_config( ) if issubclass(client_spec, DestinationClientStagingConfiguration): - spec: DestinationClientDwhConfiguration = client_spec(as_staging=as_staging) + spec: DestinationClientDwhConfiguration = client_spec( + as_staging_destination=as_staging + ) else: spec = client_spec() spec._bind_dataset_name(self.dataset_name, default_schema_name) @@ -1677,7 +1669,7 @@ def _bump_version_and_extract_state( load_package_state_update["pipeline_state"] = doc self._extract_source( extract_, - data_to_sources(data, self, schema)[0], + data_to_sources(data, self, schema=schema)[0], 1, 1, load_package_state_update=load_package_state_update, diff --git a/dlt/pipeline/trace.py b/dlt/pipeline/trace.py index 2f857e5fd5..c47926e5f4 100644 --- a/dlt/pipeline/trace.py +++ b/dlt/pipeline/trace.py @@ -3,14 +3,14 @@ import os import pickle import datetime # noqa: 251 -from typing import Any, List, NamedTuple, Optional, Protocol, Sequence +from typing import Any, List, NamedTuple, Optional, Protocol, Sequence, Union import humanize from dlt.common.pendulum import pendulum from dlt.common.configuration import is_secret_hint from dlt.common.configuration.exceptions import ContextDefaultCannotBeCreated from dlt.common.configuration.specs.config_section_context import ConfigSectionContext -from dlt.common.configuration.utils import _RESOLVED_TRACES +from dlt.common.configuration.utils import _RESOLVED_TRACES, ResolvedValueTrace from dlt.common.configuration.container import Container from dlt.common.exceptions import ExceptionTrace, ResourceNameNotAvailable from dlt.common.logger import suppress_and_warn @@ -280,8 +280,8 @@ def end_trace_step( resolved_values = map( lambda v: SerializableResolvedValueTrace( v.key, - v.value, - v.default_value, + None if is_secret_hint(v.hint) else v.value, + None if is_secret_hint(v.hint) else v.default_value, is_secret_hint(v.hint), v.sections, v.provider_name, diff --git a/dlt/pipeline/warnings.py b/dlt/pipeline/warnings.py index ac46a4eef0..a4e917f970 100644 --- a/dlt/pipeline/warnings.py +++ b/dlt/pipeline/warnings.py @@ -2,7 +2,6 @@ import warnings from dlt.common.warnings import Dlt04DeprecationWarning -from dlt.common.destination import Destination, TDestinationReferenceArg def full_refresh_argument_deprecated(caller_name: str, full_refresh: t.Optional[bool]) -> None: diff --git a/dlt/reflection/names.py b/dlt/reflection/names.py index dad7bdce92..4134e417ef 100644 --- a/dlt/reflection/names.py +++ b/dlt/reflection/names.py @@ -2,7 +2,7 @@ import dlt import dlt.destinations -from dlt import pipeline, attach, run, source, resource +from dlt import pipeline, attach, run, source, resource, transformer DLT = dlt.__name__ DESTINATIONS = dlt.destinations.__name__ @@ -11,12 +11,14 @@ RUN = run.__name__ SOURCE = source.__name__ RESOURCE = resource.__name__ +TRANSFORMER = transformer.__name__ -DETECTED_FUNCTIONS = [PIPELINE, SOURCE, RESOURCE, RUN] +DETECTED_FUNCTIONS = [PIPELINE, SOURCE, RESOURCE, RUN, TRANSFORMER] SIGNATURES = { PIPELINE: inspect.signature(pipeline), ATTACH: inspect.signature(attach), RUN: inspect.signature(run), SOURCE: inspect.signature(source), RESOURCE: inspect.signature(resource), + TRANSFORMER: inspect.signature(transformer), } diff --git a/dlt/reflection/script_visitor.py b/dlt/reflection/script_visitor.py index 52b19fe031..f4a5569ed0 100644 --- a/dlt/reflection/script_visitor.py +++ b/dlt/reflection/script_visitor.py @@ -80,6 +80,8 @@ def visit_FunctionDef(self, node: ast.FunctionDef) -> Any: self.known_sources[str(node.name)] = node elif fn == n.RESOURCE: self.known_resources[str(node.name)] = node + elif fn == n.TRANSFORMER: + self.known_resources[str(node.name)] = node super().generic_visit(node) def visit_Call(self, node: ast.Call) -> Any: diff --git a/dlt/sources/.gitignore b/dlt/sources/.gitignore new file mode 100644 index 0000000000..3b28aa3f63 --- /dev/null +++ b/dlt/sources/.gitignore @@ -0,0 +1,10 @@ +# ignore secrets, virtual environments and typical python compilation artifacts +secrets.toml +# ignore basic python artifacts +.env +**/__pycache__/ +**/*.py[cod] +**/*$py.class +# ignore duckdb +*.duckdb +*.wal \ No newline at end of file diff --git a/dlt/sources/__init__.py b/dlt/sources/__init__.py index 465467db67..dcfc281160 100644 --- a/dlt/sources/__init__.py +++ b/dlt/sources/__init__.py @@ -3,7 +3,6 @@ from dlt.extract import DltSource, DltResource, Incremental as incremental from . import credentials from . import config -from . import filesystem __all__ = [ "DltSource", @@ -13,5 +12,4 @@ "incremental", "credentials", "config", - "filesystem", ] diff --git a/dlt/sources/filesystem.py b/dlt/sources/filesystem.py deleted file mode 100644 index 23fb6a9cf3..0000000000 --- a/dlt/sources/filesystem.py +++ /dev/null @@ -1,8 +0,0 @@ -from dlt.common.storages.fsspec_filesystem import ( - FileItem, - FileItemDict, - fsspec_filesystem, - glob_files, -) - -__all__ = ["FileItem", "FileItemDict", "fsspec_filesystem", "glob_files"] diff --git a/dlt/sources/filesystem/__init__.py b/dlt/sources/filesystem/__init__.py new file mode 100644 index 0000000000..80dabe7e66 --- /dev/null +++ b/dlt/sources/filesystem/__init__.py @@ -0,0 +1,102 @@ +"""Reads files in s3, gs or azure buckets using fsspec and provides convenience resources for chunked reading of various file formats""" +from typing import Iterator, List, Optional, Tuple, Union + +import dlt +from dlt.common.storages.fsspec_filesystem import ( + FileItem, + FileItemDict, + fsspec_filesystem, + glob_files, +) +from dlt.sources import DltResource +from dlt.sources.credentials import FileSystemCredentials + +from dlt.sources.filesystem.helpers import ( + AbstractFileSystem, + FilesystemConfigurationResource, +) +from dlt.sources.filesystem.readers import ( + ReadersSource, + _read_csv, + _read_csv_duckdb, + _read_jsonl, + _read_parquet, +) +from dlt.sources.filesystem.settings import DEFAULT_CHUNK_SIZE + + +@dlt.source(_impl_cls=ReadersSource, spec=FilesystemConfigurationResource) +def readers( + bucket_url: str = dlt.secrets.value, + credentials: Union[FileSystemCredentials, AbstractFileSystem] = dlt.secrets.value, + file_glob: Optional[str] = "*", +) -> Tuple[DltResource, ...]: + """This source provides a few resources that are chunked file readers. Readers can be further parametrized before use + read_csv(chunksize, **pandas_kwargs) + read_jsonl(chunksize) + read_parquet(chunksize) + + Args: + bucket_url (str): The url to the bucket. + credentials (FileSystemCredentials | AbstractFilesystem): The credentials to the filesystem of fsspec `AbstractFilesystem` instance. + file_glob (str, optional): The filter to apply to the files in glob format. by default lists all files in bucket_url non-recursively + + """ + return ( + filesystem(bucket_url, credentials, file_glob=file_glob) + | dlt.transformer(name="read_csv")(_read_csv), + filesystem(bucket_url, credentials, file_glob=file_glob) + | dlt.transformer(name="read_jsonl")(_read_jsonl), + filesystem(bucket_url, credentials, file_glob=file_glob) + | dlt.transformer(name="read_parquet")(_read_parquet), + filesystem(bucket_url, credentials, file_glob=file_glob) + | dlt.transformer(name="read_csv_duckdb")(_read_csv_duckdb), + ) + + +@dlt.resource(primary_key="file_url", spec=FilesystemConfigurationResource, standalone=True) +def filesystem( + bucket_url: str = dlt.secrets.value, + credentials: Union[FileSystemCredentials, AbstractFileSystem] = dlt.secrets.value, + file_glob: Optional[str] = "*", + files_per_page: int = DEFAULT_CHUNK_SIZE, + extract_content: bool = False, +) -> Iterator[List[FileItem]]: + """This resource lists files in `bucket_url` using `file_glob` pattern. The files are yielded as FileItem which also + provide methods to open and read file data. It should be combined with transformers that further process (ie. load files) + + Args: + bucket_url (str): The url to the bucket. + credentials (FileSystemCredentials | AbstractFilesystem): The credentials to the filesystem of fsspec `AbstractFilesystem` instance. + file_glob (str, optional): The filter to apply to the files in glob format. by default lists all files in bucket_url non-recursively + files_per_page (int, optional): The number of files to process at once, defaults to 100. + extract_content (bool, optional): If true, the content of the file will be extracted if + false it will return a fsspec file, defaults to False. + + Returns: + Iterator[List[FileItem]]: The list of files. + """ + if isinstance(credentials, AbstractFileSystem): + fs_client = credentials + else: + fs_client = fsspec_filesystem(bucket_url, credentials)[0] + + files_chunk: List[FileItem] = [] + for file_model in glob_files(fs_client, bucket_url, file_glob): + file_dict = FileItemDict(file_model, credentials) + if extract_content: + file_dict["file_content"] = file_dict.read_bytes() + files_chunk.append(file_dict) # type: ignore + + # wait for the chunk to be full + if len(files_chunk) >= files_per_page: + yield files_chunk + files_chunk = [] + if files_chunk: + yield files_chunk + + +read_csv = dlt.transformer(standalone=True)(_read_csv) +read_jsonl = dlt.transformer(standalone=True)(_read_jsonl) +read_parquet = dlt.transformer(standalone=True)(_read_parquet) +read_csv_duckdb = dlt.transformer(standalone=True)(_read_csv_duckdb) diff --git a/dlt/sources/filesystem/helpers.py b/dlt/sources/filesystem/helpers.py new file mode 100644 index 0000000000..ebfb491197 --- /dev/null +++ b/dlt/sources/filesystem/helpers.py @@ -0,0 +1,98 @@ +"""Helpers for the filesystem resource.""" +from typing import Any, Dict, Iterable, List, Optional, Type, Union +from fsspec import AbstractFileSystem + +import dlt +from dlt.common.configuration import resolve_type +from dlt.common.typing import TDataItem + +from dlt.sources import DltResource +from dlt.sources.filesystem import fsspec_filesystem +from dlt.sources.config import configspec, with_config +from dlt.sources.credentials import ( + CredentialsConfiguration, + FilesystemConfiguration, + FileSystemCredentials, +) + +from .settings import DEFAULT_CHUNK_SIZE + + +@configspec +class FilesystemConfigurationResource(FilesystemConfiguration): + credentials: Union[FileSystemCredentials, AbstractFileSystem] = None + file_glob: Optional[str] = "*" + files_per_page: int = DEFAULT_CHUNK_SIZE + extract_content: bool = False + + @resolve_type("credentials") + def resolve_credentials_type(self) -> Type[CredentialsConfiguration]: + # use known credentials or empty credentials for unknown protocol + return Union[self.PROTOCOL_CREDENTIALS.get(self.protocol) or Optional[CredentialsConfiguration], AbstractFileSystem] # type: ignore[return-value] + + +def fsspec_from_resource(filesystem_instance: DltResource) -> AbstractFileSystem: + """Extract authorized fsspec client from a filesystem resource""" + + @with_config( + spec=FilesystemConfiguration, + sections=("sources", filesystem_instance.section, filesystem_instance.name), + ) + def _get_fsspec( + bucket_url: str, credentials: Optional[FileSystemCredentials] + ) -> AbstractFileSystem: + return fsspec_filesystem(bucket_url, credentials)[0] + + return _get_fsspec( + filesystem_instance.explicit_args.get("bucket_url", dlt.config.value), + filesystem_instance.explicit_args.get("credentials", dlt.secrets.value), + ) + + +def add_columns(columns: List[str], rows: List[List[Any]]) -> List[Dict[str, Any]]: + """Adds column names to the given rows. + + Args: + columns (List[str]): The column names. + rows (List[List[Any]]): The rows. + + Returns: + List[Dict[str, Any]]: The rows with column names. + """ + result = [] + for row in rows: + result.append(dict(zip(columns, row))) + + return result + + +def fetch_arrow(file_data, chunk_size: int) -> Iterable[TDataItem]: # type: ignore + """Fetches data from the given CSV file. + + Args: + file_data (DuckDBPyRelation): The CSV file data. + chunk_size (int): The number of rows to read at once. + + Yields: + Iterable[TDataItem]: Data items, read from the given CSV file. + """ + batcher = file_data.fetch_arrow_reader(batch_size=chunk_size) + yield from batcher + + +def fetch_json(file_data, chunk_size: int) -> List[Dict[str, Any]]: # type: ignore + """Fetches data from the given CSV file. + + Args: + file_data (DuckDBPyRelation): The CSV file data. + chunk_size (int): The number of rows to read at once. + + Yields: + Iterable[TDataItem]: Data items, read from the given CSV file. + """ + while True: + batch = file_data.fetchmany(chunk_size) + if not batch: + break + + yield add_columns(file_data.columns, batch) diff --git a/dlt/sources/filesystem/readers.py b/dlt/sources/filesystem/readers.py new file mode 100644 index 0000000000..405948b515 --- /dev/null +++ b/dlt/sources/filesystem/readers.py @@ -0,0 +1,129 @@ +from typing import TYPE_CHECKING, Any, Dict, Iterator, Optional + +from dlt.common import json +from dlt.common.typing import copy_sig_any +from dlt.sources import TDataItems, DltResource, DltSource +from dlt.sources.filesystem import FileItemDict + +from .helpers import fetch_arrow, fetch_json + + +def _read_csv( + items: Iterator[FileItemDict], chunksize: int = 10000, **pandas_kwargs: Any +) -> Iterator[TDataItems]: + """Reads csv file with Pandas chunk by chunk. + + Args: + chunksize (int): Number of records to read in one chunk + **pandas_kwargs: Additional keyword arguments passed to Pandas.read_csv + Returns: + TDataItem: The file content + """ + import pandas as pd + + # apply defaults to pandas kwargs + kwargs = {**{"header": "infer", "chunksize": chunksize}, **pandas_kwargs} + + for file_obj in items: + # Here we use pandas chunksize to read the file in chunks and avoid loading the whole file + # in memory. + with file_obj.open() as file: + for df in pd.read_csv(file, **kwargs): + yield df.to_dict(orient="records") + + +def _read_jsonl(items: Iterator[FileItemDict], chunksize: int = 1000) -> Iterator[TDataItems]: + """Reads jsonl file content and extract the data. + + Args: + chunksize (int, optional): The number of JSON lines to load and yield at once, defaults to 1000 + + Returns: + TDataItem: The file content + """ + for file_obj in items: + with file_obj.open() as f: + lines_chunk = [] + for line in f: + lines_chunk.append(json.loadb(line)) + if len(lines_chunk) >= chunksize: + yield lines_chunk + lines_chunk = [] + if lines_chunk: + yield lines_chunk + + +def _read_parquet( + items: Iterator[FileItemDict], + chunksize: int = 10, +) -> Iterator[TDataItems]: + """Reads parquet file content and extract the data. + + Args: + chunksize (int, optional): The number of files to process at once, defaults to 10. + + Returns: + TDataItem: The file content + """ + from pyarrow import parquet as pq + + for file_obj in items: + with file_obj.open() as f: + parquet_file = pq.ParquetFile(f) + for rows in parquet_file.iter_batches(batch_size=chunksize): + yield rows.to_pylist() + + +def _read_csv_duckdb( + items: Iterator[FileItemDict], + chunk_size: Optional[int] = 5000, + use_pyarrow: bool = False, + **duckdb_kwargs: Any +) -> Iterator[TDataItems]: + """A resource to extract data from the given CSV files. + + Uses DuckDB engine to import and cast CSV data. + + Args: + items (Iterator[FileItemDict]): CSV files to read. + chunk_size (Optional[int]): + The number of rows to read at once. Defaults to 5000. + use_pyarrow (bool): + Whether to use `pyarrow` to read the data and designate + data schema. If set to False (by default), JSON is used. + duckdb_kwargs (Dict): + Additional keyword arguments to pass to the `read_csv()`. + + Returns: + Iterable[TDataItem]: Data items, read from the given CSV files. + """ + import duckdb + + helper = fetch_arrow if use_pyarrow else fetch_json + + for item in items: + with item.open() as f: + file_data = duckdb.from_csv_auto(f, **duckdb_kwargs) # type: ignore + + yield from helper(file_data, chunk_size) + + +if TYPE_CHECKING: + + class ReadersSource(DltSource): + """This is a typing stub that provides docstrings and signatures to the resources in `readers" source""" + + @copy_sig_any(_read_csv) + def read_csv(self) -> DltResource: ... + + @copy_sig_any(_read_jsonl) + def read_jsonl(self) -> DltResource: ... + + @copy_sig_any(_read_parquet) + def read_parquet(self) -> DltResource: ... + + @copy_sig_any(_read_csv_duckdb) + def read_csv_duckdb(self) -> DltResource: ... + +else: + ReadersSource = DltSource diff --git a/dlt/sources/filesystem/settings.py b/dlt/sources/filesystem/settings.py new file mode 100644 index 0000000000..33fcb55b5f --- /dev/null +++ b/dlt/sources/filesystem/settings.py @@ -0,0 +1 @@ +DEFAULT_CHUNK_SIZE = 100 diff --git a/dlt/sources/filesystem_pipeline.py b/dlt/sources/filesystem_pipeline.py new file mode 100644 index 0000000000..db570487ef --- /dev/null +++ b/dlt/sources/filesystem_pipeline.py @@ -0,0 +1,196 @@ +# flake8: noqa +import os +from typing import Iterator + +import dlt +from dlt.sources import TDataItems +from dlt.sources.filesystem import FileItemDict, filesystem, readers, read_csv + + +# where the test files are, those examples work with (url) +TESTS_BUCKET_URL = "samples" + + +def stream_and_merge_csv() -> None: + """Demonstrates how to scan folder with csv files, load them in chunk and merge on date column with the previous load""" + pipeline = dlt.pipeline( + pipeline_name="standard_filesystem_csv", + destination="duckdb", + dataset_name="met_data", + ) + # met_data contains 3 columns, where "date" column contain a date on which we want to merge + # load all csvs in A801 + met_files = readers(bucket_url=TESTS_BUCKET_URL, file_glob="met_csv/A801/*.csv").read_csv() + # tell dlt to merge on date + met_files.apply_hints(write_disposition="merge", merge_key="date") + # NOTE: we load to met_csv table + load_info = pipeline.run(met_files.with_name("met_csv")) + print(load_info) + print(pipeline.last_trace.last_normalize_info) + + # now let's simulate loading on next day. not only current data appears but also updated record for the previous day are present + # all the records for previous day will be replaced with new records + met_files = readers(bucket_url=TESTS_BUCKET_URL, file_glob="met_csv/A801/*.csv").read_csv() + met_files.apply_hints(write_disposition="merge", merge_key="date") + load_info = pipeline.run(met_files.with_name("met_csv")) + + # you can also do dlt pipeline standard_filesystem_csv show to confirm that all A801 were replaced with A803 records for overlapping day + print(load_info) + print(pipeline.last_trace.last_normalize_info) + + +def read_csv_with_duckdb() -> None: + pipeline = dlt.pipeline( + pipeline_name="standard_filesystem", + destination="duckdb", + dataset_name="met_data_duckdb", + ) + + # load all the CSV data, excluding headers + met_files = readers( + bucket_url=TESTS_BUCKET_URL, file_glob="met_csv/A801/*.csv" + ).read_csv_duckdb(chunk_size=1000, header=True) + + load_info = pipeline.run(met_files) + + print(load_info) + print(pipeline.last_trace.last_normalize_info) + + +def read_csv_duckdb_compressed() -> None: + pipeline = dlt.pipeline( + pipeline_name="standard_filesystem", + destination="duckdb", + dataset_name="taxi_data", + full_refresh=True, + ) + + met_files = readers( + bucket_url=TESTS_BUCKET_URL, + file_glob="gzip/*", + ).read_csv_duckdb() + + load_info = pipeline.run(met_files) + print(load_info) + print(pipeline.last_trace.last_normalize_info) + + +def read_parquet_and_jsonl_chunked() -> None: + pipeline = dlt.pipeline( + pipeline_name="standard_filesystem", + destination="duckdb", + dataset_name="teams_data", + ) + # When using the readers resource, you can specify a filter to select only the files you + # want to load including a glob pattern. If you use a recursive glob pattern, the filenames + # will include the path to the file inside the bucket_url. + + # JSONL reading (in large chunks!) + jsonl_reader = readers(TESTS_BUCKET_URL, file_glob="**/*.jsonl").read_jsonl(chunksize=10000) + # PARQUET reading + parquet_reader = readers(TESTS_BUCKET_URL, file_glob="**/*.parquet").read_parquet() + # load both folders together to specified tables + load_info = pipeline.run( + [ + jsonl_reader.with_name("jsonl_team_data"), + parquet_reader.with_name("parquet_team_data"), + ] + ) + print(load_info) + print(pipeline.last_trace.last_normalize_info) + + +def read_custom_file_type_excel() -> None: + """Here we create an extract pipeline using filesystem resource and read_csv transformer""" + + # instantiate filesystem directly to get list of files (FileItems) and then use read_excel transformer to get + # content of excel via pandas + + @dlt.transformer(standalone=True) + def read_excel(items: Iterator[FileItemDict], sheet_name: str) -> Iterator[TDataItems]: + import pandas as pd + + for file_obj in items: + with file_obj.open() as file: + yield pd.read_excel(file, sheet_name).to_dict(orient="records") + + freshman_xls = filesystem( + bucket_url=TESTS_BUCKET_URL, file_glob="../custom/freshman_kgs.xlsx" + ) | read_excel("freshman_table") + + load_info = dlt.run( + freshman_xls.with_name("freshman"), + destination="duckdb", + dataset_name="freshman_data", + ) + print(load_info) + + +def copy_files_resource(local_folder: str) -> None: + """Demonstrates how to copy files locally by adding a step to filesystem resource and the to load the download listing to db""" + pipeline = dlt.pipeline( + pipeline_name="standard_filesystem_copy", + destination="duckdb", + dataset_name="standard_filesystem_data", + ) + + # a step that copies files into test storage + def _copy(item: FileItemDict) -> FileItemDict: + # instantiate fsspec and copy file + dest_file = os.path.join(local_folder, item["relative_path"]) + # create dest folder + os.makedirs(os.path.dirname(dest_file), exist_ok=True) + # download file + item.fsspec.download(item["file_url"], dest_file) + # return file item unchanged + return item + + # use recursive glob pattern and add file copy step + downloader = filesystem(TESTS_BUCKET_URL, file_glob="**").add_map(_copy) + + # NOTE: you do not need to load any data to execute extract, below we obtain + # a list of files in a bucket and also copy them locally + # listing = list(downloader) + # print(listing) + + # download to table "listing" + # downloader = filesystem(TESTS_BUCKET_URL, file_glob="**").add_map(_copy) + load_info = pipeline.run(downloader.with_name("listing"), write_disposition="replace") + # pretty print the information on data that was loaded + print(load_info) + print(pipeline.last_trace.last_normalize_info) + + +def read_files_incrementally_mtime() -> None: + pipeline = dlt.pipeline( + pipeline_name="standard_filesystem_incremental", + destination="duckdb", + dataset_name="file_tracker", + ) + + # here we modify filesystem resource so it will track only new csv files + # such resource may be then combined with transformer doing further processing + new_files = filesystem(bucket_url=TESTS_BUCKET_URL, file_glob="csv/*") + # add incremental on modification time + new_files.apply_hints(incremental=dlt.sources.incremental("modification_date")) + load_info = pipeline.run((new_files | read_csv()).with_name("csv_files")) + print(load_info) + print(pipeline.last_trace.last_normalize_info) + + # load again - no new files! + new_files = filesystem(bucket_url=TESTS_BUCKET_URL, file_glob="csv/*") + # add incremental on modification time + new_files.apply_hints(incremental=dlt.sources.incremental("modification_date")) + load_info = pipeline.run((new_files | read_csv()).with_name("csv_files")) + print(load_info) + print(pipeline.last_trace.last_normalize_info) + + +if __name__ == "__main__": + copy_files_resource("_storage") + stream_and_merge_csv() + read_parquet_and_jsonl_chunked() + read_custom_file_type_excel() + read_files_incrementally_mtime() + read_csv_with_duckdb() + read_csv_duckdb_compressed() diff --git a/dlt/sources/helpers/rest_client/auth.py b/dlt/sources/helpers/rest_client/auth.py index d2ca1c1ca6..31c52527da 100644 --- a/dlt/sources/helpers/rest_client/auth.py +++ b/dlt/sources/helpers/rest_client/auth.py @@ -1,6 +1,5 @@ import math import dataclasses -from abc import abstractmethod from base64 import b64encode from typing import ( TYPE_CHECKING, @@ -157,7 +156,7 @@ class OAuth2ClientCredentials(OAuth2AuthBase): def __init__( self, - access_token_url: TSecretStrValue, + access_token_url: str, client_id: TSecretStrValue, client_secret: TSecretStrValue, access_token_request_data: Dict[str, Any] = None, diff --git a/dlt/sources/pipeline_templates/.dlt/config.toml b/dlt/sources/pipeline_templates/.dlt/config.toml new file mode 100644 index 0000000000..634427baa6 --- /dev/null +++ b/dlt/sources/pipeline_templates/.dlt/config.toml @@ -0,0 +1,5 @@ +# put your configuration values here + +[runtime] +log_level="WARNING" # the system log level of dlt +# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry diff --git a/dlt/sources/pipeline_templates/.gitignore b/dlt/sources/pipeline_templates/.gitignore new file mode 100644 index 0000000000..3b28aa3f63 --- /dev/null +++ b/dlt/sources/pipeline_templates/.gitignore @@ -0,0 +1,10 @@ +# ignore secrets, virtual environments and typical python compilation artifacts +secrets.toml +# ignore basic python artifacts +.env +**/__pycache__/ +**/*.py[cod] +**/*$py.class +# ignore duckdb +*.duckdb +*.wal \ No newline at end of file diff --git a/dlt/sources/pipeline_templates/__init__.py b/dlt/sources/pipeline_templates/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/dlt/sources/pipeline_templates/arrow_pipeline.py b/dlt/sources/pipeline_templates/arrow_pipeline.py new file mode 100644 index 0000000000..92ed0664b9 --- /dev/null +++ b/dlt/sources/pipeline_templates/arrow_pipeline.py @@ -0,0 +1,60 @@ +"""The Arrow Pipeline Template will show how to load and transform arrow tables.""" + +# mypy: disable-error-code="no-untyped-def,arg-type" + +import dlt +import time +import pyarrow as pa + + +def create_example_arrow_table() -> pa.Table: + return pa.Table.from_pylist([{"name": "tom", "age": 25}, {"name": "angela", "age": 23}]) + + +@dlt.resource(write_disposition="append", name="people") +def resource(): + """One resource function will materialize as a table in the destination, wie yield example data here""" + yield create_example_arrow_table() + + +def add_updated_at(item: pa.Table): + """Map function to add an updated at column to your incoming data.""" + column_count = len(item.columns) + # you will receive and return and arrow table + return item.set_column(column_count, "updated_at", [[time.time()] * item.num_rows]) + + +# apply tranformer to resource +resource.add_map(add_updated_at) + + +@dlt.source +def source(): + """A source function groups all resources into one schema.""" + # return resources + return resource() + + +def load_arrow_tables() -> None: + # specify the pipeline name, destination and dataset name when configuring pipeline, + # otherwise the defaults will be used that are derived from the current script name + pipeline = dlt.pipeline( + pipeline_name="arrow", + destination="duckdb", + dataset_name="arrow_data", + ) + + data = list(source().people) + + # print the data yielded from resource without loading it + print(data) # noqa: T201 + + # run the pipeline with your parameters + load_info = pipeline.run(source()) + + # pretty print the information on data that was loaded + print(load_info) # noqa: T201 + + +if __name__ == "__main__": + load_arrow_tables() diff --git a/dlt/sources/pipeline_templates/dataframe_pipeline.py b/dlt/sources/pipeline_templates/dataframe_pipeline.py new file mode 100644 index 0000000000..f9f7746098 --- /dev/null +++ b/dlt/sources/pipeline_templates/dataframe_pipeline.py @@ -0,0 +1,62 @@ +"""The DataFrame Pipeline Template will show how to load and transform pandas dataframes.""" + +# mypy: disable-error-code="no-untyped-def,arg-type" + +import dlt +import time +import pandas as pd + + +def create_example_dataframe() -> pd.DataFrame: + return pd.DataFrame({"name": ["tom", "angela"], "age": [25, 23]}, columns=["name", "age"]) + + +@dlt.resource(write_disposition="append", name="people") +def resource(): + """One resource function will materialize as a table in the destination, wie yield example data here""" + yield create_example_dataframe() + + +def add_updated_at(item: pd.DataFrame): + """Map function to add an updated at column to your incoming data.""" + column_count = len(item.columns) + # you will receive and return and arrow table + item.insert(column_count, "updated_at", [time.time()] * 2, True) + return item + + +# apply tranformer to resource +resource.add_map(add_updated_at) + + +@dlt.source +def source(): + """A source function groups all resources into one schema.""" + + # return resources + return resource() + + +def load_dataframe() -> None: + # specify the pipeline name, destination and dataset name when configuring pipeline, + # otherwise the defaults will be used that are derived from the current script name + pipeline = dlt.pipeline( + pipeline_name="dataframe", + destination="duckdb", + dataset_name="dataframe_data", + ) + + data = list(source().people) + + # print the data yielded from resource without loading it + print(data) # noqa: T201 + + # run the pipeline with your parameters + load_info = pipeline.run(source()) + + # pretty print the information on data that was loaded + print(load_info) # noqa: T201 + + +if __name__ == "__main__": + load_dataframe() diff --git a/dlt/sources/pipeline_templates/debug_pipeline.py b/dlt/sources/pipeline_templates/debug_pipeline.py new file mode 100644 index 0000000000..7263962af2 --- /dev/null +++ b/dlt/sources/pipeline_templates/debug_pipeline.py @@ -0,0 +1,64 @@ +"""The Debug Pipeline Template will load a column with each datatype to your destination.""" + +# mypy: disable-error-code="no-untyped-def,arg-type" + +import dlt + +from dlt.common import Decimal + + +@dlt.resource(write_disposition="append", name="all_datatypes") +def resource(): + """this is the test data for loading validation, delete it once you yield actual data""" + yield [ + { + "col1": 989127831, + "col2": 898912.821982, + "col3": True, + "col4": "2022-05-23T13:26:45.176451+00:00", + "col5": "string data \n \r 🦆", + "col6": Decimal("2323.34"), + "col7": b"binary data \n \r ", + "col8": 2**56 + 92093890840, + "col9": { + "json": [1, 2, 3, "a"], + "link": ( + "?commen\ntU\nrn=urn%3Ali%3Acomment%3A%28acti\012 \6" + " \\vity%3A69'08444473\n\n551163392%2C6n \r 9085" + ), + }, + "col10": "2023-02-27", + "col11": "13:26:45.176451", + } + ] + + +@dlt.source +def source(): + """A source function groups all resources into one schema.""" + return resource() + + +def load_all_datatypes() -> None: + # specify the pipeline name, destination and dataset name when configuring pipeline, + # otherwise the defaults will be used that are derived from the current script name + pipeline = dlt.pipeline( + pipeline_name="debug", + destination="duckdb", + dataset_name="debug_data", + ) + + data = list(source().all_datatypes) + + # print the data yielded from resource without loading it + print(data) # noqa: T201 + + # run the pipeline with your parameters + load_info = pipeline.run(source()) + + # pretty print the information on data that was loaded + print(load_info) # noqa: T201 + + +if __name__ == "__main__": + load_all_datatypes() diff --git a/dlt/sources/pipeline_templates/default_pipeline.py b/dlt/sources/pipeline_templates/default_pipeline.py new file mode 100644 index 0000000000..9fa03f9ce5 --- /dev/null +++ b/dlt/sources/pipeline_templates/default_pipeline.py @@ -0,0 +1,51 @@ +"""The Default Pipeline Template provides a simple starting point for your dlt pipeline""" + +# mypy: disable-error-code="no-untyped-def,arg-type" + +import dlt +from dlt.common import Decimal + + +@dlt.resource(name="customers", primary_key="id") +def customers(): + """Load customer data from a simple python list.""" + yield [ + {"id": 1, "name": "simon", "city": "berlin"}, + {"id": 2, "name": "violet", "city": "london"}, + {"id": 3, "name": "tammo", "city": "new york"}, + ] + + +@dlt.resource(name="inventory", primary_key="id") +def inventory(): + """Load inventory data from a simple python list.""" + yield [ + {"id": 1, "name": "apple", "price": Decimal("1.50")}, + {"id": 2, "name": "banana", "price": Decimal("1.70")}, + {"id": 3, "name": "pear", "price": Decimal("2.50")}, + ] + + +@dlt.source(name="my_fruitshop") +def source(): + """A source function groups all resources into one schema.""" + return customers(), inventory() + + +def load_stuff() -> None: + # specify the pipeline name, destination and dataset name when configuring pipeline, + # otherwise the defaults will be used that are derived from the current script name + p = dlt.pipeline( + pipeline_name="fruitshop", + destination="duckdb", + dataset_name="fruitshop_data", + ) + + load_info = p.run(source()) + + # pretty print the information on data that was loaded + print(load_info) # noqa: T201 + + +if __name__ == "__main__": + load_stuff() diff --git a/dlt/sources/pipeline_templates/intro_pipeline.py b/dlt/sources/pipeline_templates/intro_pipeline.py new file mode 100644 index 0000000000..a4de18daba --- /dev/null +++ b/dlt/sources/pipeline_templates/intro_pipeline.py @@ -0,0 +1,82 @@ +"""The Intro Pipeline Template contains the example from the docs intro page""" + +# mypy: disable-error-code="no-untyped-def,arg-type" + +import pandas as pd +import sqlalchemy as sa + +import dlt +from dlt.sources.helpers import requests + + +def load_api_data() -> None: + """Load data from the chess api, for more complex examples use our rest_api source""" + + # Create a dlt pipeline that will load + # chess player data to the DuckDB destination + pipeline = dlt.pipeline( + pipeline_name="chess_pipeline", destination="duckdb", dataset_name="player_data" + ) + # Grab some player data from Chess.com API + data = [] + for player in ["magnuscarlsen", "rpragchess"]: + response = requests.get(f"https://api.chess.com/pub/player/{player}") + response.raise_for_status() + data.append(response.json()) + + # Extract, normalize, and load the data + load_info = pipeline.run(data, table_name="player") + print(load_info) # noqa: T201 + + +def load_pandas_data() -> None: + """Load data from a public csv via pandas""" + + owid_disasters_csv = ( + "https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/" + "Natural%20disasters%20from%201900%20to%202019%20-%20EMDAT%20(2020)/" + "Natural%20disasters%20from%201900%20to%202019%20-%20EMDAT%20(2020).csv" + ) + df = pd.read_csv(owid_disasters_csv) + data = df.to_dict(orient="records") + + pipeline = dlt.pipeline( + pipeline_name="from_csv", + destination="duckdb", + dataset_name="mydata", + ) + load_info = pipeline.run(data, table_name="natural_disasters") + + print(load_info) # noqa: T201 + + +def load_sql_data() -> None: + """Load data from a sql database with sqlalchemy, for more complex examples use our sql_database source""" + + # Use any SQL database supported by SQLAlchemy, below we use a public + # MySQL instance to get data. + # NOTE: you'll need to install pymysql with `pip install pymysql` + # NOTE: loading data from public mysql instance may take several seconds + engine = sa.create_engine("mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam") + + with engine.connect() as conn: + # Select genome table, stream data in batches of 100 elements + query = "SELECT * FROM genome LIMIT 1000" + rows = conn.execution_options(yield_per=100).exec_driver_sql(query) + + pipeline = dlt.pipeline( + pipeline_name="from_database", + destination="duckdb", + dataset_name="genome_data", + ) + + # Convert the rows into dictionaries on the fly with a map function + load_info = pipeline.run(map(lambda row: dict(row._mapping), rows), table_name="genome") + + print(load_info) # noqa: T201 + + +if __name__ == "__main__": + load_api_data() + load_pandas_data() + load_sql_data() diff --git a/dlt/sources/pipeline_templates/requests_pipeline.py b/dlt/sources/pipeline_templates/requests_pipeline.py new file mode 100644 index 0000000000..19acaa1fdb --- /dev/null +++ b/dlt/sources/pipeline_templates/requests_pipeline.py @@ -0,0 +1,61 @@ +"""The Requests Pipeline Template provides a simple starting point for a dlt pipeline with the requests library""" + +# mypy: disable-error-code="no-untyped-def,arg-type" + +from typing import Iterator, Any + +import dlt + +from dlt.sources.helpers import requests +from dlt.sources import TDataItems + + +YEAR = 2022 +MONTH = 10 +BASE_PATH = "https://api.chess.com/pub/player" + + +@dlt.resource(name="players", primary_key="player_id") +def players(): + """Load player profiles from the chess api.""" + for player_name in ["magnuscarlsen", "rpragchess"]: + path = f"{BASE_PATH}/{player_name}" + response = requests.get(path) + response.raise_for_status() + yield response.json() + + +# this resource takes data from players and returns games for the configured +@dlt.transformer(data_from=players, write_disposition="append") +def players_games(player: Any) -> Iterator[TDataItems]: + """Load all games for each player in october 2022""" + player_name = player["username"] + path = f"{BASE_PATH}/{player_name}/games/{YEAR:04d}/{MONTH:02d}" + response = requests.get(path) + response.raise_for_status() + yield response.json()["games"] + + +@dlt.source(name="chess") +def source(): + """A source function groups all resources into one schema.""" + return players(), players_games() + + +def load_chess_data() -> None: + # specify the pipeline name, destination and dataset name when configuring pipeline, + # otherwise the defaults will be used that are derived from the current script name + p = dlt.pipeline( + pipeline_name="chess", + destination="duckdb", + dataset_name="chess_data", + ) + + load_info = p.run(source()) + + # pretty print the information on data that was loaded + print(load_info) # noqa: T201 + + +if __name__ == "__main__": + load_chess_data() diff --git a/dlt/sources/rest_api/__init__.py b/dlt/sources/rest_api/__init__.py new file mode 100644 index 0000000000..fa6b691933 --- /dev/null +++ b/dlt/sources/rest_api/__init__.py @@ -0,0 +1,465 @@ +"""Generic API Source""" +from copy import deepcopy +from typing import Type, Any, Dict, List, Optional, Generator, Callable, cast, Union +import graphlib # type: ignore[import,unused-ignore] +from requests.auth import AuthBase + +import dlt +from dlt.common.validation import validate_dict +from dlt.common import jsonpath +from dlt.common.schema.schema import Schema +from dlt.common.schema.typing import TSchemaContract +from dlt.common.configuration.specs import BaseConfiguration + +from dlt.extract.incremental import Incremental +from dlt.extract.source import DltResource, DltSource + +from dlt.sources.helpers.rest_client import RESTClient +from dlt.sources.helpers.rest_client.paginators import BasePaginator +from dlt.sources.helpers.rest_client.auth import ( + HttpBasicAuth, + BearerTokenAuth, + APIKeyAuth, + AuthConfigBase, +) +from dlt.sources.helpers.rest_client.typing import HTTPMethodBasic +from .typing import ( + AuthConfig, + ClientConfig, + ResolvedParam, + ResolveParamConfig, + Endpoint, + EndpointResource, + IncrementalParamConfig, + RESTAPIConfig, + ParamBindType, + ProcessingSteps, +) +from .config_setup import ( + IncrementalParam, + create_auth, + create_paginator, + build_resource_dependency_graph, + process_parent_data_item, + setup_incremental_object, + create_response_hooks, +) +from .utils import check_connection, exclude_keys # noqa: F401 + +PARAM_TYPES: List[ParamBindType] = ["incremental", "resolve"] +MIN_SECRET_MASKING_LENGTH = 3 +SENSITIVE_KEYS: List[str] = [ + "token", + "api_key", + "username", + "password", +] + + +def rest_api_source( + config: RESTAPIConfig, + name: str = None, + section: str = None, + max_table_nesting: int = None, + root_key: bool = False, + schema: Schema = None, + schema_contract: TSchemaContract = None, + spec: Type[BaseConfiguration] = None, +) -> DltSource: + """Creates and configures a REST API source for data extraction. + + Args: + config (RESTAPIConfig): Configuration for the REST API source. + name (str, optional): Name of the source. + section (str, optional): Section of the configuration file. + max_table_nesting (int, optional): Maximum depth of nested table above which + the remaining nodes are loaded as structs or JSON. + root_key (bool, optional): Enables merging on all resources by propagating + root foreign key to child tables. This option is most useful if you + plan to change write disposition of a resource to disable/enable merge. + Defaults to False. + schema (Schema, optional): An explicit `Schema` instance to be associated + with the source. If not present, `dlt` creates a new `Schema` object + with provided `name`. If such `Schema` already exists in the same + folder as the module containing the decorated function, such schema + will be loaded from file. + schema_contract (TSchemaContract, optional): Schema contract settings + that will be applied to this resource. + spec (Type[BaseConfiguration], optional): A specification of configuration + and secret values required by the source. + + Returns: + DltSource: A configured dlt source. + + Example: + pokemon_source = rest_api_source({ + "client": { + "base_url": "https://pokeapi.co/api/v2/", + "paginator": "json_link", + }, + "endpoints": { + "pokemon": { + "params": { + "limit": 100, # Default page size is 20 + }, + "resource": { + "primary_key": "id", + } + }, + }, + }) + """ + decorated = dlt.source( + rest_api_resources, + name, + section, + max_table_nesting, + root_key, + schema, + schema_contract, + spec, + ) + + return decorated(config) + + +def rest_api_resources(config: RESTAPIConfig) -> List[DltResource]: + """Creates a list of resources from a REST API configuration. + + Args: + config (RESTAPIConfig): Configuration for the REST API source. + + Returns: + List[DltResource]: List of dlt resources. + + Example: + github_source = rest_api_resources({ + "client": { + "base_url": "https://api.github.com/repos/dlt-hub/dlt/", + "auth": { + "token": dlt.secrets["token"], + }, + }, + "resource_defaults": { + "primary_key": "id", + "write_disposition": "merge", + "endpoint": { + "params": { + "per_page": 100, + }, + }, + }, + "resources": [ + { + "name": "issues", + "endpoint": { + "path": "issues", + "params": { + "sort": "updated", + "direction": "desc", + "state": "open", + "since": { + "type": "incremental", + "cursor_path": "updated_at", + "initial_value": "2024-01-25T11:21:28Z", + }, + }, + }, + }, + { + "name": "issue_comments", + "endpoint": { + "path": "issues/{issue_number}/comments", + "params": { + "issue_number": { + "type": "resolve", + "resource": "issues", + "field": "number", + } + }, + }, + }, + ], + }) + """ + + _validate_config(config) + + client_config = config["client"] + resource_defaults = config.get("resource_defaults", {}) + resource_list = config["resources"] + + ( + dependency_graph, + endpoint_resource_map, + resolved_param_map, + ) = build_resource_dependency_graph( + resource_defaults, + resource_list, + ) + + resources = create_resources( + client_config, + dependency_graph, + endpoint_resource_map, + resolved_param_map, + ) + + return list(resources.values()) + + +def create_resources( + client_config: ClientConfig, + dependency_graph: graphlib.TopologicalSorter, + endpoint_resource_map: Dict[str, EndpointResource], + resolved_param_map: Dict[str, Optional[ResolvedParam]], +) -> Dict[str, DltResource]: + resources = {} + + for resource_name in dependency_graph.static_order(): + resource_name = cast(str, resource_name) + endpoint_resource = endpoint_resource_map[resource_name] + endpoint_config = cast(Endpoint, endpoint_resource["endpoint"]) + request_params = endpoint_config.get("params", {}) + request_json = endpoint_config.get("json", None) + paginator = create_paginator(endpoint_config.get("paginator")) + processing_steps = endpoint_resource.pop("processing_steps", []) + + resolved_param: ResolvedParam = resolved_param_map[resource_name] + + include_from_parent: List[str] = endpoint_resource.get("include_from_parent", []) + if not resolved_param and include_from_parent: + raise ValueError( + f"Resource {resource_name} has include_from_parent but is not " + "dependent on another resource" + ) + _validate_param_type(request_params) + ( + incremental_object, + incremental_param, + incremental_cursor_transform, + ) = setup_incremental_object(request_params, endpoint_config.get("incremental")) + + client = RESTClient( + base_url=client_config["base_url"], + headers=client_config.get("headers"), + auth=create_auth(client_config.get("auth")), + paginator=create_paginator(client_config.get("paginator")), + ) + + hooks = create_response_hooks(endpoint_config.get("response_actions")) + + resource_kwargs = exclude_keys(endpoint_resource, {"endpoint", "include_from_parent"}) + + def process( + resource: DltResource, + processing_steps: List[ProcessingSteps], + ) -> Any: + for step in processing_steps: + if "filter" in step: + resource.add_filter(step["filter"]) + if "map" in step: + resource.add_map(step["map"]) + return resource + + if resolved_param is None: + + def paginate_resource( + method: HTTPMethodBasic, + path: str, + params: Dict[str, Any], + json: Optional[Dict[str, Any]], + paginator: Optional[BasePaginator], + data_selector: Optional[jsonpath.TJsonPath], + hooks: Optional[Dict[str, Any]], + client: RESTClient = client, + incremental_object: Optional[Incremental[Any]] = incremental_object, + incremental_param: Optional[IncrementalParam] = incremental_param, + incremental_cursor_transform: Optional[ + Callable[..., Any] + ] = incremental_cursor_transform, + ) -> Generator[Any, None, None]: + if incremental_object: + params = _set_incremental_params( + params, + incremental_object, + incremental_param, + incremental_cursor_transform, + ) + + yield from client.paginate( + method=method, + path=path, + params=params, + json=json, + paginator=paginator, + data_selector=data_selector, + hooks=hooks, + ) + + resources[resource_name] = dlt.resource( + paginate_resource, + **resource_kwargs, # TODO: implement typing.Unpack + )( + method=endpoint_config.get("method", "get"), + path=endpoint_config.get("path"), + params=request_params, + json=request_json, + paginator=paginator, + data_selector=endpoint_config.get("data_selector"), + hooks=hooks, + ) + + resources[resource_name] = process(resources[resource_name], processing_steps) + + else: + predecessor = resources[resolved_param.resolve_config["resource"]] + + base_params = exclude_keys(request_params, {resolved_param.param_name}) + + def paginate_dependent_resource( + items: List[Dict[str, Any]], + method: HTTPMethodBasic, + path: str, + params: Dict[str, Any], + paginator: Optional[BasePaginator], + data_selector: Optional[jsonpath.TJsonPath], + hooks: Optional[Dict[str, Any]], + client: RESTClient = client, + resolved_param: ResolvedParam = resolved_param, + include_from_parent: List[str] = include_from_parent, + incremental_object: Optional[Incremental[Any]] = incremental_object, + incremental_param: Optional[IncrementalParam] = incremental_param, + incremental_cursor_transform: Optional[ + Callable[..., Any] + ] = incremental_cursor_transform, + ) -> Generator[Any, None, None]: + if incremental_object: + params = _set_incremental_params( + params, + incremental_object, + incremental_param, + incremental_cursor_transform, + ) + + for item in items: + formatted_path, parent_record = process_parent_data_item( + path, item, resolved_param, include_from_parent + ) + + for child_page in client.paginate( + method=method, + path=formatted_path, + params=params, + paginator=paginator, + data_selector=data_selector, + hooks=hooks, + ): + if parent_record: + for child_record in child_page: + child_record.update(parent_record) + yield child_page + + resources[resource_name] = dlt.resource( # type: ignore[call-overload] + paginate_dependent_resource, + data_from=predecessor, + **resource_kwargs, # TODO: implement typing.Unpack + )( + method=endpoint_config.get("method", "get"), + path=endpoint_config.get("path"), + params=base_params, + paginator=paginator, + data_selector=endpoint_config.get("data_selector"), + hooks=hooks, + ) + + resources[resource_name] = process(resources[resource_name], processing_steps) + + return resources + + +def _validate_config(config: RESTAPIConfig) -> None: + c = deepcopy(config) + client_config = c.get("client") + if client_config: + auth = client_config.get("auth") + if auth: + auth = _mask_secrets(auth) + + validate_dict(RESTAPIConfig, c, path=".") + + +def _mask_secrets(auth_config: AuthConfig) -> AuthConfig: + if isinstance(auth_config, AuthBase) and not isinstance(auth_config, AuthConfigBase): + return auth_config + + has_sensitive_key = any(key in auth_config for key in SENSITIVE_KEYS) + if isinstance(auth_config, (APIKeyAuth, BearerTokenAuth, HttpBasicAuth)) or has_sensitive_key: + return _mask_secrets_dict(auth_config) + # Here, we assume that OAuth2 and other custom classes that don't implement __get__() + # also don't print secrets in __str__() + # TODO: call auth_config.mask_secrets() when that is implemented in dlt-core + return auth_config + + +def _mask_secrets_dict(auth_config: AuthConfig) -> AuthConfig: + for sensitive_key in SENSITIVE_KEYS: + try: + auth_config[sensitive_key] = _mask_secret(auth_config[sensitive_key]) # type: ignore[literal-required, index] + except KeyError: + continue + return auth_config + + +def _mask_secret(secret: Optional[str]) -> str: + if secret is None: + return "None" + if len(secret) < MIN_SECRET_MASKING_LENGTH: + return "*****" + return f"{secret[0]}*****{secret[-1]}" + + +def _set_incremental_params( + params: Dict[str, Any], + incremental_object: Incremental[Any], + incremental_param: IncrementalParam, + transform: Optional[Callable[..., Any]], +) -> Dict[str, Any]: + def identity_func(x: Any) -> Any: + return x + + if transform is None: + transform = identity_func + params[incremental_param.start] = transform(incremental_object.last_value) + if incremental_param.end: + params[incremental_param.end] = transform(incremental_object.end_value) + return params + + +def _validate_param_type( + request_params: Dict[str, Union[ResolveParamConfig, IncrementalParamConfig, Any]] +) -> None: + for _, value in request_params.items(): + if isinstance(value, dict) and value.get("type") not in PARAM_TYPES: + raise ValueError( + f"Invalid param type: {value.get('type')}. Available options: {PARAM_TYPES}" + ) + + +# XXX: This is a workaround pass test_dlt_init.py +# since the source uses dlt.source as a function +def _register_source(source_func: Callable[..., DltSource]) -> None: + import inspect + from dlt.common.configuration import get_fun_spec + from dlt.common.source import _SOURCES, SourceInfo + + spec = get_fun_spec(source_func) + func_module = inspect.getmodule(source_func) + _SOURCES[source_func.__name__] = SourceInfo( + SPEC=spec, + f=source_func, + module=func_module, + ) + + +_register_source(rest_api_source) diff --git a/dlt/sources/rest_api/config_setup.py b/dlt/sources/rest_api/config_setup.py new file mode 100644 index 0000000000..7bf6c81634 --- /dev/null +++ b/dlt/sources/rest_api/config_setup.py @@ -0,0 +1,634 @@ +import warnings +from copy import copy +from typing import ( + Type, + Any, + Dict, + Tuple, + List, + Optional, + Union, + Callable, + cast, + NamedTuple, +) +import graphlib # type: ignore[import,unused-ignore] +import string + +import dlt +from dlt.common import logger +from dlt.common.configuration import resolve_configuration +from dlt.common.schema.utils import merge_columns +from dlt.common.utils import update_dict_nested +from dlt.common import jsonpath + +from dlt.extract.incremental import Incremental +from dlt.extract.utils import ensure_table_schema_columns + +from dlt.sources.helpers.requests import Response +from dlt.sources.helpers.rest_client.paginators import ( + BasePaginator, + SinglePagePaginator, + HeaderLinkPaginator, + JSONResponseCursorPaginator, + OffsetPaginator, + PageNumberPaginator, +) + +try: + from dlt.sources.helpers.rest_client.paginators import JSONLinkPaginator +except ImportError: + from dlt.sources.helpers.rest_client.paginators import ( + JSONResponsePaginator as JSONLinkPaginator, + ) + +from dlt.sources.helpers.rest_client.detector import single_entity_path +from dlt.sources.helpers.rest_client.exceptions import IgnoreResponseException +from dlt.sources.helpers.rest_client.auth import ( + AuthConfigBase, + HttpBasicAuth, + BearerTokenAuth, + APIKeyAuth, + OAuth2ClientCredentials, +) + +from .typing import ( + EndpointResourceBase, + AuthConfig, + IncrementalConfig, + PaginatorConfig, + ResolvedParam, + ResponseAction, + ResponseActionDict, + Endpoint, + EndpointResource, +) +from .utils import exclude_keys + + +PAGINATOR_MAP: Dict[str, Type[BasePaginator]] = { + "json_link": JSONLinkPaginator, + "json_response": ( + JSONLinkPaginator + ), # deprecated. Use json_link instead. Will be removed in upcoming release + "header_link": HeaderLinkPaginator, + "auto": None, + "single_page": SinglePagePaginator, + "cursor": JSONResponseCursorPaginator, + "offset": OffsetPaginator, + "page_number": PageNumberPaginator, +} + +AUTH_MAP: Dict[str, Type[AuthConfigBase]] = { + "bearer": BearerTokenAuth, + "api_key": APIKeyAuth, + "http_basic": HttpBasicAuth, + "oauth2_client_credentials": OAuth2ClientCredentials, +} + + +class IncrementalParam(NamedTuple): + start: str + end: Optional[str] + + +def register_paginator( + paginator_name: str, + paginator_class: Type[BasePaginator], +) -> None: + if not issubclass(paginator_class, BasePaginator): + raise ValueError( + f"Invalid paginator: {paginator_class.__name__}. " + "Your custom paginator has to be a subclass of BasePaginator" + ) + PAGINATOR_MAP[paginator_name] = paginator_class + + +def get_paginator_class(paginator_name: str) -> Type[BasePaginator]: + try: + return PAGINATOR_MAP[paginator_name] + except KeyError: + available_options = ", ".join(PAGINATOR_MAP.keys()) + raise ValueError( + f"Invalid paginator: {paginator_name}. Available options: {available_options}." + ) + + +def create_paginator( + paginator_config: Optional[PaginatorConfig], +) -> Optional[BasePaginator]: + if isinstance(paginator_config, BasePaginator): + return paginator_config + + if isinstance(paginator_config, str): + paginator_class = get_paginator_class(paginator_config) + try: + # `auto` has no associated class in `PAGINATOR_MAP` + return paginator_class() if paginator_class else None + except TypeError: + raise ValueError( + f"Paginator {paginator_config} requires arguments to create an instance. Use" + f" {paginator_class} instance instead." + ) + + if isinstance(paginator_config, dict): + paginator_type = paginator_config.get("type", "auto") + paginator_class = get_paginator_class(paginator_type) + return ( + paginator_class(**exclude_keys(paginator_config, {"type"})) if paginator_class else None + ) + + return None + + +def register_auth( + auth_name: str, + auth_class: Type[AuthConfigBase], +) -> None: + if not issubclass(auth_class, AuthConfigBase): + raise ValueError( + f"Invalid auth: {auth_class.__name__}. " + "Your custom auth has to be a subclass of AuthConfigBase" + ) + AUTH_MAP[auth_name] = auth_class + + +def get_auth_class(auth_type: str) -> Type[AuthConfigBase]: + try: + return AUTH_MAP[auth_type] + except KeyError: + available_options = ", ".join(AUTH_MAP.keys()) + raise ValueError( + f"Invalid authentication: {auth_type}. Available options: {available_options}." + ) + + +def create_auth(auth_config: Optional[AuthConfig]) -> Optional[AuthConfigBase]: + auth: AuthConfigBase = None + if isinstance(auth_config, AuthConfigBase): + auth = auth_config + + if isinstance(auth_config, str): + auth_class = get_auth_class(auth_config) + auth = auth_class() + + if isinstance(auth_config, dict): + auth_type = auth_config.get("type", "bearer") + auth_class = get_auth_class(auth_type) + auth = auth_class(**exclude_keys(auth_config, {"type"})) + + if auth: + # TODO: provide explicitly (non-default) values as explicit explicit_value=dict(auth) + # this will resolve auth which is a configuration using current section context + return resolve_configuration(auth, accept_partial=True) + + return None + + +def setup_incremental_object( + request_params: Dict[str, Any], + incremental_config: Optional[IncrementalConfig] = None, +) -> Tuple[Optional[Incremental[Any]], Optional[IncrementalParam], Optional[Callable[..., Any]]]: + incremental_params: List[str] = [] + for param_name, param_config in request_params.items(): + if ( + isinstance(param_config, dict) + and param_config.get("type") == "incremental" + or isinstance(param_config, dlt.sources.incremental) + ): + incremental_params.append(param_name) + if len(incremental_params) > 1: + raise ValueError( + "Only a single incremental parameter is allower per endpoint. Found:" + f" {incremental_params}" + ) + convert: Optional[Callable[..., Any]] + for param_name, param_config in request_params.items(): + if isinstance(param_config, dlt.sources.incremental): + if param_config.end_value is not None: + raise ValueError( + f"Only initial_value is allowed in the configuration of param: {param_name}. To" + " set end_value too use the incremental configuration at the resource level." + " See" + " https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api#incremental-loading/" + ) + return param_config, IncrementalParam(start=param_name, end=None), None + if isinstance(param_config, dict) and param_config.get("type") == "incremental": + if param_config.get("end_value") or param_config.get("end_param"): + raise ValueError( + "Only start_param and initial_value are allowed in the configuration of param:" + f" {param_name}. To set end_value too use the incremental configuration at the" + " resource level. See" + " https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api#incremental-loading" + ) + convert = parse_convert_or_deprecated_transform(param_config) + + config = exclude_keys(param_config, {"type", "convert", "transform"}) + # TODO: implement param type to bind incremental to + return ( + dlt.sources.incremental(**config), + IncrementalParam(start=param_name, end=None), + convert, + ) + if incremental_config: + convert = parse_convert_or_deprecated_transform(incremental_config) + config = exclude_keys( + incremental_config, {"start_param", "end_param", "convert", "transform"} + ) + return ( + dlt.sources.incremental(**config), + IncrementalParam( + start=incremental_config["start_param"], + end=incremental_config.get("end_param"), + ), + convert, + ) + + return None, None, None + + +def parse_convert_or_deprecated_transform( + config: Union[IncrementalConfig, Dict[str, Any]] +) -> Optional[Callable[..., Any]]: + convert = config.get("convert", None) + deprecated_transform = config.get("transform", None) + if deprecated_transform: + warnings.warn( + "The key `transform` is deprecated in the incremental configuration and it will be" + " removed. Use `convert` instead", + DeprecationWarning, + stacklevel=2, + ) + convert = deprecated_transform + return convert + + +def make_parent_key_name(resource_name: str, field_name: str) -> str: + return f"_{resource_name}_{field_name}" + + +def build_resource_dependency_graph( + resource_defaults: EndpointResourceBase, + resource_list: List[Union[str, EndpointResource]], +) -> Tuple[Any, Dict[str, EndpointResource], Dict[str, Optional[ResolvedParam]]]: + dependency_graph = graphlib.TopologicalSorter() + endpoint_resource_map: Dict[str, EndpointResource] = {} + resolved_param_map: Dict[str, ResolvedParam] = {} + + # expand all resources and index them + for resource_kwargs in resource_list: + if isinstance(resource_kwargs, dict): + # clone resource here, otherwise it needs to be cloned in several other places + # note that this clones only dict structure, keeping all instances without deepcopy + resource_kwargs = update_dict_nested({}, resource_kwargs) # type: ignore + + endpoint_resource = _make_endpoint_resource(resource_kwargs, resource_defaults) + assert isinstance(endpoint_resource["endpoint"], dict) + _setup_single_entity_endpoint(endpoint_resource["endpoint"]) + _bind_path_params(endpoint_resource) + + resource_name = endpoint_resource["name"] + assert isinstance( + resource_name, str + ), f"Resource name must be a string, got {type(resource_name)}" + + if resource_name in endpoint_resource_map: + raise ValueError(f"Resource {resource_name} has already been defined") + endpoint_resource_map[resource_name] = endpoint_resource + + # create dependency graph + for resource_name, endpoint_resource in endpoint_resource_map.items(): + assert isinstance(endpoint_resource["endpoint"], dict) + # connect transformers to resources via resolved params + resolved_params = _find_resolved_params(endpoint_resource["endpoint"]) + if len(resolved_params) > 1: + raise ValueError( + f"Multiple resolved params for resource {resource_name}: {resolved_params}" + ) + elif len(resolved_params) == 1: + resolved_param = resolved_params[0] + predecessor = resolved_param.resolve_config["resource"] + if predecessor not in endpoint_resource_map: + raise ValueError( + f"A transformer resource {resource_name} refers to non existing parent resource" + f" {predecessor} on {resolved_param}" + ) + dependency_graph.add(resource_name, predecessor) + resolved_param_map[resource_name] = resolved_param + else: + dependency_graph.add(resource_name) + resolved_param_map[resource_name] = None + + return dependency_graph, endpoint_resource_map, resolved_param_map + + +def _make_endpoint_resource( + resource: Union[str, EndpointResource], default_config: EndpointResourceBase +) -> EndpointResource: + """ + Creates an EndpointResource object based on the provided resource + definition and merges it with the default configuration. + + This function supports defining a resource in multiple formats: + - As a string: The string is interpreted as both the resource name + and its endpoint path. + - As a dictionary: The dictionary must include `name` and `endpoint` + keys. The `endpoint` can be a string representing the path, + or a dictionary for more complex configurations. If the `endpoint` + is missing the `path` key, the resource name is used as the `path`. + """ + if isinstance(resource, str): + resource = {"name": resource, "endpoint": {"path": resource}} + return _merge_resource_endpoints(default_config, resource) + + if "endpoint" in resource: + if isinstance(resource["endpoint"], str): + resource["endpoint"] = {"path": resource["endpoint"]} + else: + # endpoint is optional + resource["endpoint"] = {} + + if "path" not in resource["endpoint"]: + resource["endpoint"]["path"] = resource["name"] # type: ignore + + return _merge_resource_endpoints(default_config, resource) + + +def _bind_path_params(resource: EndpointResource) -> None: + """Binds params declared in path to params available in `params`. Pops the + bound params but. Params of type `resolve` and `incremental` are skipped + and bound later. + """ + path_params: Dict[str, Any] = {} + assert isinstance(resource["endpoint"], dict) # type guard + resolve_params = [r.param_name for r in _find_resolved_params(resource["endpoint"])] + path = resource["endpoint"]["path"] + for format_ in string.Formatter().parse(path): + name = format_[1] + if name: + params = resource["endpoint"].get("params", {}) + if name not in params and name not in path_params: + raise ValueError( + f"The path {path} defined in resource {resource['name']} requires param with" + f" name {name} but it is not found in {params}" + ) + if name in resolve_params: + resolve_params.remove(name) + if name in params: + if not isinstance(params[name], dict): + # bind resolved param and pop it from endpoint + path_params[name] = params.pop(name) + else: + param_type = params[name].get("type") + if param_type != "resolve": + raise ValueError( + f"The path {path} defined in resource {resource['name']} tries to bind" + f" param {name} with type {param_type}. Paths can only bind 'resource'" + " type params." + ) + # resolved params are bound later + path_params[name] = "{" + name + "}" + + if len(resolve_params) > 0: + raise NotImplementedError( + f"Resource {resource['name']} defines resolve params {resolve_params} that are not" + f" bound in path {path}. Resolve query params not supported yet." + ) + + resource["endpoint"]["path"] = path.format(**path_params) + + +def _setup_single_entity_endpoint(endpoint: Endpoint) -> Endpoint: + """Tries to guess if the endpoint refers to a single entity and when detected: + * if `data_selector` was not specified (or is None), "$" is selected + * if `paginator` was not specified (or is None), SinglePagePaginator is selected + + Endpoint is modified in place and returned + """ + # try to guess if list of entities or just single entity is returned + if single_entity_path(endpoint["path"]): + if endpoint.get("data_selector") is None: + endpoint["data_selector"] = "$" + if endpoint.get("paginator") is None: + endpoint["paginator"] = SinglePagePaginator() + return endpoint + + +def _find_resolved_params(endpoint_config: Endpoint) -> List[ResolvedParam]: + """ + Find all resolved params in the endpoint configuration and return + a list of ResolvedParam objects. + + Resolved params are of type ResolveParamConfig (bound param with a key "type" set to "resolve".) + """ + return [ + ResolvedParam(key, value) # type: ignore[arg-type] + for key, value in endpoint_config.get("params", {}).items() + if (isinstance(value, dict) and value.get("type") == "resolve") + ] + + +def _action_type_unless_custom_hook( + action_type: Optional[str], custom_hook: Optional[List[Callable[..., Any]]] +) -> Union[Tuple[str, Optional[List[Callable[..., Any]]]], Tuple[None, List[Callable[..., Any]]],]: + if custom_hook: + return (None, custom_hook) + return (action_type, None) + + +def _handle_response_action( + response: Response, + action: ResponseAction, +) -> Union[ + Tuple[str, Optional[List[Callable[..., Any]]]], + Tuple[None, List[Callable[..., Any]]], + Tuple[None, None], +]: + """ + Checks, based on the response, if the provided action applies. + """ + content: str = response.text + status_code = None + content_substr = None + action_type = None + custom_hooks = None + response_action = None + if callable(action): + custom_hooks = [action] + else: + action = cast(ResponseActionDict, action) + status_code = action.get("status_code") + content_substr = action.get("content") + response_action = action.get("action") + if isinstance(response_action, str): + action_type = response_action + elif callable(response_action): + custom_hooks = [response_action] + elif isinstance(response_action, list) and all( + callable(action) for action in response_action + ): + custom_hooks = response_action + else: + raise ValueError( + f"Action {response_action} does not conform to expected type. Expected: str or" + f" Callable or List[Callable]. Found: {type(response_action)}" + ) + + if status_code is not None and content_substr is not None: + if response.status_code == status_code and content_substr in content: + return _action_type_unless_custom_hook(action_type, custom_hooks) + + elif status_code is not None: + if response.status_code == status_code: + return _action_type_unless_custom_hook(action_type, custom_hooks) + + elif content_substr is not None: + if content_substr in content: + return _action_type_unless_custom_hook(action_type, custom_hooks) + + elif status_code is None and content_substr is None and custom_hooks is not None: + return (None, custom_hooks) + + return (None, None) + + +def _create_response_action_hook( + response_action: ResponseAction, +) -> Callable[[Response, Any, Any], None]: + def response_action_hook(response: Response, *args: Any, **kwargs: Any) -> None: + """ + This is the hook executed by the requests library + """ + (action_type, custom_hooks) = _handle_response_action(response, response_action) + if custom_hooks: + for hook in custom_hooks: + hook(response) + elif action_type == "ignore": + logger.info( + f"Ignoring response with code {response.status_code} " + f"and content '{response.json()}'." + ) + raise IgnoreResponseException + + # If there are hooks, then the REST client does not raise for status + # If no action has been taken and the status code indicates an error, + # raise an HTTP error based on the response status + elif not action_type: + response.raise_for_status() + + return response_action_hook + + +def create_response_hooks( + response_actions: Optional[List[ResponseAction]], +) -> Optional[Dict[str, Any]]: + """Create response hooks based on the provided response actions. Note + that if the error status code is not handled by the response actions, + the default behavior is to raise an HTTP error. + + Example: + def set_encoding(response, *args, **kwargs): + response.encoding = 'windows-1252' + return response + + def remove_field(response: Response, *args, **kwargs) -> Response: + payload = response.json() + for record in payload: + record.pop("email", None) + modified_content: bytes = json.dumps(payload).encode("utf-8") + response._content = modified_content + return response + + response_actions = [ + set_encoding, + {"status_code": 404, "action": "ignore"}, + {"content": "Not found", "action": "ignore"}, + {"status_code": 200, "content": "some text", "action": "ignore"}, + {"status_code": 200, "action": remove_field}, + ] + hooks = create_response_hooks(response_actions) + """ + if response_actions: + hooks = [_create_response_action_hook(action) for action in response_actions] + return {"response": hooks} + return None + + +def process_parent_data_item( + path: str, + item: Dict[str, Any], + resolved_param: ResolvedParam, + include_from_parent: List[str], +) -> Tuple[str, Dict[str, Any]]: + parent_resource_name = resolved_param.resolve_config["resource"] + + field_values = jsonpath.find_values(resolved_param.field_path, item) + + if not field_values: + field_path = resolved_param.resolve_config["field"] + raise ValueError( + f"Transformer expects a field '{field_path}' to be present in the incoming data from" + f" resource {parent_resource_name} in order to bind it to path param" + f" {resolved_param.param_name}. Available parent fields are {', '.join(item.keys())}" + ) + bound_path = path.format(**{resolved_param.param_name: field_values[0]}) + + parent_record: Dict[str, Any] = {} + if include_from_parent: + for parent_key in include_from_parent: + child_key = make_parent_key_name(parent_resource_name, parent_key) + if parent_key not in item: + raise ValueError( + f"Transformer expects a field '{parent_key}' to be present in the incoming data" + f" from resource {parent_resource_name} in order to include it in child records" + f" under {child_key}. Available parent fields are {', '.join(item.keys())}" + ) + parent_record[child_key] = item[parent_key] + + return bound_path, parent_record + + +def _merge_resource_endpoints( + default_config: EndpointResourceBase, config: EndpointResource +) -> EndpointResource: + """Merges `default_config` and `config`, returns new instance of EndpointResource""" + # NOTE: config is normalized and always has "endpoint" field which is a dict + # TODO: could deep merge paginators and auths of the same type + + default_endpoint = default_config.get("endpoint", Endpoint()) + assert isinstance(default_endpoint, dict) + config_endpoint = config["endpoint"] + assert isinstance(config_endpoint, dict) + + merged_endpoint: Endpoint = { + **default_endpoint, + **{k: v for k, v in config_endpoint.items() if k not in ("json", "params")}, # type: ignore[typeddict-item] + } + # merge endpoint, only params and json are allowed to deep merge + if "json" in config_endpoint: + merged_endpoint["json"] = { + **(merged_endpoint.get("json", {})), + **config_endpoint["json"], + } + if "params" in config_endpoint: + merged_endpoint["params"] = { + **(merged_endpoint.get("params", {})), + **config_endpoint["params"], + } + # merge columns + if (default_columns := default_config.get("columns")) and (columns := config.get("columns")): + # merge only native dlt formats, skip pydantic and others + if isinstance(columns, (list, dict)) and isinstance(default_columns, (list, dict)): + # normalize columns + columns = ensure_table_schema_columns(columns) + default_columns = ensure_table_schema_columns(default_columns) + # merge columns with deep merging hints + config["columns"] = merge_columns(copy(default_columns), columns, merge_columns=True) + + # no need to deep merge resources + merged_resource: EndpointResource = { + **default_config, + **config, + "endpoint": merged_endpoint, + } + return merged_resource diff --git a/dlt/sources/rest_api/exceptions.py b/dlt/sources/rest_api/exceptions.py new file mode 100644 index 0000000000..24fd5b31b0 --- /dev/null +++ b/dlt/sources/rest_api/exceptions.py @@ -0,0 +1,8 @@ +from dlt.common.exceptions import DltException + + +class RestApiException(DltException): + pass + + +# class Paginator diff --git a/dlt/sources/rest_api/typing.py b/dlt/sources/rest_api/typing.py new file mode 100644 index 0000000000..22a9560433 --- /dev/null +++ b/dlt/sources/rest_api/typing.py @@ -0,0 +1,280 @@ +from dataclasses import dataclass, field +from typing_extensions import TypedDict + +from typing import ( + Any, + Callable, + Dict, + List, + Literal, + Optional, + Union, +) + +from dlt.common import jsonpath +from dlt.common.schema.typing import ( + TAnySchemaColumns, +) +from dlt.extract.incremental.typing import IncrementalArgs +from dlt.extract.items import TTableHintTemplate +from dlt.extract.hints import TResourceHintsBase +from dlt.sources.helpers.rest_client.auth import AuthConfigBase, TApiKeyLocation + +from dataclasses import dataclass, field + +from dlt.common import jsonpath +from dlt.common.typing import TSortOrder +from dlt.common.schema.typing import ( + TColumnNames, + TTableFormat, + TAnySchemaColumns, + TWriteDispositionConfig, + TSchemaContract, +) + +from dlt.extract.items import TTableHintTemplate +from dlt.extract.incremental.typing import LastValueFunc + +from dlt.sources.helpers.rest_client.typing import HTTPMethodBasic + +from dlt.sources.helpers.rest_client.paginators import ( + BasePaginator, + HeaderLinkPaginator, + JSONResponseCursorPaginator, + OffsetPaginator, + PageNumberPaginator, + SinglePagePaginator, +) +from dlt.sources.helpers.rest_client.typing import HTTPMethodBasic + + +try: + from dlt.sources.helpers.rest_client.paginators import JSONLinkPaginator +except ImportError: + from dlt.sources.helpers.rest_client.paginators import ( + JSONResponsePaginator as JSONLinkPaginator, + ) + +from dlt.sources.helpers.rest_client.auth import ( + HttpBasicAuth, + BearerTokenAuth, + APIKeyAuth, +) + +PaginatorType = Literal[ + "json_link", + "json_response", # deprecated. Use json_link instead. Will be removed in upcoming release + "header_link", + "auto", + "single_page", + "cursor", + "offset", + "page_number", +] + + +class PaginatorTypeConfig(TypedDict, total=True): + type: PaginatorType # noqa + + +class PageNumberPaginatorConfig(PaginatorTypeConfig, total=False): + """A paginator that uses page number-based pagination strategy.""" + + base_page: Optional[int] + page_param: Optional[str] + total_path: Optional[jsonpath.TJsonPath] + maximum_page: Optional[int] + + +class OffsetPaginatorConfig(PaginatorTypeConfig, total=False): + """A paginator that uses offset-based pagination strategy.""" + + limit: int + offset: Optional[int] + offset_param: Optional[str] + limit_param: Optional[str] + total_path: Optional[jsonpath.TJsonPath] + maximum_offset: Optional[int] + + +class HeaderLinkPaginatorConfig(PaginatorTypeConfig, total=False): + """A paginator that uses the 'Link' header in HTTP responses + for pagination.""" + + links_next_key: Optional[str] + + +class JSONLinkPaginatorConfig(PaginatorTypeConfig, total=False): + """Locates the next page URL within the JSON response body. The key + containing the URL can be specified using a JSON path.""" + + next_url_path: Optional[jsonpath.TJsonPath] + + +class JSONResponseCursorPaginatorConfig(PaginatorTypeConfig, total=False): + """Uses a cursor parameter for pagination, with the cursor value found in + the JSON response body.""" + + cursor_path: Optional[jsonpath.TJsonPath] + cursor_param: Optional[str] + + +PaginatorConfig = Union[ + PaginatorType, + PageNumberPaginatorConfig, + OffsetPaginatorConfig, + HeaderLinkPaginatorConfig, + JSONLinkPaginatorConfig, + JSONResponseCursorPaginatorConfig, + BasePaginator, + SinglePagePaginator, + HeaderLinkPaginator, + JSONLinkPaginator, + JSONResponseCursorPaginator, + OffsetPaginator, + PageNumberPaginator, +] + + +AuthType = Literal["bearer", "api_key", "http_basic"] + + +class AuthTypeConfig(TypedDict, total=True): + type: AuthType # noqa + + +class BearerTokenAuthConfig(TypedDict, total=False): + """Uses `token` for Bearer authentication in "Authorization" header.""" + + # we allow for a shorthand form of bearer auth, without a type + type: Optional[AuthType] # noqa + token: str + + +class ApiKeyAuthConfig(AuthTypeConfig, total=False): + """Uses provided `api_key` to create authorization data in the specified `location` (query, param, header, cookie) under specified `name`""" + + name: Optional[str] + api_key: str + location: Optional[TApiKeyLocation] + + +class HttpBasicAuthConfig(AuthTypeConfig, total=True): + """Uses HTTP basic authentication""" + + username: str + password: str + + +# TODO: add later +# class OAuthJWTAuthConfig(AuthTypeConfig, total=True): + + +AuthConfig = Union[ + AuthConfigBase, + AuthType, + BearerTokenAuthConfig, + ApiKeyAuthConfig, + HttpBasicAuthConfig, + BearerTokenAuth, + APIKeyAuth, + HttpBasicAuth, +] + + +class ClientConfig(TypedDict, total=False): + base_url: str + headers: Optional[Dict[str, str]] + auth: Optional[AuthConfig] + paginator: Optional[PaginatorConfig] + + +class IncrementalRESTArgs(IncrementalArgs, total=False): + convert: Optional[Callable[..., Any]] + + +class IncrementalConfig(IncrementalRESTArgs, total=False): + start_param: str + end_param: Optional[str] + + +ParamBindType = Literal["resolve", "incremental"] + + +class ParamBindConfig(TypedDict): + type: ParamBindType # noqa + + +class ResolveParamConfig(ParamBindConfig): + resource: str + field: str + + +class IncrementalParamConfig(ParamBindConfig, IncrementalRESTArgs): + pass + # TODO: implement param type to bind incremental to + # param_type: Optional[Literal["start_param", "end_param"]] + + +@dataclass +class ResolvedParam: + param_name: str + resolve_config: ResolveParamConfig + field_path: jsonpath.TJsonPath = field(init=False) + + def __post_init__(self) -> None: + self.field_path = jsonpath.compile_path(self.resolve_config["field"]) + + +class ResponseActionDict(TypedDict, total=False): + status_code: Optional[Union[int, str]] + content: Optional[str] + action: Optional[Union[str, Union[Callable[..., Any], List[Callable[..., Any]]]]] + + +ResponseAction = Union[ResponseActionDict, Callable[..., Any]] + + +class Endpoint(TypedDict, total=False): + path: Optional[str] + method: Optional[HTTPMethodBasic] + params: Optional[Dict[str, Union[ResolveParamConfig, IncrementalParamConfig, Any]]] + json: Optional[Dict[str, Any]] + paginator: Optional[PaginatorConfig] + data_selector: Optional[jsonpath.TJsonPath] + response_actions: Optional[List[ResponseAction]] + incremental: Optional[IncrementalConfig] + + +class ProcessingSteps(TypedDict): + filter: Optional[Callable[[Any], bool]] # noqa: A003 + map: Optional[Callable[[Any], Any]] # noqa: A003 + + +class ResourceBase(TResourceHintsBase, total=False): + """Defines hints that may be passed to `dlt.resource` decorator""" + + table_name: Optional[TTableHintTemplate[str]] + max_table_nesting: Optional[int] + columns: Optional[TTableHintTemplate[TAnySchemaColumns]] + selected: Optional[bool] + parallelized: Optional[bool] + processing_steps: Optional[List[ProcessingSteps]] + + +class EndpointResourceBase(ResourceBase, total=False): + endpoint: Optional[Union[str, Endpoint]] + include_from_parent: Optional[List[str]] + + +class EndpointResource(EndpointResourceBase, total=False): + name: TTableHintTemplate[str] + + +class RESTAPIConfigBase(TypedDict): + client: ClientConfig + resources: List[Union[str, EndpointResource]] + + +class RESTAPIConfig(RESTAPIConfigBase, total=False): + resource_defaults: Optional[EndpointResourceBase] diff --git a/dlt/sources/rest_api/utils.py b/dlt/sources/rest_api/utils.py new file mode 100644 index 0000000000..c1ef181cca --- /dev/null +++ b/dlt/sources/rest_api/utils.py @@ -0,0 +1,35 @@ +from typing import Tuple, Dict, Any, Mapping, Iterable + +from dlt.common import logger +from dlt.extract.source import DltSource + + +def join_url(base_url: str, path: str) -> str: + if not base_url.endswith("/"): + base_url += "/" + return base_url + path.lstrip("/") + + +def exclude_keys(d: Mapping[str, Any], keys: Iterable[str]) -> Dict[str, Any]: + """Removes specified keys from a dictionary and returns a new dictionary. + + Args: + d (Mapping[str, Any]): The dictionary to remove keys from. + keys (Iterable[str]): The keys to remove. + + Returns: + Dict[str, Any]: A new dictionary with the specified keys removed. + """ + return {k: v for k, v in d.items() if k not in keys} + + +def check_connection( + source: DltSource, + *resource_names: str, +) -> Tuple[bool, str]: + try: + list(source.with_resources(*resource_names).add_limit(1)) + return (True, "") + except Exception as e: + logger.error(f"Error checking connection: {e}") + return (False, str(e)) diff --git a/dlt/sources/rest_api_pipeline.py b/dlt/sources/rest_api_pipeline.py new file mode 100644 index 0000000000..01a8828fcd --- /dev/null +++ b/dlt/sources/rest_api_pipeline.py @@ -0,0 +1,158 @@ +from typing import Any, Optional + +import dlt +from dlt.common.pendulum import pendulum +from dlt.sources.rest_api import ( + RESTAPIConfig, + check_connection, + rest_api_resources, + rest_api_source, +) + + +@dlt.source(name="github") +def github_source(access_token: Optional[str] = dlt.secrets.value) -> Any: + # Create a REST API configuration for the GitHub API + # Use RESTAPIConfig to get autocompletion and type checking + config: RESTAPIConfig = { + "client": { + "base_url": "https://api.github.com/repos/dlt-hub/dlt/", + # we add an auth config if the auth token is present + "auth": ( + { + "type": "bearer", + "token": access_token, + } + if access_token + else None + ), + }, + # The default configuration for all resources and their endpoints + "resource_defaults": { + "primary_key": "id", + "write_disposition": "merge", + "endpoint": { + "params": { + "per_page": 100, + }, + }, + }, + "resources": [ + # This is a simple resource definition, + # that uses the endpoint path as a resource name: + # "pulls", + # Alternatively, you can define the endpoint as a dictionary + # { + # "name": "pulls", # <- Name of the resource + # "endpoint": "pulls", # <- This is the endpoint path + # } + # Or use a more detailed configuration: + { + "name": "issues", + "endpoint": { + "path": "issues", + # Query parameters for the endpoint + "params": { + "sort": "updated", + "direction": "desc", + "state": "open", + # Define `since` as a special parameter + # to incrementally load data from the API. + # This works by getting the updated_at value + # from the previous response data and using this value + # for the `since` query parameter in the next request. + "since": { + "type": "incremental", + "cursor_path": "updated_at", + "initial_value": pendulum.today().subtract(days=30).to_iso8601_string(), + }, + }, + }, + }, + # The following is an example of a resource that uses + # a parent resource (`issues`) to get the `issue_number` + # and include it in the endpoint path: + { + "name": "issue_comments", + "endpoint": { + # The placeholder {issue_number} will be resolved + # from the parent resource + "path": "issues/{issue_number}/comments", + "params": { + # The value of `issue_number` will be taken + # from the `number` field in the `issues` resource + "issue_number": { + "type": "resolve", + "resource": "issues", + "field": "number", + } + }, + }, + # Include data from `id` field of the parent resource + # in the child data. The field name in the child data + # will be called `_issues_id` (_{resource_name}_{field_name}) + "include_from_parent": ["id"], + }, + ], + } + + yield from rest_api_resources(config) + + +def load_github() -> None: + pipeline = dlt.pipeline( + pipeline_name="rest_api_github", + destination="duckdb", + dataset_name="rest_api_data", + ) + + load_info = pipeline.run(github_source()) + print(load_info) # noqa: T201 + + +def load_pokemon() -> None: + pipeline = dlt.pipeline( + pipeline_name="rest_api_pokemon", + destination="duckdb", + dataset_name="rest_api_data", + ) + + pokemon_source = rest_api_source( + { + "client": { + "base_url": "https://pokeapi.co/api/v2/", + # If you leave out the paginator, it will be inferred from the API: + # "paginator": "json_link", + }, + "resource_defaults": { + "endpoint": { + "params": { + "limit": 1000, + }, + }, + }, + "resources": [ + "pokemon", + "berry", + "location", + ], + } + ) + + def check_network_and_authentication() -> None: + (can_connect, error_msg) = check_connection( + pokemon_source, + "not_existing_endpoint", + ) + if not can_connect: + pass # do something with the error message + + check_network_and_authentication() + + load_info = pipeline.run(pokemon_source) + print(load_info) # noqa: T201 + + +if __name__ == "__main__": + load_github() + load_pokemon() diff --git a/dlt/sources/sql_database/__init__.py b/dlt/sources/sql_database/__init__.py new file mode 100644 index 0000000000..f7c83b4b80 --- /dev/null +++ b/dlt/sources/sql_database/__init__.py @@ -0,0 +1,216 @@ +"""Source that loads tables form any SQLAlchemy supported database, supports batching requests and incremental loads.""" + +from typing import Callable, Dict, List, Optional, Union, Iterable, Any + +from dlt.common.libs.sql_alchemy import MetaData, Table, Engine + +import dlt +from dlt.sources import DltResource + + +from dlt.sources.credentials import ConnectionStringCredentials +from dlt.common.configuration.specs.config_section_context import ConfigSectionContext + +from .helpers import ( + table_rows, + engine_from_credentials, + TableBackend, + SqlDatabaseTableConfiguration, + SqlTableResourceConfiguration, + _detect_precision_hints_deprecated, + TQueryAdapter, +) +from .schema_types import ( + default_table_adapter, + table_to_columns, + get_primary_key, + ReflectionLevel, + TTypeAdapter, +) + + +@dlt.source +def sql_database( + credentials: Union[ConnectionStringCredentials, Engine, str] = dlt.secrets.value, + schema: Optional[str] = dlt.config.value, + metadata: Optional[MetaData] = None, + table_names: Optional[List[str]] = dlt.config.value, + chunk_size: int = 50000, + backend: TableBackend = "sqlalchemy", + detect_precision_hints: Optional[bool] = False, + reflection_level: Optional[ReflectionLevel] = "full", + defer_table_reflect: Optional[bool] = None, + table_adapter_callback: Callable[[Table], None] = None, + backend_kwargs: Dict[str, Any] = None, + include_views: bool = False, + type_adapter_callback: Optional[TTypeAdapter] = None, + query_adapter_callback: Optional[TQueryAdapter] = None, +) -> Iterable[DltResource]: + """ + A dlt source which loads data from an SQL database using SQLAlchemy. + Resources are automatically created for each table in the schema or from the given list of tables. + + Args: + credentials (Union[ConnectionStringCredentials, Engine, str]): Database credentials or an `sqlalchemy.Engine` instance. + schema (Optional[str]): Name of the database schema to load (if different from default). + metadata (Optional[MetaData]): Optional `sqlalchemy.MetaData` instance. `schema` argument is ignored when this is used. + table_names (Optional[List[str]]): A list of table names to load. By default, all tables in the schema are loaded. + chunk_size (int): Number of rows yielded in one batch. SQL Alchemy will create additional internal rows buffer twice the chunk size. + backend (TableBackend): Type of backend to generate table data. One of: "sqlalchemy", "pyarrow", "pandas" and "connectorx". + "sqlalchemy" yields batches as lists of Python dictionaries, "pyarrow" and "connectorx" yield batches as arrow tables, "pandas" yields panda frames. + "sqlalchemy" is the default and does not require additional dependencies, "pyarrow" creates stable destination schemas with correct data types, + "connectorx" is typically the fastest but ignores the "chunk_size" so you must deal with large tables yourself. + detect_precision_hints (bool): Deprecated. Use `reflection_level`. Set column precision and scale hints for supported data types in the target schema based on the columns in the source tables. + This is disabled by default. + reflection_level: (ReflectionLevel): Specifies how much information should be reflected from the source database schema. + "minimal": Only table names, nullability and primary keys are reflected. Data types are inferred from the data. + "full": Data types will be reflected on top of "minimal". `dlt` will coerce the data into reflected types if necessary. This is the default option. + "full_with_precision": Sets precision and scale on supported data types (ie. decimal, text, binary). Creates big and regular integer types. + defer_table_reflect (bool): Will connect and reflect table schema only when yielding data. Requires table_names to be explicitly passed. + Enable this option when running on Airflow. Available on dlt 0.4.4 and later. + table_adapter_callback: (Callable): Receives each reflected table. May be used to modify the list of columns that will be selected. + backend_kwargs (**kwargs): kwargs passed to table backend ie. "conn" is used to pass specialized connection string to connectorx. + include_views (bool): Reflect views as well as tables. Note view names included in `table_names` are always included regardless of this setting. + type_adapter_callback(Optional[Callable]): Callable to override type inference when reflecting columns. + Argument is a single sqlalchemy data type (`TypeEngine` instance) and it should return another sqlalchemy data type, or `None` (type will be inferred from data) + query_adapter_callback(Optional[Callable[Select, Table], Select]): Callable to override the SELECT query used to fetch data from the table. + The callback receives the sqlalchemy `Select` and corresponding `Table` objects and should return the modified `Select`. + + Returns: + Iterable[DltResource]: A list of DLT resources for each table to be loaded. + """ + # detect precision hints is deprecated + _detect_precision_hints_deprecated(detect_precision_hints) + + if detect_precision_hints: + reflection_level = "full_with_precision" + else: + reflection_level = reflection_level or "minimal" + + # set up alchemy engine + engine = engine_from_credentials(credentials) + engine.execution_options(stream_results=True, max_row_buffer=2 * chunk_size) + metadata = metadata or MetaData(schema=schema) + + # use provided tables or all tables + if table_names: + tables = [ + Table(name, metadata, autoload_with=None if defer_table_reflect else engine) + for name in table_names + ] + else: + if defer_table_reflect: + raise ValueError("You must pass table names to defer table reflection") + metadata.reflect(bind=engine, views=include_views) + tables = list(metadata.tables.values()) + + for table in tables: + yield sql_table( + credentials=credentials, + table=table.name, + schema=table.schema, + metadata=metadata, + chunk_size=chunk_size, + backend=backend, + reflection_level=reflection_level, + defer_table_reflect=defer_table_reflect, + table_adapter_callback=table_adapter_callback, + backend_kwargs=backend_kwargs, + type_adapter_callback=type_adapter_callback, + query_adapter_callback=query_adapter_callback, + ) + + +@dlt.resource(name=lambda args: args["table"], standalone=True, spec=SqlTableResourceConfiguration) +def sql_table( + credentials: Union[ConnectionStringCredentials, Engine, str] = dlt.secrets.value, + table: str = dlt.config.value, + schema: Optional[str] = dlt.config.value, + metadata: Optional[MetaData] = None, + incremental: Optional[dlt.sources.incremental[Any]] = None, + chunk_size: int = 50000, + backend: TableBackend = "sqlalchemy", + detect_precision_hints: Optional[bool] = None, + reflection_level: Optional[ReflectionLevel] = "full", + defer_table_reflect: Optional[bool] = None, + table_adapter_callback: Callable[[Table], None] = None, + backend_kwargs: Dict[str, Any] = None, + type_adapter_callback: Optional[TTypeAdapter] = None, + included_columns: Optional[List[str]] = None, + query_adapter_callback: Optional[TQueryAdapter] = None, +) -> DltResource: + """ + A dlt resource which loads data from an SQL database table using SQLAlchemy. + + Args: + credentials (Union[ConnectionStringCredentials, Engine, str]): Database credentials or an `Engine` instance representing the database connection. + table (str): Name of the table or view to load. + schema (Optional[str]): Optional name of the schema the table belongs to. + metadata (Optional[MetaData]): Optional `sqlalchemy.MetaData` instance. If provided, the `schema` argument is ignored. + incremental (Optional[dlt.sources.incremental[Any]]): Option to enable incremental loading for the table. + E.g., `incremental=dlt.sources.incremental('updated_at', pendulum.parse('2022-01-01T00:00:00Z'))` + chunk_size (int): Number of rows yielded in one batch. SQL Alchemy will create additional internal rows buffer twice the chunk size. + backend (TableBackend): Type of backend to generate table data. One of: "sqlalchemy", "pyarrow", "pandas" and "connectorx". + "sqlalchemy" yields batches as lists of Python dictionaries, "pyarrow" and "connectorx" yield batches as arrow tables, "pandas" yields panda frames. + "sqlalchemy" is the default and does not require additional dependencies, "pyarrow" creates stable destination schemas with correct data types, + "connectorx" is typically the fastest but ignores the "chunk_size" so you must deal with large tables yourself. + reflection_level: (ReflectionLevel): Specifies how much information should be reflected from the source database schema. + "minimal": Only table names, nullability and primary keys are reflected. Data types are inferred from the data. + "full": Data types will be reflected on top of "minimal". `dlt` will coerce the data into reflected types if necessary. This is the default option. + "full_with_precision": Sets precision and scale on supported data types (ie. decimal, text, binary). Creates big and regular integer types. + detect_precision_hints (bool): Deprecated. Use `reflection_level`. Set column precision and scale hints for supported data types in the target schema based on the columns in the source tables. + This is disabled by default. + defer_table_reflect (bool): Will connect and reflect table schema only when yielding data. Enable this option when running on Airflow. Available + on dlt 0.4.4 and later + table_adapter_callback: (Callable): Receives each reflected table. May be used to modify the list of columns that will be selected. + backend_kwargs (**kwargs): kwargs passed to table backend ie. "conn" is used to pass specialized connection string to connectorx. + type_adapter_callback(Optional[Callable]): Callable to override type inference when reflecting columns. + Argument is a single sqlalchemy data type (`TypeEngine` instance) and it should return another sqlalchemy data type, or `None` (type will be inferred from data) + included_columns (Optional[List[str]): List of column names to select from the table. If not provided, all columns are loaded. + query_adapter_callback(Optional[Callable[Select, Table], Select]): Callable to override the SELECT query used to fetch data from the table. + The callback receives the sqlalchemy `Select` and corresponding `Table` objects and should return the modified `Select`. + + Returns: + DltResource: The dlt resource for loading data from the SQL database table. + """ + _detect_precision_hints_deprecated(detect_precision_hints) + + if detect_precision_hints: + reflection_level = "full_with_precision" + else: + reflection_level = reflection_level or "minimal" + + engine = engine_from_credentials(credentials, may_dispose_after_use=True) + engine.execution_options(stream_results=True, max_row_buffer=2 * chunk_size) + metadata = metadata or MetaData(schema=schema) + + table_obj = metadata.tables.get("table") or Table( + table, metadata, autoload_with=None if defer_table_reflect else engine + ) + if not defer_table_reflect: + default_table_adapter(table_obj, included_columns) + if table_adapter_callback: + table_adapter_callback(table_obj) + + skip_nested_on_minimal = backend == "sqlalchemy" + return dlt.resource( + table_rows, + name=table_obj.name, + primary_key=get_primary_key(table_obj), + columns=table_to_columns( + table_obj, reflection_level, type_adapter_callback, skip_nested_on_minimal + ), + )( + engine, + table_obj, + chunk_size, + backend, + incremental=incremental, + reflection_level=reflection_level, + defer_table_reflect=defer_table_reflect, + table_adapter_callback=table_adapter_callback, + backend_kwargs=backend_kwargs, + type_adapter_callback=type_adapter_callback, + included_columns=included_columns, + query_adapter_callback=query_adapter_callback, + ) diff --git a/dlt/sources/sql_database/arrow_helpers.py b/dlt/sources/sql_database/arrow_helpers.py new file mode 100644 index 0000000000..898d8c3280 --- /dev/null +++ b/dlt/sources/sql_database/arrow_helpers.py @@ -0,0 +1,150 @@ +from typing import Any, Sequence, Optional + +from dlt.common.schema.typing import TTableSchemaColumns +from dlt.common import logger, json +from dlt.common.configuration import with_config +from dlt.common.destination import DestinationCapabilitiesContext +from dlt.common.json import custom_encode, map_nested_in_place + +from .schema_types import RowAny + + +@with_config +def columns_to_arrow( + columns_schema: TTableSchemaColumns, + caps: DestinationCapabilitiesContext = None, + tz: str = "UTC", +) -> Any: + """Converts `column_schema` to arrow schema using `caps` and `tz`. `caps` are injected from the container - which + is always the case if run within the pipeline. This will generate arrow schema compatible with the destination. + Otherwise generic capabilities are used + """ + from dlt.common.libs.pyarrow import pyarrow as pa, get_py_arrow_datatype + from dlt.common.destination.capabilities import DestinationCapabilitiesContext + + return pa.schema( + [ + pa.field( + name, + get_py_arrow_datatype( + schema_item, + caps or DestinationCapabilitiesContext.generic_capabilities(), + tz, + ), + nullable=schema_item.get("nullable", True), + ) + for name, schema_item in columns_schema.items() + if schema_item.get("data_type") is not None + ] + ) + + +def row_tuples_to_arrow(rows: Sequence[RowAny], columns: TTableSchemaColumns, tz: str) -> Any: + """Converts the rows to an arrow table using the columns schema. + Columns missing `data_type` will be inferred from the row data. + Columns with object types not supported by arrow are excluded from the resulting table. + """ + from dlt.common.libs.pyarrow import pyarrow as pa + import numpy as np + + try: + from pandas._libs import lib + + pivoted_rows = lib.to_object_array_tuples(rows).T + except ImportError: + logger.info( + "Pandas not installed, reverting to numpy.asarray to create a table which is slower" + ) + pivoted_rows = np.asarray(rows, dtype="object", order="k").T # type: ignore[call-overload] + + columnar = { + col: dat.ravel() for col, dat in zip(columns, np.vsplit(pivoted_rows, len(columns))) + } + columnar_known_types = { + col["name"]: columnar[col["name"]] + for col in columns.values() + if col.get("data_type") is not None + } + columnar_unknown_types = { + col["name"]: columnar[col["name"]] + for col in columns.values() + if col.get("data_type") is None + } + + arrow_schema = columns_to_arrow(columns, tz=tz) + + for idx in range(0, len(arrow_schema.names)): + field = arrow_schema.field(idx) + py_type = type(rows[0][idx]) + # cast double / float ndarrays to decimals if type mismatch, looks like decimals and floats are often mixed up in dialects + if pa.types.is_decimal(field.type) and issubclass(py_type, (str, float)): + logger.warning( + f"Field {field.name} was reflected as decimal type, but rows contains" + f" {py_type.__name__}. Additional cast is required which may slow down arrow table" + " generation." + ) + float_array = pa.array(columnar_known_types[field.name], type=pa.float64()) + columnar_known_types[field.name] = float_array.cast(field.type, safe=False) + if issubclass(py_type, (dict, list)): + logger.warning( + f"Field {field.name} was reflected as JSON type and needs to be serialized back to" + " string to be placed in arrow table. This will slow data extraction down. You" + " should cast JSON field to STRING in your database system ie. by creating and" + " extracting an SQL VIEW that selects with cast." + ) + json_str_array = pa.array( + [None if s is None else json.dumps(s) for s in columnar_known_types[field.name]] + ) + columnar_known_types[field.name] = json_str_array + + # If there are unknown type columns, first create a table to infer their types + if columnar_unknown_types: + new_schema_fields = [] + for key in list(columnar_unknown_types): + arrow_col: Optional[pa.Array] = None + try: + arrow_col = pa.array(columnar_unknown_types[key]) + if pa.types.is_null(arrow_col.type): + logger.warning( + f"Column {key} contains only NULL values and data type could not be" + " inferred. This column is removed from a arrow table" + ) + continue + + except pa.ArrowInvalid as e: + # Try coercing types not supported by arrow to a json friendly format + # E.g. dataclasses -> dict, UUID -> str + try: + arrow_col = pa.array( + map_nested_in_place(custom_encode, list(columnar_unknown_types[key])) + ) + logger.warning( + f"Column {key} contains a data type which is not supported by pyarrow and" + f" got converted into {arrow_col.type}. This slows down arrow table" + " generation." + ) + except (pa.ArrowInvalid, TypeError): + logger.warning( + f"Column {key} contains a data type which is not supported by pyarrow. This" + f" column will be ignored. Error: {e}" + ) + if arrow_col is not None: + columnar_known_types[key] = arrow_col + new_schema_fields.append( + pa.field( + key, + arrow_col.type, + nullable=columns[key]["nullable"], + ) + ) + + # New schema + column_order = {name: idx for idx, name in enumerate(columns)} + arrow_schema = pa.schema( + sorted( + list(arrow_schema) + new_schema_fields, + key=lambda x: column_order[x.name], + ) + ) + + return pa.Table.from_pydict(columnar_known_types, schema=arrow_schema) diff --git a/dlt/sources/sql_database/helpers.py b/dlt/sources/sql_database/helpers.py new file mode 100644 index 0000000000..1d758fe882 --- /dev/null +++ b/dlt/sources/sql_database/helpers.py @@ -0,0 +1,311 @@ +"""SQL database source helpers""" + +import warnings +from typing import ( + Callable, + Any, + Dict, + List, + Literal, + Optional, + Iterator, + Union, +) +import operator + +import dlt +from dlt.common.configuration.specs import BaseConfiguration, configspec +from dlt.common.exceptions import MissingDependencyException +from dlt.common.schema import TTableSchemaColumns +from dlt.common.typing import TDataItem, TSortOrder + +from dlt.sources.credentials import ConnectionStringCredentials + +from .arrow_helpers import row_tuples_to_arrow +from .schema_types import ( + default_table_adapter, + table_to_columns, + get_primary_key, + Table, + SelectAny, + ReflectionLevel, + TTypeAdapter, +) + +from dlt.common.libs.sql_alchemy import Engine, CompileError, create_engine + + +TableBackend = Literal["sqlalchemy", "pyarrow", "pandas", "connectorx"] +TQueryAdapter = Callable[[SelectAny, Table], SelectAny] + + +class TableLoader: + def __init__( + self, + engine: Engine, + backend: TableBackend, + table: Table, + columns: TTableSchemaColumns, + chunk_size: int = 1000, + incremental: Optional[dlt.sources.incremental[Any]] = None, + query_adapter_callback: Optional[TQueryAdapter] = None, + ) -> None: + self.engine = engine + self.backend = backend + self.table = table + self.columns = columns + self.chunk_size = chunk_size + self.query_adapter_callback = query_adapter_callback + self.incremental = incremental + if incremental: + try: + self.cursor_column = table.c[incremental.cursor_path] + except KeyError as e: + raise KeyError( + f"Cursor column '{incremental.cursor_path}' does not exist in table" + f" '{table.name}'" + ) from e + self.last_value = incremental.last_value + self.end_value = incremental.end_value + self.row_order: TSortOrder = self.incremental.row_order + else: + self.cursor_column = None + self.last_value = None + self.end_value = None + self.row_order = None + + def _make_query(self) -> SelectAny: + table = self.table + query = table.select() + if not self.incremental: + return query # type: ignore[no-any-return] + last_value_func = self.incremental.last_value_func + + # generate where + if last_value_func is max: # Query ordered and filtered according to last_value function + filter_op = operator.ge + filter_op_end = operator.lt + elif last_value_func is min: + filter_op = operator.le + filter_op_end = operator.gt + else: # Custom last_value, load everything and let incremental handle filtering + return query # type: ignore[no-any-return] + + if self.last_value is not None: + query = query.where(filter_op(self.cursor_column, self.last_value)) + if self.end_value is not None: + query = query.where(filter_op_end(self.cursor_column, self.end_value)) + + # generate order by from declared row order + order_by = None + if (self.row_order == "asc" and last_value_func is max) or ( + self.row_order == "desc" and last_value_func is min + ): + order_by = self.cursor_column.asc() + elif (self.row_order == "asc" and last_value_func is min) or ( + self.row_order == "desc" and last_value_func is max + ): + order_by = self.cursor_column.desc() + if order_by is not None: + query = query.order_by(order_by) + + return query # type: ignore[no-any-return] + + def make_query(self) -> SelectAny: + if self.query_adapter_callback: + return self.query_adapter_callback(self._make_query(), self.table) + return self._make_query() + + def load_rows(self, backend_kwargs: Dict[str, Any] = None) -> Iterator[TDataItem]: + # make copy of kwargs + backend_kwargs = dict(backend_kwargs or {}) + query = self.make_query() + if self.backend == "connectorx": + yield from self._load_rows_connectorx(query, backend_kwargs) + else: + yield from self._load_rows(query, backend_kwargs) + + def _load_rows(self, query: SelectAny, backend_kwargs: Dict[str, Any]) -> TDataItem: + with self.engine.connect() as conn: + result = conn.execution_options(yield_per=self.chunk_size).execute(query) + # NOTE: cursor returns not normalized column names! may be quite useful in case of Oracle dialect + # that normalizes columns + # columns = [c[0] for c in result.cursor.description] + columns = list(result.keys()) + for partition in result.partitions(size=self.chunk_size): + if self.backend == "sqlalchemy": + yield [dict(row._mapping) for row in partition] + elif self.backend == "pandas": + from dlt.common.libs.pandas_sql import _wrap_result + + df = _wrap_result( + partition, + columns, + **{"dtype_backend": "pyarrow", **backend_kwargs}, + ) + yield df + elif self.backend == "pyarrow": + yield row_tuples_to_arrow( + partition, self.columns, tz=backend_kwargs.get("tz", "UTC") + ) + + def _load_rows_connectorx( + self, query: SelectAny, backend_kwargs: Dict[str, Any] + ) -> Iterator[TDataItem]: + try: + import connectorx as cx + except ImportError: + raise MissingDependencyException("Connector X table backend", ["connectorx"]) + + # default settings + backend_kwargs = { + "return_type": "arrow2", + "protocol": "binary", + **backend_kwargs, + } + conn = backend_kwargs.pop( + "conn", + self.engine.url._replace( + drivername=self.engine.url.get_backend_name() + ).render_as_string(hide_password=False), + ) + try: + query_str = str(query.compile(self.engine, compile_kwargs={"literal_binds": True})) + except CompileError as ex: + raise NotImplementedError( + f"Query for table {self.table.name} could not be compiled to string to execute it" + " on ConnectorX. If you are on SQLAlchemy 1.4.x the causing exception is due to" + f" literals that cannot be rendered, upgrade to 2.x: {str(ex)}" + ) from ex + df = cx.read_sql(conn, query_str, **backend_kwargs) + yield df + + +def table_rows( + engine: Engine, + table: Table, + chunk_size: int, + backend: TableBackend, + incremental: Optional[dlt.sources.incremental[Any]] = None, + defer_table_reflect: bool = False, + table_adapter_callback: Callable[[Table], None] = None, + reflection_level: ReflectionLevel = "minimal", + backend_kwargs: Dict[str, Any] = None, + type_adapter_callback: Optional[TTypeAdapter] = None, + included_columns: Optional[List[str]] = None, + query_adapter_callback: Optional[TQueryAdapter] = None, +) -> Iterator[TDataItem]: + columns: TTableSchemaColumns = None + if defer_table_reflect: + table = Table(table.name, table.metadata, autoload_with=engine, extend_existing=True) # type: ignore[attr-defined] + default_table_adapter(table, included_columns) + if table_adapter_callback: + table_adapter_callback(table) + columns = table_to_columns(table, reflection_level, type_adapter_callback) + + # set the primary_key in the incremental + if incremental and incremental.primary_key is None: + primary_key = get_primary_key(table) + if primary_key is not None: + incremental.primary_key = primary_key + + # yield empty record to set hints + yield dlt.mark.with_hints( + [], + dlt.mark.make_hints( + primary_key=get_primary_key(table), + columns=columns, + ), + ) + else: + # table was already reflected + columns = table_to_columns(table, reflection_level, type_adapter_callback) + + loader = TableLoader( + engine, + backend, + table, + columns, + incremental=incremental, + chunk_size=chunk_size, + query_adapter_callback=query_adapter_callback, + ) + try: + yield from loader.load_rows(backend_kwargs) + finally: + # dispose the engine if created for this particular table + # NOTE: database wide engines are not disposed, not externally provided + if getattr(engine, "may_dispose_after_use", False): + engine.dispose() + + +def engine_from_credentials( + credentials: Union[ConnectionStringCredentials, Engine, str], + may_dispose_after_use: bool = False, + **backend_kwargs: Any, +) -> Engine: + if isinstance(credentials, Engine): + return credentials + if isinstance(credentials, ConnectionStringCredentials): + credentials = credentials.to_native_representation() + engine = create_engine(credentials, **backend_kwargs) + setattr(engine, "may_dispose_after_use", may_dispose_after_use) # noqa + return engine # type: ignore[no-any-return] + + +def unwrap_json_connector_x(field: str) -> TDataItem: + """Creates a transform function to be added with `add_map` that will unwrap JSON columns + ingested via connectorx. Such columns are additionally quoted and translate SQL NULL to json "null" + """ + import pyarrow.compute as pc + import pyarrow as pa + + def _unwrap(table: TDataItem) -> TDataItem: + col_index = table.column_names.index(field) + # remove quotes + column = table[field] # pc.replace_substring_regex(table[field], '"(.*)"', "\\1") + # convert json null to null + column = pc.replace_with_mask( + column, + pc.equal(column, "null").combine_chunks(), + pa.scalar(None, pa.large_string()), + ) + return table.set_column(col_index, table.schema.field(col_index), column) + + return _unwrap + + +def _detect_precision_hints_deprecated(value: Optional[bool]) -> None: + if value is None: + return + + msg = ( + "`detect_precision_hints` argument is deprecated and will be removed in a future release. " + ) + if value: + msg += "Use `reflection_level='full_with_precision'` which has the same effect instead." + + warnings.warn( + msg, + DeprecationWarning, + ) + + +@configspec +class SqlDatabaseTableConfiguration(BaseConfiguration): + incremental: Optional[dlt.sources.incremental] = None # type: ignore[type-arg] + included_columns: Optional[List[str]] = None + + +@configspec +class SqlTableResourceConfiguration(BaseConfiguration): + credentials: Union[ConnectionStringCredentials, Engine, str] = None + table: str = None + schema: Optional[str] = None + incremental: Optional[dlt.sources.incremental] = None # type: ignore[type-arg] + chunk_size: int = 50000 + backend: TableBackend = "sqlalchemy" + detect_precision_hints: Optional[bool] = None + defer_table_reflect: Optional[bool] = False + reflection_level: Optional[ReflectionLevel] = "full" + included_columns: Optional[List[str]] = None diff --git a/dlt/sources/sql_database/schema_types.py b/dlt/sources/sql_database/schema_types.py new file mode 100644 index 0000000000..2edb884d3f --- /dev/null +++ b/dlt/sources/sql_database/schema_types.py @@ -0,0 +1,163 @@ +from typing import ( + Optional, + Any, + Type, + TYPE_CHECKING, + Literal, + List, + Callable, + Union, +) +from typing_extensions import TypeAlias +from dlt.common.libs.sql_alchemy import Table, Column, Row, sqltypes, Select, TypeEngine + + +from dlt.common import logger +from dlt.common.schema.typing import TColumnSchema, TTableSchemaColumns + +ReflectionLevel = Literal["minimal", "full", "full_with_precision"] + + +# optionally create generics with any so they can be imported by dlt importer +if TYPE_CHECKING: + SelectAny: TypeAlias = Select[Any] # type: ignore[type-arg] + ColumnAny: TypeAlias = Column[Any] # type: ignore[type-arg] + RowAny: TypeAlias = Row[Any] # type: ignore[type-arg] + TypeEngineAny = TypeEngine[Any] # type: ignore[type-arg] +else: + SelectAny: TypeAlias = Type[Any] + ColumnAny: TypeAlias = Type[Any] + RowAny: TypeAlias = Type[Any] + TypeEngineAny = Type[Any] + + +TTypeAdapter = Callable[[TypeEngineAny], Optional[Union[TypeEngineAny, Type[TypeEngineAny]]]] + + +def default_table_adapter(table: Table, included_columns: Optional[List[str]]) -> None: + """Default table adapter being always called before custom one""" + if included_columns is not None: + # Delete columns not included in the load + for col in list(table._columns): # type: ignore[attr-defined] + if col.name not in included_columns: + table._columns.remove(col) # type: ignore[attr-defined] + for col in table._columns: # type: ignore[attr-defined] + sql_t = col.type + if hasattr(sqltypes, "Uuid") and isinstance(sql_t, sqltypes.Uuid): + # emit uuids as string by default + sql_t.as_uuid = False + + +def sqla_col_to_column_schema( + sql_col: ColumnAny, + reflection_level: ReflectionLevel, + type_adapter_callback: Optional[TTypeAdapter] = None, + skip_nested_columns_on_minimal: bool = False, +) -> Optional[TColumnSchema]: + """Infer dlt schema column type from an sqlalchemy type. + + If `add_precision` is set, precision and scale is inferred from that types that support it, + such as numeric, varchar, int, bigint. Numeric (decimal) types have always precision added. + """ + col: TColumnSchema = { + "name": sql_col.name, + "nullable": sql_col.nullable, + } + if reflection_level == "minimal": + # normalized into subtables + if isinstance(sql_col.type, sqltypes.JSON) and skip_nested_columns_on_minimal: + return None + return col + + sql_t = sql_col.type + + if type_adapter_callback: + sql_t = type_adapter_callback(sql_t) + # Check if sqla type class rather than instance is returned + if sql_t is not None and isinstance(sql_t, type): + sql_t = sql_t() + + if sql_t is None: + # Column ignored by callback + return col + + add_precision = reflection_level == "full_with_precision" + + if hasattr(sqltypes, "Uuid") and isinstance(sql_t, sqltypes.Uuid): + # we represent UUID as text by default, see default_table_adapter + col["data_type"] = "text" + if isinstance(sql_t, sqltypes.Numeric): + # check for Numeric type first and integer later, some numeric types (ie. Oracle) + # derive from both + # all Numeric types that are returned as floats will assume "double" type + # and returned as decimals will assume "decimal" type + if sql_t.asdecimal is False: + col["data_type"] = "double" + else: + col["data_type"] = "decimal" + if sql_t.precision is not None: + col["precision"] = sql_t.precision + # must have a precision for any meaningful scale + if sql_t.scale is not None: + col["scale"] = sql_t.scale + elif sql_t.decimal_return_scale is not None: + col["scale"] = sql_t.decimal_return_scale + elif isinstance(sql_t, sqltypes.SmallInteger): + col["data_type"] = "bigint" + if add_precision: + col["precision"] = 32 + elif isinstance(sql_t, sqltypes.Integer): + col["data_type"] = "bigint" + elif isinstance(sql_t, sqltypes.String): + col["data_type"] = "text" + if add_precision and sql_t.length: + col["precision"] = sql_t.length + elif isinstance(sql_t, sqltypes._Binary): + col["data_type"] = "binary" + if add_precision and sql_t.length: + col["precision"] = sql_t.length + elif isinstance(sql_t, sqltypes.DateTime): + col["data_type"] = "timestamp" + if add_precision: + col["timezone"] = sql_t.timezone + elif isinstance(sql_t, sqltypes.Date): + col["data_type"] = "date" + elif isinstance(sql_t, sqltypes.Time): + col["data_type"] = "time" + elif isinstance(sql_t, sqltypes.JSON): + col["data_type"] = "json" + elif isinstance(sql_t, sqltypes.Boolean): + col["data_type"] = "bool" + else: + logger.warning( + f"A column with name {sql_col.name} contains unknown data type {sql_t} which cannot be" + " mapped to `dlt` data type. When using sqlalchemy backend such data will be passed to" + " the normalizer. In case of `pyarrow` and `pandas` backend, data types are detected" + " from numpy ndarrays. In case of other backends, the behavior is backend-specific." + ) + return {key: value for key, value in col.items() if value is not None} # type: ignore[return-value] + + +def get_primary_key(table: Table) -> Optional[List[str]]: + """Create primary key or return None if no key defined""" + primary_key = [c.name for c in table.primary_key] + return primary_key if len(primary_key) > 0 else None + + +def table_to_columns( + table: Table, + reflection_level: ReflectionLevel = "full", + type_conversion_fallback: Optional[TTypeAdapter] = None, + skip_nested_columns_on_minimal: bool = False, +) -> TTableSchemaColumns: + """Convert an sqlalchemy table to a dlt table schema.""" + return { + col["name"]: col + for col in ( + sqla_col_to_column_schema( + c, reflection_level, type_conversion_fallback, skip_nested_columns_on_minimal + ) + for c in table.columns + ) + if col is not None + } diff --git a/dlt/sources/sql_database_pipeline.py b/dlt/sources/sql_database_pipeline.py new file mode 100644 index 0000000000..4b82997fd7 --- /dev/null +++ b/dlt/sources/sql_database_pipeline.py @@ -0,0 +1,361 @@ +# flake8: noqa +import humanize +from typing import Any +import os + +import dlt +from dlt.common import pendulum +from dlt.sources.credentials import ConnectionStringCredentials + +from dlt.sources.sql_database import sql_database, sql_table, Table + +from sqlalchemy.sql.sqltypes import TypeEngine +import sqlalchemy as sa + + +def load_select_tables_from_database() -> None: + """Use the sql_database source to reflect an entire database schema and load select tables from it. + + This example sources data from the public Rfam MySQL database. + """ + # Create a pipeline + pipeline = dlt.pipeline(pipeline_name="rfam", destination="duckdb", dataset_name="rfam_data") + + # Credentials for the sample database. + # Note: It is recommended to configure credentials in `.dlt/secrets.toml` under `sources.sql_database.credentials` + credentials = ConnectionStringCredentials( + "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam" + ) + # To pass the credentials from `secrets.toml`, comment out the above credentials. + # And the credentials will be automatically read from `secrets.toml`. + + # Configure the source to load a few select tables incrementally + source_1 = sql_database(credentials).with_resources("family", "clan") + + # Add incremental config to the resources. "updated" is a timestamp column in these tables that gets used as a cursor + source_1.family.apply_hints(incremental=dlt.sources.incremental("updated")) + source_1.clan.apply_hints(incremental=dlt.sources.incremental("updated")) + + # Run the pipeline. The merge write disposition merges existing rows in the destination by primary key + info = pipeline.run(source_1, write_disposition="merge") + print(info) + + # Load some other tables with replace write disposition. This overwrites the existing tables in destination + source_2 = sql_database(credentials).with_resources("features", "author") + info = pipeline.run(source_2, write_disposition="replace") + print(info) + + # Load a table incrementally with append write disposition + # this is good when a table only has new rows inserted, but not updated + source_3 = sql_database(credentials).with_resources("genome") + source_3.genome.apply_hints(incremental=dlt.sources.incremental("created")) + + info = pipeline.run(source_3, write_disposition="append") + print(info) + + +def load_entire_database() -> None: + """Use the sql_database source to completely load all tables in a database""" + pipeline = dlt.pipeline(pipeline_name="rfam", destination="duckdb", dataset_name="rfam_data") + + # By default the sql_database source reflects all tables in the schema + # The database credentials are sourced from the `.dlt/secrets.toml` configuration + source = sql_database() + + # Run the pipeline. For a large db this may take a while + info = pipeline.run(source, write_disposition="replace") + print(humanize.precisedelta(pipeline.last_trace.finished_at - pipeline.last_trace.started_at)) + print(info) + + +def load_standalone_table_resource() -> None: + """Load a few known tables with the standalone sql_table resource, request full schema and deferred + table reflection""" + pipeline = dlt.pipeline( + pipeline_name="rfam_database", + destination="duckdb", + dataset_name="rfam_data", + full_refresh=True, + ) + + # Load a table incrementally starting at a given date + # Adding incremental via argument like this makes extraction more efficient + # as only rows newer than the start date are fetched from the table + # we also use `detect_precision_hints` to get detailed column schema + # and defer_table_reflect to reflect schema only during execution + family = sql_table( + credentials=ConnectionStringCredentials( + "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam" + ), + table="family", + incremental=dlt.sources.incremental( + "updated", + ), + reflection_level="full_with_precision", + defer_table_reflect=True, + ) + # columns will be empty here due to defer_table_reflect set to True + print(family.compute_table_schema()) + + # Load all data from another table + genome = sql_table( + credentials="mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam", + table="genome", + reflection_level="full_with_precision", + defer_table_reflect=True, + ) + + # Run the resources together + info = pipeline.extract([family, genome], write_disposition="merge") + print(info) + # Show inferred columns + print(pipeline.default_schema.to_pretty_yaml()) + + +def select_columns() -> None: + """Uses table adapter callback to modify list of columns to be selected""" + pipeline = dlt.pipeline( + pipeline_name="rfam_database", + destination="duckdb", + dataset_name="rfam_data_cols", + full_refresh=True, + ) + + def table_adapter(table: Table) -> None: + print(table.name) + if table.name == "family": + # this is SqlAlchemy table. _columns are writable + # let's drop updated column + table._columns.remove(table.columns["updated"]) # type: ignore + + family = sql_table( + credentials="mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam", + table="family", + chunk_size=10, + reflection_level="full_with_precision", + table_adapter_callback=table_adapter, + ) + + # also we do not want the whole table, so we add limit to get just one chunk (10 records) + pipeline.run(family.add_limit(1)) + # only 10 rows + print(pipeline.last_trace.last_normalize_info) + # no "updated" column in "family" table + print(pipeline.default_schema.to_pretty_yaml()) + + +def select_with_end_value_and_row_order() -> None: + """Gets data from a table withing a specified range and sorts rows descending""" + pipeline = dlt.pipeline( + pipeline_name="rfam_database", + destination="duckdb", + dataset_name="rfam_data", + full_refresh=True, + ) + + # gets data from this range + start_date = pendulum.now().subtract(years=1) + end_date = pendulum.now() + + family = sql_table( + credentials="mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam", + table="family", + incremental=dlt.sources.incremental( # declares desc row order + "updated", initial_value=start_date, end_value=end_date, row_order="desc" + ), + chunk_size=10, + ) + # also we do not want the whole table, so we add limit to get just one chunk (10 records) + pipeline.run(family.add_limit(1)) + # only 10 rows + print(pipeline.last_trace.last_normalize_info) + + +def my_sql_via_pyarrow() -> None: + """Uses pyarrow backend to load tables from mysql""" + + # uncomment line below to get load_id into your data (slows pyarrow loading down) + # dlt.config["normalize.parquet_normalizer.add_dlt_load_id"] = True + + # Create a pipeline + pipeline = dlt.pipeline( + pipeline_name="rfam_cx", + destination="duckdb", + dataset_name="rfam_data_arrow_4", + ) + + def _double_as_decimal_adapter(table: sa.Table) -> None: + """Return double as double, not decimals, only works if you are using sqlalchemy 2.0""" + for column in table.columns.values(): + if hasattr(sa, "Double") and isinstance(column.type, sa.Double): + column.type.asdecimal = False + + sql_alchemy_source = sql_database( + "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam?&binary_prefix=true", + backend="pyarrow", + table_adapter_callback=_double_as_decimal_adapter, + ).with_resources("family", "genome") + + info = pipeline.run(sql_alchemy_source) + print(info) + + +def create_unsw_flow() -> None: + """Uploads UNSW_Flow dataset to postgres via csv stream skipping dlt normalizer. + You need to download the dataset from https://github.com/rdpahalavan/nids-datasets + """ + from pyarrow.parquet import ParquetFile + + # from dlt.destinations import postgres + + # use those config to get 3x speedup on parallelism + # [sources.data_writer] + # file_max_bytes=3000000 + # buffer_max_items=200000 + + # [normalize] + # workers=3 + + data_iter = ParquetFile("UNSW-NB15/Network-Flows/UNSW_Flow.parquet").iter_batches( + batch_size=128 * 1024 + ) + + pipeline = dlt.pipeline( + pipeline_name="unsw_upload", + # destination=postgres("postgres://loader:loader@localhost:5432/dlt_data"), + destination="postgres", + progress="log", + ) + pipeline.run( + data_iter, + dataset_name="speed_test", + table_name="unsw_flow_7", + loader_file_format="csv", + ) + + +def test_connectorx_speed() -> None: + """Uses unsw_flow dataset (~2mln rows, 25+ columns) to test connectorx speed""" + import os + + # from dlt.destinations import filesystem + + unsw_table = sql_table( + "postgresql://loader:loader@localhost:5432/dlt_data", + "unsw_flow_7", + "speed_test", + # this is ignored by connectorx + chunk_size=100000, + backend="connectorx", + # keep source data types + reflection_level="full_with_precision", + # just to demonstrate how to setup a separate connection string for connectorx + backend_kwargs={"conn": "postgresql://loader:loader@localhost:5432/dlt_data"}, + ) + + pipeline = dlt.pipeline( + pipeline_name="unsw_download", + destination="filesystem", + # destination=filesystem(os.path.abspath("../_storage/unsw")), + progress="log", + full_refresh=True, + ) + + info = pipeline.run( + unsw_table, + dataset_name="speed_test", + table_name="unsw_flow", + loader_file_format="parquet", + ) + print(info) + + +def test_pandas_backend_verbatim_decimals() -> None: + pipeline = dlt.pipeline( + pipeline_name="rfam_cx", + destination="duckdb", + dataset_name="rfam_data_pandas_2", + ) + + def _double_as_decimal_adapter(table: sa.Table) -> None: + """Emits decimals instead of floats.""" + for column in table.columns.values(): + if isinstance(column.type, sa.Float): + column.type.asdecimal = True + + sql_alchemy_source = sql_database( + "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam?&binary_prefix=true", + backend="pandas", + table_adapter_callback=_double_as_decimal_adapter, + chunk_size=100000, + # set coerce_float to False to represent them as string + backend_kwargs={"coerce_float": False, "dtype_backend": "numpy_nullable"}, + # preserve full typing info. this will parse + reflection_level="full_with_precision", + ).with_resources("family", "genome") + + info = pipeline.run(sql_alchemy_source) + print(info) + + +def use_type_adapter() -> None: + """Example use of type adapter to coerce unknown data types""" + pipeline = dlt.pipeline( + pipeline_name="dummy", + destination="postgres", + dataset_name="dummy", + ) + + def type_adapter(sql_type: Any) -> Any: + if isinstance(sql_type, sa.ARRAY): + return sa.JSON() # Load arrays as JSON + return sql_type + + sql_alchemy_source = sql_database( + "postgresql://loader:loader@localhost:5432/dlt_data", + backend="pyarrow", + type_adapter_callback=type_adapter, + reflection_level="full_with_precision", + ).with_resources("table_with_array_column") + + info = pipeline.run(sql_alchemy_source) + print(info) + + +def specify_columns_to_load() -> None: + """Run the SQL database source with a subset of table columns loaded""" + pipeline = dlt.pipeline( + pipeline_name="dummy", + destination="duckdb", + dataset_name="dummy", + ) + + # Columns can be specified per table in env var (json array) or in `.dlt/config.toml` + os.environ["SOURCES__SQL_DATABASE__FAMILY__INCLUDED_COLUMNS"] = '["rfam_acc", "description"]' + + sql_alchemy_source = sql_database( + "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam?&binary_prefix=true", + backend="pyarrow", + reflection_level="full_with_precision", + ).with_resources("family", "genome") + + info = pipeline.run(sql_alchemy_source) + print(info) + + +if __name__ == "__main__": + # Load selected tables with different settings + # load_select_tables_from_database() + + # load a table and select columns + # select_columns() + + # load_entire_database() + # select_with_end_value_and_row_order() + + # Load tables with the standalone table resource + load_standalone_table_resource() + + # Load all tables from the database. + # Warning: The sample database is very large + # load_entire_database() diff --git a/docs/examples/CONTRIBUTING.md b/docs/examples/CONTRIBUTING.md index 625a09d9c0..bca43ba9eb 100644 --- a/docs/examples/CONTRIBUTING.md +++ b/docs/examples/CONTRIBUTING.md @@ -10,7 +10,7 @@ Note: All paths in this guide are relative to the `dlt` repository directory. - Update the doc string which will compromise the generated markdown file, check the other examples how it is done - If your example requires any secrets, add the vars to the example.secrects.toml but do not enter the values. - Add your example code, make sure you have a `if __name__ = "__main__"` clause in which you run the example script, this will be used for testing -- You should add one or two assertions after running your example and maybe also `load_info.raise_on_failed_jobs()`, this will help greatly with testing +- You should add one or two assertions after running your example ## Testing - You can test your example simply by running your example script from your example folder. On CI a test will be automatically generated. @@ -31,4 +31,4 @@ If you use any secrets for the code snippets, e.g. Zendesk requires credentials. If your example requires any additional dependency, then you can add it - To `pyproject.toml` in the `[tool.poetry.group.docs.dependencies]` section. -- Do not forget to update your `poetry.lock` file with `poetry lock --no-update` command and commit. \ No newline at end of file +- Do not forget to update your `poetry.lock` file with `poetry lock --no-update` command and commit. diff --git a/docs/examples/_template/_template.py b/docs/examples/_template/_template.py index cdd38f8204..ae156b6f0b 100644 --- a/docs/examples/_template/_template.py +++ b/docs/examples/_template/_template.py @@ -25,6 +25,3 @@ # Extract, normalize, and load the data load_info = pipeline.run([1, 2, 3], table_name="player") print(load_info) - - # make sure nothing failed - load_info.raise_on_failed_jobs() diff --git a/docs/examples/chess/chess.py b/docs/examples/chess/chess.py index 7b577c2646..6af431b330 100644 --- a/docs/examples/chess/chess.py +++ b/docs/examples/chess/chess.py @@ -56,6 +56,3 @@ def players_games(username: Any) -> Iterator[TDataItems]: ).run(chess(max_players=5, month=9)) # display where the data went print(load_info) - - # make sure nothing failed - load_info.raise_on_failed_jobs() diff --git a/docs/examples/chess_production/chess_production.py b/docs/examples/chess_production/chess_production.py index c0f11203c8..196bc13e18 100644 --- a/docs/examples/chess_production/chess_production.py +++ b/docs/examples/chess_production/chess_production.py @@ -88,8 +88,6 @@ def load_data_with_retry(pipeline, data): load_info = pipeline.run(data) logger.info(str(load_info)) - # raise on failed jobs - load_info.raise_on_failed_jobs() # send notification send_slack_message( pipeline.runtime_config.slack_incoming_hook, "Data was successfully loaded!" @@ -169,7 +167,4 @@ def load_data_with_retry(pipeline, data): ) # get data for a few famous players data = chess(max_players=MAX_PLAYERS) - load_info = load_data_with_retry(pipeline, data) - - # make sure nothing failed - load_info.raise_on_failed_jobs() + load_data_with_retry(pipeline, data) diff --git a/docs/examples/connector_x_arrow/connector_x_arrow.py b/docs/examples/connector_x_arrow/connector_x_arrow.py index 9603fb2ba0..a321f94580 100644 --- a/docs/examples/connector_x_arrow/connector_x_arrow.py +++ b/docs/examples/connector_x_arrow/connector_x_arrow.py @@ -67,6 +67,3 @@ def genome_resource(): # check that stuff was loaded row_counts = pipeline.last_trace.last_normalize_info.row_counts assert row_counts["genome"] == 1000 - - # make sure nothing failed - load_info.raise_on_failed_jobs() diff --git a/docs/examples/custom_destination_bigquery/custom_destination_bigquery.py b/docs/examples/custom_destination_bigquery/custom_destination_bigquery.py index 48a16f15c0..8d3263ce86 100644 --- a/docs/examples/custom_destination_bigquery/custom_destination_bigquery.py +++ b/docs/examples/custom_destination_bigquery/custom_destination_bigquery.py @@ -10,7 +10,7 @@ We'll learn how to: - Use [built-in credentials.](../general-usage/credentials/complex_types#gcp-credentials) - Use the [custom destination.](../dlt-ecosystem/destinations/destination.md) -- Use pyarrow tables to create complex column types on BigQuery. +- Use pyarrow tables to create nested column types on BigQuery. - Use BigQuery `autodetect=True` for schema inference from parquet files. """ @@ -91,6 +91,3 @@ def bigquery_insert( load_info = pipeline.run(resource(url=OWID_DISASTERS_URL)) print(load_info) - - # make sure nothing failed - load_info.raise_on_failed_jobs() diff --git a/docs/examples/custom_destination_lancedb/custom_destination_lancedb.py b/docs/examples/custom_destination_lancedb/custom_destination_lancedb.py index ba815d4fcd..305c7d1f1a 100644 --- a/docs/examples/custom_destination_lancedb/custom_destination_lancedb.py +++ b/docs/examples/custom_destination_lancedb/custom_destination_lancedb.py @@ -1,6 +1,6 @@ """ --- -title: Custom Destination with LanceDB +title: Custom destination with LanceDB description: Learn how use the custom destination to load to LanceDB. keywords: [destination, credentials, example, lancedb, custom destination, vectorstore, AI, LLM] --- @@ -21,7 +21,6 @@ __source_name__ = "spotify" import datetime # noqa: I251 -import os from dataclasses import dataclass, fields from pathlib import Path from typing import Any @@ -142,7 +141,6 @@ def lancedb_destination(items: TDataItems, table: TTableSchema) -> None: ) load_info = pipeline.run(spotify_shows()) - load_info.raise_on_failed_jobs() print(load_info) row_counts = pipeline.last_trace.last_normalize_info diff --git a/docs/examples/custom_naming/custom_naming.py b/docs/examples/custom_naming/custom_naming.py index e99e582213..74feeb13ec 100644 --- a/docs/examples/custom_naming/custom_naming.py +++ b/docs/examples/custom_naming/custom_naming.py @@ -46,8 +46,6 @@ # Extract, normalize, and load the data load_info = pipeline.run([{"StückId": 1}], table_name="Ausrüstung") print(load_info) - # make sure nothing failed - load_info.raise_on_failed_jobs() with pipeline.sql_client() as client: # NOTE: we quote case sensitive identifers with client.execute_query('SELECT "StückId" FROM "Ausrüstung"') as cur: @@ -66,12 +64,10 @@ # duckdb is case insensitive so tables and columns below would clash but sql_ci_no_collision prevents that data_1 = {"ItemID": 1, "itemid": "collides"} load_info = pipeline.run([data_1], table_name="BigData") - load_info.raise_on_failed_jobs() data_2 = {"1Data": 1, "_1data": "collides"} # use colliding table load_info = pipeline.run([data_2], table_name="bigdata") - load_info.raise_on_failed_jobs() with pipeline.sql_client() as client: from duckdb import DuckDBPyConnection diff --git a/docs/examples/google_sheets/google_sheets.py b/docs/examples/google_sheets/google_sheets.py index fbc0686fb9..716009865e 100644 --- a/docs/examples/google_sheets/google_sheets.py +++ b/docs/examples/google_sheets/google_sheets.py @@ -100,6 +100,3 @@ def get_sheet(sheet_name: str) -> Iterator[DictStrAny]: print(row_counts.keys()) assert row_counts["hidden_columns_merged_cells"] == 7 assert row_counts["blank_columns"] == 21 - - # make sure nothing failed - load_info.raise_on_failed_jobs() diff --git a/docs/examples/incremental_loading/incremental_loading.py b/docs/examples/incremental_loading/incremental_loading.py index f1de4eecfe..90c5e93347 100644 --- a/docs/examples/incremental_loading/incremental_loading.py +++ b/docs/examples/incremental_loading/incremental_loading.py @@ -147,6 +147,3 @@ def get_pages( # check that stuff was loaded row_counts = pipeline.last_trace.last_normalize_info.row_counts assert row_counts["ticket_events"] == 17 - - # make sure nothing failed - load_info.raise_on_failed_jobs() diff --git a/docs/examples/nested_data/nested_data.py b/docs/examples/nested_data/nested_data.py index 046e566efd..9174bd4809 100644 --- a/docs/examples/nested_data/nested_data.py +++ b/docs/examples/nested_data/nested_data.py @@ -35,11 +35,11 @@ CHUNK_SIZE = 10000 -# You can limit how deep dlt goes when generating child tables. -# By default, the library will descend and generate child tables +# You can limit how deep dlt goes when generating nested tables. +# By default, the library will descend and generate nested tables # for all nested lists, without a limit. -# In this example, we specify that we only want to generate child tables up to level 2, -# so there will be only one level of child tables within child tables. +# In this example, we specify that we only want to generate nested tables up to level 2, +# so there will be only one level of nested tables within nested tables. @dlt.source(max_table_nesting=2) def mongodb_collection( connection_url: str = dlt.secrets.value, @@ -128,9 +128,6 @@ def convert_mongo_objs(value: Any) -> Any: tables.pop("_dlt_pipeline_state") assert len(tables) == 7, pipeline.last_trace.last_normalize_info - # make sure nothing failed - load_info.raise_on_failed_jobs() - # The second method involves setting the max_table_nesting attribute directly # on the source data object. # This allows for dynamic control over the maximum nesting @@ -149,25 +146,19 @@ def convert_mongo_objs(value: Any) -> Any: tables.pop("_dlt_pipeline_state") assert len(tables) == 1, pipeline.last_trace.last_normalize_info - # make sure nothing failed - load_info.raise_on_failed_jobs() - # The third method involves applying data type hints to specific columns in the data. # In this case, we tell dlt that column 'cast' (containing a list of actors) - # in 'movies' table should have type complex which means - # that it will be loaded as JSON/struct and not as child table. + # in 'movies' table should have type 'json' which means + # that it will be loaded as JSON/struct and not as nested table. pipeline = dlt.pipeline( pipeline_name="mongodb_pipeline", destination="duckdb", dataset_name="unpacked_data_without_cast", ) source_data = mongodb_collection(collection="movies", write_disposition="replace") - source_data.movies.apply_hints(columns={"cast": {"data_type": "complex"}}) + source_data.movies.apply_hints(columns={"cast": {"data_type": "json"}}) load_info = pipeline.run(source_data) print(load_info) tables = pipeline.last_trace.last_normalize_info.row_counts tables.pop("_dlt_pipeline_state") assert len(tables) == 6, pipeline.last_trace.last_normalize_info - - # make sure nothing failed - load_info.raise_on_failed_jobs() diff --git a/docs/examples/parent_child_relationship/test_parent_child_relationship.py b/docs/examples/parent_child_relationship/test_parent_child_relationship.py deleted file mode 100644 index 95d1bade97..0000000000 --- a/docs/examples/parent_child_relationship/test_parent_child_relationship.py +++ /dev/null @@ -1,76 +0,0 @@ -import pytest - -from tests.utils import skipifgithubfork - - -""" ---- -title: Load parent table records into child table -description: Learn how to integrate custom parent keys into child records -keywords: [parent child relationship, parent key] ---- - -This example demonstrates handling data with parent-child relationships using -the `dlt` library. You learn how to integrate specific fields (e.g., primary, -foreign keys) from a parent record into each child record. - -In this example, we'll explore how to: - -- Add `parent_id` into each child record using `add_parent_id` function -- Use the [`add_map` function](https://dlthub.com/docs/api_reference/extract/resource#add_map) to apply this -custom logic to every record in the dataset - -:::note important -Please note that dlt metadata, including `_dlt_id` and `_dlt_load_id`, will still be loaded into the tables. -::: -""" - -from typing import List, Dict, Any, Generator -import dlt - - -# Define a dlt resource with write disposition to 'merge' -@dlt.resource(name="parent_with_children", write_disposition={"disposition": "merge"}) -def data_source() -> Generator[List[Dict[str, Any]], None, None]: - # Example data - data = [ - { - "parent_id": 1, - "parent_name": "Alice", - "children": [ - {"child_id": 1, "child_name": "Child 1"}, - {"child_id": 2, "child_name": "Child 2"}, - ], - }, - { - "parent_id": 2, - "parent_name": "Bob", - "children": [{"child_id": 3, "child_name": "Child 3"}], - }, - ] - - yield data - - -# Function to add parent_id to each child record within a parent record -def add_parent_id(record: Dict[str, Any]) -> Dict[str, Any]: - parent_id_key = "parent_id" - for child in record["children"]: - child[parent_id_key] = record[parent_id_key] - return record - - -@skipifgithubfork -@pytest.mark.forked -def test_parent_child_relationship(): - # Create and configure the dlt pipeline - pipeline = dlt.pipeline( - pipeline_name="generic_pipeline", - destination="duckdb", - dataset_name="dataset", - ) - - # Run the pipeline - load_info = pipeline.run(data_source().add_map(add_parent_id), primary_key="parent_id") - # Output the load information after pipeline execution - print(load_info) diff --git a/docs/examples/pdf_to_weaviate/pdf_to_weaviate.py b/docs/examples/pdf_to_weaviate/pdf_to_weaviate.py index 5fbba98a21..76629fc612 100644 --- a/docs/examples/pdf_to_weaviate/pdf_to_weaviate.py +++ b/docs/examples/pdf_to_weaviate/pdf_to_weaviate.py @@ -80,6 +80,3 @@ def pdf_to_text(file_item, separate_pages: bool = False): client = weaviate.Client("http://localhost:8080") # get text of all the invoices in InvoiceText class we just created above print(client.query.get("InvoiceText", ["text", "file_name", "mtime", "page_id"]).do()) - - # make sure nothing failed - load_info.raise_on_failed_jobs() diff --git a/docs/examples/postgres_to_postgres/postgres_to_postgres.py b/docs/examples/postgres_to_postgres/postgres_to_postgres.py index c6502f236a..aaebb224fd 100644 --- a/docs/examples/postgres_to_postgres/postgres_to_postgres.py +++ b/docs/examples/postgres_to_postgres/postgres_to_postgres.py @@ -33,7 +33,7 @@ Install `dlt` with `duckdb` as extra, also `connectorx`, Postgres adapter and progress bar tool: ```sh -pip install dlt[duckdb] connectorx pyarrow psycopg2-binary alive-progress +pip install "dlt[duckdb]" connectorx pyarrow psycopg2-binary alive-progress ``` Run the example: @@ -214,9 +214,6 @@ def table_desc(table_name, pk, schema_name, order_date, columns="*"): assert row_counts["table_1"] == 9 assert row_counts["table_2"] == 9 - # make sure nothing failed - load_info.raise_on_failed_jobs() - if load_type == "replace": # 4. Load DuckDB local database into Postgres print("##################################### START DUCKDB LOAD ########") diff --git a/docs/examples/propagate_hints/__init__.py b/docs/examples/propagate_hints/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/docs/examples/parent_child_relationship/parent_child_relationship.py b/docs/examples/propagate_hints/propagate_hints.py similarity index 84% rename from docs/examples/parent_child_relationship/parent_child_relationship.py rename to docs/examples/propagate_hints/propagate_hints.py index 6de00ffb28..c520d9f85a 100644 --- a/docs/examples/parent_child_relationship/parent_child_relationship.py +++ b/docs/examples/propagate_hints/propagate_hints.py @@ -1,12 +1,11 @@ """ --- -title: Load parent table records into child table -description: Learn how to integrate custom parent keys into child records -keywords: [parent child relationship, parent key] +title: Propagate primary_key from root to nested tables +description: Learn how to propagate any column to nested tables +keywords: [root table, nested reference, parent key] --- -This example demonstrates handling data with parent-child relationships using the `dlt` library. -You learn how to integrate specific fields (e.g., primary, foreign keys) from a parent record into each child record. +You learn how to propagate specific fields (e.g., primary, foreign keys) from a parent record into each child record. In this example, we'll explore how to: diff --git a/docs/examples/qdrant_zendesk/qdrant_zendesk.py b/docs/examples/qdrant_zendesk/qdrant_zendesk.py index 9b6fbee150..18eea002b3 100644 --- a/docs/examples/qdrant_zendesk/qdrant_zendesk.py +++ b/docs/examples/qdrant_zendesk/qdrant_zendesk.py @@ -175,9 +175,6 @@ def get_pages( print(load_info) - # make sure nothing failed - load_info.raise_on_failed_jobs() - # getting the authenticated Qdrant client to connect to your Qdrant database with pipeline.destination_client() as destination_client: from qdrant_client import QdrantClient @@ -194,6 +191,3 @@ def get_pages( ) assert len(response) <= 3 and len(response) > 0 - - # make sure nothing failed - load_info.raise_on_failed_jobs() diff --git a/docs/examples/transformers/transformers.py b/docs/examples/transformers/transformers.py index 14d23de12d..ebf1f935ba 100644 --- a/docs/examples/transformers/transformers.py +++ b/docs/examples/transformers/transformers.py @@ -78,6 +78,3 @@ def species(pokemon_details): assert row_counts["pokemon"] == 20 assert row_counts["species"] == 20 assert "pokemon_list" not in row_counts - - # make sure nothing failed - load_info.raise_on_failed_jobs() diff --git a/docs/technical/customization_and_hacking.md b/docs/technical/customization_and_hacking.md deleted file mode 100644 index 21b5e6a9b9..0000000000 --- a/docs/technical/customization_and_hacking.md +++ /dev/null @@ -1,60 +0,0 @@ -# Customization - -Customizations allow the user to change `dlt` behaviour without modifying the source code (which we call `hacking` 😄) Most of the customizations require writing python on yaml snipppets. - -⛔ not implemented, hard to add - -☮️ not implemented, easy to add - -# in schema file - -## global settings -- default column hints, types -- column propagation -- max nesting -- choose type autodetectors -- ⛔ add custom type autodetectors - more powerful than you think - - -## table/column settings -- table and column hints -- include and exclude filters -- ⛔ last value as decorator for common cases (ie. jsonpath + max operator + automatic filtering of the results) - -# source and resource creation -when you implement new source/resource - -## source -- providing custom schema via file -- providing custom schema in the code + decorator -- providing the nesting level via decorator - -## resource -- providing table schema via hints (that includes the column definitions and column hints) -- resources may be parametrized (generators!) -- transformers also may be prametrized! (tutorial in progress) -- yielding metadata with the data items -- yielding custom data (ie. panda frames) (yes but last lambda must convert it to ) - -## extraction -- [retry with the decorator](/examples/chess/chess.py) -- [run resources and transformers in parallel threads](/examples/chess/chess.py) and test named `test_evolve_schema` -- run async resources and transformers - -# source and resource modifications -- resource selection - -## modification of sources and resources after they are created -must be done before passing to `run` method. - -- adding custom resources and transformers to the pipeline after it is created -- easy change the table name for a resource (currently the whole template must be changed) -- ☮️ adding stateles lambdas (row transformations) to the resources: map, filter, flat_map (reverse pivot) -- ☮️ adding stateful lambdas (row transformations with the write access to pipeline state) -- change the source name - - -# pipeline callbacks and hooks -those are not implemented -https://github.com/dlt-hub/dlt/issues/63 - diff --git a/docs/website/blog/2023-02-16-dlthub-mission.md b/docs/website/blog/2023-02-16-dlthub-mission.md deleted file mode 100644 index 37043a3b06..0000000000 --- a/docs/website/blog/2023-02-16-dlthub-mission.md +++ /dev/null @@ -1,28 +0,0 @@ ---- -slug: dlthub-mission -title: dltHub Mission -authors: - name: Matthaus Krzykowski - title: Co-Founder & CEO at dltHub - url: https://twitter.com/matthausk - image_url: https://pbs.twimg.com/profile_images/642282396751130624/9ixo0Opj_400x400.jpg -tags: [dlthub, mission, dlt] ---- - -**dltHub Mission** - -Since 2017, the number of Python users has been increasing by millions annually. The vast majority of these people leverage Python as a tool to solve problems at work. **Our mission is to make this next generation of Python users autonomous when they create and use data in their organizations.** For this end, we are building an open source Python library called data load tool (dlt). - -These Python practitioners, as we call them, use dlt in their scripts to turn messy, unstructured data into regularly updated datasets. dlt empowers them to create highly scalable, easy to maintain, straightforward to deploy data pipelines without having to wait for help from a data engineer. When organizations eventually bring in data engineers to help with data loading, these engineers build on their work and evolve dlt pipelines. - -We are dedicated to keeping dlt an open source project surrounded by a vibrant, engaged community. To make this sustainable, dltHub stewards dlt while also offering additional software and services that generate revenue (similar to what GitHub does with Git). - -**Why does dltHub exist?** - -We believe in a world where data loading becomes a commodity. A world where hundreds of thousands of pipelines will be created, shared, and deployed. A world where data sets, reports, and analytics will be written and shared publicly and privately. - -To achieve our mission to make this next generation of Python users autonomous when they create and use data in their organizations, we need to address the requirements of both the Python practitioner and the data engineer with a minimal Python library. We also need dltHub to become the GitHub for data pipelines, facilitating and supporting the ecosystem of pipeline creators and maintainers as well as the other data folks who consume and analyze the data loaded. - -There are lots of ETL/ELT tools available (300+!). Yet, as we engaged with Python practioners over the last one and half years, we found few Python practitioners that use traditional data ingestion tools. Only a handful have even heard of them. Very simplified, there’s two approaches in traditional data ingestion tools and neither works for this new generation: 1) SaaS solutions that handle the entire data loading process and 2) object-oriented frameworks for software engineers. - -SaaS solutions do not give Python practitioners enough credit, while frameworks expect too much of them. In other words, there's no “Jupyter Notebook, pandas, NumPy, etc. for data loading” that meets users needs. As millions of Python practioners are now entering organizations every year, we think this should exist. \ No newline at end of file diff --git a/docs/website/blog/2023-02-22-dlthub-who-we-serve.md b/docs/website/blog/2023-02-22-dlthub-who-we-serve.md deleted file mode 100644 index 114b953275..0000000000 --- a/docs/website/blog/2023-02-22-dlthub-who-we-serve.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -slug: dlthub-who-we-serve -title: Who we serve -authors: - name: Matthaus Krzykowski - title: Co-Founder & CEO at dltHub - url: https://twitter.com/matthausk - image_url: https://pbs.twimg.com/profile_images/642282396751130624/9ixo0Opj_400x400.jpg -tags: [dlthub, python practioners, data engineers, dlt] ---- - -The number of Python developers increased from [7 million in 2017](https://adtmag.com/articles/2018/09/24/developer-economics-survey.aspx) to [15.7 million in Q1 2021](https://slashdata-website-cms.s3.amazonaws.com/sample_reports/VZtJWxZw5Q9NDSAQ.pdf) and grew by [3 million (20%) between Q4 2021 and Q1 2022 alone](https://www.tiobe.com/tiobe-index/), making it the [most popular programming language in Q3 2022](https://spectrum.ieee.org/top-programming-languages-2022). A large percentage of this new group are what we call **Python practitioners**—[data folks and scripters](https://lp.jetbrains.com/python-developers-survey-2021/#PurposesUsingPython). This group uses Python to do tasks in their jobs, but they do not consider themselves to be software engineers. - -They are entering modern organizations in masse. Organizations often [employ them for data-related jobs](https://spectrum.ieee.org/top-programming-languages-2022), especially in data engineering, data science / ML, and analytics. They must work with established data sources, data stores, and data pipelines that are essential to the business of these organizations These companies, though, are not providing them with the type of tooling they learnt to expect. There’s no “Jupyter Notebook, pandas, NumPy, etc. for data loading” for them to use. - -At this stage of dlt we are focused on serving the needs of organizations with 150 employees or less. Companies of this size typically begin making their first data hires. They want data to be at their core: their CEOs may want to make their companies more “data driven” and “user feedback centric”. Their CTOs may want to “build a data warehouse for automation and self service”. They frequently are eager to take advantage of the skills of the Python practioners they have hired. - -To achieve our mission of making this next generation of Python users autonomous in these organizations, we believe we need to build dlt in a “Pythonic” way. Anyone that can write a loop in Python script should be able to write a source and load it. There should minimal learning curve. Anyone in these organizations that gets basic Python should be able to use dlt right away. - -However, we also recognize the need dlt to be loved not only by Python users but also data engineers to fulfill our mission. This is crucial because eventually these folks will be brought in to help with data loading in an organization. We need data engineers to evolve dlt pipelines rather than ripping them out and replacing them like they almost always do to scripts written by Python practitioners today. - -To develop with dlt, anyone can install it like any other Python library with `pip install dlt`. They can then run `dlt init` and be ready to go. Already today data engineers love the automatic schema inference and evolution as well as the customizability of dlt. diff --git a/docs/website/blog/2023-03-09-duckdb-1M-downloads-users.mdx b/docs/website/blog/2023-03-09-duckdb-1M-downloads-users.mdx deleted file mode 100644 index d862cb686f..0000000000 --- a/docs/website/blog/2023-03-09-duckdb-1M-downloads-users.mdx +++ /dev/null @@ -1,116 +0,0 @@ ---- -slug: duckdb-1M-downloads-users -title: As DuckDB crosses 1M downloads / month, what do its users do? -authors: - name: Matthaus Krzykowski - title: Co-Founder & CEO at dltHub - url: https://twitter.com/matthausk - image_url: https://pbs.twimg.com/profile_images/642282396751130624/9ixo0Opj_400x400.jpg -tags: [DuckDB] ---- -import { TwitterTweetEmbed } from 'react-twitter-embed'; - -# As DuckDB crosses 1M downloads / month, what do its users do? - -### Summary - -The excitement around DuckDB has steadily increased over the last year. The project has consistently crossed the 1M downloads per month mark over the last three months (Dec ‘22, Jan ‘23, Feb ‘23), a large increase from the ~250k downloads per month in February ‘22. - -Like so many others, we are excited about the project, too. Recently, we attended the DuckDB conference and spoke with many members of the community to learn why people are excited about it. We examined [issues on GitHub](https://github.com/duckdb/duckdb/issues), interviewed some of the top contributors to the project, and even experimented with DuckDB ourselves to determine [how we could contribute](https://duckdb.org/why_duckdb#other-projects). - -We aimed to identify the most popular reasons why people try out DuckDB with our research. We found five perspectives that people commonly have when trying out DuckDB. - -![Marcin watching a MotherDuck presentation](https://storage.googleapis.com/dlt-blog-images/Marcin-dltHub-DuckDB-DuckCon-Brussels.jpg) - -dltHub co-founder Marcin watching a MotherDuck presentation at DuckCon in Brussels in February - -## 1) "Normie" users love to drop-In DuckDB in all sorts of places - -Last December, folks from the data + ML community organized a great conference, [Normconf](https://normconf.com/). Presenters and attendees were invited to share stories about everyday data problems and celebrate solutions. Many of the attendees referred to themselves as "normies" during the conference. We have found many of these folks overlap with those trying out DuckDB. - -Normies have a specific way of solving problems that breaks some behavior patterns and expectations of previous generations of software engineers. As Ben Labaschin explains in his presentation [Building an HTTPS Model API for Cheap: AWS, Docker, and the Normconf API](https://www.youtube.com/watch?v=DRGxjfLVrTA), “normie software” has the following criteria: - -- It does not require domain knowledge to use. You should need almost no documentation because time is most important and all learning is investment. Before you learn, you need to know if it will pay off in the future. -- The best tools can be reused in many contexts. You learn them once, and you can apply them everywhere. -- Tools should work together. You should pick the tool that works with your other tools. - -Many at Normconf agreed that DuckDB is also a “normie tool”. It has a Python wrapper and can be dropped into any Python script, notebook, or Streamlit app. It has helpers and integrates with other libraries that are part of typical workflows (e.g. Pandas, Parquet, Arrow, etc). It is a powerful analytical database and brings local SQL execution without credentials and other hassles. You can scan data from and export data to Parquet, CSV, or JSON and query an S3 bucket directly. - -
- -
- -
- -## 2) Local Data Workflows Are Going Mainstream, and DuckDB Is at the Center - -Many people struggle to access cloud data warehouses within their organizations. Some of the problems that these users encounter include: - -1. Painful processes to obtain credentials and permissions -2. A setup that is perceived as "difficult" -3. Deployment of local composer files -4. Working on remote machines is often much less pleasant - -Instead they often use DuckDB to load data locally. DuckDB enables people to start using data by: - -1. Allowing them to learn SQL and try examples without any setup -2. Querying GCP or S3 buckets from a local machine -3. Creating notebooks or data apps with embedded DuckDB that showcase their work - -Prototyping and experimenting with production data locally on DuckDB is a popular practice. From what we learned, deployment of DuckDB to production is still quite rare. Companies seldom use local workflows as this depends on someone having their laptop turned on to function. However, many non-engineering personnel use DuckDB to access production data. - -## 3) The community is exploring various ways to use DuckDB's columnar query engine (e.g. analytics) - -As many people in the community are exploring how DuckDB could be used, the fact that the DuckDB engine provides a way to quickly query the columnar format seems to be central to multiple use cases. - -In data processing and usage, there are two types of transformations: - -- Non-time-critical, usually nightly, "transformation" jobs. These are run programmatically, the output is saved somewhere, and a business person consumes this output on demand a few hours or days later -- Time-critical "dashboard user access" jobs. A report is created as output from the former job. Now, a user wants to gain insights from it "on demand", so they aggregate this table and wait. This computation is now time-critical, because the user is actively waiting for it - -Row-based engines like Postgres are great at the first type of job, which usually involves many joins and row-based operations. However, they are not fast at aggregating data sets, as this requires them to iterate over rows by index (access the row, find its value, sum it). - -Column-based engines, such as DuckDB, the Postgres AlloyDB engine, MySQL Percona, Redshift, etc., excel at aggregation jobs, which, for example, count or sum elements in a column. - -Here, we have found evidence of two use cases where DuckDB is particularly suited: - -1. Aggregation of event data (e.g. product analytics). A dump of events could be easily scanned and aggregated into "users", "sessions", counted, etc. [By using a database architecture like an "activity schema" and a way to tag events, this would make a great "product analytics" embedded warehouse](https://duckdb.org/2022/10/12/modern-data-stack-in-a-box.html). The MotherDuck team explains some reasons why to use DuckDB for analytics [here](https://motherduck.com/blog/six-reasons-duckdb-slaps/). -2. Aggregation of "one big table" architectures that use "one big table" instead of a dimensional model. This is a common design pattern for organizations that do not tackle data architecture, such as small analyst teams. - -The amount of data processed for analytics workloads is often smaller than people think, with dashboards typically built from aggregated data. The co-founder of Motherduck suggests that a past analysis of his of [BigQuery queries found that 90% of queries in organisations processed less than 100 MB of data](https://motherduck.com/blog/big-data-is-dead/) (while the [co-founder of Ponder kind of disagrees](https://ponder.io/big-data-is-dead-long-live-big-data/)). Many people load CSV, Parquet, and JSON files sizes ranging from 50 to 200M rows into DuckDB. This includes not only one-off data loading but also ongoing demo projects. Common advantages of DuckDB we frequently heard about are speed, costs, and the usability advantages mentioned above. - -## 4) With DuckDB users bring the database engine to their data and instead of the other way around - -For most of us this behavioural pattern should ring true: - -1. “I have my data where I see fit. For example, in a AWS S3 bucket or on my laptop. My workflows deal with this well. And the workflow is fairly simple and cheap.” -2. “Now I want to process my data.” -3. “So I move my data to where a database engine is. I load it somewhere. I load it to Snowflake, BigQuery, Redshift. This is more complicated and costs significantly.” - -We encountered a different related pattern with DuckDB users. DuckDB users often bring the engine to their data: - -1. People drop it into AWS Lambda or Google Cloud Function to process data close to the engine. The few DuckDB production deployments that we have seen were all AWS Lambda centric. You can read a general example [here](https://tobilg.com/using-duckdb-in-aws-lambda) -2. The product that makes this pattern super easy for AWS is [Boiling Data](https://www.boilingdata.com/). The product also scales fairly well - -The community frequently experiments with making such a setup work. In a recent post from frequent DuckDB issue contributor Mimoune Djouallah, [he describes how he used Azure Storage, DuckDB, and an Azure ML Notebook to build a cost-effective data engineering pipeline for smaller data workloads](https://datamonkeysite.com/2023/02/23/implementing-a-poor-mans-lakehouse-in-azure/). The pipeline involves reading data from Azure Storage, running complex queries, and saving the results in a bucket. The resulting bucket can be consumed in Synapse Serverless/PowerBI/Notebook, etc. We wonder, just like Mimoune and [others](https://www.kleinerperkins.com/perspectives/infrastructure-in-23/) do, if vendors will be building more support for smaller data workloads going forward. - -It is probably worth mentioning at this stage that DuckDB and even PostgreSQL are not databases in the traditional sense. Rather, they are relational "database" management systems (RDBMS) that manage structured collections of data. While both can be used for querying internally stored data and reading external data like files or federated databases, DuckDB focuses primarily on the latter. - -This means that it functions more as an RDBMS without an attached database. Therefore, it is inaccurate to refer to DuckDB as a database. Instead, we should consider the parquet file or data lake as the actual database, with DuckDB serving as a tool for managing and querying the data. - -## 5) Some people are dropping DuckDB into their BI tooling - -A few people people we met have chosen to have always-up file storage and on-demand DuckDB embedded in a BI tool. A BI tool that [supports this use case is Metabase](https://www.metabase.com/data_sources/duckdb). It’s an interesting concept that might one day lead to a stable open source BI tool SQL cache. - -This is different than the past. Classic business intelligence tools use the OLAP concept—the business user creates a pivot table, and the tool composes a SQL query automatically and issues it to the database. This slows things down and adds additional cost. Think about Data Studio doing queries on BigQuery just because you change the granularity. - -If the part of the data is cached (in-memory or as temporary parquet file) and the BI tool has an embedded query engine, it can do following tricks, for free and in no-time: - -- change the granularity of a time dimension between different units (hour, day, week, calendar week, US calendar week, etc.) -- drill downs, drill throughs, and filters -- leveraging joins through predefined paths or UI query builders - diff --git a/docs/website/blog/2023-03-16-is-duckdb-a-database-for-ducks.mdx b/docs/website/blog/2023-03-16-is-duckdb-a-database-for-ducks.mdx deleted file mode 100644 index a44da1807c..0000000000 --- a/docs/website/blog/2023-03-16-is-duckdb-a-database-for-ducks.mdx +++ /dev/null @@ -1,64 +0,0 @@ ---- -slug: is-duckdb-a-database-for-ducks -title: Is DuckDB a database for ducks? -authors: - name: Matthaus Krzykowski - title: Co-Founder & CEO at dltHub - url: https://twitter.com/matthausk - image_url: https://pbs.twimg.com/profile_images/642282396751130624/9ixo0Opj_400x400.jpg -tags: [DuckDB, dlt, GitHub API, Google Colab] ---- - -## Using DuckDB, dlt, & GitHub to explore DuckDB - -:::tip -**TL;DR: We created a [Colab notebook](https://colab.research.google.com/drive/1BXvma_9R9MX8p_iSvHE4ebg90sUroty2) for you to learn more about [DuckDB](https://github.com/duckdb/duckdb) (or any open source repository of interest) using DuckDB, dlt, and the GitHub API 🙂** -::: - -## So is DuckDB full of data about ducks? - -Nope, you can put whatever data you want into DuckDB ✨ - -Many data analysts, data scientists, and developers prefer to work with data on their laptops. DuckDB allows them to start quickly and easily. When working only locally becomes infeasible, they can then turn this local “data pond” into a data lake, storing their data on object storage like Amazon S3, and continue to [use DuckDB as a query engine on top of the files stored there](https://datamonkeysite.com/2023/02/23/implementing-a-poor-mans-lakehouse-in-azure/). - -If you want to better understand why folks are excited about DuckDB, check out [this blog post](https://dlthub.com/docs/blog/duckdb-1M-downloads-users). - -## Perhaps ducks use DuckDB? - -Once again, the answer is also 'nein'. As far as we can tell, usually people use DuckDB 🦆 - -To determine this, we loaded emoji reaction data for DuckDB repo using [data load tool (dlt)](https://dlthub.com/docs/intro) from the GitHub API to a DuckDB instance and explored who has been reacting to issues / PRs in the open source community. This is what we learned… - -**The three issues / PRs with the most reactions all-time are** -1. [SQLAlchemy dialect #305](https://github.com/duckdb/duckdb/issues/305) -2. [Add basic support for GeoSpatial type #2836](https://github.com/duckdb/duckdb/issues/2836) -3. [Support AWS default credential provider chain #4021](https://github.com/duckdb/duckdb/issues/4021) - -**The three issues / PRs with the most reactions in 2023 are** -1. [Add support for Pivot/Unpivot statements #6387](https://github.com/duckdb/duckdb/pull/6387) -2. [Add support for a pluggable storage and catalog back-end, and add support for a SQLite back-end storage #6066](https://github.com/duckdb/duckdb/pull/6066) -3. [Add support for UPSERT (INSERT .. ON CONFLICT DO ..) syntax #5866](https://github.com/duckdb/duckdb/pull/5866) - -**Some of the most engaged users (other than the folks who work at DuckDB Labs) include** -- [@Tishj](https://github.com/Tishj), [@xhochy](https://github.com/xhochy), and [@handstuyennn](https://github.com/handstuyennn), who received the most 👍 reactions -- [@lloydtabb](https://github.com/lloydtabb), [@cboettig](https://github.com/cboettig), and [@LindsayWray](https://github.com/LindsayWray), who received the most ❤️ reactions -- [@dforsber](https://github.com/dforsber), [@ankoh](https://github.com/ankoh), and [@djouallah](https://github.com/djouallah), who gave the most total reactions - -All of these users seem to be people. Admittedly, we didn’t look at everyone though, so there *could* be ducks within the flock. You can check yourself by playing with the [Colab notebook](https://colab.research.google.com/drive/1BXvma_9R9MX8p_iSvHE4ebg90sUroty2). - -## Maybe it’s called DuckDB because you can use it to create a "data pond" that can grow into a data lake + ducks like water? - -Although this is a cool idea, it is still not the reason that it is called DuckDB 🌊 - -Using functionality offered by DuckDB to export the data loaded to it as Parquet files, you can create a small “data pond” on your local computer. To make it a data lake, you can then add these files to [Google Cloud Storage](https://cloud.google.com/storage), [Amazon S3](https://aws.amazon.com/s3/), etc. And if you want this data lake to always fill with the latest data from the GitHub API, you can [deploy the dlt pipeline](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-github-actions). - -Check this out in the [Colab notebook](https://colab.research.google.com/drive/1BXvma_9R9MX8p_iSvHE4ebg90sUroty2) and [let us know](https://github.com/dlt-hub/dlt/issues/new) if you want some help setting this up. - -## Just tell me why it is called DuckDB!!! - -Okay. It’s called DuckDB because ducks are amazing and [@hannes](https://github.com/hannes) once had a pet duck 🤣 - -![Why "Duck" DB?](https://storage.googleapis.com/dlt-blog-images/why-duckdb.png) -Source: [DuckDB: an Embeddable Analytical RDBMS](https://db.in.tum.de/teaching/ss19/moderndbs/duckdb-tum.pdf) - -## Enjoy this blog post? Give data load tool (dlt) a ⭐ on GitHub [here](https://github.com/dlt-hub/dlt) 🤜🤛 diff --git a/docs/website/blog/2023-04-27-ga4-internal-dashboard-demo.md b/docs/website/blog/2023-04-27-ga4-internal-dashboard-demo.md deleted file mode 100644 index 2d7b7de724..0000000000 --- a/docs/website/blog/2023-04-27-ga4-internal-dashboard-demo.md +++ /dev/null @@ -1,44 +0,0 @@ ---- -slug: ga4-internal-dashboard-demo -title: Internal Dashboard for Google Analytics 4 -authors: - name: Rahul Joshi - title: Data Science Intern at dltHub - url: https://github.com/rahuljo - image_url: https://avatars.githubusercontent.com/u/28861929?v=4 -tags: [internal dashboard, google analytics 4, streamlit] ---- - -:::info -**TL;DR: As of last week, there is a dlt pipeline that loads data from Google Analytics 4 (GA4). We’ve been excited about GA4 for a while now, so we decided to build some internal dashboards and show you how we did it.** -::: - -### Why GA4? - -We set out to build an internal dashboard demo based on data from Google Analytics (GA4). Google announced that they will stop processing hits for Universal Analytics (UA) on July 1st, 2023, so many people are now having to figure out how to set up analytics on top of GA4 instead of UA and struggling to do so. For example, in UA, a session represents the period of time that a user is actively engaged on your site, while in GA4, a `session_start` event generates a session ID that is associated with all future events during the session. Our hope is that this demo helps you begin this transition! - -### Initial explorations - -We decided to make a dashboard that helps us better understand data attribution for our blog posts (e.g. [As DuckDB crosses 1M downloads / month, what do its users do?](./2023-03-09-duckdb-1M-downloads-users.mdx)). Once we got our [credentials](https://dlthub.com/docs/general-usage/credentials) working, we then used the GA4 `dlt` pipeline to load data into a DuckDB instance on our laptop. This allowed us to figure out what requests we needed to make to get the necessary data to show the impact of each blog post (e.g. across different channels, what was the subsequent engagement with our docs, etc). We founded it helpful to use [GA4 Query Explorer](https://ga-dev-tools.google/ga4/query-explorer/) for this. - -### Internal dashboard - -![Dashboard 1](https://storage.googleapis.com/dlt-blog-images/g4_dashboard_screen_grab_1.jpg) ![Dashboard 2](https://storage.googleapis.com/dlt-blog-images/g4_dashboard_screen_grab_2.jpg) - -With the data loaded locally, we were able to build the dashboard on our system using Streamlit. You can also do this on your system by simply cloning [this repo](https://github.com/dlt-hub/ga4-internal-dashboard-demo) and following the steps listed [here](https://github.com/dlt-hub/ga4-internal-dashboard-demo/tree/main/intial-explorations). - -After having the pipeline and the dashboard set up just how we liked it, we were now ready to deploy it. - -### Deploying the data warehouse - -We decided to deploy our Streamlit app on a [Google Cloud VM instance](https://cloud.google.com/compute). This means that instead of storing the data locally, it would need to be in a location that could be accessed by the Streamlit app. Hence we decided to load the data onto a PostgreSQL database in the VM. [See here](https://github.com/dlt-hub/ga4-internal-dashboard-demo/tree/main/internal-dashboards) for more details on our process. - -### Deploying the `dlt` pipeline with GitHub Actions - -Once we had our data warehouse set up, we were ready to deploy the pipeline. We then followed the [deploy a pipeline](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-github-actions) walkthrough to configure and deploy a pipeline that will load the data daily onto our data warehouse. - -### Deploying the dashboard - -We finally deployed our Streamlit app on our Google Cloud VM instance by following [these steps](https://github.com/dlt-hub/ga4-internal-dashboard-demo/tree/main/internal-dashboards). - -### Enjoy this blog post? Give `dlt` a ⭐ on [GitHub](https://github.com/dlt-hub/dlt) 🤜🤛 diff --git a/docs/website/blog/2023-05-15-hacker-news-gpt-4-dashboard-demo.md b/docs/website/blog/2023-05-15-hacker-news-gpt-4-dashboard-demo.md deleted file mode 100644 index c9b2b2fcc3..0000000000 --- a/docs/website/blog/2023-05-15-hacker-news-gpt-4-dashboard-demo.md +++ /dev/null @@ -1,44 +0,0 @@ ---- -slug: hacker-news-gpt-4-dashboard-demo -title: Understanding how developers view ELT tools using the Hacker News API and GPT-4 -authors: - name: Rahul Joshi - title: Data Science Intern at dltHub - url: https://github.com/rahuljo - image_url: https://avatars.githubusercontent.com/u/28861929?v=4 -tags: [hacker news, gpt-4, streamlit] ---- -:::info -**TL;DR: We created a Hacker News -> BigQuery `dlt` pipeline to load all comments related to popular ELT keywords and then used GPT-4 to summarize the comments. We now have a live [dashboard](http://34.28.70.28:8502/) that tracks these keywords and an accompanying [GitHub repo](https://github.com/dlt-hub/hacker-news-gpt-4-dashboard-demo) detailing our process.** -::: -## Motivation - -To figure out how to improve `dlt`, we are constantly learning about how people approach extracting, loading, and transforming data (i.e. [ELT](https://docs.getdbt.com/terms/elt)). This means we are often reading posts on [Hacker News (HN)](https://news.ycombinator.com/), a forum where many developers like ourselves hang out and share their perspectives. But finding and reading the latest comments about ELT from their website has proved to be time consuming and difficult, even when using [Algolia Hacker News Search](https://hn.algolia.com/) to search. - -So we decided to set up a `dlt` pipeline to extract and load comments using keywords (e.g. Airbyte, Fivetran, Matillion, Meltano, Singer, Stitch) from the HN API. This empowered us to then set up a custom dashboard and create one sentence summaries of the comments using GPT-4, which made it much easier and faster to learn about the strengths and weaknesses of these tools. In the rest of this post, we share how we did this for `ELT`. A [GitHub repo](https://github.com/dlt-hub/hacker-news-gpt-4-dashboard-demo) accompanies this blog post, so you can clone and deploy it yourself to learn about the perspective of HN users on anything by replacing the keywords. - -## Creating a `dlt` pipeline for Hacker News - -For the dashboard to have access to the comments, we needed a data pipeline. So we built a `dlt` pipeline that could load the comments from the [Algolia Hacker News Search API](https://hn.algolia.com/api) into BigQuery. We did this by first writing the logic in Python to request the data from the API and then following [this walkthrough](https://dlthub.com/docs/walkthroughs/create-a-pipeline) to turn it into a `dlt` pipeline. - -With our `dlt` pipeline ready, we loaded all of the HN comments corresponding to the keywords from January 1st, 2022 onward. - -## Using GPT-4 to summarize the comments - -Now that the comments were loaded, we were ready to use GPT-4 to create a one sentence summary for them. We first filtered out any irrelevant comments that may have been loaded using simple heuritics in Python. Once we were left with only relevant comments, we called the `gpt-4` API and prompted it to summarize in one line what the comment was saying about the chosen keywords. If you don't have access to GPT-4 yet, you could also use the `gpt-3.5-turbo` API. - -Since these comments were posted in response to stories or other comments, we fed in the story title and any parent comments as context in the prompt. To avoid hitting rate-limit error and losing all progress, we ran this for 100 comments at a time, saving the results in the CSV file each time. We then built a streamlit app to load and display them in a dashboard. Here is what the dashboard looks like: - -![dashboard.png](https://storage.googleapis.com/dlt-blog-images/hn_gpt_dashboard.png) - -## Deploying the pipeline, Google Bigquery, and Streamlit app - -With all the comments loaded and the summaries generated in bulk, we were ready to deploy this process and have the dashboard update daily with new comments. - -We decided to deploy our streamlit app on a GCP VM. To have our app update daily with new data we did the following: -1. We first deployed our `dlt` pipeline using GitHub Actions to allow new comments to be loaded to BigQuery daily -2. We then wrote a Python script that could pull new comments from BigQuery into the VM and we scheduled to run it daily using crontab -3. This Python script also calls the `gpt-4` API to generate summaries only for the new comments -4. Finally, this Python script updates the CSV file that is being read by the streamlit app to create the dashboard. Check it out [here](http://34.28.70.28:8502/)! - -Follow the accompanying [GitHub repo](https://github.com/dlt-hub/hacker-news-gpt-4-dashboard-demo) to create your own Hacker News/GPT-4 dashboard. diff --git a/docs/website/blog/2023-05-25-postgresql-bigquery-metabase-demo.md b/docs/website/blog/2023-05-25-postgresql-bigquery-metabase-demo.md deleted file mode 100644 index 11fbc39f4d..0000000000 --- a/docs/website/blog/2023-05-25-postgresql-bigquery-metabase-demo.md +++ /dev/null @@ -1,56 +0,0 @@ ---- -slug: postgresql-bigquery-metabase-demo -title: Using Google BigQuery and Metabase to understand product usage -image: https://dlthub.com/docs/img/dlthub-logo.png -authors: - name: Rahul Joshi - title: Data Science Intern at dltHub - url: https://github.com/rahuljo - image_url: https://avatars.githubusercontent.com/u/28861929?v=4 -tags: [BigQuery, Incremental Load, Metabase, OLAP, OLTP, PostgreSQL, SQL source pipeline] ---- -:::info -TL;DR: Trying to become more user-centric and make data driven decisions? Get started with the SQL source pipeline + BigQuery + Metabase -::: -## When you have a web and / or mobile app but no data yet - -If you're a startup without a dedicated data team but a sizeable number of users on your website or mobile app, then chances are that you are collecting and storing all your product data in OLTP databases like MySQL, Postgres, etc. As you have grown, you have likely been aiming to become more user-centric, yet you find that no one at your company has information on what your users do or what their experience is like. Stakeholders should be making data-driven decisions, but they are not yet because they are unable to use the existing data to understand user behavior and experience. This is usually the point when folks realize they need a data warehouse. - -## Why a data warehouse is necessary - -OLTP databases are great because they are optimized to handle high-volume real-time transactions and maintain data integrity and consistency. However, they are not very well-suited for advanced analytics and data modelling. If you want to create reports, dashboards, and more that help you understand you users, you are going to want to extract, load, and transform (ELT) into a OLAP database like Google BigQuery, Snowflake, etc. To do this, you will need to create a data pipeline, which can be quite challenging if your company does not have a dedicated data engineering team. - -## Why a data pipeline is necessary - -Production dashboards rely on the availability of consistent, structured data, which necessitates deploying a data pipeline that is idompotent, can manage the schema and handle schema changes, can be deployed to load data incrementally, etc. For most startups, it's not obvious how to create such pipelines. This is why we decided to demonstrate how one can set up such a data pipeline and build analytics dashboards on top of it. - -## Why a reporting tool is necessary - -We chose to build our dashboard in [Metabase](https://www.metabase.com/) because it also offers an [open source edition](https://www.metabase.com/start/oss/). The advantage of reporting tools like Metabase is that they are easy and intuitive to use even for people who can't write SQL, but at the same time they are powerful enough for those who would like to use SQL. - -## How we set this up - -### 1. Creating a PostgreSQL -> BigQuery pipeline - -Our aim was to create a Metabase dashboard to explore data in a transactional database. The data set that we chose was a [sample](https://github.com/fortunewalla/dvdstore) of [The Dell DVD Store 2 database](https://linux.dell.com/files/dvdstore/), which we put into a Postgres database deployed on a Google Cloud SQL instance. To make this data available to Metabase, we needed to first load all of the data into a BigQuery instance, and for this we needed a data pipeline. We created this pipeline by doing very simple customizations on the existing `dlt` `sql_database` pipeline. See the accompanying [repo](https://github.com/dlt-hub/postgresql_bigquery_pipeline_demo) for the steps we followed. - -### 2. Building a Metabase reporting dashboard - -With the database uploaded to BigQuery, we were now ready to build a dashboard. We created a Metabase cloud account and connected it to our BigQuery instance. This made the whole database accessible to Metabase and we were able to analyze the data. - -The DVD store database contains data on the products (film DVDs), product categories, existing inventory, customers, orders, order histories etc. For the purpose of the dashboard, we decided to explore the question: *How many orders are being placed each month and which films and film categories are the highest selling?* - -![orders_chart.png](https://storage.googleapis.com/dlt-blog-images/experiment3_dashboard_orders_chart.png) ![top_selling_tables.png](https://storage.googleapis.com/dlt-blog-images/experiment3_dashboard_top_selling_tables.png) -In addition to this, we were also able to set up email alerts to get notified whenever the stock of a DVD was either empty or close to emptying. - -![low_stock_email_alert.png](https://storage.googleapis.com/dlt-blog-images/experiment3_low_stock_email_alert.png) - -### 3. Deploying the pipeline - -With our dashboard ready, all we had to do was deploy our pipeline so that the dashboard could get updated with new data daily. Since the dashboard only uses some of the tables, we needed to modify the pipeline, that was configured to load the entire database, to instead only update the necessary tables. We also wanted to make it possible for the pipeline to load tables incrementally whenever possible. - -We first started by selecting the tables that we wanted to update, namely: *orders*, *orderlines*, *products*, *categories*, *inventory*. We then decided whether we wanted to update the tables incrementally or with full replace: -- Tables *orders* and *orderlines* contain data on the orders placed. This means that they also contain a date column and hence are loaded incrementally every day. -- Tables *products*, *categories*, and *inventory* contain information on the existing products. These tables don't contain a date column and are updated whenever there is any change in inventory. Since the values of the existing data in the tables can change, these tables are not updated incrementally, but are instead fully loaded each time the pipeline is run. - -In order to specify these conditions and deploy our pipeline in production, we followed [these](https://github.com/dlt-hub/postgresql_bigquery_pipeline_demo) steps. \ No newline at end of file diff --git a/docs/website/blog/2023-05-26-structured-data-lakes-through-schema-evolution-next-generation-data-platform.md b/docs/website/blog/2023-05-26-structured-data-lakes-through-schema-evolution-next-generation-data-platform.md deleted file mode 100644 index 1192f47abf..0000000000 --- a/docs/website/blog/2023-05-26-structured-data-lakes-through-schema-evolution-next-generation-data-platform.md +++ /dev/null @@ -1,112 +0,0 @@ ---- -slug: next-generation-data-platform -title: "The structured data lake: How schema evolution enables the next generation of data platforms" -image: https://dlthub.com/docs/img/dlthub-logo.png -authors: - name: Adrian Brudaru - title: Open source data engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [data platform, structured data lake, schema evolution] ---- - -:::info -[Google Colaboratory demo](https://colab.research.google.com/drive/1H6HKFi-U1V4p0afVucw_Jzv1oiFbH2bu#scrollTo=e4y4sQ78P_OM) - -This colab demo was built and shown by our working student Rahul Joshi, for the Berlin Data meetup, where he talked about the state of schema evolution in the open source. -::: - -# What is schema evolution? - -In the fast-paced world of data, the only constant is change, and it usually comes unannounced. - -### **Schema on read** - -Schema on read means your data does not have a schema, but your consumer expects one. So when they read, they define the schema, and if the unstructured data does not have the same schema, issues happen. - -### **Schema on write** - -So, to avoid things breaking on running, you would want to define a schema upfront - hence you would structure the data. The problem with structuring data is that it’s a labor intensive process that makes people take pragmatic shortcuts of structuring only some data, which later leads to lots of maintenance. - -Schema evolution means that a schema is automatically generated on write for the data, and automatically adjusted for any changes in the data, enabling a robust and clean environment downstream. It’s an automatic data structuring process that is aimed at saving time during creation, maintenance, and recovery. - -# Why do schema evolution? - -One way or another, produced raw unstructured data becomes structured during usage. So, which paradigm should we use around structuring? - -Let’s look at the 3 existing paradigms, their complexities, and what a better solution could look like. - -## The old ways - -### **The data warehouse paradigm: Curating unstructured data upfront** - -Traditionally, many organizations have adopted a 'curate first' approach to data management, particularly when dealing with unstructured data. - -The desired outcome is that by curating the data upfront, we can directly extract value from it later. However, this approach has several pitfalls. - -**Why curating unstructured data first is a bad idea** - -1. **It's labor-intensive:** Unstructured data is inherently messy and complex. Curating it requires significant manual effort, which is time-consuming and error-prone. -2. **It's difficult to scale:** As the volume of unstructured data grows, the task of curating it becomes increasingly overwhelming. It's simply not feasible to keep up with the onslaught of new data. For example, Data Mesh paradigm tries to address this. -3. **It delays value extraction:** By focusing on upfront curation, organizations often delay the point at which they can start extracting value from their data. Valuable insights are often time-sensitive, and any delay could mean missed opportunities. -4. **It assumes we know what the stakeholders will need:** Curating data requires us to make assumptions about what data will be useful and how it should be structured. These assumptions might be wrong, leading to wasted effort or even loss of valuable information. - -### **The data lake paradigm: Schema-on-read with unstructured data** - -In an attempt to bypass upfront data structuring and curation, some organizations adopt a schema-on-read approach, especially when dealing with data lakes. While this offers flexibility, it comes with its share of issues: - -1. **Inconsistency and quality issues:** As there is no enforced structure or standard when data is ingested into the data lake, the data can be inconsistent and of varying quality. This could lead to inaccurate analysis and unreliable insights. -2. **Complexity and performance costs:** Schema-on-read pushes the cost of data processing to the read stage. Every time someone queries the data, they must parse through the unstructured data and apply the schema. This adds complexity and may impact performance, especially with large datasets. -3. **Data literacy and skill gap:** With schema-on-read, each user is responsible for understanding the data structure and using it correctly, which is unreasonable to expect with undocumented unstructured data. -4. **Lack of governance:** Without a defined structure, data governance can be a challenge. It's difficult to apply data quality, data privacy, or data lifecycle policies consistently. - -### **The hybrid approach: The lakehouse** - -- The data lakehouse uses the data lake as a staging area for creating a warehouse-like structured data store. -- This does not solve any of the previous issues with the two paradigms, but rather allows users to choose which one they apply on a case-by-case basis. - -## The new way - -### **The current solution : Structured data lakes** - -Instead of trying to curate unstructured data upfront, a more effective approach is to structure the data first with some kind of automation. By applying a structured schema to the data, we can more easily manage, query, and analyze the data. - -Here's why structuring data before curation is a good idea: - -1. **It reduces maintenance:** By automating the schema creation and maintenance, you remove 80% of maintenance events of pipelines. -2. **It simplifies the data:** By imposing a structure on the data, we can reduce its complexity, making it easier to understand, manage, and use. -3. **It enables automation:** Structured data is more amenable to automated testing and processing, including cleaning, transformation, and analysis. This can significantly reduce the manual effort required to manage the data. -4. **It facilitates value extraction:** With structured data, we can more quickly and easily extract valuable insights. We don't need to wait for the entire dataset to be curated before we start using it. -5. **It's more scalable:** Reading structured data enables us to only read the parts we care about, making it faster, cheaper, and more scalable. - -Therefore, adopting a 'structure first' approach to data management can help organizations more effectively leverage their unstructured data, minimizing the effort, time, and complexity involved in data curation and maximizing the value they can extract from their data. - -An example of such a structured lake would be parquet file data lakes, which are both, structured and inclusive of all data. However, the challenge here is creating the structured parquet files and maintaining the schemas, for which the delta lake framework provides some decent solutions, but is still far from complete. - -## The better way - -So, what if writing and merging parquet files is not for you? After all, file-based data lakes capture a minority of the data market. - -### `dlt` is the first python library in the open source to offer schema evolution - -`dlt` enables organizations to impose structure on data as it's loaded into the data lake. This approach, often termed as schema-on-load or schema-on-write, provides the best of both worlds: - -1. **Easier maintenance:** By notifying the data producer and consumer of loaded data schema changes, they can quickly decide together how to adjust downstream usage, enabling immediate recovery. -2. **Consistency and quality:** By applying structure and data typing rules during ingestion, `dlt` ensures data consistency and quality. This leads to more reliable analysis and insights. -3. **Improved performance:** With schema-on-write, the computational cost is handled during ingestion, not when querying the data. This simplifies queries and improves performance. -4. **Ease of use:** Structured data is easier to understand and use, lowering the skill barrier for users. They no longer need to understand the intricate details of the data structure. -5. **Data governance:** Having a defined schema allows for more effective data governance. Policies for data quality, data privacy, and data lifecycle can be applied consistently and automatically. - -By adopting a 'structure first' approach with `dlt`, organizations can effectively manage unstructured data in common destinations, optimizing for both, flexibility and control. It helps them overcome the challenges of schema-on-read, while reaping the benefits of a structured, scalable, and governance-friendly data environment. - -To try out schema evolution with `dlt`, check out our [colab demo.](https://colab.research.google.com/drive/1H6HKFi-U1V4p0afVucw_Jzv1oiFbH2bu#scrollTo=e4y4sQ78P_OM) - - - -![colab demo](https://storage.googleapis.com/dlt-blog-images/schema_evolution_colab_demo_light.png) - -### Want more? - -- Join our [Slack](https://dlthub.com/community) -- Read our [schema evolution blog post](https://dlthub.com/docs/blog/schema-evolution) -- Stay tuned for the next article in the series: *How to do schema evolution with* `dlt` *in the most effective way* \ No newline at end of file diff --git a/docs/website/blog/2023-06-05-google-sheets-to-data-warehouse-pipeline.md b/docs/website/blog/2023-06-05-google-sheets-to-data-warehouse-pipeline.md deleted file mode 100644 index e934f866ad..0000000000 --- a/docs/website/blog/2023-06-05-google-sheets-to-data-warehouse-pipeline.md +++ /dev/null @@ -1,49 +0,0 @@ ---- -slug: google-sheets-to-data-warehouse-pipeline -title: Using the Google Sheets `dlt` pipeline in analytics and ML workflows -image: https://storage.googleapis.com/dlt-blog-images/experiment4-blog-image.png -authors: - name: Rahul Joshi - title: Data Science Intern at dltHub - url: https://github.com/rahuljo - image_url: https://avatars.githubusercontent.com/u/28861929?v=4 -tags: [bigquery, google sheets, metabase] ---- -## Why we need a simple Google Sheets -> data warehouse pipeline - -Spreadsheets are great. They are really simple to use and offer a lot of functionality to query, explore, manipulate, import/export data. Their wide availability and ease of sharing also make them great tools for collaboration. But they have limitations and cannot be used for storage and processing of large-scale complex data. Most organizational data is actually stored in data warehouses and not spreadsheets. - -However, because of the easy set up and intuitive workflow, Google Sheets are still used by many people to track and analyze smaller datasets. But even this data often needs to be combined with the rest of the organizational data in the data warehouse for reasons like analytics, reporting etc. This is not a problem when the dataset is small and static and just needs to be exported once to the data warehouse. In most cases, however, the Google Sheets data is not static and is updated regularly, thus creating a need for an ETL pipeline, and thereby complicating an otherwise simple and intuitive workflow. - -Since `dlt` has a Google Sheets pipeline that is very easy to set up and deploy, we decided to write a blog to demonstrate how some very common use-cases of Google Sheets can be enchanced by inserting this `dlt` pipeline into the process. - -## Use-case #1: Google sheets pipeline for measuring marketing campaign ROI - -As an example of such a use-case, consider this very common scenario: You're the marketing team of a company that regularly launches social media campaigns. You track some of the information such as campaign costs in Google Sheets, whereas all of the other related data such as views, sign-ups, clicks, conversions, revenue etc. is stored in the marketing data warehouse. To optimize your marketing strategy, you decide to build a dashboard to measure the ROI for the campaigns across different channels. Hence, you would like to have all your data in one place to easily be able to connect your reporting tool to it. - -To demonstrate this process, we created some sample data where we stored costs related to some campaigns in a Google Sheet and and the rest of the related data in BigQuery. - -![campaign-roi-google-sheets](https://storage.googleapis.com/dlt-blog-images/experiment4-campaign-roi-google-sheets.png) ![campaign-roi-data-warehouse](https://storage.googleapis.com/dlt-blog-images/experiment4-campaign-roi-datawarehouse.png) - -We then used the `dlt` google sheets pipeline by following [these](https://github.com/dlt-hub/google-sheets-bigquery-pipeline) simple steps to load the Google Sheets data into BigQuery. - -With the data loaded, we finally connected Metabase to the data warehouse and created a dashboard to understand the ROIs across each platform: -![campaign-roi-dashboard-1](https://storage.googleapis.com/dlt-blog-images/experiment4-campaign-roi-dashboard-1.png) -![campaign-roi-dashboard-2](https://storage.googleapis.com/dlt-blog-images/experiment4-campaign-roi-dashboard-2.png) - -## Use-case #2: Evaluating the performance of your ML product using google sheets pipeline - -Another use-case for Google Sheets that we've come across frequently is to store annotated training data for building machine learning (ML) products. This process usually involves a human first manually doing the annotation and creating the training set in Google Sheets. Once there is sufficient data, the next step is to train and deploy the ML model. After the ML model is ready and deployed, the final step would be to create a workflow to measure its performance. Which, depending on the data and product, might involve combining the manually annotated Google Sheets data with the product usage data that is typically stored in some data warehouse - -A very common example for such a workflow is with customer support platforms that use text classfication models to categorize incoming customer support tickets into different issue categories for an efficient routing and resolution of the tickets. To illustrate this example, we created a Google Sheet with issues manually annotated with a category. We also included other manually annotated features that might help measure the effectiveness of the platform, such as priority level for the tickets and customer feedback. - -![customer-support-platform-google-sheets](https://storage.googleapis.com/dlt-blog-images/experiment4-customer-support-platform-google-sheets.png) - -We then populated a BigQuery dataset with potential product usage data, such as: the status of the ticket (open or closed), response and resolution times, whether the ticket was escalated etc. -![customer-support-platform-data-warehouse](https://storage.googleapis.com/dlt-blog-images/experiment4-customer-support-platform-data-warehouse.png) - -Then, as before, we loaded the google sheets data to the data warehouse using the `dlt` google sheets pipeline and following [these](https://github.com/dlt-hub/google-sheets-bigquery-pipeline) steps. - -Finally we connected Metabase to it and built a dashboard measuring the performance of the model over the period of a month: - -![customer-support-platform-dashboard](https://storage.googleapis.com/dlt-blog-images/experiment4-customer-support-platform-dashboard.png) \ No newline at end of file diff --git a/docs/website/blog/2023-06-10-schema-evolution.md b/docs/website/blog/2023-06-10-schema-evolution.md deleted file mode 100644 index 74402506f8..0000000000 --- a/docs/website/blog/2023-06-10-schema-evolution.md +++ /dev/null @@ -1,145 +0,0 @@ ---- -slug: schema-evolution -title: "Schema Evolution" -authors: - name: Adrian Brudaru - title: Schema Evolution - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [data engineer shortage, structured data, schema evolution] ---- - -# Schema evolution - -Schema evolution combines a technical process with a curation process, so let's understand the -process, and where the technical automation needs to be combined with human curation. - -## Whether you are aware or not, you are always getting structured data for usage - -Data used is always structured, but usually produced unstructured. - -Structuring it implicitly during reading is called "schema on read", while structuring it upfront is -called "schema on write". - -To fit unstructured data into a structured database, developers have to perform this transition -before loading. For data lake users who read unstructured data, their pipelines apply a schema -during read - if this schema is violated, the downstream software will produce bad outcomes. - -### We tried running away from our problems, but it didn't work. - -Because structuring data is difficult to deal with, people have tried to not do it. But this created -its own issues. - -- Loading json into db without typing or structuring - This anti-pattern was created to shift the - structuring of data to the analyst. While this is a good move for curation, the db support for - structuring data is minimal and unsafe. In practice, this translates to the analyst spending their - time writing lots of untested parsing code and pushing silent bugs to production. -- Loading unstructured data to lakes - This pattern pushes the curation of data to the analyst. The - problem here is similar to the one above. Unstructured data is hard to analyse and curate, and the - farther it is from the producer, the harder it is to understand. - -So no, one way or another we are using schemas. - -### If curation is hard, how can we make it easier? - -- Make data easier to discover, analyze, explore. Structuring upfront would do that. -- Simplify the human process by decentralizing data ownership and curation - the analyst can work - directly with the producer to define the dataset produced. - -## Structuring & curating data are two separate problems. Together they are more than the sum of the parts. - -The problem is that curating data is hard. - -- Typing and normalising data are technical processes. -- Curating data is a business process. - -Here's what a pipeline building process looks like: - -1. Speak with the producer to understand what the data is. Chances are the producer does not - document it and there will be many cases that need to be validated analytically. -1. Speak with the analyst or stakeholder to get their requirements. Guess which fields fulfill their - requirements. -1. Combine the 2 pieces of info to filter and structure the data so it can be loaded. -1. Type the data (for example, convert strings to datetime). -1. Load the data to warehouse. Analyst can now validate if this was the desired data with the - correct assumptions. -1. Analyst validates with stakeholder that this is the data they wanted. Stakeholder usually wants - more. -1. Possibly adjust the data filtering, normalization. -1. Repeat entire process for each adjustment. - -And when something changes, - -1. The data engineer sees something break. -1. They ask the producer about it. -1. They notify the analyst about it. -1. The analyst notifies the business that data will stop flowing until adjustments. -1. The analyst discusses with the stakeholder to get any updated requirements. -1. The analyst offers the requirements to the data engineer. -1. The data engineer checks with the producer/data how the new data should be loaded. -1. Data engineer loads the new data. -1. The analyst can now adjust their scripts, re-run them, and offer data to stakeholder. - -## Divide et impera! The two problems are technical and communicational, so let's let computers solve tech and let humans solve communication. - -Before we start solving, let's understand the problem: - -1. For usage, data needs to be structured. -1. Because structuring is hard, we try to reduce the amount we do by curating first or defering to - the analyst by loading unstructured data. -1. Now we are trying to solve two problems at once: structuring and curation, with each role - functioning as a bottleneck for the other. - -So let's de-couple these two problems and solve them appropriately: - -- The technical issue is that unstructured data needs to be structured. -- The curation issue relates to communication - so taking the engineer out of the loop would make - this easier. - -### Automate the tech: Structuring, typing, normalizing - -The only reason to keep data unstructured was the difficulty of applying structure. - -By automating schema inference, evolution, normalization, and typing, we can just load our jsons -into structured data stores, and curate it in a separate step. - -### Alert the communicators: When there is new data, alert the producer and the curator. - -To govern how data is produced and used, we need to have a definition of the data that the producer -and consumer can both refer to. This has typically been tackled with data contracts - a type of -technical test that would notify the producer and consumer of violations. - -So how would a data contract work? - -1. Human process: - 1. Humans define a data schema. - 1. Humans write a test to check if data conforms to the schema. - 1. Humans implement notifications for test fails. -1. Technical process: - 1. Data is extracted. - 1. Data is staged to somewhere where it can be tested. - 1. Data is tested: - 1. If the test fails, we notify the producer and the curator. - 1. If the test succeeds, it gets transformed to the curated form. - -So how would we do schema evolution with `dlt`? - -1. Data is extracted, `dlt` infers schema and can compare it to the previous schema. -1. Data is loaded to a structured data lake (staging area). -1. Destination schema is compared to the new incoming schema. - 1. If there are changes, we notify the producer and curator. - 1. If there are no changes, we carry on with transforming it to the curated form. - -So, schema evolution is essentially a simpler way to do a contract on schemas. If you had additional -business-logic tests, you would still need to implement them in a custom way. - -## The implementation recipe - -1. Use `dlt`. It will automatically infer and version schemas, so you can simply check if there are - changes. You can just use the [normaliser + loader](/general-usage/pipeline) or - [build extraction with dlt](/general-usage/resource). If you want to define additional - constraints, you can do so in the [schema](/general-usage/schema). -1. [Define your slack hook](/running-in-production/running#using-slack-to-send-messages) or - create your own notification function. Make sure the slack channel contains the data producer and - any stakeholders. -1. [Capture the load job info and send it to the hook](/running-in-production/running#inspect-save-and-alert-on-schema-changes). diff --git a/docs/website/blog/2023-06-14-dlthub-gpt-accelerated learning_01.md b/docs/website/blog/2023-06-14-dlthub-gpt-accelerated learning_01.md deleted file mode 100644 index 9ffcd4149c..0000000000 --- a/docs/website/blog/2023-06-14-dlthub-gpt-accelerated learning_01.md +++ /dev/null @@ -1,181 +0,0 @@ ---- -slug: training-gpt-with-opensource-codebases -title: "GPT-accelerated learning: Understanding open source codebases" -image: https://storage.googleapis.com/dlt-blog-images/blog_gpt_1.jpg -authors: - name: Tong Chen - title: Data Engineer Intern at dltHub - url: https://github.com/TongHere - image_url: https://iili.io/HP9uKIn.jpg -tags: [gpt-4, langchain, ELT] ---- - -:::info - -💡Check out the accompanying colab demo: -[Google Colaboratory demo](https://colab.research.google.com/drive/1KU1G_08Yihh5p-o1BsCuhA1OkH5zwgkf?usp=sharing) -::: - ---- - -Hi there! 👋 -In this article, I will show you a demo on how to train ChatGPT with the open-source dlt repository. Here is the article structure, and you can jump directly to the part that interests you. Let's get started! - - -I. -Introduction - -II. Walkthrough - -III. Result - -IV. Summary - -### I. Introduction -Navigating an open-source repository can be overwhelming because comprehending the intricate labyrinths of code is always a significant problem. As a person who just entered the IT industry, I found an easy way to address this problem with an ELT tool called [`dlt`](https://dlthub.com/docs/intro) (data load tool) - the Python library for loading data. - -In this article, I would love to share a use case - training GPT with an Open-Source `dlt` Repository by using the `dlt` library. In this way, I can write prompts about `dlt` and get my personalized answers. - -### II. Walkthrough - -The code provided below demonstrates training a chat-oriented GPT model using the `dlt`- hub repositories (`dlt` and pipelines). To train the GPT model, we utilized the assistance of two services: Langchain and Deeplake. In order to use these services for our project, you will need to create an account on both platforms and obtain the access token. The good news is that both services offer cost-effective options. GPT provides a $5 credit to test their API, while Deeplake offers a free tier. - -*The credit for the code goes to Langchain, which has been duly acknowledged at the end.* - - - #### 1. Run the following commands to install the necessary modules on your system. - - - -```sh -python -m pip install --upgrade langchain deeplake openai tiktoken -``` - -```py -# Create accounts on platform.openai.com and deeplake.ai. After registering, retrieve the access tokens for both platforms and securely store them for use in the next step. Enter the access tokens grabbed in the last step and enter them when prompted - -import os -import getpass - -from langchain.embeddings.openai import OpenAIEmbeddings -from langchain.vectorstores import DeepLake - -os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:') -os.environ['ACTIVELOOP_TOKEN'] = getpass.getpass('Activeloop Token:') -embeddings = OpenAIEmbeddings(disallowed_special=()) -``` - -#### 2. Create a directory to store the code for training the model. Clone the desired repositories into that. - -```sh - # making a new directory named dlt-repo -!mkdir dlt-repo -# changing the directory to dlt-repo -%cd dlt-repo -# cloning git repos into the dlt-repo directory -# dlt code base -!git clone https://github.com/dlt-hub/dlt.git -# example pipelines to help you get started -!git clone https://github.com/dlt-hub/pipelines.git -# going back to previous directory -%cd .. -``` - -#### 3. Load the files from the directory -```py -import os -from langchain.document_loaders import TextLoader - -root_dir = './dlt-repo' # load data from -docs = [] -for dirpath, dirnames, filenames in os.walk(root_dir): - for file in filenames: - try: - loader = TextLoader(os.path.join(dirpath, file), encoding='utf-8') - docs.extend(loader.load_and_split()) - except Exception as e: - pass -``` -#### 4. Load the files from the directory -```py -import os -from langchain.document_loaders import TextLoader - -root_dir = './dlt-repo' # load data from -docs = [] -for dirpath, dirnames, filenames in os.walk(root_dir): - for file in filenames: - try: - loader = TextLoader(os.path.join(dirpath, file), encoding='utf-8') - docs.extend(loader.load_and_split()) - except Exception as e: - pass -``` - -#### 5. Splitting files to chunks -```py -# This code uses CharacterTextSplitter to split documents into smaller chunksbased on character count and store the resulting chunks in the texts variable. - -from langchain.text_splitter import CharacterTextSplitter -text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) -texts = text_splitter.split_documents(docs) -``` -#### 6. Create Deeplake dataset - -```sh -# Set up your deeplake dataset by replacing the username with your Deeplake account and setting the dataset name. For example if the deeplakes username is “your_name” and the dataset is “dlt-hub-dataset” - -username = "your_deeplake_username" # replace with your username from app.activeloop.ai -db = DeepLake(dataset_path=f"hub://{username}/dlt_gpt", embedding_function=embeddings, public=True) #dataset would be publicly available -db.add_documents(texts) - -# Assign the dataset and embeddings to the variable db , using deeplake dataset. -# Replace your_username with actual username -db = DeepLake(dataset_path="hub://"your_username"/dlt_gpt", read_only=True, embedding_function=embeddings) - -# Create a retriever -retriever = db.as_retriever() -retriever.search_kwargs['distance_metric'] = 'cos' -retriever.search_kwargs['fetch_k'] = 100 -retriever.search_kwargs['maximal_marginal_relevance'] = True -retriever.search_kwargs['k'] = 10 -``` -#### 7. Initialize the GPT model -```py -from langchain.chat_models import ChatOpenAI -from langchain.chains import ConversationalRetrievalChain - -model = ChatOpenAI(model_name='gpt-3.5-turbo') -qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever) -``` -### III. Result -After the walkthrough, we can start to experiment different questions and it will output answers based on our training from dlt hub repository. - -Here, I asked " why should data teams use dlt? " - -![chatgptq1](https://storage.googleapis.com/dlt-blog-images/chatgptQ1.png) - -It outputted: - -1. It works seamlessly with Airflow and other workflow managers, making it easy to modify and maintain your code. -2. You have complete control over your data. You can rename, filter, and modify it however you want before it reaches its destination. - - -Next, I asked " Who is dlt for? " - -![chatgptq2](https://storage.googleapis.com/dlt-blog-images/chatgptQ2..png) - -It outputted: -1. `dlt` is meant to be accessible to every person on the data team, including data engineers, analysts, data scientists, and other stakeholders involved in data loading. It is designed to reduce knowledge requirements and enable collaborative working between engineers and analysts. - -### IV. Summary - -It worked! we can see how GPT can learn about an open source library by using `dlt` and utilizing the assistance of Langchain and Deeplake. Moreover, by simply follow the steps above, you can customize the GPT model training to your own needs. - -***Curious? Give the Colab [demo](https://colab.research.google.com/drive/1KU1G_08Yihh5p-o1BsCuhA1OkH5zwgkf?usp=sharing)💡 a try or share your questions with us, and we'll have ChatGPT address them in our upcoming article.*** - -*** -[ What's more? ] -- Learn more about [dlt] 👉 [here](https://dlthub.com/docs/intro) -- Need help or want to discuss? Join our [Slack community](https://dlthub.com/community) ! See you there 😊 - diff --git a/docs/website/blog/2023-06-15-automating-data-engineers.md b/docs/website/blog/2023-06-15-automating-data-engineers.md deleted file mode 100644 index 3247fd2638..0000000000 --- a/docs/website/blog/2023-06-15-automating-data-engineers.md +++ /dev/null @@ -1,126 +0,0 @@ ---- -slug: automating-data-engineers -title: "Automating the data engineer: Addressing the talent shortage" -authors: - name: Adrian Brudaru - title: Open source data engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [data engineer shortage, structured data, schema evolution] ---- - -# Automating the data engineer: Addressing the talent shortage - - -![automated pipeline automaton](https://storage.googleapis.com/dlt-blog-images/pipeline-automaton.png) - - -## Why is there a data engineer shortage? - -1. High Demand and Rapid Growth: The increasing reliance on data-driven decision-making and the rise of big data technologies have created a surge in demand for skilled data engineers. -2. Skill Gap and Specialization: Data engineering requires a unique blend of technical skills, and finding individuals with the right combination of programming, database management, and cloud computing expertise can be challenging. -3. Competition from Other Data Roles: The allure of data science and other data-related roles has attracted professionals, leading to a talent shortage in the data engineering field. - -## How big is the data engineer shortage? - - ->💡 "**In Europe there are 32K data engineers and 48K open positions to hire one. In the US the ratio is 41K to 79K**" -Source: [Linkedin data analysis blog post](https://alkrusz.medium.com/look-for-data-engineers-where-no-one-is-looking-2169ffd9fc1b) - - - -Well that doesn’t look too bad - if only we could all be about 2x as efficient :) - -## Bridging the gap: How to make your data engineers 2x more efficient? - -There are 2 ways to make the data engineers more efficient: - -### Option 1: Give them more to do, tell them how to do their jobs better! - - **For some reason, this doesn’t work out great.** All the great minds of our generation told us we should be more like them - -- do more architecture; -- learn more tech; -- use this new toy! -- learn this paradigm. -- take a step back and consider your career choices. -- write more tests; -- test the tests! -- analyse the tests :[ -- write a paper about the tests... -- do all that while alerts go off 24/7 and you are the bottleneck for everyone downstream, analysts and business people screaming. (┛ಠ_ಠ)┛彡┻━┻ - - -> “I can't do -> what ten people tell me to do. -> So I guess I'll remain the same” -> - Otis Redding, *Sittin' On The Dock Of The Bay* - - - -### Option 2: Take away unproductive work - -A data engineer has a pretty limited task repertoire - so could we give some of their work to roles we can hire? - -Let’s see what a data engineer does, according to GPT: - -- **Data curation**: Ensuring data quality, integrity, and consistency by performing data profiling, cleaning, transformation, and validation tasks. -- **Collaboration with analysts:** Working closely with data analysts to understand their requirements, provide them with clean and structured data, and assist in data exploration and analysis. -- **Collaboration with DWH architects:** Collaborating with data warehouse architects to design and optimize data models, schemas, and data pipelines for efficient data storage and retrieval. -- **Collaboration with governance managers:** Partnering with governance managers to ensure compliance with data governance policies, standards, and regulations, including data privacy, security, and data lifecycle management. -- **Structuring and loading:** Designing and developing data pipelines, ETL processes, and workflows to extract, transform, and load data from various sources into the target data structures. -- **Performance optimization:** Identifying and implementing optimizations to enhance data processing and query performance, such as indexing, partitioning, and data caching. -- **Data documentation**: Documenting data structures, data lineage, and metadata to facilitate understanding, collaboration, and data governance efforts. -- **Data troubleshooting:** Investigating and resolving data-related issues, troubleshooting data anomalies, and providing support to resolve data-related incidents or problems. -- **Data collaboration and sharing**: Facilitating data collaboration and sharing across teams, ensuring data accessibility, and promoting data-driven decision-making within the organization. -- **Continuous improvement:** Staying updated with emerging technologies, industry trends, and best practices in data engineering, and actively seeking opportunities to improve data processes, quality, and efficiency. - -### Let’s get a back of the napkin estimation of how much time they spend on those areas - - -Here’s an approximation as offered by GPT. Of course, actual numbers depend on the maturity of your team and their unique challenges. - -- **Collaboration** with others (including data **curation**): **Approximately 40-60% of their working hours**. This includes tasks such as collaborating with team members, understanding requirements, data curation activities, participating in meetings, and coordinating data-related activities. -- Data analysis: **Around 10-30% of their working hours**. This involves supporting data **exploration**, providing insights, and assisting analysts in understanding and extracting value from the data. -- Technical problem-solving (structuring, maintenance, optimization): **Roughly 30-50% of their working hours.** This includes solving data structuring problems, maintaining existing data structures, optimizing data pipelines, troubleshooting technical issues, and continuously improving processes. - -By looking at it this way, solutions become clear: - -- **Let someone else do curation.** Analysts could talk directly to producers. By removing the middle man, you improve speed and quality of the process too. -- **Automate data structuring:** While this is not as time consuming as the collaboration, it’s the second most time consuming process. -- **Let analyst do exploration** of structured data at curation, not before load. This is a minor optimisation, but 10-30% is still very significant towards our goal of reducing workload by 50%. - -### How much of their time could be saved? - -Chat GPT thinks: - -it is reasonable to expect significant time savings with the following estimates: - -1. Automation of Structuring and Maintenance: By automating the structuring and maintenance of data, data engineers **can save 30-50% or more of their time** previously spent on these tasks. This includes activities like schema evolution, data transformation, and pipeline optimization, which can be streamlined through automation. -2. Analysts and Producers Handling Curation: Shifting the responsibility of data curation to analysts and producers **can save an additional 10-30% of the data engineer's time.** This includes tasks such as data cleaning, data validation, and data quality assurance, which can be effectively performed by individuals closer to the data and its context. - -It's important to note that these estimates are approximate and can vary based on the specific circumstances and skill sets within the team. - -# 40-80% of a data engineer’s time could be spared - -
-💡 40-80% of a data engineer’s time could be spared
-
- -To achieve that, - -- Automate data structuring. -- Govern the data without the data engineer. -- Let analysts explore data as part of curation, instead of asking data engineers to do it. - -This looks good enough for solving the talent shortage. -Not only that, but doing things this way lets your team focus on what they do best. - -# A recipe to do it - -1. Use something with schema inference and evolution to load your data. -2. Notify stakeholders and producers of data changes, so they can curate it. -3. Don’t explore json with data engineers - let analyst explore structured data. - -Ready to stop the pain? Read [this explainer on how to do schema evolution with dlt](https://dlthub.com/docs/blog/schema-evolution). -Want to discuss? Join our [slack](https://dlthub.com/community). \ No newline at end of file diff --git a/docs/website/blog/2023-06-20-dlthub-gptquestion1-.md b/docs/website/blog/2023-06-20-dlthub-gptquestion1-.md deleted file mode 100644 index 6a14013775..0000000000 --- a/docs/website/blog/2023-06-20-dlthub-gptquestion1-.md +++ /dev/null @@ -1,100 +0,0 @@ ---- -slug: trained-gpt-q&a -title: "Hey GPT, tell me about dlthub!" -image: https://storage.googleapis.com/dlt-blog-images/traingptblog.jpg -authors: - name: Tong Chen - title: Data Engineer Intern at dltHub - url: https://github.com/TongHere - image_url: https://iili.io/HP9uKIn.jpg -tags: [trained gpt-4, Q&A, dlt ] - ---- -Hi there! 👋 - -In this article, we asked ChatGPT questions about `dlt`, the open source library. Questions included some basic introduction, random fun questions, and advanced `dlt` project processes. Let’s see how it answered our questions! - -#### 💡 Who can use `dlt`? ->`dlt` is designed to be accessible and useful for various roles within a data team, including: -> -> 1. Data professionals and aspiring data professionals who want to showcase their understanding and value to data teams. They can use `dlt` to build end-to-end projects, load data to databases, transform data, and prepare reporting. Read more in the Data Beginner guide. -> ->2. Data engineers who want to remove tedious work, reduce ETL maintenance, empower their team members, and enhance their productivity. `dlt` can be used to automate data loading, enable collaboration, and support natural workflows. Read more in the Data Engineer guide. -> ->3. Data analysts, data scientists, and analytics engineers who want to build and use pipelines for single use cases, core business pipelines, or heavily customized pipelines. `dlt` offers different levels of abstraction to cater to various needs and use cases. Read more in the Data Beginner guide. -> -> Overall, `dlt` is suitable for anyone working with data pipelines, from beginners to experienced professionals. - - -#### 💡 I am just starting in the data field, how can I use `dlt`? - -> If you are just starting in the data field, `dlt` can be a great tool to help you showcase your understanding and value to data teams. The first step is to get familiar with `dlt` by playing around with the Google Colab demo or installing it on your computer. Once you have `dlt` installed, you can initialize a project and pipeline to load data from an API, transform it, and load it into a database in minutes. - - -#### 💡 How can data scientists use `dlt` in data science projects? - ->Data scientists can use `dlt` in data science projects to easily load and transform data from various sources. `dlt` offers different levels of abstraction that enable and empower data scientists to build and use pipeline. -> ->For example, data scientists can use `dlt` to build pipelines for their specific use cases, such as grabbing data from an API that they need for their analysis. They can heavily customize how `dlt` sources produce data, such as selecting, filtering, renaming, anonymizing, and just about any custom operation. -> -> In addition, data scientists can use `dlt` to create highly scalable, easy to maintain, straightforward to deploy data pipelines without having to wait for help from a data engineer. This empowers them to turn messy, unstructured data into regularly updated datasets and focus on their analysis and modeling work. ->Overall, `dlt` can help data scientists be more efficient and productive in their work by simplifying the data loading process and allowing them to focus on the analysis and modeling. - -***Now, let’s start with some advanced questions:*** - -#### 💡 Is `dlt` like dbt? How to use `dlt` with dbt? Can you show me the process? - ->Yes, `dlt` and `dbt` have similar functionalities, but they differ in their approach and scope. `dlt` is a Python package that helps you load data from various sources and transform it to be ready for analysis. `dbt`, on the other hand, is a SQL transformation tool that allows you to structure and transform your data using SQL. However, you can use `dlt` with `dbt` to create a complete data pipeline from data extraction to transformation and loading. Here is an example process to use `dlt` with `dbt`: -> ->To use `dbt` with `dlt`, you can follow these steps: -> ->1. Install `dbt` and `dlt` using pip. ->2. Create a `dlt` pipeline to extract, transform, and load data into your data warehouse. You can use the `dlt` SQL client to create tables, views, and other structures that can be used for analysis. ->3. Create a `dbt` project and configure it to use the same data warehouse as your `dlt` pipeline. ->4. Write SQL queries to transform the data in your `dbt` project. ->5. Run the `dbt` project to create the necessary structures and transformations in your data warehouse. ->6. Use a BI tool to visualize and analyze the transformed data. -> ->Overall, using `dlt` with `dbt` can help you create a complete data pipeline that covers data extraction, transformation, and loading. This can help you streamline your data processes and make it easier to analyze your data. - -#### 💡 Can I use `dlt` with Streamlit? Or Sheets? Or Power BI? Metabase, Looker? What do you recommend? - ->Yes, `dlt` can be used with Streamlit, Google Sheets, Power BI, Metabase, and Looker. The choice of tool will depend on your specific use case and requirements. For example, if you want to enable business users to explore data and create their own dashboards, then Metabase or Looker might be a good choice. -> ->If you want to build a custom dashboard with Python, then Streamlit might be a good choice. If you want to use a cloud-based BI tool, then Power BI might be a good choice. In terms of using `dlt` with these tools, `dlt` can be used to load data into a database or data warehouse that can then be connected to these tools. For example, you can use `dlt` to load data into a PostgreSQL database and then use that database as a data source for Power BI. -> ->Overall, I would recommend using the tool that best fits your specific use case and requirements, and then using `dlt` to load and transform your data as needed. - -#### 💡 How can I use `dlt` to create a machine learning model? - ->`dlt` can be used to create machine learning models in the following way: -> ->1. Choose an API that produces data. ->2. Choose a use case for this data. ->3. Build a `dlt` pipeline that loads data from the API for your use case. ->4. Extract data from your pipeline. -5>. Create a machine learning model depends on your use case. ->6. Finally, you can deploy your model with Metabase dashboard. - -***Let’s try trolling :)*** - - -#### 💡 I am tired of lakehouses, can I build a beach house with dlt? or a forest house?? - ->I am sorry, but `dlt` is a data loading tool, and it is not related to building beach houses. It is meant to help you automate the process of loading and managing data in your applications. If you have any questions related to data loading or management, I will be happy to help! - -#### 💡 If data is the new oil, then what is `dlt`? newer oil? - ->`dlt` is not a type of oil. It is a data loading tool that automates time-consuming tasks, empowers stakeholders to solve pipeline maintenance issues, and offers different levels of abstraction that enable and empower data professionals to build and use pipelines. Its purpose is to make data loading a commodity and to simplify the process of loading data for organizations. - -*** -#### 💡 Conclusion - ->We intended to ask ChatGPT different questions about `dlt` and see if there are any misunderstandings. For example: how `dlt` can be intergrated in various use cases or how data teams can use `dlt` in different projects. Seems it worked really well and answered our questions precisely based on our documentation and blog! Moreover, when we tried to ask some random questions, ChatGPT also gave us proper answers! GPT really seems to understands what we were trying to communicate with it! - -**What questions you would love to ask? Share with us in our [Slack community](https://dlthub.com/community) ! See you there 😊** - -*** -[ What's more? ] -- Learn more about `dlt` 👉 [here](https://dlthub.com/docs/intro) -- Give the 👉 [Colab Demo](https://colab.research.google.com/drive/1KU1G_08Yihh5p-o1BsCuhA1OkH5zwgkf?usp=sharing) a try diff --git a/docs/website/blog/2023-06-21-open-api-spec-for-dlt-init.md b/docs/website/blog/2023-06-21-open-api-spec-for-dlt-init.md deleted file mode 100644 index 6edb0f44a7..0000000000 --- a/docs/website/blog/2023-06-21-open-api-spec-for-dlt-init.md +++ /dev/null @@ -1,48 +0,0 @@ ---- -slug: open-api-spec-for-dlt-init -title: "dlt & openAPI code generation: A step beyond APIs and towards 10,000s of live datasets" -image: https://camo.githubusercontent.com/1aca1132999dde59bc5b274aeb4d01c79eab525941362491a534ddd8d1015dce/68747470733a2f2f63646e2e6c6f6f6d2e636f6d2f73657373696f6e732f7468756d626e61696c732f32383036623837336261316334653065613338326562336234666261663830382d776974682d706c61792e676966 -authors: - name: Matthaus Krzykowski - title: Co-Founder & CEO at dltHub - url: https://twitter.com/matthausk - image_url: https://pbs.twimg.com/profile_images/642282396751130624/9ixo0Opj_400x400.jpg -tags: [fastapi, feature, openapi, dlt pipline, pokeapi] ---- -Today we are releasing a proof of concept of the [`dlt init`](https://dlthub.com/docs/walkthroughs/create-a-pipeline) extension that can [generate `dlt` pipelines from an OpenAPI specification.](https://github.com/dlt-hub/dlt-init-openapi) - -If you build APIs, for example with [FastAPI](https://fastapi.tiangolo.com/), you can, thanks to the [OpenAPI spec,](https://spec.openapis.org/oas/v3.1.0) automatically generate a [python client](https://pypi.org/project/openapi-python-client/0.6.0a4/) and give it to your users. Our demo takes this a step further and enables you to generate advanced `dlt` pipelines that, in essence, convert your API into a live dataset. - -You can see how Marcin generates such a pipeline from the OpenAPI spec using the [Pokemon API](https://pokeapi.co/) in the Loom below. -[![marcin-demo](https://storage.googleapis.com/dlt-blog-images/openapi_loom_old.png)](https://www.loom.com/share/2806b873ba1c4e0ea382eb3b4fbaf808?sid=501add8b-90a0-4734-9620-c6184d840995) - -Part of our vision is that each API will come with a `dlt` pipeline - similar to how these days often it comes with a python client. We believe that very often API users do not really want to deal with endpoints, http requests, and JSON responses. They need live, evolving datasets that they can place anywhere they want so that it's accessible to any workflow. - -We believe that API builders will bundle `dlt` pipelines with their APIs only if such a process is hassle free. One answer to that is code generation and the reuse of information from the OpenAPI spec. - -This release is a part of a bigger vision for `dlt` of a world centered around accessible data for modern data teams. In these new times code is becoming more disposable, but the data stays valuable. We eventually want to create an ecosystem where hundreds of thousands of pipelines will be created, shared, and deployed. Where datasets, reports, and analytics can be written and shared publicly and privately. [Code generation is automation on steroids](https://dlthub.com/product/#code-generation-is-automation-on-steroids) and we are going to be releasing many more features based on this principle. - -## Generating a pipeline for PokeAPI using OpenAPI spec - -In the embedded loom you saw Marcin pull data from the `dlt` pipeline created from the OpenAPI spec. The proof of concept already uses a few tricks and heuristics to generate useful code. Contrary to what you may think, PokeAPI is a complex one with a lot of linked data types and endpoints! - -- It created a resource for all endpoints that return lists of objects. -- It used heuristics to discover and extract lists wrapped in responses. -- It generated dlt transformers from all endpoints that have a matching list resource (and return the same object type). -- It used heuristics to find the right object id to pass to the transformer. -- It allowed Marcin to select endpoints using the [questionary](https://github.com/tmbo/questionary) lib in CLI. -- It listed at the top the endpoints with the most central data types (think of tables that refer to several other tables). - -As mentioned, the PoC was well tested with PokeAPI. We know it also works with many other - we just can’t guarantee that our tricks work in all cases as they were not extensively tested. - -Anyway: [Try it out yourself!](https://github.com/dlt-hub/dlt-init-openapi) - -## We plan to take this even further! - -- **We will move this feature into `dlt init` and integrate with LLM code generation!** -- **Restructuring of the python client:** We will fully restructure the underlying python client. We'll compress all the files in the `pokemon/api` folder into a single, nice, and extendable client. -- **GPT-4 friendly:** We'll allow easy addition of pagination and other injections into the client. -- **More heuristics:** Many more heuristics to extract resources, their dependencies, infer the incremental and merge loading. -- **Tight integration with FastAPI on the code level to get even more heuristics!** - -[Your feedback and help is greatly appreciated.](https://github.com/dlt-hub/dlt/blob/devel/CONTRIBUTING.md) [Join our community, and let’s build together.](https://dlthub.com/community) \ No newline at end of file diff --git a/docs/website/blog/2023-06-26-dlthub-gptquestion2.md b/docs/website/blog/2023-06-26-dlthub-gptquestion2.md deleted file mode 100644 index 75f10e362a..0000000000 --- a/docs/website/blog/2023-06-26-dlthub-gptquestion2.md +++ /dev/null @@ -1,109 +0,0 @@ ---- -slug: trained-gpt-q&a-2 -title: "dlt AI Assistant provides answers you need!" -image: https://storage.googleapis.com/dlt-blog-images/blog_gpt_QA2.jpg -authors: - name: Tong Chen - title: Data Engineer Intern at dltHub - url: https://github.com/TongHere - image_url: https://iili.io/HP9uKIn.jpg -tags: [trained gpt-4, Q&A, dlt ] - ---- -Hi there! 👋 - -Maintenance of data pipelines can often pose significant challenges for organizations. Ensuring the smooth operation of these pipelines, managing updates and changes, and addressing potential bottlenecks are just a few examples of the complexities involved. This is where `dlt` comes into play as a valuable solution, as `dlt` can efficiently alleviate these maintenance burdens and optimize data pipeline management. - -In this article, we delve into the capabilities of our `dlt` AI Assistant, the conversational companion for the dltHub docs. Our aim was to explore how `dlt` can effectively address data pipeline maintenance challenges and streamline advanced `dlt` project processes. To gain insights, we posed several questions to our AI Assistant. Let's delve into the details and discover how it responded! - -#### 💡 How does `dlt` reduce maintenance ? ->dlt reduces maintenance in several ways: ->1. **Automating structuring**: dlt automates the transition from unstructured (nested, untyped) to structured (tabular, typed) data, which eliminates the need for manual data structuring and reduces the chances of errors. ->2. **Schema evolution**: dlt can automatically adapt to changes in the source data schema, such as new fields or tables. This means that you don't have to manually update your pipelines when the source data changes. You can monitor schema evolution and get alerts on schema changes. Read more about monitoring schema evolution [here](https://dlthub.com/docs/running-in-production/running#inspect-save-and-alert-on-schema-changes). ->3. **Standardization**: By providing a standardized way of loading data, dlt enables better collaboration and communication among team members. This reduces the need for individual maintenance efforts and ensures that everyone is working with the same understanding of the data structure. ->4. **Pre-built sources**: dlt offers a collection of ready-built sources that can be easily integrated into your pipelines, reducing the need for custom development and maintenance. You can find existing sources [here](https://dlthub.com/docs/walkthroughs/add-a-verified-source). ->5. **Customization**: dlt allows for easy customization of pipelines, such as renaming columns, anonymizing data, and filtering. This means that you can adapt your pipelines to your specific needs without having to maintain complex custom code. See examples of customization [here](https://dlthub.com/docs/general-usage/customising-pipelines/renaming_columns). - ->By automating structuring, handling schema evolution, standardizing data loading, providing pre-built sources, and allowing easy customization, dlt significantly reduces the maintenance effort required for your data pipelines. - -#### 💡 How does schema evolution help companies' bottom line? - -> Schema evolution helps companies' bottom line in several ways: ->1. **Reduced maintenance costs**: By automatically adapting to changes in the source data schema, dlt reduces the need for manual updates to data pipelines. This saves time and resources, leading to lower maintenance costs. ->2. **Faster time to market**: Schema evolution allows companies to quickly adapt to changes in their data, enabling them to make data-driven decisions faster. This can lead to quicker product iterations, improved customer experiences, and ultimately, increased revenue. ->3. **Improved data quality**: By automating schema evolution, dlt helps ensure that data is consistently structured and typed, reducing the risk of errors and improving overall data quality. Better data quality leads to more accurate insights and better decision-making, which can positively impact the bottom line. ->4. **Increased collaboration and efficiency**: Schema evolution promotes better collaboration and communication among team members, as everyone operates on a shared understanding of the data structure. This can lead to increased efficiency and productivity, ultimately benefiting the company's bottom line. ->5. **Enhanced data governance**: By tracking schema changes and alerting stakeholders, dlt helps improve data governance. Better governance can lead to more responsible data usage, reduced risk of data breaches or compliance issues, and ultimately, a more stable and secure business environment. - ->Overall, schema evolution contributes to a company's bottom line by reducing maintenance costs, speeding up time to market, improving data quality, increasing collaboration and efficiency, and enhancing data governance. - - -#### 💡 How can we use `dlt` to make maintenance more efficient? - ->Using dlt to make maintenance more efficient involves several steps: ->1. **Automate structuring**: By automating the process of structuring data, dlt eliminates the need for manual data structuring, reducing the chances of errors and saving time. This makes maintenance more efficient as you don't have to spend time fixing structuring issues. ->2. **Handle schema evolution**: dlt can automatically adapt to changes in the source data schema, such as new fields or tables. This means that you don't have to manually update your pipelines when the source data changes, making maintenance more efficient. You can monitor schema evolution and get alerts on schema changes. Read more about monitoring schema evolution [here](https://dlthub.com/docs/running-in-production/running#inspect-save-and-alert-on-schema-changes). ->3. **Standardize data loading**: dlt provides a standardized way of loading data, which enables better collaboration and communication among team members. This reduces the need for individual maintenance efforts and ensures that everyone is working with the same understanding of the data structure. ->4. **Use pre-built sources**: dlt offers a collection of ready-built sources that can be easily integrated into your pipelines, reducing the need for custom development and maintenance. You can find existing sources [here](https://dlthub.com/docs/walkthroughs/add-a-verified-source). ->5. **Customize pipelines**: dlt allows for easy customization of pipelines, such as renaming columns, anonymizing data, and filtering. This means that you can adapt your pipelines to your specific needs without having to maintain complex custom code. See examples of customization [here](https://dlthub.com/docs/general-usage/customising-pipelines/renaming_columns). - ->By following these steps, you can leverage dlt to make your data pipeline maintenance more efficient, saving time and resources. - -***Now, let’s start with some advanced questions:*** - -#### 💡 Does `dlt` help deliver more data outcomes? - ->Yes, dlt helps deliver more data outcomes in several ways: ->1. **Faster time to build pipelines**: By automating the structuring and loading of data, dlt significantly reduces the time required to build data pipelines. This allows you to focus more on using the data for insights and decision-making. ->2. **Improved data quality**: dlt structures data and alerts schema changes, which leads to better data quality and governance. High-quality data enables more accurate insights and better decision-making, ultimately leading to more data-driven outcomes. ->3. **Better collaboration and communication**: Structured data promotes better collaboration and communication among team members. Since everyone operates on a shared understanding of the data structure, it becomes easier to discuss and align on data-related topics, leading to more effective data outcomes. ->4. **Simplified access**: dlt makes it easy for anyone to query structured data using their tools of choice. This enables a wider range of team members to access and analyze the data, leading to more data-driven insights and outcomes. ->5. **Integration with existing tools**: dlt can be run in various environments, such as notebooks, cloud functions, or workflow managers like Airflow. This flexibility allows you to integrate dlt into your existing data stack, making it easier to deliver data outcomes without adding complexity. - ->By automating structuring, improving data quality, promoting collaboration, simplifying access, and integrating with existing tools, dlt helps deliver more data outcomes and drive better decision-making. - -#### 💡 Does `dlt` help for data governance? - ->Yes, dlt helps with data governance in several ways: - -> 1. **Structured data**: By structuring data automatically, dlt ensures that data is consistently formatted and typed, which is essential for maintaining data quality and governance. -> 2. **Schema evolution**: dlt can automatically adapt to changes in the source data schema and alert stakeholders of these changes. This helps maintain data consistency and enables better governance. Read more about monitoring schema evolution [here](https://dlthub.com/docs/running-in-production/running#inspect-save-and-alert-on-schema-changes). ->3. **Metadata tracking**: dlt tracks metadata like data lineage and schema descriptions, which helps you stay on top of your data and maintain better governance. ->4. **Customization**: dlt allows for easy customization of pipelines, such as anonymizing data or renaming columns, which can help you comply with data privacy regulations and maintain data governance. See examples of customization [here](https://dlthub.com/docs/general-usage/customising-pipelines/renaming_columns). ->5. **Integration with data governance tools**: dlt can be integrated with other data governance tools, such as data validation libraries like Great Expectations, or data testing frameworks like dbt. This enables you to enforce data quality checks and maintain better governance across your data pipelines. - ->By structuring data, handling schema evolution, tracking metadata, allowing customization, and integrating with data governance tools, dlt helps improve data governance and ensure that your data is accurate, reliable, and secure. - -#### 💡 How can data engineers of different seniorities use `dlt`? do they build pipelines or platforms? what about complex pipelines? - ->Data engineers of different seniorities can use dlt in various ways, depending on their experience and the complexity of the pipelines or platforms they are building: - ->1. **Analysts and junior data engineers**: They can use ready-built sources or pass their unstructured data to dlt, which will create a sturdy pipeline. They can also adjust schemas and configure dlt to change loading modes or add performance hints. Examples include [adding an existing source](https://dlthub.com/docs/walkthroughs/add-a-verified-source) and [adjusting a schema](https://dlthub.com/docs/walkthroughs/adjust-a-schema). ->2. **Python-first users**: They can heavily customize how dlt sources produce data, as dlt supports selecting, filtering, renaming, anonymizing, and just about any custom operation. For example, see the [rename columns example](https://dlthub.com/docs/general-usage/customising-pipelines/renaming_columns). ->3. **Senior data engineers**: They can dig even deeper into customization options and change schemas, normalizers, the way pipelines run, such as parallelism, and build complex pipelines. They can also build data platforms by integrating dlt with other tools and services, such as orchestration tools like Apache Airflow, data validation libraries like Great Expectations, or data testing frameworks like dbt. - ->In summary, data engineers of different seniorities can use dlt to build pipelines or platforms, depending on their experience and the complexity of the project. dlt provides flexibility and customization options that cater to various skill levels and use cases, making it a valuable tool for data engineers across the board. - -***Let’s try trolling :)*** - - -#### 💡 Can I use `dlt` for my beach house maintenance? - ->I'm sorry, but I am an AI assistant specifically designed to answer questions about the `dlt` (data load tool) library. I cannot provide information or assistance for beach house maintenance. If you have any questions about the dlt library, feel free to ask! - -*** -#### 💡 Conclusion - ->During this Q&A converstation, we can understand how `dlt` helps reduce maintenance in data pipelines by automating structuring, handling schema evolution, standardizing data loading, providing pre-built sources, and allowing easy customization. It offers powerful capabilities to simplify and improve data pipeline management, reducing maintenance efforts, and driving better outcomes. - -Now we understand how `dlt` significantly improves our work efficiency! - -Want to ask your own questions to the `dlt` AI Assistant? Just click on the "Get Help" button located at the bottom right. - -![dlthelp](https://storage.googleapis.com/dlt-blog-images/dlthelp.jpg) - -*** -[ What's more? ] -- Learn more about `dlt` 👉 [here](https://dlthub.com/docs/intro) -- Want to discuss more? Join our [Slack community](https://dlthub.com/community) ! -- Try out with dlt demo 👉 [Colab Demo](https://colab.research.google.com/drive/1KU1G_08Yihh5p-o1BsCuhA1OkH5zwgkf?usp=sharing)  ! diff --git a/docs/website/blog/2023-08-14-dlt-motherduck-blog.md b/docs/website/blog/2023-08-14-dlt-motherduck-blog.md deleted file mode 100644 index 2760c99f20..0000000000 --- a/docs/website/blog/2023-08-14-dlt-motherduck-blog.md +++ /dev/null @@ -1,184 +0,0 @@ ---- -slug: dlt-motherduck-demo -title: "dlt-dbt-DuckDB-MotherDuck: My super simple and highly customizable approach to the Modern Data Stack in a box" -image: https://storage.googleapis.com/dlt-blog-images/dlt-motherduck-logos.png -authors: - name: Rahul Joshi - title: Developer Relations at dltHub - url: https://github.com/rahuljo - image_url: https://avatars.githubusercontent.com/u/28861929?v=4 -tags: [BigQuery, dbt, dlt, DuckDB, GitHub, Metabase, MotherDuck] ---- -:::info -TL;DR: I combined `dlt`, dbt, DuckDB, MotherDuck, and Metabase to create a Modern Data Stack in a box that makes it very easy to create a data pipeline from scratch and then deploy it to production. -::: - -I started working in dltHub in March 2023, right around the time when we released DuckDB as a destination for `dlt`. As a Python user, being able to create a data pipeline, load the data in my laptop, and explore and query the data all in python was awesome. - -At the time I also came across this [very cool article](https://duckdb.org/2022/10/12/modern-data-stack-in-a-box.html) by Jacob Matson in which he talks about creating a Modern Data Stack(MDS) in a box with DuckDB. I was already fascinated with `dlt` and all the other new tools that I was discovering, so reading about this approach of combining different tools to execute an end-to-end proof of concept in your laptop was especially interesting. - -Fast forward to a few weeks ago when `dlt` released MotherDuck as a destination. The first thing that I thought of was an approach to MDS in a box where you develop locally with DuckDB and deploy in the cloud with MotherDuck. I wanted to try it out. - -## What makes this awesome - -In my example, I wanted to customize reports on top of Google Analytics 4 (GA4) and combine it with data from GitHub. This is usually challenging because, while exporting data from GA4 to BigQuery is simple, combining it with other sources and creating custom analytics on top of it can get pretty complicated. - -By first pulling all the data from different sources into DuckDB files in my laptop, I was able to do my development and customization locally. - -![local-workflow](https://storage.googleapis.com/dlt-blog-images/dlt-motherduck-local-workflow.png) - -And then when I was ready to move to production, I was able to seamlessly switch from DuckDB to MotherDuck with almost no code re-writing! - -![production-workflow](https://storage.googleapis.com/dlt-blog-images/dlt-motherduck-production-workflow.png) - -Thus I got a super simple and highly customizable MDS in a box that is also close to company production setting. - -## What does this MDS in a box version look like? - -| Tool | Layer | Why it’s awesome | -| --- | --- | --- | -| [`dlt`](https://dlthub.com/docs/intro) | data ingestion | ridiculously easy to write a customized pipeline in Python to load from any source | -| [DuckDB](https://duckdb.org/) | [data warehouse in your laptop](https://dlthub.com/docs/blog/duckdb-1M-downloads-users#2-local-data-workflows-are-going-mainstream-and-duckdb-is-at-the-center) | free, fast OLAP database on your local laptop that you can explore using SQL or python | -| [MotherDuck](https://motherduck.com/) | data warehouse in the cloud | DuckDB, but in cloud: fast, OLAP database that you can connect to your local duckdb file and share it with the team in company production settings | -| [dbt](https://www.getdbt.com/) | data transformation | an amazing open source tool to package your data transformations, and it also combines well with dlt, DuckDB, and Motherduck | -| [Metabase](https://www.metabase.com/start/oss/) | reporting | open source, has support for DuckDB, and looks prettier than my Python notebook | - -## How this all works - -The example that I chose was inspired by one of my existing workflows: that of understanding `dlt`-related metrics every month. Previously, I was using only Google BigQuery and Metabase to understand `dlt`’s product usage, but now I decided to test how a migration to DuckDB and MotherDuck would look like. - -The idea is to build a dashboard to track metrics around how people are using and engaging with `dlt` on different platforms like GitHub (contributions, activity, stars etc.), `dlt` website and docs (number of website/docs visits etc.). - -This is a perfect problem to test out my new super simple and highly customizable MDS in a box because it involves combining data from different sources (GitHub API, Google Analytics 4) and tracking them in a live analytics dashboard. - -1. **Loading the data using `dlt`** - - The advantage of using `dlt` for data ingestion is that `dlt` makes it very easy to create and customize data pipelines using just Python. - - In this example, I created two data pipelines: - - - BigQuery → DuckDB: - Since all the Google Analytics 4 data is stored in BigQuery, I needed a pipeline that could load all events data from BigQuery into a local DuckDB instance. BigQuery does not exist as a verified source for `dlt`, which means that I had to write this pipeline from scratch. - - GitHub API → DuckDB: - `dlt` has an existing GitHub source that loads data around reactions, PRs, comments, and issues. To also load data on stargazers, I had to modify the existing source. - - **dlt is simple and highly customizable:** - - - **Even though Bigquery does not exist as a dlt source, dlt makes it simple to write a pipeline that uses Bigquery as a source. How this looks like**: - 1. Create a `dlt` project: - - `dlt init bigquery duckdb` - - This creates a folder with the directory structure - ```text - ├── .dlt - │ ├── config.toml - │ └── secrets.toml - ├── bigquery.py - └── requirements.txt - ``` - - 2. Add BigQuery credentials inside .dlt/secrets.toml. - 3. Add a Python function inside [bigquery.py](http://bigquery.py) that requests the data. - 4. Load the data by simply running `python bigquery.py`. - - See [the accompanying repo](https://github.com/dlt-hub/bigquery-motherduck) for a detailed step-by-step on how this was done. - - - **The data in BigQuery is nested, which dlt automatically normalizes on loading.** - - BigQuery might store data in nested structures which would need to be flattened before being loaded into the target database. This typically increases the challenge in writing data pipelines. - - `dlt` simplifies this process by automatically normalizing such nested data on load. - - ![nested-bigquery](https://storage.googleapis.com/dlt-blog-images/dlt-motherduck-nested-bigquery.png) - - Example of what the nested data in BigQuery looks like. - - ![normalized-bigquery](https://storage.googleapis.com/dlt-blog-images/dlt-motherduck-normalized-bigquery.png) - - `dlt` loads the main data into table `ga_events`, and creates another table `ga_events__event_params` for the nested data. - - - **The existing Github source does not load information on stargazers. dlt makes it easy to customize the Github source for this.** - - The way the existing GitHub verified source is written, it only loads data on GitHub issues, reactions, comments, and pull requests. To configure it to also load data on stargazers, all I had to do was to add a python function for it in the pipeline script. - - See [the accompanying repo](https://github.com/dlt-hub/github-motherduck) for a detailed step-by-step on how this was done. - -2. **Using DuckDB as the data warehouse** - **DuckDB** is open source, fast, and easy to use. It simplifies the process of validating the data after loading it with the data pipeline. - - In this example, after running the BigQuery pipeline, the data was loaded into a locally created DuckDB file called ‘bigquery.duckdb’, and this allowed me to use python to the explore the loaded data: - - ![duckdb-explore](https://storage.googleapis.com/dlt-blog-images/dlt-motherduck-duckdb-explore.png) - - The best thing about using DuckDB is that it provides a local testing and development environment. This means that you can quickly and without any additional costs test and validate your workflow before deploying it to production. - - Also, being open source, it benefits from community contributions, particularly dbt-duckdb adapter and the DuckDB Metabase driver, which make it very useful in workflows like these. - -3. **dbt for data transformations** - - Because of `dlt`’s dbt runner and DuckDB’s dbt adapter, it was very easy to insert dbt into the existing workflow. What this looked like: - - 1. I first installed dbt along with the duckdb adapter using `pip install dbt-duckdb` . - 2. I then created a dbt project inside the dlt project using `dbt init` and added any transforms as usual. - 3. Finally, I added the `dlt`’s dbt runner to my python script, and this configured my pipeline to automatically transform the data after loading it. See [the documentation](https://dlthub.com/docs/dlt-ecosystem/transformations/dbt#dbt-runner-in-dlt) for more information on the dbt runner. - -4. **Metabase for the dashboard** - - Metabase OSS has a DuckDB driver, which meant that I could simply point it to the DuckDB files in my system and build a dashboard on top of this data. - - ![dashboard-1](https://storage.googleapis.com/dlt-blog-images/dlt-motherduck-dashboard-1.png) - - ![dashboard-2](https://storage.googleapis.com/dlt-blog-images/dlt-motherduck-dashboard-2.png) - - ![dashboard-3](https://storage.googleapis.com/dlt-blog-images/dlt-motherduck-dashboard-3.png) - - ![dashboard-4](https://storage.googleapis.com/dlt-blog-images/dlt-motherduck-dashboard-4.png) - -5. **Going to production: Using MotherDuck as the destination** - - So far the process had been simple. The integrations among `dlt`, dbt, DuckDB, and Metabase made the loading, transformation, and visualization of data fairly straight-forward. But the data loaded into DuckDB existed only in my machine, and if I wanted share this data with my team, then I needed to move it to a different storage location accessible by them. - - The best and the easiest way to do this was to use MotherDuck: a serverless cloud analytics platform built on top of DuckDB, where you can host your local DuckDB databases. - - **Why choose MotherDuck** - - 1. **Go from development to production with almost no code re-writing:** - - This was my main reason for choosing MotherDuck. MotherDuck integrates with `dlt`, dbt, and Metabase just as well as DuckDB. And I was able to replace DuckDB with MotherDuck in my pipeline with almost no code re-writing! - - What this process looked like: - - 1. First, I modified the `dlt` pipelines to load to MotherDuck instead of DuckDB as follows: - 1. I added credentials for MotherDuck inside `.dlt/secrets.toml` - 2. I made a minor update to the code: i.e. just by changing `destination = "duckdb"` to `destination = "motherduck"` the pipelines were already configured to load the data into MotherDuck instead of DuckDB - 2. With this change, I was already able to deploy my pipelines with GitHub actions. - 3. After deploying, I simply changed the DuckDB path to the MotherDuck path in Metabase, and then I deployed Metabase on GCP. - - The reason this is great is because it greatly simplifies the development lifecycle. Using DuckDB + MotherDuck, you can develop and test your pipeline locally and then move seamlessly to production. - - 2. **Very easy to copy local DuckDB databases to MotherDuck** - - This was especially useful in this demo. Google Analytics 4 events data is typically large and when fetching this data from BigQuery, you are billed for the requests. - - In this example, after I ran the BigQuery -> DuckDB pipeline during development, I wanted to avoid loading the same data again when deploying the pipeline. I was able to do this by copying the complete local DuckDB database to MotherDuck, and configuring the pipeline to only load new data from BigQuery. - - 3. **Easy to share and collaborate** - - Being able to share data with the team was the main goal behind moving from DuckDB to a cloud data warehouse. MotherDuck provides a centralized storage system for the DuckDB databases which you can share with your team, allowing them to access this data from their own local DuckDB databases. - - In my example, after I load the data to MotherDuck, I can provide access to my team just by clicking on ‘Share’ in the menu of their web UI. - - ![motherduck-share](https://storage.googleapis.com/dlt-blog-images/dlt-motherduck-share.png) - -**Conclusion:** - -This was a fun and interesting exercise of creating a simple, yet powerful Modern Data Stack in a box. For me the biggest positives about this approach are: - -1. Everything was happening on my laptop during the development giving me full control. Still going to production was seamless and I didn't need to change my code and data transformations at all. -2. I really liked that I could come with my ideas on what data I need and just write the pipelines in Python using `dlt`. I was not forced to pick from a small pull of existing data extractors. Both, customizing code contributed by others and writing my bigquery source from scratch, were fun and not going beyond Python and data engineering knowledge that I had. -3. I'm impressed by how simple and customizable my version of MDS is. `dlt`, DuckDB, and MotherDuck share similar philosophy of giving full power to the local user and and making it easy to interact with them in Python. - -I repeat this entire process for the BigQuery pipeline in this video: - - diff --git a/docs/website/blog/2023-08-21-dlt-lineage-support.md b/docs/website/blog/2023-08-21-dlt-lineage-support.md deleted file mode 100644 index 7475b245c3..0000000000 --- a/docs/website/blog/2023-08-21-dlt-lineage-support.md +++ /dev/null @@ -1,127 +0,0 @@ ---- -slug: dlt-lineage-support -title: "Trust your data! Column and row level lineages, an explainer and a recipe." -image: https://storage.googleapis.com/dlt-blog-images/eye_of_data_lineage.png -authors: - name: Adrian Brudaru - title: Open source data engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [lineage, governance, column level lineage, row level lineage, metadata] ---- -:::info -TL;DR: By linking each load's metadata to the schema evolution event or schema version, we are able to assign the origin of a column to a specific load package, identified by source and time. -::: - - -## Row and Column Level Lineage with `dlt` - -### Load IDs - -Load IDs are crucial in `dlt` and are present in all the top tables (`_dlt_loads`, `load_id`, etc.). Each pipeline run creates one or more load packages, which can be identified by their `load_id`. A load package typically contains data from all resources of a particular source. The `load_id` of a particular package is added to the top data tables and to the `_dlt_loads` table with a status 0 (when the load process is fully completed). - -For more details, refer to the [Load IDs](/general-usage/destination-tables#load-ids) section of the documentation. - -### Schema Versioning https://dlthub.com/ - -Each schema file in `dlt` contains a content-based hash `version_hash` that is used to detect manual changes to the schema (i.e., user edits content) and to detect if the destination database schema is synchronized with the file schema. Each time the schema is saved, the version hash is updated. - -For more details, refer to the [Schema content hash and version](/general-usage/schema#schema-content-hash-and-version) section of the documentation. - -### Data Lineage - -Data lineage can be super relevant for architectures like the data vault architecture or when troubleshooting. Using the pipeline name and `load_id` provided out of the box by `dlt`, you are able to identify the source and time of data. - -You can save complete lineage info for a particular `load_id` including a list of loaded files, error messages (if any), elapsed times, schema changes. This can be helpful, for example, when troubleshooting problems. - -For more details, refer to the [Data lineage](/general-usage/destination-tables#data-lineage) section of the documentation. - -By combining the use of `load_id` and schema versioning, you can achieve a robust system for row and column level lineage in your data pipelines with `dlt`. - - -## Row and Column Level Lineage - -### Row Level Lineage - -Row level lineage refers to the ability to track data from its source to its destination on a row-by-row basis. This means being able to identify exactly where each row of data in your destination came from, which can be crucial for data validation, debugging, and compliance purposes. - -In `dlt`, each row in all (top level and child) data tables created by `dlt` contains a unique column named `_dlt_id`. Each child table contains a foreign key column `_dlt_parent_id` linking to a particular row (`_dlt_id`) of a parent table. This allows you to trace the lineage of each row back to its source. - -For more details, refer to the [Child and parent tables](/general-usage/destination-tables#child-and-parent-tables) section of the documentation. - -### Column Level Lineage - -Column level lineage refers to the ability to track how each column in your data has been transformed or manipulated from source to destination. This can be important for understanding how your data has been processed, ensuring data integrity, and validating data transformations. - -In `dlt`, a column schema contains properties such as `name`, `description`, `data_type`, and `is_variant`, which provide information about the column and its transformations. The `is_variant` property, for example, tells you if a column was generated as a variant of another column. - -For more details, refer to the [Tables and columns](/general-usage/destination-tables#table-and-column-names) section of the documentation. - -By combining row and column level lineage, you can have an easy overview of where your data is coming from and when changes in its structure occur. - -### Identifying the lineage with dlt - -After a pipeline run, the schema evolution info gets stored in the load info. -Load it back to the database to persist the column lineage: -```py -load_info = pipeline.run(data, - write_disposition="append", - table_name="users") - -pipeline.run([load_info], write_disposition="append", table_name="loading_status") -``` - -Loading it back to the database will produce a few status tables. - -Note the load id, which is a unix timestamp, identifying the origin of every new column. You can link it back to the load packages via the `_load_id` column. - -Below, you can find some examples of what this info looks like - Note the `_load_id` column that identifies each load, and the metadata that comes with it: - -Here is an example what load info contains in the column info of the metadata we just loaded (table `load_info__load_packages__tables__columns`): - -| nullable | partition | cluster | unique | sort | primary_key | foreign_key | root_key | merge_key | name | data_type | table_name | schema_name | load_id | _dlt_parent_id | _dlt_list_idx | _dlt_id | variant | -|----------|-----------|---------|--------|------|-------------|-------------|----------|----------|------------------------|-----------|-----------------|------------------|---------------|-------------------|---------------|-------------------|---------| -| false | false | false | false | false| false | false | false | false | version | bigint | _dlt_pipeline_state | dlt_test_pipe | 1692188651.466199 | WBS2MJRkxEn2xw | 0 | 4rQWa44uF2CKyg | | -| false | false | false | false | false| false | false | false | false | engine_version | bigint | _dlt_pipeline_state | dlt_test_pipe | 1692188651.466199 | WBS2MJRkxEn2xw | 1 | zn5zR+PKyNqJLA | | -| false | false | false | false | false| false | false | false | false | pipeline_name | text | _dlt_pipeline_state | dlt_test_pipe | 1692188651.466199 | WBS2MJRkxEn2xw | 2 | WV6DNovz7V1xBg | | -| false | false | false | false | false| false | false | false | false | state | text | _dlt_pipeline_state | dlt_test_pipe | 1692188651.466199 | WBS2MJRkxEn2xw | 3 | 77zsRk9Z5yhAwQ | | -| false | false | false | false | false| false | false | false | false | created_at | timestamp | _dlt_pipeline_state | dlt_test_pipe | 1692188651.466199 | WBS2MJRkxEn2xw | 4 | Sj5/mL9tZGlHRQ | | -| false | false | false | false | false| false | false | false | false | _dlt_load_id | text | _dlt_pipeline_state | dlt_test_pipe | 1692188651.466199 | WBS2MJRkxEn2xw | 5 | lvbvQFPbk9g0og | | -| false | false | false | false | false| false | false | false | false | load_id | text | _dlt_loads | dlt_test_pipe | 1692188651.466199 | G0HvoQ6BMNzYsw | 0 | +IeGJE0Ln0wj+w | | -| true | false | false | false | false| false | false | false | false | schema_name | text | _dlt_loads | dlt_test_pipe | 1692188651.466199 | G0HvoQ6BMNzYsw | 1 | oZ7hho/aLYJobg | | -| false | false | false | false | false| false | false | false | false | status | bigint | _dlt_loads | dlt_test_pipe | 1692188651.466199 | G0HvoQ6BMNzYsw | 2 | QrZ3e79agHFNgg | | -| false | false | false | false | false| false | false | false | false | inserted_at | timestamp | _dlt_loads | dlt_test_pipe | 1692188651.466199 | G0HvoQ6BMNzYsw | 3 | gm9kEFQuPXGwiA | | -| true | false | false | false | false| false | false | false | false | schema_version_hash | text | _dlt_loads | dlt_test_pipe | 1692188651.466199 | G0HvoQ6BMNzYsw | 4 | 4eX9BoFV5oegAg | | -| true | false | false | false | false| false | false | false | false | name | text | people | dlt_test_pipe | 1692188651.466199 | q9DzfCYuMwDjkg | 0 | ISj8XUllnHB1gA | | -| true | false | false | false | false| false | false | false | false | age | bigint | people | dlt_test_pipe | 1692188651.466199 | q9DzfCYuMwDjkg | 1 | 4YDwm8PtjtEPwA | | -| true | false | false | false | false| false | false | false | false | nationality | text | people | dlt_test_pipe | 1692188651.466199 | q9DzfCYuMwDjkg | 2 | LJTMxFWgqqyH/w | | -| true | false | false | false | false| false | false | false | false | street | text | people | dlt_test_pipe | 1692188651.466199 | q9DzfCYuMwDjkg | 3 | AmzkMpDFikafIw | | -| true | false | false | false | false| false | false | false | false | building | bigint | people | dlt_test_pipe | 1692188651.466199 | q9DzfCYuMwDjkg | 4 | GNw+E3FAuC9o5A | | -| false | false | false | false | false| false | false | false | false | _dlt_load_id | text | people | dlt_test_pipe | 1692188651.466199 | q9DzfCYuMwDjkg | 5 | 7hhoAuL9tZGlHR | | - -Here is the information contained in the `load_info` table: - -| pipeline__pipeline_name | destination_name | destination_displayable_credentials | destination_fingerprint | dataset_name | started_at | first_run | _dlt_load_id | _dlt_id | -|-------------------------|------------------|-------------------------------------------------------------|------------------------|--------------|-------------------------------|-----------|----------------------|----------------| -| dlt_test_pipe | bigquery | dlthub-loader@dlthub-analytics.iam.gserviceaccount.com@dlthub-analytics | kgecbRsVn7pCkgx5EVBi | people | 2023-08-16 12:24:09.511922 UTC | true | 1692188672.110346 | PP1cT3rrwur2pw | -| dlt_test_pipe | bigquery | dlthub-loader@dlthub-analytics.iam.gserviceaccount.com@dlthub-analytics | kgecbRsVn7pCkgx5EVBi | people | 2023-08-16 12:25:12.789753 UTC | false | 1692188728.938733 | WcBNyAKI3NdVzg | - - -## Conclusion - -In conclusion, implementing row and column level lineage within data processing is crucial for maintaining data integrity, validation, and troubleshooting. The `dlt` framework offers a robust solution for achieving both forms of lineage, providing a comprehensive understanding of data transformations and origins. - -- **Row level lineage**: Utilizing unique identifiers like `_dlt_id` and `_dlt_parent_id`, `dlt` enables precise tracing of data from source to destination. This level of detail is essential for tasks like data validation, debugging, and compliance. - -- **Column level lineage**: By leveraging column schema properties such as name, data type, and `is_variant`, `dlt` reveals column transformations, offering insights into data manipulation throughout the pipeline. - -- **Extend lineage into transformation**: To maintain dlt lineage into transformations, log metadata at each transformation step, including transformation type, logic, and timestamps, while extending lineage columns to represent transformed data's lineage and relationships. - -Combining row and column level lineage provides data professionals with a holistic view of data's journey, enhancing comprehension of its source, transformations, and changes. The lineage information stored in `dlt` facilitates effective troubleshooting, validation, and compliance checks, bolstering governance of data pipelines. - -In summary, the integration of lineage through `dlt` empowers organizations to construct transparent and reliable data pipelines. This practice ensures data quality, cultivating a foundation for accurate and trustworthy data-driven decisions. - -## Start using dlt today -What are you waiting for? -* Dive into our [getting started docs](https://dlthub.com/docs/getting-started) -* [Join the slack community for discussion and help!](https://dlthub.com/community) \ No newline at end of file diff --git a/docs/website/blog/2023-08-24-dlt-etlt.md b/docs/website/blog/2023-08-24-dlt-etlt.md deleted file mode 100644 index b7b087e657..0000000000 --- a/docs/website/blog/2023-08-24-dlt-etlt.md +++ /dev/null @@ -1,220 +0,0 @@ ---- -slug: dlt-etlt -title: "The return of ETL in the Python age" -image: https://storage.googleapis.com/dlt-blog-images/went-full-etltlt.png -authors: - name: Adrian Brudaru - title: Open source data engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [ETL, ELT, EtlT, ] ---- -:::info -PSSSST! You do ELT, right? not ETL? asking for a friend... -::: - -## ETL vs ELT? A vendor driven story. - - - -One of the earliest tooling for "ETL" data was Pentaho Kettle. -Kettle stands for "Kettle Extraction Transformation Transport Load Environment" and signifies that it transforms the data before loading it. -It was usually used to load data which was later transformed in SQL via "SQL scripts", while still in the tool, or via database triggers or views outside of the tool. - -Indeed, the tool creators imagined some folks would write java to transform before loading, but the vast majority of data users just wanted to use SQL. - -Sounds familiar? This is not so different to today's "ELT", is it? - -## Why did we call it ELT? - -### The people - -Well, first of all SQL is much more accessible and very powerful for transforming tables, -columns and rows - where programming handles single values. -So before purpose built tooling existed, data people were already doing the transform in SQL - it just made sense. - -### The "EL" vendors -In the decade following Pentaho, Saas solutions started offering pipelines that load data into your database, removing the option for you to tinker with it before loading. -For this reason, they would call it "ELT". - -### The db vendors -The concept also resonated with MPP DBs (massive parallel processing), such as Snowflake, Redshift, Bigquery, which were more than happy to encourage doing all the compute on their side. - -### The "T in ELT" vendors - -Another puzzle piece was dbt, a tool purpose built for SQL transform. So if there's a question of -ETL or ELT, dbt can only answer ELT. In dbt's word view, data starts dirty in your warehouse, where you "rename, cast, join, enrich" - a true ELT. -To make the drudgery of data cleaning in SQL easier, dbt offers some python support to enable generating some of the typing and renaming SQL. -They also offer a litte bit of python support for scalar operations in some db vendor systems. - -## What do we really do? - -Most of us do a little bit of both - we extract with python, and the next steps are loading, -cleaning and curation. In some cases, cleaning and curation are optional. For example, -when we load a report from another platform we will probably not need to clean or curate anything. - -### Where do we clean data? - -Data cleaning usually refers to normalising the data into correct types, usable names, etc. -Doing this in SQL results in writing a lot of manual code that needs to be maintained. -On the other hand, sturcturing data in python isn't easy either, -it's just less technically difficult, but when metadata is missing, it becomes guesswork. - -So, technically the easier place to clean data is in python, but likely the majority will do it in SQL as they are more practiced in SQL. - -### Where do we transform data? -When it comes to working with tables, SQL is still the better place to be. -Joins and aggregations are the core operations that will happen here and they would be much harder to handle scalably in python. - - -# `dlt` puts the small t back in EtlT, let's see how. - -So, python is still superior at a few operations -- Typing, renaming, normalising, unpacking -- complex scalar operations - -While we will leave the aggregations and joins to the big T, SQL. - -### Normalisation, typing, unpacking - -`dlt` does this well out of the box. Automatic typing, renaming, -flattening, and ddl deployment are all handled by the schema inference and evolution engine. -This engine is configurable in both how it works and what it does, -you can read more here: [Normaliser, schema settings](https://dlthub.com/docs/general-usage/schema#data-normalizer) - -Here is a usage example (it's built into the pipeline): -```py - -import dlt - -# Json, dataframes, iterables, all good -# the data will be auto typed and normalised -data = [{'id': 1, 'name': 'John'}] - -# open connection -pipe = dlt.pipeline(destination='bigquery', - dataset_name='raw_data') - -# self-explanatory declarative interface -job_status = pipe.run(data, - write_disposition="merge", - primary_key="id", - table_name="users") - -# optionally load schema and metadata -pipe.run([job_status], - write_disposition="append", - table_name="loading_status") - -``` - -### Scalar operations - -Sometimes we need to edit a column's value in some very specific way for which SQL doesn't quite cut it. -Sometimes, we have data we need to pseudonymise before loading for regulatory reasons. - -Because `dlt` is a library, it means you can easily change how the data stream is produced or ingested. -Besides your own customisations, `dlt` also supports injecting your transform code inside the event stream, -[see an example here](https://dlthub.com/docs/general-usage/customising-pipelines/renaming_columns#renaming-columns-by-replacing-the-special-characters) - -Here is a code example of pseudonymisation, a common case where data needs to be transformed before loading: - -```py -import dlt -import hashlib - -@dlt.source -def dummy_source(prefix: str = None): - @dlt.resource - def dummy_data(): - for _ in range(3): - yield {'id':_, 'name': f'Jane Washington {_}'} - return dummy_data(), - -def pseudonymize_name(doc): - ''' - Pseudonmyisation is a deterministic type of PII-obscuring - Its role is to allow identifying users by their hash, - without revealing the underlying info. - ''' - # add a constant salt to generate - salt = 'WI@N57%zZrmk#88c' - salted_string = doc['name'] + salt - sh = hashlib.sha256() - sh.update(salted_string.encode()) - hashed_string = sh.digest().hex() - doc['name'] = hashed_string - return doc - - -# 1. Create an instance of the source so you can edit it. -data_source = dummy_source() -# 2. Modify this source instance's resource -data_resource = data_source.dummy_data().add_map(pseudonymize_name) -# 3. Inspect your result -for row in data_resource: - print(row) -#{'id': 0, 'name': '96259edb2b28b48bebce8278c550e99fbdc4a3fac8189e6b90f183ecff01c442'} -#{'id': 1, 'name': '92d3972b625cbd21f28782fb5c89552ce1aa09281892a2ab32aee8feeb3544a1'} -#{'id': 2, 'name': '443679926a7cff506a3b5d5d094dc7734861352b9e0791af5d39db5a7356d11a'} - -pipeline = dlt.pipeline(pipeline_name='example', destination='bigquery', dataset_name='normalized_data') -load_info = pipeline.run(data_resource) - -``` - -### The big T - -Finally, once you have clean data loaded, you will probably prefer to use SQL and one of the standard tools. -`dlt` offers a dbt runner to get you started easily with your transformation package. - -```py -pipeline = dlt.pipeline( - pipeline_name='pipedrive', - destination='bigquery', - dataset_name='pipedrive_dbt' -) - -# make or restore venv for dbt, using latest dbt version -venv = dlt.dbt.get_venv(pipeline) - -# get runner, optionally pass the venv -dbt = dlt.dbt.package( - pipeline, - "pipedrive/dbt_pipedrive/pipedrive", # or use public git "https://github.com/dbt-labs/jaffle_shop.git" - venv=venv -) - -# run the models and collect any info -# If running fails, the error will be raised with full stack trace -models = dbt.run_all() - -#optionally log dbt status -pipeline.run([models], - write_disposition="append", - table_name="_models_log") - -``` - - -## In conclusion - -ETL vs ELT was never really a debate. -With some exceptions almost everyone transforms the data in SQL - -but what they call this process depends on who's telling the story. - -While it's easier to do most of the transformation in SQL, the tedious is completely automatable in python, -and the dirty data doesn't need manual normalisation. With `dlt`, you can do ETL or ELT, or even better, both, as EtLT - - -Or, if you're feeling funny, you can add [duckdb](https://dlthub.com/docs/dlt-ecosystem/destinations/duckdb) in the middle and go full EtLTLT -where you have an additional T step in the middle for the kinds of operations that could be done locally. -And afterwards you could load to operational systems to add one more L to the name :) - -Fundamentally, we all agree it's all ETL, with the flavors simply designating specific sub-types. - - -## Start using `dlt` today -What are you waiting for? -* Dive into our [getting started docs](https://dlthub.com/docs/getting-started) -* [Join the ⭐Slack Community⭐ for discussion and help!](https://dlthub.com/community) diff --git a/docs/website/blog/2023-09-05-mongo-etl.md b/docs/website/blog/2023-09-05-mongo-etl.md deleted file mode 100644 index 8dfd953be4..0000000000 --- a/docs/website/blog/2023-09-05-mongo-etl.md +++ /dev/null @@ -1,202 +0,0 @@ ---- -slug: mongo-etl -title: "Dumpster diving for data: The MongoDB experience" -image: https://storage.googleapis.com/dlt-blog-images/data-dumpster.png -authors: - name: Adrian Brudaru - title: Open source data engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [mongodb, mongo, nosql, bson] ---- -:::info -💡 TIL: BSON stands for binary JSON, not for BS Object Notation /s -::: - - -## What will you learn in this article? - -The scope of this article is to look at and discuss the process of extracting data from MongoDB and -making it available in a SQL store. We will focus on the difficulties around ingesting the data into -a SQL database, while we will look at MongoDB only from a source perspective. - -The focus is the data in its different states, not the underlying technologies. - -## Why the harsh title? - -The title may sound harsh, but it accurately reflects the challenges often encountered when dealing -with MongoDB. - -Also referred to as `/dev/null`, Mongo offers a flexible schema and document-based storage can lead -to data inconsistencies and complexities, resembling a metaphorical "dumpster" where data might be -scattered and difficult to organise. - -Analytical consumers of MongoDB will be forced to invest extra effort in data modelling, schema -design, and querying optimisation to ensure data quality and retrieval efficiency. - -## Is this a common problem? - -It's inevitable. An analytical system has multiple requirements which force us to move data from -places such as MongoDB to a SQL store. - -Let’s look at those requirements: - -- **Business User access**: Most data is used by business users to improve operations. Business - users access data via dashboards, or pivot-table like interfaces that enable them to do custom - aggregations. The tooling that exists to do this is created for SQL, as a place where relational - queries can be executed. -- **Ecosystem of integrations and tools**: In analytics, having a diverse ecosystem of integrations - and tools is crucial. SQL databases seamlessly fit into this ecosystem, offering compatibility - with a wide array of data warehousing, data integration, and data governance tools. This - comprehensive ecosystem enhances the analytics infrastructure, ensuring that data can be - efficiently managed, transformed, and accessed by various stakeholders. -- **Standardization for consistency**: Maintaining data consistency is paramount in analytics. SQL's - widespread adoption and standardized query language enable analysts and data professionals to work - with data consistently across different systems and platforms. This standardization ensures that - data is interpreted and manipulated uniformly, reducing the risk of errors and discrepancies in - analytical processes. -- **Data transformation & modelling capabilities**: Effective data transformation and modelling are - prerequisites for meaningful analytics. SQL provides a robust toolkit for these tasks, enabling - data professionals to perform complex operations such as joins, filtering, aggregation, and - intricate calculations. These capabilities are essential for preparing raw data into structured - formats that can be readily used for in-depth analysis, reporting, and decision-making in the - analytics domain. - -So, after looking at what is needed for analytics, it becomes clear that going off the beaten path -will lead to some pretty gnarly limitations and outcomes. - -## Mongo in particular: BSON vs JSON - -How is Mongo different from semi-structure data like JSON, and is MongoDB particularly hard to -ingest from? - -### Bson is for performance, json is for transmission. - -The differences stem from the fact that MongoDB uses BSON under the hood, as opposed to JSON. BSON -is a binary object notation optimised for performance, while JSON is a standard interchange format. - -Similarly, Mongo also supports custom and more complex data types, such as geospatial, dates, regex, -etc, that json does not. Additionally, BSON supports character encodings. All these benefits enable -MongoDB to be a faster and better database, but the cost is additional hurdles that must be crossed -before we can use this data elsewhere. - -So how do you solve these issues? Well, hopefully your development team didn't go overboard, and you -can just simply convert the BSON to JSON. If you are unlucky, you will need to create your own -mappers that follow whatever your team did. - -## From JSON to DB - -Once you have converted your mongo BSON into JSON, you are able to use its wide support to have it -ingested. - -JSON enjoys widespread support across various data processing tools and systems, making it a -versatile choice for data ingestion. With your data in JSON, you can seamlessly integrate it into -your database, leveraging its compatibility to efficiently manage and analyze your information. - -### Cleaning and typing - -Data typing is essential in ensuring data integrity. It involves assigning appropriate data types to -JSON fields, like converting numerical values into integers or floats, representing dates as -datetime types, and labeling text data as string data types. This step guarantees that the database -accurately stores and processes information. - -### Do we unpack? - -The choice between unpacking nested JSON into tables or keeping it as JSON depends on your specific -needs. Unpacking enhances query performance, indexing, and data manipulation within relational -databases. However, native JSON support in some databases can suffice for simpler scenarios, -preserving the original hierarchical structure. Your decision should align with data analysis, -retrieval requirements, and your chosen database's capabilities. - -Simply put, if you plan to use the data, you should probably unpack it to benefit from what -relational dbs have to offer. But if you simply need to store and retrieve the json, do not convert -it. - -### Querying unpacked data is cheaper and more robust than maintaining wet json_extract() code - -Unpacking nested JSON into separate tables within a relational database is essential for robustness -and query efficiency. Relational databases are optimized for tabular data and typed columns, making -it challenging and error prone to handle complex nested structures directly. - -By breaking down nested JSON into separate tables and establishing relationships through foreign -keys, the data becomes more structured, ensuring robust data management and enhancing query -efficiency. This simplification streamlines data retrieval and manipulation, aligning it with -standard SQL operations for efficient and effective use. - -## Start using `dlt` to load Mongo to SQL today - -To help with the challenges of loading Mongo data, we created a dlt source that reads your mongo -collections and throws flat sql tables on the other side. - -The benefit of using dlt is that you get flat tables in your sql database that adapt to match the -Mongo schema. - -Here's a code explanation of how it works under the hood: - -1. It grabs data from Mongo and turns it into JSON. - -1. From json, dlt leverages schema inference and evolution to make sense of the data. Here is an - example of how this nested data could look: - - ```json - { - "id": 1, - "name": "Alice", - "job": { - "company": "ScaleVector", - "title": "Data Scientist" - }, - "children": [ - { - "id": 1, - "name": "Eve" - }, - { - "id": 2, - "name": "Wendy" - } - ] - } - ``` - -1. We can load the data to a supported destination declaratively: - - ```py - import dlt - - pipeline = dlt.pipeline( - pipeline_name='from_json', - destination='duckdb', - dataset_name='mydata', - dev_mode=True, - ) - # dlt works with lists of dicts, so wrap data to the list - load_info = pipeline.run([data], table_name="json_data") - print(load_info) - ``` - -1. Now we can use the data, these are two tables: - - *json_data* - - | index | id | name | job\_\_company | job\_\_title | \_dlt_load_id | \_dlt_id | - | ----- | --- | ----- | -------------- | -------------- | ----------------- | -------------- | - | 0 | 1 | Alice | ScaleVector | Data Scientist | 1693922245.602667 | 0ZbCzK7Ra2tWMQ | - - *json_data\_\_children* - - | index | id | name | \_dlt_parent_id | \_dlt_list_idx | \_dlt_id | - | ----- | --- | ----- | --------------- | -------------- | -------------- | - | 0 | 1 | Eve | 0ZbCzK7Ra2tWMQ | 0 | TjzpGZ+dwrrQhg | - | 1 | 2 | Wendy | 0ZbCzK7Ra2tWMQ | 1 | RdqpN1luoKxQTA | - - Note that the original json got unpacked into tables that are now joinable via generated keys - `child._dlt_parent_id = parent._dlt_id`. - -Read more about it here: -[Mongo verified source.](https://dlthub.com/docs/dlt-ecosystem/verified-sources/mongodb) - -What are you waiting for? - -- Dive into our [Getting Started.](https://dlthub.com/docs/getting-started) -- [Join the ⭐Slack Community⭐ for discussion and help!](https://dlthub.com/community) diff --git a/docs/website/blog/2023-09-20-data-engineering-cv.md b/docs/website/blog/2023-09-20-data-engineering-cv.md deleted file mode 100644 index 7010434a81..0000000000 --- a/docs/website/blog/2023-09-20-data-engineering-cv.md +++ /dev/null @@ -1,104 +0,0 @@ ---- -slug: data-engineering-cv -title: "How to write a data engineering CV for Europe and America - A hiring manager’s perspective" -image: https://storage.googleapis.com/dlt-blog-images/dall-e-de-cv.png -authors: - name: Adrian Brudaru - title: Open source data engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [data engineer, cv] ---- - -# How to write a data engineering CV for European and US companies - A hiring manager’s perspective - -I recently reviewed someone’s anonymized CV on Reddit as they complained they applied to dozens of jobs but never heard back. Shortly after, my inbox blew up with review requests. - -My experience comes from 10+ years in the industry, and having reviewed thousands of CVs myself, having hired for multiple companies. I also asked the opinion of other hiring managers who have hired data engineers. - -Why European and US companies? Because, in my experience, other parts of the world work more in enterprises than startups or SMEs, and as a consequence they work with different tool sets. They often do “outsourcing” work, often on technologies used 5-20 years ago and less modern techs. Even when the tech used is new, the role is usually very limited in an enterprise and generally lacks the end-to-end capabilities requirement. For example, it might mean doing drag and drop tools + SQL stored procedures, or it might mean doing only data loading without architecture and generally without an orchestrator, etc. - -So, let’s cut to the chase - what is a hiring manager looking for in an application? - -### Cover letter - -Let’s start with the shortest bit - cover letter. Definitely write one. If you apply by email, put it in the email body. If not, attach it to the application. - -What should it contain? A couple of paragraphs about - -- why this company you are applying for is interesting to you -- your motivation for applying and what you hope to achieve here -- why you would be a good fit for the role. - -Keep it honest. Write about the role and your intent, do not make it about you. - -A definite **don’t** is a generic letter that talks about you, but doesn’t mention the role or company. This shows a lack of investment in the application and gives wrong signals. So if you cannot review the company and say why you like it, then the reviewer might feel the same way about you. - -### The CV - -Rules of thumb: - -- Space is your friend. It drives focus to what matters. Don’t fill it with junk. -- “Soft skills” are important. Show the human behind the role, and have a bio section that should contain info about you. -- Focus shows consideration to the reviewer. Tell me what’s relevant. -- If you are junior, demonstrate potential. List your initiatives, learning, etc. -- If you are senior, demonstrate results. Focus less on techs and more on outcomes. -- Use a two-pager if you can. First page - everything else. Second page: your experience. If you do not have experience, stick to a one-pager. If you are in the US, you might be required to use a one-pager, in which case make your job descriptions shorter and more focused, dropping the non-relevant. - -### The parts - -1. **Bio:** name, where you come from, your values. - - Image. You are a human talking to a human, don’t make it less, it doesn’t help. Be confident, friendly, smile. - - “Values”. Your [values](https://en.wikipedia.org/wiki/Values_(Western_philosophy)) are a significant predictor of your “direction”, behavior and interactions. List the top 3, not more. No BS. - - “Motivation”. It indicates what is the fuel to your fire so the company knows if they can do that for you. One sentence. - - “Mission” or what you are looking to do, indicates if your goals may align with role goals. One or two sentences max. - -1. **Skills:** The easiest thing to filter on, so list the relevant skills for the role you are applying. Do not list the entire list of something you have ever used or tried. This only creates noise and the impression that you don’t have any practiced skills. Mention the skills that are relevant first, and consider skipping the rest. For example: “Python, SQL, orchestrator, GitHub, others.” instead of “Java, Javascript, Ruby, Go, R, C++, SQL, Python, Tableau, Excel, Looker, BigQuery, Bash, Basic, Assembly, MsWord, Google Sheets, PowerBI, ” Make sure those skills are clearly displayed and optionally also mentioned in the listed jobs so people can judge how much you used them. - - You can personalize the list based on the job you apply for. If skills are listed outside the job description, look at who else works or worked there, they might list the tools or skills on their LinkedIn. - - If you are starting and need to gain the skills, make them happen. Take a course, learn them, and list them. - -2. **Job experience**. Format: - - - - What are we presenting? The context (company, team), the achievements (X, Y, Z), the takeaways (A, B, C, X, Y, Z experience) - - What are we **not** presenting? Things are irrelevant to the role we are applying for or our values. Less is more as it shifts focus to the important. Also, no vanity achievements or internal company awards, which are often offered in consultancies. These are just tools to motivate you, and nobody cares externally. - -3. **Education, studies, self-studies:** Some groups care about the school. It is less critical, as the school does not teach real-life data engineering, but it will help you understand where you come from. -4. **Projects, courses, volunteering:** I care more about the demonstrated learning experience. I want to see that you keep learning. If not on the job, then I want to see courses or projects here. Volunteering also paints a picture of a “doer”. - -### The skills you need - -This depends on the role, but most DE roles building standard platforms need the following: - -- Python -- orchestrator (Airflow usually) -- dimensional modeling and other data architectures -- SQL -- software best practices - versioning, cicd, testing, etc. - -### And the magic sauce… - -… goes into the food, keep it out of the CV :) There’s no magic here, just clear communication. - -Your three levers are - -- better communication -- better skills -- more applications - -The rest is a matter of opportunities, depending on your location and the job market. If the opportunities are not available, consider entering the field via an adjacent role like an analytics engineer. - -Screening CVs is a rapid process, so you need to ensure you check off the requirements while removing (not adding) any doubts. Any hire is a risk, so the screener wants to minimize that - help them do it by showing a good understanding of your role and what is expected of you. Don’t add info that isn’t needed, as that might add doubts. - -I wish you good luck on your application process and encourage you to ask for feedback from the community if you get stuck. diff --git a/docs/website/blog/2023-09-26-verba-dlt-zendesk.md b/docs/website/blog/2023-09-26-verba-dlt-zendesk.md deleted file mode 100644 index bf724778ec..0000000000 --- a/docs/website/blog/2023-09-26-verba-dlt-zendesk.md +++ /dev/null @@ -1,295 +0,0 @@ ---- -slug: verba-dlt-zendesk -title: "Talk to your Zendesk tickets with Weaviate’s Verba and dlt: A Step by Step Guide" -image: https://storage.googleapis.com/dlt-blog-images/dlt-business-knowledge-retrieval-augmented-generation-diagram.png -authors: - name: Anton Burnashev - title: Software Engineer - url: https://github.com/burnash - image_url: https://avatars.githubusercontent.com/u/264674?v=4 -tags: [Verba, dlt, Zendesk, RAG, Generative Search, Weaviate, OpenAI] ---- - -*tl;dr: In this blog post, we'll build a RAG chatbot for Zendesk Support data using Verba and dlt.* - -As businesses scale and the volume of internal knowledge grows, it becomes increasingly difficult for everyone in the company to find the right information at the right time. - -With the latest advancements in large language models (LLMs) and [vector databases](https://weaviate.io/blog/what-is-a-vector-database), it's now possible to build a new class of tools that can help get insights from this data. One approach to do so is Retrieval-Augmented Generation (RAG). The idea behind RAGs is to retrieve relevant information from your database and use LLMs to generate a customised response to a question. Leveraging RAG enables the LLM to tailor its responses based on your proprietary data. - -![Diagram illustrating the process of internal business knowledge retrieval and augmented generation (RAG), involving components like Salesforce, Zendesk, Asana, Jira, Notion, Slack and HubSpot, to answer user queries and generate responses.](https://storage.googleapis.com/dlt-blog-images/dlt-business-knowledge-retrieval-augmented-generation-diagram.png) - -One such source of internal knowledge is help desk software. It contains a wealth of information about the company's customers and their interactions with the support team. - -In this blog post, we'll guide you through the process of building a RAG application for Zendesk Support data, a popular help desk software. We’re going to use dlt, Weaviate, Verba and OpenAI. - -[**dlt**](https://github.com/dlt-hub/dlt) is an open-source Python library that simplifies the process of loading data from various sources. It does not requires extensive setup or maintenance and particularly useful for CRM data: highly tailored to the needs of the business and changes frequently. - -[**Weaviate**](https://weaviate.io/) is an open-source, AI-native vector database that is redefining the foundation of AI-powered applications. With capabilities for vector, hybrid, and generative search over billions of data objects, Weaviate serves as the critical infrastructure for organizations building sophisticated AI solutions and exceptional user experiences. - -[**Verba**](https://github.com/weaviate/Verba) is an open-source chatbot powered by Weaviate. It's built on top of Weaviate's state-of-the-art [Generative Search](https://weaviate.io/developers/weaviate/search/generative) technology. Verba includes a web interface and a query engine that uses Weaviate database. - -## Prerequisites - -1. A URL and an API key of a Weaviate instance. We're using the hosted version of Weaviate to store our data. Head over to the [Weaviate Cloud Services](https://console.weaviate.cloud/dashboard) and create a new cluster. You can create a free sandbox, but keep in mind your cluster will expire and your data will be deleted after 14 days. In the "Details" of your cluster you'll find the Cluster URL and the API key. -2. An OpenAI account and API key. Verba utilizes OpenAI's models to generate answers to user's questions and Weaviate uses them to [vectorize](https://weaviate.io/developers/weaviate/concepts/vector-index) text before storing it in the database. You can sign up for an account on [OpenAI's website](https://platform.openai.com/). -3. A Zendesk account and API credentials. - -## Let’s get started - -### Step 1. Set up Verba - -Create a new folder for your project and install Verba: - -```sh -mkdir verba-dlt-zendesk -cd verba-dlt-zendesk -python -m venv venv -source venv/bin/activate -pip install goldenverba -``` - -To configure Verba, we need to set the following environment variables: - -```sh -VERBA_URL=https://your-cluster.weaviate.network # your Weaviate instance URL -VERBA_API_KEY=F8...i4WK # the API key of your Weaviate instance -OPENAI_API_KEY=sk-...R # your OpenAI API key - -``` - -You can put them in a `.env` file in the root of your project or export them in your shell. - -Let's test that Verba is installed correctly: - -```sh -verba start -``` - -You should see the following output: - -```sh -INFO: Uvicorn running on (Press CTRL+C to quit) -ℹ Setting up client -✔ Client connected to Weaviate Cluster -INFO: Started server process [50252] -INFO: Waiting for application startup. -INFO: Application startup complete. -``` - -Now, open your browser and navigate to [http://localhost:8000](http://localhost:8000/). - -![A user interface screenshot showing Verba, retrieval and augmented generation chatbot, powered by Weaviate](https://storage.googleapis.com/dlt-blog-images/dlt-weaviate-verba-ui-1.png) - -Great! Verba is up and running. - -If you try to ask a question now, you'll get an error in return. That's because we haven't imported any data yet. We'll do that in the next steps. - -### Step 2. Install dlt with Zendesk source - -We get our data from Zendesk using dlt. Let's install it along with the Weaviate extra: - -```sh -pip install "dlt[weaviate]" -``` - -This also installs a handy CLI tool called `dlt`. It will help us initialize the [Zendesk verified data source](https://dlthub.com/docs/dlt-ecosystem/verified-sources/zendesk)—a connector to Zendesk Support API. - -Let's initialize the verified source: - -```sh -dlt init zendesk weaviate -``` - -`dlt init` pulls the latest version of the connector from the [verified source repository](https://github.com/dlt-hub/verified-sources) and creates a credentials file for it. The credentials file is called `secrets.toml` and it's located in the `.dlt` directory. - -To make things easier, we'll use the email address and password authentication method for Zendesk API. Let's add our credentials to `secrets.toml`: - -```toml -[sources.zendesk.credentials] -password = "your-password" -subdomain = "your-subdomain" -email = "your-email@example.com" -``` - -We also need to specify the URL and the API key of our Weaviate instance. Copy the credentials for the Weaviate instance you created earlier and add them to `secrets.toml`: - -```toml -[destination.weaviate.credentials] -url = "https://your-cluster.weaviate.network" -api_key = "F8.....i4WK" - -[destination.weaviate.credentials.additional_headers] -X-OpenAI-Api-Key = "sk-....." -``` - -All the components are now in place and configured. Let's set up a pipeline to import data from Zendesk. - -### Step 3. Set up a dlt pipeline - -Open your favorite text editor and create a file called `zendesk_verba.py`. Add the following code to it: - -```py -import itertools - -import dlt -from weaviate.util import generate_uuid5 -from dlt.destinations.adapters import weaviate_adapter - -from zendesk import zendesk_support - -def to_verba_document(ticket): - # The document id is the ticket id. - # dlt will use this to generate a UUID for the document in Weaviate. - return { - "doc_id": ticket["id"], - "doc_name": ticket["subject"], - "doc_type": "Zendesk ticket", - "doc_link": ticket["url"], - "text": ticket["description"], - } - -def to_verba_chunk(ticket): - # We link the chunk to the document by using the document (ticket) id. - return { - "chunk_id": 0, - "doc_id": ticket["id"], - "doc_name": ticket["subject"], - "doc_type": "Zendesk ticket", - "doc_uuid": generate_uuid5(ticket["id"], "Document"), - "text": ticket["description"], - } - -def main(): - pipeline = dlt.pipeline( - pipeline_name="zendesk_verba", - destination="weaviate", - ) - - # Zendesk Support has data tickets, users, groups, etc. - zendesk_source = zendesk_support(load_all=False) - - # Here we get a dlt resource containing only the tickets - tickets = zendesk_source.tickets - - # Split the tickets into two streams - tickets1, tickets2 = itertools.tee(tickets, 2) - - @dlt.resource(primary_key="doc_id", write_disposition="merge") - def document(): - # Map over the tickets and convert them to Verba documents - # primary_key is the field that will be used to generate - # a UUID for the document in Weaviate. - yield from weaviate_adapter( - map(to_verba_document, tickets1), - vectorize="text", - ) - - @dlt.resource(primary_key="doc_id", write_disposition="merge") - def chunk(): - yield from weaviate_adapter( - map(to_verba_chunk, tickets2), - vectorize="text", - ) - - info = pipeline.run([document, chunk]) - - return info - -if __name__ == "__main__": - load_info = main() - print(load_info) -``` - -There's a lot going on here, so let's break it down. - -First, in `main()` we create a dlt pipeline and add a Weaviate destination to it. We'll use it to store our data. - -Next, we create a Zendesk Support source. It will fetch data from Zendesk Support API. - -To match the data model of Zendesk Support to the internal data model of Verba, we need to convert Zendesk tickets to Verba documents and chunks. We do that by defining two functions: `to_verba_document` and `to_verba_chunk`. We also create two streams of tickets. We'll use them to create two dlt resources: `document` and `chunk`. These will populate the `Document` and `Chunk` classes in Verba. In both resources we instruct dlt which fields to vectorize using the `weaviate_adapter()` function. - -We specify `primary_key` and `write_disposition` for both resources. `primary_key` is the field that will be used to generate a UUID for the document in Weaviate. `write_disposition` tells dlt how to handle duplicate documents. In our case, we want to merge them: if a document already exists in Weaviate, we want to update it with the new data. - -Finally, we run the pipeline and print the load info. - -### Step 4. Load data into Verba - -Let's run the pipeline: - -```sh -python zendesk_verba.py -``` - -You should see the following output: - -```sh -Pipeline zendesk_verba completed in 8.27 seconds -1 load package(s) were loaded to destination weaviate and into dataset None -The weaviate destination used location to store data -Load package 1695726495.383148 is LOADED and contains no failed jobs - -``` - -Verba is now populated with data from Zendesk Support. However there are a couple of classes that need to be created in Verba: Cache and Suggestion. We can do that using the Verba CLI `init` command. When it runs it will ask us if we want to create Verba classes. Make sure to answer "n" to the question about the Document class — we don't want to overwrite it. - -Run the following command: - -```sh -verba init -``` - -You should see the following output: - -```sh -===================== Creating Document and Chunk class ===================== -ℹ Setting up client -✔ Client connected to Weaviate Cluster -Document class already exists, do you want to overwrite it? (y/n): n -⚠ Skipped deleting Document and Chunk schema, nothing changed -ℹ Done - -============================ Creating Cache class ============================ -ℹ Setting up client -✔ Client connected to Weaviate Cluster -✔ 'Cache' schema created -ℹ Done - -========================= Creating Suggestion class ========================= -ℹ Setting up client -✔ Client connected to Weaviate Cluster -✔ 'Suggestion' schema created -ℹ Done -``` - -We're almost there! Let's start Verba: - -```sh -verba start -``` - -### Step 4. Ask Verba a question - -Head back to [http://localhost:8000](http://localhost:8000/) and ask Verba a question. For example, "What are common issues our users report?". - -![A user interface screenshot of Verba showing Zendesk tickets with different issues like API problems and update failures, with responses powered by Weaviate](https://storage.googleapis.com/dlt-blog-images/dlt-weaviate-verba-ui-2.png) - -As you can see, Verba is able to retrieve relevant information from Zendesk Support and generate an answer to our question. It also displays the list of relevant documents for the question. You can click on them to see the full text. - -## Conclusion - -In this blog post, we've built a RAG application for Zendesk Support using Verba and dlt. We've learned: - -- How easy it is to get started with Verba. -- How to build dlt pipeline with a ready-to-use data source. -- How to customize the pipeline so it matches the data model of Verba. - -## Where to go next? - -- **Ensure your data is up-to-date.** With `dlt deploy` you can [deploy your pipeline](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline) to Google's Cloud Composer or GitHub Actions and run it on a schedule. -- **Build a Verba RAG for other data sources.** Interested in building a RAG that queries other internal business knowledge than Zendesk? With dlt you can easily switch your data source. Other dlt verified sources of internal business knowledge include [Asana](https://dlthub.com/docs/dlt-ecosystem/verified-sources/asana), [Hubspot](https://dlthub.com/docs/dlt-ecosystem/verified-sources/hubspot), [Jira](https://dlthub.com/docs/dlt-ecosystem/verified-sources/jira), [Notion](https://dlthub.com/docs/dlt-ecosystem/verified-sources/notion), [Slack](https://github.com/dlt-hub/verified-sources/tree/master/sources/slack) and [Salesforce](https://dlthub.com/docs/dlt-ecosystem/verified-sources/salesforce). However, dlt isn’t just about ready-to-use data sources; many of our users choose to implement [their own custom data sources](https://dlthub.com/docs/build-a-pipeline-tutorial#why-build-pipelines-with-dlt). -- **Learn more about how Weaviate works.** Check out [Zero to MVP](https://weaviate.io/developers/academy/zero_to_mvp) course to learn more about Weaviate database and how to use it to build your own applications. -- **Request more features**. A careful reader might have noticed that we used both Document and Chunk classes in Verba for the same type of data. For simplicity's sake, we assumed that the ticket data is small enough to fit into a single chunk. However, if you if you're dealing with larger documents, you might consider splitting them into chunks. Should we add chunking support to dlt? Or perhaps you have other feature suggestions? If so, please consider opening a feature request in the [dlt repo](https://github.com/dlt-hub/dlt/issues) to discuss your ideas! - -## Let's stay in touch - -If you have any questions or feedback, please reach out to us on the [dltHub Slack](https://dlthub.com/community). diff --git a/docs/website/blog/2023-10-06-dlt-holistics.md b/docs/website/blog/2023-10-06-dlt-holistics.md deleted file mode 100644 index def0ffbf5e..0000000000 --- a/docs/website/blog/2023-10-06-dlt-holistics.md +++ /dev/null @@ -1,474 +0,0 @@ ---- -slug: MongoDB-dlt-Holistics -title: "Modeling Unstructured Data for Self-Service Analytics with dlt and Holistics" -image: https://storage.googleapis.com/dlt-blog-images/dlt_holistics_overview.jpg -authors: - name: Zaeem Athar - title: Junior Data Engineer - url: https://github.com/zem360 - image_url: https://images.ctfassets.net/c4lg2g5jju60/5tZn4cCBIesUYid17g226X/a044d2d471ebd466db32f7868d5c0cc8/Zaeem.jpg?w=400&h=400&q=50&fm=webp -tags: [MongoDB, dlt, Holistics, Unstructured Data, Transformation] ---- -:::info -TL;DR: A modern analytics stack with dlt and Holistics to transform and ingest unstructured production data from MongoDB to flat tables in BigQuery for self-service analytics. -::: - -If you’re a CTO, then you probably love MongoDB: it’s scalable, production-ready, and a great dump for unstructured, and semi-structured data. If you’re however a data scientist or data analyst and you need to run analytics on top of MongoDB data dumps, then you’re probably not a fan. The data in MongoDB needs to be transformed and stored in a data warehouse before it is ready for analytics. The process of transforming and storing the data can become quite tedious due to the unstructured nature of the data. - -In this blog, we will show you how you can combine `dlt` and **Holistics** and create a modern data stack that makes the process of extracting unstructured data from MongoDB, and running self-service analytics on the data simple and straightforward. We will use `dlt` to ingest the [Movie Flix Dataset](https://www.mongodb.com/docs/atlas/sample-data/sample-mflix/) into BigQuery from MongoDB and use **Holistics** to transform the data and run self-service analytics. - -## An Overview of the MongoDB Modern Analytics Stack - -![Diagram illustrating the inner workings of our Modern Analytics Stack](https://storage.googleapis.com/dlt-blog-images/dlt_holistics_overview.jpg) - - -| Tool | Layer | Why it’s awesome | -| --- | --- | --- | -| [MongoDB](https://www.mongodb.com/) | Production | Sometimes used as a data dump by CTOs. Often stores unstructured, semi-structured production data that stakeholders want to access. | -| [dlt](https://dlthub.com/docs/intro) | Data Ingestion | Mongo is great, but then others struggle to analyze the data. dlt extracts data from MongoDB, creates schema in BigQuery, and loads normalized MongoDB data into BigQuery. | -| [BigQuery](https://cloud.google.com/bigquery?hl=en) | Data Warehouse | Because of its pricing model, it’s a good data warehouse choice to store structured MongoDB data so it can be used by BI tools like Holistics for self-service analytics. | -| [Holistics](https://www.holistics.io/) | Data Modeling for Self-Service Analytics | Holistics makes it easy for data teams to setup and govern an end-user self-service analytics platform using DevOps best practices | - -In our stack, `dlt` resides in the data ingestion layer. It takes in unstructured data from MongoDB normalizes the data and populates it into BigQuery. - -In the data modeling layer, Holistics accesses the data from BigQuery builds relationships, transforms the data, and creates datasets to access the transformations. In the reporting layer, Holistics allows stakeholders to self-service their data by utilizing the created datasets to build reports and create visualizations. - -## MongoDB is loved by CTOs, but its usage creates issues for stakeholders. - -NoSQL databases such as MongoDB have gained widespread popularity due to their capacity to store data in formats that align more seamlessly with application usage, necessitating fewer data transformations during storage and retrieval. - -MongoDB is optimized for performance and uses BSON (Binary Javascript Object Notation) under the hood as compared to JSON. This allows MongoDB to support custom and more complex data types, such as geospatial data, dates, and regex. Additionally, BSON supports character encodings. - -All these benefits enable MongoDB to be a faster and better database, but the advantages of the flexibility offered by MongoDB are sometimes abused by developers and CTOs who use it as a dump for all types of unstructured and semi-structured data. This makes this data inaccessible to stakeholders and unfit for analytics purposes. - -Moreover, the unique nature of MongoDB with its BSON types and its usage as a data dump in current times mean that additional hurdles must be crossed before data from MongoDB can be moved elsewhere. - -## How does our Modern data stack solve the MongoDB problem? - -In the data ingestion layer, `dlt` utilizes the MongoDB verified source to ingest data into BigQuery. Initializing the MongoDB verified source setups default code needed to run the pipeline. We just have to setup the credentials and specify the collections in MongoDB to ingest into BigQuery. Once the pipeline is run `dlt` takes care of all the steps from extracting unstructured data from MongoDB, normalizing the data, creating schema, and populating the data into BigQuery. - -Getting your data cleaned and ingested into a data warehouse is just one part of the analytics pipeline puzzle. Before the data is ready to be used by the entire organization the data team must model the data and document the context of data. This means defining the relationships between tables, adding column descriptions, and implementing the necessary transformations. This is where Holistics shines. With analytics-as-code as first-class citizens, Holistics allows data teams to adopt software engineering best practices in their analytics development workflows. This helps data teams govern a centralized curated set of semantic datasets that any business users can use to extract data from the data warehouse. - -## Why is dlt useful when you want to ingest data from a production database such as MongoDB? - -Writing a Python-based data ingestion pipeline for sources such as MongoDB is quite a tedious task as it involves a lot of overhead to set up. The data needs to be cleaned before it is ready for ingestion. Moreover, MongoDB is a NoSQL database meaning it stores data in a JSON-like data structure. So if you want to query it with SQL natively, you will need to transform this JSON-like data structure into flat tables. Let's look at how this transformation and cleaning can be done: - -- Create a Data Model based on the MongoDB data we intend to ingest. -- Create tables in the data warehouse based on the defined Data Model. -- Extract the data from MongoDB and perform necessary transformations such as Data Type conversion (BSON to JSON), and flattening of nested data. -- Insert the transformed data into the corresponding SQL tables. -- Define relationships between tables by setting up primary and foreign keys. - -Using the `dlt` MongoDB verified source we can forgo the above-mentioned steps. dlt takes care of all the steps from transforming the JSON data into relational data, to creating the schema in the SQL database. - -To get started with `dlt` we would need to set some basic configurations, while everything else would be automated. `dlt` takes care of all the steps from creating schema to transforming the JSON data into relational data. The workflow for creating such a data pipeline in `dlt` would look something like this: - -- Initialize a MongoDB source to copy the default code. -- Set up the credentials for the source and destination. -- Define the MongoDB collection to ingest (or default to all). -- Optionally configure incremental loading based on source logic. - -## What is useful about Holistics in this project? -Holistics is a Business Intelligence platform with the goal of enabling self-service analytics for entire organizations. Holistics works by connecting to an SQL data warehouse. This allows it to build SQL queries and execute them against the data warehouse. In essence, Holistics utilizes the storage and processing capabilities of the data warehouse and the data never leaves the data warehouse. - -To enable self-service Holistics introduces a modeling layer. The data teams use this layer to define table relationships, data transformations, metrics, and data logic. The entire organization can utilize these metrics and data logic defined in this layer to self-service their data needs. - -In addition to the transformation layer, Holistics provides advanced features such as defining models using code through Holistics’ **analytics-as-code languages** (AMQL) and utilizing **Git version control** systems to manage code changes. Moreover, data teams can **integrate with dbt** to streamline the data transformations. - -The overall Holistics workflow looks something like this: - -![Holistics Overview](https://storage.googleapis.com/dlt-blog-images/holistics_overview.png) - -- Connect Holistics to an existing SQL data warehouse. -- Data teams use Holistics Data Modeling to model and transform analytics data. This model layer is reusable across reports & datasets. -- Non-technical users can self-service explore data based on predefined datasets prepared by data teams. They can save their explorations into dashboards for future use. -- Dashboards can be shared with others, or pushed to other platforms (email, Slack, webhooks, etc.). - -## Code Walkthrough - -In this section, we walk through how to set up a MongoDB data pipeline using `dlt`. We will be using the MongoDB verified source you can find [here](https://dlthub.com/docs/dlt-ecosystem/verified-sources/mongodb). - -### 1. Setting up the dlt pipeline - -Use the command below to install `dlt`. - -```sh -pip3 install -U dlt -``` - -Consider setting up a virtual environment for your projects and installing the project-related libraries and dependencies inside the environment. For best installation practices visit the `dlt` [installation guide](https://dlthub.com/docs/reference/installation). - -Once we have `dlt` installed, we can go ahead and initialize a verified MongoDB pipeline with the destination set to Google BigQuery. First, create a project directory and then execute the command below: - -```sh -dlt init mongodb bigquery -``` - -The above command will create a local ready-made pipeline that we can customize to our needs. After executing the command your project directory will look as follows: - -```text -. -├── .dlt -│ ├── config.toml -│ └── secrets.toml -├── mongodb -│ ├── README.md -│ ├── __init__.py -│ └── helpers.py -├── mongodb_pipeline.py -└── requirements.txt -``` - -The `__init__.py` file in the **`mongodb`** directory contains dlt functions we call `resources` that yield the data from MongoDB. The yielded data is passed to a `dlt.pipeline` that normalizes the data and forms the connection to move the data to your destination. To get a better intuition of the different dlt concepts have a look at the [docs](https://dlthub.com/docs/intro). - -As the next step, we set up the credentials for MongoDB. You can find detailed information on setting up the credentials in the MongoDB verified sources [documentation](https://dlthub.com/docs/dlt-ecosystem/verified-sources/mongodb). - -We also need to set up the GCP service account credentials to get permissions to BigQuery. You can find detailed explanations on setting up the service account in the dlt docs under [Destination Google BigQuery.](https://dlthub.com/docs/dlt-ecosystem/destinations/bigquery) - -Once all the credentials are set add them to the `secrets.toml` file. Your file should look something like this: - -```toml -# put your secret values and credentials here. do not share this file and do not push it to github -[sources.mongodb] -connection_url = "mongodb+srv://:@.cvanypn.mongodb.net" # please set me up! -database = "sample_mflix" - -[destination.bigquery] -location = "US" -[destination.bigquery.credentials] -project_id = "analytics" # please set me up! -private_key = "very secret can't show" -client_email = "@analytics.iam.gserviceaccount.com" # please set me up! -``` - -The `mongodb_pipeline.py` at the root of your project directory is the script that runs the pipeline. It contains many functions that provide different ways of loading the data. The selection of the function depends on your specific use case, but for this demo, we try to keep it simple and use the `load_entire_database` function. - -```py -def load_entire_database(pipeline: Pipeline = None) -> LoadInfo: - """Use the mongo source to completely load all collection in a database""" - if pipeline is None: - # Create a pipeline - pipeline = dlt.pipeline( - pipeline_name="local_mongo", - destination='bigquery', - dataset_name="mongo_database", - ) - - # By default the mongo source reflects all collections in the database - source = mongodb() - - # Run the pipeline. For a large db this may take a while - info = pipeline.run(source, write_disposition="replace") - - return info -``` - -Before we execute the pipeline script let's install the dependencies for the pipeline by executing the `requirements.txt` file. - -```sh -pip install -r requirements.txt -``` - -Finally, we are ready to execute the script. In the main function uncomment the `load_entire_database` function call and run the script. - -```sh -python mongodb_pipeline.py -``` - -If you followed the instructions correctly the pipeline should run successfully and the data should be loaded in Google BigQuery. - -### 2. The result: Comparing MongoDB data with the data loaded in BigQuery. - -To get a sense of what we accomplished let's examine what the unstructured data looked like in MongoDB against what is loaded in BigQuery. Below you can see the sample document in MongoDB. - -```json -{ - "_id": { - "$oid": "573a1390f29313caabcd42e8" - }, - "plot": "A group of bandits stage a brazen train hold-up, only to find a determined posse hot on their heels.", - "genres": [ - "Short", - "Western" - ], - "runtime": { - "$numberInt": "11" - }, - "cast": [ - "A.C. Abadie", - "Gilbert M. 'Broncho Billy' Anderson", - "George Barnes", - "Justus D. Barnes" - ], - "poster": "https://m.media-amazon.com/images/M/MV5BMTU3NjE5NzYtYTYyNS00MDVmLWIwYjgtMmYwYWIxZDYyNzU2XkEyXkFqcGdeQXVyNzQzNzQxNzI@._V1_SY1000_SX677_AL_.jpg", - "title": "The Great Train Robbery", - "fullplot": "Among the earliest existing films in American cinema - notable as the first film that presented a narrative story to tell - it depicts a group of cowboy outlaws who hold up a train and rob the passengers. They are then pursued by a Sheriff's posse. Several scenes have color included - all hand tinted.", - "languages": [ - "English" - ], - "released": { - "$date": { - "$numberLong": "-2085523200000" - } - }, - "directors": [ - "Edwin S. Porter" - ], - "rated": "TV-G", - "awards": { - "wins": { - "$numberInt": "1" - }, - "nominations": { - "$numberInt": "0" - }, - "text": "1 win." - }, - "lastupdated": "2015-08-13 00:27:59.177000000", - "year": { - "$numberInt": "1903" - }, - "imdb": { - "rating": { - "$numberDouble": "7.4" - }, - "votes": { - "$numberInt": "9847" - }, - "id": { - "$numberInt": "439" - } - }, - "countries": [ - "USA" - ], - "type": "movie", - "tomatoes": { - "viewer": { - "rating": { - "$numberDouble": "3.7" - }, - "numReviews": { - "$numberInt": "2559" - }, - "meter": { - "$numberInt": "75" - } - }, - "fresh": { - "$numberInt": "6" - }, - "critic": { - "rating": { - "$numberDouble": "7.6" - }, - "numReviews": { - "$numberInt": "6" - }, - "meter": { - "$numberInt": "100" - } - }, - "rotten": { - "$numberInt": "0" - }, - "lastUpdated": { - "$date": { - "$numberLong": "1439061370000" - } - } - }, - "num_mflix_comments": { - "$numberInt": "0" - } -} -``` - -This is a typical way data is structured in a NoSQL database. The data is in a JSON-like format and contains nested data. Now, let's look at what is loaded in BigQuery. Below you can see the same data in BigQuery. - -![BigQuery Data Overview](https://storage.googleapis.com/dlt-blog-images/dlt_holistics_bigquery_data.png) - -The ddl (data definition language) for the movies table in BigQuery can be seen below: - -```sql -CREATE TABLE `dlthub-analytics.mongo_database.movies` -( - _id STRING NOT NULL, - plot STRING, - runtime INT64, - poster STRING, - title STRING, - fullplot STRING, - released TIMESTAMP, - rated STRING, - awards__wins INT64, - awards__nominations INT64, - awards__text STRING, - lastupdated TIMESTAMP, - year INT64, - imdb__rating FLOAT64, - imdb__votes INT64, - imdb__id INT64, - type STRING, - tomatoes__viewer__rating FLOAT64, - tomatoes__viewer__num_reviews INT64, - tomatoes__viewer__meter INT64, - tomatoes__fresh INT64, - tomatoes__critic__rating FLOAT64, - tomatoes__critic__num_reviews INT64, - tomatoes__critic__meter INT64, - tomatoes__rotten INT64, - tomatoes__last_updated TIMESTAMP, - num_mflix_comments INT64, - _dlt_load_id STRING NOT NULL, - _dlt_id STRING NOT NULL, - tomatoes__dvd TIMESTAMP, - tomatoes__website STRING, - tomatoes__production STRING, - tomatoes__consensus STRING, - metacritic INT64, - tomatoes__box_office STRING, - imdb__rating__v_text STRING, - imdb__votes__v_text STRING, - year__v_text STRING -); -``` - -If you compare the ddl against the sample document in MongoDB you will notice that the nested arrays such as **CAST** are missing from the ddl in BigQuery. This is because of how dlt handles nested arrays. If we look at our database in BigQuery you can see the **CAST** is loaded as a separate table. - -![BigQuery Table Overview](https://storage.googleapis.com/dlt-blog-images/dlt_holistics_bigquery_table.png) - -`dlt` normalises nested data by populating them in separate tables and creates relationships between the tables, so they can be combined together using normal SQL joins. All this is taken care of by `dlt` and we need not worry about how transformations are handled. In short, the transformation steps we discussed in [Why is dlt useful when you want to ingest data from a production database such as MongoDB?](#why-is-dlt-useful-when-you-want-to-ingest-data-from-a-production-database-such-as-mongodb) are taken care of by dlt, making the data analyst's life easier. - -To better understand how `dlt` does this transformation, refer to the [docs](/general-usage/destination-tables#child-and-parent-tables). - -### 3. Self-service analytics for MongoDB with Holistics. - -After `dlt` ingests the data into your data warehouse, you can connect Holistics to the data warehouse and model, govern, and set up your self-service analytics platform for end-user consumption. - -By combining `dlt` with Holistics we get the best of both worlds. The flexibility of an open source library for data ingestion that we can customize based on changing data needs, and a self-service BI tool in Holistics that can not only be used for analytics but also introduces a data modeling layer where metrics and data logic can be defined. Holistics also has support for **Git version control** to track code changes and can integrate with **dbt for streamlining data transformations**. - -We took care of the data ingestion step in the previous section. We can now connect to our SQL data warehouse, and start transforming the data using the modeling layer in Holistics. We will be using the newest version of Holistics, **Holistics 4.0** for this purpose. - -In Holistics, add a new data source click on the plus sign (+) on the top menu, and then select **Connect Data Sources.** Select **New Data Sources** and in the database type select Google BigQuery. We need to provide the service account credentials that were generated above when we connected `dlt` to BigQuery. For more detailed instructions on connecting BigQuery to Hollistics refer to this [guide](https://docs.holistics.io/docs/connect/databases/bigquery). - -Once the BigQuery source is added we are ready to import the schemas from BigQuery into Holistics. The schema(`dataset_name`) name under which dlt loaded the MongoDB data is defined in the `load_entire_database` function when we create the MongoDB pipeline. - -```sh -# Create a pipeline -pipeline = dlt.pipeline( - pipeline_name="local_mongo", - destination='bigquery', - dataset_name="mongo_database", # Schema Name -) -``` - -### 4. Modeling the Data and Relationships with Holistics. - -To use the data, we will define a data model and the join paths that Holistics can use to build the semantic datasets. - -**[A data model is an abstract view on top of a physical database table](https://www.holistics.io/books/setup-analytics/data-modeling-layer-and-concepts/)** that you may manipulate without directly affecting the underlying data. It allows you to store additional metadata that may enrich the underlying data in the data table. - -In Holistics, go to the **Modelling 4.0** section from the top bar. We will be greeted with the Start page as we have created no models or datasets. We will turn on the development mode from the top left corner. The development model will allow you to experiment with the data without affecting the production datasets and reporting. To keep things organized let’s create two folders called **Models** and **Datasets**. - -#### **Adding Holistics Data Model(s):** - -Under the Models folder, let's add the MongoDB data from BigQuery as Table Models. Hover over the Models folder and click on the (+) sign then select **Add Table Model.** In the **Data Sources** select the BigQuery Source we created before and then select the relevant table models to import into Holistics. In this case, we are importing the `movies`, `movies_cast` and `movies_directors` tables. - -![Holistics Add Model](https://storage.googleapis.com/dlt-blog-images/holistics_add_model.png) - -#### **Adding Holistics Dataset(s) and Relationships:** - -After the Data Models have been added, we can create a Dataset with these models and use them for reporting. - -:::info -A [Dataset](https://docs.holistics.io/docs/datasets) is a "container" holding several [Data Models](https://docs.holistics.io/docs/data-model) together so they can be explored together, and dictating which join path to be used in a particular analytics use case. - -Datasets works like a data marts, except that it exists only on the semantic layer. You publish these datasets to your business users to let them build dashboards, or explore existing data. -::: - -Hover over the Datasets folder, click on the (+) sign, and then select **Add Datasets.** Select the previously created Table Models under this dataset, and **Create Dataset**. - -![Holistics Create Dataset](https://storage.googleapis.com/dlt-blog-images/holistics_add_dataset.png) - -We will then be asked to create relationships between the models. We create a **Many-to-one (n - 1)** relationship between the `cast` and the `movies` models. - -![Add Relationship between Models](https://storage.googleapis.com/dlt-blog-images/holistics_add_relationship.png) - -The resulting relationship can seen As Code using the Holistics 4.0 Analytics as Code feature. To activate this feature click on the newly created dataset and select the **View as Code** option from the top right. For more detailed instructions on setting up relationships between models refer to the model relationship [guide](https://docs.holistics.io/docs/relationships#automatic-relationship-creation). - -Previously, we created the relationship between the `cast` and the `movies` tables using GUI, now let’s add the relationship between the `directors` and `movies` tables using the Analytics as Code feature. In the `dataset.aml` file append the relationships block with the following line of code: - -```py -relationship(model__mongo_database_movies_directors.dlt_parent_id > model__mongo_database_movies.dlt_id, true) -``` - -After the change, the `dataset.aml` file should look like this: - -```sh -import '../Models/mongo_database_movies.model.aml' { - mongo_database_movies as model__mongo_database_movies -} -import '../Models/mongo_database_movies_cast.model.aml' { - mongo_database_movies_cast as model__mongo_database_movies_cast -} -import '../Models/mongo_database_movies_directors.model.aml' { - mongo_database_movies_directors as model__mongo_database_movies_directors -} - -Dataset movies { - label: 'Movies' - description: '' - data_source_name: 'bigquery_mongo' - models: [ - model__mongo_database_movies, - model__mongo_database_movies_cast, - model__mongo_database_movies_directors - ] - relationships: [ - relationship(model__mongo_database_movies_cast.dlt_parent_id > model__mongo_database_movies.dlt_id, true), - relationship(model__mongo_database_movies_directors.dlt_parent_id > model__mongo_database_movies.dlt_id, true) - ] - owner: 'zaeem@dlthub.com' -} -``` - -The corresponding view for the `dataset.aml` file in the GUI looks like this: - -![Add Relationship GUI](https://storage.googleapis.com/dlt-blog-images/holistics_relationship_gui.png) - -Once the relationships between the tables have been defined we are all set to create some visualizations. We can select the **Preview** option from next to the View as Code toggle to create some visualization in the development mode. This comes in handy if we have connected an external git repository to track our changes, this way we could test out the dataset in preview mode before committing and pushing changes, and deploying the dataset to production. - -In the current scenario, we will just directly deploy the dataset to production as we have not integrated a Git Repository. For more information on connecting a Git Repository refer to the Holistics [docs](https://docs.holistics.io/as-code/git-version-control/external-git). - -The Movies dataset should now be available in the Reporting section. We will create a simple visualization that shows the workload of the cast and directors. In simple words, **How many movies did an actor or director work on in a single year?** - -#### **Visualization and Self-Service Analytics with Holistics:** - -The visualization part is pretty self-explanatory and is mostly drag and drop as we took the time to define the relationships between the tables. Below we create a simple table in Holistics that shows the actors that have appeared in most movies since the year 2000. - -![Holistics Create Visualization](https://storage.googleapis.com/dlt-blog-images/Holistics_new.gif) - -Similarly, we can add other reports and combine them into a dashboard. The resulting dashboard can be seen below: - -![Holistics Dashboard](https://storage.googleapis.com/dlt-blog-images/holistics_dashboard.png) - -## Conclusion - -In this blog, we have introduced a modern data stack that uses `dlt` and **Holistics** to address the MongoDB data accessibility issue. - -We leverage **`dlt`**, to extract, normalize, create schemas, and load data into BigQuery, making it more structured and accessible. Additionally, **Holistics** provides the means to transform and model this data, adding relationships between various datasets, and ultimately enabling self-service analytics for the broader range of stakeholders in the organization. - -This modern data stack offers an efficient and effective way to bridge the gap between MongoDB's unstructured data storage capabilities and the diverse needs of business, operations, and data science professionals, thereby unlocking the full potential of the data within MongoDB for the entire Company. - -## Additional Resources: - -- Want to discuss `dlt`? Join the `dlt` [Slack Community](https://dlthub.com/community) -- Check out our friends over at [Holistics](https://www.holistics.io/). -- [`dlt` MongoDB Source](https://dlthub.com/docs/dlt-ecosystem/verified-sources/mongodb). -- Holistics 4.0: [Analytics as Code](https://docs.holistics.io/as-code/get-started). -- Holistics: [Data Modelling.](https://docs.holistics.io/docs/modeling/) -- Holistics: [Model Relationship](https://docs.holistics.io/docs/relationships#automatic-relationship-creation). -- Holistics 4.0: [Production vs. Development Mode](https://docs.holistics.io/as-code/development/dev-prod-mode). -- Holistics 4.0: [Git Version Control](https://docs.holistics.io/as-code/git-version-control/). -- Holistics 4.0: [dbt Integration](https://docs.holistics.io/as-code/dbt-integration/). \ No newline at end of file diff --git a/docs/website/blog/2023-10-09-dlt-ops-startups.md b/docs/website/blog/2023-10-09-dlt-ops-startups.md deleted file mode 100644 index 648747047c..0000000000 --- a/docs/website/blog/2023-10-09-dlt-ops-startups.md +++ /dev/null @@ -1,165 +0,0 @@ ---- -slug: dlt-ops-startups -title: "PDF invoices → Real-time financial insights: How I stopped relying on an engineer to automate my workflow and learnt to do it myself" -image: https://storage.googleapis.com/dlt-blog-images/invoice_flowchart.png -authors: - name: Anna Hoffmann - title: COO - url: https://github.com/ahoffix - image_url: https://avatars.githubusercontent.com/u/12297835?v=4 -tags: [PDF parsing, PDF invoice, PDF to DWH] ---- - -:::info -**TL;DR** *I set up a data pipeline that automatically extracts data from PDF invoices in my Gmail and puts it all in a single Google Sheet where I can easily keep of track of it. I did this using the python library [dlt](https://dlthub.com/docs/intro) that uses [langchain](https://www.langchain.com/) and LLMs to read PDF data and converts it into structured tables.* -::: - -I am Anna, co-founder & COO of dltHub. As an ops lead with many years of running SMB-size startups, I find myself juggling a myriad of tasks, from administration, finance, and people to customer support or sales. These tasks come with their own data, all of which are crucial for making decisions. This creates a huge scope for automation, but unfortunately getting engineering support is not always easy. Whether it's integrating tools with APIs or managing data efficiently, the waiting game can be frustrating. - -So, I often end up doing manual tasks such as budgeting, cost estimation, updating CRM, or preparing audits. I have been dreaming about automating these processes. - -For example, I need to analyze expenses in order to prepare a budget estimation. I get numerous PDFs daily in a dedicated Gmail group inbox. I was wondering to which extent [dlt](https://github.com/dlt-hub/dlt) can help fulfill my automation dream. I decided to work with Alena from our data team on an internal project. - -![invoice flow chart](https://storage.googleapis.com/dlt-blog-images/invoice_flowchart.png) - -## Use Case - -Imagine this scenario: your team receives numerous invoices as email attachments daily. You need to extract and analyze the data within these invoices to gain insights crucial to your operations. This is where the data load tool (`dlt`) steps in. - -Alena created a [pipeline](https://github.com/dlt-hub/dlt_invoices) using `dlt` that automates the process of translating invoices received as email attachments in a specific Google email group and stores them in a database (for example, [BigQuery](https://dlthub.com/docs/dlt-ecosystem/destinations/bigquery) or [DuckDB](https://dlthub.com/docs/dlt-ecosystem/destinations/duckdb)). - -As a non-coder working in tech startups for a long time, I finally got a chance to learn how to use the terminal and run a simple pipeline. - -Here's a summary of how it works. - -## Let’s get started - -In this article, I will show you an example of loading structured data from invoices received by email into [BigQuery](https://dlthub.com/docs/dlt-ecosystem/destinations/bigquery). For more details, check the [README.md](https://github.com/dlt-hub/dlt_invoices) in the GitHub repository. - -### Step 1. Preparation - -Make sure that you have all you need: - -- Make sure you have Python 3.x installed on your system. -- Use a virtual environment (more details on how to [set up the environment](https://dlthub.com/docs/reference/installation#set-up-environment)). -- Install the `dlt` library by using `pip install "dlt[bigquery]"`. -- Create a project folder on your laptop. I called mine “unstructured_data_pipeline”. -- We will need access to LLM, Langchain will use OpenAI models by default, so we also used an OpenAI API token. -- Using a tool like Visual Studio makes it easier. - -### Step 2. Initiate the pipeline - -To create the pipeline, we will use the `dlt` verified source [unstructured_data](https://github.com/dlt-hub/verified-sources/blob/master/sources/unstructured_data/README.md), which includes the verified source [inbox](https://github.com/dlt-hub/verified-sources/blob/master/sources/unstructured_data/inbox/README.md). - -- Init the pipeline by using `dlt init unstructured_data bigquery`. -- Install necessary requirements `pip install -r requirements.txt`. - -### Step 3. Set up your credentials - -The `dlt` [init command](https://dlthub.com/docs/reference/command-line-interface#dlt-init) creates folder `.dlt` in your project directory, and clones the source code from the [verified-sources repository](https://github.com/dlt-hub/verified-sources). - -- Open `.dlt/secrets.toml` file on your laptop. -- Enter the OpenAI secrets: - - ```toml - [sources.unstructured_data] - openai_api_key = "openai_api_key" - ``` - -- Enter your email account secrets in the same section `[sources.unstructured_data]`: - - ```toml - host = 'imap.example.com' - email_account = "example@example.com" - password = 'set me up!' - ``` - - Check [here](https://github.com/dlt-hub/dlt_invoices#configure-inbox-source) how to configure the inbox source. - -- Enter the BigQuery secrets: - - ```toml - [destination.bigquery] - location = "US" - [destination.bigquery.credentials] - project_id = "set me up!" - private_key = "set me up!" - client_email = "set me up!" - ``` - - -Read more about [dlt credentials](https://dlthub.com/docs/general-usage/credentials) and [BigQuery credentials](https://dlthub.com/docs/dlt-ecosystem/destinations/bigquery). - -### Step 5: Define your queries - -This is the part where you can define what you’d like to see as an outcome. - -Queries example: - -```py -INVOICE_QUERIES = { - "recipient_company_name": "Who is the recipient of the invoice? Just return the name. If you don't know, then return None", - "invoice_amount": "What is the total amount of the invoice? Just return the amount as decimal number, no currency or text. If you don't know, then return None", - "invoice_date": "What is the date of the invoice? Just return the date. If you don't know, then return None", - "invoice_number": "What is the invoice number? Just return the number. If you don't know, then return None", - "service_description": "What is the description of the service that this invoice is for? Just return the description. If you don't know, then return None", -} -``` - -Customize the INVOICE_QUERIES dictionary in the `unstructured_data/settings.py` file if you want to extract other information, or if your invoices have a different structure. - -### Step 6: Run the pipeline! - -And now the magic happens. Use the following command to run the pipeline: - -```sh -python unstructured_data_pipeline.py -``` - -In the next step, `dlt` will save all processed structured data to the database (in my case, BigQuery). - -### Step 7: Check the outcome in BigQuery - -If you load it to BigQuery like I did in my example, then you can look at your data using BigQuery UI or export it directly to a Google sheet. - -### Step 8: Deploy - -Now you can [deploy this script with GitHub Actions](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-github-actions) as we did, so that it checks your incoming email every day and processes invoices automatically. - -# Outcome**:** - -Here’s how the result looks like in BigQuery: - -![screenshot 1](https://storage.googleapis.com/dlt-blog-images/pdf_parse_outcome_1.png) - -…and as a Google Sheet. You can easily export this table from BigQuery to Google Sheets using the Export button in the top right corner. - -![screenshot 2](https://storage.googleapis.com/dlt-blog-images/pdf_parse_outcome_2.png) - -Bonus: In order to have a Google Sheet with live updates, you can go to the Data tab in your -Spreadsheet → Data Connectors → BigQuery → choose your database and voila, your data will be updated automatically. - -![screenshot 3](https://storage.googleapis.com/dlt-blog-images/pdf_parse_outcome_3.png) - -# **Conclusion:** - -### **This worked well:** - -- `dlt` was good at extracting the data I needed, and it indeed worked in real-time. I needed some support from Alena when running the pipeline for the first time, but that’s because I had never coded before. 😊 -- I was able to see the details that are relevant to my workaround budgeting. - -### **This did not work well:** - -- Some PDFs don’t get transformed correctly. Some details were missing or misspelled. That depends on the LLM, which extracts structured data from a raw text. And also on the invoice structure. -- And it only worked well with digital PDFs, though not with JPG/scanned versions. Luckily, 99% of all the invoices are of the former kind. However, you can even set up this process for the other kinds of documents by making modifications to [unstructured.io](https://unstructured.io/). - -# Where to go next? - -It was definitely a great start, and we will test it further. And I already have many other use cases where dlt pipelines could help with ops automation processes. E.g.: - -- In creating a list of all contracts based on PDFs in a Google Drive folder (super useful for audits). -- In moving specific data to CRM (e.g. invoice related information about the customers). - -This specific example illustrates just one way in which Operations Leads can harness the power of `dlt` to analyze data efficiently without relying on engineers for extensive support. By automating data processes and enabling real-time insights, `dlt` empowers small startups to make informed decisions and stay competitive in their respective markets. - -In the startup world where time is of the essence, dlt has a chance to be the key to unlock data's full potential and accelerate operational efficiency. I’m looking forward to saying goodbye to endless waiting and hello to a world where Operations Leads can take control of their data needs, all thanks to `dlt`. diff --git a/docs/website/blog/2023-10-10-data-product-docs.md b/docs/website/blog/2023-10-10-data-product-docs.md deleted file mode 100644 index 2adfd42675..0000000000 --- a/docs/website/blog/2023-10-10-data-product-docs.md +++ /dev/null @@ -1,112 +0,0 @@ ---- -slug: data-product-docs -title: "The role of docs in data products" -image: https://storage.googleapis.com/dlt-blog-images/parrot-baby.gif -authors: - name: Adrian Brudaru - title: Open source data engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [data product, data as a product, data documentation, data product documentation] ---- - - -# Lip service to docs - -We often see people talk about data products or data as a product, and they usually tackle the topics of: - -- Concepts and how to think about data products. -- Users and producers: Roles, responsibilities and blame around data products. -- Data: Data quality and governance that is part of those products, data as a product. -- Code: The code or technology powering the pipelines. -- Infra: the infrastructure data products are run on. - -What we do **not** see is any practical advices or examples of how to implement these products. -While the concepts often define data products as something with a use case, -they fail to discuss the importance a user manual, or documentation. - -# The role of the user manual - -### So what is a data product? - -A data product is a self-contained piece of data-powered software that serves a single use case. For example, it could be a pipeline that loads Salesforce data to Snowflake, or it could be an ML model hosted behind an api. -Many talk about data products as some kind of inter-company exchange - like one company does it and another reuses it. -However, the prevalent case is when we have a team building it and another using it - just like a "production backend", these internal data tools help the business run their processes and are an integral part of the company and their product. - -Always consider the use case for the description of the product, but the entire technical stack as part of the product - so, the code and data responsible for enabling the use case are part of the product. - -Examples of data products: -- Lead ranking algorithm that helps the sales team prioritise their leads based on rules and maybe data. -- ROI calculator that enables the marketing team to optimise profits or expansion via better bidding and reinvestment efforts. -- Data pipeline that creates a report that is core for the finance team, containing things the finance team defines and wants. -- A data contract that alerts if salesforce leads do not have a corresponding company in the production system. -- A series of calculations that segment customers by various features we can use for targetting. -- A data mart that enables the CRM team select subsets of users by ad-hoc defined behavior. -- A data pipeline that provides externals with data. -- A report which we offer via api for external consumption. -- An api endpoint that produces a content recommendation for a particular website slot. -- A dashboard that enables the Account Management team to prioritise who they should reach out to, to enable them to reach their goals. - - - -### What makes a data pipeline a data product? - -The term product assumes more than just some code. -A "quick and dirty" pipeline is what you would call a "proof of concept" in the product world and far from a product. - -![Who the duck wrote this garbage??? Ah nvm… it was me…](https://storage.googleapis.com/dlt-blog-images/parrot-baby.gif) -> Who the duck wrote this trash??? Ahhhhh it was me :( ... - -To create a product, you need to consider how it will be used, by whom, and enable that usage by others. - -A product is something that you can pick up and use and is thus different from someone’s python spaghetti. - -For example, a product is: - -- Reusable: The first thing needed here is a **solid documentation** that will enable other users to understand how to use the product. -- Robust: Nothing kills the trust in data faster than bad numbers. To be maintainable, code must be simple, explicit, tested, and **documented** :) -- Secure: Everything from credentials to data should be secure. Depending on their requirements, that could mean keeping data on your side (no 3rd party tools), controlling data access, using SOC2 compliant credential stores, etc. -- Observable: Is it working? how do you know? you can automate a large part of this question by monitoring the volume of data and schema changes, or whatever other important run parameters or changes you might have. -- Operationizable: Can we use it? do we need a rocket scientist, or can [little Bobby Tables](https://xkcd.com/327/) use it? That will largely depend on docs and the product itself. - -### So what is a data product made of? - -Let’s look at the high-level components: - -1. Structured data: A data product needs data. The code and data are tightly connected - an ML model or data pipeline cannot be trained or operate without data. Why structured? because our code will expect a structured input, so the data is going to be either explicitly structured upfront (”schema on write”), or structured implicitly on read (”schema on read”). -2. Code. -3. Docs for usage - Without a user manual, a complex piece of code is next to unusable. - -### And which docs are needed? - -We will need top level docs, plus some for each of the parts described above. - -1. Top level: **Purpose** of existence for the data product. The code describes the what and how - So focus the readme on the “why” and top level “what”. Similar to a problem description, this document explains what problem your product solves and enables the reader to understand the cost and impact it might have to use your product. -2. Structured data: - 1. A **data dictionary** enables users to gain literacy on the dataset. - 2. **Maintenance info:** information about the source, schema, tests, responsible person, how to monitor, etc. -3. Code & Usage manual: This one is harder. You need to convey a lot of information effectively, and depending on who your user is, you need to convey that information in a different format. According to the **[best practices on the topic of docs](https://documentation.divio.com/introduction.html)**, these are the **4 relevant formats you should consider.** They will enable you to write high-quality, comprehensive and understandable docs that cover the user’s intention. - - learning-oriented tutorials; - - goal-oriented how-to guides; - - understanding-oriented discussions; - - information-oriented reference material. - -# Some examples from dlt - -Dlt is a library that enables us to build data pipelines. By building with dlt, you benefit from simple declarative code and accessible docs for anyone maintaining later. -Assuming you use dlt or your own loading approach in your data platform, you will want to document both the tool used, to enable people to modify things, and the popelines themselves to describe semantically what is being loaded. -Here are some examples of how you could do that: - -- Top level: Here is our attempt for dlt itself - the [intro doc](https://dlthub.com/docs/intro). You could describe the problem or use case that the pipeline solves. -- Data dictionary: Schema info belongs to each pipeline and can be found [here](https://dlthub.com/docs/blog/dlt-lineage-support). To get sample values, you could write a query. We plan to enable its generation in the future via a “describe” command. -- Maintenance info: See [how to set up schema evolution alerts](https://dlthub.com/docs/blog/schema-evolution#whether-you-are-aware-or-not-you-are-always-getting-structured-data-for-usage). You can also capture load info such as row counts to monitor loaded volume for abnormalities. -- Code and usage: We are structuring all our [docs](https://dlthub.com/docs/intro) to follow the best practices around the 4 types of docs, generating a comprehensive, recognisable documentation. We also have a GPT assistant on docs, and we answer questions in Slack for conversational help. - -# In conclusion - -Stop thinking about data, code and docs in isolation - they do not function independently, they are different parts of the same product. To produce quality documentation, focus on the why, let the code show the what and how. and use [standard formats](https://documentation.divio.com/introduction.html) for teaching complex tooling. - -Want to create data products with dlt? What are you waiting for? - -- Dive into our [Getting Started.](https://dlthub.com/docs/getting-started) -- [Join the ⭐Slack Community⭐ for discussion and help!](https://dlthub.com/community) diff --git a/docs/website/blog/2023-10-16-first-data-warehouse.md b/docs/website/blog/2023-10-16-first-data-warehouse.md deleted file mode 100644 index c03c72a00b..0000000000 --- a/docs/website/blog/2023-10-16-first-data-warehouse.md +++ /dev/null @@ -1,171 +0,0 @@ ---- -slug: first-data-warehouse -title: "Your first data warehouse: A practical approach" -image: https://storage.googleapis.com/dlt-blog-images/oil-painted-dashboard.png -authors: - name: Adrian Brudaru - title: Open source data engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [first data warehouse] ---- - -> In this article I will not discuss the best data warehouse you could in theory build. Instead, I will describe how data warehousing projects pragmatically start in order to have an easy time building and improving without running into early limits. -> - -Building a data warehouse is a complex endeavor, often too intricate to navigate flawlessly in the initial attempt. In this article, we'll provide insights and pointers to guide you in choosing the right stack for your data warehouse. - -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/oil-painted-dashboard.png) - - - -## The Business requirements - -Understanding the business's performance is the initial priority, and achieving this necessitates a comprehensive understanding of the business model and its various intricacies. Tracking key processes and Key Performance Indicators (KPIs) is fundamental as they provide insights into the business's health and performance across various aspects such as sales, marketing, customer engagement, operational efficiency, and financial health. - -Collaboration with different departments is crucial to comprehensively grasp their unique perspectives and priorities. Engaging with stakeholders ensures that the data warehouse is designed to cater to a wide array of informational needs, aligning with the organizational goals and objectives. - -Furthermore, identifying pivotal business drivers is essential. Beyond explicit feedback, it's crucial to recognize the primary business levers often represented by cross-departmental data. These drivers shed light on the core aspects that significantly impact the business's success. For instance, in an e-commerce business, the main levers might focus on increasing customer lifetime value, improving conversion rates, and optimizing ad spend to align with the customer's worth. - -## The Tech stack - -### Orchestration - -Orchestration functions as the central control mechanism, overseeing and coordinating the execution of diverse data workflows. - -For your first data warehouse build, opting for a managed solution often proves pragmatic. -Major cloud platforms provide managed versions of orchestrators like Airflow, ensuring reliability and relieving the burden of self-hosting. -While this convenience comes at some cost, the investment is justified considering the potential intricacies and management efforts associated with self-hosting, which could potentially outweigh server expenses. -Keep in mind that cloud vendors like gcp will only charge for the rented services/hardware, and so their managed airflow is priced the same as the one you would manage. - -The most well known orchestrator is Airflow which is open source maintained by an open source community. - -There are many newer orchestrators that improve on Airflow’s design and shortcomings, with varying features and approaches. -Prefect, Dagster, Mage, and Kestra stand out as prominent contenders, introducing unique features and approaches that push the boundaries of orchestration. - -Besides standards, you can always go for simplicity by looking out of the box - Github Actions is actually an orchestrator and while not particularly feature rich, it is often sufficient for a basic load-trasnform setup. - -### Ingestion - -Future-proofing your data warehouse cannot be done by relying on the hope that vendors will fulfill your requirements. While it is easy to start with a SaaS pipeline solution, they are generally expensive and end up vendor locking you to their schema, creating migration pains if you want to move and improve. -There are also reasons to use SaaS such as not having the in-house python team or deciding to suffer the cost and outsource the effort. - -But one way or another, you end up building custom pipelines for reasons like: - -- SQL pipelines are simple to create but cost a ton on SaaS services. -- The vendor does not have all the endpoints and too few customers asked for it for them to care. -- You start using a new service the vendor doesn’t offer. - -So, to have a clean setup, you would be better off standardizing a custom ingester. Here, you can write your own, or use the dlt library which is purpose-made and will generate database agnostic schemas, enabling migration between databases at the flip of a switch - making your test setups even easier. - -If you do write your own, choose a common interchange format and create it to load from that (such as json) and have all your extractors output json. - -You could also consider customizable solutions like Airbyte or Meltano. However, they follow their own paradigms, which ultimately create difficulties when trying to maintain or keep a stable, robust stack. - -### Transforming Data - -Transforming raw data into a structured, analytical format is a pivotal step in the data pipeline. In this domain, **dbt** stands out as a robust solution with widespread adoption, extensive documentation, and now, a standard tool. However, it's not the only player. Alternatives like **SQLMesh** are evolving this space, introducing enhancements tailored to specific use cases. For instance, SQLMesh's innovation in achieving database agnosticism through the use of sqlglot under the hood sets it apart. - -When it comes to data modeling, **star schemas** emerge as the preferred choice for many due to their advantages, including efficient and clear code and support for ROLAP tools. However, it's crucial to note that the transformation code is both quantitative and complex, making adherence to best practices imperative for maintenance and scalability. - -### Reverse ETL - -While implementing **Reverse ETL** might not be an initial priority, it's essential to demystify the process. -For those new to pushing data via an API, it may seem intimidating. -Let's simplify - sending data to an API endpoint for loading or updating an object is similar to making a `GET` request. -Here's a straightforward example in Python: - -```py -# Assuming data is in this format -import requests -# assume we have a table of contacts we want to push to Pipedrive. -data_table = [{'name': 'abc', 'email': 'abc@d.com'},] - -# Post the data to this endpoint -API_URL = f'https://api.pipedrive.com/v1/persons?api_token={YOUR_API_TOKEN}&pipeline_id={PIPELINE_ID}' -for row in data_table: - response = requests.post(API_URL, headers=headers, data=json.dumps(row)) -``` - -For those seeking tools, Census and Hightouch are prominent players in this space. - -## Dashboards & their usage paradigms - -When it comes to dashboards, each tool follows its distinctive paradigm. For example, Tableau and PowerBI are good for analysts to make polished dashboards for business users, while Metabase offers more simplicity and self service for more technically able business users. - -If you're uncertain about your choice, starting with something simple and rooted in ROLAP (Relational Online Analytical Processing) is a sound approach. -ROLAP plays a pivotal role and should not be underestimated—it's the linchpin for leveraging star schemas. - -But what exactly is ROLAP? ROLAP lets you define links between tables, -allowing the tool to present data as if it's pre-joined, -performing actual joins only when needed. - -Essentially, ROLAP transforms a star schema into what appears to be a single table for the end user. -This setup empowers users to navigate and explore data seamlessly using a "pivot-table" like interface -commonly found in BI tools. - -By using ROLAP, we are able to maintain single versions of dimension tables, -and reduce maintenance efforts while increasing flexibility and velocity for the end user. - -## Data stack Governance - -This section sheds light on strategies for efficient management of your data stack. Here are key tips to get you started: - -- **Version control is essential:** - Version control, like using Git, is akin to having a safety net for your code. It ensures you can track changes, collaborate seamlessly, and revert to previous versions if needed. - -- **Early alert setup:** - Implementing alert mechanisms from the get-go is like having a diligent watchdog for your data. It helps you catch issues early, preserving trust in your data. Check out this [guide on using dlt to send alerts to Slack](https://dlthub.com/docs/running-in-production/running#using-slack-to-send-messages). - -- **Streamlined workflows and CI/CD:** - Streamlining your workflows and embracing CI/CD is like putting your data operations on an express lane. It speeds up development, minimizes errors, and ensures a smoother deployment process. If you're using Airflow on GCP, this [simple setup guide](https://dlthub.com/docs/reference/explainers/airflow-gcp-cloud-composer) is your friend. - -- **Assumption testing:** - Adding comprehensive tests is akin to having a safety net beneath a trapeze artist. It gives you the confidence to make changes or additions without fearing a crash. - -- **Goal-oriented KPI definition:** - When defining KPIs, always keep the end goal in mind. Tailor your KPIs to what matters most for each business function. Marketing may dance to the tune of signups, Finance to contracts, and Operations to active users. - -- **Implement lineage for faster Troubleshooting:** - Implementing lineage is like having a well-organized toolbox. It helps you trace and understand the journey of your data, making troubleshooting and model iteration a breeze. - -These foundational practices form the cornerstone of effective data stack governance, ensuring a sturdy structure that grows with your data needs. - -## In Conclusion: a simple beginning, a challenging growth - -Initiating a data warehouse project doesn't have to be an uphill struggle. -In fact, starting with simplicity can often be the wisest path. -With minimal effort, you can accomplish a great deal of what a data team requires in the initial stages. - -The true test lies in scaling—the journey from a streamlined beginning to a comprehensive, -organization-wide data infrastructure. -This evolution is where most of the challenge happens - adoption, stakeholder education and culture change happen in this step too. -However, it's worth noting that having an entire team of data experts right at the start of this journey is a rarity. -Therefore, while scaling is a critical aspect, delving into the intricacies of extensive -team and organizational scaling ventures beyond the scope of this article. - - -## Resources - -If you're building on Google Cloud Platform (GCP), here are some tutorials and resources that can aid you in your data warehouse setup: - -1. **Deploy Cloud Composer with CI/CD from GitHub Repo** - Tutorial Link: [Deploy Cloud Composer with CI/CD](https://dlthub.com/docs/reference/explainers/airflow-gcp-cloud-composer) - -2. **Deploy DLT to Cloud Composer** - Tutorial Link: [Deploy dlt to Cloud Composer](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer) - -3. **Deploy dbt to Cloud Composer** - Tutorial Link: [Deploy dbt to Cloud Composer](https://dlthub.com/docs/dlt-ecosystem/transformations/dbt) - -4. **Setting up Alerts to Slack** - Tutorial Link: [Setting up Alerts to Slack](https://dlthub.com/docs/running-in-production/running#using-slack-to-send-messages). For integrating it into on-failure callbacks, refer to the [Apache Airflow documentation](https://airflow.apache.org/docs/apache-airflow/stable/administration-and-deployment/logging-monitoring/callbacks.html) - -5. **Example ROLAP Definition on Holistics Tool** - Tutorial Link: [Example ROLAP Definition on Holistics Tool](https://dlthub.com/docs/blog/MongoDB-dlt-Holistics) - - -Want to discuss dlt and data lakes or warehouses? - -- Dive into our [Getting Started.](https://dlthub.com/docs/getting-started) -- [Join the ⭐Slack Community⭐ for discussion and help!](https://dlthub.com/community) diff --git a/docs/website/blog/2023-10-19-dbt-runners.md b/docs/website/blog/2023-10-19-dbt-runners.md deleted file mode 100644 index 8173cdd28b..0000000000 --- a/docs/website/blog/2023-10-19-dbt-runners.md +++ /dev/null @@ -1,243 +0,0 @@ ---- -slug: dbt-runners-usage -title: "Running dbt Cloud or core from python - use cases and simple solutions" -image: https://storage.googleapis.com/dlt-blog-images/purple-python-spoderweb.png -authors: - name: Adrian Brudaru - title: Open source data engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [dbt runner, dbt cloud runner, dbt core runner] ---- - -> tl;dr: You can kick off dbt jobs from Python - either by wrapping dbt Core, or by wrapping the Cloud API. -> But why should you use one over the other, and how to best do it to keep things simple? - - -# Outline: - -1. **What is dbt, and what’s the use case for Core and Cloud?** - - **The Problem dbt Solves** - - **What is dbt Core?** - - **What is dbt Cloud?** - - **When to Use One or the Other** - - **Use Cases of dbt Cloud Over Core** - -2. **What are the use cases for running dbt core or Cloud from Python?** - - **Case 1: Analytics Engineering and Data Engineering Teams** - - **Case 2: Real-time Data Processing and Analytics** - - **Case 3: Avoiding Library Conflicts** - -3. **Introducing dlt’s dbt runners - how the Extract and Load steps can trigger the Transform.** - - **The Cloud runner** - - **The Core runner** - -4. **A short demo on how to do that with dlt’s dbt runner.** - - **dbt Cloud Runner Demo** - - **dbt Core Runner Demo** - ---- - -### 1. **What is dbt, and what’s the use case for Core and Cloud?** - -**dbt (data build tool)** is an open-source software that plays a crucial role in the data transformation process. -It empowers data analysts and engineers to create, manage, and document data transformation workflows using SQL (Structured Query Language). -dbt primarily focuses on solving the transformation aspect in ELT (Extract, Load, Transform) data processing. - -### **The Problem dbt Solves** - -dbt addresses the challenge of efficient data transformation, streamlining the 'Transform' stage in ELT workflows. -Traditionally, transforming raw data into a structured, analyzable format has been complex and laborious. -dbt simplifies and automates this process, allowing users to define data transformations through SQL queries. - -### **What is dbt Core?** - -dbt Core is the fundamental open-source version of dbt. It provides the essential features and functionalities -for developing and running data transformation workflows using SQL scripts. -dbt Core offers local execution capabilities, making it suitable for small to medium-scale projects run within a user's environment. - -### **What is dbt Cloud?** - -dbt Cloud is a cloud-based platform provided by Fishtown Analytics, the company behind dbt. -dbt Cloud offers a managed environment for running dbt, providing additional features and capabilities beyond what dbt Core offers. -It is hosted on the cloud, providing a centralized, collaborative, and scalable solution for data transformation needs. - -### **When to Use One or the Other?** - -The choice between dbt Core and dbt Cloud depends on various factors, including the scale of your data transformation needs, collaboration requirements, and resource constraints. - -- **Use dbt Core:** - - For small to medium-sized projects. - - When you prefer to manage and execute dbt locally within your environment. - - If you have specific security or compliance requirements that necessitate an on-premises solution. -- **Use dbt Cloud:** - - For larger, enterprise-scale projects with significant data transformation demands. - - When you require a managed, cloud-hosted solution to reduce operational overhead. - - If you value collaborative features, centralized project management, and simplified access control. - -But, dbt Core is free and open source, where dbt Cloud is paid. So let’s look into why we would use the paid service: - -### **Use Cases of dbt Cloud Over Core** - -We could summarize this as: Cloud is the best solution if your Analytics engineer team wants analytics engineer specific -tooling and does not want to concern itself with data-engineer specific tooling. - -1. **Scalability and Performance:** dbt Cloud provides seamless scalability to handle large-scale data transformation workloads efficiently. -2. **Collaboration and Team Management:** dbt Cloud offers centralized project management and collaboration features, enhancing team productivity and coordination. -3. **Automated Task Scheduling:** dbt Cloud allows for automated scheduling of dbt jobs, streamlining data transformation processes. -4. **Easy Integration with Cloud Data Warehouses:** dbt Cloud integrates seamlessly with various cloud data warehouses, facilitating simplified setup and configuration. - -So dbt Cloud is kind of like a standalone orchestrator, IDE and more. - -## 2. What are the use cases for running dbt Core or Cloud from Python? - -### Case 1: You have an Analytics engineering team and a data engineering team that work with different tools. - -This is a normal case to have in an enterprise teams, where we have a clear separation of responsibilities and tooling based on team preferences and competencies. - -In this case, the Analyics Engineering team will use dbt Cloud for its convenient features, making them more effective. - -However, the Data Engineers will want to ensure that the dbt models only run after new data has been loaded - not before, -not after, and not at all in case the data did not load. -So how to coordinate this? - -To avoid race conditions, or dbt starting despite a broken loading pipeline, the data engineer needs to be able to trigger the dbt run and wait for it. - -Of course, this is a case for the dbt **Cloud** runner. - -### **Case 2: Real-time Data Processing and Analytics** - -In scenarios where you require real-time or near real-time data processing and analytics, -integrating dbt with Python allows for dynamic and immediate transformations based on incoming data. - -If you only refresh data once a day, you do not need the runners - you can set the loads to start at midnight, and the transforms to start at 7 AM. -The hours in between are typically more than enough for loading to happen, and so you will have time to deliver the transformed data by 9 AM. - -However, if you want to refresh data every 5, 15, 60 minutes or something similar, -you will want to have fine grained control over calling the transform after loading the new increment. - -Such, we have to be able to kick off the dbt job and wait for it, before starting the next refresh cycle. - -Here, both the dbt **Cloud** and **Core** runners would fit. - -### Case 3. Avoiding Library conflicts between dbt Core and run environment. - -If you are running dbt from some orchestrators, such as Airflow, you might find that you cannot, because installing dbt causes library conflicts with the base environment. - -In such cases, you would want to create a venv or run the job off the orchestrator. - -Such, both the **Cloud** runner and the **Core** runner with virtual env would fit well here. - -## 3. Introducing the dbt runners we have created in open source - -Here at dlt we solve the EL in the ELT - so naturally we want to kick off dbt to solve the T. - -dlt is an open source library made for easily building data pipelines for Python first people. - -The dlt library auto cleans data and generates database-agnostic schemas before loading - so regardless of which database we use, our schema is the same. -This provides a unique opportunity to standardise dbt packages on top using cross db macros. - -So let’s look at the 2 runners we offer: - -### The Cloud runner - -Docs link: [dbt Cloud runner docs.](https://dlthub.com/docs/dlt-ecosystem/transformations/dbt/dbt_cloud) - -The Cloud runner we support can do the following: - -- Start a dbt job in your dbt Cloud account, optionally wait for it to finish. -- Check the status of a dbt job in your account. - -Code example: -```py -from dlt.helpers.dbt_cloud import run_dbt_cloud_job - -# Trigger a job run with additional data -additional_data = { - "git_sha": "abcd1234", - "schema_override": "custom_schema", - # ... other parameters -} -status = run_dbt_cloud_job(job_id=1234, data=additional_data, wait_for_outcome=True) -print(f"Job run status: {status['status_humanized']}") -``` - -Read more about the additional data dbt accepts [in their docs.](https://docs.getdbt.com/dbt-cloud/api-v2#/operations/Trigger%20Job%20Run) - -### The core runner - -Docs link: [dbt Core runner docs.](https://dlthub.com/docs/dlt-ecosystem/transformations/dbt) - -The core runner does the following: - -- Run dbt core from a local or repository package path. -- Set up the running: - - Optionally install a venv. - - Install dbt if not exists. - - Copy over the remote package. - - Inject credentials from dlt (which can be passed via env, vaults, or directly). - - Execute the package and report the outcome. - -Code example: -```py -# Create a transformation on a new dataset called 'pipedrive_dbt' -# we created a local dbt package -# and added pipedrive_raw to its sources.yml -# the destination for the transformation is passed in the pipeline -pipeline = dlt.pipeline( - pipeline_name='pipedrive', - destination='bigquery', - dataset_name='pipedrive_dbt' -) - -# make or restore venv for dbt, using latest dbt version -venv = dlt.dbt.get_venv(pipeline) - -# get runner, optionally pass the venv -dbt = dlt.dbt.package( - pipeline, - "pipedrive/dbt_pipedrive/pipedrive", - venv=venv -) - -# run the models and collect any info -# If running fails, the error will be raised with full stack trace -models = dbt.run_all() - -# on success print outcome -for m in models: - print( - f"Model {m.model_name} materialized" + - f"in {m.time}" + - f"with status {m.status}" + - f"and message {m.message}") -``` - -## 4. A short demo on how to do that with dlt’s dbt runner. - -### dbt Cloud runner - -In this example, we start from the Pokemon API, load some data with dlt, and then kick off the dbt run in our dbt Cloud account. - -GitHub repo: [dbt Cloud runner example.](https://github.com/dlt-hub/dlt_dbt_cloud) - - -### dbt Core runner - -In this example, we copy GA4 events data from BigQuery into DuckDB, and run a dbt package to calculate metrics. - -Article: [BQ-dlt-dbt_core-MotherDuck.](https://dlthub.com/docs/blog/dlt-motherduck-demo) - -Accompanying GitHub repo: [dbt Core runner example.](https://github.com/dlt-hub/bigquery-motherduck) - - -## In conclusion - -Running dbt from Python is an obvious necessity for a data team that also uses Python for ingestion, orchestration, or analysis. -Having the 2 options to run Cloud or Core versions of dbt enables better integration between the Transform component and the rest of the data stack. - -Want more? - -- [Join the ⭐Slack Community⭐ for discussion and help!](https://dlthub.com/community) -- Dive into our [Getting Started.](https://dlthub.com/docs/getting-started) -- Star us on [GitHub](https://github.com/dlt-hub/dlt)! diff --git a/docs/website/blog/2023-10-23-arrow-loading.md b/docs/website/blog/2023-10-23-arrow-loading.md deleted file mode 100644 index 25962c932e..0000000000 --- a/docs/website/blog/2023-10-23-arrow-loading.md +++ /dev/null @@ -1,141 +0,0 @@ ---- -slug: dlt-arrow-loading -title: "Get 30x speedups when reading databases with ConnectorX + Arrow + dlt" -image: https://storage.googleapis.com/dlt-blog-images/arrow_30x_faster.png -authors: - name: Marcin Rudolf - title: dltHub CTO - url: https://github.com/rudolfix - image_url: https://avatars.githubusercontent.com/u/17202864 -tags: [arrow, Rust, ConnectorX] ---- - -If rust + arrow + duckb is a new data engineering stack, now you can get a feel of it with `dlt`. We recently added native arrow tables (and panda frames) loading. What it means? You can pass an Arrow table to `dlt` **pipeline.run** or **pipeline.extract** methods, have it normalized, saved to parquet and loaded to your destination. - -Here we achieved ~30x speedups when loading data from (local) postgres database using ConnectorX + Arrow compared to SqlAlchemy + json. (both use dlt as an engine, read disclaimer at the end!) - -### Load postgres table with Arrow - -We’ll start with [ConnectorX library](https://github.com/sfu-db/connector-x) that creates Arrow tables from SQL queries on most of the popular database engines. - -```sh -pip install connectorx -``` - -Lib has Rust inside, zero copy extraction and is amazingly fast. We’ll extract and normalize 10 000 000 [test rows](https://github.com/dlt-hub/verified-sources/blob/master/tests/sql_database/sql_source.py#L88) from local postgresql. The table **chat_message** looks like Slack messages dump. Messages have unique autoincrement **id** which we use to load in chunks: - -```py -import connectorx as cx -import dlt -from dlt.sources.credentials import ConnectionStringCredentials - -def read_sql_x( - conn_str: str -): - # load in chunks by one million - for _id in range(1, 10_000_001, 1_000_000): - table = cx.read_sql(conn_str, - "SELECT * FROM arrow_test_2.chat_message WHERE id BETWEEN %i AND %i" % (_id, _id + 1000000 - 1), - return_type="arrow2", - protocol="binary" - ) - yield table - -chat_messages = dlt.resource( - read_sql_x, - name="chat_messages" -)("postgresql://loader:loader@localhost:5432/dlt_data") -``` - -In this demo I just extract and normalize data and skip the loading step. - -```py -pipeline = dlt.pipeline(destination="duckdb", dev_mode=True) -# extract first -pipeline.extract(chat_messages) -info = pipeline.normalize() -# print count of items normalized -print(info) -# print the execution trace -print(pipeline.last_trace) -``` - -Let’s run it: - -```sh -$ PROGRESS=enlighten python connector_x_speed.py -Items 10000001 [00:00, 241940483.70/s] -Normalized data for the following tables: -- _dlt_pipeline_state: 1 row(s) -- chat_messages: 10000000 row(s) - -Run started at 2023-10-23T19:06:55.527176+00:00 and COMPLETED in 16.17 seconds with 2 steps. -Step extract COMPLETED in 16.09 seconds. - -Step normalize COMPLETED in 0.08 seconds. -``` -### Load postgres table with SqlAlchemy - -Here’s corresponding code working with **SqlAlchemy**. We process 10 000 000 rows, yielding in 100k rows packs and normalize to parquet in 3 parallel processes. - -```py -from itertools import islice -import dlt -from sqlalchemy import create_engine - -CHUNK_SIZE=100000 - -def read_sql_a(conn_str: str): - engine = create_engine(conn_str) - with engine.connect() as conn: - rows = conn.execution_options(yield_per=CHUNK_SIZE).exec_driver_sql("SELECT * FROM arrow_test_2.chat_message") - while rows_slice := list(islice(map(lambda row: dict(row._mapping), rows), CHUNK_SIZE)): - yield rows_slice - -chat_messages = dlt.resource( - read_sql_a, - name="chat_messages", - write_disposition="append", -)("postgresql://loader:loader@localhost:5432/dlt_data") - -pipeline = dlt.pipeline(destination="duckdb", dev_mode=True) -# extract first -pipeline.extract(chat_messages) -info = pipeline.normalize(workers=3, loader_file_format="parquet") -print(info) -print(pipeline.last_trace) -``` - -Let’s run it: - -```sh -$ PROGRESS=enlighten python sql_alchemy_speed.py -Normalized data for the following tables: -- _dlt_pipeline_state: 1 row(s) -- chat_messages: 10000000 row(s) - -Run started at 2023-10-23T19:13:55.898598+00:00 and COMPLETED in 8 minutes and 12.97 seconds with 2 steps. -Step extract COMPLETED in 3 minutes and 32.75 seconds. - -Step normalize COMPLETED in 3 minutes and 40.22 seconds. -Normalized data for the following tables: -- _dlt_pipeline_state: 1 row(s) -- chat_messages: 10000000 row(s) -``` - -### Results - -So we can see **~30x overall speedup on extract and normalize steps** (~16 seconds vs ~8 minutes). The **extract step is ~13x faster**, while **normalize is few thousand times faster**. Arrow normalizer is just checking the schemas and moves parquet files around. JSON normalizer is inspecting every row to first infer the schema and then to validate the data. - -As the output in both of methods is the same (parquet files) - the actual load step takes the same time in both cases and is not compared. I could easily push the load packages (parquet files) to [any of supported destinations](https://dlthub.com/docs/dlt-ecosystem/verified-sources/arrow-pandas#destinations-that-support-parquet-for-direct-loading) - -### What’s next: -- [Reads our docs on Arrow](https://dlthub.com/docs/dlt-ecosystem/verified-sources/arrow-pandas) -- [Add merge and incremental loads to code above](https://dlthub.com/docs/examples/connector_x_arrow/) -- I'm on [dltHub Slack](https://dlthub.com/community) all the time. - -### Disclaimers - -- Playing field is not level. classical (sql alchemy) `dlt` run is processing data row by row, inferring and validating schema. that’s why it so slow. The Arrow version benefits from the fact, that data is already structured in the source. -- We load from local database. That means that network roundtrip during extraction is not included. That isolates Arrow speedups well. In case of remote database engine, the speedups will be smaller. -- You could optimize extract (both classical and arrow) by reading data from **postgres** [in parallel](https://dlthub.com/docs/examples/transformers/#using-transformers-with-the-pokemon-api) or use partitions in ConnectorX \ No newline at end of file diff --git a/docs/website/blog/2023-10-25-dlt-deepnote.md b/docs/website/blog/2023-10-25-dlt-deepnote.md deleted file mode 100644 index 4251be39d2..0000000000 --- a/docs/website/blog/2023-10-25-dlt-deepnote.md +++ /dev/null @@ -1,240 +0,0 @@ ---- -slug: deepnote-women-wellness-violence-tends -title: "DLT & Deepnote in women's wellness and violence trends: A Visual Analysis" -image: https://storage.googleapis.com/dlt-blog-images/blog_deepnote_improved_flow.png -authors: - name: Hiba Jamal - title: Data Science intern at dlthub - url: https://github.com/hibajamal - image_url: https://avatars.githubusercontent.com/u/35984866?v=4 -tags: [dbt runner, dbt cloud runner, dbt core runner] ---- - -# DLT & Deepnote in women's wellness and violence trends: A Visual Analysis - - - -What’s in this article: - -1. [⌛The Problem; The bulk of time spent in a data science project is on the transformation of data itself.](#data-trans1) - 1. [The usual flow of data for data science projects](#ds-project-usual-flow) - 2. [A peak into the datasets 👀](#dataset-peak) -2. [⚰️The Classical Solution; using pandas to model complicated data for your analytics workflows isn’t the fastest way out.](#classical-solution) -3. [💫The Revised Solution; Revisualizing the flow of data with dlt & Deepnote](#revised-solution) - 1. [Introducing dlt; the data cleaner I wish I had](#introducing-dlt) - 2. [Deepnote - the iPython Notebook turned Dashboarding tool](#Deepnote-the-iPython-Notebook-turned-Dashboarding-tool) -4. [🌍Clustering countries based on their wellness indicators](#Clustering-countries-based-on-their-wellness-indicators) -5. [🔧Technical Conclusion; dlt & Deepnote are the data science dream team](#technical-conclusion) -6. [🎆Analytical Conclusion; Leave women in dangerous situations for extended periods of time and they’ll begin to justify the violence committed against themselves!](#analytical-conclusion) - -# ⌛The Problem; The bulk of time spent in a data science project is on the transformation of data itself. - -If you are a data analyst, data scientist or a machine learning engineer, then more -likely than not, you spend more time fixing data pipelines or data formats then you do -on ML algorithms or dashboard designs. We aren’t always lucky enough to get structured -data to work with. Imagine a world where your training data is just this statement without no prior work: - -```sql -select * from -``` - -What a world that would be. - -Unfortunately, before we get to writing this `select` statement, we need to go through -some very important but time consuming first steps. To describe what this journey looks -like, let’s list down the steps we usually undergo. - -### The usual flow of data for data science projects - -![usual flow](https://storage.googleapis.com/dlt-blog-images/blog_deepnote_usual_flow.png) - -We sign up for our jobs because we enjoy the last two activities the most. These parts have -all the pretty charts, the flashy animations, and, if the stars align, include watching your -hunches turn out to be statistically significant! - -However, the journey to reach these stages is stretched much longer due to the time spent on data formats and pipelines. It would be such a load off my mind if they would get sorted themselves and we could skip to the good part. Sure, ipython notebooks with `pandas` and `numpy` help us in getting along, but what if there was something even simpler? Let’s explore different solutions. - -### A peak into the datasets 👀 - -The two datasets that we are using are nested json files, with further lists of dictionaries, and are survey results with wellness indicators for women. Here’s what the first element of one dataset looks like: - -
- -
- -

Looks like it is a nested json, nested further with more lists of dictionaries.

- -# ⚰️The Classical Solution; using pandas to model complicated data for your analytics workflows isn’t the fastest way out. - -Usually, `json_normalize` can be used to unnest a json file while loading it into pandas. However, the nested lists inside dictionaries do not unravel quite well. Nonetheless, let’s see how the pandas normalizer works on our dataset. - -
- -
- -

Conclusion from looking at the data: pandas successfully flattened dictionaries but did not unnest lists. Perhaps because in order to unpack these lists, one might need to create new tables, essentially create a data model entirely. But, that is something pandas does not do for us. So, to be able to use it, let’s flatten the data further into arrays and tables. Particularly, let’s pay attention to the amount of code required to achieve this task.

- -To start off, using the `pandas` `explode` function might be a good way to flatten these lists: - -
- -
- -

And now, putting one of the nested variables into a pandas data frame:

- -
- -
- -

And this little exercise needs to be repeated for each of the columns that we had to “explode” in the first place.

- -Our next step could be using a visualization package like `matplotlib`, and other `pandas` and `numpy` based functions to conduct a thorough exploratory analysis on the data. However, if we use the code above and plot two variables against each other on a scatter plot, for example, `marriage_related` and `work_related`, then joining this data wouldn’t be simple. We would have to be wary of the list indices (or something that can be used as foreign keys) that will match rows together across different tables. Otherwise, we would end up with mismatched data points on the scatter plot. We’ll get more into this in the [Know your data model](#know-your-data-model) section. - -# 💫The Revised Solution; Revisualizing the flow of data with dlt & Deepnote - -We can reimagine the flow of data with dlt and Deepnote in the following way: - -![revised flow](https://storage.googleapis.com/dlt-blog-images/blog_deepnote_improved_flow.png) - -We leave the loading of the raw data to dlt, while we leave the data exploration and visualization to the Deepnote interface. - -## Introducing dlt; the data cleaner I wish I had - -Imagine this: you initialize a data pipeline in one line of code, and pass complicated raw data in another to be modelled, unnested and formatted. Now, watch that come to reality: - -
- -
- -
- -
- -And that’s pretty much it. Notice the difference in the effort you had to put in? - -The data has been loaded into a pipeline with `duckdb` as its destination. `duckdb` was chosen as it is an OLAP database, perfect for usage in our analytics workflow. The data has been unnested and formatted. To explore what exactly was stored in that destination, a `duckdb` connector (`conn`) is set up, and the `SHOW ALL TABLES` command is executed. - -
- -
- -In a first look, we understand that both the datasets `violence` and `wellness` have their own base tables. One of the child tables is shown below: - -
- -
- -### Know your data model; connect the unnested tables using dlt’s pre-assigned primary and foreign keys: - -The child tables, like `violence__value` or `wellness__age_related` are the unnested lists of dictionaries from the original json files. The `_dlt_id` column as shown in the table above serves as a **primary key**. This will help us in connecting the children tables with ease. The `parent_id` column in the children tables serve as **foreign keys** to the base tables. If more then one child table needs to be joined together, we make use of the `_dlt_list_idx` column; - -
- -
- -## Deepnote - the iPython Notebook turned Dashboarding tool - -Take your average Notebook experience, and combine it with the powers of a collaborative and interactive dashboarding tool and you get Deepnote. Now that we focus on analytics portion of this article, let’s check out how Deepnote helps along the way. - -### One step visualizations - -At this point, we would probably move towards a `plt.plot` or `plt.bar` function. However, with Deepnote, the little Visualize button on top of any data frame will help us jump straight to an easy figure. Clicking on the Visualize button takes you to a new cell block, where you can choose your parameters, types of charts, and customization settings in the sidebar. The following chart is built from the `joined` data frame we defined above. - -![chart](https://storage.googleapis.com/dlt-blog-images/blog_deepnote_chart.png) - -And a stacked bar chart came into existence! A little note about the query results; the **value** column corresponds to how much (in %) a person justifies violence against women. An interesting yet disturbing insight from the above plot: in many countries, women condone violence against women as often if not more often than men do! - -The next figure slices the data further by gender and demographic. The normalized bar chart is sliced by 2 parameters, gender and demographic. The two colors represent genders. While different widths of the rectangles represent the different demographics, and the different heights represent that demographic’s justification of violence in %. The taller the rectangle, the greater the % average. It tells us that most women think that violence on them is justified for the reasons mentioned, as shown by the fact that the blue rectangles make up more than 50% of respondents who say ‘yes’ to each reason shown on the x-axis. If you hover over the blocks, you will see the gender and demographic represented in each differently sized rectangle, alongside that subset’s percentage of justification of violence. - -Let’s examine the differences in women’s responses for two demographic types: employment vs education levels. We can see that the blue rectangles for “employed for cash” vs “employed for kind” don’t really vary in size. However, when we select “higher” vs “no education”, we see that the former is merely a speck when compared to the rectangles for the latter. This comparison between employment and education differences demonstrates that education plays a much larger role in likelihood to influence women’s levels of violence justification. - -
- -
- -Let’s look at one last plot created by Deepnote for the other dataset with wellness indicators. The upward moving trend shows us that women are much less likely to have a final say on their health if they are less educated. - -
- -
- -# 🌍 Clustering countries based on their wellness indicators - -Lastly, based on these indicators of wellness and violence about women, let’s use KMEANS to cluster these countries to see how the algorithm groups which countries together. The intersection of the ‘countries’ columns in both datasets results in the availability of data for 45 countries. The columns used in this model indicate per country: - -- the average years of education for women -- % of women who have a final say over their health matters -- % of women who have control over their finances -- % of women working -- % of violence justification - - Within these countries, the KMEANs algorithm converges to 4 clusters. - -![clustering](https://storage.googleapis.com/dlt-blog-images/blog_deepnote_animation.gif) - -The color bar shows us which color is associated to which cluster. Namely; 1: purple, 2: blue, 3: green, and 4: yellow. - -To understand briefly what each cluster represents, let’s look at the averages for each indicator across all clusters; - -
- -
- -This tells us that according to these datasets, cluster 2 (highlighted blue) is the cluster that is performing the best in terms of wellness of women. It has the lowest levels of justifications of violence, highest average years of education, and almost the highest percentage of women who have control over their health and finances. This is followed by clusters 3, 1, and 4 respectively; countries like the Philippines, Peru, Mozambique, Indonesia and Bolivia are comparatively better than countries like South Africa, Egypt, Zambia, Guatemala & all South Asian countries, in regards to how they treat women. - -## 🔧Technical Conclusion; dlt & Deepnote are the data science dream team - -It is safe to say that dlt is a dream come true for all data scientists who do not want to 1. W**ait for a data engineer to fix data pipeline issues** and model discrepancies, or 2. **Spend time studying the format of a dataset** and find ways to structure and unnest it. The library supports many different [sources](https://dlthub.com/docs/dlt-ecosystem/verified-sources/) and can pick up the dreadful data cleaning tasks you don’t want to do. - -Next, let’s talk about the coding tool of choice for this article—Deepnote. With code blocks that come with **AI code generation and debugging capabilities**, and the **built-in ability to use SQL on your Python DataFrame**, you can quickly **create multiple plots out of a given DataFrame**. You can also easily slice your visualizations by various dimensions using Python-based visualization libraries like seaborn, matplotlib and plotly. - -Using both of these tools together made the critical tasks of data loading and data exploration much easier for a data scientist or analyst by automating much of the upfront data preparation steps! - -## 🎆Analytical Conclusion; Leave women in dangerous situations for extended periods of time and they’ll begin to justify the violence committed against themselves! - -The data we explored in the plots above demonstrated that women often justify violent acts committed against themselves almost as equally as men do. Particularly, women who are less educated are more likely to fall into the shackles of these beliefs when compared to their more educated counterparts. - -Additionally, the data also shows us women who are less educated have less input on the fate of their personal health. Thus, misogyny is often internalized and condoned by women themselves, especially by those who are less educated. It is not enough to be kinder toward women—we need to advocate for their education to be able to fight the sexism and prejudice that often start within women themselves. - ---- - -P.S. If you want to explore this notebook on your own, then here’s the [link](https://deepnote.com/workspace/dlthub-9af36282-cfc1-4352-a24c-c703ff0ca26e/project/Hiba-Jamals-Untitled-project-5fc0e511-cc64-4c44-a71c-a36c8c18ef62/notebook/Article-48645544ae4740ce8e49fb6e0c1db925) to it! \ No newline at end of file diff --git a/docs/website/blog/2023-10-26-dlt-prefect.md b/docs/website/blog/2023-10-26-dlt-prefect.md deleted file mode 100644 index 8ceb51ec81..0000000000 --- a/docs/website/blog/2023-10-26-dlt-prefect.md +++ /dev/null @@ -1,277 +0,0 @@ ---- -slug: dlt-prefect -title: "Building resilient pipelines in minutes with dlt + Prefect" -meta: - - name: canonical - content: https://www.prefect.io/blog/building-resilient-data-pipelines-in-minutes-with-dlt-prefect -image: https://storage.googleapis.com/dlt-blog-images/prefect-dlt.png -authors: - name: Dylan Hughes & Chris Reuter - title: Engineering & Community at Prefect.io - url: https://github.com/dylanbhughes - image_url: https://avatars.githubusercontent.com/u/2325367?v=4 -tags: [dbt runner, dbt cloud runner, dbt core runner] ---- - - - - - -This article is reposted from Prefect.io blog, and you can read the original [there](https://www.prefect.io/blog/building-resilient-data-pipelines-in-minutes-with-dlt-prefect). - ->The hardest part about writing a blog is getting started - writing the outline and filling out the first few key points. The same can be said for writing data pipelines: you need to inspect docs, determine data structures, write tests, etc. -> ->What if you could build a resilient, production-ready data pipeline that is scheduled and running in just a few minutes? We’ll show you how to do just that with dlt and Prefect. - -## dlt - -dlt is an open-source library that you can add to your Python scripts to load data from various and -often messy data sources into well-structured, live datasets. It abstracts away the need to hunt -through docs, interpret APIs, and reinvent the wheel every time. Instead of writing a custom pipeline, -you can use dlt to build a framework for your pipelines for any combination of tools. - -### Moving Slack data into BigQuery - -We use BigQuery as our data warehouse, and try to centralize as much information there as possible. -Given our Slack community is over 25,000 people, it makes sense to use that information to better our -community. We can identify the types of questions our users struggle with the most, and take action to -improve Prefect by using Slack data. - -If you Google “load Slack into BigQuery,” you’ll see a bunch of listings for no-code tools like -Zapier that can help you move data… for a fee, of course. What if you want to do this yourself? -Slack has an API, but [check it out](https://api.slack.com/methods/users.list). -It would take some effort to interpret even a simple response like this one for users: - -```json -{ - "ok": true, - "members": [ - { - "id": "W012A3CDE", - "team_id": "T012AB3C4", - "name": "spengler", - "deleted": false, - "color": "9f69e7", - "real_name": "spengler", - "tz": "America/Los_Angeles", - "tz_label": "Pacific Daylight Time", - "tz_offset": -25200, - "profile": { - "avatar_hash": "ge3b51ca72de", - "status_text": "Print is dead", - "status_emoji": ":books:", - "real_name": "Egon Spengler", - "display_name": "spengler", - "real_name_normalized": "Egon Spengler", - "display_name_normalized": "spengler", - "email": "spengler@ghostbusters.example.com", - "image_24": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", - "image_32": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", - "image_48": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", - "image_72": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", - "image_192": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", - "image_512": "https://.../avatar/e3b51ca72dee4ef87916ae2b9240df50.jpg", - "team": "T012AB3C4" - }, - "is_admin": true, - "is_owner": false, - "is_primary_owner": false, - "is_restricted": false, - "is_ultra_restricted": false, - "is_bot": false, - "updated": 1502138686, - "is_app_user": false, - "has_2fa": false - } - ] -} -``` -### With dlt -You can use dlt to build a Slack to BigQuery pipeline in just a few seconds with a single command. -Seriously, it is that simple. -In preparation, let’s make sure to install what we need: - -```sh -pip install dlt -pip install prefect -```` - -Then just run a simple init command: - -```sh - -dlt init slack bigquery -``` -In the `.dlt/secrets.toml` file, enter your Slack and BigQuery credentials: - -```toml -[sources.slack] -access_token="*****" - -[destinations.bigquery] -location = "US" - -[destination.bigquery.credentials] -project_id = "*****" -private_key = "*****" -client_email = "*****" -``` - -With a single command + adding some credentials, -we now have the framework of a pipeline! [Look at what has been generated](https://github.com/dylanbhughes/dlt_slack_pipeline/blob/main/slack_pipeline.py), -with a couple of small customizations: - -Note that we are redacting some of the code in the preview for brevity, -to follow along completely navigate to the repo. - - -```py -# Pipeline to load Slack into BigQuery - -from typing import List - -import dlt -import pendulum - -from slack import slack_source - -def load_channels() -> None: - """Execute a pipeline that will load a list of all the Slack channels in the workspace to BigQuery""" - # ... - -def get_resources() -> List[str]: - """Fetch a list of available dlt resources so we can fetch them one at a time""" - # ... - -def load_channel_history(channel: str, start_date: Date) -> None: - """Execute a pipeline that will load the given Slack channel incrementally beginning at the given start date.""" - # ... - -def get_users() -> None: - """Execute a pipeline that will load Slack users list.""" - # ... - -if __name__ == "__main__": - channels = None - start_date = pendulum.now().subtract(days=1).date() - - load_channels() - - resources = get_resources() - for resource in resources: - if channels is not None and resource not in channels: - continue - - load_channel_history(resource, start_date=start_date) - - get_users() -``` - -### What if it fails? -Great, we’ve got a pipeline that moves data from Slack to BigQuery, -and we didn’t have to format any JSON - that alone is a win. -However, there may be some issues. What if Slack rate limits you? -What if BigQuery is down (😅)? What about a networking issue? -What if the execution environment where this script lives isn’t working? - -These questions are the difference between a pipeline and a resilient pipeline. -They’re the difference between you getting sleep at night and you looking like a hero (or a dummy) -to your stakeholders. - - -## Adding Prefect - -Prefect is a workflow orchestration tool for turning your pipelines into scheduled, repeatable, -and resilient workflows. With Prefect you get scheduling, observability, and automations -that can make sure your pipelines aren’t causing you stress in the middle of the night. - -Make sure you’re logged in to Prefect Cloud by [signing up](https://app.prefect.cloud/?utm_source=dltblog) -and using the following command: - -```sh -prefect cloud login -``` -Luckily, Prefect is also incredibly Pythonic. Turning any pipeline into an observable, scheduled -Prefect flow is as simple as adding decorators to your functions and `serving` it up. -Here’s our `dlt` generated pipeline, scheduled daily: - -```py -from typing import List - -import dlt -import pendulum -from prefect import flow, task -from slack import slack_source - -@task -def load_channels() -> None: - ... - -@task -def get_resources() -> List[str]: - ... - -@task -def load_channel_history(channel: str, start_date: pendulum.Date) -> None: - ... - -@task -def get_users() -> None: - ... - -@flow -def slack_pipeline( - channels=None, start_date=pendulum.now().subtract(days=1).date() -) -> None: - load_channels() - - resources = get_resources() - for resource in resources: - if channels is not None and resource not in channels: - continue - - load_channel_history(resource, start_date=start_date) - - get_users() - -if __name__ == "__main__": - slack_pipeline.serve("slack_pipeline", cron="0 0 * * *") -``` - -We’ve added `@task` to our individual functions. -These will be treated as individual units of work by Prefect when they are executed. -We decorate our primary function (slack_pipeline) with @flow, which references -our task functions. We will schedule and kick off flows, which in turn will -execute tasks based on the decorators within them. - -Finally, adding `.serve` to our `if __name__ == "__main__":` call means that a Prefect -deployment will be automatically created and scheduled to run daily at noon. We can -see our deployment and scheduled runs in the [Prefect UI](https://app.prefect.cloud), and we’ll know when it ran or, -more importantly, if they didn't. We can further extend our pipeline by: - -- Setting up [remote infrastructure with workers](https://docs.prefect.io/latest/tutorial/workers/) -- [Adding automations](https://docs.prefect.io/latest/concepts/automations/) to notify us when the pipeline has failed -- [Setting up retries](https://docs.prefect.io/latest/concepts/tasks/#custom-retry-behavior) - -### Where to handle failure - -There are many levels of failure, you could say, from "accidentally liking your ex's social media post from five years ago" to "trying to assemble IKEA furniture without instructions," up to "asking for the Wi-Fi password at a funeral." So which ones should we handle where, and what are some quick solutions? - -With dlt, your pipelines are resilient at the API level. From [schema changes](https://dlthub.com/docs/blog/schema-evolution) -to network issues or [memory overflow](https://dlthub.com/docs/reference/performance#memorydisk-management), -there is automated resiliency and recovery that is specific to working with the pesky APIs of your tools. - -With Prefect, your pipelines become resilient at the function level. -If your workflows never run, break and fail, [or break and never end](https://docs.prefect.io/latest/api-ref/server/api/flow_runs/?h=lateness#prefect.server.api.flow_runs.average_flow_run_lateness), Prefect will be your backstop - -notifying you and taking the appropriate action in case of failure. - -### Building resilient pipelines faster with dlt + Prefect -Getting into production is hard. First you need to build your pipeline, and then you need to make it -resilient. With this tutorial, we’ve shown you how to quickly build pipelines with dlt and then turn -that pipeline into a resilient, repeatable workflow with Prefect. - -Prefect makes complex workflows simpler, not harder. -Try [Prefect Cloud](https://app.prefect.cloud/) for free for yourself, -download our [open source package](https://github.com/PrefectHQ/prefect), join our -[Slack community](https://www.prefect.io/slack), or [talk to one of our engineers](https://calendly.com/prefect-experts/prefect-product-advocates) to learn more. - diff --git a/docs/website/blog/2023-10-30-data-modelling-tools.md b/docs/website/blog/2023-10-30-data-modelling-tools.md deleted file mode 100644 index 9ab3a96007..0000000000 --- a/docs/website/blog/2023-10-30-data-modelling-tools.md +++ /dev/null @@ -1,583 +0,0 @@ ---- -slug: semantic-modeling-tools-comparison -title: "Semantic Modeling Capabilities of Power BI, GoodData & Metabase: A Comparison" -image: https://storage.googleapis.com/dlt-blog-images/people-stuck-with-tables-2.jpg -authors: - name: Hiba Jamal - title: Data Science intern at dlthub - url: https://github.com/hibajamal - image_url: https://avatars.githubusercontent.com/u/35984866?v=4 -tags: [data modelling] ---- - -![cover](https://storage.googleapis.com/dlt-blog-images/people-stuck-with-tables-2.jpg) -DeepAI Image with prompt: People stuck with tables. - - - -#### What’s in this article: - -1. [Depending on your role, data modelling can mean different things](#dm-meaning) -2. [Introducing the three dashboarding tools](#dm-tools) -3. [Introducing our database](#dataset) -4. [Comparison Metrics & Table](#comp-table) -5. [In depth comparison](#indepth-comp) - -# Depending on your role, data modelling can mean different things. - -## For Data & Analytics Engineers - -For some of us who have spent our fair share of time working with databases, the words `data model` illustrates a bunch of tables on a canvas. Behind those tables we see discussions of whether or not they should be floating there by themselves or tied together by lines that say `1` or **`*`** on the corners. - -If you are a data engineer, maybe you do a data vault model for ingestion, while if you are an analytics engineer you might do a dimensional model for supporting reporting requirements. - -After figuring out what sort of **entities, constraints and relationships** we need to define, we dive further into the **data types** of each of the fields within those entities. This makes the recipe for a good data model. This model is then implemented in the database, and deployed to be run against new data coming in. Lastly, to avoid the ill-fated incident of an analyst being lost in the complex structure and pipeline of the data, it must be [**documented**](https://dlthub.com/docs/blog/data-product-docs)! - -## For Data Analysts - -## - -For the dashboard creators, the initial data model has (hopefully) already been set up. A subset of the tables visualized by the engineers are to be handpicked and dropped onto a **dashboard**. Some tools do you the favor of **detecting relationships** between tables, if not, you can find a way to do it on the dashboarding tool itself. The data modelling for analysts includes building **aggregated measures**, **calculated columns**, semantic types definition to define the actions the tool allows on the field, and finding the best **read, cache and refresh** options for the data. - -If you have big data, the connected dashboards might be slow and need **optimization**. This is when you would be pushed to make the decision to fix the problem either before or after it reaches the dashboard. This means creating **aggregated tables** with a different **data granularity,** either in the source db or in the tool cache db. - -# Introducing the three dashboarding tools - -The three data reporting or dashboarding tools we’ll be diving into are Power BI, GoodData and Metabase. All three have a substantial following among business intelligence teams and analytics experts, and the tools come with their own set of data modelling capabilities. - -## Introducing Power BI - -Power BI is a powerful data visualization tool trusted by [97% of Fortune 500](https://powerbi.microsoft.com/en-us/blog/microsoft-named-a-leader-in-2021-gartner-magic-quadrant-for-analytics-and-bi-platforms/) companies by 2021. It's available as both desktop and online versions, but being a Microsoft product, it's limited to Windows. You can connect it to various data sources, including files like CSV and JSON, and databases like BigQuery and AWS Athena, and about 40 others! It offers a variety of visual elements for creating reports, and it also supports Python and R integration. - -While its primary purpose is generating actionable reports for businesses, it's user-friendly for data exploration and modeling. It's affordable for BI analysts, with pricing ranging from free to $10-$20 per user per month, or premium bundles from $262.80 to $4,995 per month. - -## Introducing GoodData - -GoodData prides itself as the #1 embedded analytics vendor, and currently in 2023, has [3.2 million](https://www.gooddata.com/customers/) end users worldwide. Established in 2008, it started with data exploration and visualization tools and has since evolved. In 2022, it introduced its cloud platform with enhanced features (the version referenced in this article). GoodData currently supports 10 data sources and 2 data source managers. - -The user-friendly dashboard makes managing data, creating metrics, visuals, and dashboards quite clean and easy. Pricing varies based on the selected product, with both predefined and customizable options to suit an organization's needs. - -## Introducing Metabase - -Metabase is a BI tool that is now about 4 years old, with a user base of almost [50,000 organizations](https://www.metabase.com/case_studies) that use it to work with their data. The tool has interesting terms to showcase its abilities to the “data democratization” crowd. For example, while loading visualizations or calculations, it tells you it’s: doing science ✨, which is a playful way to appeal to non-devs. Additionally, if you want to extract SQL-defined data from a source, Metabase calls it 'asking a question' to that source. - -This tool serves as a foundation for embedded analytics and offers data organization through model creation and query building. With 26 official data source connectors, it also supports raw data imports. Metabase's pricing varies based on whether it's used as a managed service or self-managed. Self-management can include using it as an open-source tool, and otherwise it has pricing options that extend up to $500, along with custom pricing options. - -# The dataset we’ll be using for our experiments; modeled by dlt - -Our database is based on the data published by [LivWell](https://www.nature.com/articles/s41597-022-01824-2), containing wellness indicators for women all around the world. It can also be found as a flattened CSV on Kaggle, [here](https://www.kaggle.com/datasets/konradb/wellbeing-of-women-in-52-countries). It is a compilation of surveys collected from women internationally. - -Sample input structure: - -```py -[{"survey_id": "AM2000DHS", -"country": "Armenia", -"marriage_related": [{...}, {...}, ...], -"work_related": [{...}, {...}, ...], -"education_related": [{...}, {...}, ...], -"money_related": [{...}, {...}, ...], -"health_related": [{...}, {...}, ...], -"age_related": [{...}, {...}, ...] -}, - {...}, {...}, {...}, {...}] -``` - -To break it up into proper tables representing the different sections of the surveys, we gave this data to **dlt** to unpack it into a flat relational structure into BigQuery. dlt automatically unpacked the original data into connected tables. The various child tables link to the parent table `wellness` using foreign keys. `Wellness` contains surveys identified by ID and country. The final setup of indicators broken up into different categories can be found below, as displayed by Power BI. This structured database has been used to experiment with all three dashboarding tools in this article. - -![pbi-modelled-livewell](https://storage.googleapis.com/dlt-blog-images/pbi-modelled-livewell.png) -The database schema as presented by a Power BI Model. - -# Comparison Metrics & Table - -The database hosted on BigQuery was loaded into all three dashboarding tools via their own respective connectors. We came up with some metrics to compare things. - -Before delving into detailed analyses on those metrics, here's an overview of what'll be discussed: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Power BIGoodDataMetabase
Data TypesIt lets you use types like Decimals, Whole Numbers, Percentages for columns, various date and time formats, and binary objects for conditional setups.GoodData categorizes data as facts, attributes, and tables for efficient organization in a dimensional model.It uses the same data types as the source, such as integers or strings, and also adds user-friendly "field types" for better understanding.
Data DictionariesPower BI allows column property editing but lacks a built-in data dictionary view, accessible via the performance analyzer.GoodData Cloud provides a simplified data dictionary with column properties for easy fact-label categorization, including source data mappings.Metabase has a robust data dictionary in the admin panel, enabling column-level property and description configurations.
Table Properties & DescriptionsPower BI shows table descriptions right under the “Model View” tab, this can be used as a means for table level documentation.GoodData displays table descriptions in the "Data" tab, emphasizing data source mapping over table-level documentation.Metabase provides descriptions through the "Learn about this table" feature, offering insights on the table's significance and important details.
Inter Table Relationships Simplifies data modeling in Model View with drag-and-drop relationships, auto or manual detection, and cardinality editing.GoodData separates date fields into distinct tables, creating a star schema, automatically identifies keys using source naming conventions, and allows drag-and-drop relationships creation.Metabase lets you specify keys at the table level, globally in the admin panel, or within Models and questions, connecting tables through SQL queries or models.
Custom Query language Power BI developers use DAX for measures and fields and Power Query M for data import and transformation.GoodData uses MAQL, a unique query language for multi-dimensional models, unlike traditional SQL for relational databases.Metabase uses SQL for custom models and expressions, seamlessly integrating code with visualizations.
Data granularity Management: Column Creation & Aggregation capabilities Power BI permits the creation of custom fields, and tables, facilitating data granularity adjustments and customized aggregation.Custom calculated fields and other transformations can be achieved with a SQL query under the dataset. Datetime granularity is simplified with custom truncation settings.Like Power BI, it allows users to create models with custom aggregation levels and add custom fields through Custom Expressions.
Defining Local or Central Metrics Power BI Measures can be made in various ways, with DAX for reusable aggregations and has a central "Metrics Hub" in the Power BI service.GoodData uses MAQL for custom metric creation, easily added in the "Analyze" tab. Reusable/central metrics are managed in the Metrics tab.Custom metrics can be crafted through SQL, Questions, Models, and admin-defined metrics can be used in reports with suitable access.
Data Refresh and Loading capabilitiesPower BI data updates vary by loading method: Imported data uses refresh options, while DirectQuery/LiveConnect relies on cache.GoodData has a refresh button for updating source data, with a focus on cache refresh. An automated notification process helps clear old cache data and load the new.Metabase automatically updates data. You can import files for ad hoc analysis and connect dashboards to hosted databases for regular syncing. It has caching abilities too.
- -# In-Depth Comparison - -## 1. Data Types - -When designing databases, or even coding in languages that require the “type” of a variable to be declared, we think of data types like `int`, `float`, `double`, `char`, `varchar`, `string` etc. The story becomes slightly different within dashboarding tools. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-data-types-power-bi.png) - -
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- -### Power BI - -The column types as declared in Power BI in the first image here show that instead of saying double or int, it says Decimal and Whole number. We also have options for visualisation formats such as percentage or different datetime notations. It also has a binary type which is supported in the editor to enable conversion to friendlier types for the end user. -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/data-types-gooddata.png) - -
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- -### GoodData - -While there is a wide range of [data types supported](https://www.gooddata.com/docs/cloud/model-data/prepare-your-data/) in the GoodData pipeline, they are mostly semantic, so relating to their usage not form. It takes all numeric type columns and sets them as facts, the date type columns and creates another table from them, and all text or character based columns and sets them as attributes. This also helps the tool in splitting the columns up into tables in a dimensional model - which will be discussed further in the inter-table relationships section. -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/data-types-metabase.png) - -
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- -### Metabase - -Interestingly, in Metabase, the data type is defined as it exists in the source, like an integer or string. But, the “field type” isn’t that straightforward; these are not `int`, `float`, `varchar`, or even `percentage` that we are used to when declaring dashboard columns, but types that are recognizable to any user. These are semantic types, rather than data types. For example, if a column contains numeric data, the categories available to select are Quantity, Price, Cost, Score, etc. - -
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- -## 2. Data Dictionaries -In order for an end user to use data, they need to have data literacy. That is the ability to understand what the data they look at actually represents. To enable that, having a data dictionary is a first step. This includes column definitions and the ability to manipulate them, which can be a basic requirement for any dashboard creator. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-data-dictionaries-pbi.png) - -
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- -### Power BI - -It allows users to edit column level properties on both its main dashboard and on the “Transform Data” window that shows up on the “Model View” tab. This allows you to select the data type of the column, to edit the name, format, and other sorting and aggregation functions you might want to apply to the column. However, this does not have the “data dictionary document” view that one might look for, as one has to click on each column to see its properties. In order to see the proper “data dictionary” document, it can be extracted through Power BI’s [performance analyzer](https://aginic.com/blog/building-a-data-dictionary-for-powerbi/). - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-data-dictionaries-gooddata.png) - -
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- -### GoodData - -In GoodData Cloud, they increase the level of simplicity to read a data dictionary, and it has only a subset of options presented in the other two tools. The column level properties entail converting the field to a fact or label, or moving the field to another table. It is the only tool here that shows the actual column name and mapping for each column in the logical model as it maps to the data source. This helps us understand which fact and label is matched to which database field in the source data, and how it was perceived under the naming convention in the source. This convention will be discussed more under table relationships. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-data-dictionaries-metabase.png) - -
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- -### Metabase - -Metabase allows users to view the data dictionary for all tables in the admin panel. This includes setting column properties as well as field settings to be adopted into analytics flows. There are also other aspects to view and change column properties. The first is that after using the little book icon that says “Learn about this table”, we are taken to some documentation that would be available on what that table is (if it was filled in before). After which, we can click on the “Fields in this table” category and that is where the field type of columns can be updated. The second place one we can change the field type is in the meta data of “Questions” or “Models” created. These can be excerpts of data with particular elements of different tables in the selected database. Lastly, Metabase is also the only tool among all, that has the ability to add column level descriptions - that is an amazing level of **documentation** that one can have available. - -
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- - -## 3. Table Properties & Descriptions - -For an analyst, navigating extensive databases within dashboards can be a challenging endeavor. Ideally, one should be able to discern the purpose of each table by its name alone. While this might be feasible for analysts who were involved in creating and configuring the database, it can be quite perplexing for newcomers to the organization. In such cases, comprehensive documentation becomes an invaluable resource, aiding them in their data exploration journey. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-table-prop-pbi.png) - -
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- -### Power BI - -All tools show table level descriptions in some shape or form. Power BI shows table descriptions right under the “Model View” tab, this can be used as a means for table level documentation. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-table-prop-gooddata.png) - -
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- -### GoodData - -GoodData on the other hand shows it in the “Data” tab, under “More” > “View” details option on each table. This does not show a documentation level of description for each table as the other two tools. But includes the data source mapping as discussed in the column details section. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-table-prop-metabase.png) - -
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- -### Metabase - -Metabase shows descriptions and ways to add them in the “Learn about this table” option on each table name, then takes it one step further and adds more information by asking “what makes this table interesting” and “things to be aware of”. - -
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- -## 4. Inter Table Relationships - -In order to create metrics and visuals that involve data from multiple tables and/or datasets, each dashboarding tool needs to be able to detect or define relationships if they exist. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-table-rel-pbi.png) - -
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- -### Power BI - -Power BI has one of the most popular setups for data modelling, all contained within its Model View. It has the ability to both auto-detect relationships and the functionality to define them inside the tool in a very easy, drag and drop method. The cardinality for relationships is mostly detected itself even if the relationship is defined, but can also be edited. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-table-rel-gooddata.png) - -
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- -### GoodData - -As for GoodData, the logical modelling layer is quite different than the other two. As discussed in the data types section, and shown in the image, the date type fields are taken and defined as separate tables (or datasets). The reason for doing so is in the spirit of creating a star schema; where one date table serves every table that requires a date dimension. GoodData takes into consideration the star and snowflake schemas as it splits all fields up into facts, labels and attributes. GoodData requires that fields be named according to a particular convention in the source to be recognized as keys automatically. However, it is also possible to assign primary and foreign keys by drag and drop methods. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-table-rel-metabase.png) - -
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- -### Metabase - -For Metabase, a primary or foreign key can be stated as such in the metadata (or field type display/settings) of a table. This can be either be done globally through the admin panel, through field settings in the data dictionary as discussed above, or per visual within Models and questions, through joins. Which means that in order to create a visual out of two or more connected tables, they need to be defined in some sort of SQL Query or Model (if not already connected in the global metadata). There is no ERD level view of table relationships as defined in GoodData and PowerBI. - -
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- -## 5. Custom Query Language - -When all drag and drop methodologies for defining metrics just aren’t cutting it anymore, one craves SQL and must resort to code. However, different dashboarding tools have different custom query languages. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-language-pbi.png) - -
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- -### Power BI - -Power BI has two custom languages known to its developers. One of them is DAX - Data Analysis Expression, and the other is Power Query M - Microsoft Power Query. DAX helps to build formulas and easy-to-complex expressions for measures and fields. Power Query is a powerful import defining tool. This can include filtering through one data source while loading it, or combining multiple data sources to your own need. This sets itself apart from other custom query tools as it is helpful during data loading, as compared to metric creation for visuals. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-language-gooddata.png) - -
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- -### GoodData - -GoodData has its own query language called MAQL, or Multi Dimension Analytical Query Language. It is what is used to define metrics, expressions, functions, or other simple or statistical queries. It works on top of the logical data models defined, and hence is aware of the table relationships and dimensions. That is what sets is apart from SQL, which is for relational databases, while MAQL is designed to perform for multi-dimensional models. SQL can still be used to specify a [SQL dataset](https://www.gooddata.com/docs/cloud/model-data/create-logical-data-model/create-sql-datasets/) in scope of the logical data model. MAQL is then used on top of this stored query instead of a physical table. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-language-metabase.png) - -
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- -### Metabase - -Metabase sticks to the basics and uses everything SQL! It uses SQL to define custom models and expressions. This includes both writing code to create aggregations and metrics, and the interactive SQL form that they have created. The non-code SQL allows users to do everything one can with SQL, with very well thought-out frontend capabilities. The interwovenness of SQL can be seen when code creates visualizations, and vice versa! Meaning, the aggregations created directly on visualizations can be converted into SQL code - as shown in the image. - -
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- -## 6. Data granularity Management: Column Creation & Aggregation capabilities - -In foundational database courses, we learn the importance of normalization and how great it is to keep the integrity of your data. However, as we go deeper into normalization levels, the data may become redundant and that is a problem for dashboarding tools, because the data becomes unnecessarily heavy to load. Different tools provide different methods to overcome this problem. That can either look like reducing data granularity, creating custom fields or aggregating tables. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-granularity-pbi.png) - -
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- -### Power BI - -Power BI introduces the ability the create custom fields and columns where you might be able to truncate redundant data; like the granularity of time into chunks. On top of which, another table can be built, aggregated on the granularity level you require. This can go beyond chunks of time, into categorizations of any nature, which is a great level of customization that is available in Power BI; the power to make custom calculated fields in the Transform Data section of the tool. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-granularity-gooddata.png) - -
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- -### GoodData - -GoodData allows you to switch each dataset from being mapped to a physical table to being defined by a custom SQL query. This feature enables you to add custom-calculated fields, filter out specific data, or even join multiple tables as needed. You can then map the fields to the results of that SQL query. Additionally, GoodData helps manage granularity for datetime fields directly by a setting your own custom truncation to them. This can be done so easily by viewing the details on the datetime objects that are cast as a separate table/dataset by GoodData. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-granularity-metabase.png) - -
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- -### Metabase - -The same methodology can be followed in Metabase. Where it is easily possible to create Models with your own defined level of aggregation, as well as custom fields that you can introduce to the tables. Custom Fields are created using Custom Expressions in Metabase - which can be done through the query builder. - -
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- -## 7. Defining Local or Central Metrics - -One of the main responsibilities of BI experts is to track metrics, align them with the company’s expectations, flag them if they go over or under their expected magnitudes. This, according to some data professionals calls for [centrally defined definitions](https://www.metabase.com/community_posts/what-is-a-metrics-layer-and-how-your-company-can-benefit-from-it) that others can use and follow, rather than defining them on their own and possibly misleading analytics flows. The ability to predefine metrics, or aggregations in a dashboard are known as the key abilities of any dashboarding tool! Alongside the ability to simply define these metrics, let’s also explore the ability the define central definitions of metrics as well. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-metrics-pbi.png) - -
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- -### Power BI - -In Power BI, these metrics are known as Measures, and can be created from both the fields pane and the calculations view on the Home tab. Either the options given on the Fields pane can be directly utilized to create a metric on a visual, or DAX can be used to create a reusable aggregation as another field under a table. Additionally, the power BI service has a “Metrics Hub”, where users can create metrics and set the scope for which other users can use them. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-metrics-gooddata.png) - -
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- -### GoodData - -Involving its own query language, GoodData uses MAQL to create custom metrics that can be dragged on to the visuals in the “Analyze” tab easily. This functionality can be found under the Metrics tab, where all metrics can be created and managed. Since these metrics are saved, this can act as a **central** service to manage and use metrics too! - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-metrics-metabase.png) - -
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- -### Metabase - -In Metabase, the Summarize functionality serves the same function as aggregated metrics-creation. This can be found after you click on any table in a selected database. Furthermore the functionality for creation of custom metrics can be extended to an SQL query, Metabase Question or Model. Additionally, in the Metabase admin panel, one can create centrally defined metrics as well. These can be adopted into reports that anyone can create, as long as granted the right access! - -
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- -## 8. Data Refresh and Loading capabilities - -Whether a dashboard is being built for the first time, or is fully furnished but needs to be periodically updated, data loading capabilities of dashboards must be carefully considered for successful reporting. All three tools have very clear methods to add data and support various sources including custom json and csv loaders. How the data can be manipulated after that has been discussed in depth above. We lastly talk about updates. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-refresh-pbi.png) - -
- -
- -### Power BI - -Coming to data updates and refresh capabilities, it depends on how data was loaded onto Power BI. If the data has been imported, then the refresh button and scheduled refresh would work fine to update the dashboards. However, if the loading has been through DirectQuery or LiveConnect, then it does not make sense to add an additional refresh functionality as it does not apply. What does end up being needed is cache availability. Which is provided on Premium offers of the product. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-refresh-gooddata.png) - -
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- -### GoodData - -GoodData also has a clear refresh button and methodology to refresh sources in the tool. But, unlike Power BI, GoodData refreshes it’s cache as opposed to the entire database. The tool stores computed results and data used in visuals and dashboards in an internal cache. If data is to be refreshed, the cache needs to be refreshed. In this process, it is recommended by GoodData that an automated notification process be set up to clear up the cache from the old data, and load into the new one. - -
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- -![hard coded dashboard](https://storage.googleapis.com/dlt-blog-images/blog-modelling-refresh-metabase.png) - -
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- -### Metabase - -As established above, data need only be refreshed if it is stored. Metabase establishes a direct connection to the source, so it doesn’t need a refresh option. Unless the data is a file based import, then Metabase recommends that it be used for ad hoc analysis. As for periodic database syncing, one should rather connect their dashboards to a hosted database. To manage overly frequent refreshes and its impact on dashboards, Metabase offers a Result cache for dashboard charts and a Model cache for modelled data. - -
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diff --git a/docs/website/blog/2023-11-01-dlt-dagster.md b/docs/website/blog/2023-11-01-dlt-dagster.md deleted file mode 100644 index 687e8444c4..0000000000 --- a/docs/website/blog/2023-11-01-dlt-dagster.md +++ /dev/null @@ -1,421 +0,0 @@ ---- -slug: dlt-dagster -title: "Orchestrating unstructured data pipeline with Dagster and dlt." -image: https://d1ice69yfovmhk.cloudfront.net/images/dlt-dagster_overview.jpg -authors: - name: Zaeem Athar - title: Junior Data Engineer - url: https://github.com/zem360 - image_url: https://images.ctfassets.net/c4lg2g5jju60/5tZn4cCBIesUYid17g226X/a044d2d471ebd466db32f7868d5c0cc8/Zaeem.jpg?w=400&h=400&q=50&fm=webp -tags: [Dagster, dlt, Asset Factory, Unstructured Data] ---- -:::info -TL;DR: In this blog post, we'll build data piplines using [dlt](https://dlthub.com/) and orchestrate them using [Dagster](https://dagster.io/). -::: - -`dlt` is an open-source Python library that allows you to declaratively load messy data sources into well-structured tables or datasets, through automatic schema inference and evolution. It simplifies building data pipelines by providing functionality to support the entire extract and load process. - -It does so in a scalable way, enabling you to run it on both micro workers or in highly parallelized setups. `dlt` also offers robustness on extraction by providing state management for incremental extraction, drop-in requests replacement with retries, and many other helpers for common and uncommon extraction cases. - -To start with `dlt`, you can install it using pip: `pip install dlt`. Afterward, import `dlt` in your Python script and start building your data pipeline. There's no need to start any backends or containers. - -## Project Overview: - -In this example, we will ingest GitHub issue data from a repository and store the data in BigQuery. We will use `dlt` to create a data pipeline and orchestrate it using Dagster. - -Initially, we will start by creating a simple data pipeline using `dlt`. We will then orchestrate the pipeline using Dagster. Finally, we will add more features to this pipeline by using the dlt schema evolution and Dagster asset metadata to educate the users about their data pipeline. - -The project code is available on [GitHub](https://github.com/dlt-hub/dlt-dagster-demo/tree/main). - -![Project Overview](https://d1ice69yfovmhk.cloudfront.net/images/dlt-dagster_overview.jpg) - -As we will be ingesting data into BigQuery we first need to create service account credentials for BigQuery. You can find more info on setting up a service account in the `dlt` [docs](https://dlthub.com/docs/dlt-ecosystem/destinations/bigquery). - -Once we have the credentials we are ready to begin. Let’s first install Dagster and `dlt`. The below commands should install both. - -```sh -pip install dlt -pip install dagster dagster-webserver -``` - -## Simple dlt Pipeline: - -As a first step, we will create the GitHub issues pipeline using `dlt`. - -```sh -dlt init github_issues bigquery -``` - -This will generate a template for us to create a new pipeline. Under `.dlt/secrets.toml` add the service account credentials for BigQuery. Then in the `github_issues.py` delete the generated code and add the following: - -```py -@dlt.resource(write_disposition="append") -def github_issues_resource(api_secret_key=dlt.secrets.value): - owner = 'dlt-hub' - repo = 'dlt' - url = f"https://api.github.com/repos/{owner}/{repo}/issues" - headers = {"Accept": "application/vnd.github.raw+json"} - - while url: - response = requests.get(url, headers=headers) - response.raise_for_status() # raise exception if invalid response - issues = response.json() - yield issues - - if 'link' in response.headers: - if 'rel="next"' not in response.headers['link']: - break - - url = response.links['next']['url'] # fetch next page of stargazers - else: - break - time.sleep(2) # sleep for 2 seconds to respect rate limits - -if __name__ == "__main__": - # configure the pipeline with your destination details - pipeline = dlt.pipeline( - pipeline_name='github_issues', destination='bigquery', dataset_name='github_issues_data' - ) - - # run the pipeline with your parameters - load_info = pipeline.run(github_issues_resource()) - - #print the information on data that was loaded - print(load_info) -``` - -The above code creates a simple **github_issues** pipeline that gets the issues data from the defined repository and loads it into BigQuery. The `dlt.resources` yields the data while the `dlt.pipeline` normalizes the nested data and loads it into the defined destination. To read more about the technical details refer to the `dlt` [docs](https://dlthub.com/docs/intro). - -To run the pipeline execute the below commands: - -```sh -pip install -r requirements.txt -python github_issues.py -``` - -We now have a running pipeline and are ready to orchestrate it using Dagster. - -## Orchestrating using Dagster: - -We will need to adjust our pipeline a bit to orchestrate it using Dagster. - -### Step 1: Create a Dagster project - -- Create a new directory for your Dagster project and scaffold the basic structure: - -```sh -mkdir dagster_github_issues -cd dagster_github_issues -dagster project scaffold --name github-issues -``` - -This will generate the default files for Dagster that we will use as a starting point for our data pipeline. - -### Step 2: Set up the directory structure - -- Inside the `github-issues/github_issues` directory create the following folders: `assets`, `resources`, and `dlt`. - -```sh -. -├── README.md -├── github_issues -│ ├── __init__.py -│ ├── assets -│ │ ├── __init__.py -│ ├── dlt -│ │ ├── __init__.py -│ └── resources -│ ├── __init__.py -├── github_issues_tests -│ ├── __init__.py -│ └── test_assets.py -├── pyproject.toml -├── setup.cfg -└── setup.py -``` - -### Step 3: Add dlt Resources and environment variables - -- Copy the previously created **github_issues_resource** code into `dlt/__init__.py` under the `dlt` folder. Remove the `dlt.secrets.value` parameter, as we'll pass the credentials through a `.env` file. -- Create a `.env` file in the root directory. This is the directory where the `pyproject.toml` file exits. Copy the credentials into the `.env` and follow the correct naming convention. For more info on setting up the `.env` file have a look at the [docs](https://dlthub.com/docs/walkthroughs/add_credentials#reading-credentials-from-environment-variables). - -### Step 4: Add configurable resources and define the asset - -- Define a `DDltResource` class in `resources/__init__.py` as a Dagster configurable resource. This class allows you to reuse pipeline code inside an asset. - -```py -from dagster import ConfigurableResource -import dlt - -class DDltResource(ConfigurableResource): - pipeline_name: str - dataset_name: str - destination: str - - def create_pipeline(self, resource_data, table_name): - - # configure the pipeline with your destination details - pipeline = dlt.pipeline( - pipeline_name=self.pipeline_name, destination=self.destination, dataset_name=self.dataset_name - ) - - # run the pipeline with your parameters - load_info = pipeline.run(dlt_resource, table_name=table_name) - - return load_info -``` - -- Define the asset, `issues_pipeline`, in `assets/__init__.py`. This asset uses the configurable resource to create a dlt pipeline and ingests data into BigQuery. - -```py -from dagster import asset, get_dagster_logger -from ..resources import DDltResource -from ..dlt import github_issues_resource - -@asset -def issues_pipeline(pipeline: DDltResource): - - logger = get_dagster_logger() - results = pipeline.create_pipeline(github_issues_resource, table_name='github_issues') - logger.info(results) -``` - -The defined asset (**issues_pipeline**) takes as input the configurable resource (**DDltResource**). In the asset, we use the configurable resource to create a dlt pipeline by using an instance of the configurable resource (**DDltResource**) to call the `create_pipeline` function. The `dlt.resource` (**github_issues_resource**) is passed to the `create_pipeline` function. The `create_pipeline` function normalizes the data and ingests it into BigQuery. - -### Step 5: Handle Schema Evolution - -`dlt` provides the feature of schema evolution that monitors changes in the defined table schema. Suppose GitHub adds a new column or changes a datatype of a column this small change can break pipelines and transformations. The schema evolution feature works amazingly well with Dagster. - -- Add the schema evolution code to the asset to make our pipelines more resilient to changes. - -```py -from dagster import AssetExecutionContext -@asset -def issues_pipeline(context: AssetExecutionContext, pipeline: DDltResource): - ... - md_content="" - for package in result.load_packages: - for table_name, table in package.schema_update.items(): - for column_name, column in table["columns"].items(): - md_content= f"\tTable updated: {table_name}: Column changed: {column_name}: {column['data_type']}" - - # Attach the Markdown content as metadata to the asset - context.add_output_metadata(metadata={"Updates": MetadataValue.md(md_content)}) -``` - -### Step 6: Define Definitions - -- In the `__init.py__` under the **github_issues** folder add the definitions: - -```py -all_assets = load_assets_from_modules([assets]) -simple_pipeline = define_asset_job(name="simple_pipeline", selection= ['issues_pipeline']) - -defs = Definitions( - assets=all_assets, - jobs=[simple_pipeline], - resources={ - "pipeline": DDltResource( - pipeline_name = "github_issues", - dataset_name = "dagster_github_issues", - destination = "bigquery", - table_name= "github_issues" - ), - } -) -``` - -### Step 7: Run the Web Server and materialize the asset - -- In the root directory (**github-issues**) run the `dagster dev` command to run the web server and materialize the asset. - -![GitHub Asset](https://d1ice69yfovmhk.cloudfront.net/images/dlt-dagster_asset.png) - -### Step 8: View the populated Metadata and ingested data in BigQuery - -Once the asset has been successfully materialized go to the Assets tab from the top and select the **Issues_pipeline**. In the Metadata you can see the Tables, Columns, and Data Types that have been updated. In this case, the changes are related to internal dlt tables. - -Any subsequent changes in the GitHub issues schema can be tracked from the metadata. You can set up [Slack notifications](https://dlthub.com/docs/running-in-production/running#using-slack-to-send-messages) to be alerted to schema changes. - -![Meatadata loaded in Asset](https://d1ice69yfovmhk.cloudfront.net/images/dlt-dagster_metadata.png) - -Let's finally have a look in BigQuery to view the ingested data. - -![Data Loaded in Bigquery](https://d1ice69yfovmhk.cloudfront.net/images/dlt-dagster_bigquery_data.png) - -The **github_issues** is the parent table that contains the data from the root level of the JSON returned by the GitHub API. The subsequent table **github_issues_assignees** is a child table that was nested in the original JSON. `dlt` normalizes nested data by populating them in separate tables and creates relationships between the tables. To learn more about how `dlt` created these relationships refer to the [docs](https://dlthub.com/docs/general-usage/destination-tables#child-and-parent-tables). - -## Orchestrating verified dlt source using Dagster: - -`dlt` provides a list of verified sources that can be initialized to fast-track the pipeline-building process. You can find a list of sources provided in the `dlt` [docs](https://dlthub.com/docs/dlt-ecosystem/verified-sources/). - -One of the main strengths of `dlt` lies in its ability to extract, normalize, and ingest unstructured and semi-structured data from various sources. One of the most commonly used verified source is MongoDB. Let’s quickly look at how we can orchestrate MongoDB source using Dagster. - -### Step 1: Setting up a Dagster project - -- Start by creating a new Dagster project scaffold: - -```sh -dagster project scaffold --name mongodb-dlt -``` - -- Follow the steps mentioned earlier and create an `assets`, and `resources` directory under `mongodb-dlt/mongodb_dlt`. -- Initialize a `dlt` MongoDB pipeline in the same directory: - -```sh -dlt init mongodb bigquery -``` - -This will create a template with all the necessary logic implemented for extracting data from MongoDB. After running the command your directory structure should be as follows: - -```text -. -├── README.md -├── mongodb_dlt -│ ├── __init__.py -│ ├── assets -│ │ ├── __init__.py -│ │ └── assets.py -│ ├── mongodb -│ │ ├── README.md -│ │ ├── __init__.py -│ │ └── helpers.py -│ ├── mongodb_pipeline.py -│ ├── requirements.txt -│ └── resources -│ ├── __init__.py -├── mongodb_dlt_tests -│ ├── __init__.py -│ └── test_assets.py -├── pyproject.toml -├── setup.cfg -└── setup.py -``` - -### Step 2: Configuring MongoDB Atlas and Credentials - -For this example, we are using MongoDB Atlas. Set up the account for MongoDB Atlas and use the test [Movie Flix Dataset](https://www.mongodb.com/docs/atlas/sample-data/sample-mflix/). You can find detailed information on setting up the credentials in the MongoDB verified sources [documentation](https://dlthub.com/docs/dlt-ecosystem/verified-sources/mongodb). - -Next, create a `.env` file and add the BigQuery and MongoDB credentials to the file. The `.env` file should reside in the root directory. - - -### Step 3: Adding the DDltResource - - Create a `DltResouce` under the **resources** directory. Add the following code to the `__init__.py`: - -```py -from dagster import ConfigurableResource - -import dlt - -class DDltResource(ConfigurableResource): - pipeline_name: str - dataset_name: str - destination: str - - def load_collection(self, resource_data, database): - - # configure the pipeline with your destination details - pipeline = dlt.pipeline( - pipeline_name=f"{database}_{self.pipeline_name}", destination=self.destination, dataset_name=f"{self.dataset_name}_{database}" - ) - - load_info = pipeline.run(resource_data, write_disposition="replace") - - return load_info -``` - -### Step 4: Defining an Asset Factory - -The structure of data in MongoDB is such that under each database you will find multiple collections. When writing a data pipeline it is important to separate the data loading for each collection. - -Dagster provides the feature of `@multi_asset` declaration that will allow us to convert each collection under a database into a separate asset. This will make our pipeline easy to debug in case of failure and the collections independent of each other. - -In the `mongodb_pipeline.py` file, locate the `load_select_collection_hint_db` function. We will use this function to create the asset factory. - - In the `__init__.py` file under the **assets** directory, define the `dlt_asset_factory`: - -```py -from ..mongodb import mongodb -from ..resources import DDltResource - -import dlt -import os - -URL = os.getenv('SOURCES__MONGODB__CONNECTION__URL') - -DATABASE_COLLECTIONS = { - "sample_mflix": [ - "comments", - "embedded_movies", - ], -} - -def dlt_asset_factory(collection_list): - multi_assets = [] - - for db, collection_name in collection_list.items(): - @multi_asset( - name=db, - group_name=db, - outs={ - stream: AssetOut(key_prefix=[f'raw_{db}']) - for stream in collection_name} - - ) - def collections_asset(context: OpExecutionContext, pipeline: DDltResource): - - # Getting Data From MongoDB - data = mongodb(URL, db).with_resources(*collection_name) - - logger = get_dagster_logger() - results = pipeline.load_collection(data, db) - logger.info(results) - - return tuple([None for _ in context.selected_output_names]) - - multi_assets.append(collections_asset) - - return multi_assets - - -dlt_assets = dlt_asset_factory(DATABASE_COLLECTIONS) -``` - -### Step 5: Definitions and Running the Web Server - -Add the definitions in the `__init__.py` in the root directory: - -```py -from dagster import Definitions - -from .assets import dlt_assets -from .resources import DDltResource - -defs = Definitions( - assets=dlt_assets, - resources={ - "pipeline": DDltResource( - pipeline_name = "mongo", - dataset_name = "dagster_mongo", - destination = "bigquery" - ), - } -) -``` - -We can run the `dagster dev` command to start the web server. We can see that each collection is converted into a separate asset by Dagster. We can materialize our assets to ingest the data into BigQuery. - -![Asset Factory](https://d1ice69yfovmhk.cloudfront.net/images/dlt-dagster_asset_factory.png) - -The resulting data in BigQuery: - -![Data Ingestion in BigQuery from MongoDB](https://d1ice69yfovmhk.cloudfront.net/images/dlt-dagster_mongo_bigquery.png) - -## Conclusion: - -In this demo, we looked at how to orchestrate dlt pipelines using Dagster. We started off by creating a simple dlt pipeline and then converted the pipeline into an asset and resource before orchestrating. - -We also looked at how we can orchestrate dlt MongoDB verified sources using Dagster. We utilized the Dagster `@multi_asset` feature to create a `dlt_asset_factory` which converts each collection under a database to a separate asset allowing us to create more robust data pipelines. - -Both `dlt` and Dagster can be easily run on local machines. By combining the two we can build data pipelines at great speed and rigorously test them before shipping to production. \ No newline at end of file diff --git a/docs/website/blog/2023-11-08-solving-ingestion.md b/docs/website/blog/2023-11-08-solving-ingestion.md deleted file mode 100644 index 88f020d7f5..0000000000 --- a/docs/website/blog/2023-11-08-solving-ingestion.md +++ /dev/null @@ -1,125 +0,0 @@ ---- -slug: solving-data-ingestion-python -title: "Solving data ingestion for Python coders" -image: https://storage.googleapis.com/dlt-blog-images/blog-ingestion-etl-tools-users.png -authors: - name: Adrian Brudaru - title: Open source data engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [data ingestion, python sdk, ETL, python data pipelines, Open Source, Developer Tools] ---- - - - -In a recent [article](https://kestra.io/blogs/2023-10-11-why-ingestion-will-never-be-solved), Anna Geller, product manager at Kestra, highlighted why data ingestion will never be solved. In her article, she described the many obstacles around data ingestion, and detailed how various companies and open-source tools approached this problem. - -I’m Adrian, data builder. Before starting dlthub, I was building data warehouses and teams for startups and corporations. Since I was such a power-builder, I have been looking for many years into how this space could be solved. - -The conviction on which we started dlt is that, to solve the data ingestion problem, we need to identify the motivated problem solver and turbo charge them with the right tooling. - -# The current state of data ingestion: dependent on vendors or engineers. - -When building a data pipeline, we can start from scratch, or we can look for existing solutions. - -## How can we build an ingestion pipeline? - -- SaaS tools: We could use ready-made pipelines or use building blocks to configure a new API call. -- SDKs: We could ask a software developer to build a Singer or Airbyte source. Or we could learn object-oriented programming and the SDKs and become the software developer - but the latter is an unreasonable pathway for most. -- Custom pipelines: We could ask a data engineer to build custom pipelines. Unfortunately, everyone is building from scratch, so we usually end up reinventing the flat tire. Pipelines often break and have a high maintenance effort, bottlenecking the amount that can be built and maintained per data engineer. - -Besides the persona-tool fit, in the current tooling, there is a major trade-off between complexity. For example, SaaS tools or SaaS SDKs offer “building blocks” and leave little room for customizations. On the other hand, custom pipelines enable one to do anything they could want but come with a high burden of code, complexity, and maintenance. And classic SDKs are simply too difficult for the majority of data people. - -![etl_by_others.png](https://storage.googleapis.com/dlt-blog-images/blog-ingestion-etl-tools-users.png) - -# So how can we solve ingestion? - -Ask first, **who** should solve ingestion. Afterwards, we can look into the right tools. - -## The builder persona should be invested in solving the problem, not into preserving it. - -UI first? We already established that people dependent on a UI with building blocks are non-builders - they use what exists. They are part of the demand, not part of the solution. - -SDK first? Further, having a community of software engineers for which the only reason to maintain pipelines is financial incentives also doesn’t work. For example, Singer has a large community of agencies that will help - for a price. But the open-source sources are not maintained, PRs are not accepted, etc. It’s just another indirect vendor community for whom the problem is desired. - -The reasonable approach is to offer something to a **person who wants to use the data but also has some capability to do something about it, and willingness to make an effort.** So the problem has to be solved in code, and it logically follows that if we want the data person to use this without friction, it has to be Python. - -## So the existing tools are a dead end: What do custom pipeline builders do? - -Unfortunately, the industry has very little standardization, but we can note some patterns. - -### df.to_sql() was a great first step - -For the Python-first users, pandas df.to_sql() automated loading dataframes to SQL without having to worry about database-specific commands or APIs. - -Unfortunately, this way of loading is limited and not very robust. There is no support for merge/upsert loading or for advanced configuration like performance hints. The automatic typing might sometimes also lead to issues over time with incremental loading. - -Additionally, putting the data into a dataframe means loading it into memory, leading to limitations. So a data engineer considering how to create a boilerplate loading solution would not end up relying on this method because it would offer too little while taking away fine-grain control. - -So while this method works well for quick and dirty work, it doesn’t work so well in production. And for a data engineer, this method adds little while taking away a lot. The good news: we can all use it; The bad news: it’s not engineering-ready. - -### Inserting JSON directly is a common antipattern. However, many developers use it because it solves a real problem. - -Inserting JSON “as is” is a common antipattern in data loading. We do it because it’s a quick fix for compatibility issues between untyped semi-structured data and strongly typed databases. This enables us to just feed raw data to the analyst who can sort through it and clean it and curate it, which in turn enables the data team to not get bottlenecked at the data engineer. - -So, inserting JSON is not all bad. It solves some real problems, but it has some unpleasant side effects: - -- Without an explicit schema, you do not know if there are schema changes in the data. -- Without an explicit schema, you don’t know if your JSON extract path is unique. Many applications output inconsistent types, for example, a dictionary for a single record or a list of dicts for multiple records, causing JSON path inconsistencies. -- Without an explicit schema, data discovery and exploration are harder, requiring more effort. -- Reading a JSON record in a database usually scans the entire record, multiplying cost or degrading performance significantly. -- Without types, you might incorrectly guess and suffer from frequent maintenance or incorrect parsing. -- Dashboarding tools usually cannot handle nested data - but they often have options to model tabular data. - -### Boilerplate code vs one-offs - -Companies who have the capacity will generally create some kind of common, boilerplate methods that enable their team to re-use the same glue code. This has major advantages but also disadvantages: building something like this in-house is hard, and the result is often a major cause of frustration for the users. What we usually see implemented is a solution to a problem, but is usually immature to be a nice technology and far from being a good product that people can use. - -One-offs have their advantage: they are easy to create and can generally take a shortened path to loading data. However, as soon as you have more of them, you will want to have a single point of maintenance as above. - -# The solution: A pipeline-building dev tool for the Python layman - -Let’s let Drake recap for us: - -![what would drake do](https://storage.googleapis.com/dlt-blog-images/blog-what_would_drake_do.png) - -So what does our desired solution look like? - -- Usable by **any Python user** in **any Python environment**, like df.to_sql() -- **Automate difficult things:** Normalize JSON into relational tables automatically. Alert schema changes or contract violations. Add robustness, scaling. -- **Keep code low:** Declarative hints are better than imperative spaghetti. -- **Enable fine-grained control:** Builders should be enabled to control finer aspects such as performance, cost, compliance. -- **Community:** Builders should be enabled to share content that they create - -We formulated our [product principles](https://dlthub.com/product/) and went from there. - -And how far did we get? - -- dlt is usable by any Python user and has a very shallow learning curve. -- dlt runs where Python runs: Cloud functions, notebooks, etc. -- Automate difficult things: Dlt’s schema automations and extraction helpers do 80% of the pipeline work. -- Keep code low: by automating a large chunk and offering declarative configuration, dlt keeps code as short as it can be. -- Fine-grained control: Engineers with advanced requirements can easily fulfill them by using building blocks or custom code. -- Community: We have a sharing mechanism (add a source to dlt’s sources) but it’s too complex for the target audience. There is a trade-off between the quality of code and strictness of requirements which we will continue exploring. We are also considering how LLMs can be used to assist with code quality and pipeline generation in the future. - -What about automating the builder further? - -LLMs are changing the world. They are particularly well-suited at language tasks. Here, a library shines over any other tool - simple code like you would write with dlt can automatically be written by GPT. - -The same cannot be said for SDK code or UI tools: because they use abstractions like classes or configurations, they deviate much further from natural language, significantly increasing the complexity of using LLMs to generate for them. - -LLMs aside, technology is advancing faster than our ability to build better interfaces - and a UI builder has been for years an obsolete choice. With the advent of self-documenting APIs following OpenAPI standard, there is no more need for a human to use a UI to compose building blocks - the entire code can be generated even without LLM assistance ([demo of how we do it](https://www.loom.com/share/2806b873ba1c4e0ea382eb3b4fbaf808?sid=b9d6bcdc-35d0-4349-a564-1e852278ea55)). An LLM could then possibly improve it from there. And if the APIs do not follow the standard, the building blocks of a UI builder are even less useful, while an LLM could read the docs and brute-force solutions. - -# So, will data ingestion ever be a fully solved problem? **Yes, by you and us together.** - -In summary, data ingestion is a complex challenge that has seen various attempts at solutions, from SDKs to custom pipelines. The landscape is marked by trade-offs, with existing tools often lacking the perfect balance between simplicity and flexibility. - -dlt, as a pipeline-building dev tool designed for Python users, aims to bridge this gap by offering an approachable, yet powerful solution. It enables users to automate complex tasks, keep their code concise, and maintain fine-grained control over their data pipelines. The community aspect is also a crucial part of the dlt vision, allowing builders to share their content and insights. - -The journey toward solving data ingestion challenges is not just possible; it's promising, and it's one that data professionals together with dlt are uniquely equipped to undertake. - -### Resources: - -- [Join the ⭐Slack Community⭐ for discussion and help!](https://dlthub.com/community) -- Dive into our [Getting Started](https://dlthub.com/docs/getting-started). -- Star us on [GitHub](https://github.com/dlt-hub/dlt)! diff --git a/docs/website/blog/2023-11-22-dlt-webhooks-event-based-ingestion.md b/docs/website/blog/2023-11-22-dlt-webhooks-event-based-ingestion.md deleted file mode 100644 index 94fb89790e..0000000000 --- a/docs/website/blog/2023-11-22-dlt-webhooks-event-based-ingestion.md +++ /dev/null @@ -1,268 +0,0 @@ ---- -slug: dlt-webhooks-on-cloud-functions-for-event-capture -title: "Deploy Google Cloud Functions as webhooks to capture event-based data from GitHub, Slack, or Hubspot" -image: https://dlt-static.s3.eu-central-1.amazonaws.com/images/webhook_blog_image.jpeg -authors: - name: Aman Gupta - title: Junior Data Engineer - url: https://github.com/dat-a-man - image_url: https://dlt-static.s3.eu-central-1.amazonaws.com/images/aman.png -tags: [data ingestion, python sdk, ETL, python data pipelines, Open Source, Developer Tools, Streaming] ---- - -💡 This article explores methods for monitoring transactional events, allowing immediate action and data capture -that might be lost otherwise. We focus on Github, Slack, and Hubspot, demonstrating techniques applicable to -low-volume transactional events (under 500k/month) within the free tier. For clickstream tracking or higher -volumes, we recommend more scalable solutions. - -There’s more than one way to sync data. Pulling data after it has been collected from APIs is a -classic way, but some types of data are better transmitted as an event at the time of happening. Our -approach is event-triggered and can include actions like: - -| Application | Action | -|-------------|---------------------------------------| -| Slack | Sending messages in Slack | -| Github | Commit, comment, or PR actions | -| Hubspot | Object creation or meeting specific criteria | - - -These actions initiate a webhook that sends a POST request to trigger a DLT pipeline for event -ingestion. The data is then loaded into BigQuery. - -![pictorial_demonstration](https://dlt-static.s3.eu-central-1.amazonaws.com/images/webhook_blog_image.jpeg) - -This setup enables real-time alerts or event storage for later use. For example, let’s say you want -to alert every time something happens - you’d want to be able to capture an event being sent to you -and act on it. Or, in some cases, you store it for later use. This guide covers a use case for -deploying and setting up webhooks. - -### Why do we use webhooks? - -Whenever we want to receive an event from an external source, we need a “recipient address” to which -they can send the data. To solve this problem, an effortless way is to use a URL as the address and -accept a payload as data. - -### Why cloud functions? - -The key reasons for using cloud functions include: - -1. To have a ***URL up and accept the data payload***, we would need some service or API always to be - up and ready to listen for the data. - -1. Creating our application for this would be cumbersome and expensive. It makes sense to use some - serverless service for low volumes of events. - -1. On AWS, you would use API gateway + lambda to handle incoming events, but for GCP users, the - option is more straightforward: Google Cloud functions come with an HTTP trigger, which enables - you to ***create a URL and accept a payload.*** - -1. The pricing for cloud functions is unbeatable for low volumes: For ingesting an event with a minor - function, assuming processing time to be a few seconds, we could invoke a few hundred thousand - calls every month for free. For more pricing details, see the - [GCP pricing page for cloud functions.](https://cloud.google.com/functions/pricing) - -Let's dive into the deployment of webhooks and app setup, focusing next on triggers from GitHub, -Slack, and HubSpot for use cases discussed above. - -## 1. GitHub Webhook - -This GitHub webhook is triggered upon specified events such as pull requests (PRs), commits, or -comments. It relays relevant data to BigQuery. Set up the GitHub webhook by creating the cloud -function URL and configuring it in the GitHub repository settings. - -### 1.1 Initialize GitHub webhook deployment - -To set up the webhook, start by creating a cloud function. Follow these brief steps, and for an -in-depth guide, please refer to the detailed documentation. - -1. Log into GCP and activate the Cloud Functions API. -1. Click 'Create Function' in Cloud Functions, and select your region and environment setup. -1. Choose HTTP as the trigger, enable 'Allow unauthenticated invocations', save, and click 'Next'. -1. Set the environment to Python 3.10 and prepare to insert code into main.py: - ```py - import dlt - import time - from google.cloud import bigquery - from dlt.common import json - - def github_webhook(request): - # Extract relevant data from the request payload - data = request.get_json() - - Event = [data] - - pipeline = dlt.pipeline( - pipeline_name='platform_to_bigquery', - destination='bigquery', - dataset_name='github_data', - ) - - pipeline.run(Event, table_name='webhook') #table_name can be customized - return 'Event received and processed successfully.' - ``` -1. Name the function entry point "github_webhook" and list required modules in requirements.txt. - ```text - # requirements.txt - dlt[bigquery] - ``` -1. Post-deployment, a webhook URL is generated, typically following a specific format. - ```sh - https://{region]-{project-id}.cloudfunctions.net/{cloud-function-name} - ``` - -Once the cloud function is configured, it provides a URL for GitHub webhooks to send POST requests, -funneling data directly into BigQuery. - -### 1.2 Configure the repository webhook in GitHub - -Set up a GitHub repository webhook to trigger the cloud function on specified events by following -these steps: - -1. Log into GitHub and go to your repository. -1. Click "Settings" > "Webhooks" > "Add webhook." -1. Enter the cloud function URL in "Payload URL." -1. Choose "Content-Type" and select events to trigger the webhook, or select "Just send me - everything." -1. Click "Add webhook." - -With these steps complete, any chosen events in the repository will push data to BigQuery, ready for -analysis. - -## 2. Slack Webhook - -This Slack webhook fires when a user sends a message in a channel where the Slack app is installed. -To set it up, set up a cloud function as below and obtain the URL, then configure the message events -in Slack App settings. - -### 2.1 Initialize Slack webhook deployment - -Set up the webhook by creating a cloud function, using the same steps as for the [GitHub webhook.](#1-github-webhook) - - -1. Here’s what `main.py` looks like: - ```py - import dlt - from flask import jsonify - - def slack_webhook(request): - # Handles webhook POST requests - if request.method == 'POST': - data = request.get_json() - - # Responds to Slack's verification challenge - if 'challenge' in data: - return jsonify({'challenge': data['challenge']}) - - # Processes a message event - if 'event' in data and 'channel' in data['event']: - message_data = process_webhook_event(data['event']) - - # Configures and initiates a DLT pipeline - pipeline = dlt.pipeline( - pipeline_name='platform_to_bigquery', - destination='bigquery', - dataset_name='slack_data', - ) - - # Runs the pipeline with the processed event data - pipeline.run([message_data], table_name='webhook') - return 'Event processed.' - else: - return 'Event type not supported', 400 - else: - return 'Only POST requests are accepted', 405 - - def process_webhook_event(event_data): - # Formats the event data for the DLT pipeline - message_data = { - 'channel': event_data.get('channel'), - 'user': event_data.get('user'), - 'text': event_data.get('text'), - 'ts': event_data.get('ts'), - # Potentially add more fields according to event_data structure - } - return message_data - ``` -1. Name the entry point "slack_webhook" and include the necessary modules in **`requirements.txt`**, - the same as the GitHub webhook setup. -1. Once the cloud function is configured, you get a URL for Slack events to send POST requests, - funneling data directly into BigQuery. - -### 2.2 Set up and configure a Slack app - -Create and install a Slack app in your workspace to link channel messages from Slack to BigQuery as -follows: - -1. Go to "Manage apps" in workspace settings; click "Build" and "Create New App". -1. Choose "from scratch", name the app, select the workspace, and create the app. -1. Under "Features", select "Event Subscription", enable it, and input the Cloud Function URL. -1. Add `message.channels` under "Subscribe to bot events". -1. Save and integrate the app to the desired channel. - -With these steps complete, any message sent on the channel will push data to BigQuery, ready for -analysis. - -## 3. Hubspot webhook - -A Hubspot webhook can be configured within an automation workflow, applicable to contacts, -companies, deals, tickets, quotes, conversations, feedback submissions, goals and invoices. It -triggers upon specific conditions or data filters. To establish it, create a cloud function, -retrieve its URL, and input this in Hubspot's automation workflow settings for message events. - -### 3.1 Initialize Hubspot webhook deployment - -Set up the webhook by creating a cloud function, using the same steps as for the [GitHub webhook.](#1-github-webhook) - - -1. Here’s what `main.py`looks like: - ```py - import dlt - from flask import jsonify - - def hubspot_webhook(request): - # Endpoint for handling webhook POST requests from Hubspot - if request.method == 'POST': - # Get JSON data from the POST request - data = request.get_json() - - # Initialize and configure the DLT pipeline - pipeline = dlt.pipeline( - pipeline_name="hubspot", - destination='bigquery', # Destination service for the data - dataset_name='hubspot_webhooks_dataset', # BigQuery dataset name - ) - - # Execute the pipeline with the incoming data - pipeline.run([data], table_name='hubspot_contact_events') - - # Return a success response - return jsonify(message='HubSpot event processed.'), 200 - else: - # Return an error response for non-POST requests - return jsonify(error='Only POST requests are accepted'), 405 - - ``` -1. Name the entry point "your_webhook" and include the necessary modules in **`requirements.txt`**, - the same as the GitHub webhook setup. -1. Once the cloud function is configured, you get a URL for Slack events to send POST requests, - funneling data directly into BigQuery. - -### 3.2 Configure a Hubspot automation workflow - -To activate a Hubspot workflow with your webhook: - -1. Go to Hubspot: "Automation" > "Workflows" > "Create workflow". -1. Start from scratch; choose "Company-based" for this example. -1. Set "Object created" as the trigger. -1. Add the "Send a webhook" action, use the "POST" method, and input your webhook URL. -1. Select the company properties to include, test, and save. - -This triggers the webhook upon new company creation, sending data to Bigquery via DLT. - -### In conclusion - -Setting up a webhook is straightforward. - -Using dlt with schema evolution, we can accept the events without worrying about their schema. -However, for events with custom schemas or vulnerable to bad data quality or abuse, consider using -dlt’s data contracts. diff --git a/docs/website/blog/2023-11-27-dlt-data-lineage.md b/docs/website/blog/2023-11-27-dlt-data-lineage.md deleted file mode 100644 index d91659eb6b..0000000000 --- a/docs/website/blog/2023-11-27-dlt-data-lineage.md +++ /dev/null @@ -1,302 +0,0 @@ ---- -slug: dlt-data-lineage -title: "Data Lineage using dlt and dbt." -image: https://d1ice69yfovmhk.cloudfront.net/images/data_lineage_overview.jpeg -authors: - name: Zaeem Athar - title: Junior Data Engineer - url: https://github.com/zem360 - image_url: https://images.ctfassets.net/c4lg2g5jju60/5tZn4cCBIesUYid17g226X/a044d2d471ebd466db32f7868d5c0cc8/Zaeem.jpg?w=400&h=400&q=50&fm=webp -tags: [dlt, dbt, Data Lineage] ---- -:::info -TL;DR: In this blog, we'll create a data lineage view for our ingested data by utlizing the `dlt` **load_info**. -::: - -## Why data lineage is important? - -Data lineage is an important tool in an arsenal of a data engineer. It showcases the journey of data from its source to its destination. It captures all the pitstops made and can help identify issues in the data pipelines by offering a birds eye view of the data. - -As data engineers, data lineage enables us to trace and troubleshoot the datapoints we offer to our stakeholders. It is also an important tool that can be used to meet regulation regarding privacy. Moreover, it can help us evaluate how any changes upstream in a pipeline effects the downstream source. There are many types of data lineage, the most commonly used types are the following: - -- Table lineage, it shows the raw data sources that are used to form a new table. It tracks the flow of data, showing how data moves forward through various processes and transformations. -- Row lineage reveals the data flow at a more granular level. It refers to tracking and understanding of individual rows of data as they move through various stages in a data processing pipeline. It is a subset of table lineage that focuses specifically on the journey of individual records or rows rather than the entire dataset. -- Column lineage specifically focuses on tracking and documenting the flow and transformation of individual columns or fields within different tables and views in the data. - - -## Project Overview - -In this demo, we showcase how you can leverage the `dlt` pipeline **[load_info](https://dlthub.com/docs/running-in-production/running#inspect-and-save-the-load-info-and-trace)** to create table, row and column lineage for your data. The code for the demo is available on [GitHub](https://github.com/dlt-hub/demo-data-lineage). - -The `dlt` **load_info** encapsulates useful information pertaining the loaded data. It contains the pipeline, dataset name, the destination information and list of loaded packages among other elements. Within the **load_info** packages, you will find a list of all tables and columns created at the destination during loading of the data. It can be used to display all the schema changes that occur during data ingestion and implement data lineage. - -We will work with the example of a skate shop that runs an online shop using Shopify, in addition to its physical stores. The data from both sources is extracted using `dlt` and loaded into BigQuery. - -![Data Lineage Overview](https://d1ice69yfovmhk.cloudfront.net/images/data_lineage_overview.jpeg) - -In order to run analytics workloads, we will create a transformed **fact_sales** table using dbt and the extracted raw data. The **fact_sales** table can be used to answer all the sales related queries for the business. - -The **load_info** produced by `dlt` for both pipelines is also populated into BigQuery. We will use this information to create a Dashboard in Metabase that shows the data lineage for the **fact_sales** table. - -## Implementing Data Lineage - -To get started install `dlt` and dbt: - -```sh -pip install dlt -pip install dbt-bigquery -``` - -As we will be ingesting data into BigQuery, we first need to create service account credentials for BigQuery. You can find more info on setting up a service account in the `dlt` [docs](https://dlthub.com/docs/dlt-ecosystem/destinations/bigquery). - -We use the following CSV files as our data sources for this demo: -- Test Shopify data -- [Kaggle Supermarket](https://www.kaggle.com/datasets/aungpyaeap/supermarket-sales) dataset. - -`dlt` provides [verified Shopify source](https://dlthub.com/docs/dlt-ecosystem/verified-sources/shopify) to directly extract data from the Shopify API. - -### Step 1: Initialize a dlt pipeline - -To get started we initialize a dlt pipeline and selecting BigQuery as our destination by running the following command: - -```sh -dlt init data_lineage bigquery -``` - -This will create default scaffolding to build our pipeline. Install the dependencies by running the following command: - -```sh -pip install -r requirements.txt -``` - -## Loading the data -As a first step, we will load the sales data from the online and physical store of the skate shop into BigQuery. In addition to the sales data, we will also ingest the dlt **load_info** into BigQuery. This will help us track changes in our pipeline. - -### Step 2: Adding the dlt pipeline code - -In the `data_lineage.py` file remove the default code and add the following: - -```py -FILEPATH = "data/supermarket_sales.csv" -FILEPATH_SHOPIFY = "data/orders_export_1.csv" - -class Data_Pipeline: - def __init__(self, pipeline_name, destination, dataset_name): - self.pipeline_name = pipeline_name - self.destination = destination - self.dataset_name = dataset_name - - def run_pipeline(self, data, table_name, write_disposition): - # Configure the pipeline with your destination details - pipeline = dlt.pipeline( - pipeline_name=self.pipeline_name, - destination=self.destination, - dataset_name=self.dataset_name - ) - # Run the pipeline with the provided data - load_info = pipeline.run( - data, - table_name=table_name, - write_disposition=write_disposition - ) - - # Pretty print the information on data that was loaded - print(load_info) - return load_info -``` - -Any changes in the underlying data are captured by the dlt **load_info**. To showcase this, we will filter the data to remove the **Branch** and **Tags** columns from Store and Shopify data respectively and run the pipeline. Later we will add back the columns and rerun the pipeline. These new columns added will be recorded in the **load_info** packages. - -We will add the **load_info** back to BigQuery to use in our Dashboard. The Dashboard will provide an overview data lineage for our ingested data. - -```py -if __name__ == "__main__": - - data_store = pd.read_csv(FILEPATH) - data_shopify = pd.read_csv(FILEPATH_SHOPIFY) - - #filtering some data. - select_c_data_store = data_store.loc[ - :, data_store.columns.difference(['Branch']) - ] - select_c_data_shopify = data_shopify.loc[ - :, data_shopify.columns.difference(['Tags']) - ] - - pipeline_store = Data_Pipeline( - pipeline_name='pipeline_store', - destination='bigquery', - dataset_name='sales_store' - ) - pipeline_shopify = Data_Pipeline( - pipeline_name='pipeline_shopify', - destination='bigquery', - dataset_name='sales_shopify' - ) - - load_a = pipeline_store.run_pipeline( - data=select_c_data_store, - table_name='sales_info', - write_disposition='replace' - ) - load_b = pipeline_shopify.run_pipeline( - data=select_c_data_shopify, - table_name='sales_info', - write_disposition='replace' - ) - - pipeline_store.run_pipeline( - data=load_a.load_packages, - table_name="load_info", - write_disposition="append" - ) - pipeline_shopify.run_pipeline( - data=load_b.load_packages, - table_name='load_info', - write_disposition="append" - ) -``` - -### Step 3: Run the dlt pipeline - -To run the pipeline, execute the following command: - -```sh -python data_lineage.py -``` - -This will load the data into BigQuery. We now need to remove the column filters from the code and rerun the pipeline. This will add the filtered columns to the tables in BigQuery. The change will be captured by `dlt`. - -## Data Transformation and Lineage - -Now that both the Shopify and Store data are available in BigQuery, we will use **dbt** to transform the data. - -### Step 4: Initialize a dbt project and define model - -To get started initialize a dbt project in the root directory: - -```sh -dbt init sales_dbt -``` - -Next, in the `sales_dbt/models` we define the dbt models. The first model will be the `fact_sales.sql`. The skate shop has two data sources: the online Shopify source and the physical Store source. We need to combine the data from both sources to create a unified reporting feed. The **fact_sales** table will be our unified source. - -Code for `fact_sales.sql`: - -```sql -{{ config(materialized='table') }} - -select - invoice_id, - city, - unit_price, - quantity, - total, - date, - payment, - info._dlt_id, - info._dlt_load_id, - loads.schema_name, - loads.inserted_at -from {{source('store', 'sales_info')}} as info -left join {{source('store', '_dlt_loads')}} as loads -on info._dlt_load_id = loads.load_id - -union all - -select - name as invoice_id, - billing_city, - lineitem_price, - lineitem_quantity, - total, - created_at, - payment_method, - info._dlt_id, - info._dlt_load_id, - loads.schema_name, - loads.inserted_at -from {{source('shopify', 'sales_info')}} as info -left join {{source('shopify', '_dlt_loads')}} as loads -on info._dlt_load_id = loads.load_id -where financial_status = 'paid' -``` - -In the query, we join the sales information for each source with its dlt **load_info**. This will help us keep track of the number of rows added with each pipeline run. The `schema_name` identifies the source that populated the table and helps establish the table lineage. While the `_dlt_load_id` identifies the pipeline run that populated the each row and helps establish row level lineage. The sources are combined to create a **fact_sales** table by doing a union over both sources. - -Next, we define the `schema_change.sql` model to capture the changes in the table schema using a following query: - -```sql -{{ config(materialized='table') }} - -select * -from {{source('store', 'load_info__tables__columns')}} - -union all - -select * -from {{source('shopify', 'load_info__tables__columns')}} -``` - -In the query, we combine the **load_info** for both sources by doing a union over the sources. The resulting **schema_change** table contains records of the column changes that occur on each pipeline run. This will help us track the column lineage and will be used to create our Data Lineage Dashboard. - -### Step 5: Run the dbt package - -In the `data_lineage.py` add the code to run the dbt package using `dlt`. - -```py -pipeline_transform = dlt.pipeline( - pipeline_name='pipeline_transform', - destination='bigquery', - dataset_name='sales_transform' -) - -venv = Venv.restore_current() -here = os.path.dirname(os.path.realpath(__file__)) - -dbt = dlt.dbt.package( - pipeline_transform, - os.path.join(here, "sales_dbt/"), - venv=venv -) - -models = dbt.run_all() - -for m in models: - print( - f"Model {m.model_name} materialized in {m.time} - " - f"Status {m.status} and message {m.message}" - ) -``` - -Next, run the pipeline using the following command: - -```sh -python data_lineage.py -``` - -Once the pipeline is run, a new dataset called **sales_transform** will be created in BigQuery, which will contain the **fact_sales** and **schema_changes** tables that we defined in the dbt package. - -### Step 6: Visualising the lineage in Metabase - -To access the BigQuery data in Metabase, we need to connect BigQuery to Metabase. Follow the Metabase [docs](https://www.metabase.com/docs/latest/databases/connections/bigquery) to connect BigQuery to Metabase. - -Once BigQuery is connected with Metabase, use the SQL Editor to create the first table. The **Data Load Overview** table gives an overview of the dlt pipelines that populated the **fact_sales** table. It shows the pipeline names and the number of rows loaded into the **fact_sales** table by each pipeline. - -![Metabase Report](https://d1ice69yfovmhk.cloudfront.net/images/data_lineage_metabase_report.png) - -This can be used to track the rows loaded by each pipeline. An upper and lower threshold can be set, and when our pipelines add rows above or below the threshold, that can act as our canary in the coal mine. - -Next, we will visualize the **fact_sales** and the **schema_changes** as a table and add the `dlt_load_id` as a filter. The resulting Data Lineage Dashboard will give us an overview of the table, row and column level lineage for our data. - -![Data Lineage Dashboard](https://d1ice69yfovmhk.cloudfront.net/images/data_lineage_dashboard.gif) - -When we filter by the **dlt_load_id** the dashboard will filter for the specific pipeline run. In the **Fact Sales** table the column *schema_name* identifies the raw sources that populated the table (Table lineage). The table also shows only the rows that were added for the pipeline run (Row Lineage). Lastly, the **Updated Columns** table revels the columns that were added for filtered pipeline run (Column Lineage). - -When we ran the pipeline initially, we filtered out the **Tags** column and later reintroduced it and ran the pipeline again. The **Updated Columns** shows that the Tags column was added to the Fact Sales table with the new pipeline run. - -## Conclusion - -Data lineage provides an overview of the data journey from the source to destination. It is an important tool that can help troubleshoot a pipeline. dlt **load_info** provides an alternative solution to visualizing data lineage by tracking changes in the underlying data. - -Although `dlt` currently does not support data flow diagrams, it tracks changes in the data schema that can be used to create dashboards that provides an overview of table, row and column lineage for the loaded data. \ No newline at end of file diff --git a/docs/website/blog/2023-11-29-exploring-data-replication-of-sap-hana-to-snowflake-using-dlt.md b/docs/website/blog/2023-11-29-exploring-data-replication-of-sap-hana-to-snowflake-using-dlt.md deleted file mode 100644 index 2daca41161..0000000000 --- a/docs/website/blog/2023-11-29-exploring-data-replication-of-sap-hana-to-snowflake-using-dlt.md +++ /dev/null @@ -1,76 +0,0 @@ ---- -slug: sap-hana-to-snowflake-demo-blog -title: "Exploring data replication of SAP HANA to Snowflake using dlt" -image: https://dlt-static.s3.eu-central-1.amazonaws.com/images/sap_snowflake_blog_data_link_image.png -authors: - name: Rahul Joshi - title: Developer Relations at dltHub - url: https://github.com/rahuljo - image_url: https://avatars.githubusercontent.com/u/28861929?v=4 -tags: [SAP, SAP HANA, Snowflake, Cloud, ETL] ---- -:::info -**TL;DR: While most companies continue to build their businesses on top of SAP, when it comes to analytics, they prefer to take advantage of the price and elastic compute of modern cloud infrastructure. As a consequence, we get several dlt users asking for a simple and low-cost way to migrate from SAP to cloud data warehouses like Snowflake. In this blog, I show how you can build a custom SAP connector with dlt and use it to load SAP HANA tables into Snowflake.** -::: - -![Blog image](https://dlt-static.s3.eu-central-1.amazonaws.com/images/sap_snowflake_blog_data_link_image.png) - -In case you haven’t figured it out already, we at dltHub love creating blogs and demos. It’s fun, creative, and gives us a chance to play around with many new tools. We are able to do this mostly because, like any other modern tooling, dlt just *fits* in the modern ecosystem. Not only does dlt have existing [integrations](https://dlthub.com/docs/dlt-ecosystem) (to, for example, GCP, AWS, dbt, airflow etc.) that can simply be “plugged in”, but it is also very simple to customize it to integrate with almost any other modern tool (such as [Metabase](https://dlthub.com/docs/blog/postgresql-bigquery-metabase-demo), [Holistics](https://dlthub.com/docs/blog/MongoDB-dlt-Holistics), [Dagster](https://dlthub.com/docs/blog/dlt-dagster), [Prefect](https://dlthub.com/docs/blog/dlt-prefect) etc.). - -But what about enterprise systems like SAP? They are, after all, the most ubiquitous tooling out there: according to SAP [data](https://assets.cdn.sap.com/sapcom/docs/2017/04/4666ecdd-b67c-0010-82c7-eda71af511fa.pdf), 99 out of 100 largest companies are SAP customers. A huge part of the reason for this is that their ERP system is still the gold standard in terms of effectivity and reliability. However, when it comes to OLAP workloads like analytics, machine learning, predictive modelling etc., [many companies prefer the convenience and cost savings of modern cloud solutions](https://www.statista.com/statistics/967365/worldwide-cloud-infrastructure-services-market-share-vendor/) like GCP, AWS, Azure, etc.. - -So, wouldn’t it be nice to be able to integrate SAP into the modern ecosystem? - -Unfortunately, this is not that simple. SAP does not integrate easily with non-SAP systems, and migrating data out from SAP is complicated and/or costly. This often means that ERP data stays separate from analytics data. - -## Creating a dlt integration - -Our users have been asking for SAP HANA data, hence I decided to create a custom dlt integration to SAP’s in-memory data warehouse: SAP HANA. Given its SQL backend and [Python API](https://developers.sap.com/tutorials/hana-clients-python.html), I figured dlt should also have no problem connecting to it. - -I then use this pipeline to load SAP HANA tables into Snowflake, since Snowflake is cloud agnostic and can be run in different environments (such AWS, GCP, Azure, or any combination of the three). This is how I did it: - -**Step 1: I created an instance in [SAP HANA cloud](https://www.sap.com/products/technology-platform/hana.html).** - -(*I used [this helpful tutorial](https://www.youtube.com/watch?v=hEQCGBAn7Tc&list=PLkzo92owKnVwtyoQRRN2LsQlTHzNE-0US) to navigate SAP HANA.*) - -![SAP instance](https://dlt-static.s3.eu-central-1.amazonaws.com/images/sap_snowflake_blog_creating_sap_instance.png) - -**Step 2: I inserted some sample data.** -![SAP insert data](https://dlt-static.s3.eu-central-1.amazonaws.com/images/sap_snowflake_blog_inserting_data_in_sap.png) - -**Step 3: With tables created in SAP HANA, I was now ready to create a dlt pipeline to extract it into Snowflake:** - -Since SAP HANA has a SQL backend, I decided to extract the data using dlt’s [SQL source](https://dlthub.com/docs/dlt-ecosystem/verified-sources/sql_database) - -1. I first created a dlt pipeline - - `dlt init sql_database snowflake` - -2. I then passed the connection string for my HANA instance inside the loading function in `sql_database_pipeline.py`. (Optional: I also specified the tables that I wanted to load in `sql_database().with_resources("v_city", "v_hotel", "room")` ) -3. Before running the pipeline I installed all necessary requirements using - - `pip install -r requirements.txt` - - The dependencies inside `requirements.txt` are for the general SQL source. To extract data specifically from HANA, I also installed the packages `hdbcli` and `sqlalchemy-hana`. - - -**Step 4: I finally ran the pipeline using `python sql_database_pipeline.py`. This loaded the tables into Snowflake.** - -![Data in Snowflake](https://dlt-static.s3.eu-central-1.amazonaws.com/images/sap_snowflake_blog_data_loaded_into_snowflake.png) - -## Takeaway - -The dlt SAP HANA connector constructed in this demo works like any other dlt connector, and is able to successfully load data from SAP HANA into data warehouses like Snowflake. - -Furthermore, the demo only used a toy example, but the SQL source is a production-ready source with incremental loading, merges, data contracts etc., which means that this pipeline could also be configured for production use-cases. - -Finally, the dlt-SAP integration has bigger consequences: it allows you to add other tools like dbt, airflow etc. easily into an SAP workflow, since all of these tools integrate well with dlt. - -## Next steps - -This was a just first step into exploring what’s possible. Creating a custom dlt connector worked pretty well for SAP HANA, and there are several possible next steps, such as converting this to a verified source, or building other SAP connectors. - -1. **Creating a verified source for SAP HANA:** This should be pretty straight-forward since it would require a small modification of the existing SQL source. -2. **Creating a dlt connector for SAP S/4 HANA:** S/4 HANA is SAP’s ERP software that runs on the HANA database. The use case would be to load ERP tables from S/4 HANA into other data warehouses like Snowflake. Depending on the requirements, there are two ways to go about it: - 1. **Low volume data:** This would again be straight-forward. SAP offers [REST API end points](https://api.sap.com/products/SAPS4HANACloud/apis/ODATA) to access ERP tables, and dlt is designed to be able to load data from any such end point. - 2. **High volume data:** dlt can also be configured for the use case of migrating large volumes of data with fast incremental or merge syncs. But this would require some additional steps, such as configuring the pipeline to access HANA backend directly from Python hdbcli. diff --git a/docs/website/blog/2023-12-01-dlt-kestra-demo.md b/docs/website/blog/2023-12-01-dlt-kestra-demo.md deleted file mode 100644 index 1b1c79562d..0000000000 --- a/docs/website/blog/2023-12-01-dlt-kestra-demo.md +++ /dev/null @@ -1,170 +0,0 @@ ---- -slug: dlt-kestra-demo-blog -title: "From Inbox to Insights: AI-enhanced email analysis with dlt and Kestra" -image: https://storage.googleapis.com/dlt-blog-images/dlt_kestra_workflow_overview.png -authors: - name: Anuun Chinbat - title: Data Science Intern at dltHub - url: https://github.com/anuunchin - image_url: https://avatars.githubusercontent.com/u/88698977?s=96&v=4 -tags: [Kestra, Automation, dlt, Orchestration, Slack, BigQuery, OpenAI] ---- ---- -## THE PROBLEM - -There are two types of people: those who hoard thousands of unread emails in their inbox and those who open them immediately to avoid the ominous red notification. But one thing unites us all: everyone hates emails. The reasons are clear: - -- They're often unnecessarily wordy, making them time-consuming. -- SPAM (obviously). -- They become black holes of lost communication because CC/BCC-ing people doesn't always work. -- Sometimes, there are just too many. - -So, this post will explore a possible remedy to the whole email issue involving AI. - ---- - -## THE SOLUTION - -Don't worry; it's nothing overly complex, but it does involve some cool tools that everyone could benefit from. - ->💡 **In a nutshell**, I created two flows (a main flow and a subflow) in [Kestra](https://github.com/kestra-io/kestra) : ->- **The main flow** extracts email data from Gmail and loads it into BigQuery using **`dlt`**, checks for new emails, and, if found, triggers the subflow for further processing. ->- **The subflow** utilizes OpenAI to summarize and analyze the sentiment of an email, loads the results into BigQuery, and then notifies about the details via Slack. - - -Just so you're aware: - -- **[`Kestra`](https://github.com/kestra-io/kestra)** is an open-source automation tool that makes both scheduled and event-driven workflows easy. -- **[`dlt`](https://github.com/dlt-hub/dlt)** is an open-source library that you can add to your Python scripts to load data from various and often messy data sources into well-structured, live datasets. - -:::tip -Wanna jump to the [GitHub repo](https://github.com/dlt-hub/dlt-kestra-demo)? -::: - ---- - -## HOW IT WORKS - -To lay it all out clearly: Everything's automated in **`Kestra`**, with hassle-free data loading thanks to **`dlt`**, and the analytical thinking handled by OpenAI. Here's a diagram to help you understand the general outline of the entire process. - -![overview](https://storage.googleapis.com/dlt-blog-images/dlt_kestra_workflow_overview.png) - -Now, let's delve into specific parts of the implementation. - -### The environment: - ->💡 The two flows in Kestra are set up in a very straightforward and intuitive manner. Simply follow the Prerequisites and Setup guidelines in the [repo](https://github.com/dlt-hub/dlt-kestra-demo). It should take no more than 15 minutes. - -Once you’ve opened [http://localhost:8080/](http://localhost:8080/) in your browser, this is what you’ll see on your screen: - -![Kestra](https://storage.googleapis.com/dlt-blog-images/dlt_kestra_kestra_ui.png) - -Now, all you need to do is [create your flows](https://github.com/dlt-hub/dlt-kestra-demo/blob/main/README.md) and execute them. - -The great thing about **`Kestra`** is its ease of use - it's UI-based, declarative, and language-agnostic. Unless you're using a task like a [Python script](https://kestra.io/plugins/plugin-script-python/tasks/io.kestra.plugin.scripts.python.script), you don't even need to know how to code. - - -:::tip -If you're already considering ways to use **`Kestra`** for your projects, consult their [documentation](https://kestra.io/docs) and the [plugin](https://kestra.io/plugins) pages for further insights. -::: - -### The data loading part - -> 💡 This is entirely managed by **`dlt`** in just five lines of code. - -I set up a pipeline using the **[Inbox](https://dlthub.com/docs/dlt-ecosystem/verified-sources/inbox)** source – a regularly tested and verified source from **`dlt`** – with BigQuery as the destination. - -In my scenario, the email data doesn't have nested structures, so there's no need for flattening. However, if you encounter nested structures in a different use case, **`dlt`** can automatically normalize them during loading. - -Here's how the pipeline is defined and subsequently run in the first task of the main flow in **`Kestra`**: - -```py -# Run dlt pipeline to load email data from gmail to BigQuery -pipeline = dlt.pipeline( - pipeline_name="standard_inbox", - destination='bigquery', - dataset_name="messages_data", - dev_mode=False, -) - -# Set table name -table_name = "my_inbox" -# Get messages resource from the source -messages = inbox_source(start_date = pendulum.datetime(2023, 11, 15)).messages -# Configure the messages resource to get bodies of the emails -messages = messages(include_body=True).with_name(table_name) -# Load data to the "my_inbox" table -load_info = pipeline.run(messages) -``` - -In this setup ☝️, **`dlt`** loads all email data into the table “my_inbox”, with the email body specifically stored in the “body” column. After executing your flow in **`Kestra`**, the table in BigQuery should appear as shown below: - -![bigquery_my_inbox](https://storage.googleapis.com/dlt-blog-images/dlt_kestra_bigquery_my_inbox.png) - -:::tip -This implementation doesn't handle email attachments, but if you need to analyze, for instance, invoice PDFs from your inbox, you can read about how to automate this with **`dlt`** [here](https://dlthub.com/docs/blog/dlt-ops-startups). -::: - -### The AI part - ->💡 In this day and age, how can we not incorporate AI into everything? 😆 - -But seriously, if you're familiar with OpenAI, it's a matter of an API call to the chat completion endpoint. What simplifies it even further is `Kestra`’s [OpenAI plugin](https://kestra.io/plugins/plugin-openai). - -In my [subflow](https://github.com/dlt-hub/dlt-kestra-demo/blob/main/subflow.yml), I used it to obtain both the summary and sentiment analysis of each email body. Here's a glimpse of how it's implemented: - -```yaml -- id: get_summary - type: io.kestra.plugin.openai.ChatCompletion - apiKey: "{{ secret('OPENAI_API') }}" - model: gpt-3.5-turbo - prompt: "Summarize the email content in one sentence with less than 30 words: {{inputs.data[0]['body']}}" - messages: [{"role": "system", "content": "You are a tool that summarizes emails."}] -``` - -:::info -**`Kestra`** also includes Slack, as well as BigQuery plugins, which I used in my flows. Additionally, there is a wide variety of [other plugins](https://kestra.io/plugins) available. -::: - -### The automation part - ->💡 **`Kestra`** triggers are the ideal solution! - -I’ve used a [schedule trigger](https://kestra.io/docs/developer-guide/triggers) that allows you to execute your flow on a regular cadence e.g. using a CRON expression: - -```yaml -triggers: - - id: schedule - type: io.kestra.core.models.triggers.types.Schedule - cron: "0 9-18 * * 1-5" -``` - -This configuration ensures that your flows are executed hourly on workdays from 9 AM to 6 PM. - ---- - -## THE OUTCOME - -A Slack assistant that delivers crisp inbox insights right at your fingertips: - -![slack.png](https://storage.googleapis.com/dlt-blog-images/dlt_kestra_slack.png) - -And a well-organized table in BigQuery, ready for you to dive into a more complex analysis: - -![bigquery_test.png](https://storage.googleapis.com/dlt-blog-images/dlt_kestra_bigquery_test.png) - -In essence, using **`Kestra`** and **`dlt`** offers a trio of advantages for refining email analysis and data workflows: - -1. **Efficient automation**: **`Kestra`** effortlessly orchestrates intricate workflows, integrating smoothly with tools like **`dlt`**, OpenAI, and BigQuery. This process reduces manual intervention while eliminating errors, and freeing up more time for you. -2. **User-friendly and versatile**: Both **`Kestra`** and **`dlt`** are crafted for ease of use, accommodating a range of skill levels. Their adaptability extends to various use cases. -3. **Seamless scaling**: **`Kestra`**, powered by Kafka and Elasticsearch, adeptly manages large-scale data and complex workflows. Coupled with **`dlt`**'s solid data integration capabilities, it ensures a stable and reliable solution for diverse requirements. - ---- - -## HOW IT COULD WORK ELSEWHERE - -Basically, you can apply the architecture discussed in this post whenever you need to automate a business process! - -For detailed examples of how **`Kestra`** can be utilized in various business environments, you can explore [Kestra's use cases](https://kestra.io/use-cases). - -Embrace automation, where the only limit is your imagination! 😛 diff --git a/docs/website/blog/2023-12-13-dlt-aws-taktile-blog.md b/docs/website/blog/2023-12-13-dlt-aws-taktile-blog.md deleted file mode 100644 index 296d303dcb..0000000000 --- a/docs/website/blog/2023-12-13-dlt-aws-taktile-blog.md +++ /dev/null @@ -1,137 +0,0 @@ ---- -slug: dlt-aws-taktile-blog -title: "Why Taktile runs dlt on AWS Lambda to process millions of daily tracking events" -image: https://dlt-static.s3.eu-central-1.amazonaws.com/images/aws-taktile-blog-simon-meetup-image.jpg -authors: - name: Simon Bumm - title: Data and Analytics Lead at Taktile - url: https://github.com/codingcyclist - image_url: https://media.licdn.com/dms/image/C4E03AQHjlnKE9zCmXQ/profile-displayphoto-shrink_400_400/0/1650447289892?e=1707955200&v=beta&t=w9KR2GfXxjU4e3e2rL69wNr0ZwuD4YlPWDy1YOpjC2I -tags: [AWS, AWS Lambda, Serverless Compute, Taktile, Streaming] ---- -:::info -**TL;DR: Combining dlt and AWS Lambda creates a secure, scalable, lightweight, and powerful instrumentation engine that Taktile uses for its low-code, high-volume data processing platform. I explain why dlt and AWS Lambda work together so well and how to get everything set up in less than one hour. If you want to jump to the code right away, you can find the accompanying GitHub repo [here](https://github.com/codingcyclist/dlt-aws-lambda).** -::: - -An important aspect of being a data person today is being able to navigate and choose from among many tools when setting up your company’s infrastructure. (And there are *many* tools out there!). While there is no one-size-fits-all when it comes to the right tooling, choosing ones that are powerful, flexible, and easily compatible with other tools empowers you to tailor your setup to your specific use case. - -I am leading Data and Analytics at [Taktile](https://www.taktile.com/): a low-code platform used by global credit- and risk teams to design, build, and evaluate automated decision flows at scale. [It’s the leading decision intelligence platform for the financial service industry today](https://www.taktile.com/articles/cnbc-recognizes-taktile-as-one-of-the-world-s-top-fintechs). To run our business effectively, we need an instrumentation mechanism that can anonymize and load millions of events and user actions each day into our Snowflake Data Warehouse. Inside the Warehouse, business users will use the data to run product analytics, build financial reports, set up automations, etc. - -![Taktile Flow Chart](https://dlt-static.s3.eu-central-1.amazonaws.com/images/aws-taktile-blog-taktile-flow-chart.png) - -### Choosing the right instrumentation engine is non-trivial - -Setting up the infrastructure to instrument a secured, high-volume data processing platform like Taktile is complicated and there are essential considerations that need to be made: - -1. **Data security:** Each day, Taktile processes millions of high-stakes financial decisions for banks and Fintechs around the world. In such an environment, keeping sensitive data safe is crucial. Hence, Taktile only loads a subset of non-sensitive events into its warehouse and cannot rely on external vendors accessing decision data. -2. **Handling irregular traffic volumes:** Taktile’s platform is being used for both batch and real-time decision-making, which means that traffic spikes are common and hard to anticipate. Such irregular traffic mandates an instrumentation engine that can quickly scale out and guarantee timely event ingestion into the warehouse, even under high load. -3. **Maintenance:** a fast-growing company like Taktile needs to focus on its core product and on tools that don't create additional overhead. - -### dlt and AWS Lambda as the secure, scalable, and lightweight solution - -AWS Lambda is Amazon’s serverless compute service. dlt is a lightweight python ETL library that runs on any infrastructure. dlt fits neatly into the AWS Lambda paradigm, and by just adding a simple REST API and a few lines of python, it converts your Lambda function into a powerful and scalable event ingestion engine. - -- **Security:** Lambda functions and dlt run within the perimeter of your own AWS infrastructure, hence there are no dependencies on external vendors. -- **Scalability**: serverless compute services like AWS Lambda are great at handling traffic volatility through built-in horizontal scaling. -- **Maintenance:** not only does AWS Lambda take care of provisioning and managing servers, but inserting dlt into the mix, also adds production-ready capabilities such as: - - Automatic schema detection and evolution - - Automatic normalization of unstructured data - - Easy provisioning of staging destinations - -![Tools workflow](https://dlt-static.s3.eu-central-1.amazonaws.com/images/aws-taktile-blog-data-tools-workflow.png) - -### Get started with dlt on AWS Lambda using SAM (AWS Serverless Application Model) - -SAM is a lightweight Infrastructure-As-Code framework provided by AWS. Using SAM, you simply declare serverless resources like Lambda functions, API Gateways, etc. in a `template.yml` file and deploy them to your AWS account with a lightweight CLI. - -1. Install the SAM CLI [add link or command here] - - ```sh - pip install aws-sam-cli - ``` - -2. Define your resources in a `template.yml` file - - ```text - AWSTemplateFormatVersion: "2010-09-09" - Transform: AWS::Serverless-2016-10-31 - - Resources: - ApiGateway: - Type: AWS::Serverless::Api - Properties: - Name: DLT Api Gateway - StageName: v1 - DltFunction: - Type: AWS::Serverless::Function - Properties: - PackageType: Image - Timeout: 30 # default is 3 seconds, which is usually too little - MemorySize: 512 # default is 128mb, which is too little - Events: - HelloWorldApi: - Type: Api - Properties: - RestApiId: !Ref ApiGateway - Path: /collect - Method: POST - Environment: - Variables: - DLT_PROJECT_DIR: "/tmp" # the only writeable directory on a Lambda - DLT_DATA_DIR: "/tmp" # the only writeable directory on a Lambda - DLT_PIPELINE_DIR: "/tmp" # the only writeable directory on a Lambda - Policies: - - Statement: - - Sid: AllowDLTSecretAccess - Effect: Allow - Action: - - secretsmanager:GetSecretValue - Resource: !Sub "arn:aws:secretsmanager:${AWS::Region}:${AWS::AccountId}:secret:DLT_*" - Metadata: - DockerTag: dlt-aws - DockerContext: . - Dockerfile: Dockerfile - Outputs: - ApiGateway: - Description: "API Gateway endpoint URL for Staging stage for Hello World function" - Value: !Sub "https://${ApiGateway}.execute-api.${AWS::Region}.amazonaws.com/v1/collect/" - ``` - -3. Build a deployment package - - ```sh - sam build - ``` - -4. Test your setup locally - - ```sh - sam local start-api - - # in a second terminal window - curl -X POST http://127.0.0.1:3000/collect -d '{"hello":"world"}' - ``` - -5. Deploy your resources to AWS - - ```sh - sam deploy --stack-name= --resolve-image-repos --resolve-s3 --capabilities CAPABILITY_IAM - ``` - - -### Caveats to be aware of when setting up dlt on AWS Lambda: - -No worries, all caveats described below are already being taken care of in the sample repo: https://github.com/codingcyclist/dlt-aws-lambda. I still recommend you read through them to be aware of what’s going on. - -1. **Local files:** When running a pipeline, dlt usually stores a schema and other local files under your users’ home directory. On AWS Lambda, however, `/tmp` is the only directory into which files can be written. Simply tell dlt to use `/tmp` instead of the home directory by setting the `DLT_PROJECT_DIR`, `DLT_DATA_DIR`, `DLT_PIPELINE_DIR` environment variables to `/tmp`. -2. **Database Secrets:** dlt usually recommends providing database credentials via TOML files or environment variables. However, given that AWS Lambda does not support masking files or environment variables as secrets, I recommend you read database credentials from an external secret manager like AWS Secretsmanager (ASM). -3. **Large dependencies:** Usually, the code for a Lambda function gets uploaded as a `.zip` archive that cannot be larger than 250 MB in total (uncompressed). Given that dbt has a ~400 MB memory footprint (including Snowflake dependencies), the dlt Lambda function needs to be deployed as a Docker image, which can be up to 10 GB in size. - -### dlt and AWS Lambda are a great foundation for building a production-grade instrumentation engine - -dlt and AWS Lambda are a very powerful setup already. At Taktile, we still decided to add a few more components to our production setup to get even better resilience, scalability, and observability: - -1. **SQS message queue:** An SQS message queue between the API gateway and the Lambda function is useful for three reasons. First, the queue serves as an additional buffer for sudden traffic spikes. Events can just fill the queue until the Lambda function picks them up and loads them into the destination. Second, an SQS queue comes with built-in batching so that the whole setup becomes even more cost-efficient. A batch of events only gets dispatched to the Lambda function when it reaches a certain size or has already been waiting in the queue for a specific period. Third, there is a dead-letter queue attached to make sure no events get dropped, even if the Lambda function fails. Failed events end up in the dead-letter queue and are sent back to the Lambda function once the root cause of the failure has been fixed. -2. **Slack Notifications:** Slack messages help a great deal in improving observability when [running dlt in production](https://dlthub.com/docs/examples/chess_production/). Taktile has set up Slack notifications for both schema changes and pipeline failures to always have transparency over the health status of their pipeline. - -No matter whether you want to save time, cost, or both on your instrumentation setup, I hope you give dlt and AWS Lambda a try. It’s a modern, powerful, and lightweight combination of tools that has served us exceptionally well at Taktile. diff --git a/docs/website/blog/2024-01-08-streaming-pubsub-json-gcp.md b/docs/website/blog/2024-01-08-streaming-pubsub-json-gcp.md deleted file mode 100644 index e6e7d2ba18..0000000000 --- a/docs/website/blog/2024-01-08-streaming-pubsub-json-gcp.md +++ /dev/null @@ -1,230 +0,0 @@ ---- -slug: streaming-pubsub-json-gcp -title: Streaming Pub/Sub JSON to Cloud SQL PostgreSQL on GCP -summary: 'How and why to use a low-cost instance group to stream JSON events from Pub/Sub to PostgreSQL running on Cloud SQL.' -image: https://storage.googleapis.com/dlt-blog-images/pubsub_dlt/pubsub_backlog_performance.png -meta: - - name: canonical - content: https://www.dataroc.ca/blog/streaming-pubsub-json-to-cloud-sql-postgresql-on-gcp -authors: - name: William Laroche - title: GCP cloud architect * Backend and data engineer - url: https://www.linkedin.com/in/william-laroche/ - image_url: https://avatars.githubusercontent.com/u/10359701?v=4 -tags: [dlthub, python, dlt, pub/sub, gcp, event ingestion, Streaming] ---- - - - - - -:::info -**TL;DR: William, a gcp data consultant, shares an article about the work he did with dlt and GCP to create a secure, scalable, lightweight, and powerful high-volume event ingestion engine.** - -He explores several alternatives before offering a solution, and he benchmarks the solution after a few weeks of running. - -Read the original post here: [dataroc.ca blog](https://www.dataroc.ca/blog/streaming-pubsub-json-to-cloud-sql-postgresql-on-gcp). -Or find/hire William on [Linkedin](https://www.linkedin.com/in/william-laroche/). -::: - - -In the ever-evolving landscape of cloud computing, optimizing data workflows is -paramount for achieving efficiency and scalability. Even though Google Cloud Platform -offers the powerful Dataflow service to process data at scale, sometimes the simplest solution -is worth a shot. - -In cases with a relatively high Pub/Sub volume (>10 messages per second), a pull -subscription with a continuously running worker is more cost-efficient and quicker than -a push subscription. Using a combination of Docker, Instance Templates and Instance -Groups, it is pretty simple to set up an auto-scaling group of instances that will -process Pub/Sub messages. - -This guide will walk you through the process of configuring GCP infrastructure that -efficiently pulls JSON messages from a Pub/Sub subscription, infers schema, and inserts -them directly into a Cloud SQL PostgreSQL database using micro-batch processing. - - - -# The issue at hand - -In my current role at WishRoll, I was faced with the issue of processing a high amount -of events and store them in the production database directly. - -Imagine the scene: the server application produces analytics-style events such as "user -logged-in", and "task X was completed" (among others). Eventually, for example, we want -to run analytics queries on those events to count how many times a user logs in to -better tailor their experience. - -## A. The trivial solution: synchronous insert - -The trivial solution is to synchronously insert these events directly in the database. A -simple implementation would mean that each event fired results in a single insert to the -database. This comes with 2 main drawbacks: - -- Every API call that produces an event becomes slower. I.e. the /login endpoint needs to insert a record in the database -- The database is now hit with a very high amount of insert queries - -With our most basic need of 2 event types, we were looking at about 200 to 500 events -per second. I concluded this solution would not be scalable. To make it so, 2 things -would be necessary: (1) make the event firing mechanism asynchronous and (2) bulk events -together before insertion. - -## B. The serverless asynchronous solution - -A second solution is to use a Pub/Sub push subscription to trigger an HTTP endpoint when -a message comes in. This would've been easy in my case because we already have a -worker-style autoscaled App Engine service that could've hosted this. However, this only -solves the 1st problem of the trivial solution; the events still come in one at a -time to the HTTP service. - -Although it's possible to implement some sort of bulking mechanism in a push endpoint, -it's much easier to have a worker pull many messages at once instead. - -## C. The serverless, fully-managed Dataflow solution - -This led me to implement a complete streaming pipeline using GCP's streaming service: -Dataflow. Spoiler: this was way overkill and led to weird bugs with DLT ([data load -tool](https://dlthub.com/)). If you're curious, [I've open-sourced that code -too](https://github.com/dataroche/pubsub-dataflow-dlt). - -This solved both issues of the trivial solution, but proved pretty expensive and hard to -debug and monitor. - -## D. An autoscaled asynchronous pull worker - -Disclaimer: I had never considered standalone machines from cloud providers (AWS EC2, GCP Compute -Engine) to be a viable solution to my cloud problems. In my head, they seemed like -outdated, manually provisioned services that could instead be replaced by managed -services. - -But here I was, with a need to have a continuously running worker. I decided to bite the -bullet and try my luck with GCP Compute Engine. What I realized to my surprise, is that -by using instance templates and instance groups, you can easily set up a cluster of workers -that will autoscale. - -The code is simple: run a loop forever that pulls messages from a Pub/Sub subscription, -bulk the messages together, and then insert them in the database. Repeat. - -Then deploy that code as an instance group that auto-scales based on the need to process -messages. - -# Code walkthrough - -[The complete source code is available here.](https://github.com/dataroche/pubsub-dlt-stream) - -Summarily, the code is comprised of 2 main parts: - -- The pulling and batching logic to accumulate and group messages from Pub/Sub based on - their destination table -- The load logic to infer the schema and bulk insert the records into the database. This - part leverages DLT for destination compatibility and schema inference - -## Main loop - -By using this micro-batch architecture, we strive to maintain a balance of database -insert efficiency (by writing multiple records at a time) with near real-time insertion -(by keeping the window size around 5 seconds). - -```py - -pipeline = dlt.pipeline( - pipeline_name="pubsub_dlt", - destination=DESTINATION_NAME, - dataset_name=DATASET_NAME, -) - -pull = StreamingPull(PUBSUB_INPUT_SUBCRIPTION) -pull.start() - -try: - while pull.is_running: - bundle = pull.bundle(timeout=WINDOW_SIZE_SECS) - if len(bundle): - load_info = pipeline.run(bundle.dlt_source()) - bundle.ack_bundle() - # pretty print the information on data that was loaded - print(load_info) - else: - print(f"No messages received in the last {WINDOW_SIZE_SECS} seconds") - -finally: - pull.stop() - -``` - -## How to deploy - -[The GitHub repo explains how to deploy the project as an instance group](https://github.com/dataroche/pubsub-dlt-stream?tab=readme-ov-file#example-deployment-as-a-gcp-instance-group). - -## Database concerns - -Using DLT has the major advantage of inferring the schema of your JSON data -automatically. This also comes with some caveats: - -- The output schema of these analytics tables might change based on events -- If your events have a lot of possible properties, the resulting tables could become - very wide (lots of columns) which is not something desirable in an OLTP database - -Given these caveats, I make sure that all events fired by our app are fully typed and -limited in scope. Moreover, using the `table_name_data_key` configuration of the code I -wrote, it's possible to separate different events with different schemas into different -tables. - -[See this README -section](https://github.com/dataroche/pubsub-dlt-stream?tab=readme-ov-file#output-table-format) -for an example of application code and the resulting table. - -# Performance and cost - -After running this code and doing backfills for a couple of weeks, I was able to -benchmark the overall efficiency and cost of this solution. - -## Throughput capacity - -The pull worker performance - - -The Pub/Sub subscription metrics. Message throughput ranges between 200 and 300 -per second, while the oldest message is usually between 5 and 8 seconds with occasional spikes. - - -I am running a preemptible (SPOT) instance group of n1-standard-1 machines that -auto-scales between 2 and 10 instances. In normal operation, a single worker can handle -our load easily. However, because of the preemptible nature of the instances, I set the -minimum number to 2 to avoid periods where no worker is running. - -## Maximum capacity - -When deploying the solution with a backlog of messages to process (15 hours worth of -messages), 10 instances were spawned and cleared the backlog in about 25 minutes. - - - - -The Pub/Sub subscription throughput metrics when a 15-hour backlog was cleared. The -instance group gradually reached 10 instances at about 10:30AM, then cleared the -backlog by 10:50AM. - - -Between 7000 and 10000 messages per second were processed on average by these 10 -instances, resulting in a minimum throughput capacity of 700 messages/s per worker. - -## Cost - -Using n1-standard-1 spot machines, this cluster costs $8.03/mth per active machine. With -a minimum cluster size of 2, this means $16.06 per month. - -# Conclusion - -Using more "primitive" GCP services around Compute Engine provides a straightforward and -cost-effective way to process a high throughput of Pub/Sub messages from a pull -subscription. - -:::info -PS from dlt team: -* We just added [data contracts](https://dlthub.com/docs/general-usage/schema-contracts) enabling to manage schema evolution behavior. -* Are you on aws? Check out this AWS SAM & Lambda event ingestion pipeline [here](https://dlthub.com/docs/blog/dlt-aws-taktile-blog). -::: diff --git a/docs/website/blog/2024-01-10-dlt-mode.md b/docs/website/blog/2024-01-10-dlt-mode.md deleted file mode 100644 index 1d6bf8ca0e..0000000000 --- a/docs/website/blog/2024-01-10-dlt-mode.md +++ /dev/null @@ -1,157 +0,0 @@ ---- -slug: dlt-mode-blog -title: "The Modern Data Stack with dlt & Mode" -image: https://storage.googleapis.com/dlt-blog-images/blog-mode-dataflow1.png -authors: - name: Hiba Jamal - title: Data Science intern at dlthub - url: https://github.com/hibajamal - image_url: https://avatars.githubusercontent.com/u/35984866?v=4 -tags: [notebook, reporting, sql] ---- - -# The Modern Data Stack with dlt & Mode - - - -## Mode - Not another BI tool - -**Empowering people by making data work simple** - a value dlt embodies, and so does Mode. Both tools enable a person to build powerful things “on-the-fly”. Hence, when Mode positions itself as a self-service analytics platform, it delivers on that commitment by offering a user-friendly and **familiar** interface, and holistic experience. - -### 👨🏻‍🦰, 👨🏻‍🦱, and 🧕🏻 from Logistics need to know what happened on the 1st of August, now! - -The sad story of most data and analytics teams are as follows: they are frequently burdened with routine (or [ad-hoc](https://mode.com/ad-hoc-analysis)) data requests, often involving simple SQL commands exported to Excel for external use. Despite the apparent simplicity, handling multiple requests simultaneously creates unnecessary workload. This detracts from the analytics team's capacity to engage in more constructive tasks such as cohort analysis, predictive analysis, hypothesis testing, creating funky plots - the fun stuff! - -Nevertheless, employees outside the data team should not be blamed for making data requests without meaningful access to the data. If they were empowered to access and utilize the necessary data independently, individuals like 👨🏻‍🦰, 👨🏻‍🦱, and 🧕🏻 could filter user data from August 1st without relying on analysts. - -### Don’t know where you stand as a company, with data? Ask Mode - -You can start  your company’s journey with Mode by utilizing their [data maturity test](https://mode.com/maturity-model-assessment). It will tell you where you stand on your **data democracy** practices. A quick survey of user experiences showed exactly how Mode empowered companies of different sizes to become data thinkers. It has been adopted into 50% of Fortune 500 companies already! - -Contrary to common belief, fostering a company-wide embrace of data thinking doesn't necessarily entail teaching everyone programming or conducting data science courses. Mode identifies four pivotal factors—**people, processes, culture, and tools**—that can empower companies to cultivate data thinkers. However, there are more reasons contributing to Mode's success in facilitating the emergence of company-wide "data heroes”. Let’s explore them. - -## The ease of adopting Mode - -### 👀 Familiarity & Simple UX - -Whether intentional or not, the table view on Mode, alongside by its green and white interface, evokes a sense of familiarity to *original BI tool:* **Excel**. Additionally, the platform offers the flexibility of having an SQL-only space and extending that functionality to incorporate Python (and R), providing a user experience similar to utilizing **Databricks’** notebook & SQL environment. Lastly, the interface of the dashboarding spaces are the (simplified) experiences of tools like **Power BI** or **Tableau**. - -When a tool *feels* familiar, people might embrace it faster. In Mode, all these familiar experiences are combined and simplified into one platform, and this holistic offering could be why Mode is: 1) easy to use and attracts users, and 2) easy to adopt across a company. - -### 🔓 Access Paradigms - -Talking about company-wide adoption of a data tool, Mode offers various levels of access tailored to different user roles. - -This aligns with the idea behind data democracy, ensuring that individuals throughout the company can engage with data. In Mode, this includes both viewing reports and deriving insights from them, and also viewing the underlying data collection (or datasets). Notably, access can be fine-tuned based on user distinctions, such as developers and business users. This is accomplished through nuanced permission settings and user grouping. By defining specific permissions, one can specify the actions users are empowered to perform. Now, let's explore the specific depth of what these users can actually do with all this power, in the next sections. - -### 💽 SQL & Datasets - -Mode stores in itself “datasets”. This goes one step beyond writing a bajillion queries with joins and either saving them as code or saving them as materialized views in your database. You can use SQL and create datasets that are reusable and power a variety of different reports. - -Contrast this with the user experience offered by other BI tools, even though they do offer the workspace for table creation, they lack robust documentation and centralization of these tables. It then becomes challenging for other teams (and in a couple of months, yourself) to comprehend the purpose and content of these tables - let alone use them across different reports. - -There's no need to switch to a different database engine environment for [SQL writing](https://mode.com/sql-tutorial/sql-in-mode); Mode provides this functionality within its own environment. While tools like **Databricks** also offer this feature, Mode stands out by seamlessly utilizing it to generate shareable reports, much like the functionality seen in Metabase. Moreover, Mode goes a step further with its integration of Python and R, a capability present in **Power BI** but notably lacking the user-friendly interface of Mode's notebook environment. - -### 🦉 A single source of truth! - -In creating these replicable datasets that can be accessed through different ways, Mode creates a single source of truth. This eliminates the need to search for disparate queries, streamlining the data retrieval (and access) process. - -When we discuss data centralization, it typically involves cloud-hosted data warehouses that are accessible to authorized users at any time. This concept extends to business intelligence (BI) as well. Analysts within a company may utilize various tools, different source tables and SQL implementations, such as **Apache Superset** for business users, and **Presto SQL** for BI developers in their exploration, this leads to differences in loading and accessing data. Mode, in positioning itself as a central hub for data, resolves this by ensuring uniformity – everyone interacts with the same data source, eliminating variations in querying methods and results. - -### 🔦 Semantic Layers (& dbt) - -Speaking of running around for different definitions, we come to the importance of the semantic layer in a data workflow. - -In 2022, dbt introduced its semantic layer to address the challenge faced by BI developers and other stakeholders alike, in standardizing metric and indicator definitions across a company. This aimed to resolve issues arising from different individuals querying and defining these metrics, a process prone to human error (*or logical code error*) that can lead to inconsistencies. The significance of company-wide metrics lies in their impact on investors and their role in guiding teams on measuring growth and determining actions based on that growth. - -
-
- -![semantic layer](https://storage.googleapis.com/dlt-blog-images/blog-mode-semantic-layer-dbt.jpg) - -
- -
- -This concept bears some resemblance to the centralized metrics approach described here. However it is integrated into data products, its significance remains crucial. Therefore, incorporating dbt into your pipeline and linking it with Mode can significantly contribute to your journey of data centralization and governance. -
- -
- - -## Creating the Modern Data Stack with dlt & Mode - -Both dlt and Mode share the core value of data democracy, a cornerstone of the Modern Data Stack. When discussing the modern data stack, we are referring to the integration of various modular components that collaboratively create an accessible central system. Typically, this stack begins with a cloud data warehouse, where data is loaded, and updated by a data pipeline tool, like `dlt`. This process often involves a transformation layer, such as `dbt`, followed by the utilization of business intelligence (BI) tools like Mode. - -In the context of a Python-based environment, one can employ dlt to ingest data into either a database or warehouse destination. Whether this Python environment is within Mode or external to it, `dlt` stands as its own independent data pipeline tool, responsible for managing the extract and load phases of the ETL process. Additionally, `dlt` has the ability to structure unstructured data within a few lines of code - this empowers individuals or developers to work independently. - -With simplicity, centralization, and governance at its core, the combination of `dlt` and Mode, alongside a robust data warehouse, establishes two important elements within the modern data stack. Together, they handle data pipeline processes and analytics, contributing to a comprehensive and powerful modern data ecosystem. - -There are two ways to use dlt and Mode to uncomplicate your workflows. - -### 1. Extract, Normalize and Load with dlt and Visualize with Mode - -![data flow 1](https://storage.googleapis.com/dlt-blog-images/blog-mode-dataflow1.png) - -The data we are looking at comes from the source: Shopify. The configuration to initialize a Shopify source can be found in the dltHub docs. Once a dlt pipeline is initialized for Shopify, data from the source can be streamed into the destination of your choice. In this demo, we have chosen for it to be BigQuery destination. From where, it is connected to Mode. Mode’s SQL editor is where you can model your data for reports - removing all unnecessary columns or adding/subtracting the tables you want to be available to teams. - -![sql editor](https://storage.googleapis.com/dlt-blog-images/blog-mode-editor.png) - -This stage can be perceived as Mode’s own data transformation layer, or semantic modelling layer, depending on which team/designation the user belongs to. Next, the reporting step is also simplified in Mode. - -
-
- -![data flow 1](https://storage.googleapis.com/dlt-blog-images/blog-mode-report1.png) - -
- -
- -With the model we just created, called Products, a chart can be instantly created and shared via Mode’s Visual Explorer. Once created, it can easily be added to the Report Builder, and added onto a larger dashboard. -
- -
- - -### 2. Use dlt from within the python workspace in Mode - -![data flow 2](https://storage.googleapis.com/dlt-blog-images/blog-mode-dataflow2.png) - -In this demo, we’ll forego the authentication issues of connecting to a data warehouse, and choose the DuckDB destination to show how the Python environment within Mode can be used to initialize a data pipeline and dump normalized data into a destination. In order to see how it works, we first install dlt[duckdb] into the Python environment. - -```sh -!pip install dlt[duckdb] -``` - -Next, we initialize the dlt pipeline: - -```py -# initializing the dlt pipeline with your -# data warehouse destination -pipeline = dlt.pipeline( - pipeline_name="mode_example_pipeline", - destination="duckdb", - dataset_name="staging_data") -``` - -And then, we pass our data into the pipeline, and check out the load information. Let's look at what the Mode cell outputs: - -![load information](https://storage.googleapis.com/dlt-blog-images/blog-mode-load-info.png) - -Let’s check if our pipeline exists within the Mode ecosystem: - -![mode file system](https://storage.googleapis.com/dlt-blog-images/blog-mode-env-dir.png) - -Here we see the pipeline surely exists. Courtesy of Mode, anything that exists within the pipeline that we can query through Python can also be added to the final report or dashboard using the “Add to Report” button. - -![add to report button](https://storage.googleapis.com/dlt-blog-images/blog-mode-add-to-report.png) - -Once a pipeline is initialized within Mode’s Python environment, the Notebook cell can be frozen, and every consecutive run of the notebook can be a call to the data source, updating the data warehouse and reports altogether! - -## Conclusion - -dlt and Mode can be used together using either method, and make way for seamless data workflows. The first method mentioned in this article is the more traditional method of creating a data stack, where each tool serves a specific purpose. The second method, however utilizes the availability of a Python workspace within Mode to also serve the ETL process within Mode as well. This can be used for either ad-hoc reports and ad hoc data sources that need to be viewed visually, or, can be utilized as a proper pipeline creation and maintenance tool. diff --git a/docs/website/blog/2024-01-15-dlt-dbt-runner-on-cloud-functions.md b/docs/website/blog/2024-01-15-dlt-dbt-runner-on-cloud-functions.md deleted file mode 100644 index 059dd97a06..0000000000 --- a/docs/website/blog/2024-01-15-dlt-dbt-runner-on-cloud-functions.md +++ /dev/null @@ -1,377 +0,0 @@ ---- -slug: dlt-dbt-runner-on-cloud-functions -title: "Comparison running dbt-core and dlt-dbt runner on Google Cloud Functions" -image: https://storage.googleapis.com/dlt-blog-images/dlt-dbt-runner-on-cloud-functions.png -authors: - name: Aman Gupta - title: Junior Data Engineer - url: https://github.com/dat-a-man - image_url: https://dlt-static.s3.eu-central-1.amazonaws.com/images/aman.png -tags: [dbt, dlt-dbt-runner, cloud functions, ETL, data modeling] ---- - -:::info -TL;DR: This article compares deploying dbt-core standalone and using dlt-dbt runner on Google Cloud Functions. The comparison covers various aspects, along with a step-by-step deployment guide. -::: - -dbt or “data build tool” has become a standard for transforming data in analytical environments. -Most data pipelines nowadays start with ingestion and finish with running a dbt package. - -dlt or “data load tool” is an open-source Python library for easily creating data ingestion -pipelines. And of course, after ingesting the data, we want to transform it into an analytical -model. For this reason, dlt offers a dbt runner that’s able to just run a dbt model on top of where -dlt loaded the data, without setting up any additional things like dbt credentials. - -### Using dbt in Google Cloud functions - -To use dbt in cloud functions, we employed two methods: - -1. `dbt-core` on GCP cloud functions. -1. `dlt-dbt runner` on GCP cloud functions. - -Let’s discuss these methods one by one. - -### 1. Deploying dbt-core on Google Cloud functions - -Let's dive into running dbt-core up on cloud functions. - -You should use this option for scenarios where you have already collected and housed your data in a -data warehouse, and you need further transformations or modeling of the data. This is a good option -if you have used dbt before and want to leverage the power of dbt-core. If you are new to dbt, please -refer to dbt documentation: [Link Here.](https://docs.getdbt.com/docs/core/installation-overview) - -Let’s start with setting up the following directory structure: - -```text -dbt_setup -|-- main.py -|-- requirements.txt -|-- profiles.yml -|-- dbt_project.yml -|-- dbt_transform - |-- models - | |-- model1.sql - | |-- model2.sql - | |-- sources.yml - |-- (other dbt related contents, if required) -``` - -> You can setup the contents in `dbt_transform` folder by initing a new dbt project, for details -> refer to -> [documentation.](https://docs.getdbt.com/reference/commands/init#:~:text=When%20using%20dbt%20init%20to,does%20not%20exist%20in%20profiles.) - -:::note -We recommend setting up and testing dbt-core locally before using it in cloud functions. -::: - -**To run dbt-core on GCP cloud functions:** - -1. Once you've tested the dbt-core package locally, update the `profiles.yml` before migrating the - folder to the cloud function as follows: - - ```yaml - dbt_gcp: # project name - target: dev # environment - outputs: - dev: - type: bigquery - method: oauth - project: please_set_me_up! # your GCP project name - dataset: please_set_me_up! # your project dataset name - threads: 4 - impersonate_service_account: please_set_me_up! # GCP service account - ``` - - > This service account should have bigquery read and write permissions. - -1. Next, modify the `main.py` as follows: - - ```py - import os - import subprocess - import logging - - # Configure logging - logging.basicConfig(level=logging.INFO) - - def run_dbt(request): - try: - # Set your dbt profiles directory (assuming it's in /workspace) - os.environ['DBT_PROFILES_DIR'] = '/workspace/dbt_transform' - - # Log the current working directory and list files - dbt_project_dir = '/workspace/dbt_transform' - os.chdir(dbt_project_dir) - - # Log the current working directory and list files - logging.info(f"Current working directory: {os.getcwd()}") - logging.info(f"Files in the current directory: {os.listdir('.')}") - - # Run dbt command (e.g., dbt run) - - result = subprocess.run( - ['dbt', 'run'], - capture_output=True, - text=True - ) - - # Return dbt output - return result.stdout - - except Exception as e: - logging.error(f"Error running dbt: {str(e)}") - return f"Error running dbt: {str(e)}" - ``` - -1. Next, list runtime-installable modules in `requirements.txt`: - - ```text - dbt-core - dbt-bigquery - ``` - -1. Finally, you can deploy the function using gcloud CLI as: - - ```sh - gcloud functions deploy YOUR_FUNCTION_NAME \ - --gen2 \ - --region=YOUR_REGION \ - --runtime=python310 \ - --source=YOUR_SOURCE_LOCATION \ - --entry-point=YOUR_CODE_ENTRYPOINT \ - TRIGGER_FLAGS - ``` - - > You have option to deploy the function via GCP Cloud Functions' GUI. - -### 2. Deploying function using dlt-dbt runner - -The second option is running dbt using data load tool(dlt). - -I work at dlthub and often create dlt pipelines. These often need dbt for modeling the data, making -the dlt-dbt combination highly effective. For using this combination on cloud functions, we used -[dlt-dbt runner](https://dlthub.com/docs/api_reference/helpers/dbt/runner#create_runner) developed -at dlthub. - -The main reason I use this runner is because I load data with dlt and can re-use dlt’s connection to -the warehouse to run my dbt package, saving me the time and code complexity I’d need to set up and -run dbt standalone. - -To integrate dlt and dbt in cloud functions, use the dlt-dbt runner; here’s how: - -1. Lets start by creating the following directory structure: - - ```text - dbt_setup - |-- main.py - |-- requirements.txt - |-- dbt_project.yml - |-- dbt_transform - |-- models - | |-- model1.sql - | |-- model2.sql - | |-- sources.yml - |-- (other dbt related contents, if required) - ``` - - > You can set up the dbt by initing a new project, for details refer to - > [documentation](https://docs.getdbt.com/reference/commands/init#:~:text=When%20using%20dbt%20init%20to,does%20not%20exist%20in%20profiles.). - - :::note - With the dlt-dbt runner configuration, setting up a `profiles.yml` is unnecessary. DLT seamlessly - shares credentials with dbt, and on Google Cloud Functions, it automatically retrieves service - account credentials, if none are provided. - ::: - -1. Next, configure the `dbt_projects.yml` and set the model directory, for example: - - ```yaml - model-paths: ["dbt_transform/models"] - ``` - -1. Next, configure the `main.py` as follows: - - ```py - import dlt - import logging - from flask import jsonify - from dlt.common.runtime.slack import send_slack_message - from dlt.common import json - - def run_pipeline(request): - """ - Set up and execute a data processing pipeline, returning its status - and model information. - - This function initializes a dlt pipeline with pre-defined settings, - runs the pipeline with a sample dataset, and then applies dbt - transformations. It compiles and returns the information about - each dbt model's execution. - - Args: - request: The Flask request object. Not used in this function. - - Returns: - Flask Response: A JSON response with the pipeline's status - and dbt model information. - """ - try: - # Sample data to be processed - data = [{"name": "Alice Smith", "id": 1, "country": "Germany"}, - {"name": "Carlos Ruiz", "id": 2, "country": "Romania"}, - {"name": "Sunita Gupta", "id": 3, "country": "India"}] - - # Initialize a dlt pipeline with specified settings - pipeline = dlt.pipeline( - pipeline_name="user_data_pipeline", - destination="bigquery", - dataset_name="dlt_dbt_test" - ) - - # Run the pipeline with the sample data - pipeline.run(data, table_name="sample_data") - - # Apply dbt transformations and collect model information - models = transform_data(pipeline) - model_info = [ - { - "model_name": m.model_name, - "time": m.time, - "status": m.status, - "message": m.message - } - for m in models - ] - - # Convert the model information to a string - model_info_str = json.dumps(model_info) - - # Send the model information to Slack - send_slack_message( - pipeline.runtime_config.slack_incoming_hook, - model_info_str - ) - - # Return a success response with model information - return jsonify({"status": "success", "model_info": model_info}) - except Exception as e: - # Log and return an error response in case of any exceptions - logging.error(f"Error in running pipeline: {e}", exc_info=True) - - return jsonify({"status": "error", "error": str(e)}), 500 - - def transform_data(pipeline): - """ - Execute dbt models for data transformation within a dlt pipeline. - - This function packages and runs all dbt models associated with the - pipeline, applying defined transformations to the data. - - Args: - pipeline (dlt.Pipeline): The pipeline object for which dbt - transformations are run. - - Returns: - list: A list of dbt model run information, indicating the - outcome of each model. - - Raises: - Exception: If there is an error in running the dbt models. - """ - try: - # Initialize dbt with the given pipeline and virtual environment - dbt = dlt.dbt.package( - pipeline, - "/workspace/dbt_transform", - venv=dlt.dbt.get_venv(pipeline) - ) - logging.info("Running dbt models...") - # Run all dbt models and return their run information - return dbt.run_all() - except Exception as e: - # Log and re-raise any errors encountered during dbt model - # execution - logging.error(f"Error in running dbt models: {e}", exc_info=True) - raise - - # Main execution block - if __name__ == "__main__": - # Execute the pipeline function. - run_pipeline(None) - ``` - -1. The send_slack_message function is utilized for sending messages to Slack, triggered by - both success and error events. For setup instructions, please refer to the official - [documentation here.](https://dlthub.com/docs/running-in-production/running#using-slack-to-send-messages) - > `RUNTIME__SLACK_INCOMING_HOOK` was set up as environment variable in the above code. - -1. Next, list runtime-installable modules in `requirements.txt`: - - ```sh - dbt-core - dbt-bigquery - ``` - -1. Finally, you can deploy the function using gcloud CLI as: - - ```sh - gcloud functions deploy YOUR_FUNCTION_NAME \ - --gen2 \ - --region=YOUR_REGION \ - --runtime=python310 \ - --source=YOUR_SOURCE_LOCATION \ - --entry-point=YOUR_CODE_ENTRYPOINT \ - TRIGGER_FLAGS - ``` - -The merit of this method is that it can be used to load and transform data simultaneously. Using dlt -for data loading and dbt for modeling makes using dlt-dbt a killer combination for data engineers -and scientists, and my preferred choice. This method is especially effective for batched data and -event-driven pipelines with small to medium workloads. For larger data loads nearing timeout limits, -consider separating dlt and dbt into different cloud functions. - -> For more info on using `dlt-dbt runner` , please refer to the -> [official documentation by clicking here.](https://dlthub.com/docs/api_reference/helpers/dbt/runner#dbtpackagerunner-objects) - -### Deployment considerations: How does cloud functions compare to Git Actions? - -At dlthub we already natively support deploying to GitHub Actions, enabling you to have a serverless setup with a 1-command deployment. - -GitHub actions is an orchestrator that most would not find suitable for a data warehouse setup - but -it certainly could do the job for a minimalistic setup. GitHub actions provide 2000 free minutes per -month, so if our pipelines run for 66 minutes per day, we fit in the free tier. If our pipelines -took another 1h per day, we would need to pay ~15 USD/month for the smallest machine (2 vCPUs) but you -can see how that would be expensive if we wanted to run it continuously or had multiple pipelines always-on in parallel. - -Cloud functions are serverless lightweight computing solutions that can handle small computational -workloads and are cost-effective. dbt doesn't require the high computing power of the machine -because it uses the computing power of the data warehouse to perform the transformations. This makes -running dbt-core on cloud functions a good choice. The free tier would suffice for about 1.5h per -day of running a 1 vCPU and 2 GB RAM machine, and if we wanted an additional 1h -per day for this hardware it would cost us around 3-5 USD/month. - -![DLT-DBT-RUNNER_IMAGE](https://storage.googleapis.com/dlt-blog-images/dlt-dbt-runner-on-cloud-functions.png) - -When deploying dbt-core on cloud functions, there are certain constraints to keep in mind. For instance, -there is a 9-minute time-out limit for all 1st Gen functions. For 2nd Gen functions, there is a 9-minute -limit for event-driven functions and a 60-minute limit for HTTP functions. Since dbt works on the processing -power of the data warehouse it's operating on, 60 minutes is sufficient for most cases with small to medium -workloads. However, it is important to remember the 9-minute cap when using event-driven functions. - - -### Conclusion - -When creating lightweight pipelines, using the two tools together on one cloud function makes a lot -of sense, simplifying the setup process and the handover between loading and transformation. - -However, for more resource-intensive pipelines, we might want to improve resource utilisation by -separating the dlt loading from the dbt running because while dbt’s run speed is determined by the -database, dlt can utilize the cloud function’s hardware resources. - -When it comes to setting up just a dbt package to run on cloud functions, I guess it comes to -personal preference: I prefer dlt as it simplifies credential management. It automatically shares -credentials with dbt, making setup easier. Streamlining the process further, dlt on Google Cloud -functions, efficiently retrieves service account credentials, when none are provided. I also -used dlt’s [Slack error reporting function](https://dlthub.com/docs/running-in-production/running#using-slack-to-send-messages) -that sends success and error notifications from your runs directly to your Slack channel, -helping me manage and monitor my runs. diff --git a/docs/website/blog/2024-01-16-dlt-dbt-semantic-layer.md b/docs/website/blog/2024-01-16-dlt-dbt-semantic-layer.md deleted file mode 100644 index 415a55f9b9..0000000000 --- a/docs/website/blog/2024-01-16-dlt-dbt-semantic-layer.md +++ /dev/null @@ -1,136 +0,0 @@ ---- -slug: dlt-dbt-semantic-layer -title: "dlt & dbt in Semantic Modelling" -image: https://dlt-static.s3.eu-central-1.amazonaws.com/images/2024-01-11-dlt-dbt-semantic-layer/blog-dbt_sem-data-people.png -authors: - name: Hiba Jamal - title: Data Science intern at dltHub - url: https://github.com/hibajamal - image_url: https://avatars.githubusercontent.com/u/35984866?v=4 -tags: [semantic modelling] ---- - - - -## The Chinese Whisper of Data - -In the context of constructing a **modern data stack** through the development of various modular components for a data pipeline, our attention turns to the centralization of metrics and their definitions. - -For the purposes of this demo, we’ll be looking specifically at how `dlt` and `dbt` come together to solve the problem of the data flow from data engineer → analytics engineer → data analyst → business user. That’s quite a journey. And just like any game of *Chinese whisper*, things certainly do get lost in translation. - -
- -![cover](https://dlt-static.s3.eu-central-1.amazonaws.com/images/2024-01-11-dlt-dbt-semantic-layer/blog-dbt_sem-data-people.png) -
Taken from the real or fictitious book called '5th grade data engineering, 1998'.
- -
- -To solve this problem, both these tools come together and seamlessly integrate to create everything from data sources to uniform metric definitions, that can be handled centrally, and hence are a big aid to the data democracy practices of your company! - -Here’s how a pipeline could look: -1. Extract and load with `dlt`: `dlt` will automate data cleaning and normalization leaving you with clean data you can just use. -2. Create SQL models that simplify sources, if needed. This can include renaming and/or eliminating columns, identifying and setting down key constraints, fixing data types, etc. -3. Create and manage central metric definitions with the semantic layer. - -## 1. Extract, Structure, & Load with dlt - -The data being used is of a questionnaire, which includes questions, the options of those questions, respondents and responses. This data is contained within a nested json object, that we’ll pass as a raw source to `dlt` to structure, normalize and dump into a BigQuery destination. - -```py -# initializing the dlt pipeline with your data warehouse destination -pipeline = dlt.pipeline( - pipeline_name="survey_pipeline", - destination="bigquery", - dataset_name="questionnaire" -) - -# running the pipeline (into a structured model) -# the dataset variable contains unstructured data -pipeline.run(dataset, table_name='survey') -``` - -The extract and load steps of an ETL pipeline have been taken care of with these steps. Here’s what the final structure looks like in BigQuery: - -![bigquery tables](https://dlt-static.s3.eu-central-1.amazonaws.com/images/2024-01-11-dlt-dbt-semantic-layer/blog-dbt_sem-data-bqtables.png) - -`questionnaire` is a well structured dataset with a base table, and child tables. The `survey__questions` and `survey_questions__options` are normalized tables with, the individual questions and options of those questions, respectively, connected by a foreign key. The same structure is followed with the `..__respondents` tables, with `survey__respondents__responses` as our fact table. - -## 2. Transformation with dbt - -For transformation, we head to `dbt`. - -- The tables created by `dlt` are loaded as sources into `dbt`, with the same columns and structure as created by `dlt`. -- Since not much change is required to our original data, we can utilize the model creation ability of `dbt` to create a metric, whose results can directly be pulled by users. - -Say, we would like to find the average age of people by their favorite color. First, we’d create an SQL model to find the age per person. The sources used are presented in the following image: - -![dag 1](https://dlt-static.s3.eu-central-1.amazonaws.com/images/2024-01-11-dlt-dbt-semantic-layer/blog-dbt_sem-dag1.png) - -Next, using this information, we can find the average age for each favorite color. The sources used are as follows: - -![dag 2](https://dlt-static.s3.eu-central-1.amazonaws.com/images/2024-01-11-dlt-dbt-semantic-layer/blog-dbt_sem-dag2.png) - -This is one method of centralizing a metric definition or formula, that you create a model out of it for people to directly pull into their reports. - -## 3. Central Metric Definitions & Semantic Modelling with dbt - -The other method of creating a metric definition, powered by MetricFlow, is the `dbt` semantic layer. Using MetricFlow we define our metrics in yaml files and then directly query them from any different reporting tool. Hence, ensuring that no one gets a different result when they are trying to query company metrics and defining formulas and filters for themselves. For example, we created a semantic model named questionnaire, defining different entities, dimensions and measures. Like as follows: - -```yaml -model: ref('fact_table') # where the columns referred in this model will be taken from -# possible joining key columns -entities: - - name: id - type: primary -# where in SQL you would: create the aggregation column -measures: - - name: surveys_total - description: The total surveys for each --dimension. - agg: count - # if all rows need to be counted then expr = 1 - expr: 1 -# where in SQL you would: group by columns -dimensions: - # default dbt requirement - - name: surveyed_at - type: time - type_params: - time_granularity: day - # count entry per answer - - name: people_per_color - type: categorical - expr: answer - # count entry per question - - name: question - type: categorical - expr: question -``` - -Next, a metric is created from it: - -```yaml -metrics: - - name: favorite_color - description: Number of people with favorite colors. - type: simple - label: Favorite Colors - type_params: - # reference of the measure created in the semantic model - measure: surveys_total - filter: | # adding a filter on the "question" column for asking about favorite color - {{ Dimension('id__question') }} = 'What is your favorite color?' -``` - -The DAG then looks like this: - -![dag 3](https://dlt-static.s3.eu-central-1.amazonaws.com/images/2024-01-11-dlt-dbt-semantic-layer/blog-dbt_sem-dag3.png) - -We can now [query](https://docs.getdbt.com/docs/use-dbt-semantic-layer/quickstart-sl#test-and-query-metrics) this query, using whichever dimension we want. For example, here is a sample query: `dbt sl query --metrics favorite_color --group-by id__people_per_color` - -The result of which is: - -![query result](https://dlt-static.s3.eu-central-1.amazonaws.com/images/2024-01-11-dlt-dbt-semantic-layer/blog-dbt_sem-query-result.png) - -And just like that, the confusion of multiple people querying or pulling from different sources and different definitions get resolved. With aliases for different dimensions, the question of which column and table to pull from can be hidden - it adds a necessary level of abstraction for the average business end user. diff --git a/docs/website/blog/2024-02-06-practice-api-sources.md b/docs/website/blog/2024-02-06-practice-api-sources.md deleted file mode 100644 index 248d4ae647..0000000000 --- a/docs/website/blog/2024-02-06-practice-api-sources.md +++ /dev/null @@ -1,292 +0,0 @@ ---- -slug: practice-api-sources -title: "API playground: Free APIs for personal data projects" -image: https://storage.googleapis.com/dlt-blog-images/blog-api.png -authors: - name: Adrian Brudaru - title: Open source Data Engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [elt, free apis] ---- - -## Free APIs for Data Engineering - -Practicing data engineering is better with real data sources. -If you are considering doing a data engineering project, consider the following: -- Ideally, your data has entities and activities, so you can model dimensions and facts. -- Ideally, the APIs have no auth, so they can be easily tested. -- Ideally, the API should have some use case that you are modelling and showing the data for. -- Ideally, you build end-to-end pipelines to showcase extraction, ingestion, modelling and displaying data. - -This article outlines 10 APIs, detailing their use cases, any free tier limitations, and authentication needs. - - -## Material teaching data loading with dlt: - -### Data talks club data engineering zoomcamp -* [Video](https://www.youtube.com/watch?v=oLXhBM7nf2Q) -* [Course step by step](https://github.com/DataTalksClub/data-engineering-zoomcamp/blob/main/cohorts/2024/workshops/dlt_resources/data_ingestion_workshop.md) -* [Colab notebook](https://colab.research.google.com/drive/1kLyD3AL-tYf_HqCXYnA3ZLwHGpzbLmoj#scrollTo=5aPjk0O3S_Ag&forceEdit=true&sandboxMode=true) - -### Data talks club open source spotlight -* [Video](https://www.youtube.com/watch?v=eMbhyOECpcE) -* [Notebook](https://github.com/dlt-hub/dlt_demos/blob/main/spotlight_demo.ipynb) -* DTC Learners showcase (review again) - -### Docs -* [Getting started](https://dlthub.com/docs/getting-started) -* [Advanced pipeline tutorial](https://dlthub.com/docs/build-a-pipeline-tutorial) - - -## APIs Overview - -### 1. **PokeAPI** -- **URL:** [PokeAPI](https://pokeapi.co/). -- **Use:** Import Pokémon data for projects on data relationships and stats visualization. -- **Free:** Rate-limited to 100 requests/IP/minute. -- **Auth:** None. - -### 2. **REST Countries API** -- **URL:** [REST Countries](https://restcountries.com/). -- **Use:** Access country data for projects analyzing global metrics. -- **Free:** Unlimited. -- **Auth:** None. - -### 3. **OpenWeather API** -- **URL:** [OpenWeather](https://openweathermap.org/api). -- **Use:** Fetch weather data for climate analysis and predictive modeling. -- **Free:** Limited requests and features. -- **Auth:** API key. - -### 4. **JSONPlaceholder API** -- **URL:** [JSONPlaceholder](https://jsonplaceholder.typicode.com/). -- **Use:** Ideal for testing and prototyping with fake data. Use it to simulate CRUD operations on posts, comments, and user data. -- **Free:** Unlimited. -- **Auth:** None required. - -### 5. **Quandl API** -- **URL:** [Quandl](https://www.quandl.com/tools/api). -- **Use:** For financial market trends and economic indicators analysis. -- **Free:** Some datasets require premium. -- **Auth:** API key. - -### 6. **GitHub API** -- **URL:** [GitHub API](https://docs.github.com/en/rest) -- **Use:** Analyze open-source trends, collaborations, or stargazers data. You can use it from our [verified sources](https://dlthub.com/docs/dlt-ecosystem/verified-sources/) [repository](https://github.com/dlt-hub/verified-sources/tree/master/sources/github). -- **Free:** 60 requests/hour unauthenticated, 5000 authenticated. -- **Auth:** OAuth or personal access token. - -### 7. **NASA API** -- **URL:** [NASA API](https://api.nasa.gov/). -- **Use:** Space-related data for projects on space exploration or earth science. -- **Free:** Rate-limited. -- **Auth:** API key. - -### 8. **The Movie Database (TMDb) API** -- **URL:** [TMDb API](https://www.themoviedb.org/documentation/api). -- **Use:** Movie and TV data for entertainment industry trend analysis. -- **Free:** Requires attribution. -- **Auth:** API key. - -### 9. **CoinGecko API** -- **URL:** [CoinGecko API](https://www.coingecko.com/en/api). -- **Use:** Cryptocurrency data for market trend analysis or predictive modeling. -- **Free:** Rate-limited. -- **Auth:** None. - -### 10. Public APIs GitHub list -- **URL:** [Public APIs list](https://github.com/public-apis/public-apis). -- **Use:** Discover APIs for various projects. A meta-resource. -- **Free:** Varies by API. -- **Auth:** Depends on API. - -### 11. News API -- **URL**: [News API](https://newsapi.ai/). -- **Use**: Get datasets containing current and historic news articles. -- **Free**: Access to current news articles. -- **Auth**: API-Key. - -### 12. Exchangerates API -- **URL**: [Exchangerate API](https://exchangeratesapi.io/). -- **Use**: Get realtime, intraday and historic currency rates. -- **Free**: 250 monthly requests. -- **Auth**: API-Key. - -### 13. Spotify API -- **URL**: [Spotify API](https://developer.spotify.com/documentation/web-api). -- **Use**: Get spotify content and metadata about songs. -- **Free**: Rate limit. -- **Auth**: API-Key. - -### 14. Football API -- **URL**: [FootBall API](https://www.api-football.com/). -- **Use**: Get information about Football Leagues & Cups. -- **Free**: 100 requests/day. -- **Auth**: API-Key. - -### 15. Yahoo Finance API -- **URL**: [Yahoo Finance API](https://rapidapi.com/sparior/api/yahoo-finance15/details). -- **Use**: Access a wide range of financial data. -- **Free**: 500 requests/month. -- **Auth**: API-Key. - -### 16. Basketball API - -- URL: [Basketball API](https://www.api-basketball.com/). -- Use: Get information about basketball leagues & cups. -- Free: 100 requests/day. -- Auth: API-Key. - -### 17. NY Times API - -- URL: [NY Times API](https://developer.nytimes.com/apis). -- Use: Get info about articles, books, movies and more. -- Free: 500 requests/day or 5 requests/minute. -- Auth: API-Key. - -### 18. Spoonacular API - -- URL: [Spoonacular API](https://spoonacular.com/food-api). -- Use: Get info about ingredients, recipes, products and menu items. -- Free: 150 requests/day and 1 request/sec. -- Auth: API-Key. - -### 19. Movie database alternative API - -- URL: [Movie database alternative API](https://rapidapi.com/rapidapi/api/movie-database-alternative/pricing). -- Use: Movie data for entertainment industry trend analysis. -- Free: 1000 requests/day and 10 requests/sec. -- Auth: API-Key. - -### 20. RAWG Video games database API - -- URL: [RAWG Video Games Database](https://rawg.io/apidocs). -- Use: Gather video game data, such as release dates, platforms, genres, and reviews. -- Free: Unlimited requests for limited endpoints. -- Auth: API key. - -### 21. Jikan API - -- **URL:** [Jikan API](https://jikan.moe/). -- **Use:** Access data from MyAnimeList for anime and manga projects. -- **Free:** Rate-limited. -- **Auth:** None. - -### 22. Open Library Books API - -- URL: [Open Library Books API](https://openlibrary.org/dev/docs/api/books). -- Use: Access data about millions of books, including titles, authors, and publication dates. -- Free: Unlimited. -- Auth: None. - -### 23. YouTube Data API - -- URL: [YouTube Data API](https://developers.google.com/youtube/v3/docs/search/list). -- Use: Access YouTube video data, channels, playlists, etc. -- Free: Limited quota. -- Auth: Google API key and OAuth 2.0. - -### 24. Reddit API - -- URL: [Reddit API](https://www.reddit.com/dev/api/). -- Use: Access Reddit data for social media analysis or content retrieval. -- Free: Rate-limited. -- Auth: OAuth 2.0. - -### 25. World Bank API - -- URL: [World bank API](https://documents.worldbank.org/en/publication/documents-reports/api). -- Use: Access economic and development data from the World Bank. -- Free: Unlimited. -- Auth: None. - -Each API offers unique insights for data engineering, from ingestion to visualization. Check each API's documentation for up-to-date details on limitations and authentication. - -## Using the above sources - -You can create a pipeline for the APIs discussed above by using `dlt's` REST API source. Let’s create a PokeAPI pipeline as an example. Follow these steps: - -1. Create a Rest API source: - - ```sh - dlt init rest_api duckdb - ``` - -2. The following directory structure gets generated: - - ```sh - rest_api_pipeline/ - ├── .dlt/ - │ ├── config.toml # configs for your pipeline - │ └── secrets.toml # secrets for your pipeline - ├── rest_api/ # folder with source-specific files - │ └── ... - ├── rest_api_pipeline.py # your main pipeline script - ├── requirements.txt # dependencies for your pipeline - └── .gitignore # ignore files for git (not required) - ``` - -3. Configure the source in `rest_api_pipeline.py`: - - ```py - def load_pokemon() -> None: - pipeline = dlt.pipeline( - pipeline_name="rest_api_pokemon", - destination='duckdb', - dataset_name="rest_api_data", - ) - - pokemon_source = rest_api_source( - { - "client": { - "base_url": "https://pokeapi.co/api/v2/", - }, - "resource_defaults": { - "endpoint": { - "params": { - "limit": 1000, - }, - }, - }, - "resources": [ - "pokemon", - "berry", - "location", - ], - } - ) - - ``` - -For a detailed guide on creating a pipeline using the Rest API source, please read the Rest API source [documentation here](https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api). - -## Example projects - -Here are some examples from dlt users and working students: -- A pipeline that pulls data from an API and produces a dashboard in the [dbt blog](https://docs.getdbt.com/blog/serverless-dlt-dbt-stack). -- A [streaming pipeline on GCP](https://dlthub.com/docs/blog/streaming-pubsub-json-gcp) that replaces expensive tools such as Segment/5tran with a setup 50-100x cheaper. -- Another [streaming pipeline on AWS ](https://dlthub.com/docs/blog/dlt-aws-taktile-blog)for a slightly different use case. -- [Orchestrator + email + AI ](https://dlthub.com/docs/blog/dlt-kestra-demo-blog) + Slack to summarize emails. -- Evaluate a frontend tool to show your ability to [deliver end-to-end](https://dlthub.com/docs/blog/dlt-mode-blog). -- An end-to-end [data lineage implementation](https://dlthub.com/docs/blog/dlt-data-lineage) from extraction to dashboard. -- A bird pipeline and the associated schema management that ensures smooth operation [Part 1](https://publish.obsidian.md/lough-on-data/blogs/bird-finder-via-dlt-i), [Part 2](https://publish.obsidian.md/lough-on-data/blogs/bird-finder-via-dlt-ii). -- Japanese language demos [Notion calendar](https://stable.co.jp/blog/notion-calendar-dlt) and [exploring csv to bigquery with dlt](https://soonraah.github.io/posts/load-csv-data-into-bq-by-dlt/). -- Demos with [Dagster](https://dagster.io/blog/dagster-dlt) and [Prefect](https://www.prefect.io/blog/building-resilient-data-pipelines-in-minutes-with-dlt-prefect). - -## DTC learners showcase -Check out the incredible projects from our DTC learners: - -1. [e2e_de_project](https://github.com/scpkobayashi/e2e_de_project/tree/153d485bba3ea8f640d0ccf3ec9593790259a646) by [scpkobayashi](https://github.com/scpkobayashi). -2. [de-zoomcamp-project](https://github.com/theDataFixer/de-zoomcamp-project/tree/1737b6a9d556348c2d7d48a91e2a43bb6e12f594) by [theDataFixer](https://github.com/theDataFixer). -3. [data-engineering-zoomcamp2024-project2](https://github.com/pavlokurochka/data-engineering-zoomcamp2024-project2/tree/f336ed00870a74cb93cbd9783dbff594393654b8) by [pavlokurochka](https://github.com/pavlokurochka). -4. [de-zoomcamp-2024](https://github.com/snehangsude/de-zoomcamp-2024) by [snehangsude](https://github.com/snehangsude). -5. [zoomcamp-data-engineer-2024](https://github.com/eokwukwe/zoomcamp-data-engineer-2024) by [eokwukwe](https://github.com/eokwukwe). -6. [data-engineering-zoomcamp-alex](https://github.com/aaalexlit/data-engineering-zoomcamp-alex) by [aaalexlit](https://github.com/aaalexlit). -7. [Zoomcamp2024](https://github.com/alfredzou/Zoomcamp2024) by [alfredzou](https://github.com/alfredzou). -8. [data-engineering-zoomcamp](https://github.com/el-grudge/data-engineering-zoomcamp) by [el-grudge](https://github.com/el-grudge). - -Explore these projects to see the innovative solutions and hard work the learners have put into their data engineering journeys! - -## Showcase your project -If you want your project to be featured, let us know in the [#sharing-and-contributing channel of our community Slack](https://dlthub.com/community). \ No newline at end of file diff --git a/docs/website/blog/2024-02-21-pipelines-single-pane-of-glass.md b/docs/website/blog/2024-02-21-pipelines-single-pane-of-glass.md deleted file mode 100644 index ff54c463bd..0000000000 --- a/docs/website/blog/2024-02-21-pipelines-single-pane-of-glass.md +++ /dev/null @@ -1,84 +0,0 @@ ---- -slug: single-pane-glass -title: "Single pane of glass for pipelines running on various orchestrators" -image: https://storage.googleapis.com/dlt-blog-images/single-pane-glass.png -authors: - name: Adrian Brudaru - title: Open source Data Engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [data observability, data pipeline observability] ---- - -# The challenge in discussion - -In large organisations, there are often many data teams that serve different departments. These data teams usually cannot agree where to run their infrastructure, and everyone ends up doing something else. For example: - -- 40 generated GCP projects with various services used on each -- Native AWS services under no particular orchestrator -- That on-prem machine that’s the only gateway to some strange corporate data -- and of course that SaaS orchestrator from the marketing team -- together with the event tracking lambdas from product -- don’t forget the notebooks someone scheduled - -So, what’s going on? Where is the data flowing? what data is it? - -# The case at hand - -At dltHub, we are data people, and use data in our daily work. - -One of our sources is our community slack, which we use in 2 ways: - -1. We are on free tier Slack, where messages expire quickly. We refer to them in our github issues and plan to use the technical answers for training our GPT helper. For these purposes, we archive the conversations daily. We run this pipeline on github actions ([docs](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-github-actions)) which is a serverless runner that does not have a short time limit like cloud functions. -2. We measure the growth rate of the dlt community - for this, it helps to understand when people join Slack. Because we are on free tier, we cannot request this information from the API, but can capture the event via a webhook. This runs serverless on cloud functions, set up as in this [documentation](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-gcp-cloud-function-as-webhook). - -So already we have 2 different serverless run environments, each with their own “run reporting”. - -Not fun to manage. So how do we achieve a single pane of glass? - -### Alerts are better than monitoring - -Since “checking” things can be tedious, we rather forget about it and be notified. For this, we can use slack to send messages. Docs [here](https://dlthub.com/docs/running-in-production/running#using-slack-to-send-messages). - -Here’s a gist of how to use it - -```py -from dlt.common.runtime.slack import send_slack_message - -def run_pipeline_and_notify(pipeline, data): - try: - load_info = pipeline.run(data) - except Exception as e: - send_slack_message( - pipeline.runtime_config.slack_incoming_hook, - f"Pipeline {pipeline.pipeline_name} failed! \n Error: {str(e)}") - raise -``` - -### Monitoring load metrics is cheaper than scanning entire data sets - -As for monitoring, we could always run some queries to count the amount of loaded rows ad hoc - but this would scan a lot of data and cost significantly on larger volumes. - -A better way would be to leverage runtime metrics collected by the pipeline such as row counts. You can find docs on how to do that [here](https://dlthub.com/docs/running-in-production/monitoring#data-monitoring). - -### If we care, governance is doable too - -Now, not everything needs to be governed. But for the slack pipelines we want to tag which columns have personally identifiable information, so we can delete that information and stay compliant. - -One simple way to stay compliant is to annotate your raw data schema and use views for the transformed data, so if you delete the data at source, it’s gone everywhere. - -If you are materialising your transformed tables, you would need to have column level lineage in the transform layer to facilitate the documentation and deletion of the data. [Here’s](https://dlthub.com/docs/blog/dlt-lineage-support) a write up of how to capture that info. There are also other ways to grab a schema and annotate it, read more [here](https://dlthub.com/docs/general-usage/schema). - -# In conclusion - -There are many reasons why you’d end up running pipelines in different places, from organisational disagreements, to skillset differences, or simply technical restrictions. - -Having a single pane of glass is not just beneficial but essential for operational coherence. - -While solutions exist for different parts of this problem, the data collection still needs to be standardised and supported across different locations. - -By using a tool like dlt, standardisation is introduced with ingestion, enabling cross-orchestrator observability and monitoring. - -### Want to discuss? - -[Join our slack community](https://dlthub.com/community) to take part in the conversation. \ No newline at end of file diff --git a/docs/website/blog/2024-02-28-what-is-pyairbyte.md b/docs/website/blog/2024-02-28-what-is-pyairbyte.md deleted file mode 100644 index 02ab1b6de3..0000000000 --- a/docs/website/blog/2024-02-28-what-is-pyairbyte.md +++ /dev/null @@ -1,40 +0,0 @@ ---- -slug: what-is-pyairbyte -title: "PyAirbyte - what it is and what it’s not" -image: https://storage.googleapis.com/dlt-blog-images/pysquid.png -authors: - name: Adrian Brudaru - title: Open source Data Engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [data observability, data pipeline observability] ---- - -## Intro - -Here at dltHub, we work on the python library for data ingestion. So when I heard from Airbyte that they are building a library, I was intrigued and decided to investigate. - -# What is PyAirbyte? - -PyAirbyte is an interesting Airbyte’s initiative - similar to the one that Meltano had undertook 3 years ago. It provides a convenient way to download and install Airbyte sources and run them locally storing the data in a cache dataset. Users are allowed to then read the data from this cache. - -A Python wrapper on the Airbyte source is quite nice and has a feeling close to [Alto](https://github.com/z3z1ma/alto). The whole process of cloning/pip installing the repository, spawning a separate process to run Airbyte connector and read the data via UNIX pipe is hidden behind Pythonic interface. - -Note that this library is not an Airbyte replacement - the loaders of Airbyte and the library are very different. The library loader uses pandas.to_sql and sql alchemy and is not a replacement for Airbyte destinations that are available in Open Source Airbyte - -# Questions I had, answered - -- Can I run Airbyte sources with PyAirbyte? A subset of them. -- Can I use PyAirbyte to run a demo pipeline in a colab notebook? Yes. -- Would my colab demo have a compatible schema with Airbyte? No. -- Is PyAirbyte a replacement for Airbyte? No. -- Can I use PyAirbyte to develop or test during development Airbyte sources? No. -- Can I develop pipelines with PyAirbyte? no - -# In conclusion - -In wrapping up, it's clear that PyAirbyte is a neat little addition to the toolkit for those of us who enjoy tinkering with data in more casual or exploratory settings. I think this is an interesting initiative from Airbyte that will enable new usage patterns. - -### Want to discuss? - -[Join our slack community](https://dlthub.com/community) to take part in the conversation. \ No newline at end of file diff --git a/docs/website/blog/2024-03-07-openapi-generation-chargebee.md b/docs/website/blog/2024-03-07-openapi-generation-chargebee.md deleted file mode 100644 index 97fc6e4865..0000000000 --- a/docs/website/blog/2024-03-07-openapi-generation-chargebee.md +++ /dev/null @@ -1,159 +0,0 @@ ---- -slug: openapi-generation-chargebee -title: "Saving 75% of work for a Chargebee Custom Source via pipeline code generation with dlt" -image: https://storage.googleapis.com/dlt-blog-images/openapi-generation.png -authors: - name: Adrian Brudaru & Violetta Mishechkina - title: Data Engineer & ML Engineer - url: https://github.com/dlt-hub/dlt - image_url: https://avatars.githubusercontent.com/u/89419010?s=48&v=4 -tags: [data observability, data pipeline observability, openapi] ---- - -At dltHub, we have been pioneering the future of data pipeline generation, [making complex processes simple and scalable.](https://dlthub.com/product/#multiply-don't-add-to-our-productivity) We have not only been building dlt for humans, but also LLMs. - -Pipeline generation on a simple level is already possible directly in ChatGPT chats - just ask for it. But doing it at scale, correctly, and producing comprehensive, good quality pipelines is a much more complex endeavour. - -# Our early exploration with code generation - -As LLMs became available at the end of 2023, we were already uniquely positioned to be part of the wave. By being a library, a LLM could use dlt to build pipelines without the complexities of traditional ETL tools. - -This raised from the start the question - what are the different levels of pipeline quality? For example, how does a user code snippet, which formerly had value, compare to LLM snippets which can be generated en-masse? What does a perfect pipeline look like now, and what can LLMs do? - -We were only able to answer some of these questions, but we had some concrete outcomes that we carry into the future. - -### In June ‘23 we added a GPT-4 docs helper that generates snippets - -- try it on our docs; it's widely used as code troubleshooter -![gpt-4 dhelp](https://storage.googleapis.com/dlt-blog-images/dhelp.png) - -### We created an OpenAPI based pipeline generator - -- Blog: https://dlthub.com/docs/blog/open-api-spec-for-dlt-init -- OpenApi spec describes the api; Just as we can create swagger docs or a python api wrapper, we can create pipelines - - -[![marcin-demo](https://storage.googleapis.com/dlt-blog-images/openapi_loom_old.png)](https://www.loom.com/share/2806b873ba1c4e0ea382eb3b4fbaf808?sid=501add8b-90a0-4734-9620-c6184d840995) - - - -### Running into early limits of LLM automation: A manual last mile is needed - -Ideally, we would love to point a tool at an API or doc of the API, and just have the pipeline generated. - -However, the OpenApi spec does not contain complete information for generating a complete pipeline. There’s many challenges to overcome and gaps that need to be handled manually. - -While LLM automation can handle the bulk, some customisation remains manual, generating requirements towards our ongoing efforts of full automation. - -# Why revisit code generation at dlt now? - -### Growth drives a need for faster onboarding - -The dlt community has been growing steadily in recent months. In February alone we had a 25% growth on Slack and even more in usage. - -New users generate a lot of questions and some of them used our onboarding program, where we speed-run users through any obstacles, learning how to make things smoother on the dlt product side along the way. - -### Onboarding usually means building a pipeline POC fast - -During onboarding, most companies want to understand if dlt fits their use cases. For these purposes, building a POC pipeline is pretty typical. - -This is where code generation can prove invaluable - and reducing a build time from 2-3d to 0.5 would lower the workload for both users and our team. -💡 *To join our onboarding program, fill this [form](https://forms.gle/oMgiTqhnrFrYrfyD7) to request a call.* - - -# **Case Study: How our solution engineer Violetta used our PoC to generate a production-grade Chargebee pipeline within hours** - -In a recent case, one of our users wanted to try dlt with a source we did not list in our [public sources](https://dlthub.com/docs/dlt-ecosystem/verified-sources/) - Chargebee. - -Since the Chargebee API uses the OpenAPI standard, we used the OpenAPI PoC dlt pipeline code generator that we built last year. - -### Starting resources - -POC for getting started, human for the last mile. - -- Blog post with a video guide https://dlthub.com/docs/blog/open-api-spec-for-dlt-init -- OpenAPI Proof of Concept pipeline generator: https://github.com/dlt-hub/dlt-init-openapi -- Chargebee openapi spec https://github.com/chargebee/openapi -- Understanding of how to make web requests -- And 4 hours of time - this was part of our new hire Violetta’s onboarding tasks at dltHub so it was her first time using dlt and the code generator. - -Onward, let’s look at how our new colleague Violetta, ML Engineer, used this PoC to generate PoCs for our users. - -### Violetta shares her experience: - -So the first thing I found extremely attractive — the code generator actually created a very simple and clean structure to begin with. - -I was able to understand what was happening in each part of the code. What unfortunately differs from one API to another — is the authentication method and pagination. This needed some tuning. Also, there were other minor inconveniences which I needed to handle. - -There were no great challenges. The most ~~difficult~~ tedious probably was to manually change pagination in different sources and rename each table. - -1) Authentication -The provided Authentication was a bit off. The generated code assumed the using of a username and password but what was actually required was — an empty username + api_key as a password. So super easy fix was changing - -```py -def to_http_params(self) -> CredentialsHttpParams: - cred = f"{self.api_key}:{self.password}" if self.password else f"{self.username}" - encoded = b64encode(f"{cred}".encode()).decode() - return dict(cookies={}, headers={"Authorization": "Basic " + encoded}, params={}) -``` - -to - -```py -def to_http_params(self) -> CredentialsHttpParams: - encoded = b64encode(f"{self.api_key}".encode()).decode() - return dict(cookies={}, headers={"Authorization": "Basic " + encoded}, params={}) -``` - -Also I was pleasantly surprised that generator had several different authentication methods built in and I could easily replace `BasicAuth` with `BearerAuth` of `OAuth2` for example. - -2) Pagination - -For the code generator it’s hard to guess a pagination method by OpenAPI specification, so the generated code has no pagination 😞. So I had to replace a line - -```py -def f(): - yield _build_response(requests.request(**kwargs)) -``` - - with yielding form a 6-lines `get_page` function - -```py -def get_pages(kwargs: Dict[str, Any], data_json_path): - has_more = True - while has_more: - response = _build_response(requests.request(**kwargs)) - yield extract_nested_data(response.parsed, data_json_path) - kwargs["params"]["offset"] = response.parsed.get("next_offset", None) - has_more = kwargs["params"]["offset"] is not None -``` - -The downside — I had to do it for each resource. - -3) Too many files - -The code wouldn’t run because it wasn’t able to find some models. I found a commented line in generator script - -```py -# self._build_models() -``` - -I regenerated code with uncommented line and understood why it was commented. Code created 224 `.py` files under the `models` directory. Turned out I needed only two of them. Those were models used in api code. So I just removed other 222 garbage files and forgot about them. - -4) Namings - -The only problem I was left with — namings. The generated table names were like -`ListEventsResponse200ListItem` or `ListInvoicesForACustomerResponse200ListItem` . I had to go and change them to something more appropriate like `events` and `invoices` . - -# The result - -Result: https://github.com/dlt-hub/chargebee-source - -I did a walk-through with our user. Some additional context started to appear. For example, which endpoints needed to be used with `replace` write disposition, which would require specifying the `merge` keys. So in the end this source would still require some testing to be performed and some fine-tuning from the user. -I think the silver lining here is how to start. I don’t know how much time I would’ve spent on this source if I started from scratch. Probably, for the first couple of hours, I would be trying to decide where should the authentication code go, or going through the docs searching for information on how to use dlt configs. I would certainly need to go through all API endpoints in the documentation to be able to find the one I needed. There are a lot of different things which could be difficult especially if you’re doing it for the first time. -I think in the end if I had done it from scratch, I would’ve got cleaner code but spent a couple of days. With the generator, even with finetuning, I spent about half a day. And the structure was already there, so it was overall easier to work with and I didn’t have to consider everything upfront. - -### We are currently working on making full generation a reality. - -* Stay tuned for more, or -* [Join our slack community](https://dlthub.com/community) to take part in the conversation. \ No newline at end of file diff --git a/docs/website/blog/2024-03-11-moving-away-from-segment.md b/docs/website/blog/2024-03-11-moving-away-from-segment.md deleted file mode 100644 index e3e44ce027..0000000000 --- a/docs/website/blog/2024-03-11-moving-away-from-segment.md +++ /dev/null @@ -1,233 +0,0 @@ ---- -slug: dlt-segment-migration -title: "Moving away from Segment to a cost-effective do-it-yourself event streaming pipeline with Cloud Pub/Sub and dlt." -image: https://storage.googleapis.com/dlt-blog-images/dlt-segment-migration.jpeg -authors: - name: Zaeem Athar - title: Junior Data Engineer - url: https://github.com/zem360 - image_url: https://images.ctfassets.net/c4lg2g5jju60/5tZn4cCBIesUYid17g226X/a044d2d471ebd466db32f7868d5c0cc8/Zaeem.jpg?w=400&h=400&q=50&fm=webp -tags: [Pub/Sub, dlt, Segment, Streaming] ---- -:::info -TL;DR: This blog post introduces a cost-effective solution for event streaming that results in up to 18x savings. The solution leverages Cloud Pub/Sub and dlt to build an efficient event streaming pipeline. -::: - -## The Segment Problem -Event tracking is a complicated problem for which there exist many solutions. One such solution is Segment, which offers ample startup credits to organizations looking to set up event ingestion pipelines. Segment is used for a variety of purposes, including web analytics. - -:::note - -💡 With Segment, you pay 1-1.2 cents for every tracked users. - -Let’s take a back-of-napkin example: for 100.000 users, ingesting their events data would cost **$1000.** - -**The bill:** -* **Minimum 10,000 monthly tracked users (0-10K)** + $120. -* **Additional 1,000 monthly tracked users (10K - 25K)** + $12 / 1000 user. -* **Additional 1,000 monthly tracked users (25k - 100K)** + $11 / 1000 user. -* **Additional 1,000 monthly tracked users (100k +)** + $10 / 1000 user. - -::: - -The price of **$1000/month** for 100k tracked users doesn’t seem excessive, given the complexity of the task at hand. - -However, similar results can be achieved on GCP by combining different services. If those 100k users produce 1-2m events, **those costs would stay in the $10-60 range.** - -In the following sections, we will look at which GCP services can be combined to create a cost-effective event ingestion pipeline that doesn’t break the bank. - -![goodbye segment](https://storage.googleapis.com/dlt-blog-images/goodbye_segment.gif) - -## The Solution to the Segment Problem -Our proposed solution to replace Segment involves using dlt with Cloud Pub/Sub to create a simple, scalable event streaming pipeline. The pipeline's overall architecture is as follows: - -![pubsub_dlt-pipeline](https://storage.googleapis.com/dlt-blog-images/dlt-segment-migration.jpeg) - -In this architecture, a publisher initiates the process by pushing events to a Pub/Sub topic. Specifically, in the context of dlt, the library acts as the publisher, directing user telemetry data to a designated topic within Pub/Sub. - -A subscriber is attached to the topic. Pub/Sub offers a push-based [subscriber](https://cloud.google.com/pubsub/docs/subscription-overview) that proactively receives messages from the topic and writes them to Cloud Storage. The subscriber is configured to aggregate all messages received within a 10-minute window and then forward them to a designated storage bucket. - -Once the data is written to the Cloud Storage this triggers a Cloud Function. The Cloud Function reads the data from the storage bucket and uses dlt to ingest the data into BigQuery. - -## Code Walkthrough -This section dives into a comprehensive code walkthrough that illustrates the step-by-step process of implementing our proposed event streaming pipeline. - -Implementing the pipeline requires the setup of various resources, including storage buckets and serverless functions. To streamline the procurement of these resources, we'll leverage Terraform—an Infrastructure as Code (IaC) tool. - -### Prerequisites - -Before we embark on setting up the pipeline, there are essential tools that need to be installed to ensure a smooth implementation process. - -- **Firstly**, follow the official guide to install [Terraform](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli), a tool for automating the deployment of cloud infrastructure. -- **Secondly**, install the [Google Cloud Pub/Sub API client library](https://cloud.google.com/sdk/docs/install) which is required for publishing events to Cloud Pub/Sub. - -### Permissions - -Next, we focus on establishing the necessary permissions for our pipeline. A crucial step involves creating service account credentials, enabling Terraform to create and manage resources within Google Cloud seamlessly. - -Please refer to the Google Cloud documentation [here](https://cloud.google.com/iam/docs/service-accounts-create#console) to set up a service account. Once created, it's important to assign the necessary permissions to the service account. The project [README](https://github.com/dlt-hub/dlt_pubsub_demo) lists the necessary permissions. Finally, generate a key for the created service account and download the JSON file. Pass the credentials as environment variables in the project root directory. - -```sh -export GOOGLE_APPLICATION_CREDENTIALS="/path/to/keyfile.json" -``` - -### Setting Up The Event Streaming Pipeline - -To set up our pipeline, start by cloning the [GitHub Repository](https://github.com/dlt-hub/dlt_pubsub_demo). The repository contains all the necessary components, structured as follows: - -```sh -. -├── README.md -├── cloud_functions -│ ├── main.py -│ └── requirements.txt -├── publisher.py -├── requirement.txt -├── terraform -│ ├── backend.tf -│ ├── cloud_functions.tf -│ ├── main.tf -│ ├── provider.tf -│ ├── pubsub.tf -│ ├── storage_buckets.tf -│ └── variables.tf -``` - -Within this structure, the **Terraform** directory houses all the Terraform code required to set up the necessary resources on Google Cloud. - -Meanwhile, the **cloud_functions** folder includes the code for the Cloud Function that will be deployed. This function will read the data from storage and use dlt to ingest data into BigQuery. The code for the function can be found in `cloud_functions/main.py` file. - -### Step 1: Configure Service Account Credentials - -To begin, integrate the service account credentials with Terraform to enable authorization and resource management on Google Cloud. Edit the `terraform/main.tf` file to include the path to your service account's credentials file as follows: - - -```sh -provider "google" { - credentials = file("./../credentials.json") - project = var.project_id - region = var.region -} -``` - -### Step 2: **Define Required Variables** - -Next, in the `terraform/variables.tf` define the required variables. These variables correspond to details within your `credentials.json` file and include your project's ID, the region for resource deployment, and any other parameters required by your Terraform configuration: - -```sh -variable "project_id" { - type = string - default = "Add Project ID" -} - -variable "region" { - type = string - default = "Add Region" -} - -variable "service_account_email" { - type = string - default = "Add Service Account Email" -} -``` - -### Step 3: Procure Cloud Resources - -We are now ready to set up some cloud resources. To get started, navigate into the **terraform** directory and `terraform init`. The command initializes the working directory containing Terraform configuration files. - -With the initialization complete, you're ready to proceed with the creation of your cloud resources. To do this, run the following Terraform commands in sequence. These commands instruct Terraform to plan and apply the configurations defined in your `.tf` files, setting up the infrastructure on Google Cloud as specified. - -```sh -terraform plan -terraform apply -``` - -This `terraform plan` command previews the actions Terraform intends to take based on your configuration files. It's a good practice to review this output to ensure the planned actions align with your expectations. - -After reviewing the plan, execute the `terraform apply` command. This command prompts Terraform to create or update resources on Google Cloud according to your configurations. - -The following resources are created on Google Cloud once `terraform apply` command is executed: - -| Name | Type | Description | -| --- | --- | --- | -| tel_storage | Bucket | Bucket for storage of telemetry data. | -| pubsub_cfunctions | Bucket | Bucket for storage of Cloud Function source code. | -| storage_bigquery | Cloud Function | The Cloud Function that runs dlt to ingest data into BigQuery. | -| telemetry_data_tera | Pub/Sub Topic | Pub/Sub topic for telemetry data. | -| push_sub_storage | Pub/Sub Subscriber | Pub/Sub subscriber that pushes data to Cloud Storage. | - -### Step 4: Run the Publisher - -Now that our cloud infrastructure is in place, it's time to activate the event publisher. Look for the `publisher.py` file in the project root directory. You'll need to provide specific details to enable the publisher to send events to the correct Pub/Sub topic. Update the file with the following: - -```py -# TODO(developer) -project_id = "Add GCP Project ID" -topic_id = "telemetry_data_tera" -``` - -The `publisher.py` script is designed to generate dummy events, simulating real-world data, and then sends these events to the specified Pub/Sub topic. This process is crucial for testing the end-to-end functionality of our event streaming pipeline, ensuring that data flows from the source (the publisher) to our intended destinations (BigQuery, via the Cloud Function and dlt). To run the publisher execute the following command: - -```sh -python publisher.py -``` - -### Step 5: Results - -Once the publisher sends events to the Pub/Sub Topic, the pipeline is activated. These are asynchronous calls, so there's a delay between message publication and their appearance in BigQuery. - -The average completion time of the pipeline is approximately 12 minutes, accounting for the 10-minute time interval after which the subscriber pushes data to storage plus the Cloud Function execution time. The push interval of the subscriber can be adjusted by changing the **max_duration** in `pubsub.tf` - -```sh -cloud_storage_config { - bucket = google_storage_bucket.tel_bucket_storage.name - - filename_prefix = "telemetry-" - - max_duration = "600s" - - } -``` - -## Our Cost Estimation -On average the cost for our proposed pipeline are as follows: - -- 100k users tracked on Segment would cost **$1000**. -- 1 million events ingested via our setup **$37**. -- Our web tracking user:event ratio is 1:15, so the Segment cost equivalent would be **$55**. -- Our telemetry device:event ratio is 1:60, so the Segment cost equivalent would be **$220**. - -So with our setup, as long as we keep events-to-user ratio **under 270**, we will have cost savings over Segment. In reality, it gets even better because GCP offers a very generous free tier that resets every month, where Segment costs more at low volumes. - -**GCP Cost Calculation:** -Currently, our telemetry tracks 50,000 anonymized devices each month on a 1:60 device-to-event ratio. Based on these data volumes we can estimate the cost of our proposed pipeline. - -Cloud Functions is by far the most expensive service used by our pipeline. It is billed based on the vCPU / memory, compute time, and number of invocations. - -:::note -💡 The cost of compute for 512MB / .333vCPU machine time for 1000ms is as follows - -| Metric | Unit Price | -| --- | --- | -| GB-seconds (Memory) | $0.000925 | -| GHz-seconds (vCPU) | $0.001295 | -| Invocation | $0.0000004 | -| Total | 0.0022 | - -This puts the **monthly cost of ingesting 1 million events with Cloud Functions at:** -- (1 million / 60) * 0.0022 cents = **$37** -::: - - - -## In Conclusion - -Event streaming pipelines don’t need to be expensive. In this demo, we present an alternative to Segment that offers up to **18x** in savings in practice. Our proposed solution leverages Cloud Pub/Sub and dlt to deliver a cost-effective streaming pipeline. - -Following this demo requires knowledge of the publisher-subscriber model, dlt, and GCP. It took about 4 hours to set up the pipeline from scratch, but we went through the trouble and set up Terraform to procure infrastructure. - -Use `terraform apply` to set up the needed infrastructure for running the pipeline. This can be done in 30 minutes, allowing you to evaluate the proposed solution's efficacy without spending extra time on setup. Please do share your feedback. - -P.S: We will soon be migrating from Segment. Stay tuned for future posts where we document the migration process and provide a detailed analysis of the associated human and financial costs. diff --git a/docs/website/blog/2024-03-12-source-libraries.md b/docs/website/blog/2024-03-12-source-libraries.md deleted file mode 100644 index 10e5d23244..0000000000 --- a/docs/website/blog/2024-03-12-source-libraries.md +++ /dev/null @@ -1,60 +0,0 @@ ---- -slug: code-vs-buy -title: Coding data pipelines is faster than renting connector catalogs -image: https://storage.googleapis.com/dlt-blog-images/source-lib-1.png -authors: - name: Matthaus Krzykowski - title: Co-Founder & CEO at dltHub - url: https://twitter.com/matthausk - image_url: https://pbs.twimg.com/profile_images/642282396751130624/9ixo0Opj_400x400.jpg -tags: [dlthub, source libraries, dlt] ---- - -## **Celebrating over 500 ad hoc custom sources written by the** `dlt` **community in February** - -Today it is easier to pip install dlt and write a custom source than to setup and configure a traditional ETL platform. - -The wider community is increasingly noticing these benefits. In February the community wrote over 500 `dlt` custom sources. Last week we crossed 2000 `dlt` total custom sources created since we launched dlt last summer. - -![custom sources](https://storage.googleapis.com/dlt-blog-images/source-lib-1.png) - - -A custom `dlt` source is something new for our industry. With `dlt` we automated the majority of the work data engineers tasks that are usually done in traditional ETL platforms. Hence, creating an ad hoc [`dlt` pipeline and source](https://dlthub.com/docs/walkthroughs/create-a-pipeline) is a [dramatically simpler.](https://dlthub.com/docs/build-a-pipeline-tutorial#the-simplest-pipeline-1-liner-to-load-data-with-schema-evolution) Maintaining a custom `dlt` source in production is relatively easy as most of the common [pipeline maintenance issues are handled.](https://dlthub.com/docs/build-a-pipeline-tutorial#governance-support-in-dlt-pipelines) - -Today [`dlt` users](https://dlthub.com/docs/blog/dlthub-who-we-serve) pick dlt because it is the fastest way to create a dataset. As we frequently hear it from all of you “dlt is pip install and go”. This is in line with our [mission to make this next generation of Python users autonomous when they create and use data in their organizations](https://dlthub.com/docs/blog/dlthub-mission). - -## How to get to 50,000 sources: let’s remove the dependency on source catalogs and move forward to ad hoc code - -We think that “Pip install ETLs” or “EL as code” tools such as dlt are ushering a new era of ad hoc code. ad hoc code allows for automation and customization of very specific tasks. - -Most of the market today is educated by Saas ETLs on the value of “source”/”connector” catalogs. The core is a short-tail catalog market of +-20 sources (product database replication, some popular CRMs and ads APIs) with the highest profit margins and intense competition among vendors. The long-tail source catalog market, depending on the vendor, is usually up to 400 sources, with much smaller support. - -We think that source catalogs will become more and more irrelevant in the era of LLMs and ad hoc code. “EL as code” allows users to work with source catalog. From the beginning the dlt community has been writing [wrappers for taps/connectors from other vendors](https://github.com/z3z1ma/alto/blob/main/example_proj/asana_pipeline.py), usually to migrate to a dlt pipeline at some point, as we documented in the [customer story how Harness adopted dlt](https://dlthub.com/success-stories/harness/). - -![moving away from sources](https://storage.googleapis.com/dlt-blog-images/source-lib-2.png) - -Even for short-tail, high quality catalog sources “EL as code” allows for fixes of hidden gotchas and customisation that makes data pipelines production-ready. - -We also believe that these are all early steps in “EL as code”. [Huggingface hosts over 116k datasets](https://huggingface.co/datasets) as of March ‘24. We at dltHub think that the ‘real’ Pythonic ETL market is a market of 100k of APIs and millions of datasets. - -## dlt has been built for humans and LLMs from the get go and this will make coding data pipelines even faster - -Since the inception of dlt, we have believed that the adoption `dlt`among the next generation of Python users will depend on its compatibility with code generation tools, including Codex, ChatGPT, and any new tools that emerge on the market.. - -We have not only been building `dlt` for humans, but also LLMs. - -Back in March ‘23 we released [dlt init](https://dlthub.com/docs/getting-started) as the simplest way to add a pipeline/initialize a project in `dlt`. We rebuilt the `dlt` library in such a way that it performs well with LLMs. At the end of May ‘23 we opened up our `dltHub` Slack to the broader community. - - -[Back in June ‘23 we released a proof of concept](https://dlthub.com/docs/blog/open-api-spec-for-dlt-init) of the ['dlt init'](https://dlthub.com/docs/walkthroughs/create-a-pipeline) extension that can [generate `dlt` pipelines from an OpenAPI specification.](https://github.com/dlt-hub/dlt-init-openapi) As we said at that time, if you build APIs, for example with [FastAPI](https://fastapi.tiangolo.com/), you can, thanks to the [OpenAPI spec,](https://spec.openapis.org/oas/v3.1.0) automatically generate a [Python client](https://pypi.org/project/openapi-python-client/0.6.0a4/) and give it to your users. If you have 3min time watch how a demo Marcin generates such a pipeline from the OpenAPI spec using the [Pokemon API](https://pokeapi.co/) in [this Loom](https://www.loom.com/share/2806b873ba1c4e0ea382eb3b4fbaf808?sid=501add8b-90a0-4734-9620-c6184d840995). This demo took things a step further and enables users to generate advanced `dlt` pipelines that, in essence, convert your API into a live dataset. - - -However, it takes a long time to go from a LLM PoC to production-grade code. We know much of our user base is already using ChatPGT and comparable tools to generate sources. We hear our community's excitement about the promise of LLMs for this task. The automation promise is in both in building and configuring pipelines. Anything seems possible, but if any of you have played around this task with ChatPGT - usually the results are janky. Along these lines in the last couple of months we have been dog fooding the PoC that can generate `dlt` pipelines from an OpenAPI specification. - -![comics](https://storage.googleapis.com/dlt-blog-images/source-lib-3.png) - -https://twitter.com/xatkit/status/1763973816798138370 - -You can read a case study [on how our solution engineer Violetta used an iterated version of the PoC to generate a production-grade Chargebee `dlt` within hours instead of 2,3 days here](https://dlthub.com/docs/blog/openapi-generation-chargebee). - -We think that at this stage we are a few weeks away from releasing our next product that makes coding data pipelines faster than renting connector catalog: a `dlt` code generation tool that allows `dlt` users create datasets from the REST API in the coming weeks. \ No newline at end of file diff --git a/docs/website/blog/2024-03-25-reverse_etl_dlt.md b/docs/website/blog/2024-03-25-reverse_etl_dlt.md deleted file mode 100644 index 2dc32cc914..0000000000 --- a/docs/website/blog/2024-03-25-reverse_etl_dlt.md +++ /dev/null @@ -1,225 +0,0 @@ ---- -slug: reverse-etl-dlt -title: "dlt adds Reverse ETL - build a custom destination in minutes" -image: https://storage.googleapis.com/dlt-blog-images/reverse-etl.png -authors: - name: Adrian Brudaru - title: Open source Data Engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [reverse etl, pythonic] ---- - -# Pythonic reverse ETL is here - -## Why Python is the right approach for doing Reverse ETL - -Reverse ETL is generally about putting data into a business application. This data would often come from a SQL database used as a middle layer for data integrations and calculations. - -That’s fine - but nowadays most data people speak Python, and the types of things we want to put into an operational application don’t always come from a DB, they often come from other business applications, or from things like a dataframe on which we did some scoring, etc. - -![reverse etl](https://storage.googleapis.com/dlt-blog-images/reverse-etl.png) - -### The full potential of Reverse ETL is in the flexibility of sources - -SQL databases are a good start, but in reality very often our data source is something else. More often than not, it’s a Python analyst’s implementation of some scoring or some business calculation. - -Other times, it’s a business application - for example, we might have a form that sends the response data to a webhook, from where it could end up in Salesforce, DWH, and Slack as a notification. And of course, if this is done by a data person it will be done in Python. - -Such, it follows that if we want to cater to the data crowd, we need to be Pythonic. - -## There’s synergy with ETL - -Reverse ETL is ultimately ETL. Data is extracted from a source, its transformed, and then loaded to a destination. The challenges are similar, the most notable difference being that pulling data from a strongly typed environment like a DB and converting it to weakly typed JSON is MUCH easier than the other way around. You can argue that Reverse ETL is simpler than ETL. - -### Flavors of Reverse ETL - -Just like we have ETL and ELT, we also have flavors of Reverse ETL - -- **Reverse ETL or TEL:** Transform the data to a specification, read it from DB, and send it to an application. -- **Tool Reverse ETL or ETL:** Extract from DB, map fields to destination in the tool, load to destination. -- **Pythonic Freestyle Reverse ETL:** You extract data from wherever you want and put it anywhere except storage/DB. Transformations are optional. - -Examples of Python reverse ETL - -- Read data from Mongo, do anomaly detection, and notify anomalies to Slack. -- Read membership data from Stripe, calculate the chance to churn, and upload to CRM for account managers. -- Capture a form response with a webhook and send the information to CRM, DWH, and Slack. - -## Add python? - new skills unlocked! - -So why is it much better to do reverse ETL in Python? - -- **Live Streaming and Flexibility**: Python's ability to handle live data streams and integrate with various APIs and services surpasses the capabilities of SQL-based data warehouses designed for batch processing. -- **End-to-End Workflow**: Employing Python from data extraction to operational integration facilitates a streamlined workflow, enabling data teams to maintain consistency and efficiency across the pipeline. -- **Customization and Scalability**: Python's versatility allows for tailored solutions that can scale with minimal overhead, reducing the reliance on additional tools and simplifying maintenance. -- **Collaboration and Governance**: By keeping the entire data workflow within Python, teams can ensure better governance, compliance, and collaboration, leveraging common tools and repositories. - -## Example: Building a Custom Destination and a pipeline in under 1h - -Documentation used: -Building a destination: [docs](https://dlthub.com/devel/dlt-ecosystem/destinations/destination) -SQL source: [docs](https://dlthub.com/devel/dlt-ecosystem/verified-sources/sql_database) -In this example, you will see why it’s faster to build a custom destination than set up a separate tool. - -dlt allows you to define custom destination functions. You'll write a function that extracts the relevant data from your dataframe and formats it for the Notion API. - -This example assumes you have set up Google Sheets API access and obtained the necessary credentials to authenticate. - -### Step 1: Setting Up Google Sheets API (10min) - -1. Enable the Google Sheets API in the Google Developers Console. -2. Download the credentials JSON file. -3. Share the target Google Sheet with the email address found in your credentials JSON file. - -### Step 2: Define the Destination method in its own file `sheets_destination.py` (20min) - -Install the required package for the Google API client: - -```sh -pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib - -``` - -Here’s how to define a destination function to update a Google Sheet. -In our case we wrote a slightly complex function that checks the headers and aligns the columns with the existing ones before inserting: - -```py -import dlt -from google.oauth2.service_account import Credentials -from googleapiclient.discovery import build - - -@dlt.destination(batch_size=100) -def google_sheets(items, - table_schema, - sheets_id: str = dlt.config.value, - credentials_json: dict = dlt.secrets.value, - range_name: str = 'Sheet1'): - """ - Send data to a Google Sheet. - :param items: Batch of items to send. - :param table_schema: Schema of the table (unused in this example but required by dlt). - :param sheets_id: ID of the Google Sheet, retrieved from config. - :param credentials_json: Google Service Account credentials, retrieved from secrets. - :param range_name: The specific range within the Sheet where data should be appended. - """ - credentials = Credentials.from_service_account_info(credentials_json) - service = build('sheets', 'v4', credentials=credentials) - - # Fetch existing headers from the sheet - existing_headers_result = service.spreadsheets().values().get( - spreadsheetId=sheets_id, range="Sheet1!A1:1" - ).execute() - existing_headers = existing_headers_result.get('values', [[]])[0] if existing_headers_result.get('values') else [] - - # Determine new headers from items - new_keys = set().union(*(d.keys() for d in items)) - # Identify headers that need to be added (not already existing) - headers_to_add = [key for key in new_keys if key not in existing_headers] - # New comprehensive headers list, preserving the order of existing headers and adding new ones at the end - comprehensive_headers = existing_headers + headers_to_add - - # If there are headers to add, update the first row with comprehensive headers - if headers_to_add: - update_body = {'values': [comprehensive_headers]} - service.spreadsheets().values().update( - spreadsheetId=sheets_id, range="Sheet1!A1", - valueInputOption='RAW', body=update_body - ).execute() - - # Prepare the data rows according to the comprehensive headers list - values = [] - for item in items: - row = [item.get(header, "") for header in comprehensive_headers] # Fill missing keys with empty string - values.append(row) - - body = {'values': values} - - # Append the data rows - if values: - append_body = {'values': values} - append_result = service.spreadsheets().values().append( - spreadsheetId=sheets_id, range=range_name, - valueInputOption='RAW', insertDataOption='INSERT_ROWS', body=append_body - ).execute() - print(f"{append_result.get('updates').get('updatedRows')} rows have been added to the sheet.") - - -``` - -### Step 3: Configure secrets (5min) - -For the custom destination, you can follow this example. Configure the source as instructed in the source [documentation](https://dlthub.com/devel/dlt-ecosystem/verified-sources/shopify). - - **`secrets.toml`** - -```toml -[destination.google_sheets] -credentials_json = ''' -{ - "type": "service_account", - "project_id": "your_project_id", - "private_key_id": "your_private_key_id", - ... -} -''' -``` - -**`config.toml`** - -```toml -sheets_id = "1xj6APSKhepp8-sJIucbD9DDx7eyBt4UI2KlAYaQ9EKs" -``` - -### Step 4: Running the pipeline in `sheets_destination.py`(10min) - -Now, assuming you have a source function **`dict_row_generator()`**, you can set up and run your pipeline as follows: - -```py -# ... destination code from above - -# pass some destination arguments explicitly (`range_name`) -pipeline = dlt.pipeline("my_google_sheets_pipeline", destination=google_sheets(range_name="named_range")) - -# Use the source function and specify the resource "people_report" -def dict_row_generator(): - yield {"row": 1, 'a': "a"} - yield {"row": 2, 'b': "b"} - yield {"row": 3, 'c': "c"} - yield {"row": 1, 'a': 1} - yield {"row": 2, 'b': 2} - yield {"row": 3, 'c': 3} - - - -# Now, run the pipeline with the specified source -info = pipeline.run(dict_row_generator) - -``` - -In this setup, **`append_to_google_sheets`** acts as a custom destination within your dlt pipeline, pushing the fetched data to the specified Google Sheet. This method enables streamlined and secure data operations, fully utilizing Python's capabilities for Reverse ETL processes into Google Sheets. - -## What does dlt do for me here? - -Using dlt for reverse ETL instead of plain Python, especially with its **`@dlt.destination`** decorator, provides a structured framework that streamlines the process of data integrating into various destinations. Here’s how the dlt decorator specifically aids you compared to crafting everything from scratch in plain Python: - -### Faster time to Production grade pipelines - -The **`@dlt.destination`** decorator significantly reduces the need for custom boilerplate code. It provides a structured approach to manage batch processing, error handling, and retries, which would otherwise require complex custom implementations in plain Python. This built-in functionality ensures reliability and resilience in your data pipelines. - -### Focus on custom business logic and adding value - -The flexibility of creating custom destinations with dlt shifts the focus from the possibilities to the necessities of your specific use case. This empowers you to concentrate on implementing the best solutions for your unique business requirements. - -### Scalability and efficient resource use - -dlt facilitates efficient handling of large data loads through chunking and batching, allowing for optimal use of computing resources. This means even small worker machines can stream data effectively into your chosen endpoint instead of wasting a large machine waiting for the network. The library design supports easy scaling and adjustments. Making changes to batch sizes or configurations is straightforward, ensuring your data pipelines can grow and evolve with minimal effort. This approach simplifies maintenance and ensures that once a solution is implemented, it's broadly applicable across your projects. - -### In Conclusion - -Reverse ETL is just a piece of the ETL puzzle. It could be done cleaner and better when done in Python end to end. - -Tools will always appeal to the non-technical folks. However, anyone with the ability to do Python pipelines can do Reverse ETL pipelines too, bringing typical benefits of code vs tool to a dev team - customisation, collaboration, best practices, etc. - -So read more about [how to built a dlt destination](https://dlthub.com/devel/dlt-ecosystem/destinations/destination) and consider giving it a try in your next reverse ETL pipeline. diff --git a/docs/website/blog/2024-03-25-smart-dashboarding-tools.md b/docs/website/blog/2024-03-25-smart-dashboarding-tools.md deleted file mode 100644 index de97f7f2b1..0000000000 --- a/docs/website/blog/2024-03-25-smart-dashboarding-tools.md +++ /dev/null @@ -1,140 +0,0 @@ ---- -slug: smart-dashboarding-tools -title: "What is so smart about smart dashboarding tools?" -image: https://storage.googleapis.com/dlt-blog-images/ai_sql.png -authors: - name: Hiba Jamal - title: Data Science intern at dltHub - url: https://github.com/hibajamal - image_url: https://avatars.githubusercontent.com/u/35984866?v=4 -tags: [dashboarding, analyst, LLMs] ---- - - - -## The advent of ChatGPT... -...sparked widespread speculation about the future of *many* professions, analytics included. Now, over a year later, and with an array of GPT and LLM-powered analytics tools at our disposal, we're in a stronger position to assess their intelligence and capabilities. - -In this article, we explore ThoughtSpot, known for its simplicity and strong data democracy practices. However, our focus narrows to Sage, its LLM assistant, and examining how it can empower or replace analytics techniques carried out by different analysts. - -## Analysts: Fallen victims of AI or not? - -The data analyst's role encompasses various job descriptions – from data science to dashboarding, data pipeline management, and even ML engineering. However, for this blog, we'll focus on the four key analytics components or techniques that help a company achieve its goals, as outlined by [Gartner](https://www.gartner.com/en/topics/data-and-analytics#q8). - -**Gartner’s categories:** - -| Categories | What does it solve? | Techniques | Example | -|--------------|---------------------|------------|--------------------------------------------------------------------------------------------------------------------------| -| Descriptive | Answers the “What happened/is happening?” questions | Data aggregation, pivot tables, mean, median, mode, variance, etc. | What was our top selling product of 2023? | -| Diagnostic | Answers the “Why/How?” questions | Drill down, correlation, Regression/multivariate analysis (for understanding relationships), segmentation, etc. | Why is this our top selling product (what are the common characteristics)? Or, What is the relationship between X and Y? | -| Prescriptive | Answers the “Now what?” | Setting rules/thresholds/goals based on the other 3 categories’ results. | Based on past retention curves of different user segments, which segments should we chase more? | -| Predictive | Forecasting and predicting outcomes | Probability distributions, Regression analysis (for prediction), other ML, etc. | What is our potential revenue for the next 2 years? | - -There have been solutions around utilizing LLMs to solve these analytics strategies, some of these attempts can be found on opensource sources and others as commercial products. - -For example, [Mariya Mansurova at Wise](https://towardsdatascience.com/can-llms-replace-data-analysts-building-an-llm-powered-analyst-851578fa10ce) created a GPT driven LLM agent that can do descriptive analytics and other reporting tasks. - -Other commercially existing solutions include: -- [ThoughtSpot’s Sage AI](https://www.thoughtspot.com/product/sage), an LLM analyst you can ask questions to about your data in simple language. -- [Pecan.ai](http://Pecan.ai), which creates predictive models based on cases described in simple language. -- [SnapLogic](https://www.snaplogic.com/), which designs data workflows based on reporting needs through its generative integration capabilities. - -ThoughtSpot’s Sage is powered by GPT - and as easy as GPT has made our lives, every GPT or any LLM user understands the importance of engineering a prompt good enough to actually get the answer one wants from the LLM. This might be a challenge open to AI driven analytics tools on how they cater to different types of users; for example, a business user can ask the same question differently than an analyst. - -In this article, we've chosen ThoughtSpot's Sage as our emblem for AI-driven analytics. We'll assess its performance across various analytics scenarios, aligned with the four categories previously defined. Our discussion will explore whether AI, through Sage, serves to replace or enhance analysts these analytics domains. The style of questions we will ask Sage will be a mix of what can be posed in the language of a business user and an analyst. - -## The data & data model - -The data that’ll be used for this experiment will be from the HubSpot CRM, regarding various deals, companies and contacts and different stages of their journey. This data was populated and then pulled via the [HubSpot API in Python](https://developers.hubspot.com/docs/api/overview) and then [loaded into BigQuery](https://dlthub.com/docs/dlt-ecosystem/verified-sources/hubspot) via `dlt`. The data was structured into different tables by `dlt` and final model looks as follows: - -![data model](https://storage.googleapis.com/dlt-blog-images/smart-dashboarding-data-model.png) - -It is important to note how the child table, `companies__deals` show the [association](https://developers.hubspot.com/docs/api/crm/associations) between the `deals` and `companies` tables. In other words, it shows which deal exists in the pipeline for which company. This model will be useful while trying to do some diagnostic analysis of different deals. - -## Evaluating Sage AI - -Before diving in, it's important to note that ThoughtSpot can connect to [dbt's semantic layer](https://dlthub.com/docs/blog/dlt-dbt-semantic-layer), which helps contextualize the questions asked - for example, by making clear what a certain success metric might mean. However, we haven't set up any semantic definitions or pre-built dashboards for this data. Our analysis will solely rely on raw data modeled by `dlt`. This approach might limit our ability to fully showcase ThoughtSpot's capabilities and the potential of combining AI in analytics with semantic modeling. Nonetheless, our focus here is on evaluating Sage AI's inference capabilities with raw data, across all categories except for prescriptive analytics, which leans more towards strategic business decision-making. - -The lack of semantic definitions and other dashboards meant that for each question asked to Sage, we had to specify exactly which table it should look into, to find a result. For example: - -![example question](https://storage.googleapis.com/dlt-blog-images/smart-dashboarding-question-example.png) - -Let’s begin asking questions and see how Sage answers! The framing of each question is exactly as it was asked to Sage. - -### Descriptive Analytics - -1. How many companies do we have? ✅ -2. How many companies do we serve? ✅ -3. How many deals do we have by month? ✅ -4. Deals broken by industry, shown as percentages ❌ -5. Revenue of a deal by stage ❌ Proportion of each deal stage’s revenue ❌ -6. What percentage of companies is in the computer software industry? ✅ -7. Show revenue by month ✅ **Worked even though the revenue column is named “amount” - it could infer!** -8. How many deals are signed by each company? **✅** - - This was by far the most astounding result! The underlying model is `companies__deals`, and it contains the unnested information for what deals belong to which company. The schema looks like this: -

- - ![schema](https://storage.googleapis.com/dlt-blog-images/smart-dashboarding-data-model-2.png) - - a. Unnested means that there is a parent table, which here is `companies`, and a child table, which here is `deals`. A company can have several deals. - - b. The `_dlt_parent_id` then refers to a company id, saved in the companies table. This is a `dlt` assigned primary key to a company. Whereas, the `value` field is a HubSpot defined primary key to a company. Both saved as foreign keys in this table. - - c. The `deals_id` is also therefore present in the `deals` table. - - d. Whereas, `_dlt_list_idx` is another column to keep track of rows during the process of unnesting - courtesy of `dlt`. - Perhaps given the naming convention of the table; `companies__deals`, and the word parent in the columns, Sage was able to infer the connection between the two. Here is the result: - - ![outcome](https://storage.googleapis.com/dlt-blog-images/smart-dashboarding-outcome.png) - - To extend this result to include company names, I added joins in the ThoughtSpot data model as allowed by the tool, but it still did not seem to make a difference when it came to replacing the foreign keys with names of the companies. Nonetheless, the child table that `dlt` put into place still served its purpose for Sage to understand what it is, and that is a remarkable feat for both the tools! -9. Best deals **✅** - - Showed by revenue/amount in descending order. -10. Least successful industry? **✅** - - **Showed by deals lost.** Solving this question by using the status of deals (won/lost), rather than the revenue, as in the last prompt, shows the versatility of Sage and its understanding of data models. - -**Summary**: Worked impressively well on the different types of questions asked, unless speaking on proportions. - -### Diagnostic Analytics - -1. What are the shared characteristics of top 5 deals? ❌ - it tried by showing all columns of 5 highest amounts. -2. Drill through most successful deals **✅ - showed characteristics of most successful deals by revenue, and success of deal closing**. -3. What do the most successful deals have in common? ❌ - showed individual deal information as above. -4. Cluster deals ❌ - showed all data. -5. Segment deals ❌ - showed all data. -6. Cluster deals by amount and industry ❌ - showed a useful graph between the two columns but no grouping. -7. Relationship between amounts in closed-won and closed-lost ❌ -8. Regression with closed-won and closed-lost ❌ - -**Summary**: Does not work fairly well, will not work at all for business users. The area in ThoughtSpot where queries can be crafted with AI will answer most of these questions, but this tool would more so empower analysts than business users. - -### Predictive Analytics - -1. Probability of closing deals ❌ - showed by industry (however, it is a simple probability calculation, not a future prediction). -2. Probability of closing deals in 2024/in the future/in the next 3 months ❌ -3. Predict/estimate/forecast revenue for 2024 ❌ -4. If we keep acquiring deals at the same rate as historical, how many will we have in 2024? ❌ - -**Summary**: Works well for probabilities but not predictions - but that was a bit of a stretch anyway, it would be something that would fall into the Forte of Pecan.ai. Sage instead relied on probability and aggregate values for existing data (filtered on future dates, like 2024). - -## Scores -The summary of our findings was quite predictable: Sage excelled at answering descriptive analytics questions more so than in diagnostic or predictive contexts. Its performance in interpreting descriptive queries was notably impressive. - -The score: - -- Descriptive analytics: **6/8 (75%)** -- Diagnostic Analytics: **1/8 (12.5%)** -- Predictive Analytics: **0/4 (0%)** - -# Conclusion - -Regarding the debate on whether smart dashboarding or AI driven analytics tools could displace analysts, it appears that for basic descriptive tasks, especially when combined with **semantic data definitions**, analysts could potentially be replaced. This ties back to how much clarity in context is given to LLMs. - -The importance of a **solid data model** was highlighted, proving essential for an LLM like Sage to accurately understand or deduce business user needs. However, when it comes to diagnostic tasks, AI in LLMs still has progress to make before they can fully replace analysts. Instead, they are more likely to empower analysts by offering solutions via interfaces like ChatGPT and handling descriptive tasks to save time. - -In translating this to an **analysts toolkit of capabilities**, Sage worked well in instances where single **simple SQL statements** can be executed. This excludes the usage of calculations that can be made to work on window functions and other partition statements in SQL. Whereas, joins have to be specified in the data model if the data is to be pulled from multiple tables. From these SQL statements, Sage was able to construct **good visualizations** as well. - -However, where the metrics or calculations surpassed the complexity of simple SQL statements to Python scripts, LLMs like ChatGPT can be used for **script generation purposes**. Since, as it stands today, GPT is better at writing code to calculate residuals or function coefficients than actually calculating these things itself. \ No newline at end of file diff --git a/docs/website/blog/2024-03-26-second-data-setup b/docs/website/blog/2024-03-26-second-data-setup deleted file mode 100644 index 12b032eef2..0000000000 --- a/docs/website/blog/2024-03-26-second-data-setup +++ /dev/null @@ -1,123 +0,0 @@ ---- -slug: second-data-setup -title: The Second Data Warehouse, aka the "disaster recovery" project -image: https://storage.googleapis.com/dlt-blog-images/second_house.png -authors: - name: Adrian Brudaru - title: Open source Data Engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [data setup, disaster recovery] ---- - -# The things i've seen - -The last 5 years before working on dlt, I spent as a data engineering freelancer. -Before freelancing, I was working for "sexy but poor" startups where building fast and cheap was a religion. - -In this time, I had the pleasure of doing many first time setups, and a few "rebuilds" or "second time setups". - -In fact, my first freelancing project was a "disaster recovery" one. - -A "second time build" or "disaster recovery project" refers to the process of re-designing, re-building, or significantly -overhauling a data warehouse or data infrastructure after the initial setup has failed to meet the organization's needs. - -![dipping your toes in disaster](https://storage.googleapis.com/dlt-blog-images/disaster-2.png) - -## The first time builds gone wrong - -There's usually no need for a second time build, if the first time build works. Rather, a migration might cut it. -A second time build usually happens only if -- the first time build does not work, either now or for the next requirements. -- the first time build cannot be "migrated" or "fixed" due to fundamental flaws. - -Let's take some examples from my experiences. -Example 1: A serial talker takes a lead role at a large, growing startup. They speak like management, so management trusts. A few years later - - half the pipelines are running on Pentaho + windows, the other are python 2, 3 and written by agencies. - - The data engineering team quit. They had enough. - - The remaining data engineers do what they want - a custom framework - or they threaten to quit, taking the only knowledge of the pipelines with them. - - Solution: Re-write all pipelines in python3, replace custom framework with airflow, add tests, github, and other best pratices. - -Example 2: A large international manufacturing company needed a data warehouse. - - Microsoft sold them their tech+ consultants. - - 2 years later, it's done but doesn't work (query time impossible) - - Solution: Teach the home DE team to use redshift and migrate. - -Example 3: A non technical professional takes a lead data role and uses a tool to do everything. - - same as above but the person also hired a team of juniors - - since there was no sudden ragequit, the situation persisted for a few years - - after they left, the remaining team removed the tool and re-built. - -Example 4: A first time data hire introduces a platform-like tool that's sql centric and has no versioning, api, or programmatic control. - - after writing 30k+ lines of wet sql, scheduling and making them dependent on each other in this UI tool (without lineage), the person can no longer maintain the reports - - Quits after arguing with management. - - Solution: Reverse engineer existing reports, account for bugs and unfulfilled requirements, build them from scratch, occasionally searching the mass of sql. Outcome was under 2k lines. - -Example 5: A VC company wants to make a tool that reads metrics from business apps like google ads, Stripe. - - They end up at the largest local agency, who recommends them a single - tenant SaaS MDS for 90k to set up and a pathway from there - - They agreed and then asked me to review. The agency person was aggressive and queried my knowledge on unrelated things, in an attempt to dismiss my assessment. - - Turns out the agency was selling "installing 5tran and cleaning the data" for 5k+ per source, and some implementation partners time. - - I think the VC later hired a non technical freelancer to do the work. - -# Who can build a first time setup that scales into the future? - -The non-negotiable skills needed are -- Programming. You can use ETL tools for ingestion, but they rarely solve the problem fully (under 20% in my respondent network - these are generally <30 people companies) -- Modelling. Architecture first, sql second, tools third. -- Requirement collection. You should consult your stakeholders on the data available to represent their process, and reach a good result. Usually the stakeholders are not experts and will not be able to give good requirements. - -## Who's to blame and what can we do about it? - -I believe the blame is quite shared. The common denominators seem to be -- A lack of technical knowledge, -- tools to fill the gap. -- and a warped or dishonest self representation (by vendor or professional) - -As for what to do about it: -If you were a hiring manager, ensure that your first data hire has all the skills at their disposal, and make sure they don't just talk the talk but walk the walk. Ask for references or test them. - -But you aren't a hiring manager - those folks don't read data blogs. - -So here's what you can do -- Ensure all 3 skills are available - they do not need to all be in one person. You could hire a freelance DE to build first, and a technical analyst to fulfil requests and extend the stack. -- Let vendors write about first data hire, and "follow the money" - Check if the advice aligns with their financial incentive. If it does, get a second opinion. -- Choose tooling that scales across different stages of a data stack lifecycle, so the problem doesn't occur. -- Use vendor agnostic components where possible (for example, dlt + sqlmesh + sql glot can create a db-agnostic stack that enables you to switch between dbs) -- Behave better - the temptation to oversell yourself is there, but you could check yourself and look for a position where you can learn. Your professional network could be your biggest help in your career, don't screw them over. -- Use independent freelancers for consulting. They live off reputation, so look for the recommended ones. - -## How to do a disaster recovery? - -The problem usually originates from the lack of a skill, which downstreams into implementations that don't scale. -However, the solution is often not as simple as adding the skill, because various workarounds were created to bridge that gap, and those workarounds have people working on them. - -Simply adding that missing skill to the team to build the missing piece would create a redundancy, which in its resolution would kick out the existing workarounds. -But workarounds are maintained by roles, so the original implementer will usually feel their position threatened; -This can easily escalate to a people conflict which often leads with the workaround maker quitting (or getting fired). - -How to manage the emotions? -- Be considerate of people's feelings - you are brought in to replace their work, so make it a cooperative experience where they can be the hero. -- Ask for help when you are not sure about who has the decision over an area. - -How to manage the technical side? -- Ensure you have all the skills needed to deliver a data stack on the team. -- If the existing solution produces correct results, use it as requirements for the next - for example, you could write tests that check that business rules are correctly implemented. -- Clarify with stakeholders how much the old solution should be maintained - it will likely free up people to work on the new one. -- Identify team skills that can help towards the new solution and consider them when choosing the technology stack. - - -## What I wish I knew - -Each "disaster recovery" project was more than just a technical reboot; it was a testament to the team's adaptability, -the foresight in planning for scalability, and, importantly, the humility to recognize and rectify mistakes. -"What I Wish I Knew Then" is about the understanding that building a data infrastructure is as much about -building a culture of continuous learning and improvement as it is about the code and systems themselves. - - -### Want to discuss? - -Agencies and freelancers are often the heavy-lifters that are brought in to do such setups. -Is this something you are currently doing? -Tell us about your challenges so we may better support you. - -[Join our slack community](https://dlthub.com/community) to take part in the conversation. \ No newline at end of file diff --git a/docs/website/blog/2024-03-28-easy-hard-possible.md b/docs/website/blog/2024-03-28-easy-hard-possible.md deleted file mode 100644 index edfadebcac..0000000000 --- a/docs/website/blog/2024-03-28-easy-hard-possible.md +++ /dev/null @@ -1,127 +0,0 @@ ---- -slug: yes-code-elt -title: "Yes code ELT: dlt make easy things easy, and hard things possible" -image: https://storage.googleapis.com/dlt-blog-images/easy-things-easy.png -authors: - name: Adrian Brudaru - title: Open source Data Engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [full code etl, yes code etl, etl, pythonic] ---- - -# There's code and then there's code - -The concept of simplicity and automation in a programming language is not new. -Perl scripting language had the motto "Perl makes easy things easy and hard things possible". - -The reason for this motto, was the difficulty of working with C, which requires more manual -handling of resources and also a compilation step. - -Perl scripts could be written and executed rapidly, making it ideal for tasks that needed -quick development cycles. This ease of use and ability to handle complex tasks without -cumbersome syntax made Perl incredibly popular in its heyday. - -Perl was introduced as a scripting language that emphasized getting things done. -It was created as a practical extraction and reporting tool, which quickly found -its place in system administration, web development, and network programming. - -## History repeats, Python is a language for humans - -![human-building](https://storage.googleapis.com/dlt-blog-images/easy-things-easy.png) - -Python took the philosophy of making programming more accessible and human-friendly even further. -Guido van Rossum created Python with the goal of removing the drudgery from coding, choosing to -prioritize readability and simplicity. This design philosophy makes Python an intuitive language -not just for seasoned developers but for beginners as well. Its syntax is clean and expressive, -allowing developers to write fewer lines of code for tasks that would require more in Perl or other languages. -Python's extensive standard library, along with its powerful data structures, contribute to its -ability to handle complex applications with ease. - -Python's widespread adoption across various domains, from web development to data science and machine -learning, is largely attributed to its accessibility. - -Its simple syntax resembles natural language, which lowers the barrier to entry for programming. -Compared to Perl, Python offers an even more organized and readable approach to coding, -making it an ideal teaching language that prepares new developers for future challenges in software development. - -And just like perl, it's used for data extraction and visualisation - but now it's done by normie humans, -not sysadmins or devs. - -# dlt makes easy things fast, and hard things accessible - -Following the principles of Perl and Python, dlt aimed to simplify the data engineering process. -dlt focuses on making the extraction and loading of data as straightforward as possible. - -## dlt makes easy things fast - -Starting from a simple abstraction like `pipeline.run(data, table_name="table")`, -where data can be any iterable such as a generator or dataframe, dlt enables robust loading. -Here is what the above function does, so you don't have to. -- It will (optionally) unpack nested lists into separate tables with generated join keys, and flatten nested dictionaries into a main row. -- If given a generator, it will consume it via microbatching, buffering to disk or external drives, never running out of memory (customisable). -- it will create "extract packages" of extracted data so if the downstream steps fail, it can resume/retry later. -- It will normalise the data into a shape that naturally fits the database (customisable). -- It will create "load packages" of normalised data so if the downstream steps fail, it can retry later. -- It infers and loads with the correct data types, for example from ISO timestamp strings (configurable). -- It can accept different types of write dispositions declaratively such as 'append', 'merge' and 'replace'. -- It will evolve the schema if we load a second time something with new columns, and it can alert the schema changes. -- It will even create type variant columns if data types change (and alert if desired). -- Or you can stop the schema from evolving and use the inferred schema or a modified one as a data contract -- It will report load packages associated with new columns, enabling passing down column level lineage - -That's a lot of development and maintenance pain solved only at its simplest. You could say, the dlt loader doesn't break, as long as it encounters common data types. -If an obscure type is in your data, it would need to be added to dlt or converted beforehand. - -### From robust loading to robust extraction - -Building on the simple loading abstraction, dlt is more than a tool for simple things. - -The next step in dlt usage is to leverage it for extraction. dlt offers the concepts of 'source' and 'resource', -A resource is the equivalent of a single data source, while a source is the group we put resources in to bundle them for usage. - -For example, an API extractor from a single API with multiple endpoints, would be built as a source with multiple resources. - -Resources enable you to easily configure how the data in that resource is loaded. You can create a resource by -decorating a method with the '@resource' decorator, or you can generate them dynamically. - -Examples of dynamic resources -- If we have an api with multiple endpoints, we can put the endpoints in a list and iterate over it to generate resources -- If we have an endpoint that gives us datapoints with different schemas, we could split them by a column in the data. -- Similarly, if we have a webhook that listens to multiple types of events, it can dispatch each event type to its own table based on the data. -- Or, if we want to shard a data stream into day-shards, we could append a date suffix in the resource name dynamically. - -Once we group resources into a source, we can run them together (or, we could still run the resources independently) - -Examples of reasons to group resources into sources. -- We want to run (load) them together on the same schedule -- We want to configure them together or keep their schemas together -- They represent a single API and we want to publish them in a coherent, easy to use way. - -So what are the efforts you spare when using dlt here? -- A source can function similar to a class, but simpler, encouraging code reuse and simplicity. -- Resources offer more granular configuration options -- Resources can also be transformers, passing data between them in a microbatched way enabling patters like enrichments or list/detail endpoints. -- Source schemas can be configured with various options such as pushing down top level columns into nested structures -- dlt's requests replacement has built in retries for non-permanent error codes. This safeguards the progress of long extraction jobs that could otherwise break over and over (if retried as a whole) due to network or source api issues. - - -### What else does dlt bring to the table? - -Beyond the ease of data extraction and loading, dlt introduces several advanced features that further simplify data engineering tasks: - -Asynchronous operations: dlt harnesses the power of asynchronous programming to manage I/O-bound and network operations efficiently. This means faster data processing, better resource utilization, and more responsive applications, especially when dealing with high volumes of data or remote data sources. - -Flexible destinations and reverse ETL: dlt isn't just about pulling data in; it's about sending it where it needs to go. Whether it's a SQL database, a data lake, or a cloud-based storage solution or a custom reverse etl destination, dlt provides the flexibility to integrate with various destinations. - -Optional T in ETL: With dlt, transformations are not an afterthought but a core feature. You can define transformations as part of your data pipelines, ensuring that the data is not just moved but refined, enriched, and shaped to fit your analytical needs. This capability allows for more sophisticated data modeling and preparation tasks to be streamlined within your ELT processes. - -Data quality and observability: dlt places a strong emphasis on data quality and observability. It includes features for schema evolution tracking, data type validation, and error handling, and data contracts, which are critical for maintaining the integrity of your data ecosystem. Observability tools integrated within dlt help monitor the health and performance of your pipelines, providing insights into data flows, bottlenecks, and potential issues before they escalate. - -Community and ecosystem: One of the most significant advantages of dlt is its growing community and ecosystem. Similar to Python, dlt benefits from contributions that extend its capabilities, including connectors, plugins, and integrations. This collaborative environment ensures that dlt remains at the forefront of data engineering innovation, adapting to new challenges and opportunities. - -In essence, dlt is not just a tool but a comprehensive one stop shop that addresses the end-to-end needs of modern data ingestion. By combining the simplicity of Python with the robustness of enterprise-grade tools, dlt democratizes data engineering, making it accessible to a broader audience. Whether you're a data scientist, analyst, or engineer, dlt empowers you to focus on what matters most: deriving insights and value from your data. - -## Conclusion - -As Perl and Python have made programming more accessible, dlt is set to transform data engineering by making sophisticated data operations accessible to all. This marks a significant shift towards the democratization of technology, enabling more individuals to contribute to and benefit from the digital landscape. dlt isn't just about making easy things fast and hard things accessible; it's about preparing a future where data engineering becomes an integral part of every data professional's toolkit. diff --git a/docs/website/blog/2024-04-05-governance-democracy-mesh.md b/docs/website/blog/2024-04-05-governance-democracy-mesh.md deleted file mode 100644 index 4eb5471bf0..0000000000 --- a/docs/website/blog/2024-04-05-governance-democracy-mesh.md +++ /dev/null @@ -1,227 +0,0 @@ ---- -slug: governance-democracy-mesh -title: "Shift Left Data Democracy: the link between democracy, governance, data contracts and data mesh." -image: https://storage.googleapis.com/dlt-blog-images/shift-left-democracy.png -authors: - name: Adrian Brudaru - title: Open source Data Engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [data mesh, shift left, data democracy, pythonic] ---- - - -![shift-left-data-democracy](https://storage.googleapis.com/dlt-blog-images/shift-left-democracy.png) - - -Definitions of how I use the terms: - -Data Governance: A system of oversight and guidance over the data, much like a government is a system of oversight and guidance for a country. The opposite of governance is anarchy, chaos, and entropy. - -Data Democracy: A type of governance that ensures stakeholders are part of the governance. - -Shift left: Assuming data flows from left to right, shift left represents a focus towards the origin. - -Data Mesh: A decentralized data management strategy that treats data as a product, with domain-specific teams managing its quality, governance, and lifecycle. - -## Shift Left Data Democracy: From Access to Involvement - -In the traditional view, data democracy was largely about democratizing access—ensuring that everyone across the organization could easily retrieve and analyze data. This was a crucial step forward, breaking down silos and making information more available than ever before. However, as we've evolved, so too has our understanding of what true data democracy entails. - -Shift left data democracy represents a more profound change. It's not just about making data accessible post-factum; it's about involving a broader spectrum of roles in the very processes of data ingestion, processing, and management. This approach extends the principles of democracy to the entire data lifecycle, beginning with data ingestion. - -It's a shift from mere consumption to participation, where access is just the beginning. - -### Data mesh is the driver - -Just as the data mesh concept emerged to address the complexities of managing data in a distributed, domain-oriented environment, we now see a need for technology to evolve in parallel. The goal? -To put data sources directly in the hands of the people who use them. This means giving teams the tools and autonomy to manage and process their data, ensuring governance and quality from the outset and throughout the data's lifecycle. - -This shift left approach to data democracy aligns with the idea behind data mesh, -recognizing that effective data management and governance are not centralized activities but -distributed responsibilities. By involving more stakeholders from the very start of the data flows, -we're not just democratizing access; we're democratizing the entire data flows. - -## Governance, from a power game, to a team sport; A brief history of how we got here - -Building a data warehouse is a beaten path - but how to go from technical solution to organisation-wide application? - -Building a data warehouse for reporting on some business processes is a good start, but in order to leverage that data we need a culture -to do so and the skills to do it correctly. - -While a centralised solution enables a skilled team to deliver results, these results are often inflexible without hands on help - so how can the -organisation be expected to become data driven? The process of tracking a goal, creating hypotheses, starting an experiment -and then tracking outcomes is much more complex than that of tracking a metric in a dashboard. - -Cue, the move to democratic data access. - - -### From Monarchy to Democracy: Data access for the people! - -The move from a centralised system to a democratic system comes from the competitive use of data. In a centralised system where only management has access, -data is used to keep track of goals. To enable people to use that data to do something about the goals, the user must have access and understanding of the data. - -As with anything, the first step is obvious: Give people access - without it, there is no progress. -However, once we do that, the reality rears its ugly head: Access is not enough! - -Democratic access is great but as long as the data producers are not providing clean documented data , we don't have a democracy. -Instead what we have is reality-adjusted communism - we all have plentiful access to the same black box or garbage that the big central team put in. - - -![monarchy-to-democracy](https://storage.googleapis.com/dlt-blog-images/sld-monarchy-to-democracy.png) - - -So, after democratizing data access, the next challenge was to answer the obvious question: So what does this data mean? - -Turns out, the central team doesn't quite know either - it's rather the owner of the process we track, the data producer, -that understands how the data they emit links to the real life process it tracks. - -So how do we go from having data to understanding what it means? - -### From democratizing access to democratizing literacy though embedded analysts - -One easy way to help teams understand the data is to give them an analyst resource. And what better than someone who knows their domain? - -Cue the embedded analysts. These folks are crucial in bridging the gap between data capabilities and domain-specific needs. -By positioning data experts within specific business units, organizations can ensure that the insights generated are highly -relevant and immediately applicable to the domain's unique challenges and opportunities. - -![democracy-to-embedded](https://storage.googleapis.com/dlt-blog-images/sld_demoracy_to_embedded.png) - - -This placement helps in several key ways: -* Domain expertise meets data munging: Embedded analysts develop a deep understanding of the specific challenges and workflows of the business unit they are part of, which enables them to tailor data models and analytics strategies effectively. -* Data literacy: These analysts act as champions of data within their teams, educating and training non-data savvy members on data-driven decision-making processes. This upskills the team and increases the overall data literacy within the unit. -* Faster response times: Being close to the operational realities of the business unit, embedded analysts can deliver faster, more targeted responses to data queries and needs, reducing the time from question to insight. - -And just as we started, we solve another increment of the problem, which reveals the next. - -Now that we can analyse the data, we need the data. But, it turns out the data we have is dirty, and we are missing some outright. - -So let's solve the next problem: Data sources and quality. - -### The Advent of Data Mesh: Solving the data source problem - -Wow, well we went quite a way to get here, and a decade after talking about democratization, we are starting to -recognize that **governance is an activity, not a process.** And democracy is more work than we originally thought. - - -![embedded-to-mesh](https://storage.googleapis.com/dlt-blog-images/sld_embedded_to_mesh.png) - - -The data mesh architecture marks a significant evolution in the data democratization journey. Data mesh advances the principles of -embedded analysts by decentralizing data ownership entirely, promoting domain-specific control over data assets. - -This architectural approach is based on the idea that data should not only be accessible but also actionable across various -sections of an organization without bottlenecks. - -And just like governments hire a lot of people, turns out, a governance system also needs people to work for it. - -Data mesh tries to solve much of that by embracing domain-oriented decentralization. In this model, data is treated as a product -with the domain teams as the product owners. These teams are responsible for ensuring their data's quality and relevance, -significantly reducing the latency issues found in centralized systems by eliminating the lengthy processes of data cleansing and approval. - -Further, data mesh empowers teams with the necessary tools and authority to manage their data effectively, fostering a culture where data -is a valuable asset across all levels of the organization. This approach not only supports rapid decision-making and innovation -within teams but also offers scalability and flexibility as organizational data needs evolve, allowing domains to independently expand -their data operations without a comprehensive overhaul of the central data infrastructure. - -Of course, at this point having a complete or partial data platform that offers some governance starts to become very important as we don't -want individual business units to be burdened with responsibity but without proper tooling - or the outcome will be high entropy. - -### From retrofitting governance to applying it from the start: Shift left data democracy! - - -![mesh-to-sldd](https://storage.googleapis.com/dlt-blog-images/mesh_to_sldd.png) - - -Imagine a world where your company's data sources can just be picked and unpacked in the destination of your choice by analysts - not through -an external saas tool, but via an internal service portal. - -Shift-Left Data Democracy (SLDD) is a concept in data management that advocates for integrating data governance early in the data lifecycle. -This approach shifts governance practices from being a retrospective or reactionary activity to an integral part of the initial design and -development phases of data systems. By doing so, SLDD aims to embed governance, quality controls, and compliance measures at the point of data -creation and throughout its subsequent handling. - -By embedding governance early in the data lifecycle, SLDD eliminates the complex and costly process of retrofitting governance frameworks -to mature datasets and systems. This proactive approach leads to streamlined operations, reducing both the complexity and the cost -traditionally associated with late-stage governance implementation. - -This early incorporation of governance enhances transparency throughout the entire process. Stakeholders gain a clear understanding of how data -is managed and governed from the start, building trust and ensuring compliance. - -What's revolutionary about SLDD is that a governed data source can easily be unfolded into a normalised or analytical model. - -This "ad hoc data mart" can be used without central bottlenecks and easily customised to fit specific cases without having to reach modelling consensus with other teams. -This built-in modularity avoids the creation of more bottlenecks downstream, enabling fast research and development where needed. - -Further, a well-defined governance framework enables greater innovation within safe boundaries. Teams can explore and innovate knowing -they are aligned with compliance and operational standards, which speeds up experimentation and development cycles. This environment encourages -a more dynamic approach to data handling, where creativity is not stifled by fear of violating governance protocols. By treating governance -as an integral part of the data management process rather than a hindrance, SLDD fosters a culture where data truly drives innovation. - - -### Distinction between data mesh and shift-left data democracy - -While both concepts advocate for decentralized governance, they focus on different aspects of the data lifecycle. -Data mesh architecture emphasizes the structural and operational decentralization of data management, granting autonomy -to domain-specific teams. Shift-left data democracy, on the other hand, extends this decentralization to the very -beginning of the data lifecycle, advocating for early involvement and broad stakeholder participation in governance processes. - -The main difference is: Mesh is applied post-factum. For newly built systems, starting with governance as a technical universal standard is less complex. -And while mesh grants autonomy, the entropy raises complexities and cost; on the other hand formalising and standardising responsibilities from the start of data production reduces entropy. - -## Practicing shift-left data democracy - -So how do we do it? Is this a future or can we already do it? - -We asked ourselves the same and we are working towards fully supporting the standard. - -### Ensuring quality at the source - -Start with having quality control embedded in the source. Here's what I mean - start with a clear schema for your data, and ensure you have a strategy to adapt to change. -One such strategy could be having data contracts, refusing and data that does not fit the defined schema. The other strategy, would be evolving the schema into a staging -layer and notifying changes, so the engineering analyst can look into the data to understand what happened and correctly deal with the change. - -At dlt we support schema evolution and data contracts. [docs](https://dlthub.com/docs/general-usage/schema-contracts). - -### Metadata for full lineage - -Column and row level lineage are a basic necessity of development and traceability, so ensure each ingested package is annotated with source and time. Keep track of when -columns are added to a source. Associate those schema changes with the corresponding load package to achieve column and row level lineage already from the ingestion layer, -referring to a source defined as `pipeline` code, not `table`. - -Besides data lineage, you want semantic metadata. What does a source actually represent as a business entity or process? To govern data semantically, we would need semantic -tags at the source. This would enable us to know how to work with the data. For example, we could generate data vault, 3nf, star schema or activity schema models -algorithmically starting from annotated json documents. - -Besides business entities, domains or processes, semantic tags could also designate PII, security policies, or anything actionable. -For example, PII tags could enable automatic lineage documentation and governance, while access tags could enable automatic access policies or automatic data modelling. - -dlt currently supports column and row level lineage, as well as schema comments - which could be used as annotations. - -### The role of the Data platform engineer will grow - -In a shift left data democracy, the data platform engineer is a key character, as much as a CTO is in an organisation. By having a data platform engineer you ensure your -data governance is done with automated tooling, to support implementation and compliance. - -These data platform engineer becomes pivotal in empowering the democratization of data, providing the essential tooling and -infrastructure that allow teams across the organization to manage their data autonomously. - -Data platform engineers become enablers and facilitators, embedding governance and quality controls right from the start of the data lifecycle. Their work supports -the organization by ensuring that data management practices are not only compliant and secure but also accessible and intuitive for non-specialists (democratic). -This shift underlines a transition from centralized control to distributed empowerment, where data platform engineers support the broader goal of making data -accessible, manageable, and governable across the entire spectrum of the organization. - -## The future of data management - -![history_to_future](https://storage.googleapis.com/dlt-blog-images/sld_data_sociocracy.png) - -Are we heading towards semantically annotated data marts as code? Why not? We're in the age of serverless infrastructures, after all. -Could data sociocracy become the future? Would we eventually encourage the entire organisation to annotate data sources with their learnings? -Only time will tell. - -### Want to discuss? - -[Join the dlt slack community](https://dlthub.com/community) to take part in the conversation. - diff --git a/docs/website/blog/2024-04-11-second-data-setup.md b/docs/website/blog/2024-04-11-second-data-setup.md deleted file mode 100644 index c60eb2c5a5..0000000000 --- a/docs/website/blog/2024-04-11-second-data-setup.md +++ /dev/null @@ -1,121 +0,0 @@ ---- -slug: second-data-setup -title: The Second Data Warehouse, aka the "disaster recovery" project -image: https://storage.googleapis.com/dlt-blog-images/second_house.png -authors: - name: Adrian Brudaru - title: Open source Data Engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [data setup, disaster recovery] ---- - -# The things i've seen - -The last 5 years before working on dlt, I spent as a data engineering freelancer. -Before freelancing, I was working for "sexy but poor" startups where building fast and cheap was a religion. - -In this time, I had the pleasure of doing many first time setups, and a few "rebuilds" or "second time setups". - -In fact, my first freelancing project was a "disaster recovery" one. - -A "second time build" or "disaster recovery project" refers to the process of re-designing, re-building, or significantly -overhauling a data warehouse or data infrastructure after the initial setup has failed to meet the organization's needs. - -![dipping your toes in disaster](https://storage.googleapis.com/dlt-blog-images/disaster-2.png) - -## The first time builds gone wrong - -There's usually no need for a second time build, if the first time build works. Rather, a migration might cut it. -A second time build usually happens only if -* the first time build does not work, either now or for the next requirements. -* the first time build cannot be "migrated" or "fixed" due to fundamental flaws. - -Let's take some examples from my experiences. -Example 1: A serial talker takes a lead role at a large, growing startup. They speak like management, so management trusts. A few years later -* half the pipelines are running on Pentaho + windows, the other are python 2, 3 and written by agencies. -* The data engineering team quit. They had enough. -* The remaining data engineers do what they want - a custom framework - or they threaten to quit, taking the only knowledge of the pipelines with them. -* Solution: Re-write all pipelines in python3, replace custom framework with airflow, add tests, github, and other best pratices. - -Example 2: A large international manufacturing company needed a data warehouse. -* Microsoft sold them their tech+ consultants. -* 2 years later, it's done but doesn't work (query time impossible) -* Solution: Teach the home DE team to use redshift and migrate. - -Example 3: A non technical professional takes a lead data role and uses a tool to do everything. -* same as above but the person also hired a team of juniors -* since there was no sudden ragequit, the situation persisted for a few years -* after they left, the remaining team removed the tool and re-built. - -Example 4: A first time data hire introduces a platform-like tool that's sql centric and has no versioning, api, or programmatic control. -* after writing 30k+ lines of wet sql, scheduling and making them dependent on each other in this UI tool (without lineage), the person can no longer maintain the reports -* Quits after arguing with management. -* Solution: Reverse engineer existing reports, account for bugs and unfulfilled requirements, build them from scratch, occasionally searching the mass of sql. Outcome was under 2k lines. - -Example 5: A VC company wants to make a tool that reads metrics from business apps like google ads, Stripe. -* They end up at the largest local agency, who recommends them a single - tenant SaaS MDS for 90k to set up and a pathway from there -* They agreed and then asked me to review. The agency person was aggressive and queried my knowledge on unrelated things, in an attempt to dismiss my assessment. -* Turns out the agency was selling "installing 5tran and cleaning the data" for 5k+ per source, and some implementation partners time. -* The VC later hired a non technical freelancer to do the work. - -# Who can build a first time setup that scales into the future? - -The non-negotiable skills needed are -* Programming. You can use ETL tools for ingestion, but they rarely solve the problem fully (under 20% in my respondent network - these are generally <30 people companies) -* Modelling. Architecture first, sql second, tools third. -* Requirement collection. You should consult your stakeholders on the data available to represent their process, and reach a good result. Usually the stakeholders are not experts and will not be able to give good requirements. - -## Who's to blame and what can we do about it? - -I believe the blame is quite shared. The common denominators seem to be -* A lack of technical knowledge, -* tools to fill the gap. -* and a warped or dishonest self representation (by vendor or professional) - -As for what to do about it: -If you were a hiring manager, ensure that your first data hire has all the skills at their disposal, and make sure they don't just talk the talk but walk the walk. Ask for references or test them. - -But you aren't a hiring manager (those folks don't read this blog). - -So here's what you can do -* Ensure all 3 skills are available - they do not need to all be in one person. You could hire a freelance DE to build first, and a technical analyst to fulfil requests and extend the stack. -* Let vendors write about first data hire, and "follow the money" - Check if the advice aligns with their financial incentive. If it does, get a second opinion. -* Choose tooling that scales across different stages of a data stack lifecycle, so the problem doesn't occur. -* Use vendor agnostic components where possible (for example, dlt + sqlmesh + sql glot can create a db-agnostic stack that enables you to switch between dbs) -* Behave better - the temptation to oversell yourself is there, but you could check yourself and look for a position where you can learn. Your professional network could be your biggest help in your career, don't screw them over. -* Use independent freelancers for consulting. They live off reputation, so look for the recommended ones. - -## How to do a disaster recovery? - -The problem usually originates from the lack of a skill, which downstreams into implementations that don't scale. -However, the solution is often not as simple as adding the skill, because various workarounds were created to bridge that gap, and those workarounds have people working on them. - -Simply adding that missing skill to the team to build the missing piece would create a redundancy, which in its resolution would kick out the existing workarounds. -But workarounds are maintained by roles, so the original implementer will usually feel their position threatened; -This can easily escalate to a people conflict which often leads with the workaround maker quitting (or getting fired). - -How to manage the emotions? -* Be considerate of people's feelings - you are brought in to replace their work, so make it a cooperative experience where they can be the hero. -* Ask for help when you are not sure about who has the decision over an area. - -How to manage the technical side? -* Ensure you have all the skills needed to deliver a data stack on the team. -* If the existing solution produces correct results, use it as requirements for the next - for example, you could write tests that check that business rules are correctly implemented. -* Clarify with stakeholders how much the old solution should be maintained - it will likely free up people to work on the new one. -* Identify team skills that can help towards the new solution and consider them when choosing the technology stack. - - -## What I wish I knew - -Each "disaster recovery" project was more than just a technical reboot; it was a test to the team's adaptability and to their the humility to recognize and rectify mistakes. -What I wish I knew is that building a data infrastructure is as much about building a culture of continuous learning and improvement as it is about the code and systems themselves, and that they need to be fixed together - otherwise, one will break the other. - - -### Want to discuss? - -Agencies and freelancers are often the heavy-lifters that are brought in to do such setups. -Is this something you are currently doing? -Tell us about your challenges, so we may better support you. - -[Join our slack community](https://dlthub.com/community) to take part in the conversation. diff --git a/docs/website/blog/2024-04-12-portable-etl.md b/docs/website/blog/2024-04-12-portable-etl.md deleted file mode 100644 index 0e693fee6b..0000000000 --- a/docs/website/blog/2024-04-12-portable-etl.md +++ /dev/null @@ -1,155 +0,0 @@ ---- -slug: portable-elt -title: "Portable, embeddable ETL - what if pipelines could run anywhere?" -image: https://storage.googleapis.com/dlt-blog-images/embeddable-etl.png -authors: - name: Adrian Brudaru - title: Open source Data Engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [full code etl, yes code etl, etl, pythonic] ---- - -# Portable, embeddable ETL - what if pipelines could run anywhere? - -![embeddable etl](https://storage.googleapis.com/dlt-blog-images/embeddable-etl.png) - -## The versatility that enables "one way to rule them all"... requires a devtool - -A unified approach to ETL processes centers around standardization without compromising flexibility. -To achieve this, we need to be enabled to build and run custom code, bu also have helpers to enable us to standardise and simplify our work. - -In the data space, we have a few custom code options, some of which portable. But what is needed to achieve -universality and portability is more than just a code standard. - -So what do we expect from such a tool? -- It should be created for our developers -- it should be easily pluggable into existing tools and workflows -- it should perform across a variety of hardware and environments. - -## Data teams don't speak Object Oriented Programming (OOP) - -Connectors are nice, but when don't exist or break, what do we do? We need to be able to build and maintain those connectors simply, as we work with the rest of our scripts. - -The data person has a very mixed spectrum of activities and responsibilities, and programming is often a minor one. Thus, across a data team, while some members -can read or even speak OOP, the team will not be able to do so without sacrificing other capabilities. - -This means that in order to be able to cater to a data team as a dev team, we need to aknowledge a different abstraction is needed. - -### Goodbye OOP, hello `@decorators`! - -Data teams often navigate complex systems and workflows that prioritize functional clarity over object-oriented -programming (OOP) principles. They require tools that simplify process definition, enabling quick, readable, -and maintainable data transformation and movement. Decorators serve this purpose well, providing a straightforward -way to extend functionality without the overhead of class hierarchies and inheritance. - -Decorators in Python allow data teams to annotate functions with metadata and operational characteristics, -effectively wrapping additional behavior around core logic. This approach aligns with the procedural mindset -commonly found in data workflows, where the emphasis is on the transformation steps and data flow rather than the objects that encapsulate them. - -By leveraging decorators, data engineers can focus on defining what each part of the ETL process does—extract, -transform, load—without delving into the complexities of OOP. This simplification makes the code more accessible -to professionals who may not be OOP experts but are deeply involved in the practicalities of data handling and analysis. - -## The ability to run embedded is more than just scalability - -Most traditional ETL frameworks are architected with the assumption of relatively abundant computational resources. -This makes sense given the resource-intensive nature of ETL tasks when dealing with massive datasets. - -However, this assumption often overlooks the potential for running these processes on smaller, more constrained infrastructures, -such as directly embedded within an orchestrator or on edge devices. - -The perspective that ETL processes necessarily require large-scale infrastructure is ripe for challenge. In fact, -there is a compelling argument to be made for the efficiency and simplicity of executing ETL tasks, particularly web -requests for data integration, on smaller systems. This approach can offer significant cost savings and agility, -especially when dealing with less intensive data loads or when seeking to maintain a smaller digital footprint. - -Small infrastructure ETL runs can be particularly efficient in situations where real-time data processing is not -required, or where data volumes are modest. By utilizing the orchestrator's inherent scheduling and management -capabilities, one can execute ETL jobs in a leaner, more cost-effective manner. This can be an excellent fit for -organizations that have variable data processing needs, where the infrastructure can scale down to match lower demands, -thereby avoiding the costs associated with maintaining larger, underutilized systems. - -### Running on small workers is easier than spinning up infra - -Running ETL processes directly on an orchestrator can simplify architecture by reducing the number of -moving parts and dependencies. It allows data teams to quickly integrate new data sources and destinations with minimal -overhead. This methodology promotes a more agile and responsive data architecture, enabling businesses to adapt more swiftly -to changing data requirements. - -It's important to recognize that this lean approach won't be suitable for all scenarios, particularly where data volumes -are large or where the complexity of transformations requires the robust computational capabilities of larger systems. -Nevertheless, for a significant subset of ETL tasks, particularly those involving straightforward data integrations via web requests, -running on smaller infrastructures presents an appealing alternative that is both cost-effective and simplifies the -overall data processing landscape. - -### Dealing with spiky loads is easier on highly parallel infras like serverless functions - -Serverless functions are particularly adept at managing spiky data loads due to their highly parallel and elastic nature. -These platforms automatically scale up to handle bursts of data requests and scale down immediately after processing, -ensuring that resources are utilized only when necessary. This dynamic scaling not only improves resource efficiency -but also reduces costs, as billing is based on actual usage rather than reserved capacity. - -The stateless design of serverless functions allows them to process multiple, independent tasks concurrently. -This capability is crucial for handling simultaneous data streams during peak times, facilitating rapid data processing -that aligns with sudden increases in load. Each function operates in isolation, mitigating the risk of one process impacting another, -which enhances overall system reliability and performance. - -Moreover, serverless architectures eliminate the need for ongoing server management and capacity planning. -Data engineers can focus solely on the development of ETL logic without concerning themselves with underlying infrastructure issues. -This shift away from operational overhead to pure development accelerates deployment cycles and fosters innovation. - -## Some examples of embedded portability with dlt - -### Dagster's embedded ETL now supports `dlt` - enabling devs to do what they love - build. - -The "Stop Reinventing Orchestration: Embedded ELT in the Orchestrator" blog post by Pedram from Dagster Labs, -introduces the concept of Embedded ELT within an orchestration framework, highlighting the transition in data engineering from bulky, -complex systems towards more streamlined, embedded solutions that simplify data ingestion and management. This evolution is seen in -the move away from heavy tools like Airbyte or Meltano towards utilizing lightweight, performant libraries which integrate seamlessly into existing -orchestration platforms, reducing deployment complexity and operational overhead. This approach leverages the inherent capabilities of -orchestration systems to handle concerns typical to data ingestion, such as state management, error handling, and observability, -thereby enhancing efficiency and developer experience. - -dlt was built for just such a scenario and we are happy to be adopted into it. Besides adding connectors, dlt adds a simple way to build custom pipelines. - -Read more about it on [Dagster blog post on dlt](https://dagster.io/blog/expanding-dagsters-embedded-elt-ecosystem-with-dlthub-for-data-ingestion). - - -### Dagworks' `dlt` + `duckdb` + `ibis` + `Hamilton` demo - -The DAGWorks Substack post introduces a highly portable pipeline of all libraries, and leverages a blend of open-source Python libraries: dlt, Ibis, and Hamilton. -This integration exemplifies the trend towards modular, decentralized data systems, where each component specializes in a segment of the data handling process—dlt for extraction and loading, -Ibis for transformation, and Hamilton for orchestrating complex data flows. These technologies are not just tools but represent a -paradigm shift in data engineering, promoting agility, scalability, and cost-efficiency in deploying serverless microservices. - -The post not only highlights the technical prowess of combining these libraries to solve practical problems like message -retention and thread summarization on Slack but also delves into the meta aspects of such integrations. It reflects on the broader -implications of adopting a lightweight stack that can operate within diverse infrastructures, from cloud environments to embedded systems, -underscoring the shift towards interoperability and backend agnosticism in data engineering practices. This approach illustrates a shift -in the data landscape, moving from monolithic systems to flexible, adaptive solutions that can meet specific organizational needs -without heavy dependencies or extensive infrastructure. - -Read more about it on [Dagworks blog post on dlt](https://blog.dagworks.io/p/slack-summary-pipeline-with-dlt-ibis). - - -## Closing thoughts - -The concepts discussed here—portability, simplicity, and scalability—are central to modern data engineering practices. They reflect a shift towards -tools that not only perform well but also integrate seamlessly across different environments, from high-powered servers to minimal infrastructures like -edge devices. This shift emphasizes the importance of adaptability in tools used by data teams, catering to a broad spectrum of deployment scenarios without -sacrificing performance. - -In this landscape, dlt exemplifies the type of tool that embodies these principles. It's not just about being another platform; it's about providing a -framework that supports the diverse needs of developers and engineers. dlt's design allows it to be embedded directly within various architectures, -enabling teams to implement robust data processes with minimal overhead. This approach reduces complexity and fosters an environment where innovation is -not hindered by the constraints of traditional data platforms. - -We invite the community to engage with these concepts through dlt, contributing to its evolution and refinement. By participating in this collaborative -effort, you can help ensure that the tool remains at the forefront of data engineering technology, providing effective solutions that address the real-world -challenges of data management and integration. - -Join the conversation and share your insights in our [Slack community](https://dlthub.com/community) or contribute directly to the growing list of [projects using us](https://github.com/dlt-hub/dlt/network/dependents). Your expertise can drive -the continuous improvement of dlt, shaping it into a tool that not only meets current demands but also anticipates future needs in the data engineering field. - - diff --git a/docs/website/blog/2024-04-20-sdmx-source.md b/docs/website/blog/2024-04-20-sdmx-source.md deleted file mode 100644 index 1786b84df2..0000000000 --- a/docs/website/blog/2024-04-20-sdmx-source.md +++ /dev/null @@ -1,81 +0,0 @@ ---- -slug: source-sdmx -title: "Easy loading from statistical data metadata exchange to dbs" -image: https://storage.googleapis.com/dlt-blog-images/sdmx.png -authors: - name: Adrian Brudaru - title: Open source Data Engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [source, python etl, etl, sdmx] ---- - -# Simplifying SDMX Data Integration with Python - -Statistical Data and Metadata eXchange (SDMX) is an international standard used extensively by global organizations, government agencies, and financial institutions to facilitate the efficient exchange, sharing, and processing of statistical data. - -Utilizing SDMX enables seamless integration and access to a broad spectrum of statistical datasets covering economics, finance, population demographics, health, and education, among others. - -These capabilities make it invaluable for creating robust, data-driven solutions that rely on accurate and comprehensive data sources. - -![embeddable etl](https://storage.googleapis.com/dlt-blog-images/sdmx.png) - -## Why SDMX? - -SDMX not only standardizes data formats across disparate systems but also simplifies the access to data provided by institutions such as Eurostat, the ECB (European Central Bank), the IMF (International Monetary Fund), and many national statistics offices. - -This standardization allows data engineers and scientists to focus more on analyzing data rather than spending time on data cleaning and preparation. - -### Installation and Basic Usage -To start integrating SDMX data sources into your Python applications, install the sdmx library using pip: - -```sh -pip install sdmx1 -``` - -Here's an example of how to fetch data from multiple SDMX sources, illustrating the diversity of data flows and the ease of access: - -```py -from sdmx_source import sdmx_source - -source = sdmx_source([ - {"data_source": "ESTAT", "dataflow": "PRC_PPP_IND", "key": {"freq": "A", "na_item": "PLI_EU28", "ppp_cat": "A0101", "geo": ["EE", "FI"]}, "table_name": "food_price_index"}, - {"data_source": "ESTAT", "dataflow": "sts_inpr_m", "key": "M.PROD.B-D+C+D.CA.I15+I10.EE"}, - {"data_source": "ECB", "dataflow": "EXR", "key": {"FREQ": "A", "CURRENCY": "USD"}} -]) -print(list(source)) -``` -This configuration retrieves data from: - -* Eurostat (ESTAT) for the Purchasing Power Parity (PPP) and Price Level Indices providing insights into economic factors across different regions. -* Eurostat's short-term statistics (sts_inpr_m) on industrial production, which is crucial for economic analysis. -* European Central Bank (ECB) for exchange rates, essential for financial and trade-related analyses. - -## Loading the data with dlt, leveraging best practices - -After retrieving data using the sdmx library, the next challenge is effectively integrating this data into databases. -The dlt library excels in this area by offering a robust solution for data loading that adheres to best practices in several key ways: - -* Automated schema management -> dlt infers types and evolves schema as needed. It automatically handles nested structures too. You can customise this behavior, or turn the schema into a data contract. -* Declarative configuration -> You can easily switch between write dispositions (append/replace/merge) or destinations. -* Scalability -> dlt is designed to handle large volumes of data efficiently, making it suitable for enterprise-level applications and high-volume data streams. This scalability ensures that as your data needs grow, your data processing pipeline can grow with them without requiring significant redesign or resource allocation. - -Martin Salo, CTO at Yummy, a food logistics company, uses dlt to efficiently manage complex data flows from SDMX sources. -By leveraging dlt, Martin ensures that his data pipelines are not only easy to build, robust and error-resistant but also optimized for performance and scalability. - -View [Martin Salo's implementation](https://gist.github.com/salomartin/d4ee7170f678b0b44554af46fe8efb3f) - -Martin Salo's implementation of the sdmx_source package effectively simplifies the retrieval of statistical data from diverse SDMX data sources using the Python sdmx library. -The design is user-friendly, allowing both simple and complex data queries, and integrates the results directly into pandas DataFrames for immediate analysis. - -This implementation enhances data accessibility and prepares it for analytical applications, with built-in logging and error handling to improve reliability. - -## Conclusion -Integrating sdmx and dlt into your data pipelines significantly enhances data management practices, ensuring operations are robust, -scalable, and efficient. These tools provide essential capabilities for data professionals looking to seamlessly integrate -complex statistical data into their workflows, enabling more effective data-driven decision-making. - -By engaging with the data engineering community and sharing strategies and insights on effective data integration, -data engineers can continue to refine their practices and achieve better outcomes in their projects. - -Join the conversation and share your insights in our [Slack community](https://dlthub.com/community). diff --git a/docs/website/blog/2024-04-23-replacing-saas-with-python-etl.md b/docs/website/blog/2024-04-23-replacing-saas-with-python-etl.md deleted file mode 100644 index eac2d6908f..0000000000 --- a/docs/website/blog/2024-04-23-replacing-saas-with-python-etl.md +++ /dev/null @@ -1,69 +0,0 @@ ---- -slug: replacing-saas-elt -title: "Replacing Saas ETL with Python dlt: A painless experience for Yummy.eu" -image: https://storage.googleapis.com/dlt-blog-images/martin_salo_tweet.png -authors: - name: Adrian Brudaru - title: Open source Data Engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [full code etl, yes code etl, etl, python elt] ---- - -About [Yummy.eu](https://about.yummy.eu/) - -Yummy is a Lean-ops meal-kit company streamlines the entire food preparation process for customers in emerging markets by providing personalized recipes, -nutritional guidance, and even shopping services. Their innovative approach ensures a hassle-free, nutritionally optimized meal experience, -making daily cooking convenient and enjoyable. - -Yummy is a food box business. At the intersection of gastronomy and logistics, this market is very competitive. -To make it in this market, Yummy needs to be fast and informed in their operations. - -### Pipelines are not yet a commodity. - -At Yummy, efficiency and timeliness are paramount. Initially, Martin, Yummy’s CTO, chose to purchase data pipelining tools for their operational and analytical -needs, aiming to maximize time efficiency. However, the real-world performance of these purchased solutions did not meet expectations, which -led to a reassessment of their approach. - -### What’s important: Velocity, Reliability, Speed, time. Money is secondary. - -Martin was initially satisfied with the ease of setup provided by the SaaS services. - -The tipping point came when an update to Yummy’s database introduced a new log table, leading to unexpectedly high fees due to the vendor’s default settings that automatically replicated new tables fully on every refresh. This situation highlighted the need for greater control over data management processes and prompted a shift towards more transparent and cost-effective solutions. - - - - -## 10x faster, 182x cheaper with dlt + async + modal - -Motivated to find a solution that balanced cost with performance, Martin explored using dlt, a tool known for its simplicity in building data pipelines. -By combining dlt with asynchronous operations and using [Modal](https://modal.com/) for managed execution, the improvements were substantial: - -* Data processing speed increased tenfold. -* Cost reduced by 182 times compared to the traditional SaaS tool. -* The new system supports extracting data once and writing to multiple destinations without additional costs. - -For a peek into on how Martin implemented this solution, [please see Martin's async Postgres source on GitHub.](https://gist.github.com/salomartin/c0d4b0b5510feb0894da9369b5e649ff). - - -[![salo-martin-tweet](https://storage.googleapis.com/dlt-blog-images/martin_salo_tweet.png)](https://twitter.com/salomartin/status/1755146404773658660) - -## Taking back control with open source has never been easier - -Taking control of your data stack is more accessible than ever with the broad array of open-source tools available. SQL copy pipelines, often seen as a basic utility in data management, do not generally differ significantly between platforms. They perform similar transformations and schema management, making them a commodity available at minimal cost. - -SQL to SQL copy pipelines are widespread, yet many service providers charge exorbitant fees for these simple tasks. In contrast, these pipelines can often be set up and run at a fraction of the cost—sometimes just the price of a few coffees. - -At dltHub, we advocate for leveraging straightforward, freely available resources to regain control over your data processes and budget effectively. - -Setting up a SQL pipeline can take just a few minutes with the right tools. Explore these resources to enhance your data operations: - -- [30+ SQL database sources](https://dlthub.com/docs/dlt-ecosystem/verified-sources/sql_database) -- [Martin’s async PostgreSQL source](https://gist.github.com/salomartin/c0d4b0b5510feb0894da9369b5e649ff) -- [Arrow + connectorx](https://www.notion.so/Martin-Salo-Yummy-2061c3139e8e4b7fa355255cc994bba5?pvs=21) for up to 30x faster data transfers - -For additional support or to connect with fellow data professionals, [join our community](https://dlthub.com/community). diff --git a/docs/website/blog/2024-05-07-on-orchestrators.md b/docs/website/blog/2024-05-07-on-orchestrators.md deleted file mode 100644 index 8424cece98..0000000000 --- a/docs/website/blog/2024-05-07-on-orchestrators.md +++ /dev/null @@ -1,204 +0,0 @@ ---- -slug: on-orchestrators -title: "On Orchestrators: You Are All Right, But You Are All Wrong Too" -image: https://storage.googleapis.com/dlt-blog-images/dlt_dagster_snowflake_demo_overview.png -authors: - name: Anuun Chinbat - title: Data Science Intern at dltHub - url: https://github.com/anuunchin - image_url: https://avatars.githubusercontent.com/u/88698977?s=96&v=4 -tags: [Orchestration, Automation, dlt, OpenAI, Snowflake, Dagster] ---- ---- - -It's been nearly half a century since cron was first introduced, and now we have a handful orchestration tools that go way beyond just scheduling tasks. With data folks constantly debating about which tools are top-notch and which ones should leave the scene, it's like we're at a turning point in the evolution of these tools. By that I mean the term 'orchestrator' has become kind of a catch-all, and that's causing some confusion because we're using this one word to talk about a bunch of different things. - -![dates](https://storage.googleapis.com/dlt-blog-images/blog-on-orchestrators-dates.png) - -Think about the word “date.” It can mean a fruit, a romantic outing, or a day on the calendar, right? We usually figure out which one it is from the context, but what does context mean when it comes to orchestration? It might sound like a simple question, but it's pretty important to get this straight. - -> And here's a funny thing: some people, after eating an odd-tasting date (the fruit, of course), are so put off that they naively swear off going on romantic dates altogether. It's an overly exaggerated figurative way of looking at it, but it shows how one bad experience can color our view of something completely different. That's kind of what's happening with orchestration tools. If someone had a bad time with one tool, they might be overly critical towards another, even though it might be a totally different experience. - -So the context in terms of orchestration tools seems to be primarily defined by one thing - WHEN a specific tool was first introduced to the market (*aside from the obvious factors like the technical background of the person discussing these tools and their tendency to be a chronic complainer* 🙄). - - ---- - -## IT'S ALL ABOUT TIMING! - - -![evolution-of-data-orchestration](https://storage.googleapis.com/dlt-blog-images/blog-on-orchestrators-evolution.png) - -### The Illegitimate Child - -Cron was initially released in 1975 and is undoubtedly the father of all scheduling tools, including orchestrators, but I’m assuming Cron didn’t anticipate this many offspring in the field of data (or perhaps it did). As Oracle brought the first commercial relational database to market in 1979, people started to realize that data needs to be moved on schedule, and without manual effort. And it was doable, with the help of Control-M, though it was more of a general workflow automation tool that didn’t pay special attention to data workflows. - -Basically, since the solutions weren’t data driven at that time, it was more “The job gets done, but without a guarantee of data quality.” - -### Finally Adopted - -Unlike Control-M, Informatica was designed for data operations in mind from the beginning. As data started to spread across entire companies, advanced OLAPs started to emerge with a broad use of datawarehousing. Now data not only needed to be moved, but integrated across many systems and users. The data orchestration solution from Informatica was inevitably influenced by the rising popularity of the contemporary drag-and-drop concept, that is, to the detriment of many modern data engineers who would recommend to skip Informatica and other GUI based ETL tools that offer ‘visual programming’. - -> As the creator of Airflow, Max Beauchemin, said: “There's a multitude of reasons why complex pieces of software are not developed using drag and drop tools: **it's that ultimately code is the best abstraction there is for software.**” - -### To Be Free, That Is, Diverse - -With traditional ETL tools, such as IBM DataStage and Talend, becoming well-established in the 1990s and early 2000s, the big data movement started gaining its momentum with Hadoop as the main star. Oozie, later made open-source in 2011, was tasked with workflow scheduling of Hadoop jobs, with closed-source solutions, like K2View starting to operate behind the curtains. - -Fast forward a bit, and the scene exploded, with Airflow quickly becoming the heavyweight champ, while every big data service out there began rolling out their own orchestrators. This burst brought diversity, but with diversity came a maze of complexity. All of a sudden, there’s an orchestrator for everyone — whether you’re chasing features or just trying to make your budget work 👀 — picking the perfect one for your needs has gotten even trickier. - -![types](https://storage.googleapis.com/dlt-blog-images/blog-on-orchestrators-types.png) - - -### The Bottom Line - -The thing is that every tool out there has some inconvenient truths, and real question isn't about escaping the headache — it's about choosing your type of headache. Hence, the endless sea of “versus” articles, blog posts, and guides trying to help you pick your personal battle. - -> A Redditor: [“Everyone has hated all orchestration tools for all time. People just hated Airflow less and it took off.“](https://www.reddit.com/r/dataengineering/comments/10ttbvl/comment/j7a4685/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button) - -What I'm getting at is this: we're all a bit biased by the "law of the instrument." You know, the whole “If all you have is a hammer, everything looks like a nail” thing. Most engineers probably grabbed the latest or most hyped tool when they first dipped their toes into data orchestration and have stuck with it ever since. Sure, Airflow is the belle of the ball for the community, but there's a whole lineup of contenders vying for the spotlight. - -![law-of-instrument](https://storage.googleapis.com/dlt-blog-images/blog-on-orchestrators-perspectives.png) - -And there are obviously those who would relate to the following: - -[![reddit-screenshot](https://storage.googleapis.com/dlt-blog-images/blog-on-orchestrators-reddit-screenshot.png)](https://www.reddit.com/r/dataengineering/comments/168p757/comment/jyx9gs7/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button) - ---- - -## A HANDY DETOUR POUR TOI 💐 - -### The Fundamentals - -- [A Brief History of Workflow Orchestration](https://www.prefect.io/blog/brief-history-of-workflow-orchestration) by Prefect. -- [What is Data Orchestration and why is it misunderstood?](https://medium.com/@hugolu87/what-is-data-orchestration-and-why-is-it-misunderstood-844878ac8c0a) by Hugo Lu. -- [The evolution of data orchestration: Part 1 - the past and present](https://jonathanneo.substack.com/p/the-evolution-of-data-orchestration) by Jonathan Neo. -- [The evolution of data orchestration: Part 2 - the future](https://jonathanneo.substack.com/p/the-evolution-of-data-orchestration-002) by Jonathan Neo. -- [Bash-Script vs. Stored Procedure vs. Traditional ETL Tools vs. Python-Script](https://www.dedp.online/part-2/4-ce/bash-stored-procedure-etl-python-script.html) by Simon Späti. - -### About Airflow - -- [6 inconvenient truths about Apache Airflow (and what to do about them)](https://www.ibm.com/blog/6-issues-with-airflow/) by IBM. -- [Airflow Survey 2022](https://airflow.apache.org/blog/airflow-survey-2022/) by Airflow. - -### Miscellaneous - -- [Picking A Kubernetes Orchestrator: Airflow, Argo, and Prefect](https://medium.com/arthur-engineering/picking-a-kubernetes-orchestrator-airflow-argo-and-prefect-83539ecc69b) by Ian McGraw. -- [Airflow, Prefect, and Dagster: An Inside Look](https://towardsdatascience.com/airflow-prefect-and-dagster-an-inside-look-6074781c9b77) by Pedram Navid. - ---- - -## WHAT THE FUTURE HOLDS... - -I'm no oracle or tech guru, but it's pretty obvious that at their core, most data orchestration tools are pretty similar. They're like building blocks that can be put together in different ways—some features come, some go, and users are always learning something new or dropping something old. So, what's really going to make a difference down the line is NOT just about having the coolest features. It's more about having a strong community that's all in on making the product better, a welcoming onboarding process that doesn't feel like rocket science, and finding that sweet spot between making things simple to use and letting users tweak things just the way they like. - -In other words, it's not just about what the tools can do, but how people feel about using them, learning them, contributing to them, and obviously how much they spend to maintain them. That's likely where the future winners in the data orchestration game will stand out. But don’t get me wrong, features are important — it's just that there are other things equally important. - ---- - -## SO WHO'S ACTUALLY TRENDING? - -I’ve been working on this article for a WHILE now, and, honestly, it's been a bit of a headache trying to gather any solid, objective info on which data orchestration tool tops the charts. The more I think about it, the more I realise it's probably because trying to measure "the best" or "most popular" is a bit like trying to catch smoke with your bare hands — pretty subjective by nature. Plus, only testing them with non-production level data probably wasn't my brightest move. - -However, I did create a fun little project where I analysed the sentiment of comments on articles about selected data orchestrators on Hacker News and gathered Google Trends data for the past year. - -Just a heads-up, though: the results are BY NO MEANS reliable and are skewed due to some fun with words. For instance, searching for “Prefect” kept leading me to articles about Japanese prefectures, “Keboola” resulted in Kool-Aid content, and “Luigi”... well, let’s just say I ran into Mario’s brother more than once 😂. - ---- - -## THE FUN LITTLE PROJECT - -> Straight to the [GitHub repo](https://github.com/dlt-hub/dlt_demos/tree/main/dlt-dagster-snowflake). - -I used Dagster and `dlt` to load data into Snowflake, and since both of them have integrations with Snowflake, it was easy to set things up and have them all running: - -![Pipeline overview](https://storage.googleapis.com/dlt-blog-images/dlt_dagster_snowflake_demo_overview.png) - -This project is very minimal, including just what's needed to run Dagster locally with `dlt`. Here's a quick breakdown of the repo’s structure: - -1. `.dlt`: Utilized by the `dlt` library for storing configuration and sensitive information. The Dagster project is set up to fetch secret values from this directory as well. -2. `charts`: Used to store chart images generated by assets. -3. `dlt_dagster_snowflake_demo`: Your Dagster package, comprising Dagster assets, `dlt` resources, Dagster resources, and general project configurations. - -### Dagster Resources Explained - -In the `resources` folder, the following two Dagster resources are defined as classes: - -1. `DltPipeline`: This is our `dlt` object defined as a Dagster ConfigurableResource that creates and runs a `dlt` pipeline with the specified data and table name. It will later be used in our Dagster assets to load data into Snowflake. - - ```py - class DltPipeline(ConfigurableResource): - # Initialize resource with pipeline details - pipeline_name: str - dataset_name: str - destination: str - - def create_pipeline(self, resource_data, table_name): - """ - Creates and runs a dlt pipeline with specified data and table name. - - Args: - resource_data: The data to be processed by the pipeline. - table_name: The name of the table where data will be loaded. - - Returns: - The result of the pipeline execution. - """ - - # Configure the dlt pipeline with your destination details - pipeline = dlt.pipeline( - pipeline_name=self.pipeline_name, - destination=self.destination, - dataset_name=self.dataset_name - ) - - # Run the pipeline with your parameters - load_info = pipeline.run(resource_data, table_name=table_name) - return load_info - ``` - -2. `LocalFileStorage`: Manages the local file storage, ensuring the storage directory exists and allowing data to be written to files within it. It will be later used in our Dagster assets to save images into the `charts` folder. - -### `dlt` Explained - -In the dlt folder within dlt_dagster_snowflake_demo, necessary dlt resources and sources are defined. Below is a visual representation illustrating the functionality of dlt: - -![dlt explained](https://storage.googleapis.com/dlt-blog-images/dlt_dagster_snowflake_demo_dlt.png) - -1. `hacker_news`: A `dlt` resource that yields stories related to specified orchestration tools from Hackernews. For each tool, it retrieves the top 5 stories that have at least one comment. The stories are then appended to the existing data. - - Note that the `write_disposition` can also be set to `merge` or `replace`: - - - The merge write disposition merges the new data from the resource with the existing data at the destination. It requires a `primary_key` to be specified for the resource. More details can be found here. - - The replace write disposition replaces the data in the destination with the data from the resource. It deletes all the classes and objects and recreates the schema before loading the data. - - More details can be found [here](https://dlthub.com/docs/general-usage/resource). - -2. `comments`: A `dlt` transformer - a resource that receives data from another resource. It fetches comments for each story yielded by the `hacker_news` function. -3. `hacker_news_full`: A `dlt` source that extracts data from the source location using one or more resource components, such as `hacker_news` and `comments`. To illustrate, if the source is a database, a resource corresponds to a table within that database. -4. `google_trends`: A `dlt` resource that fetches Google Trends data for specified orchestration tools. It attempts to retrieve the data multiple times in case of failures or empty responses. The retrieved data is then appended to the existing data. - -As you may have noticed, the `dlt` library is designed to handle the unnesting of data internally. When you retrieve data from APIs like Hacker News or Google Trends, `dlt` automatically unpacks the nested structures into relational tables, creating and linking child and parent tables. This is achieved through unique identifiers (`_dlt_id` and `_dlt_parent_id`) that link child tables to specific rows in the parent table. However, it's important to note that you have control over [how this unnesting is done](https://dlthub.com/docs/general-usage/destination-tables). - - -### The Results - -Alright, so once you've got your Dagster assets all materialized and data loaded into Snowflake, let's take a peek at what you might see: - -![sentiment counts](https://storage.googleapis.com/dlt-blog-images/blog-on-orchestrators-chart.png) - -I understand if you're scratching your head at first glance, but let me clear things up. Remember those sneaky issues I mentioned with Keboola and Luigi earlier? Well, I've masked their charts with the respective “culprits”. - -Now, onto the bars. Each trio of bars illustrates the count of negative, neutral, and positive comments on articles sourced from Hacker News that have at least one comment and were returned when searched for a specific orchestration tool, categorized accordingly by the specific data orchestration tool. - -What's the big reveal? It seems like Hacker News readers tend to spread more positivity than negativity, though neutral comments hold their ground. - -And, as is often the case with utilizing LLMs, this data should be taken with a grain of salt. It's more of a whimsical exploration than a rigorous analysis. However, if you take a peek behind Kool Aid and Luigi, it's intriguing to note that articles related to them seem to attract a disproportionate amount of negativity. 😂 - ---- - -## IF YOU'RE STILL HERE - -… and you're just dipping your toes into the world of data orchestration, don’t sweat it. It's totally normal if it doesn't immediately click for you. For beginners, it can be tricky to grasp because in small projects, there isn't always that immediate need for things to happen "automatically" - you build your pipeline, run it once, and then bask in the satisfaction of your results - just like I did in my project. However, if you start playing around with one of these tools now, it could make it much easier to work with them later on. So, don't hesitate to dive in and experiment! - -… And hey, if you're a seasoned pro about to drop some knowledge bombs, feel free to go for it - because what doesn’t challenge us, doesn’t change us 🥹. *(\*Cries in Gen Z\*)* diff --git a/docs/website/blog/2024-05-14-rest-api-source-client.md b/docs/website/blog/2024-05-14-rest-api-source-client.md deleted file mode 100644 index ee20b43b41..0000000000 --- a/docs/website/blog/2024-05-14-rest-api-source-client.md +++ /dev/null @@ -1,238 +0,0 @@ ---- -slug: rest-api-source-client -title: "Announcing: REST API Source toolkit from dltHub - A Python-only high level approach to pipelines" -image: https://storage.googleapis.com/dlt-blog-images/rest-img.png -authors: - name: Adrian Brudaru - title: Open source Data Engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [rest-api, declarative etl] ---- - -## What is the REST API Source toolkit? -:::tip -tl;dr: You are probably familiar with REST APIs. - -- Our new **REST API Source** is a short, declarative configuration driven way of creating sources. -- Our new **REST API Client** is a collection of Python helpers used by the above source, which you can also use as a standalone, config-free, imperative high-level abstraction for building pipelines. - -Want to skip to docs? Links at the [bottom of the post.](#next-steps) -::: - -### Why REST configuration pipeline? Obviously, we need one! - -But of course! Why repeat write all this code for requests and loading, when we could write it once and re-use it with different APIs with different configs? - -Once you have built a few pipelines from REST APIs, you can recognise we could, instead of writing code, write configuration. - -**We can call such an obvious next step in ETL tools a “[focal point](https://en.wikipedia.org/wiki/Focal_point_(game_theory))” of “[convergent evolution](https://en.wikipedia.org/wiki/Convergent_evolution)”.** - -And if you’ve been in a few larger more mature companies, you will have seen a variety of home-grown solutions that look similar. You might also have seen such solutions as commercial products or offerings. - -### But ours will be better… - -So far we have seen many REST API configurators and products — they suffer from predictable flaws: - -- Local homebrewed flavors are local for a reason: They aren’t suitable for the broad audience. And often if you ask the users/beneficiaries of these frameworks, they will sometimes argue that they aren’t suitable for anyone at all. -- Commercial products are yet another data product that doesn’t plug into your stack, brings black boxes and removes autonomy, so they simply aren’t an acceptable solution in many cases. - -So how can `dlt` do better? - -Because it can keep the best of both worlds: the autonomy of a library, the quality of a commercial product. - -As you will see further, we created not just a standalone “configuration-based source builder” but we also expose the REST API client used enabling its use directly in code. - -## Hey community, you made us do it! - -The push for this is coming from you, the community. While we had considered the concept before, there were many things `dlt` needed before creating a new way to build pipelines. A declarative extractor after all, would not make `dlt` easier to adopt, because a declarative approach requires more upfront knowledge. - -Credits: - -- So, thank you Alex Butler for building a first version of this and donating it to us back in August ‘23: https://github.com/dlt-hub/dlt-init-openapi/pull/2. -- And thank you Francesco Mucio and Willi Müller for re-opening the topic, and creating video [tutorials](https://www.youtube.com/playlist?list=PLpTgUMBCn15rs2NkB4ise780UxLKImZTh). -- And last but not least, thank you to `dlt` team’s Anton Burnashev (also known for [gspread](https://github.com/burnash/gspread) library) for building it out! - -## The outcome? Two Python-only interfaces, one declarative, one imperative. - -- **dlt’s REST API Source** is a Python dictionary-first declarative source builder, that has enhanced flexibility, supports callable passes, native config validations via python dictionaries, and composability directly in your scripts. It enables generating sources dynamically during runtime, enabling straightforward, manual or automated workflows for adapting sources to changes. -- **dlt’s REST API Client** is the low-level abstraction that powers the REST API Source. You can use it in your imperative code for more automation and brevity, if you do not wish to use the higher level declarative interface. - -### Useful for those who frequently build new pipelines - -If you are on a team with 2-3 pipelines that never change much you likely won’t see much benefit from our latest tool. -What we observe from early feedback a declarative extractor is great at is enabling easier work at scale. -We heard excitement about the **REST API Source** from: - -- companies with many pipelines that frequently create new pipelines, -- data platform teams, -- freelancers and agencies, -- folks who want to generate pipelines with LLMs and need a simple interface. - -## How to use the REST API Source? - -Since this is a declarative interface, we can’t make things up as we go along, and instead need to understand what we want to do upfront and declare that. - -In some cases, we might not have the information upfront, so we will show you how to get that info during your development workflow. - -Depending on how you learn better, you can either watch the videos that our community members made, or follow the walkthrough below. - -## **Video walkthroughs** - -In these videos, you will learn at a leisurely pace how to use the new interface. -[Playlist link.](https://www.youtube.com/playlist?list=PLpTgUMBCn15rs2NkB4ise780UxLKImZTh) - - -## Workflow walkthrough: Step by step - -If you prefer to do things at your own pace, try the workflow walkthrough, which will show you the workflow of using the declarative interface. - -In the example below, we will show how to create an API integration with 2 endpoints. One of these is a child resource, using the data from the parent endpoint to make a new request. - -### Configuration Checklist: Before getting started - -In the following, we will use the GitHub API as an example. - -We will also provide links to examples from this [Google Colab tutorial.](https://colab.research.google.com/drive/1qnzIM2N4iUL8AOX1oBUypzwoM3Hj5hhG#scrollTo=SCr8ACUtyfBN&forceEdit=true&sandboxMode=true) - - -1. Collect your api url and endpoints, [Colab example](https://colab.research.google.com/drive/1qnzIM2N4iUL8AOX1oBUypzwoM3Hj5hhG#scrollTo=bKthJGV6Mg6C): - - An URL is the base of the request, for example: `https://api.github.com/`. - - An endpoint is the path of an individual resource such as: - - `/repos/{OWNER}/{REPO}/issues`; - - or `/repos/{OWNER}/{REPO}/issues/{issue_number}/comments` which would require the issue number from the above endpoint; - - or `/users/{username}/starred` etc. -2. Identify the authentication methods, [Colab example](https://colab.research.google.com/drive/1qnzIM2N4iUL8AOX1oBUypzwoM3Hj5hhG#scrollTo=mViSDre8McI7): - - GitHub uses bearer tokens for auth, but we can also skip it for public endpoints https://docs.github.com/en/rest/authentication/authenticating-to-the-rest-api?apiVersion=2022-11-28. -3. Identify if you have any dependent request patterns such as first get ids in a list, then use id for requesting details. - For GitHub, we might do the below or any other dependent requests. [Colab example.](https://colab.research.google.com/drive/1qnzIM2N4iUL8AOX1oBUypzwoM3Hj5hhG#scrollTo=vw7JJ0BlpFyh): - 1. Get all repos of an org `https://api.github.com/orgs/{org}/repos`. - 2. Then get all contributors `https://api.github.com/repos/{owner}/{repo}/contributors`. - -4. How does pagination work? Is there any? Do we know the exact pattern? [Colab example.](https://colab.research.google.com/drive/1qnzIM2N4iUL8AOX1oBUypzwoM3Hj5hhG#scrollTo=rqqJhUoCB9F3) - - On GitHub, we have consistent [pagination](https://docs.github.com/en/rest/using-the-rest-api/using-pagination-in-the-rest-api?apiVersion=2022-11-28) between endpoints that looks like this `link_header = response.headers.get('Link', None)`. -5. Identify the necessary information for incremental loading, [Colab example](https://colab.research.google.com/drive/1qnzIM2N4iUL8AOX1oBUypzwoM3Hj5hhG#scrollTo=fsd_SPZD7nBj): - - Will any endpoints be loaded incrementally? - - What columns will you use for incremental extraction and loading? - - GitHub example: We can extract new issues by requesting issues after a particular time: `https://api.github.com/repos/{repo_owner}/{repo_name}/issues?since={since}`. - -### Configuration Checklist: Checking responses during development - -1. Data path: - - You could print the source and see what is yielded. [Colab example.](https://colab.research.google.com/drive/1qnzIM2N4iUL8AOX1oBUypzwoM3Hj5hhG#scrollTo=oJ9uWLb8ZYto&line=6&uniqifier=1) -2. Unless you had full documentation at point 4 (which we did), you likely need to still figure out some details on how pagination works. - 1. To do that, we suggest using `curl` or a second python script to do a request and inspect the response. This gives you flexibility to try anything. [Colab example.](https://colab.research.google.com/drive/1qnzIM2N4iUL8AOX1oBUypzwoM3Hj5hhG#scrollTo=tFZ3SrZIMTKH) - 2. Or you could print the source as above - but if there is metadata in headers etc, you might miss it. - -### Applying the configuration - -Here’s what a configured example could look like: - -1. Base URL and endpoints. -2. Authentication. -3. Pagination. -4. Incremental configuration. -5. Dependent resource (child) configuration. - -If you are using a narrow screen, scroll the snippet below to look for the numbers designating each component `(n)`. - -```py -# This source has 2 resources: -# - issues: Parent resource, retrieves issues incl. issue number -# - issues_comments: Child resource which needs the issue number from parent. - -import os -from rest_api import RESTAPIConfig - -github_config: RESTAPIConfig = { - "client": { - "base_url": "https://api.github.com/repos/dlt-hub/dlt/", #(1) - # Optional auth for improving rate limits - # "auth": { #(2) - # "token": os.environ.get('GITHUB_TOKEN'), - # }, - }, - # The paginator is autodetected, but we can pass it explicitly #(3) - # "paginator": { - # "type": "header_link", - # "next_url_path": "paging.link", - # } - # We can declare generic settings in one place - # Our data is stateful so we load it incrementally by merging on id - "resource_defaults": { - "primary_key": "id", #(4) - "write_disposition": "merge", #(4) - # these are request params specific to GitHub - "endpoint": { - "params": { - "per_page": 10, - }, - }, - }, - "resources": [ - # This is the first resource - issues - { - "name": "issues", - "endpoint": { - "path": "issues", #(1) - "params": { - "sort": "updated", - "direction": "desc", - "state": "open", - "since": { - "type": "incremental", #(4) - "cursor_path": "updated_at", #(4) - "initial_value": "2024-01-25T11:21:28Z", #(4) - }, - } - }, - }, - # Configuration for fetching comments on issues #(5) - # This is a child resource - as in, it needs something from another - { - "name": "issue_comments", - "endpoint": { - "path": "issues/{issue_number}/comments", #(1) - # For child resources, you can use values from the parent resource for params. - "params": { - "issue_number": { - # Use type "resolve" to define child endpoint wich should be resolved - "type": "resolve", - # Parent endpoint - "resource": "issues", - # The specific field in the issues resource to use for resolution - "field": "number", - } - }, - }, - # A list of fields, from the parent resource, which will be included in the child resource output. - "include_from_parent": ["id"], - }, - ], -} -``` - -## And that’s a wrap — what else should you know? - -- As we mentioned, there’s also a **REST Client** - an imperative way to use the same abstractions, for example, the auto-paginator - check out this runnable snippet: - - ```py - from dlt.sources.helpers.rest_client import RESTClient - - # Initialize the RESTClient with the Pokémon API base URL - client = RESTClient(base_url="https://pokeapi.co/api/v2") - - # Using the paginate method to automatically handle pagination - for page in client.paginate("/pokemon"): - print(page) - ``` - -- We are going to generate a bunch of sources from OpenAPI specs — stay tuned for an update in a couple of weeks! - -## Next steps -- Share back your work! Instructions: **[dltHub-Community-Sources-Snippets](https://www.notion.so/7a7f7ddb39334743b1ba3debbdfb8d7f?pvs=21)** -- Read more about the - - **[REST API Source](https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api)** and - - **[REST API Client](https://dlthub.com/docs/general-usage/http/rest-client),** - - and the related **[API helpers](https://dlthub.com/devel/general-usage/http/overview)** and **[requests](https://dlthub.com/docs/general-usage/http/requests)** helper. -- **[Join our community](https://dlthub.com/community)** and give us feedback! diff --git a/docs/website/blog/2024-05-23-contributed-first-pipeline.md b/docs/website/blog/2024-05-23-contributed-first-pipeline.md deleted file mode 100644 index c6d9252da3..0000000000 --- a/docs/website/blog/2024-05-23-contributed-first-pipeline.md +++ /dev/null @@ -1,90 +0,0 @@ ---- -slug: contributed-first-pipeline -title: "How I Contributed to My First Open Source Data Pipeline" -image: https://storage.googleapis.com/dlt-blog-images/blog_my_first_data_pipeline.png -authors: - name: Aman Gupta - title: Junior Data Engineer - url: https://github.com/dat-a-man - image_url: https://dlt-static.s3.eu-central-1.amazonaws.com/images/aman.png -tags: [data ingestion, python sdk, ETL, python data pipelines, Open Source, Developer Tools] ---- - -Hello, I'm Aman Gupta. Over the past eight years, I have navigated the structured world of civil engineering, but recently, I have found myself captivated by data engineering. Initially, I knew how to stack bricks and build structural pipelines. But this newfound interest has helped me build data pipelines, and most of all, it was sparked by a workshop hosted by **dlt.** - -:::info -dlt (data loading tool) is an open-source library that you can add to your Python scripts to load data from various and often messy data sources into well-structured, live datasets. -::: - -The `dlt` workshop took place in November 2022, co-hosted by Adrian Brudaru, my former mentor and co-founder of `dlt`. - -An opportunity arose when another client needed data migration from FreshDesk to BigQuery. I crafted a basic pipeline version, initially designed to support my use case. Upon presenting my basic pipeline to the dlt team, Alena Astrakhatseva, a team member, generously offered to review it and refine it into a community-verified source. - -![image](https://storage.googleapis.com/dlt-blog-images/blog_my_first_data_pipeline.png) - -My first iteration was straightforward—loading data in [replace mode](https://dlthub.com/docs/general-usage/incremental-loading#the-3-write-dispositions). While adequate for initial purposes, a verified source demanded features like [pagination](https://dlthub.com/docs/general-usage/http/overview#explicitly-specifying-pagination-parameters) and [incremental loading](https://dlthub.com/docs/general-usage/incremental-loading). To achieve this, I developed an API client tailored for the Freshdesk API, integrating rate limit handling and pagination: - -```py -class FreshdeskClient: - """ - Client for making authenticated requests to the Freshdesk API. It incorporates API requests with - rate limit and pagination. - """ - - def __init__(self, api_key: str, domain: str): - # Contains stuff like domain, credentials and base URL. - pass - - def _request_with_rate_limit(self, url: str, **kwargs: Any) -> requests.Response: - # Handles rate limits in HTTP requests and ensures that the client doesn't exceed the limit set by the server. - pass - - def paginated_response( - self, - endpoint: str, - per_page: int, - updated_at: Optional[str] = None, - ) -> Iterable[TDataItem]: - # Fetches a paginated response from a specified endpoint. - pass -``` - -To further make the pipeline effective, I developed dlt [resources](https://dlthub.com/docs/general-usage/resource) that could handle incremental data loading. This involved creating resources that used **`dlt`**'s incremental functionality to fetch only new or updated data: - -```py -def incremental_resource( - endpoint: str, - updated_at: Optional[Any] = dlt.sources.incremental( - "updated_at", initial_value="2022-01-01T00:00:00Z" - ), -) -> Generator[Dict[Any, Any], Any, None]: - """ - Fetches and yields paginated data from a specified API endpoint. - Each page of data is fetched based on the `updated_at` timestamp - to ensure incremental loading. - """ - - # Retrieve the last updated timestamp to fetch only new or updated records. - updated_at = updated_at.last_value - - # Use the FreshdeskClient instance to fetch paginated responses - yield from freshdesk.paginated_response( - endpoint=endpoint, - per_page=per_page, - updated_at=updated_at, - ) -``` - -With the steps defined above, I was able to load the data from Freshdesk to BigQuery and use the pipeline in production. Here’s a summary of the steps I followed: - -1. Created a Freshdesk API token with sufficient privileges. -2. Created an API client to make requests to the Freshdesk API with rate limit and pagination. -3. Made incremental requests to this client based on the “updated_at” field in the response. -4. Ran the pipeline using the Python script. - - -While my journey from civil engineering to data engineering was initially intimidating, it has proved to be a profound learning experience. Writing a pipeline with **`dlt`** mirrors the simplicity of a GET request: you request data, yield it, and it flows from the source to its destination. Now, I help other clients integrate **`dlt`** to streamline their data workflows, which has been an invaluable part of my professional growth. - -In conclusion, diving into data engineering has expanded my technical skill set and provided a new lens through which I view challenges and solutions. As for me, the lens view mainly was concrete and steel a couple of years back, which has now begun to notice the pipelines of the data world. - -Data engineering has proved both challenging, satisfying, and a good career option for me till now. For those interested in the detailed workings of these pipelines, I encourage exploring dlt's [GitHub repository](https://github.com/dlt-hub/verified-sources) or diving into the [documentation](https://dlthub.com/docs/dlt-ecosystem/verified-sources/freshdesk). \ No newline at end of file diff --git a/docs/website/blog/2024-05-28-openapi-pipeline.md b/docs/website/blog/2024-05-28-openapi-pipeline.md deleted file mode 100644 index 60faa062e0..0000000000 --- a/docs/website/blog/2024-05-28-openapi-pipeline.md +++ /dev/null @@ -1,97 +0,0 @@ ---- -slug: openapi-pipeline -title: "Instant pipelines with dlt-init-openapi" -image: https://storage.googleapis.com/dlt-blog-images/openapi.png -authors: - name: Adrian Brudaru - title: Open source Data Engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [openapi] ---- - -# The Future of Data Pipelines starts now. - -Dear dltHub Community, - -We are thrilled to announce the launch of our groundbreaking pipeline generator tool. - -We call it `dlt-init-openapi`. - -Just point it to an OpenAPI spec, select your endpoints, and you're done! - - -### What's OpenAPI again? - -[OpenAPI](https://www.openapis.org/) is the world's most widely used API description standard. You may have heard about swagger docs? those are docs generated from the spec. -In 2021 an information-security company named Assetnote scanned the web and unearthed [200,000 public -OpenAPI files](https://www.assetnote.io/resources/research/contextual-content-discovery-youve-forgotten-about-the-api-endpoints). -Modern API frameworks like [FastAPI](https://pypi.org/project/fastapi/) generate such specifications automatically. - -## How does it work? - -**A pipeline is a series of datapoints or decisions about how to extract and load the data**, expressed as code or config. I say decisions because building a pipeline can be boiled down to inspecting a documentation or response and deciding how to write the code. - -Our tool does its best to pick out the necessary details and detect the rest to generate the complete pipeline for you. - -The information required for taking those decisions comes from: -- The OpenAPI [Spec](https://github.com/dlt-hub/openapi-specs) (endpoints, auth) -- The dlt [REST API Source](https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api) which attempts to detect pagination -- The [dlt init OpenAPI generator](https://dlthub.com/docs/dlt-ecosystem/verified-sources/openapi-generator) which attempts to detect incremental logic and dependent requests. - -### How well does it work? - -This is something we are also learning about. We did an internal hackathon where we each built a few pipelines with this generator. In our experiments with APIs for which we had credentials, it worked pretty well. - -However, we cannot undertake a big detour from our work to manually test each possible pipeline, so your feedback will be invaluable. -So please, if you try it, let us know how well it worked - and ideally, add the spec you used to our [repository](https://github.com/dlt-hub/openapi-specs). - -### What to do if it doesn't work? - -Once a pipeline is created, it is a **fully configurable instance of the REST API Source**. -So if anything did not go smoothly, you can make the final tweaks. -You can learn how to adjust the generated pipeline by reading our [REST API Source documentation](https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api). - -### Are we using LLMS under the hood? - -No. This is a potential future enhancement, so maybe later. - -The pipelines are generated algorithmically with deterministic outcomes. This way, we have more control over the quality of the decisions. - -If we took an LLM-first approach, the errors would compound and put the burden back on the data person. - -We are however considering using LLM-assists for the things that the algorithmic approach can't detect. Another avenue could be generating the OpenAPI spec from website docs. -So we are eager to get feedback from you on what works and what needs work, enabling us to improve it. - -## Try it out now! - -**Video Walkthrough:** - - - - -**[Colab demo](https://colab.research.google.com/drive/1MRZvguOTZj1MlkEGzjiso8lQ_wr1MJRI?usp=sharing)** - Load data from Stripe API to DuckDB using dlt and OpenAPI - -**[Docs](https://dlthub.com/docs/dlt-ecosystem/verified-sources/openapi-generator)** for `dlt-init-openapi` - -dlt init openapi **[code repo.](https://github.com/dlt-hub/dlt-init-openapi)** - -**[Specs repository you can generate from.](https://github.com/dlt-hub/openapi-specs)** - -Showcase your pipeline in the community sources **[here](https://www.notion.so/dlthub/dltHub-Community-Sources-Snippets-7a7f7ddb39334743b1ba3debbdfb8d7f) - -## Next steps: Feedback, discussion and sharing. - -Solving data engineering headaches in the open source is a team sport. -We got this far with your feedback and help (especially on [REST API source](https://dlthub.com/docs/blog/rest-api-source-client)), and are counting on your continuous usage and engagement -to steer our pushing of what's possible into uncharted, but needed directions. - -So here's our call to action: - -- We're excited to see how you will use our new pipeline generator and we are -eager for your feedback. **[Join our community and let us know how we can improve dlt-init-openapi](https://dlthub.com/community)** -- Got an OpenAPI spec? **[Add it to our specs repository](https://github.com/dlt-hub/openapi-specs)** so others may use it. If the spec doesn't work, please note that in the PR and we will use it for R&D. - -*Thank you for being part of our community and for building the future of ETL together!* - -*- dltHub Team* diff --git a/docs/website/blog/2024-06-12-from-pandas-to-production.md b/docs/website/blog/2024-06-12-from-pandas-to-production.md deleted file mode 100644 index 5dbd494a3e..0000000000 --- a/docs/website/blog/2024-06-12-from-pandas-to-production.md +++ /dev/null @@ -1,211 +0,0 @@ ---- -slug: pandas-to-production -title: "From Pandas to Production: How we built dlt as the right ELT tool for Normies" -image: https://storage.googleapis.com/dlt-blog-images/i-am-normal.png -authors: - name: Adrian Brudaru - title: Open source Data Engineer - url: https://github.com/adrianbr - image_url: https://avatars.githubusercontent.com/u/5762770?v=4 -tags: [pandas, production, etl, etl] ---- - - - -:::info -**TL;DR: dlt is a library for Normies: Problem solvers with antipathy for black boxes, gratuitous complexity and external dependencies.** - -**This post tells the story of how we got here.** - -Try it in colab: -* [Schema evolution](https://colab.research.google.com/drive/1H6HKFi-U1V4p0afVucw_Jzv1oiFbH2bu#scrollTo=e4y4sQ78P_OM) -* [Data Talks Club Open Source Spotlight](https://colab.research.google.com/drive/1D39_koejvi-eTtA_8AI33AHhMGGOklfb) + [Video](https://www.youtube.com/playlist?list=PL3MmuxUbc_hJ5t5nnjzC0F2zan76Dpsz0) -* [Hackernews Api demo](https://colab.research.google.com/drive/1DhaKW0tiSTHDCVmPjM-eoyL47BJ30xmP) -* [LLM-free pipeline generation demo](https://colab.research.google.com/drive/1MRZvguOTZj1MlkEGzjiso8lQ_wr1MJRI) +[4min Video](https://www.youtube.com/watch?v=b99qv9je12Q) - -But if you want to load pandas dfs to production databases, with all the best practices built-in, check out this [documentation](https://dlthub.com/docs/dlt-ecosystem/verified-sources/arrow-pandas) or this colab notebook that shows [easy handling of complex api data](https://colab.research.google.com/drive/1DhaKW0tiSTHDCVmPjM-eoyL47BJ30xmP#scrollTo=1wf1R0yQh7pv). - -Or check out more resources [at the end of the article](#call-to-action) -::: - -## I. The background story: Normal people load data too - -Hey, I’m Adrian, cofounder of dlt. I’ve been working in the data industry since 2012, doing all kinds of end-to-end things. - -In 2017, a hiring team called me a data engineer. As I saw that title brought me a lot of work offers, I kept it and went with it. - -But was I doing data engineering? Yes and no. Since my studies were not technical, I always felt some impostor syndrome calling myself a data engineer. I had started as an analyst, did more and more and became an end to end data professional that does everything from building the tech stack, collecting requirements, getting managers to agree on the metrics used 🙄, creating roadmap and hiring a team. - -Back in 2022 there was an online conference called [Normconf](https://normconf.com/) and I ‘felt seen’. As [I watched Normconf participants](https://www.youtube.com/@normconf), I could relate more to them than to the data engineer label. No, I am not just writing code and pushing best practices - I am actually just trying to get things done without getting bogged down in bad practice gotchas. And it seemed at this conference that many people felt this way. - -![normal](https://storage.googleapis.com/dlt-blog-images/i-am-normal.png) - -### Normies: Problem solvers with antipathy for black boxes, gratuitous complexity and external dependencies - -At Normconf, "normie" participants often embodied the three fundamental psychological needs identified in Self-Determination Theory: autonomy, competence, and relatedness. - -They talked about how they autonomously solved all kinds of problems, related on the pains and gains of their roles, and showed off their competence across the board, in solving problems. - -What they did, was what I also did as a data engineer: We start from a business problem, and work back through what needs to be done to understand and solve it. - -By very definition, Normie is someone not very specialised at one thing or another, and in our field, even data engineers are jacks of all trades. - -What undermines the Normie mission are things that clash with the basic needs, from uncustomisable products, to vendors that add bottlenecks and unreliable dependencies. - -### Encountering friction between data engineers and Python-first analysts - -Before becoming a co-founder of dlt I had 5 interesting years as a startup employee, a half-year nightmare in a corporation with no autonomy or mastery (I got fired for refusing the madness, and it was such a huge relief), followed by 5 fun, rewarding and adventure-filled years of freelancing. Much of my work was “build&hire” which usually meant building a first time data warehouse and hiring a team for it. The setups that I did were bespoke to the businesses that were getting them, including the teams - Meaning, the technical complexity was also tailored to the (lack of) technical culture of the companies I was building for. - -In this time, I saw an acute friction between data engineers and Python-first analysts, mostly around the fact that data engineers easily become a bottleneck and data scientists are forced to pick up the slack. And of course, this causes other issues that might further complicate the life of the data engineer, while still not being a good solution for the data consumers. - -So at this point I started building boilerplate code for data warehouses and learning how to better cater to the entire team. - - -### II. The initial idea: pandas.df.to_sql() with data engineering best practices - -After a few attempts I ended up with the hypothesis that df.to_sql() is the natural abstraction a data person would use - I have a table here, I want a table there, shouldn’t be harder than a function call right? - -Right. - -Except that particular function call is anything but data engineering complete. A single run will do what it promises. A production pipeline will also have many additional requirements. In the early days, we wrote up an ideal list of features that should be auto-handled (spoiler alert: today dlt does all that and more). Read on for the wish list: - -### Our dream: a tool that meets production pipelines requirements - -- Wouldn’t it be nice if we could auto-flatten and unpack nested structures into tables with generated join keys? - - -- Wouldn’t it be nice if data types were properly defined and managed? -- Wouldn’t it be nice if we could load the data incrementally, meaning retain some state to know where to start from? -- Wouldn’t it be nice if this incremental load was bound to a way to do incremental extraction? -- Wouldn’t it be nice if we didn’t run out of memory? -- Wouldn’t it be nice if we got alerted/notified when schemas change? -- Wouldn’t it be nice if schema changes were self healing? -- Wouldn’t it be nice if I could run it all in parallel, or do async calls? -- Wouldn’t it be nice if it ran on different databases too, from dev to prod? -- Wouldn’t it be nice if it offered requests with built in retries for those nasty unreliable apis (Hey Zendesk, why you fail on call 99998/100000?) -- Wouldn’t it be nice if we had some extraction helpers like pagination detection? - -Auto typing and unpacking with generated keys: -![keys](https://storage.googleapis.com/dlt-blog-images/generated_keys.png) - -Performance [docs](https://dlthub.com/docs/reference/performance) - - -### The initial steps - -How did we go about it? At first dlt was created as an engine to iron out its functionality. During this time, it was deployed it in several projects, from startups to enterprises, particularly to accelerate data pipeline building in a robust way. - -A while later, to prepare this engine for the general public, we created the current interface on top of it. We then tested it in a workshop with many “Normies” of which over 50% were pre-employment learners. - -For the workshop we broke down the steps to build an incremental pipeline into 20 steps. In the 6 hour workshop we asked people to react on Slack to each “checkpoint”. We then exported the slack data and loaded it with dlt, exposing the completion rate per checkpoint. Turns out, it was 100%. -Everyone who started, managed to build the pipeline. “This is it!” we thought, and spend the next 6 months preparing our docs and adding some plugins for easy deployment. - -## III. Launching dlt - -We finally launched dlt mid 2023 to the general public. Our initial community was mostly data engineers who had been using dlt without docs, -managing from reading code. As we hoped a lot of “normies” are using dlt, too! - -## dlt = code + docs + Slack support - -A product is a sum of many parts. For us dlt is not only the dlt library and interface, but also our docs and Slack community and the support and discussions there. - -In the early days of dlt we talked to Sebastian Ramirez from FastAPI who told us that he spends 2/3 of his FastAPI time writing documentation. - -In this vein, from the beginning docs were very important to us and we quickly adopted our own [docs standard](https://www.writethedocs.org/videos/eu/2017/the-four-kinds-of-documentation-and-why-you-need-to-understand-what-they-are-daniele-procida/). - -However, when we originally launched dlt, we found that different user types, especially Normies, expect different things from our docs, and because we asked for feedback, they told us. - -So overall, we were not satisfied to stop there. - -### "Can you make your docs more like my favorite tool's docs?" - -To this end we built and embedded our own docs helper in our docs. - -The result? The docs helper has been running for a year and we currently see around **300 questions per day.** Comparing this to other communities that do AI support on Slack, that’s almost 2 orders of magnitude difference in question volume by community size. - -We think this is a good thing, and a result of several factors. - -- Embedded in docs means at the right place at the right time. Available to anyone, whether they use Slack or not. -- Conversations are private and anonymous. This reduces the emotional barrier of asking. We suspect this is great for the many “Normies” / “problem solvers” that work in data. -- The questions are different than in our Slack community: Many questions are around “Setup and configuration”, “Troubleshooting” and “General questions” about dlt architecture. In Slack, we see the questions that our docs or assistant could not answer. -- The bot is conversational and will remember recent context, enabling it to be particularly helpful. This is different from the “question answering service” that many Slack bots offer, which do not keep context once a question was answered. By retaining context, it’s possible to reach a useful outcome even if it doesn’t come in the first reply. - -### dlt = “pip install and go” - the fastest way to create a pipeline and source - -dlt offers a small number of verified sources, but encourages you to build your own. As we -mentioned, creating an ad hoc dlt [pipeline and source](https://dlthub.com/docs/tutorial/load-data-from-an-api) is -[dramatically simpler](https://dlthub.com/docs/build-a-pipeline-tutorial#the-simplest-pipeline-1-liner-to-load-data-with-schema-evolution) compared to other python libraries. -Maintaining a custom dlt source in production takes no time at all because the pipeline won't break unless the source stops existing. - -The sources you build and run that are not shared back into the verified sources are what we call “private sources”. - -By the end of 2023, our community had already built 1,000 private sources, [2,000 by early March](https://dlthub.com/docs/blog/code-vs-buy). We -are now at the end of q2 2024 and we see 5,000 private sources. - -### Embracing LLM-free code generation - -We recently launched additional tooling that helps our users build sources. If you wish to try our python-first -dict-based declarative approach to building sources, check out the relevant post. - -- Rest api connector -- Openapi based pipeline generator that configures the rest api connector. - -Alena introduces the generator and troubleshoots the outcome in 4min: - - -Community videos for rest api source: [playlist](https://www.youtube.com/playlist?list=PLpTgUMBCn15rs2NkB4ise780UxLKImZTh). - -Both tools are LLM-free pipeline generators. I stress LLM free, because in our experience, GPT can -do some things to some extent - so if we ask it to complete 10 tasks to produce a pipeline, each -having 50-90% accuracy, we can expect very low success rates. - -To get around this problem, we built from the OpenAPI standard which contains information that can -be turned into a pipeline algorithmically. OpenAPI is an Api spec that’s also used by FastAPI and -constantly growing in popularity, with around 50% of apis currently supporting it. - -By leveraging the data in the spec, we are able to have a basic pipeline. Our generator also infers -some other pieces of information algorithmically to make the pipeline incremental and add some other useful details. - -### When generation doesn’t work - -Of course, generation doesn’t always work but you can take the generated pipeline and make the final -adjustments to have a standard REST API config-based pipeline that won’t suffer from code smells. - -### The benefit of minimalistic sources - -The real benefit of this declarative source is not at building time - A declarative interface requires -more upfront knowledge. Instead, by having this option, we enable minimalistic pipelines that anyone could -maintain, including non coders or human-assisted LLMs. After all, LLMs are particularly proficient at translating configurations back and forth. - -Want to influence us? we listen, so you’re welcome to discuss with us in our slack channel [**#4-discussions**](https://dlthub.com/community) - -### Towards a paid offering - -dlt is an open core product, meaning it won’t be gated to push you to the paid version at some point. -Instead, much like Kafka and Confluent, we will offer things around dlt to help you leverage it in your context. - -If you are interested to help us research what’s needed, you can apply for our design partnership -program, that aims to help you deploy dlt, while helping us learn about your challenges. - -## Call to action. - -If you like the idea of dlt, there is one thing that would help us: - -**Set aside 30min and try it.** - -See resource below. - -We often hear variations of “oh i postponed dlt so long but it only took a few minutes to get going, wish I hadn’t -installed [other tool] which took 2 weeks to set up properly and now we need to maintain or replace”, so don't be that guy. - - -Here are some notebooks and docs to open your appetite: - - -- An [API pipeline step by step tutorial](https://dlthub.com/docs/tutorial/load-data-from-an-api) to build a production pipeline from an api -- A colab demo of [schema evolution](https://colab.research.google.com/drive/1H6HKFi-U1V4p0afVucw_Jzv1oiFbH2bu#scrollTo=e4y4sQ78P_OM) (2min read) -- Docs: RestClient, the imperative class that powers the REST API source, featuring auto pagination https://dlthub.com/docs/general-usage/http/rest-client -- Docs: [Build a simple pipeline](https://dlthub.com/docs/walkthroughs/create-a-pipeline) -- Docs: [Build a complex pipeline](https://dlthub.com/docs/walkthroughs/create-a-pipeline) -- Docs: [capabilities overview](https://dlthub.com/docs/build-a-pipeline-tutorial) hub page -- Community & Help: [Slack join link.](https://dlthub.com/community) \ No newline at end of file diff --git a/docs/website/blog/2024-06-19-scd2-and-incremental-loading.md b/docs/website/blog/2024-06-19-scd2-and-incremental-loading.md deleted file mode 100644 index 11c858c076..0000000000 --- a/docs/website/blog/2024-06-19-scd2-and-incremental-loading.md +++ /dev/null @@ -1,132 +0,0 @@ ---- -slug: scd2-and-incremental-loading -title: "Slowly Changing Dimension Type2: Explanation and code" -image: https://storage.googleapis.com/dlt-blog-images/flowchart_for_scd2.png -authors: - name: Aman Gupta - title: Junior Data Engineer - url: https://github.com/dat-a-man - image_url: https://dlt-static.s3.eu-central-1.amazonaws.com/images/aman.png -tags: [scd2, incremental loading, slowly changing dimensions, python data pipelines] ---- - - - -:::info -**Check [this Colab Notebook](https://colab.research.google.com/drive/115cRdw1qvekZbXIQSXYkAZzLAqD9_x_I) for a short and sweet demo.** -::: - -# What is a slowly changing dimension? - -Slowly changing dimensions are a dimensional modelling technique created for historising changes in data. - -This technique only works if the dimensions change slower than we read the data, since we would not be able to track changes happening between reads. -For example, if someone changes their address once in a blue moon, we will capture the changes with daily loads - but if -they change their address 3x in a day, we will only see the last state and only capture 2 of the 4 versions of the address. - -However, they enable you to track things you could not before such as - -- Hard deletes -- Most of the changes and when they occurred -- Different versions of entities valid at different historical times - -## What is Slowly Changing Dimension Type 2 (SCD2)? and why use it? - -The Type 2 subtype of Slowly Changing Dimensions (SCD) manages changes in data over time. -When data changes, a new record is added to the database, but the old record remains unchanged. -Each record includes a timestamp or version number. This allows you to view both the historical -data and the most current data separately. - -Traditional data loading methods often involve updating existing records with new information, which results in the loss of historical data. - -SCD2 not only preserves an audit trail of data changes but also allows for accurate historical analysis and reporting. - -## SCD2 applications - -[Colab demo](https://colab.research.google.com/drive/115cRdw1qvekZbXIQSXYkAZzLAqD9_x_I) - -### Use Case 1: Versioning a record that changes - -In environments where maintaining a complete historical record of data changes is crucial, -such as in financial services or healthcare, SCD Type 2 plays a vital role. For instance, if a -customer's address changes, SCD2 ensures that the old address is preserved in historical -records while the new address is available for current transactions. This ability to view the -evolution of data over time supports auditing, tracking changes, and analyzing trends without losing -the context of past information. It allows organizations to track the lifecycle of a data -entity across different states. - -Here's an example with the customer address change. - -Before: - -| `_dlt_valid_from` | `_dlt_valid_to` | `customer_key` | `c1` | `c2` | -|-----------------------------|-----------------|----------------|-------------|------| -| 2024-04-09 18:27:53.734235 | NULL | 1 | 123 Elm St | TN | - -After update: - -| `_dlt_valid_from` | `_dlt_valid_to` | `customer_key` | `c1` | `c2` | -|-----------------------------|-----------------------------|----------------|-------------|------| -| 2024-04-09 18:27:53.734235 | 2024-05-01 17:00:00.000000 | 1 | 123 Elm St | TN | -| 2024-05-02 08:00:00.000000 | NULL | 1 | 456 Oak Ave | TN | - -In the updated state, the previous address record is closed with an `_dlt_valid_to` timestamp, and a new record is created -with the new address "456 Oak Ave" effective from May 2, 2024. The NULL in the `_dlt_valid_to` field for this -new record signifies that it is the current and active address. - -### Use Case 2: Tracking deletions - -This approach ensures that historical data is preserved for audit and compliance purposes, even though the -record is no longer active in the current dataset. It allows businesses to maintain integrity and a full -historical trail of their data changes. - -State Before Deletion: Customer Record Active - -| `_dlt_valid_from` | `_dlt_valid_to` | `customer_key` | `c1` | `c2` | -|-----------------------------|-----------------|----------------|-------------|------| -| 2024-04-09 18:27:53.734235 | NULL | 1 | 123 Elm St | TN | -This table shows the customer record when it was active, with an address at "123 Elm St". The `_dlt_valid_to` field is NULL, indicating that the record is currently active. - -State after deletion: Customer record marked as deleted - -| `_dlt_valid_from` | `_dlt_valid_to` | `customer_key` | `c1` | `c2` | -|-----------------------------|-----------------------------|----------------|-------------|------| -| 2024-04-09 18:27:53.734235 | 2024-06-01 10:00:00.000000 | 1 | 123 Elm St | TN | - -In this updated table, the record that was previously active is marked as deleted by updating the `_dlt_valid_to` field -to reflect the timestamp when the deletion was recognized, in this case, June 1, 2024, at 10:00 AM. The presence -of a non-NULL `_dlt_valid_to` date indicates that this record is no longer active as of that timestamp. - - -Learn how to customise your column names and validity dates in our [SDC2 docs](https://dlthub.com/docs/general-usage/incremental-loading#scd2-strategy). - - -### Surrogate keys, what are they? Why use? - -Every record in the SCD2 table needs its own id. We call this a surrogate key. We use it to identify the specific -record or version of an entity, and we can use it when joining to our fact tables for performance (as opposed to joining on entity id + validity time). - -### Simple steps to determine data loading strategy and write disposition - -This decision flowchart helps determine the most suitable data loading strategy and write disposition: - -1. Is your data stateful? Stateful data is subject to change, like your age. Stateless data does not change, for example, events that happened in the past are stateless. - - 1. If your data is stateless, such as logs, you can just increment by appending new logs. - 2. If it is stateful, do you need to track changes to it? - 1. If yes, then use SCD2 to track changes. - 2. If no, - 1. Can you extract it incrementally (new changes only)? - 1. If yes, load incrementally via merge. - 2. If no, re-load fully via replace. - -Below is a visual representation of steps discussed above: -![Image](https://storage.googleapis.com/dlt-blog-images/flowchart_for_scd2.png) - -### **Conclusion** - -Use SCD2 where it makes sense but keep in mind the shortcomings related to the read vs update frequency. -Use dlt to do it at loading and keep everything downstream clean and simple. - -Want to discuss? -[Join the dlt slack community!](https://dlthub.com/community) diff --git a/docs/website/blog/2024-06-21-google-forms-to-notion.md b/docs/website/blog/2024-06-21-google-forms-to-notion.md deleted file mode 100644 index ec1631bc44..0000000000 --- a/docs/website/blog/2024-06-21-google-forms-to-notion.md +++ /dev/null @@ -1,142 +0,0 @@ ---- -slug: google-forms-to-notion -title: "Syncing Google Forms data with Notion using dlt" -authors: - name: Aman Gupta - title: Junior Data Engineer - url: https://github.com/dat-a-man - image_url: https://dlt-static.s3.eu-central-1.amazonaws.com/images/aman.png -tags: [google forms, cloud functions, google-forms-to-notion] ---- - -## Why do we do it? - -Hello, I'm Aman, and I assist the dlthub team with various data-related tasks. In a recent project, the Operations team needed to gather information through Google Forms and integrate it into a Notion database. Initially, they tried using the Zapier connector as a quick and cost-effective solution, but it didn’t work as expected. Since we’re at dlthub, where everyone is empowered to create pipelines, I stepped in to develop one that would automate this process. - -The solution involved setting up a workflow to automatically sync data from Google Forms to a Notion database. This was achieved using Google Sheets, Google Apps Script, and a `dlt` pipeline, ensuring that every new form submission was seamlessly transferred to the Notion database without the need for manual intervention. - -## Implementation - -So here are a few steps followed: - -**Step 1: Link Google Form to Google Sheet** - -Link the Google Form to a Google Sheet to save responses in the sheet. Follow [Google's documentation](https://support.google.com/docs/answer/2917686?hl=en#zippy=%2Cchoose-where-to-store-responses) for setup. - -**Step 2: Google Apps Script for Data Transfer** - -Create a Google Apps Script to send data from Google Sheets to a Notion database via a webhook. This script triggers every time a form response is saved. - -**Google Apps Script code:** - -```text -function sendWebhookOnEdit(e) { - var sheet = SpreadsheetApp.getActiveSpreadsheet().getActiveSheet(); - var range = sheet.getActiveRange(); - var updatedRow = range.getRow(); - var lastColumn = sheet.getLastColumn(); - var headers = sheet.getRange(1, 1, 1, lastColumn).getValues()[0]; - var updatedFields = {}; - var rowValues = sheet.getRange(updatedRow, 1, 1, lastColumn).getValues()[0]; - - for (var i = 0; i < headers.length; i++) { - updatedFields[headers[i]] = rowValues[i]; - } - - var jsonPayload = JSON.stringify(updatedFields); - Logger.log('JSON Payload: ' + jsonPayload); - - var url = 'https://your-webhook.cloudfunctions.net/to_notion_from_google_forms'; // Replace with your Cloud Function URL - var options = { - 'method': 'post', - 'contentType': 'application/json', - 'payload': jsonPayload - }; - - try { - var response = UrlFetchApp.fetch(url, options); - Logger.log('Response: ' + response.getContentText()); - } catch (error) { - Logger.log('Failed to send webhook: ' + error.toString()); - } -} -``` - -**Step 3: Deploying the ETL Pipeline** - -Deploy a `dlt` pipeline to Google Cloud Functions to handle data transfer from Google Sheets to the Notion database. The pipeline is triggered by the Google Apps Script. - -1. Create a Google Cloud function. -2. Create `main.py` with the Python code below. -3. Ensure `requirements.txt` includes `dlt`. -4. Deploy the pipeline to Google Cloud Functions. -5. Use the function URL in the Google Apps Script. - -:::note -This pipeline uses `@dlt.destination` decorator which is used to set up custom destinations. Using custom destinations is a part of `dlt's` reverse ETL capabilities. To read more about `dlt's` reverse ETL pipelines, please read the [documentation here.](https://dlthub.com/docs/dlt-ecosystem/destinations/destination) -::: - -**Python code for `main.py` (Google cloud functions) :** - -```py -import dlt -from dlt.common import json -from dlt.common.typing import TDataItems -from dlt.common.schema import TTableSchema -from datetime import datetime -from dlt.sources.helpers import requests - -@dlt.destination(name="notion", batch_size=1, naming_convention="direct", skip_dlt_columns_and_tables=True) -def insert_into_notion(items: TDataItems, table: TTableSchema) -> None: - api_key = dlt.secrets.value # Add your notion API key to "secrets.toml" - database_id = "your_notion_database_id" # Replace with your Notion Database ID - url = "https://api.notion.com/v1/pages" - headers = { - "Authorization": f"Bearer {api_key}", - "Content-Type": "application/json", - "Notion-Version": "2022-02-22" - } - - for item in items: - if isinstance(item.get('Timestamp'), datetime): - item['Timestamp'] = item['Timestamp'].isoformat() - data = { - "parent": {"database_id": database_id}, - "properties": { - "Timestamp": { - "title": [{ - "text": {"content": item.get('Timestamp')} - }] - }, - # Add other properties here - } - } - response = requests.post(url, headers=headers, data=json.dumps(data)) - print(response.status_code, response.text) - -def your_webhook(request): - data = request.get_json() - Event = [data] - - pipeline = dlt.pipeline( - pipeline_name='platform_to_notion', - destination=insert_into_notion, - dataset_name='webhooks', - full_refresh=True - ) - - pipeline.run(Event, table_name='webhook') - return 'Event received and processed successfully.' -``` - -### Step 4: Automation and Real-Time updates - -With everything set up, the workflow automates data transfer as follows: - -1. Form submission saves data in Google Sheets. -2. Google Apps Script sends a POST request to the Cloud Function. -3. The `dlt` pipeline processes the data and updates the Notion database. - -# Conclusion - -We initially considered using Zapier for this small task, but ultimately, handling it ourselves proved to be quite effective. Since we already use an orchestrator for our other automations, the only expense was the time I spent writing and testing the code. This experience demonstrates that `dlt` is a straightforward and flexible tool, suitable for a variety of scenarios. Essentially, wherever Python can be used, `dlt` can be applied effectively for data loading, provided it meets your specific needs. \ No newline at end of file diff --git a/docs/website/blog/2024-07-11-how-dlt-uses-apache-arrow.md b/docs/website/blog/2024-07-11-how-dlt-uses-apache-arrow.md deleted file mode 100644 index 4ae6f12013..0000000000 --- a/docs/website/blog/2024-07-11-how-dlt-uses-apache-arrow.md +++ /dev/null @@ -1,308 +0,0 @@ ---- -slug: how-dlt-uses-apache-arrow -title: "How dlt uses Apache Arrow" -image: https://storage.googleapis.com/dlt-blog-images/blog_data_engineering_with_jorrit.png -authors: - name: Jorrit Sandbrink - title: Open Source Software Engineer - url: https://github.com/jorritsandbrink - tags: [apache arrow, dlt] -canonical_url: "https://jorritsandbrink.substack.com/p/how-dlt-uses-apache-arrow-for-fast-pipelines" ---- - -:::tip TL;DR: -`dlt` uses Apache Arrow to make pipelines faster. The Arrow format is a better way -to represent tabular data in memory than native Python objects (list of dictionaries). It enables -offloading computation to Arrow’s fast C++ library, and prevents processing rows one by one. -::: - -Speed matters. Pipelines should move data quickly and efficiently. The bigger the data, the more -that holds true. Growing data volumes force performance optimization upon data processing tools. In -this blog I describe how `dlt` uses Arrow and why it makes data pipelines faster. - -## What is `dlt`? - -`dlt` is an open source Python library that lets you build data pipelines as code. It tries to make -data movement between systems easier. It gives data engineers a set of abstractions (e.g. source, -destination, pipeline) and a declarative API that saves them from writing lower level code. - -`dlt` doesn’t use a backend server/database. It’s “just a library” that can be embedded in a Python -process. `pip install dlt` and `import dlt` is all it takes. - -An example use case is loading data from a REST API (the source) into a data warehouse (the -destination) with a `dlt` pipeline that runs in a serverless cloud function (e.g. AWS Lambda). - -## What is Arrow? - -Arrow is an Apache project that standardizes data analytics systems. Among other things, it -specifies a format to represent analytics data in memory. - -Format characteristics: - -- language agnostic → it’s the same in C++, Rust, Python, or any other language - -- columnar → values for a column are stored contiguously - -- lightweight encoding → no general purpose compression (e.g. Snappy) or complex encodings - -- O(1) (constant-time) random access - -System interoperability and performance are two of the benefits of having this standard. - -## How `dlt` works - -Before explaining how `dlt` uses Arrow, I will first describe how `dlt` works at a high level. - -Pipeline steps - -A basic `dlt` pipeline has three main steps: - -1. extract - -1. normalize - -1. load - -**extract →** fetch data from source system and write to local disk - -**normalize →** read extracted data from local disk infer schema and transform data in memory write -transformed data to local disk - -**load →** read normalized data from local disk and ingest into destination system - -### Extraction - -extract is I/O intensive. - -`dlt` uses a Python generator function that fetches data from a source system and yields it into the -pipeline. This function is called a resource. - -### Normalization - -Steps 1 and 3 of normalize are I/O intensive. Step 2 is compute intensive. Step 2 has several -“substeps”: - - -1. identify tables, columns and their data types - -2. apply naming convention (e.g. snake_case) to table and column identifiers - -3. add system columns → e.g. `_dlt_id` (row identifier) and `_dlt_load_id` (load identifier) - -4. split nested data into parent and child tables - - -> Some of these substeps are already done during extract when using the Arrow route, as I explain -> later in this blog. - -### Loading - -Load is I/O intensive (and in some cases also compute intensive). - -The data files persisted during normalize are loaded into the destination. How this is done differs -per destination. - -## How `dlt` uses Arrow - -`dlt` currently supports two different pipeline “routes”: - -1. The traditional route → has existed since earliest versions of `dlt` - -1. The Arrow route → was added later as improvement - -The user decides which route is taken. It’s an implicit choice that depends on the type of object -yielded by the resource. -![Picture](https://storage.googleapis.com/dlt-blog-images/blog_data_engineering_with_jorrit.png) - -## Traditional route - -The traditional route uses native Python objects and row orientation to represent tabular data in -memory. - -```py -@dlt.resource -def my_traditional_resource(): - - # native Python objects as table - table = [ - {"foo": 23, "bar": True}, - {"foo": 7, "bar": False} - ] - - yield table - -pipeline.run(my_traditional_resource()) -``` - -### extract - -The resource yields Python dictionaries or lists of dictionaries into the pipeline. Each dictionary -is a row: keys are column names, values are column values. A list of such dictionaries can be seen -as a table. - -The pipeline serializes the Python objects into a JSON-like byte-stream (using orjson) and persists -to binary disk files with .typed-jsonl extension. - -### normalize - -The pipeline reads the extracted data from .typed-jsonl files back into memory and deserializes it. -It iterates over all table values in a nested for loop. The outer loop iterates over the rows, the -inner loop iterates over the columns. While looping, the pipeline performs the steps mentioned in -the paragraph called Normalization. - -The normalized data is persisted to disk in a format that works well for the destination it will be -loaded into. For example, two of the formats are: - -- jsonl → JSON Lines—default for filesystem destination - -- insert_values → a file storing INSERT SQL statements, compressed by default—default for some of - the SQL destinations - -### load - -As mentioned, this step differs per destination. It also depends on the format of the file persisted -during normalize. Here are two examples to give an idea: - -- jsonl files and filesystem destination → use PUT operation - -- insert_values files and SQL destination (e.g. postgres) → execute SQL statements on SQL engine - -### Arrow route - -The Arrow route uses columnar Arrow objects to represent tabular data in memory. It relies on the -pyarrow Python libary. - -```py -import pyarrow as pa - -@dlt.resource -def my_arrow_resource(): - - ... # some process that creates a Pandas DataFrame - - # Arrow object as table - table = pa.Table.from_pandas(df) - - yield table - -pipeline.run(my_arrow_resource()) -``` - -### extract - -The resource yields Arrow objects into the pipeline. These can be Arrow tables (pyarrow.Table) or -Arrow record batches (pyarrow.RecordBatch). Arrow objects are schema aware, meaning they store -column names and data types alongside the data. - -The pipeline serializes the Arrow objects into Parquet files on disk. This is done with pyarrow’s -Parquet writer (pyarrow.parquet.ParquetWriter). Like Arrow objects, Parquet files are schema aware. -The Parquet writer simply translates the Arrow schema to a Parquet schema and persists it in the -file. - -> The yielded Arrow objects are slightly normalized in the extract step. This prevents a rewrite in -> the normalize step. The normalization done here are cheap metadata operations that don’t add much -> overhead to extract. For example, column names are adjusted if they don’t match the naming -> convention and column order is adjusted if it doesn’t match the table schema. - -### normalize - -Schema inference is not needed because the table schema can be read from the Parquet file. - -There are tree cases—in the ideal case, data does not need to be transformed: - -1. **destination supports Parquet loading — no normalization (ideal):** the extracted Parquet - files are simply “moved” to the load folder using an atomic rename. This is a cheap metadata - operation. Data is not transformed and the data doesn’t actually move. `dlt` does not add row and - load identifier columns. - -1. **destination supports Parquet loading — yes normalization (okay):** the extracted Parquet - files are loaded into memory in Arrow format. The necessary transformations (e.g. adding system - columns or renaming column identifiers) are done using pyarrow methods. These operations are - relatively cheap. Parquet and Arrow are both columnar and have similar data layouts. - Transformations are done in batch, not on individual rows. Computations are done in C++, because - pyarrow is a wrapper around the Arrow C++ library. - -1. **destination does not support Parquet loading (not good):** the extracted Parquet files are - read in memory and converted to a format supported by the destination (e.g. insert_values). This - is an expensive operation. Parquet’s columnar format needs to be converted to row orientation. - The rows are iterated over one by one to generate the load file. - -### load - -This step is the same as in the traditional route. - -## Main differences - -The most important differences between the traditional and Arrow routes are as follows. - -- **in memory format** - - - traditional → native Python objects - - Arrow → pyarrow objects - -- **on disk format for normalized data** - - - traditional → defaults to jsonl - - Arrow → defaults to parquet - -- **schema inference** - - - traditional → handled by `dlt` during normalize—done in Python while iterating over rows - - Arrow → two cases: - - source system provides Arrow data: schema taken from source (no schema inference needed) - - source system does not provide Arrow data: handled by pyarrow during extract when data is - converted into Arrow objects, done in C++ - -- **data transformation for normalization** - - - traditional → handled by dlt—done in Python while iterating over rows - - Arrow → handled by pyarrow—done in C++ on columnar batches of rows - -## Why `dlt` uses Arrow - -`dlt` uses Arrow to make pipelines faster. The normalize step in particular can be much more efficient -in the Arrow route. - -Using pyarrow objects for tabular data is faster than using native Python objects (lists of -dictionaries), because they are: - -- schema aware - -- columnar - -- computed in C++ - -Generally speaking, C++ is much faster than Python. Moreover, Arrow’s C++ implementation can use -vectorization (SIMD) thanks to the columnar data layout. The Arrow route can process batches of -values concurrently in C++, while `dlt’s` traditional route needs iteration over values one by one in -a nested Python loop. - -Schema aware Arrow objects prevents `dlt` from having to infer column types from column values. - -## Further thoughts - -A potential optimization I can think of (but haven’t tested) is to use the Arrow IPC File Format to -serialize data between extract and normalize. This saves two format conversions: - -1. Arrow → Parquet (serialization at the end of extract) - -1. Parquet → Arrow (deserialization at the start of normalize) - -Although Arrow and Parquet have relatively similar layouts (especially when using Parquet without -general purpose compression), removing the (de)serialization steps might still improve performance -significantly. - -Simply disabling compression when writing the Parquet file could be an easier way to achieve similar -results. - -## Context - -I contribute to the open source `dlt` library, but didn’t implement the core framework logic related -to extraction, normalization, and loading described in this post. I’m enthusiastic about Arrow and -its implications for the data ecosystem, but haven’t contributed to their open source libraries. - -# Call to action -Try the SQL connector here with the various backends: [Docs](https://dlthub.com/docs/dlt-ecosystem/verified-sources/sql_database#pick-the-right-backend-to-load-table-data) - -Want to discuss performance? -[Join the dlt slack community!](https://dlthub.com/community) diff --git a/docs/website/blog/authors.yml b/docs/website/blog/authors.yml deleted file mode 100644 index 42ef10d123..0000000000 --- a/docs/website/blog/authors.yml +++ /dev/null @@ -1,5 +0,0 @@ -matthaus: - name: Matthaus Krzykowski - title: Co-Founder & CEO at dltHub - url: https://twitter.com/matthausk - image_url: https://pbs.twimg.com/profile_images/642282396751130624/9ixo0Opj_400x400.jpg \ No newline at end of file diff --git a/docs/website/docs/dlt-ecosystem/destinations/athena.md b/docs/website/docs/dlt-ecosystem/destinations/athena.md index 2a8b8c6b9d..e6f99adc48 100644 --- a/docs/website/docs/dlt-ecosystem/destinations/athena.md +++ b/docs/website/docs/dlt-ecosystem/destinations/athena.md @@ -131,13 +131,6 @@ def data() -> Iterable[TDataItem]: ... ``` -Alternatively, you can set all tables to use the iceberg format with a config variable: - -```toml -[destination.athena] -force_iceberg = "True" -``` - For every table created as an iceberg table, the Athena destination will create a regular Athena table in the staging dataset of both the filesystem and the Athena glue catalog, and then copy all data into the final iceberg table that lives with the non-iceberg tables in the same dataset on both the filesystem and the glue catalog. Switching from iceberg to regular table or vice versa is not supported. #### `merge` support diff --git a/docs/website/docs/dlt-ecosystem/destinations/clickhouse.md b/docs/website/docs/dlt-ecosystem/destinations/clickhouse.md index bf8e2bce02..8752c571b1 100644 --- a/docs/website/docs/dlt-ecosystem/destinations/clickhouse.md +++ b/docs/website/docs/dlt-ecosystem/destinations/clickhouse.md @@ -133,7 +133,7 @@ destination. The `clickhouse` destination has a few specific deviations from the default sql destinations: -1. `Clickhouse` has an experimental `object` datatype, but we've found it to be a bit unpredictable, so the dlt clickhouse destination will load the complex datatype to a `text` column. +1. `Clickhouse` has an experimental `object` datatype, but we've found it to be a bit unpredictable, so the dlt clickhouse destination will load the `json` datatype to a `text` column. If you need this feature, get in touch with our Slack community, and we will consider adding it. 2. `Clickhouse` does not support the `time` datatype. Time will be loaded to a `text` column. diff --git a/docs/website/docs/dlt-ecosystem/destinations/databricks.md b/docs/website/docs/dlt-ecosystem/destinations/databricks.md index ddb82c95b2..12b267c9d6 100644 --- a/docs/website/docs/dlt-ecosystem/destinations/databricks.md +++ b/docs/website/docs/dlt-ecosystem/destinations/databricks.md @@ -136,7 +136,7 @@ For more information on staging, see the [staging support](#staging-support) sec The `jsonl` format has some limitations when used with Databricks: 1. Compression must be disabled to load jsonl files in Databricks. Set `data_writer.disable_compression` to `true` in dlt config when using this format. -2. The following data types are not supported when using `jsonl` format with `databricks`: `decimal`, `complex`, `date`, `binary`. Use `parquet` if your data contains these types. +2. The following data types are not supported when using `jsonl` format with `databricks`: `decimal`, `json`, `date`, `binary`. Use `parquet` if your data contains these types. 3. `bigint` data type with precision is not supported with `jsonl` format diff --git a/docs/website/docs/dlt-ecosystem/destinations/destination.md b/docs/website/docs/dlt-ecosystem/destinations/destination.md index 6ffc13ad74..bd26aa366b 100644 --- a/docs/website/docs/dlt-ecosystem/destinations/destination.md +++ b/docs/website/docs/dlt-ecosystem/destinations/destination.md @@ -68,7 +68,7 @@ def my_destination(items: TDataItems, table: TTableSchema) -> None: * The `loader_file_format` parameter on the destination decorator defines in which format files are stored in the load package before being sent to the destination function. This can be `jsonl` or `parquet`. * The `name` parameter on the destination decorator defines the name of the destination that gets created by the destination decorator. * The `naming_convention` parameter on the destination decorator defines the name of the destination that gets created by the destination decorator. This controls how table and column names are normalized. The default is `direct`, which will keep all names the same. -* The `max_nesting_level` parameter on the destination decorator defines how deep the normalizer will go to normalize complex fields on your data to create subtables. This overwrites any settings on your `source` and is set to zero to not create any nested tables by default. +* The `max_nesting_level` parameter on the destination decorator defines how deep the normalizer will go to normalize nested fields on your data to create subtables. This overwrites any settings on your `source` and is set to zero to not create any nested tables by default. * The `skip_dlt_columns_and_tables` parameter on the destination decorator defines whether internal tables and columns will be fed into the custom destination function. This is set to `True` by default. * The `max_parallel_load_jobs` parameter will define how many load jobs will run in parallel in threads, if you have a destination that only allows five connections at a time, you can set this value to 5 for example * The `loader_parallelism_strategy` parameter will control how load jobs are parallelized. Set to `parallel`, the default, jobs will be parallelized no matter which table is being loaded to. `table-sequential` will parallelize loading but only ever have one load job per table at a time, `sequential` will run all load jobs sequentially on the main thread. diff --git a/docs/website/docs/dlt-ecosystem/destinations/duckdb.md b/docs/website/docs/dlt-ecosystem/destinations/duckdb.md index 19cef92f9d..4b8ecec4ca 100644 --- a/docs/website/docs/dlt-ecosystem/destinations/duckdb.md +++ b/docs/website/docs/dlt-ecosystem/destinations/duckdb.md @@ -35,6 +35,42 @@ All write dispositions are supported. ## Data loading `dlt` will load data using large INSERT VALUES statements by default. Loading is multithreaded (20 threads by default). If you are okay with installing `pyarrow`, we suggest switching to `parquet` as the file format. Loading is faster (and also multithreaded). +### Data types +`duckdb` supports various [timestamp types](https://duckdb.org/docs/sql/data_types/timestamp.html). These can be configured using the column flags `timezone` and `precision` in the `dlt.resource` decorator or the `pipeline.run` method. + +- **Precision**: supported precision values are 0, 3, 6, and 9 for fractional seconds. Note that `timezone` and `precision` cannot be used together; attempting to combine them will result in an error. +- **Timezone**: + - Setting `timezone=False` maps to `TIMESTAMP`. + - Setting `timezone=True` (or omitting the flag, which defaults to `True`) maps to `TIMESTAMP WITH TIME ZONE` (`TIMESTAMPTZ`). + +#### Example precision: TIMESTAMP_MS + +```py +@dlt.resource( + columns={"event_tstamp": {"data_type": "timestamp", "precision": 3}}, + primary_key="event_id", +) +def events(): + yield [{"event_id": 1, "event_tstamp": "2024-07-30T10:00:00.123"}] + +pipeline = dlt.pipeline(destination="duckdb") +pipeline.run(events()) +``` + +#### Example timezone: TIMESTAMP + +```py +@dlt.resource( + columns={"event_tstamp": {"data_type": "timestamp", "timezone": False}}, + primary_key="event_id", +) +def events(): + yield [{"event_id": 1, "event_tstamp": "2024-07-30T10:00:00.123+00:00"}] + +pipeline = dlt.pipeline(destination="duckdb") +pipeline.run(events()) +``` + ### Names normalization `dlt` uses the standard **snake_case** naming convention to keep identical table and column identifiers across all destinations. If you want to use the **duckdb** wide range of characters (i.e., emojis) for table and column names, you can switch to the **duck_case** naming convention, which accepts almost any string as an identifier: * `\n` `\r` and `"` are translated to `_` @@ -77,7 +113,8 @@ to disable tz adjustments. ::: ## Supported column hints -`duckdb` may create unique indexes for all columns with `unique` hints, but this behavior **is disabled by default** because it slows the loading down significantly. + +`duckdb` can create unique indexes for columns with `unique` hints. However, **this feature is disabled by default** as it can significantly slow down data loading. ## Destination Configuration diff --git a/docs/website/docs/dlt-ecosystem/destinations/filesystem.md b/docs/website/docs/dlt-ecosystem/destinations/filesystem.md index 018b838363..cfeb03655c 100644 --- a/docs/website/docs/dlt-ecosystem/destinations/filesystem.md +++ b/docs/website/docs/dlt-ecosystem/destinations/filesystem.md @@ -1,10 +1,14 @@ -# Filesystem & buckets -The Filesystem destination stores data in remote file systems and bucket storages like **S3**, **Google Storage**, or **Azure Blob Storage**. Underneath, it uses [fsspec](https://github.com/fsspec/filesystem_spec) to abstract file operations. Its primary role is to be used as a staging for other destinations, but you can also quickly build a data lake with it. +# Cloud storage and filesystem +The filesystem destination stores data in remote file systems and cloud storage services like **AWS S3**, **Google Cloud Storage**, or **Azure Blob Storage**. Underneath, it uses [fsspec](https://github.com/fsspec/filesystem_spec) to abstract file operations. Its primary role is to be used as a staging for other destinations, but you can also quickly build a data lake with it. -> 💡 Please read the notes on the layout of the data files. Currently, we are getting feedback on it. Please join our Slack (icon at the top of the page) and help us find the optimal layout. +:::tip +Please read the notes on the layout of the data files. Currently, we are getting feedback on it. Please join our Slack (icon at the top of the page) and help us find the optimal layout. +::: ## Install dlt with filesystem -**To install the dlt library with filesystem dependencies:** + +Install the dlt library with filesystem dependencies: + ```sh pip install "dlt[filesystem]" ``` @@ -24,14 +28,15 @@ so pip does not fail on backtracking. ## Initialise the dlt project Let's start by initializing a new dlt project as follows: - ```sh - dlt init chess filesystem - ``` +```sh +dlt init chess filesystem +``` + :::note -This command will initialize your pipeline with chess as the source and the AWS S3 filesystem as the destination. +This command will initialize your pipeline with chess as the source and the AWS S3 as the destination. ::: -## Set up bucket storage and credentials +## Set up the destination and credentials ### AWS S3 The command above creates a sample `secrets.toml` and requirements file for AWS S3 bucket. You can install those dependencies by running: @@ -39,7 +44,8 @@ The command above creates a sample `secrets.toml` and requirements file for AWS pip install -r requirements.txt ``` -To edit the `dlt` credentials file with your secret info, open `.dlt/secrets.toml`, which looks like this: +To edit the dlt credentials file with your secret info, open `.dlt/secrets.toml`, which looks like this: + ```toml [destination.filesystem] bucket_url = "s3://[your_bucket_name]" # replace with your bucket name, @@ -49,19 +55,21 @@ aws_access_key_id = "please set me up!" # copy the access key here aws_secret_access_key = "please set me up!" # copy the secret access key here ``` -If you have your credentials stored in `~/.aws/credentials`, just remove the **[destination.filesystem.credentials]** section above, and `dlt` will fall back to your **default** profile in local credentials. If you want to switch the profile, pass the profile name as follows (here: `dlt-ci-user`): +If you have your credentials stored in `~/.aws/credentials`, just remove the **[destination.filesystem.credentials]** section above, and dlt will fall back to your **default** profile in local credentials. If you want to switch the profile, pass the profile name as follows (here: `dlt-ci-user`): + ```toml [destination.filesystem.credentials] profile_name="dlt-ci-user" ``` -You can also pass an AWS region: +You can also specify an AWS region: + ```toml [destination.filesystem.credentials] region_name="eu-central-1" ``` -You need to create an S3 bucket and a user who can access that bucket. `dlt` does not create buckets automatically. +You need to create an S3 bucket and a user who can access that bucket. dlt does not create buckets automatically. 1. You can create the S3 bucket in the AWS console by clicking on "Create Bucket" in S3 and assigning the appropriate name and permissions to the bucket. 2. Once the bucket is created, you'll have the bucket URL. For example, If the bucket name is `dlt-ci-test-bucket`, then the bucket URL will be: @@ -71,7 +79,7 @@ You need to create an S3 bucket and a user who can access that bucket. `dlt` doe ``` 3. To grant permissions to the user being used to access the S3 bucket, go to the IAM > Users, and click on “Add Permissions”. -4. Below you can find a sample policy that gives a minimum permission required by `dlt` to a bucket we created above. The policy contains permissions to list files in a bucket, get, put, and delete objects. **Remember to place your bucket name in the Resource section of the policy!** +4. Below you can find a sample policy that gives a minimum permission required by dlt to a bucket we created above. The policy contains permissions to list files in a bucket, get, put, and delete objects. **Remember to place your bucket name in the Resource section of the policy!** ```json { @@ -146,6 +154,7 @@ if you have default google cloud credentials in your environment (i.e. on cloud Use **Cloud Storage** admin to create a new bucket. Then assign the **Storage Object Admin** role to your service account. ### Azure Blob Storage + Run `pip install "dlt[az]"` which will install the `adlfs` package to interface with Azure Blob Storage. Edit the credentials in `.dlt/secrets.toml`, you'll see AWS credentials by default replace them with your Azure credentials. @@ -186,7 +195,17 @@ azure_client_secret = "client_secret" azure_tenant_id = "tenant_id" # please set me up! ``` +:::caution +**Concurrent blob uploads** +`dlt` limits the number of concurrent connections for a single uploaded blob to 1. By default `adlfs` that we use, splits blobs into 4 MB chunks and uploads them concurrently which leads to gigabytes of used memory and thousands of connections for a larger load packages. You can increase the maximum concurrency as follows: +```toml +[destination.filesystem.kwargs] +max_concurrency=3 +``` +::: + ### Local file system + If for any reason you want to have those files in a local folder, set up the `bucket_url` as follows (you are free to use `config.toml` for that as there are no secrets required) ```toml @@ -259,6 +278,98 @@ bucket_url='\\?\UNC\localhost\c$\a\b\c' ``` ::: +### SFTP +Run `pip install "dlt[sftp]` which will install the `paramiko` package alongside `dlt`, enabling secure SFTP transfers. + +Configure your SFTP credentials by editing the `.dlt/secrets.toml` file. By default, the file contains placeholders for AWS credentials. You should replace these with your SFTP credentials. + +Below are the possible fields for SFTP credentials configuration: + +```text +sftp_port # The port for SFTP, defaults to 22 (standard for SSH/SFTP) +sftp_username # Your SFTP username, defaults to None +sftp_password # Your SFTP password (if using password-based auth), defaults to None +sftp_key_filename # Path to your private key file for key-based authentication, defaults to None +sftp_key_passphrase # Passphrase for your private key (if applicable), defaults to None +sftp_timeout # Timeout for establishing a connection, defaults to None +sftp_banner_timeout # Timeout for receiving the banner during authentication, defaults to None +sftp_auth_timeout # Authentication timeout, defaults to None +sftp_channel_timeout # Channel timeout for SFTP operations, defaults to None +sftp_allow_agent # Use SSH agent for key management (if available), defaults to True +sftp_look_for_keys # Search for SSH keys in the default SSH directory (~/.ssh/), defaults to True +sftp_compress # Enable compression (can improve performance over slow networks), defaults to False +sftp_gss_auth # Use GSS-API for authentication, defaults to False +sftp_gss_kex # Use GSS-API for key exchange, defaults to False +sftp_gss_deleg_creds # Delegate credentials with GSS-API, defaults to True +sftp_gss_host # Host for GSS-API, defaults to None +sftp_gss_trust_dns # Trust DNS for GSS-API, defaults to True +``` +> For more information about credentials parameters: https://docs.paramiko.org/en/3.3/api/client.html#paramiko.client.SSHClient.connect + +### Authentication Methods + +SFTP authentication is attempted in the following order of priority: + +1. **Key-based authentication**: If you provide a `key_filename` containing the path to a private key or a corresponding OpenSSH public certificate (e.g., `id_rsa` and `id_rsa-cert.pub`), these will be used for authentication. If the private key requires a passphrase, you can specify it via `sftp_key_passphrase`. If your private key requires a passphrase to unlock, and you’ve provided one, it will be used to attempt to unlock the key. + +2. **SSH Agent-based authentication**: If `allow_agent=True` (default), Paramiko will look for any SSH keys stored in your local SSH agent (such as `id_rsa`, `id_dsa`, or `id_ecdsa` keys stored in `~/.ssh/`). + +3. **Username/Password authentication**: If a password is provided (`sftp_password`), plain username/password authentication will be attempted. + +4. **GSS-API authentication**: If GSS-API (Kerberos) is enabled (sftp_gss_auth=True), authentication will use the Kerberos protocol. GSS-API may also be used for key exchange (sftp_gss_kex=True) and credential delegation (sftp_gss_deleg_creds=True). This method is useful in environments where Kerberos is set up, often in enterprise networks. + + +#### 1. **Key-based Authentication** + +If you use an SSH key instead of a password, you can specify the path to your private key in the configuration. + +```toml +[destination.filesystem] +bucket_url = "sftp://[hostname]/[path]" +file_glob = "*" + +[destination.filesystem.credentials] +sftp_username = "foo" +sftp_key_filename = "/path/to/id_rsa" # Replace with the path to your private key file +sftp_key_passphrase = "your_passphrase" # Optional: passphrase for your private key +``` + +#### 2. **SSH Agent-based Authentication** + +If you have an SSH agent running with loaded keys, you can allow Paramiko to use these keys automatically. You can omit the password and key fields if you're relying on the SSH agent. + +```toml +[destination.filesystem] +bucket_url = "sftp://[hostname]/[path]" +file_glob = "*" + +[destination.filesystem.credentials] +sftp_username = "foo" +sftp_key_passphrase = "your_passphrase" # Optional: passphrase for your private key +``` +The loaded key must be one of the following types stored in ~/.ssh/: id_rsa, id_dsa, or id_ecdsa. + +#### 3. **Username/Password Authentication** + +This is the simplest form of authentication, where you supply a username and password directly. + +```toml +[destination.filesystem] +bucket_url = "sftp://[hostname]/[path]" # The hostname of your SFTP server and the remote path +file_glob = "*" # Pattern to match the files you want to upload/download + +[destination.filesystem.credentials] +sftp_username = "foo" # Replace "foo" with your SFTP username +sftp_password = "pass" # Replace "pass" with your SFTP password +``` + + +### Notes: +- **Key-based Authentication**: Make sure your private key has the correct permissions (`chmod 600`), or SSH will refuse to use it. +- **Timeouts**: It's important to adjust timeout values based on your network conditions to avoid connection issues. + +This configuration allows flexible SFTP authentication, whether you're using passwords, keys, or agents, and ensures secure communication between your local environment and the SFTP server. + ## Write disposition The filesystem destination handles the write dispositions as follows: - `append` - files belonging to such tables are added to the dataset folder @@ -285,8 +396,8 @@ def my_upsert_resource(): #### Known limitations - `hard_delete` hint not supported -- deleting records from child tables not supported - - This means updates to complex columns that involve element removals are not propagated. For example, if you first load `{"key": 1, "complex": [1, 2]}` and then load `{"key": 1, "complex": [1]}`, then the record for element `2` will not be deleted from the child table. +- deleting records from nested tables not supported + - This means updates to json columns that involve element removals are not propagated. For example, if you first load `{"key": 1, "nested": [1, 2]}` and then load `{"key": 1, "nested": [1]}`, then the record for element `2` will not be deleted from the nested table. ## File Compression @@ -309,7 +420,7 @@ For more details on managing file compression, please visit our documentation on All the files are stored in a single folder with the name of the dataset that you passed to the `run` or `load` methods of the `pipeline`. In our example chess pipeline, it is **chess_players_games_data**. :::note -Bucket storages are, in fact, key-blob storage so the folder structure is emulated by splitting file names into components by separator (`/`). +Object storages are, in fact, key-blob storage so the folder structure is emulated by splitting file names into components by separator (`/`). ::: You can control files layout by specifying the desired configuration. There are several ways to do this. diff --git a/docs/website/docs/dlt-ecosystem/destinations/lancedb.md b/docs/website/docs/dlt-ecosystem/destinations/lancedb.md index 8b7f3854ee..0d726508e6 100644 --- a/docs/website/docs/dlt-ecosystem/destinations/lancedb.md +++ b/docs/website/docs/dlt-ecosystem/destinations/lancedb.md @@ -201,7 +201,7 @@ This is the default disposition. It will append the data to the existing data in ## Additional Destination Options - `dataset_separator`: The character used to separate the dataset name from table names. Defaults to "___". -- `vector_field_name`: The name of the special field to store vector embeddings. Defaults to "vector__". +- `vector_field_name`: The name of the special field to store vector embeddings. Defaults to "vector". - `id_field_name`: The name of the special field used for deduplication and merging. Defaults to "id__". - `max_retries`: The maximum number of retries for embedding operations. Set to 0 to disable retries. Defaults to 3. @@ -216,11 +216,21 @@ The LanceDB destination supports syncing of the `dlt` state. ## Current Limitations +### In-Memory Tables + Adding new fields to an existing LanceDB table requires loading the entire table data into memory as a PyArrow table. This is because PyArrow tables are immutable, so adding fields requires creating a new table with the updated schema. For huge tables, this may impact performance and memory usage since the full table must be loaded into memory to add the new fields. Keep these considerations in mind when working with large datasets and monitor memory usage if adding fields to sizable existing tables. +### Null string handling for OpenAI embeddings + +OpenAI embedding service doesn't accept empty string bodies. We deal with this by replacing empty strings with a placeholder that should be very semantically dissimilar to 99.9% of queries. + +If your source column (column which is embedded) has empty values, it is important to consider the impact of this. There might be a _slight_ change that semantic queries can hit these empty strings. + +We reported this issue to LanceDB: https://github.com/lancedb/lancedb/issues/1577. + diff --git a/docs/website/docs/dlt-ecosystem/destinations/postgres.md b/docs/website/docs/dlt-ecosystem/destinations/postgres.md index 1281298312..e506eb79fe 100644 --- a/docs/website/docs/dlt-ecosystem/destinations/postgres.md +++ b/docs/website/docs/dlt-ecosystem/destinations/postgres.md @@ -82,6 +82,27 @@ If you set the [`replace` strategy](../../general-usage/full-loading.md) to `sta ## Data loading `dlt` will load data using large INSERT VALUES statements by default. Loading is multithreaded (20 threads by default). +### Data types +`postgres` supports various timestamp types, which can be configured using the column flags `timezone` and `precision` in the `dlt.resource` decorator or the `pipeline.run` method. + +- **Precision**: allows you to specify the number of decimal places for fractional seconds, ranging from 0 to 6. It can be used in combination with the `timezone` flag. +- **Timezone**: + - Setting `timezone=False` maps to `TIMESTAMP WITHOUT TIME ZONE`. + - Setting `timezone=True` (or omitting the flag, which defaults to `True`) maps to `TIMESTAMP WITH TIME ZONE`. + +#### Example precision and timezone: TIMESTAMP (3) WITHOUT TIME ZONE +```py +@dlt.resource( + columns={"event_tstamp": {"data_type": "timestamp", "precision": 3, "timezone": False}}, + primary_key="event_id", +) +def events(): + yield [{"event_id": 1, "event_tstamp": "2024-07-30T10:00:00.123"}] + +pipeline = dlt.pipeline(destination="postgres") +pipeline.run(events()) +``` + ### Fast loading with arrow tables and csv You can use [arrow tables](../verified-sources/arrow-pandas.md) and [csv](../file-formats/csv.md) to quickly load tabular data. Pick the `csv` loader file format like below diff --git a/docs/website/docs/dlt-ecosystem/destinations/redshift.md b/docs/website/docs/dlt-ecosystem/destinations/redshift.md index bb92d651f2..529424a198 100644 --- a/docs/website/docs/dlt-ecosystem/destinations/redshift.md +++ b/docs/website/docs/dlt-ecosystem/destinations/redshift.md @@ -84,7 +84,7 @@ When staging is enabled: > ❗ **Redshift cannot detect compression type from `json` files**. `dlt` assumes that `jsonl` files are gzip compressed, which is the default. -> ❗ **Redshift loads `complex` types as strings into SUPER with `parquet`**. Use `jsonl` format to store JSON in SUPER natively or transform your SUPER columns with `PARSE_JSON`. +> ❗ **Redshift loads `json` types as strings into SUPER with `parquet`**. Use `jsonl` format to store JSON in SUPER natively or transform your SUPER columns with `PARSE_JSON`. ## Supported column hints diff --git a/docs/website/docs/dlt-ecosystem/destinations/snowflake.md b/docs/website/docs/dlt-ecosystem/destinations/snowflake.md index 57e6db311d..74688ba7fa 100644 --- a/docs/website/docs/dlt-ecosystem/destinations/snowflake.md +++ b/docs/website/docs/dlt-ecosystem/destinations/snowflake.md @@ -143,6 +143,27 @@ The data is loaded using an internal Snowflake stage. We use the `PUT` command a keep_staged_files = false ``` +### Data types +`snowflake` supports various timestamp types, which can be configured using the column flags `timezone` and `precision` in the `dlt.resource` decorator or the `pipeline.run` method. + +- **Precision**: allows you to specify the number of decimal places for fractional seconds, ranging from 0 to 9. It can be used in combination with the `timezone` flag. +- **Timezone**: + - Setting `timezone=False` maps to `TIMESTAMP_NTZ`. + - Setting `timezone=True` (or omitting the flag, which defaults to `True`) maps to `TIMESTAMP_TZ`. + +#### Example precision and timezone: TIMESTAMP_NTZ(3) +```py +@dlt.resource( + columns={"event_tstamp": {"data_type": "timestamp", "precision": 3, "timezone": False}}, + primary_key="event_id", +) +def events(): + yield [{"event_id": 1, "event_tstamp": "2024-07-30T10:00:00.123"}] + +pipeline = dlt.pipeline(destination="snowflake") +pipeline.run(events()) +``` + ## Supported file formats * [insert-values](../file-formats/insert-format.md) is used by default * [parquet](../file-formats/parquet.md) is supported @@ -155,7 +176,7 @@ When staging is enabled: * [csv](../file-formats/csv.md) is supported :::caution -When loading from `parquet`, Snowflake will store `complex` types (JSON) in `VARIANT` as a string. Use the `jsonl` format instead or use `PARSE_JSON` to update the `VARIANT` field after loading. +When loading from `parquet`, Snowflake will store `json` types (JSON) in `VARIANT` as a string. Use the `jsonl` format instead or use `PARSE_JSON` to update the `VARIANT` field after loading. ::: ### Custom csv formats diff --git a/docs/website/docs/dlt-ecosystem/destinations/synapse.md b/docs/website/docs/dlt-ecosystem/destinations/synapse.md index 6cfcb1ef8f..0d50924cdf 100644 --- a/docs/website/docs/dlt-ecosystem/destinations/synapse.md +++ b/docs/website/docs/dlt-ecosystem/destinations/synapse.md @@ -142,7 +142,7 @@ Data is loaded via `INSERT` statements by default. ## Data type limitations * **Synapse cannot load `TIME` columns from `parquet` files**. `dlt` will fail such jobs permanently. Use the `insert_values` file format instead, or convert `datetime.time` objects to `str` or `datetime.datetime`, to load `TIME` columns. -* **Synapse does not have a complex/JSON/struct data type**. The `dlt` `complex` data type is mapped to the `nvarchar` type in Synapse. +* **Synapse does not have a nested/JSON/struct data type**. The `dlt` `json` data type is mapped to the `nvarchar` type in Synapse. ## Table index type The [table index type](https://learn.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-index) of the created tables can be configured at the resource level with the `synapse_adapter`: diff --git a/docs/website/docs/dlt-ecosystem/destinations/weaviate.md b/docs/website/docs/dlt-ecosystem/destinations/weaviate.md index 43bd85ce41..962239b7e6 100644 --- a/docs/website/docs/dlt-ecosystem/destinations/weaviate.md +++ b/docs/website/docs/dlt-ecosystem/destinations/weaviate.md @@ -116,7 +116,7 @@ weaviate_adapter( tokenization={"title": "word", "description": "whitespace"}, ) ``` -When using the `weaviate_adapter`, it's important to apply it directly to resources, not to the whole source. Here's an example: +When using the `weaviate_adapter`, it's important to apply it directly to resources, not to the whole source. Here's an example: ```py products_tables = sql_database().with_resources("products", "customers") @@ -131,7 +131,7 @@ weaviate_adapter(products_tables.products, vectorize="description") weaviate_adapter(products_tables.customers, vectorize="bio") info = pipeline.run(products_tables) -``` +``` :::tip @@ -196,18 +196,18 @@ Loading data into Weaviate from different sources requires a proper understandin Data loaded into Weaviate from various sources might have different types. To ensure compatibility with Weaviate's schema, there's a predefined mapping between the [dlt types](../../general-usage/schema.md#data-types) and [Weaviate's native types](https://weaviate.io/developers/weaviate/config-refs/datatypes): -| dlt Type | Weaviate Type | -|------------|---------------| -| text | text | -| double | number | -| bool | boolean | -| timestamp | date | -| date | date | -| bigint | int | -| binary | blob | -| decimal | text | -| wei | number | -| complex | text | +| dlt Type | Weaviate Type | +| --------- | ------------- | +| text | text | +| double | number | +| bool | boolean | +| timestamp | date | +| date | date | +| bigint | int | +| binary | blob | +| decimal | text | +| wei | number | +| json | text | ### Dataset name diff --git a/docs/website/docs/dlt-ecosystem/file-formats/csv.md b/docs/website/docs/dlt-ecosystem/file-formats/csv.md index 242a8282d1..05a0c2e50d 100644 --- a/docs/website/docs/dlt-ecosystem/file-formats/csv.md +++ b/docs/website/docs/dlt-ecosystem/file-formats/csv.md @@ -82,9 +82,9 @@ You'll need those setting when [importing external files](../../general-usage/re **arrow writer** * binary columns are supported only if they contain valid UTF-8 characters -* complex (nested, struct) types are not supported +* json (nested, struct) types are not supported **csv writer** * binary columns are supported only if they contain valid UTF-8 characters (easy to add more encodings) -* complex columns dumped with json.dumps +* json columns dumped with json.dumps * **None** values are always quoted \ No newline at end of file diff --git a/docs/website/docs/dlt-ecosystem/file-formats/insert-format.md b/docs/website/docs/dlt-ecosystem/file-formats/insert-format.md index c6742c2584..3e58b5a25d 100644 --- a/docs/website/docs/dlt-ecosystem/file-formats/insert-format.md +++ b/docs/website/docs/dlt-ecosystem/file-formats/insert-format.md @@ -14,15 +14,15 @@ Additional data types are stored as follows: - `datetime` and `date` are stored as ISO strings; - `decimal` is stored as a text representation of a decimal number; - `binary` storage depends on the format accepted by the destination; -- `complex` storage also depends on the format accepted by the destination. +- `json` storage also depends on the format accepted by the destination. This file format is [compressed](../../reference/performance.md#disabling-and-enabling-file-compression) by default. ## Supported Destinations -This format is used by default by: **DuckDB**, **Postgres**, **Redshift**. +This format is used by default by: **DuckDB**, **Postgres**, **Redshift**, **Synapse**, **MSSQL**, **Motherduck** -It is also supported by: **Filesystem**. +It is also supported by: **Filesystem** if you'd like to store INSERT VALUES statements for some reason ## How to configure diff --git a/docs/website/docs/dlt-ecosystem/file-formats/jsonl.md b/docs/website/docs/dlt-ecosystem/file-formats/jsonl.md index 72168b38f0..5957ccc8ad 100644 --- a/docs/website/docs/dlt-ecosystem/file-formats/jsonl.md +++ b/docs/website/docs/dlt-ecosystem/file-formats/jsonl.md @@ -16,7 +16,7 @@ Additional data types are stored as follows: - `decimal` is stored as a text representation of a decimal number; - `binary` is stored as a base64 encoded string; - `HexBytes` is stored as a hex encoded string; -- `complex` is serialized as a string. +- `json` is serialized as a string. This file format is [compressed](../../reference/performance.md#disabling-and-enabling-file-compression) by default. diff --git a/docs/website/docs/dlt-ecosystem/file-formats/parquet.md b/docs/website/docs/dlt-ecosystem/file-formats/parquet.md index 5d85b7a557..30f7051386 100644 --- a/docs/website/docs/dlt-ecosystem/file-formats/parquet.md +++ b/docs/website/docs/dlt-ecosystem/file-formats/parquet.md @@ -35,6 +35,7 @@ Under the hood, `dlt` uses the [pyarrow parquet writer](https://arrow.apache.org - `flavor`: Sanitize schema or set other compatibility options to work with various target systems. Defaults to None which is **pyarrow** default. - `version`: Determine which Parquet logical types are available for use, whether the reduced set from the Parquet 1.x.x format or the expanded logical types added in later format versions. Defaults to "2.6". - `data_page_size`: Set a target threshold for the approximate encoded size of data pages within a column chunk (in bytes). Defaults to None which is **pyarrow** default. +- `row_group_size`: Set the number of rows in a row group. [See here](#row-group-size) how this can optimize parallel processing of queries on your destination over the default setting of `pyarrow`. - `timestamp_timezone`: A string specifying timezone, default is UTC. - `coerce_timestamps`: resolution to which coerce timestamps, choose from **s**, **ms**, **us**, **ns** - `allow_truncated_timestamps` - will raise if precision is lost on truncated timestamp. @@ -76,3 +77,19 @@ You can generate parquet files without timezone adjustment information in two wa 2. Set the **timestamp_timezone** to empty string (ie. `DATA_WRITER__TIMESTAMP_TIMEZONE=""`) to generate logical type without UTC adjustment. To our best knowledge, arrow will convert your timezone aware DateTime(s) to UTC and store them in parquet without timezone information. + + +### Row group size +The `pyarrow` parquet writer writes each item, i.e. table or record batch, in a separate row group. +This may lead to many small row groups which may not be optimal for certain query engines. For example, `duckdb` parallelizes on a row group. +`dlt` allows controlling the size of the row group by +[buffering and concatenating tables](../../reference/performance.md#controlling-in-memory-buffers) and batches before they are written. The concatenation is done as a zero-copy to save memory. +You can control the size of the row group by setting the maximum number of rows kept in the buffer. +```toml +[extract.data_writer] +buffer_max_items=10e6 +``` +Mind that `dlt` holds the tables in memory. Thus, 1,000,000 rows in the example above may consume a significant amount of RAM. + +`row_group_size` configuration setting has limited utility with `pyarrow` writer. It may be useful when you write single very large pyarrow tables +or when your in memory buffer is really large. \ No newline at end of file diff --git a/docs/website/docs/dlt-ecosystem/index.md b/docs/website/docs/dlt-ecosystem/index.md deleted file mode 100644 index 740a3a3a39..0000000000 --- a/docs/website/docs/dlt-ecosystem/index.md +++ /dev/null @@ -1,18 +0,0 @@ ---- -title: Integrations -description: List of integrations -keywords: ['integrations, sources, destinations'] ---- -import DocCardList from '@theme/DocCardList'; -import Link from '../_book-onboarding-call.md'; - -Speed up the process of creating data pipelines by using dlt's multiple pre-built sources and destinations: - -- Each [dlt verified source](verified-sources) allows you to create [pipelines](../general-usage/pipeline) that extract data from a particular source: a database, a cloud service, or an API. -- [Destinations](destinations) are where you want to load your data. dlt supports a variety of destinations, including databases, data warehouses, and data lakes. - - - -:::tip -Most source-destination pairs work seamlessly together. If the merge [write disposition](../general-usage/incremental-loading#choosing-a-write-disposition) is not supported by a destination (for example, [file sytem destination](destinations/filesystem)), dlt will automatically fall back to the [append](../general-usage/incremental-loading#append) write disposition. -::: \ No newline at end of file diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/arrow-pandas.md b/docs/website/docs/dlt-ecosystem/verified-sources/arrow-pandas.md index cb14db7ae7..4a5cdd2f71 100644 --- a/docs/website/docs/dlt-ecosystem/verified-sources/arrow-pandas.md +++ b/docs/website/docs/dlt-ecosystem/verified-sources/arrow-pandas.md @@ -123,10 +123,10 @@ pipeline.run(orders) Look at the [Connector X + Arrow Example](../../examples/connector_x_arrow/) to see how to load data from production databases fast. ::: -## Loading `json` documents -If you want to skip default `dlt` JSON normalizer, you can use any available method to convert json documents into tabular data. +## Loading JSON documents +If you want to skip default `dlt` JSON normalizer, you can use any available method to convert JSON documents into tabular data. * **pandas** has `read_json` and `json_normalize` methods -* **pyarrow** can infer table schema and convert json files into tables with `read_json` +* **pyarrow** can infer table schema and convert JSON files into tables with `read_json` * **duckdb** can do the same with `read_json_auto` ```py @@ -153,12 +153,12 @@ The Arrow data types are translated to dlt data types as follows: | `int` | `bigint` | Precision is determined by the bit width. | | `binary` | `binary` | | | `decimal` | `decimal` | Precision and scale are determined by the type properties. | -| `struct` | `complex` | | +| `struct` | `json` | | | | | | ## Loading nested types -All struct types are represented as `complex` and will be loaded as JSON (if destination permits) or a string. Currently we do not support **struct** types, +All struct types are represented as `json` and will be loaded as JSON (if destination permits) or a string. Currently we do not support **struct** types, even if they are present in the destination (except **BigQuery** which can be [configured to handle them](../destinations/bigquery.md#use-bigquery-schema-autodetect-for-nested-fields)) If you want to represent nested data as separated tables, you must yield panda frames and arrow tables as records. In the examples above: diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/filesystem.md b/docs/website/docs/dlt-ecosystem/verified-sources/filesystem.md deleted file mode 100644 index 7552a0acb2..0000000000 --- a/docs/website/docs/dlt-ecosystem/verified-sources/filesystem.md +++ /dev/null @@ -1,535 +0,0 @@ ---- -title: Filesystem -description: dlt verified source for Readers Source and Filesystem -keywords: [readers source and filesystem, filesystem, readers source] ---- -import Header from './_source-info-header.md'; - -# Readers Source and Filesystem - -
- -This verified source easily streams files from AWS S3, Google Cloud Storage, Google Drive, Azure, or local filesystem using the reader source. - -Sources and resources that can be used with this verified source are: - -| Name | Type | Description | -|--------------|----------------------|---------------------------------------------------------------------------| -| readers | Source | Lists and reads files with resource `filesystem` and readers transformers | -| filesystem | Resource | Lists files in `bucket_url` using `file_glob` pattern | -| read_csv | Resource-transformer | Reads csv file with **Pandas** chunk by chunk | -| read_jsonl | Resource-transformer | Reads jsonl file content and extract the data | -| read_parquet | Resource-transformer | Reads parquet file content and extract the data with **Pyarrow** | - -## Setup Guide - -### Grab credentials - -This source can access various bucket types, including: - -- AWS S3. -- Google Cloud Storage. -- Google Drive. -- Azure Blob Storage. -- Local Storage - -To access these, you'll need secret credentials: - -#### AWS S3 credentials - -To get AWS keys for S3 access: - -1. Access IAM in AWS Console. -2. Select "Users", choose a user, and open "Security credentials". -3. Click "Create access key" for AWS ID and Secret Key. - -For more info, see -[AWS official documentation.](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html) - -#### Google Cloud Storage / Google Drive credentials - -To get GCS/GDrive access: - -1. Log in to [console.cloud.google.com](http://console.cloud.google.com/). -2. Create a [service account](https://cloud.google.com/iam/docs/service-accounts-create#creating). -3. Enable "Cloud Storage API" / "Google Drive API"; see - [Google's guide](https://support.google.com/googleapi/answer/6158841?hl=en). -4. In IAM & Admin > Service Accounts, find your account, click the three-dot menu > "Manage Keys" > - "ADD KEY" > "CREATE" to get a JSON credential file. -5. Grant the service account appropriate permissions for cloud storage access. - -For more info, see how to -[create service account](https://support.google.com/a/answer/7378726?hl=en). - -#### Azure Blob Storage credentials - -To obtain Azure blob storage access: - -1. Go to Azure Portal (portal.azure.com). -2. Select "Storage accounts" > your storage. -3. Click "Settings" > "Access keys". -4. View account name and two keys (primary/secondary). Keep keys confidential. - -For more info, see -[Azure official documentation](https://learn.microsoft.com/en-us/azure/storage/common/storage-account-keys-manage?tabs=azure-portal). - -### Initialize the verified source - -To get started with your data pipeline, follow these steps: - -1. Enter the following command: - - ```sh - dlt init filesystem duckdb - ``` - - [This command](../../reference/command-line-interface) will initialize - [the pipeline example](https://github.com/dlt-hub/verified-sources/blob/master/sources/filesystem_pipeline.py) - with filesystem as the [source](../../general-usage/source) and - [duckdb](../destinations/duckdb.md) as the [destination](../destinations). - -2. If you'd like to use a different destination, simply replace `duckdb` with the name of your - preferred [destination](../destinations). - -3. After running this command, a new directory will be created with the necessary files and - configuration settings to get started. - -For more information, read the -[Walkthrough: Add a verified source.](../../walkthroughs/add-a-verified-source) - -### Add credentials - -1. In the `.dlt` folder, there's a file called `secrets.toml`. It's where you store sensitive - information securely, like access tokens. Keep this file safe. Here's its format for service - account authentication: - - ```toml - [sources.filesystem.credentials] # use [sources.readers.credentials] for the "readers" source - # For AWS S3 access: - aws_access_key_id="Please set me up!" - aws_secret_access_key="Please set me up!" - - # For GCS bucket / Google Drive access: - client_email="Please set me up!" - private_key="Please set me up!" - project_id="Please set me up!" - - # For Azure blob storage access: - azure_storage_account_name="Please set me up!" - azure_storage_account_key="Please set me up!" - ``` - -2. Finally, enter credentials for your chosen destination as per the [docs](../destinations/). - -3. You can pass the bucket URL and glob pattern or use `config.toml`. For local filesystems, use - `file://` as follows: - - ```toml - [sources.filesystem] # use [sources.readers.credentials] for the "readers" source - bucket_url='file://Users/admin/Documents/csv_files' - file_glob="*" - ``` - or skip the schema and provide the local path in a format native for your operating system as follows: - - ```toml - [sources.filesystem] # use [sources.readers.credentials] for the "readers" source - bucket_url='~\Documents\csv_files\' - file_glob="*" - ``` - - In the example above we use Windows path to current user's Documents folder. Mind that literal toml string (single quotes) - was used to conveniently use the backslashes without need to escape. - - For remote file systems you need to add the schema, it will be used to get the protocol being - used. The protocols that can be used are: - - - For Azure blob storage - ```toml - [sources.filesystem] # use [sources.readers.credentials] for the "readers" source - bucket_url="az:////" - ``` - - - `az://` indicates the Azure Blob Storage protocol. - - `container_name` is the name of the container. - - `path_to_files/` is a directory path within the container. - - `CAUTION: For Azure, use adlfs>=2023.9.0. Older versions mishandle globs.` - - - For Google Drive - ```toml - [sources.filesystem] # use [sources.readers.credentials] for the "readers" source - bucket_url="gdrive:////" - ``` - - - `gdrive://` indicates that the Google Drive protocol. - - `folder_name` refers to a folder within Google Drive. - - `subfolder_or_file_path/` is a sub-folder or directory path within the my-bucket folder. - - - For Google Storage - ```toml - [sources.filesystem] # use [sources.readers.credentials] for the "readers" source - bucket_url="gs:////" - ``` - - - `gs://` indicates the Google Cloud Storage protocol. - - `bucket_name` is the name of the bucket. - - `path_to_files/` is a directory path within the bucket. - - - For AWS S3 - ```toml - [sources.filesystem] # use [sources.readers.credentials] for the "readers" source - bucket_url="s3:////" - ``` - - - `s3://` indicates the AWS S3 protocol. - - `bucket_name` is the name of the bucket. - - `path_to_files/` is a directory path within the bucket. - -### Use local file system paths -You can use both native local file system paths and in form of `file:` uri. Absolute, relative and UNC Windows paths are supported. -You can find relevant examples in [filesystem destination documentation](../destinations/filesystem.md#local-file-system) which follows -the same rules to specify the `bucket_url`. - -:::caution -Windows supports paths up to 255 characters. When you access a path longer than 255 characters you'll see `FileNotFound` exception. - - To go over this limit you can use [extended paths](https://learn.microsoft.com/en-us/windows/win32/fileio/maximum-file-path-limitation?tabs=registry). - **Note that Python glob does not work with extended UNC paths** so you will not be able to use them - -```toml -[sources.filesystem] -bucket_url = '\\?\C:\a\b\c' -``` -::: - -## Run the pipeline - -1. Before running the pipeline, ensure that you have installed all the necessary dependencies by - running the command: - - ```sh - pip install -r requirements.txt - ``` - -2. Install optional modules: - - - For AWS S3: - ```sh - pip install s3fs - ``` - - For Azure blob: - ```sh - pip install adlfs>=2023.9.0 - ``` - - GCS storage: No separate module needed. - -3. You're now ready to run the pipeline! To get started, run the following command: - - ```sh - python filesystem_pipeline.py - ``` - -4. Once the pipeline has finished running, you can verify that everything loaded correctly by using - the following command: - - ```sh - dlt pipeline show - ``` - - For example, the `pipeline_name` for the above pipeline example is `standard_filesystem`, you may - also use any custom name instead. - -For more information, read the [Walkthrough: Run a pipeline](../../walkthroughs/run-a-pipeline). - -## Sources and resources - -`dlt` works on the principle of [sources](../../general-usage/source) and -[resources](../../general-usage/resource). - -### Source `readers` - -This source offers chunked file readers as resources, which can be optionally customized. Provided resources include: - -- `read_csv()` -- `read_jsonl()` -- `read_parquet()` - -```py -@dlt.source(_impl_cls=ReadersSource, spec=FilesystemConfigurationResource) -def readers( - bucket_url: str = dlt.secrets.value, - credentials: Union[FileSystemCredentials, AbstractFileSystem] = dlt.secrets.value, - file_glob: Optional[str] = "*", -) -> Tuple[DltResource, ...]: - ... -``` - -- `bucket_url`: The url to the bucket. -- `credentials`: The credentials to the filesystem of fsspec `AbstractFilesystem` instance. -- `file_glob`: Glob filter for files. Defaults to non-recursive listing in the bucket. - -:::tip -We advise that you give each resource a -[specific name](../../general-usage/resource#duplicate-and-rename-resources) -before loading with `pipeline.run`. This will make sure that data goes to a table with the name you -want and that each pipeline uses a -[separate state for incremental loading.](../../general-usage/state#read-and-write-pipeline-state-in-a-resource) -::: - - -### Resource `filesystem` - -This resource lists files in `bucket_url` based on the `file_glob` pattern, returning them as -[FileItem](https://github.com/dlt-hub/dlt/blob/devel/dlt/common/storages/fsspec_filesystem.py#L22) -with data access methods. These can be paired with transformers for enhanced processing. - -```py -@dlt.resource( - primary_key="file_url", spec=FilesystemConfigurationResource, standalone=True -) -def filesystem( - bucket_url: str = dlt.secrets.value, - credentials: Union[FileSystemCredentials, AbstractFileSystem] = dlt.secrets.value, - file_glob: Optional[str] = "*", - files_per_page: int = DEFAULT_CHUNK_SIZE, - extract_content: bool = False, -) -> Iterator[List[FileItem]]: - ... -``` - -- `bucket_url`: URL of the bucket. -- `credentials`: Filesystem credentials of `AbstractFilesystem` instance. -- `file_glob`: File filter in glob format. Defaults to listing all non-recursive files -in bucket URL. -- `files_per_page`: Number of files processed at once. Default: 100. -- `extract_content`: If true, the content of the file will be read and returned in the resource. Default: False. - - -## Filesystem Integration and Data Extraction Guide - -### Filesystem Usage - -- The filesystem tool enumerates files in a selected bucket using a glob pattern, returning details as FileInfo in customizable page sizes. - -- This resource integrates with transform functions and transformers for customized extraction pipelines. - -To load data into a specific table (instead of the default filesystem table), see the snippet below: - -```py -@dlt.transformer(standalone=True) -def read_csv(items, chunksize: int = 15): - """Reads csv file with Pandas chunk by chunk.""" - ... - -# list only the *.csv in specific folder and pass the file items to read_csv() -met_files = ( - filesystem(bucket_url="s3://my_bucket/data", file_glob="csv_folder/*.csv") - | read_csv() -) -# load to met_csv table using with_name() -pipeline.run(met_files.with_name("csv_data")) -``` - -Use the -[standalone filesystem](../../general-usage/resource#declare-a-standalone-resource) -resource to list files in s3, GCS, and Azure buckets. This allows you to customize file readers or -manage files using [fsspec](https://filesystem-spec.readthedocs.io/en/latest/index.html). -```py -files = filesystem(bucket_url="s3://my_bucket/data", file_glob="csv_folder/*.csv") -pipeline.run(files) -``` -The filesystem ensures consistent file representation across bucket types and offers methods to access and read -data. You can quickly build pipelines to: - -- Extract text from PDFs. -- Stream large file content directly from buckets. -- Copy files locally. - -### `FileItem` Representation - -- All dlt sources/resources that yield files follow the [FileItem](https://github.com/dlt-hub/dlt/blob/devel/dlt/common/storages/fsspec_filesystem.py#L22) contract. -- File content is typically not loaded; instead, full file info and methods to access content are - available. -- Users can request an authenticated [fsspec AbstractFileSystem](https://filesystem-spec.readthedocs.io/en/latest/_modules/fsspec/spec.html#AbstractFileSystem) instance. - -#### `FileItem` Fields: - -- `file_url` - Complete URL of the file; also the primary key (e.g. `s3://bucket-name/path/file`). -- `file_name` - Name of the file from the bucket URL. -- `relative_path` - Set when doing `glob`, is a relative path to a `bucket_url` argument. -- `mime_type` - File's mime type; sourced from the bucket provider or inferred from its extension. -- `modification_date` - File's last modification time (format: `pendulum.DateTime`). -- `size_in_bytes` - File size. -- `file_content` - Content, provided upon request. - -:::info -When using a nested or recursive glob pattern, `relative_path` will include the file's path relative to `bucket_url`. For -instance, using the resource: -`filesystem("az://dlt-ci-test-bucket/standard_source/samples", file_glob="met_csv/A801/*.csv")` -will produce file names relative to the `/standard_source/samples` path, such as -`met_csv/A801/A881_20230920.csv`. For local filesystems, POSIX paths (using "/" as separator) are returned. -::: - -### File Manipulation - -[FileItem](https://github.com/dlt-hub/dlt/blob/devel/dlt/common/storages/fsspec_filesystem.py#L22), backed by a dictionary implementation, offers these helper methods: - -- `read_bytes()`: Returns the file content as bytes. -- `open()`: Provides a file object when opened. -- `filesystem`: Gives access to an authorized `AbstractFilesystem` with standard fsspec methods. - -## Customization - -### Create your own pipeline - -If you wish to create your own pipelines, you can leverage source and resource methods from this -verified source. - -1. Configure the pipeline by specifying the pipeline name, destination, and dataset as follows: - - ```py - pipeline = dlt.pipeline( - pipeline_name="standard_filesystem", # Use a custom name if desired - destination="duckdb", # Choose the appropriate destination (e.g., duckdb, redshift, post) - dataset_name="filesystem_data_csv" # Use a custom name if desired - ) - ``` - -1. To read and load CSV files: - - ```py - BUCKET_URL = "YOUR_BUCKET_PATH_HERE" # path of the bucket url or local destination - met_files = readers( - bucket_url=BUCKET_URL, file_glob="directory/*.csv" - ).read_csv() - # tell dlt to merge on date - met_files.apply_hints(write_disposition="merge", merge_key="date") - # We load the data into the met_csv table - load_info = pipeline.run(met_files.with_name("table_name")) - print(load_info) - print(pipeline.last_trace.last_normalize_info) - ``` - - - The `file_glob` parameter targets all CSVs in the "met_csv/A801" directory. - - The `print(pipeline.last_trace.last_normalize_info)` line displays the data normalization details from the pipeline's last trace. - - :::info - If you have a default bucket URL set in `.dlt/config.toml`, you can omit the `bucket_url` parameter. - ::: -1. To load only new CSV files with [incremental loading](../../general-usage/incremental-loading): - - ```py - # This configuration will only consider new csv files - new_files = filesystem(bucket_url=BUCKET_URL, file_glob="directory/*.csv") - # add incremental on modification time - new_files.apply_hints(incremental=dlt.sources.incremental("modification_date")) - load_info = pipeline.run((new_files | read_csv()).with_name("csv_files")) - print(load_info) - print(pipeline.last_trace.last_normalize_info) - ``` - -1. To read and load Parquet and JSONL from a bucket: - ```py - jsonl_reader = readers(BUCKET_URL, file_glob="**/*.jsonl").read_jsonl( - chunksize=10000 - ) - # PARQUET reading - parquet_reader = readers(BUCKET_URL, file_glob="**/*.parquet").read_parquet() - # load both folders together to specified tables - load_info = pipeline.run( - [ - jsonl_reader.with_name("jsonl_data"), - parquet_reader.with_name("parquet_data"), - ] - ) - print(load_info) - print(pipeline.last_trace.last_normalize_info) - ``` - - The `file_glob`: Specifies file pattern; reads all JSONL and Parquet files across directories. - - The `chunksize`: Set to 10,000; data read in chunks of 10,000 records each. - - `print(pipeline.last_trace.last_normalize_info)`: Displays the data normalization details from the pipeline's last trace. - -1. To set up a pipeline that reads from an Excel file using a standalone transformer: - - ```py - # Define a standalone transformer to read data from an Excel file. - @dlt.transformer(standalone=True) - def read_excel( - items: Iterator[FileItemDict], sheet_name: str - ) -> Iterator[TDataItems]: - # Import the required pandas library. - import pandas as pd - - # Iterate through each file item. - for file_obj in items: - # Open the file object. - with file_obj.open() as file: - # Read from the Excel file and yield its content as dictionary records. - yield pd.read_excel(file, sheet_name).to_dict(orient="records") - - # Set up the pipeline to fetch a specific Excel file from a filesystem (bucket). - example_xls = filesystem( - bucket_url=BUCKET_URL, file_glob="../directory/example.xlsx" - ) | read_excel("example_table") # Pass the data through the transformer to read the "example_table" sheet. - - # Execute the pipeline and load the extracted data into the "duckdb" destination. - load_info = dlt.run( - example_xls.with_name("example_xls_data"), - destination="duckdb", - dataset_name="example_xls_data", - ) - - # Print the loading information. - print(load_info) - ``` - - The code loads data from `example.xlsx` into the `duckdb` destination. - -1. To copy files locally, add a step in the filesystem resource and then load the listing to the database: - - ```py - def _copy(item: FileItemDict) -> FileItemDict: - # instantiate fsspec and copy file - dest_file = os.path.join(local_folder, item["file_name"]) - # create dest folder - os.makedirs(os.path.dirname(dest_file), exist_ok=True) - # download file - item.fsspec.download(item["file_url"], dest_file) - # return file item unchanged - return item - - # use recursive glob pattern and add file copy step - downloader = filesystem(BUCKET_URL, file_glob="**").add_map(_copy) - - # NOTE: you do not need to load any data to execute extract, below we obtain - # a list of files in a bucket and also copy them locally - listing = list(downloader) - print(listing) - # download to table "listing" - load_info = pipeline.run( - downloader.with_name("listing"), write_disposition="replace" - ) - # pretty print the information on data that was loaded - print(load_info) - print(listing) - print(pipeline.last_trace.last_normalize_info) - ``` - -1. Cleanup after loading: - - You can get a fsspec client from filesystem resource after it was extracted i.e. in order to delete processed files etc. - The filesystem module contains a convenient method `fsspec_from_resource` that can be used as follows: - - ```py - from filesystem import filesystem, fsspec_from_resource - # get filesystem source - gs_resource = filesystem("gs://ci-test-bucket/") - # extract files - pipeline.run(gs_resource | read_csv) - # get fs client - fs_client = fsspec_from_resource(gs_resource) - # do any operation - fs_client.ls("ci-test-bucket/standard_source/samples") - ``` - - \ No newline at end of file diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/filesystem/advanced.md b/docs/website/docs/dlt-ecosystem/verified-sources/filesystem/advanced.md new file mode 100644 index 0000000000..be08e9ff44 --- /dev/null +++ b/docs/website/docs/dlt-ecosystem/verified-sources/filesystem/advanced.md @@ -0,0 +1,196 @@ +--- +title: Advanced Filesystem Usage +description: Use filesystem source as a building block +keywords: [readers source and filesystem, files, filesystem, readers source, cloud storage] +--- + +The filesystem source provides the building blocks to load data from files. This section explains how you can customize the filesystem source for your use case. + +## Standalone filesystem resource + +You can use the [standalone filesystem](../../../general-usage/resource#declare-a-standalone-resource) resource to list files in cloud storage or a local filesystem. This allows you to customize file readers or manage files using [fsspec](https://filesystem-spec.readthedocs.io/en/latest/index.html). + +```py +from dlt.sources.filesystem import filesystem + +pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb") +files = filesystem(bucket_url="s3://my_bucket/data", file_glob="csv_folder/*.csv") +pipeline.run(files) +``` + +The filesystem ensures consistent file representation across bucket types and offers methods to access and read data. You can quickly build pipelines to: + +- Extract text from PDFs ([unstructured data source](https://github.com/dlt-hub/verified-sources/tree/master/sources/unstructured_data)). +- Stream large file content directly from buckets. +- Copy files locally ([copy files](#copy-files-locally)) + +### `FileItem` representation + +- All dlt sources/resources that yield files follow the [FileItem](https://github.com/dlt-hub/dlt/blob/devel/dlt/common/storages/fsspec_filesystem.py#L40) contract. +- File content is typically not loaded (you can control it with the `extract_content` parameter of the filesystem resource). Instead, full file info and methods to access content are available. +- Users can request an authenticated [fsspec AbstractFileSystem](https://filesystem-spec.readthedocs.io/en/latest/_modules/fsspec/spec.html#AbstractFileSystem) instance. + +#### `FileItem` fields + +- `file_url` - complete URL of the file (e.g. `s3://bucket-name/path/file`). This field serves as a primary key. +- `file_name` - name of the file from the bucket URL. +- `relative_path` - set when doing `glob`, is a relative path to a `bucket_url` argument. +- `mime_type` - file's mime type. It is sourced from the bucket provider or inferred from its extension. +- `modification_date` - file's last modification time (format: `pendulum.DateTime`). +- `size_in_bytes` - file size. +- `file_content` - content, provided upon request. + +:::info +When using a nested or recursive glob pattern, `relative_path` will include the file's path relative to `bucket_url`. For instance, using the resource: `filesystem("az://dlt-ci-test-bucket/standard_source/samples", file_glob="met_csv/A801/*.csv")` will produce file names relative to the `/standard_source/samples` path, such as `met_csv/A801/A881_20230920.csv`. For local filesystems, POSIX paths (using "/" as separator) are returned. +::: + +### File manipulation + +[FileItem](https://github.com/dlt-hub/dlt/blob/devel/dlt/common/storages/fsspec_filesystem.py#L40), backed by a dictionary implementation, offers these helpers: + +- `read_bytes()` - method, which returns the file content as bytes. +- `open()` - method which provides a file object when opened. +- `filesystem` - field, which gives access to authorized `AbstractFilesystem` with standard fsspec methods. + +## Create your own transformer + +Although the `filesystem` resource yields the files from cloud storage or a local filesystem, you need to apply a transformer resource to retrieve the records from files. `dlt` natively supports three file types: `csv`, `parquet`, and `jsonl` (more details in [filesystem transformer resource](../filesystem/basic#2-choose-the-right-transformer-resource)). + +But you can easily create your own. In order to do this, you just need a function that takes as input a `FileItemDict` iterator and yields a list of records (recommended for performance) or individual records. + +### Example: read data from Excel files + +The code below sets up a pipeline that reads from an Excel file using a standalone transformer: + +```py +import dlt +from dlt.common.storages.fsspec_filesystem import FileItemDict +from dlt.common.typing import TDataItems +from dlt.sources.filesystem import filesystem + +BUCKET_URL = "s3://my_bucket/data" + +# Define a standalone transformer to read data from an Excel file. +@dlt.transformer(standalone=True) +def read_excel( + items: Iterator[FileItemDict], sheet_name: str +) -> Iterator[TDataItems]: + # Import the required pandas library. + import pandas as pd + + # Iterate through each file item. + for file_obj in items: + # Open the file object. + with file_obj.open() as file: + # Read from the Excel file and yield its content as dictionary records. + yield pd.read_excel(file, sheet_name).to_dict(orient="records") + +# Set up the pipeline to fetch a specific Excel file from a filesystem (bucket). +example_xls = filesystem( + bucket_url=BUCKET_URL, file_glob="../directory/example.xlsx" +) | read_excel("example_table") # Pass the data through the transformer to read the "example_table" sheet. + +pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb", dataset_name="example_xls_data",) +# Execute the pipeline and load the extracted data into the "duckdb" destination. +load_info = pipeline.run(example_xls.with_name("example_xls_data")) +# Print the loading information. +print(load_info) +``` + +### Example: read data from XML files + +You can use any third-party library to parse an `xml` file (e.g., [BeautifulSoup](https://pypi.org/project/beautifulsoup4/), [pandas](https://pandas.pydata.org/docs/reference/api/pandas.read_xml.html)). In the following example, we will be using the [xmltodict](https://pypi.org/project/xmltodict/) Python library. + +```py +import dlt +from dlt.common.storages.fsspec_filesystem import FileItemDict +from dlt.common.typing import TDataItems +from dlt.sources.filesystem import filesystem + +BUCKET_URL = "s3://my_bucket/data" + +# Define a standalone transformer to read data from an XML file. +@dlt.transformer(standalone=True) +def read_xml(items: Iterator[FileItemDict]) -> Iterator[TDataItems]: + # Import the required xmltodict library. + import xmltodict + + # Iterate through each file item. + for file_obj in items: + # Open the file object. + with file_obj.open() as file: + # Parse the file to dict records + yield xmltodict.parse(file.read()) + +# Set up the pipeline to fetch a specific XML file from a filesystem (bucket). +example_xml = filesystem( + bucket_url=BUCKET_URL, file_glob="../directory/example.xml" +) | read_xml() # Pass the data through the transformer + +pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb", dataset_name="example_xml_data") +# Execute the pipeline and load the extracted data into the "duckdb" destination. +load_info = pipeline.run(example_xml.with_name("example_xml_data")) + +# Print the loading information. +print(load_info) +``` + +## Clean files after loading + +You can get an fsspec client from the filesystem resource after it was extracted, i.e., in order to delete processed files, etc. The filesystem module contains a convenient method `fsspec_from_resource` that can be used as follows: + +```py +from dlt.sources.filesystem import filesystem, read_csv +from dlt.sources.filesystem.helpers import fsspec_from_resource + +# get filesystem source +gs_resource = filesystem("gs://ci-test-bucket/") +# extract files +pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb") +pipeline.run(gs_resource | read_csv()) +# get fs client +fs_client = fsspec_from_resource(gs_resource) +# do any operation +fs_client.ls("ci-test-bucket/standard_source/samples") +``` + +## Copy files locally + +To copy files locally, add a step in the filesystem resource and then load the listing to the database: + +```py +import os + +import dlt +from dlt.common.storages.fsspec_filesystem import FileItemDict +from dlt.sources.filesystem import filesystem + +def _copy(item: FileItemDict) -> FileItemDict: + # instantiate fsspec and copy file + dest_file = os.path.join(local_folder, item["file_name"]) + # create dest folder + os.makedirs(os.path.dirname(dest_file), exist_ok=True) + # download file + item.fsspec.download(item["file_url"], dest_file) + # return file item unchanged + return item + +BUCKET_URL = "gs://ci-test-bucket/" + +# use recursive glob pattern and add file copy step +downloader = filesystem(BUCKET_URL, file_glob="**").add_map(_copy) + +# NOTE: you do not need to load any data to execute extract, below we obtain +# a list of files in a bucket and also copy them locally +listing = list(downloader) +print(listing) +# download to table "listing" +pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb") +load_info = pipeline.run( + downloader.with_name("listing"), write_disposition="replace" +) +# pretty print the information on data that was loaded +print(load_info) +print(listing) +print(pipeline.last_trace.last_normalize_info) +``` \ No newline at end of file diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/filesystem/basic.md b/docs/website/docs/dlt-ecosystem/verified-sources/filesystem/basic.md new file mode 100644 index 0000000000..359ebb5088 --- /dev/null +++ b/docs/website/docs/dlt-ecosystem/verified-sources/filesystem/basic.md @@ -0,0 +1,485 @@ +--- +title: Filesystem source +description: Learn how to set up and configure +keywords: [readers source and filesystem, files, filesystem, readers source, cloud storage] +--- +import Header from '../_source-info-header.md'; +
+ +Filesystem source allows loading files from remote locations (AWS S3, Google Cloud Storage, Google Drive, Azure) or the local filesystem seamlessly. Filesystem source natively supports `csv`, `parquet`, and `jsonl` files and allows customization for loading any type of structured files. + +To load unstructured data (`.pdf`, `.txt`, e-mail), please refer to the [unstructured data source](https://github.com/dlt-hub/verified-sources/tree/master/sources/unstructured_data). + +## How Filesystem source works? + +The Filesystem source doesn't just give you an easy way to load data from both remote and local files — it also comes with a powerful set of tools that let you customize the loading process to fit your specific needs. + +Filesystem source loads data in two steps: +1. It [accesses the files](#1-initialize-a-filesystem-resource) in your remote or local file storage without actually reading the content yet. At this point, you can [filter files by metadata or name](#6-filter-files). You can also set up [incremental loading](#5-incremental-loading) to load only new files. +2. [The transformer](#2-choose-the-right-transformer-resource) reads the files' content and yields the records. At this step, you can filter out the actual data, enrich records with metadata from files, or [perform incremental loading](#load-new-records-based-on-a-specific-column) based on the file content. + +## Quick example + +```py +import dlt +from dlt.sources.filesystem import filesystem, read_parquet + +filesystem_resource = filesystem( + bucket_url="file://Users/admin/Documents/parquet_files", + file_glob="**/*.parquet" +) +filesystem_pipe = filesystem_resource | read_parquet() +filesystem_pipe.apply_hints(incremental=dlt.sources.incremental("modification_date")) + +# We load the data into the table_name table +pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb") +load_info = pipeline.run(filesystem_pipe.with_name("table_name")) +print(load_info) +print(pipeline.last_trace.last_normalize_info) +``` + +## Setup + +### Prerequisites + +Please make sure the `dlt` library is installed. Refer to the [installation guide](../../../getting-started). + +### Initialize the filesystem source + +To get started with your data pipeline, follow these steps: + +1. Enter the following command: + + ```sh + dlt init filesystem duckdb + ``` + + [dlt init command](../../../reference/command-line-interface) will initialize + [the pipeline example](https://github.com/dlt-hub/verified-sources/blob/master/sources/filesystem_pipeline.py) + with the filesystem as the source and [duckdb](../../destinations/duckdb.md) as the destination. + +2. If you would like to use a different destination, simply replace `duckdb` with the name of your + preferred [destination](../../destinations). + +3. After running this command, a new directory will be created with the necessary files and + configuration settings to get started. + +## Configuration + +### Get credentials + + + + + +To get AWS keys for S3 access: + +1. Access IAM in the AWS Console. +2. Select "Users", choose a user, and open "Security credentials". +3. Click "Create access key" for AWS ID and Secret Key. + +For more info, see +[AWS official documentation.](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html) + + + + + +To get GCS/GDrive access: + +1. Log in to [console.cloud.google.com](http://console.cloud.google.com/). +2. Create a [service account](https://cloud.google.com/iam/docs/service-accounts-create#creating). +3. Enable "Cloud Storage API" / "Google Drive API"; see + [Google's guide](https://support.google.com/googleapi/answer/6158841?hl=en). +4. In IAM & Admin > Service Accounts, find your account, click the three-dot menu > "Manage Keys" > + "ADD KEY" > "CREATE" to get a JSON credential file. +5. Grant the service account appropriate permissions for cloud storage access. + +For more info, see how to +[create a service account](https://support.google.com/a/answer/7378726?hl=en). + + + + + +To obtain Azure blob storage access: + +1. Go to the Azure Portal (portal.azure.com). +2. Select "Storage accounts" > your storage. +3. Click "Settings" > "Access keys". +4. View the account name and two keys (primary/secondary). Keep keys confidential. + +For more info, see +[Azure official documentation](https://learn.microsoft.com/en-us/azure/storage/common/storage-account-keys-manage?tabs=azure-portal). + + + + +You don't need any credentials for the local filesystem. + + + + +### Add credentials to dlt pipeline + +To provide credentials to the filesystem source, you can use [any method available](../../../general-usage/credentials/setup#available-config-providers) in `dlt`. +One of the easiest ways is to use configuration files. The `.dlt` folder in your working directory +contains two files: `config.toml` and `secrets.toml`. Sensitive information, like passwords and +access tokens, should only be put into `secrets.toml`, while any other configuration, like the path to +a bucket, can be specified in `config.toml`. + + + + + +```toml +# secrets.toml +[sources.filesystem.credentials] +aws_access_key_id="Please set me up!" +aws_secret_access_key="Please set me up!" + +# config.toml +[sources.filesystem] +bucket_url="s3:////" +``` + + + + +```toml +# secrets.toml +[sources.filesystem.credentials] +azure_storage_account_name="Please set me up!" +azure_storage_account_key="Please set me up!" + +# config.toml +[sources.filesystem] # use [sources.readers.credentials] for the "readers" source +bucket_url="az:////" +``` + + + + +```toml +# secrets.toml +[sources.filesystem.credentials] +client_email="Please set me up!" +private_key="Please set me up!" +project_id="Please set me up!" + +# config.toml +# gdrive +[gdrive_pipeline_name.sources.filesystem] +bucket_url="gdrive:////" + +# config.toml +# Google storage +[gstorage_pipeline_name.sources.filesystem] +bucket_url="gs:////" +``` + + + + +You can use both native local filesystem paths and `file://` URI. Absolute, relative, and UNC Windows paths are supported. + +You could provide an absolute filepath: + +```toml +# config.toml +[sources.filesystem] +bucket_url='file://Users/admin/Documents/csv_files' +``` + +Or skip the schema and provide the local path in a format native for your operating system. For example, for Windows: + +```toml +[sources.filesystem] +bucket_url='~\Documents\csv_files\' +``` + + + + + +You can also specify the credentials using Environment variables. The name of the corresponding environment +variable should be slightly different than the corresponding name in the `toml` file. Simply replace dots `.` with double +underscores `__`: + +```sh +export SOURCES__FILESYSTEM__AWS_ACCESS_KEY_ID = "Please set me up!" +export SOURCES__FILESYSTEM__AWS_SECRET_ACCESS_KEY = "Please set me up!" +``` + +:::tip +`dlt` supports more ways of authorizing with the cloud storage, including identity-based +and default credentials. To learn more about adding credentials to your pipeline, please refer to the +[Configuration and secrets section](../../../general-usage/credentials/complex_types#gcp-credentials). +::: + +## Usage + +The filesystem source is quite unique since it provides you with building blocks for loading data from files. +First, it iterates over files in the storage and then processes each file to yield the records. +Usually, you need two resources: + +1. The `filesystem` resource enumerates files in a selected bucket using a glob pattern, returning details as `FileItem` in customizable page sizes. +2. One of the available transformer resources to process each file in a specific transforming function and yield the records. + +### 1. Initialize a `filesystem` resource + +:::note +If you use just the `filesystem` resource, it will only list files in the storage based on glob parameters and yield the +files [metadata](advanced#fileitem-fields). The `filesystem` resource itself does not read or copy files. +::: + +All parameters of the resource can be specified directly in code: +```py +from dlt.sources.filesystem import filesystem + +filesystem_source = filesystem( + bucket_url="file://Users/admin/Documents/csv_files", + file_glob="*.csv" +) +``` +or taken from the config: + +* python code: + + ```py + from dlt.sources.filesystem import filesystem + + filesystem_source = filesystem() + ``` + +* configuration file: + ```toml + [sources.filesystem] + bucket_url="file://Users/admin/Documents/csv_files" + file_glob="*.csv" + ``` + +Full list of `filesystem` resource parameters: + +* `bucket_url` - full URL of the bucket (could be a relative path in the case of the local filesystem). +* `credentials` - cloud storage credentials of `AbstractFilesystem` instance (should be empty for the local filesystem). We recommend not to specify this parameter in the code, but put it in secrets file instead. +* `file_glob` - file filter in glob format. Defaults to listing all non-recursive files in the bucket URL. +* `files_per_page` - number of files processed at once. The default value is `100`. +* `extract_content` - if true, the content of the file will be read and returned in the resource. The default value is `False`. + +### 2. Choose the right transformer resource + +The current implementation of the filesystem source natively supports three file types: `csv`, `parquet`, and `jsonl`. +You can apply any of the above or [create your own transformer](advanced#create-your-own-transformer). To apply the selected transformer +resource, use pipe notation `|`: + +```py +from dlt.sources.filesystem import filesystem, read_csv + +filesystem_pipe = filesystem( + bucket_url="file://Users/admin/Documents/csv_files", + file_glob="*.csv" +) | read_csv() +``` + +#### Available transformers + +- `read_csv()` - process `csv` files using `pandas` +- `read_jsonl()` - process `jsonl` files chuck by chunk +- `read_parquet()` - process `parquet` files using `pyarrow` +- `read_csv_duckdb()` - this transformer process `csv` files using DuckDB, which usually shows better performance, than `pandas`. + +:::tip +We advise that you give each resource a +[specific name](../../../general-usage/resource#duplicate-and-rename-resources) +before loading with `pipeline.run`. This will make sure that data goes to a table with the name you +want and that each pipeline uses a +[separate state for incremental loading.](../../../general-usage/state#read-and-write-pipeline-state-in-a-resource) +::: + +### 3. Create and run a pipeline + +```py +import dlt +from dlt.sources.filesystem import filesystem, read_csv + +filesystem_pipe = filesystem(bucket_url="file://Users/admin/Documents/csv_files", file_glob="*.csv") | read_csv() +pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb") +info = pipeline.run(filesystem_pipe) +print(info) +``` + +For more information on how to create and run the pipeline, read the [Walkthrough: Run a pipeline](../../../walkthroughs/run-a-pipeline). + +### 4. Apply hints + +```py +import dlt +from dlt.sources.filesystem import filesystem, read_csv + +filesystem_pipe = filesystem(bucket_url="file://Users/admin/Documents/csv_files", file_glob="*.csv") | read_csv() +# tell dlt to merge on date +filesystem_pipe.apply_hints(write_disposition="merge", merge_key="date") + +# We load the data into the table_name table +pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb") +load_info = pipeline.run(filesystem_pipe.with_name("table_name")) +print(load_info) +``` + +### 5. Incremental loading + +Here are a few simple ways to load your data incrementally: + +1. [Load files based on modification date](#load-files-based-on-modification-date). Only load files that have been updated since the last time `dlt` processed them. `dlt` checks the files' metadata (like the modification date) and skips those that haven't changed. +2. [Load new records based on a specific column](#load-new-records-based-on-a-specific-column). You can load only the new or updated records by looking at a specific column, like `updated_at`. Unlike the first method, this approach would read all files every time and then filter the records which was updated. +3. [Combine loading only updated files and records](#combine-loading-only-updated-files-and-records). Finally, you can combine both methods. It could be useful if new records could be added to existing files, so you not only want to filter the modified files, but modified records as well. + +#### Load files based on modification date +For example, to load only new CSV files with [incremental loading](../../../general-usage/incremental-loading) you can use `apply_hints` method. + +```py +import dlt +from dlt.sources.filesystem import filesystem, read_csv + +# This configuration will only consider new csv files +new_files = filesystem(bucket_url="s3://bucket_name", file_glob="directory/*.csv") +# add incremental on modification time +new_files.apply_hints(incremental=dlt.sources.incremental("modification_date")) + +pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb") +load_info = pipeline.run((new_files | read_csv()).with_name("csv_files")) +print(load_info) +``` + +#### Load new records based on a specific column + +In this example we load only new records based on the field called `updated_at`. This method may be useful if you are not able to +filter files by modification date because for example, all files are modified each time new record is appeared. +```py +import dlt +from dlt.sources.filesystem import filesystem, read_csv + +# We consider all csv files +all_files = filesystem(bucket_url="s3://bucket_name", file_glob="directory/*.csv") + +# But filter out only updated records +filesystem_pipe = (all_files | read_csv()) +filesystem_pipe.apply_hints(incremental=dlt.sources.incremental("updated_at")) +pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb") +load_info = pipeline.run(filesystem_pipe) +print(load_info) +``` + +#### Combine loading only updated files and records + +```py +import dlt +from dlt.sources.filesystem import filesystem, read_csv + +# This configuration will only consider modified csv files +new_files = filesystem(bucket_url="s3://bucket_name", file_glob="directory/*.csv") +new_files.apply_hints(incremental=dlt.sources.incremental("modification_date")) + +# And in each modified file we filter out only updated records +filesystem_pipe = (new_files | read_csv()) +filesystem_pipe.apply_hints(incremental=dlt.sources.incremental("updated_at")) +pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb") +load_info = pipeline.run(filesystem_pipe) +print(load_info) +``` + +### 6. Filter files + +If you need to filter out files based on their metadata, you can easily do this using the `add_filter` method. +Within your filtering function, you'll have access to [any field](advanced#fileitem-fields) of the `FileItem` representation. + +#### Filter by name +To filter only files that have `London` and `Berlin` in their names, you can do the following: +```py +import dlt +from dlt.sources.filesystem import filesystem, read_csv + +# Filter files accessing file_name field +filtered_files = filesystem(bucket_url="s3://bucket_name", file_glob="directory/*.csv") +filtered_files.add_filter(lambda item: ("London" in item["file_name"]) or ("Berlin" in item["file_name"])) + +filesystem_pipe = (filtered_files | read_csv()) +pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb") +load_info = pipeline.run(filesystem_pipe) +print(load_info) +``` + +:::tip +You could also use `file_glob` to filter files by names. It works very well in simple cases, for example, filtering by extention: +```py +from dlt.sources.filesystem import filesystem + +filtered_files = filesystem(bucket_url="s3://bucket_name", file_glob="**/*.json") +``` +::: + +#### Filter by size + +If for some reason you only want to load small files, you can also do that: + +```py +import dlt +from dlt.sources.filesystem import filesystem, read_csv + +MAX_SIZE_IN_BYTES = 10 + +# Filter files accessing size_in_bytes field +filtered_files = filesystem(bucket_url="s3://bucket_name", file_glob="directory/*.csv") +filtered_files.add_filter(lambda item: item["size_in_bytes"] < MAX_SIZE_IN_BYTES) + +filesystem_pipe = (filtered_files | read_csv()) +pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb") +load_info = pipeline.run(filesystem_pipe) +print(load_info) +``` + +## Troubleshooting + +### Access extremely long file paths + +Windows supports paths up to 255 characters. When you access a path longer than 255 characters, you'll see a `FileNotFound` exception. + + To go over this limit, you can use [extended paths](https://learn.microsoft.com/en-us/windows/win32/fileio/maximum-file-path-limitation?tabs=registry). + **Note that Python glob does not work with extended UNC paths**, so you will not be able to use them + +```toml +[sources.filesystem] +bucket_url = '\\?\C:\a\b\c' +``` + +### If you get an empty list of files + +If you are running a `dlt` pipeline with the filesystem source and get zero records, we recommend you check +the configuration of `bucket_url` and `file_glob` parameters. + +For example, with Azure Blob storage, people sometimes mistake the account name for the container name. Make sure +you've set up a URL as `"az:///"`. + +Also, please reference the [glob](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.glob) +function to configure the resource correctly. Use `**` to include recursive files. Note that the local +filesystem supports full Python [glob](https://docs.python.org/3/library/glob.html#glob.glob) functionality, +while cloud storage supports a restricted `fsspec` [version](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.glob). + + \ No newline at end of file diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/filesystem/index.md b/docs/website/docs/dlt-ecosystem/verified-sources/filesystem/index.md new file mode 100644 index 0000000000..32e0df77c2 --- /dev/null +++ b/docs/website/docs/dlt-ecosystem/verified-sources/filesystem/index.md @@ -0,0 +1,18 @@ +--- +title: Filesystem & Buckets +description: dlt-verified source for Filesystem & Buckets +keywords: [readers source and filesystem, files, filesystem, readers source, cloud storage] +--- + +The Filesystem source allows seamless loading of files from the following locations: +* AWS S3 +* Google Cloud Storage +* Google Drive +* Azure +* local filesystem + +The Filesystem source natively supports `csv`, `parquet`, and `jsonl` files and allows customization for loading any type of structured files. + +import DocCardList from '@theme/DocCardList'; + + \ No newline at end of file diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/github.md b/docs/website/docs/dlt-ecosystem/verified-sources/github.md index a5a338666e..830f4035d8 100644 --- a/docs/website/docs/dlt-ecosystem/verified-sources/github.md +++ b/docs/website/docs/dlt-ecosystem/verified-sources/github.md @@ -206,7 +206,7 @@ def github_repo_events( `access_token`: Optional classic or fine-grained access token. If not provided, calls are made anonymously. -`max_table_nesting=2` sets the maximum nesting level of child tables to 2. +`max_table_nesting=2` sets the maximum nesting level to 2. Read more about [nesting levels](../../general-usage/source#reduce-the-nesting-level-of-generated-tables). diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/index.md b/docs/website/docs/dlt-ecosystem/verified-sources/index.md index d105dccb9c..c846a73eb2 100644 --- a/docs/website/docs/dlt-ecosystem/verified-sources/index.md +++ b/docs/website/docs/dlt-ecosystem/verified-sources/index.md @@ -1,34 +1,48 @@ --- -title: Verified sources -description: List of verified sources -keywords: ['verified source'] +title: Sources +description: Available sources +keywords: ['source'] --- -import DocCardList from '@theme/DocCardList'; import Link from '../../_book-onboarding-call.md'; +import DocCardList from '@theme/DocCardList'; +import {useCurrentSidebarCategory} from '@docusaurus/theme-common'; -Choose from our collection of verified sources, developed and maintained by the dlt team and community. Each source is rigorously tested against a real API and provided as Python code for easy customization. - -Planning to use dlt in production and need a source that isn't listed? We're happy to help you build it: . +Planning to use `dlt` in production and need a source that isn't listed? We're happy to help you build it: . -### Popular sources +### Core sources -- [SQL databases](sql_database). Supports PostgreSQL, MySQL, MS SQL Server, BigQuery, Redshift, and more. -- [REST API generic source](rest_api). Loads data from REST APIs using declarative configuration. -- [OpenAPI source generator](openapi-generator). Generates a source from an OpenAPI 3.x spec using the REST API source. -- [Cloud and local storage](filesystem). Retrieves data from AWS S3, Google Cloud Storage, Azure Blob Storage, local files, and more. + item.label === '30+ SQL Databases' || item.label === 'REST APIs' || item.label === 'Filesystem & buckets' +)} /> -### Full list of verified sources +### Verified sources - +Choose from our collection of verified sources, developed and maintained by the `dlt` team and community. Each source is rigorously tested against a real API and provided as Python code for easy customization. :::tip -If you're looking for a source that isn't listed and it provides a REST API, be sure to check out our [REST API generic source](rest_api) - source. +If you couldn't find a source implementation, you can easily create your own, check out the [resource page](../../general-usage/resource) to learn how! ::: + item.label !== '30+ SQL Databases' && item.label !== 'REST API generic source'&& item.label !== 'Filesystem & buckets' +)} /> + +### What's the difference between core and verified sources? + +The main difference between the [core sources](#core-sources) and [verified sources](#verified-sources) lies in their structure. +Core sources are generic collections, meaning they can connect to a variety of systems. For example, the [SQL Database source](sql_database) can connect to any +database which supports SQLAlchemy. + +According to our telemetry, core sources are the most widely used among our users! + +It's also important to note that core sources are integrated into the `dlt` core library, +whereas verified sources are maintained in a separate [repository](https://github.com/dlt-hub/verified-sources). +To use a verified source, you need to run the `dlt` init command, which will download the verified source code to +your working directory. + ### Get help * Source missing? [Request a new verified source.](https://github.com/dlt-hub/verified-sources/issues/new?template=source-request.md) * Missing endpoint or a feature? [Request or contribute](https://github.com/dlt-hub/verified-sources/issues/new?template=extend-a-source.md) -* [Join our Slack community](https://dlthub.com/community) and ask in the technical-help channel. +* [Join our Slack community](https://dlthub.com/community) and ask in the technical-help channel. \ No newline at end of file diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/mongodb.md b/docs/website/docs/dlt-ecosystem/verified-sources/mongodb.md index f6d57a5ba2..0a6ba8c632 100644 --- a/docs/website/docs/dlt-ecosystem/verified-sources/mongodb.md +++ b/docs/website/docs/dlt-ecosystem/verified-sources/mongodb.md @@ -103,7 +103,7 @@ nested data. It employs a flexible schema, and its key terms include: `Databases`: Containers for collections; a single MongoDB server can have multiple databases. -The `dlt` converts nested data into relational tables, deduces data types, and defines parent-child +The `dlt` converts nested data into relational tables, deduces data types, and defines nested relationships, creating an adaptive schema for future data adjustments. ### Initialize the verified source diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/rest_api/advanced.md b/docs/website/docs/dlt-ecosystem/verified-sources/rest_api/advanced.md new file mode 100644 index 0000000000..fa663b9ca5 --- /dev/null +++ b/docs/website/docs/dlt-ecosystem/verified-sources/rest_api/advanced.md @@ -0,0 +1,121 @@ +--- +title: Advanced configuration +description: Learn custom response processing +keywords: [rest api, restful api] +--- + +`rest_api_source()` function creates the [dlt source](../../../general-usage/source.md) and lets you configure the following parameters: + +- `config`: The REST API configuration dictionary. +- `name`: An optional name for the source. +- `section`: An optional section name in the configuration file. +- `max_table_nesting`: Sets the maximum depth of nested table above which the remaining nodes are loaded as structs or JSON. +- `root_key` (bool): Enables merging on all resources by propagating root foreign key to nested tables. This option is most useful if you plan to change write disposition of a resource to disable/enable merge. Defaults to False. +- `schema_contract`: Schema contract settings that will be applied to this resource. +- `spec`: A specification of configuration and secret values required by the source. + +### Response actions + +The `response_actions` field in the endpoint configuration allows you to specify how to handle specific responses or all responses from the API. For example, responses with specific status codes or content substrings can be ignored. +Additionally, all responses or only responses with specific status codes or content substrings can be transformed with a custom callable, such as a function. This callable is passed on to the requests library as a [response hook](https://requests.readthedocs.io/en/latest/user/advanced/#event-hooks). The callable can modify the response object and has to return it for the modifications to take effect. + +:::caution Experimental Feature +This is an experimental feature and may change in future releases. +::: + +**Fields:** + +- `status_code` (int, optional): The HTTP status code to match. +- `content` (str, optional): A substring to search for in the response content. +- `action` (str or Callable or List[Callable], optional): The action to take when the condition is met. Currently supported actions: + - `"ignore"`: Ignore the response. + - a callable accepting and returning the response object. + - a list of callables, each accepting and returning the response object. + + +#### Example A + +```py +{ + "path": "issues", + "response_actions": [ + {"status_code": 404, "action": "ignore"}, + {"content": "Not found", "action": "ignore"}, + {"status_code": 200, "content": "some text", "action": "ignore"}, + ], +} +``` + +In this example, the source will ignore responses with a status code of 404, responses with the content "Not found", and responses with a status code of 200 _and_ content "some text". + +#### Example B + +```py +def set_encoding(response, *args, **kwargs): + # sets the encoding in case it's not correctly detected + response.encoding = 'windows-1252' + return response + + +def add_and_remove_fields(response: Response, *args, **kwargs) -> Response: + payload = response.json() + for record in payload["data"]: + record["custom_field"] = "foobar" + record.pop("email", None) + modified_content: bytes = json.dumps(payload).encode("utf-8") + response._content = modified_content + return response + + +source_config = { + "client": { + # ... + }, + "resources": [ + { + "name": "issues", + "endpoint": { + "path": "issues", + "response_actions": [ + set_encoding, + { + "status_code": 200, + "content": "some text", + "action": add_and_remove_fields, + }, + ], + }, + }, + ], +} +``` + +In this example, the resource will set the correct encoding for all responses first. Thereafter, for all responses that have the status code 200, we will add a field `custom_field` and remove the field `email`. + +#### Example C + +```py +def set_encoding(response, *args, **kwargs): + # sets the encoding in case it's not correctly detected + response.encoding = 'windows-1252' + return response + +source_config = { + "client": { + # ... + }, + "resources": [ + { + "name": "issues", + "endpoint": { + "path": "issues", + "response_actions": [ + set_encoding, + ], + }, + }, + ], +} +``` + +In this example, the resource will set the correct encoding for all responses. More callables can be added to the list of response_actions. diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/rest_api.md b/docs/website/docs/dlt-ecosystem/verified-sources/rest_api/basic.md similarity index 72% rename from docs/website/docs/dlt-ecosystem/verified-sources/rest_api.md rename to docs/website/docs/dlt-ecosystem/verified-sources/rest_api/basic.md index e1cd9ce88e..1a28fe4602 100644 --- a/docs/website/docs/dlt-ecosystem/verified-sources/rest_api.md +++ b/docs/website/docs/dlt-ecosystem/verified-sources/rest_api/basic.md @@ -1,13 +1,13 @@ --- -title: REST API generic source -description: dlt verified source for REST APIs +title: REST API source +description: Learn how to set up and configure keywords: [rest api, restful api] --- -import Header from './_source-info-header.md'; +import Header from '../_source-info-header.md';
-This is a generic dlt source you can use to extract data from any REST API. It uses [declarative configuration](#source-configuration) to define the API endpoints, their [relationships](#define-resource-relationships), how to handle [pagination](#pagination), and [authentication](#authentication). +This is a dlt source you can use to extract data from any REST API. It uses [declarative configuration](#source-configuration) to define the API endpoints, their [relationships](#define-resource-relationships), how to handle [pagination](#pagination), and [authentication](#authentication). ### Quick example @@ -15,7 +15,7 @@ Here's an example of how to configure the REST API source to load posts and rela ```py import dlt -from rest_api import rest_api_source +from dlt.sources.rest_api import rest_api_source source = rest_api_source({ "client": { @@ -64,9 +64,13 @@ load_info = pipeline.run(source) Running this pipeline will create two tables in the DuckDB: `posts` and `comments` with the data from the respective API endpoints. The `comments` resource will fetch comments for each post by using the `id` field from the `posts` resource. -## Setup guide +## Setup -### Initialize the verified source +### Prerequisites + +Please make sure the `dlt` library is installed. Refer to the [installation guide](../../../getting-started). + +### Initialize the REST API source Enter the following command in your terminal: @@ -74,7 +78,7 @@ Enter the following command in your terminal: dlt init rest_api duckdb ``` -[dlt init](../../reference/command-line-interface) will initialize the pipeline examples for REST API as the [source](../../general-usage/source) and [duckdb](../destinations/duckdb.md) as the [destination](../destinations). +[dlt init](../../../reference/command-line-interface) will initialize the pipeline examples for REST API as the [source](../../../general-usage/source) and [duckdb](../../destinations/duckdb.md) as the [destination](../../destinations). Running `dlt init` creates the following in the current folder: - `rest_api_pipeline.py` file with a sample pipelines definition: @@ -91,7 +95,7 @@ Change the REST API source to your needs by modifying the `rest_api_pipeline.py` For the rest of the guide, we will use the [GitHub API](https://docs.github.com/en/rest?apiVersion=2022-11-28) and [Pokemon API](https://pokeapi.co/) as example sources. ::: -This source is based on the [RESTClient class](../../general-usage/http/rest-client.md). +This source is based on the [RESTClient class](../../../general-usage/http/rest-client.md). ### Add credentials @@ -102,7 +106,7 @@ The GitHub API [requires an access token](https://docs.github.com/en/rest/authen After you get the token, add it to the `secrets.toml` file: ```toml -[sources.rest_api.github] +[sources.rest_api_pipeline.github_source] github_token = "your_github_token" ``` @@ -133,7 +137,7 @@ github_token = "your_github_token" Let's take a look at the GitHub example in `rest_api_pipeline.py` file: ```py -from rest_api import RESTAPIConfig, rest_api_resources +from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def github_source(github_token=dlt.secrets.value): @@ -205,8 +209,8 @@ The declarative resource configuration is defined in the `config` dictionary. It 1. `client`: Defines the base URL and authentication method for the API. In this case it uses token-based authentication. The token is stored in the `secrets.toml` file. 2. `resource_defaults`: Contains default settings for all [resources](#resource-configuration). In this example, we define that all resources: - - Have `id` as the [primary key](../../general-usage/resource#define-schema) - - Use the `merge` [write disposition](../../general-usage/incremental-loading#choosing-a-write-disposition) to merge the data with the existing data in the destination. + - Have `id` as the [primary key](../../../general-usage/resource#define-schema) + - Use the `merge` [write disposition](../../../general-usage/incremental-loading#choosing-a-write-disposition) to merge the data with the existing data in the destination. - Send a `per_page` query parameter with each request to 100 to get more results per page. 3. `resources`: A list of [resources](#resource-configuration) to be loaded. Here, we have two resources: `issues` and `issue_comments`, which correspond to the GitHub API endpoints for [repository issues](https://docs.github.com/en/rest/issues/issues?apiVersion=2022-11-28#list-repository-issues) and [issue comments](https://docs.github.com/en/rest/issues/comments?apiVersion=2022-11-28#list-issue-comments). Note that we need a in issue number to fetch comments for each issue. This number is taken from the `issues` resource. More on this in the [resource relationships](#define-resource-relationships) section. @@ -219,7 +223,7 @@ Let's break down the configuration in more detail. Import the `RESTAPIConfig` type from the `rest_api` module to have convenient hints in your editor/IDE and use it to define the configuration object. ```py -from rest_api import RESTAPIConfig +from dlt.sources.rest_api import RESTAPIConfig ``` ::: @@ -297,7 +301,7 @@ This is a list of resource configurations that define the API endpoints to be lo ### Resource configuration -A resource configuration is used to define a [dlt resource](../../general-usage/resource.md) for the data to be loaded from an API endpoint. It contains the following key fields: +A resource configuration is used to define a [dlt resource](../../../general-usage/resource.md) for the data to be loaded from an API endpoint. It contains the following key fields: - `endpoint`: The endpoint configuration for the resource. It can be a string or a dict representing the endpoint settings. See the [endpoint configuration](#endpoint-configuration) section for more details. - `write_disposition`: The write disposition for the resource. @@ -305,7 +309,7 @@ A resource configuration is used to define a [dlt resource](../../general-usage/ - `include_from_parent`: A list of fields from the parent resource to be included in the resource output. See the [resource relationships](#include-fields-from-the-parent-resource) section for more details. - `selected`: A flag to indicate if the resource is selected for loading. This could be useful when you want to load data only from child resources and not from the parent resource. -You can also pass additional resource parameters that will be used to configure the dlt resource. See [dlt resource API reference](../../api_reference/extract/decorators.md#resource) for more details. +You can also pass additional resource parameters that will be used to configure the dlt resource. See [dlt resource API reference](../../../api_reference/extract/decorators.md#resource) for more details. ### Endpoint configuration @@ -346,7 +350,7 @@ The REST API source will try to automatically handle pagination for you. This wo In some special cases, you may need to specify the pagination configuration explicitly. -To specify the pagination configuration, use the `paginator` field in the [client](#client) or [endpoint](#endpoint-configuration) configurations. You may either use a dictionary with a string alias in the `type` field along with the required parameters, or use a [paginator class instance](../../general-usage/http/rest-client.md#paginators). +To specify the pagination configuration, use the `paginator` field in the [client](#client) or [endpoint](#endpoint-configuration) configurations. You may either use a dictionary with a string alias in the `type` field along with the required parameters, or use a [paginator class instance](../../../general-usage/http/rest-client.md#paginators). #### Example @@ -393,22 +397,22 @@ from dlt.sources.helpers.rest_client.paginators import JSONLinkPaginator ``` :::note -Currently pagination is supported only for GET requests. To handle POST requests with pagination, you need to implement a [custom paginator](../../general-usage/http/rest-client.md#custom-paginator). +Currently pagination is supported only for GET requests. To handle POST requests with pagination, you need to implement a [custom paginator](../../../general-usage/http/rest-client.md#custom-paginator). ::: These are the available paginators: | `type` | Paginator class | Description | | ------------ | -------------- | ----------- | -| `json_link` | [JSONLinkPaginator](../../general-usage/http/rest-client.md#jsonresponsepaginator) | The link to the next page is in the body (JSON) of the response.
*Parameters:*
  • `next_url_path` (str) - the JSONPath to the next page URL
| -| `header_link` | [HeaderLinkPaginator](../../general-usage/http/rest-client.md#headerlinkpaginator) | The links to the next page are in the response headers.
*Parameters:*
  • `link_header` (str) - the name of the header containing the links. Default is "next".
| -| `offset` | [OffsetPaginator](../../general-usage/http/rest-client.md#offsetpaginator) | The pagination is based on an offset parameter. With total items count either in the response body or explicitly provided.
*Parameters:*
  • `limit` (int) - the maximum number of items to retrieve in each request
  • `offset` (int) - the initial offset for the first request. Defaults to `0`
  • `offset_param` (str) - the name of the query parameter used to specify the offset. Defaults to "offset"
  • `limit_param` (str) - the name of the query parameter used to specify the limit. Defaults to "limit"
  • `total_path` (str) - a JSONPath expression for the total number of items. If not provided, pagination is controlled by `maximum_offset` and `stop_after_empty_page`
  • `maximum_offset` (int) - optional maximum offset value. Limits pagination even without total count
  • `stop_after_empty_page` (bool) - Whether pagination should stop when a page contains no result items. Defaults to `True`
| -| `page_number` | [PageNumberPaginator](../../general-usage/http/rest-client.md#pagenumberpaginator) | The pagination is based on a page number parameter. With total pages count either in the response body or explicitly provided.
*Parameters:*
  • `base_page` (int) - the starting page number. Defaults to `0`
  • `page_param` (str) - the query parameter name for the page number. Defaults to "page"
  • `total_path` (str) - a JSONPath expression for the total number of pages. If not provided, pagination is controlled by `maximum_page` and `stop_after_empty_page`
  • `maximum_page` (int) - optional maximum page number. Stops pagination once this page is reached
  • `stop_after_empty_page` (bool) - Whether pagination should stop when a page contains no result items. Defaults to `True`
| -| `cursor` | [JSONResponseCursorPaginator](../../general-usage/http/rest-client.md#jsonresponsecursorpaginator) | The pagination is based on a cursor parameter. The value of the cursor is in the response body (JSON).
*Parameters:*
  • `cursor_path` (str) - the JSONPath to the cursor value. Defaults to "cursors.next"
  • `cursor_param` (str) - the query parameter name for the cursor. Defaults to "after"
| +| `json_link` | [JSONLinkPaginator](../../../general-usage/http/rest-client.md#jsonresponsepaginator) | The link to the next page is in the body (JSON) of the response.
*Parameters:*
  • `next_url_path` (str) - the JSONPath to the next page URL
| +| `header_link` | [HeaderLinkPaginator](../../../general-usage/http/rest-client.md#headerlinkpaginator) | The links to the next page are in the response headers.
*Parameters:*
  • `link_header` (str) - the name of the header containing the links. Default is "next".
| +| `offset` | [OffsetPaginator](../../../general-usage/http/rest-client.md#offsetpaginator) | The pagination is based on an offset parameter. With total items count either in the response body or explicitly provided.
*Parameters:*
  • `limit` (int) - the maximum number of items to retrieve in each request
  • `offset` (int) - the initial offset for the first request. Defaults to `0`
  • `offset_param` (str) - the name of the query parameter used to specify the offset. Defaults to "offset"
  • `limit_param` (str) - the name of the query parameter used to specify the limit. Defaults to "limit"
  • `total_path` (str) - a JSONPath expression for the total number of items. If not provided, pagination is controlled by `maximum_offset` and `stop_after_empty_page`
  • `maximum_offset` (int) - optional maximum offset value. Limits pagination even without total count
  • `stop_after_empty_page` (bool) - Whether pagination should stop when a page contains no result items. Defaults to `True`
| +| `page_number` | [PageNumberPaginator](../../../general-usage/http/rest-client.md#pagenumberpaginator) | The pagination is based on a page number parameter. With total pages count either in the response body or explicitly provided.
*Parameters:*
  • `base_page` (int) - the starting page number. Defaults to `0`
  • `page_param` (str) - the query parameter name for the page number. Defaults to "page"
  • `total_path` (str) - a JSONPath expression for the total number of pages. If not provided, pagination is controlled by `maximum_page` and `stop_after_empty_page`
  • `maximum_page` (int) - optional maximum page number. Stops pagination once this page is reached
  • `stop_after_empty_page` (bool) - Whether pagination should stop when a page contains no result items. Defaults to `True`
| +| `cursor` | [JSONResponseCursorPaginator](../../../general-usage/http/rest-client.md#jsonresponsecursorpaginator) | The pagination is based on a cursor parameter. The value of the cursor is in the response body (JSON).
*Parameters:*
  • `cursor_path` (str) - the JSONPath to the cursor value. Defaults to "cursors.next"
  • `cursor_param` (str) - the query parameter name for the cursor. Defaults to "after"
| | `single_page` | SinglePagePaginator | The response will be interpreted as a single-page response, ignoring possible pagination metadata. | | `auto` | `None` | Explicitly specify that the source should automatically detect the pagination method. | -For more complex pagination methods, you can implement a [custom paginator](../../general-usage/http/rest-client.md#implementing-a-custom-paginator), instantiate it, and use it in the configuration. +For more complex pagination methods, you can implement a [custom paginator](../../../general-usage/http/rest-client.md#implementing-a-custom-paginator), instantiate it, and use it in the configuration. Alternatively, you can use the dictionary configuration syntax also for custom paginators. For this, you need to register your custom paginator: @@ -479,7 +483,7 @@ Read more about [JSONPath syntax](https://github.com/h2non/jsonpath-ng?tab=readm ### Authentication -For APIs that require authentication to access their endpoints, the REST API source supports various authentication methods, including token-based authentication, query parameters, basic authentication, and custom authentication. The authentication configuration is specified in the `auth` field of the [client](#client) either as a dictionary or as an instance of the [authentication class](../../general-usage/http/rest-client.md#authentication). +For APIs that require authentication to access their endpoints, the REST API source supports various authentication methods, including token-based authentication, query parameters, basic authentication, and custom authentication. The authentication configuration is specified in the `auth` field of the [client](#client) either as a dictionary or as an instance of the [authentication class](../../../general-usage/http/rest-client.md#authentication). #### Quick example @@ -505,10 +509,10 @@ Available authentication types: | Authentication class | String Alias (`type`) | Description | | ------------------- | ----------- | ----------- | -| [BearTokenAuth](../../general-usage/http/rest-client.md#bearer-token-authentication) | `bearer` | Bearer token authentication. | -| [HTTPBasicAuth](../../general-usage/http/rest-client.md#http-basic-authentication) | `http_basic` | Basic HTTP authentication. | -| [APIKeyAuth](../../general-usage/http/rest-client.md#api-key-authentication) | `api_key` | API key authentication with key defined in the query parameters or in the headers. | -| [OAuth2ClientCredentials](../../general-usage/http/rest-client.md#oauth20-authorization) | N/A | OAuth 2.0 authorization with a temporary access token obtained from the authorization server. | +| [BearTokenAuth](../../../general-usage/http/rest-client.md#bearer-token-authentication) | `bearer` | Bearer token authentication. | +| [HTTPBasicAuth](../../../general-usage/http/rest-client.md#http-basic-authentication) | `http_basic` | Basic HTTP authentication. | +| [APIKeyAuth](../../../general-usage/http/rest-client.md#api-key-authentication) | `api_key` | API key authentication with key defined in the query parameters or in the headers. | +| [OAuth2ClientCredentials](../../../general-usage/http/rest-client.md#oauth20-authorization) | N/A | OAuth 2.0 authorization with a temporary access token obtained from the authorization server. | To specify the authentication configuration, use the `auth` field in the [client](#client) configuration: @@ -546,12 +550,12 @@ Available authentication types: | `type` | Authentication class | Description | | ----------- | ------------------- | ----------- | -| `bearer` | [BearTokenAuth](../../general-usage/http/rest-client.md#bearer-token-authentication) | Bearer token authentication.
Parameters:
  • `token` (str)
| -| `http_basic` | [HTTPBasicAuth](../../general-usage/http/rest-client.md#http-basic-authentication) | Basic HTTP authentication.
Parameters:
  • `username` (str)
  • `password` (str)
| -| `api_key` | [APIKeyAuth](../../general-usage/http/rest-client.md#api-key-authentication) | API key authentication with key defined in the query parameters or in the headers.
Parameters:
  • `name` (str) - the name of the query parameter or header
  • `api_key` (str) - the API key value
  • `location` (str, optional) - the location of the API key in the request. Can be `query` or `header`. Default is `header`
| +| `bearer` | [BearTokenAuth](../../../general-usage/http/rest-client.md#bearer-token-authentication) | Bearer token authentication.
Parameters:
  • `token` (str)
| +| `http_basic` | [HTTPBasicAuth](../../../general-usage/http/rest-client.md#http-basic-authentication) | Basic HTTP authentication.
Parameters:
  • `username` (str)
  • `password` (str)
| +| `api_key` | [APIKeyAuth](../../../general-usage/http/rest-client.md#api-key-authentication) | API key authentication with key defined in the query parameters or in the headers.
Parameters:
  • `name` (str) - the name of the query parameter or header
  • `api_key` (str) - the API key value
  • `location` (str, optional) - the location of the API key in the request. Can be `query` or `header`. Default is `header`
| -For more complex authentication methods, you can implement a [custom authentication class](../../general-usage/http/rest-client.md#implementing-custom-authentication) and use it in the configuration. +For more complex authentication methods, you can implement a [custom authentication class](../../../general-usage/http/rest-client.md#implementing-custom-authentication) and use it in the configuration. You can use the dictionary configuration syntax also for custom authentication classes after registering them as follows: @@ -632,7 +636,7 @@ The syntax for the `resolve` field in parameter configuration is: The `field` value can be specified as a [JSONPath](https://github.com/h2non/jsonpath-ng?tab=readme-ov-file#jsonpath-syntax) to select a nested field in the parent resource data. For example: `"field": "items[0].id"`. -Under the hood, dlt handles this by using a [transformer resource](../../general-usage/resource.md#process-resources-with-dlttransformer). +Under the hood, dlt handles this by using a [transformer resource](../../../general-usage/resource.md#process-resources-with-dlttransformer). #### Include fields from the parent resource @@ -653,7 +657,7 @@ This will include the `id`, `title`, and `created_at` fields from the `issues` r ## Incremental loading Some APIs provide a way to fetch only new or changed data (most often by using a timestamp field like `updated_at`, `created_at`, or incremental IDs). -This is called [incremental loading](../../general-usage/incremental-loading.md) and is very useful as it allows you to reduce the load time and the amount of data transferred. +This is called [incremental loading](../../../general-usage/incremental-loading.md) and is very useful as it allows you to reduce the load time and the amount of data transferred. When the API endpoint supports incremental loading, you can configure dlt to load only the new or changed data using these two methods: @@ -762,9 +766,9 @@ The fields are: - `end_value` (str): The end value for the cursor to stop the incremental loading. This is optional and can be omitted if you only need to track the start condition. If you set this field, `initial_value` needs to be set as well. - `convert` (callable): A callable that converts the cursor value into the format that the query parameter requires. For example, a UNIX timestamp can be converted into an ISO 8601 date or a date can be converted into `created_at+gt+{date}`. -See the [incremental loading](../../general-usage/incremental-loading.md#incremental-loading-with-a-cursor-field) guide for more details. +See the [incremental loading](../../../general-usage/incremental-loading.md#incremental-loading-with-a-cursor-field) guide for more details. -If you encounter issues with incremental loading, see the [troubleshooting section](../../general-usage/incremental-loading.md#troubleshooting) in the incremental loading guide. +If you encounter issues with incremental loading, see the [troubleshooting section](../../../general-usage/incremental-loading.md#troubleshooting) in the incremental loading guide. ### Convert the incremental value before calling the API @@ -798,128 +802,10 @@ Incremental loading using the `incremental` field: } ``` -## Advanced configuration - -`rest_api_source()` function creates the [dlt source](../../general-usage/source.md) and lets you configure the following parameters: - -- `config`: The REST API configuration dictionary. -- `name`: An optional name for the source. -- `section`: An optional section name in the configuration file. -- `max_table_nesting`: Sets the maximum depth of nested table above which the remaining nodes are loaded as structs or JSON. -- `root_key` (bool): Enables merging on all resources by propagating root foreign key to child tables. This option is most useful if you plan to change write disposition of a resource to disable/enable merge. Defaults to False. -- `schema_contract`: Schema contract settings that will be applied to this resource. -- `spec`: A specification of configuration and secret values required by the source. - -### Response actions - -The `response_actions` field in the endpoint configuration allows you to specify how to handle specific responses or all responses from the API. For example, responses with specific status codes or content substrings can be ignored. -Additionally, all responses or only responses with specific status codes or content substrings can be transformed with a custom callable, such as a function. This callable is passed on to the requests library as a [response hook](https://requests.readthedocs.io/en/latest/user/advanced/#event-hooks). The callable can modify the response object and has to return it for the modifications to take effect. - -:::caution Experimental Feature -This is an experimental feature and may change in future releases. -::: - -**Fields:** - -- `status_code` (int, optional): The HTTP status code to match. -- `content` (str, optional): A substring to search for in the response content. -- `action` (str or Callable or List[Callable], optional): The action to take when the condition is met. Currently supported actions: - - `"ignore"`: Ignore the response. - - a callable accepting and returning the response object. - - a list of callables, each accepting and returning the response object. - - -#### Example A - -```py -{ - "path": "issues", - "response_actions": [ - {"status_code": 404, "action": "ignore"}, - {"content": "Not found", "action": "ignore"}, - {"status_code": 200, "content": "some text", "action": "ignore"}, - ], -} -``` - -In this example, the source will ignore responses with a status code of 404, responses with the content "Not found", and responses with a status code of 200 _and_ content "some text". - -#### Example B - -```py -def set_encoding(response, *args, **kwargs): - # sets the encoding in case it's not correctly detected - response.encoding = 'windows-1252' - return response - - -def add_and_remove_fields(response: Response, *args, **kwargs) -> Response: - payload = response.json() - for record in payload["data"]: - record["custom_field"] = "foobar" - record.pop("email", None) - modified_content: bytes = json.dumps(payload).encode("utf-8") - response._content = modified_content - return response - - -source_config = { - "client": { - # ... - }, - "resources": [ - { - "name": "issues", - "endpoint": { - "path": "issues", - "response_actions": [ - set_encoding, - { - "status_code": 200, - "content": "some text", - "action": add_and_remove_fields, - }, - ], - }, - }, - ], -} -``` - -In this example, the resource will set the correct encoding for all responses first. Thereafter, for all responses that have the status code 200, we will add a field `custom_field` and remove the field `email`. - -#### Example C - -```py -def set_encoding(response, *args, **kwargs): - # sets the encoding in case it's not correctly detected - response.encoding = 'windows-1252' - return response - -source_config = { - "client": { - # ... - }, - "resources": [ - { - "name": "issues", - "endpoint": { - "path": "issues", - "response_actions": [ - set_encoding, - ], - }, - }, - ], -} -``` - -In this example, the resource will set the correct encoding for all responses. More callables can be added to the list of response_actions. - ## Troubleshooting -If you encounter issues while running the pipeline, enable [logging](../../running-in-production/running.md#set-the-log-level-and-format) for detailed information about the execution: +If you encounter issues while running the pipeline, enable [logging](../../../running-in-production/running.md#set-the-log-level-and-format) for detailed information about the execution: ```sh RUNTIME__LOG_LEVEL=INFO python my_script.py @@ -973,7 +859,7 @@ It means that in the first resource configuration (`resources[0]`), the `params` Import the `RESTAPIConfig` type from the `rest_api` module to have convenient hints in your editor/IDE and use it to define the configuration object. ```py -from rest_api import RESTAPIConfig +from dlt.sources.rest_api import RESTAPIConfig ``` ::: @@ -983,11 +869,11 @@ If incorrect data is received from an endpoint, check the `data_selector` field #### Getting insufficient data or incorrect pagination -Check the `paginator` field in the configuration. When not explicitly specified, the source tries to auto-detect the pagination method. If auto-detection fails, or the system is unsure, a warning is logged. For production environments, we recommend to specify an explicit paginator in the configuration. See the [pagination](#pagination) section for more details. Some APIs may have non-standard pagination methods, and you may need to implement a [custom paginator](../../general-usage/http/rest-client.md#implementing-a-custom-paginator). +Check the `paginator` field in the configuration. When not explicitly specified, the source tries to auto-detect the pagination method. If auto-detection fails, or the system is unsure, a warning is logged. For production environments, we recommend to specify an explicit paginator in the configuration. See the [pagination](#pagination) section for more details. Some APIs may have non-standard pagination methods, and you may need to implement a [custom paginator](../../../general-usage/http/rest-client.md#implementing-a-custom-paginator). #### Incremental loading not working -See the [troubleshooting guide](../../general-usage/incremental-loading.md#troubleshooting) for incremental loading issues. +See the [troubleshooting guide](../../../general-usage/incremental-loading.md#troubleshooting) for incremental loading issues. #### Getting HTTP 404 errors @@ -997,11 +883,11 @@ Some API may return 404 errors for resources that do not exist or have no data. If experiencing 401 (Unauthorized) errors, this could indicate: -- Incorrect authorization credentials. Verify credentials in the `secrets.toml`. Refer to [Secret and configs](../../general-usage/credentials/setup#understanding-the-exceptions) for more information. -- An incorrect authentication type. Consult the API documentation for the proper method. See the [authentication](#authentication) section for details. For some APIs, a [custom authentication method](../../general-usage/http/rest-client.md#custom-authentication) may be required. +- Incorrect authorization credentials. Verify credentials in the `secrets.toml`. Refer to [Secret and configs](../../../general-usage/credentials/setup#understanding-the-exceptions) for more information. +- An incorrect authentication type. Consult the API documentation for the proper method. See the [authentication](#authentication) section for details. For some APIs, a [custom authentication method](../../../general-usage/http/rest-client.md#custom-authentication) may be required. ### General guidelines -The `rest_api` source uses the [RESTClient](../../general-usage/http/rest-client.md) class for HTTP requests. Refer to the RESTClient [troubleshooting guide](../../general-usage/http/rest-client.md#troubleshooting) for debugging tips. +The `rest_api` source uses the [RESTClient](../../../general-usage/http/rest-client.md) class for HTTP requests. Refer to the RESTClient [troubleshooting guide](../../../general-usage/http/rest-client.md#troubleshooting) for debugging tips. For further assistance, join our [Slack community](https://dlthub.com/community). We're here to help! diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/rest_api/index.md b/docs/website/docs/dlt-ecosystem/verified-sources/rest_api/index.md new file mode 100644 index 0000000000..dd9a77e297 --- /dev/null +++ b/docs/website/docs/dlt-ecosystem/verified-sources/rest_api/index.md @@ -0,0 +1,18 @@ +--- +title: REST APIs +description: Loads data from REST APIs using a declarative configuration +keywords: [rest api, restful api] +--- + +You can use the REST API source to extract data from any REST API. Using a [declarative configuration](./basic.md#source-configuration), you can define: + +* the API endpoints to pull data from, +* their [relationships](./basic.md#define-resource-relationships), +* how to handle [pagination](./basic.md#pagination), +* [authentication](./basic.md#authentication). + +dlt will take care of the rest: unnesting the data, inferring the schema etc, and writing to the destination. + +import DocCardList from '@theme/DocCardList'; + + \ No newline at end of file diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/sql_database.md b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database.md deleted file mode 100644 index c89a63a524..0000000000 --- a/docs/website/docs/dlt-ecosystem/verified-sources/sql_database.md +++ /dev/null @@ -1,657 +0,0 @@ ---- -title: 30+ SQL Databases -description: dlt pipeline for SQL Database -keywords: [sql connector, sql database pipeline, sql database] ---- -import Header from './_source-info-header.md'; - -# 30+ SQL Databases - -
- -SQL databases are management systems (DBMS) that store data in a structured format, commonly used -for efficient and reliable data retrieval. - -Our SQL Database verified source loads data to your specified destination using SQLAlchemy, pyarrow, pandas or ConnectorX - -:::tip -View the pipeline example [here](https://github.com/dlt-hub/verified-sources/blob/master/sources/sql_database_pipeline.py). -::: - -Sources and resources that can be loaded using this verified source are: - -| Name | Description | -| ------------ | -------------------------------------------------------------------- | -| sql_database | Reflects the tables and views in SQL database and retrieves the data | -| sql_table | Retrieves data from a particular SQL database table | -| | | - -### Supported databases - -We support all [SQLAlchemy dialects](https://docs.sqlalchemy.org/en/20/dialects/), which include, but are not limited to, the following database engines: - -* PostgreSQL -* MySQL -* SQLite -* Oracle -* Microsoft SQL Server -* MariaDB -* IBM DB2 and Informix -* Google BigQuery -* Snowflake -* Redshift -* Apache Hive and Presto -* SAP Hana -* CockroachDB -* Firebird -* Teradata Vantage - -:::note -Note that there many unofficial dialects, such as [DuckDB](https://duckdb.org/). -::: - -## Setup Guide - -1. ### Initialize the verified source - -To get started with your data pipeline, follow these steps: - -1. Enter the following command: - - ```sh - dlt init sql_database duckdb - ``` - - It will initialize - [the pipeline example](https://github.com/dlt-hub/verified-sources/blob/master/sources/sql_database_pipeline.py) - with an SQL database as the [source](../../general-usage/source) and - [DuckDB](../destinations/duckdb.md) as the [destination](../destinations). - - :::tip - If you'd like to use a different destination, simply replace `duckdb` with the name of your preferred [destination](../destinations). - ::: - -1. After running this command, a new directory will be created with the necessary files and - configuration settings to get started. - -For more information, read the guide on [how to add a verified source](../../walkthroughs/add-a-verified-source). - -2. ### Add credentials - -1. In the `.dlt` folder, there's a file called `secrets.toml`. It's where you store sensitive - information securely, like access tokens. Keep this file safe. - - Here's what the `secrets.toml` looks like: - - ```toml - [sources.sql_database.credentials] - drivername = "mysql+pymysql" # driver name for the database - database = "Rfam" # database name - username = "rfamro" # username associated with the database - host = "mysql-rfam-public.ebi.ac.uk" # host address - port = "4497" # port required for connection - ``` - -1. Alternatively, you can also provide credentials in "secrets.toml" as: - - ```toml - [sources.sql_database] - credentials="mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam" - ``` - > See - > [pipeline example](https://github.com/dlt-hub/verified-sources/blob/master/sources/sql_database_pipeline.py) - > for details. - -1. Finally, follow the instructions in [Destinations](../destinations/) to add credentials for your chosen destination. This will ensure that your data is properly routed. - -For more information, read the [General Usage: Credentials.](../../general-usage/credentials) - -#### Credentials format - -`sql_database` uses SQLAlchemy to create database connections and reflect table schemas. You can pass credentials using -[database urls](https://docs.sqlalchemy.org/en/20/core/engines.html#database-urls). For example: - -"mysql+pymysql://rfamro:PWD@mysql-rfam-public.ebi.ac.uk:4497/Rfam"` - -will connect to `myssql` database with a name `Rfam` using `pymysql` dialect. The database host is at `mysql-rfam-public.ebi.ac.uk`, port `4497`. -User name is `rfmaro` and password is `PWD`. - -3. ### Run the pipeline - -1. Install the necessary dependencies by running the following command: - - ```sh - pip install -r requirements.txt - ``` - -1. Run the verified source by entering: - - ```sh - python sql_database_pipeline.py - ``` - -1. Make sure that everything is loaded as expected with: - - ```sh - dlt pipeline show - ``` - - :::note - The pipeline_name for the above example is `rfam`, you may also use any - custom name instead. - ::: - -## Source and resource functions -Import `sql_database` and `sql_table` functions as follows: -```py -from sql_database import sql_database, sql_table -``` -and read the docstrings to learn about available options. - -:::tip -We intend our sources to be fully hackable. Feel free to change the code of the source to customize it to your needs -::: - -## Pick the right backend to load table data -Table backends convert stream of rows from database tables into batches in various formats. The default backend **sqlalchemy** is following standard `dlt` behavior of -extracting and normalizing Python dictionaries. We recommend it for smaller tables, initial development work and when minimal dependencies or pure Python environment is required. This backend is also the slowest. -Database tables are structured data and other backends speed up dealing with such data significantly. The **pyarrow** will convert rows into `arrow` tables, has -good performance, preserves exact database types and we recommend it for large tables. - -### **sqlalchemy** backend - -**sqlalchemy** (the default) yields table data as list of Python dictionaries. This data goes through regular extract -and normalize steps and does not require additional dependencies to be installed. It is the most robust (works with any destination, correctly represents data types) but also the slowest. You can use `reflection_level="full_with_precision"` to pass exact database types to `dlt` schema. - -### **pyarrow** backend - -**pyarrow** yields data as Arrow tables. It uses **SqlAlchemy** to read rows in batches but then immediately converts them into `ndarray`, transposes it and uses to set columns in an arrow table. This backend always fully -reflects the database table and preserves original types ie. **decimal** / **numeric** will be extracted without loss of precision. If the destination loads parquet files, this backend will skip `dlt` normalizer and you can gain two orders of magnitude (20x - 30x) speed increase. - -Note that if **pandas** is installed, we'll use it to convert SqlAlchemy tuples into **ndarray** as it seems to be 20-30% faster than using **numpy** directly. - -```py -import sqlalchemy as sa -pipeline = dlt.pipeline( - pipeline_name="rfam_cx", destination="postgres", dataset_name="rfam_data_arrow" -) - -def _double_as_decimal_adapter(table: sa.Table) -> None: - """Emits decimals instead of floats.""" - for column in table.columns.values(): - if isinstance(column.type, sa.Float): - column.type.asdecimal = False - -sql_alchemy_source = sql_database( - "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam?&binary_prefix=true", - backend="pyarrow", - table_adapter_callback=_double_as_decimal_adapter -).with_resources("family", "genome") - -info = pipeline.run(sql_alchemy_source) -print(info) -``` - -### **pandas** backend - -**pandas** backend yield data as data frames using the `pandas.io.sql` module. `dlt` use **pyarrow** dtypes by default as they generate more stable typing. - -With default settings, several database types will be coerced to dtypes in yielded data frame: -* **decimal** are mapped to doubles so it is possible to lose precision. -* **date** and **time** are mapped to strings -* all types are nullable. - -Note: `dlt` will still use the reflected source database types to create destination tables. It is up to the destination to reconcile / parse -type differences. Most of the destinations will be able to parse date/time strings and convert doubles into decimals (Please note that you' still lose precision on decimals with default settings.). **However we strongly suggest -not to use pandas backend if your source tables contain date, time or decimal columns** - -Example: Use `backend_kwargs` to pass [backend-specific settings](https://pandas.pydata.org/docs/reference/api/pandas.read_sql_table.html) ie. `coerce_float`. Internally dlt uses `pandas.io.sql._wrap_result` to generate panda frames. - -```py -import sqlalchemy as sa -pipeline = dlt.pipeline( - pipeline_name="rfam_cx", destination="postgres", dataset_name="rfam_data_pandas_2" -) - -def _double_as_decimal_adapter(table: sa.Table) -> None: - """Emits decimals instead of floats.""" - for column in table.columns.values(): - if isinstance(column.type, sa.Float): - column.type.asdecimal = True - -sql_alchemy_source = sql_database( - "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam?&binary_prefix=true", - backend="pandas", - table_adapter_callback=_double_as_decimal_adapter, - chunk_size=100000, - # set coerce_float to False to represent them as string - backend_kwargs={"coerce_float": False, "dtype_backend": "numpy_nullable"}, -).with_resources("family", "genome") - -info = pipeline.run(sql_alchemy_source) -print(info) -``` - -### **connectorx** backend -[connectorx](https://sfu-db.github.io/connector-x/intro.html) backend completely skips **sqlalchemy** when reading table rows, in favor of doing that in rust. This is claimed to be significantly faster than any other method (confirmed only on postgres - see next chapter). With the default settings it will emit **pyarrow** tables, but you can configure it via **backend_kwargs**. - -There are certain limitations when using this backend: -* it will ignore `chunk_size`. **connectorx** cannot yield data in batches. -* in many cases it requires a connection string that differs from **sqlalchemy** connection string. Use `conn` argument in **backend_kwargs** to set it up. -* it will convert **decimals** to **doubles** so you'll will lose precision. -* nullability of the columns is ignored (always true) -* it uses different database type mappings for each database type. [check here for more details](https://sfu-db.github.io/connector-x/databases.html) -* JSON fields (at least those coming from postgres) are double wrapped in strings. Here's a transform to be added with `add_map` that will unwrap it: - -```py -from sources.sql_database.helpers import unwrap_json_connector_x -``` - -Note: dlt will still use the reflected source database types to create destination tables. It is up to the destination to reconcile / parse type differences. Please note that you' still lose precision on decimals with default settings. - -```py -"""Uses unsw_flow dataset (~2mln rows, 25+ columns) to test connectorx speed""" -import os -from dlt.destinations import filesystem - -unsw_table = sql_table( - "postgresql://loader:loader@localhost:5432/dlt_data", - "unsw_flow_7", - "speed_test", - # this is ignored by connectorx - chunk_size=100000, - backend="connectorx", - # keep source data types - reflection_level="full_with_precision", - # just to demonstrate how to setup a separate connection string for connectorx - backend_kwargs={"conn": "postgresql://loader:loader@localhost:5432/dlt_data"} -) - -pipeline = dlt.pipeline( - pipeline_name="unsw_download", - destination=filesystem(os.path.abspath("../_storage/unsw")), - progress="log", - dev_mode=True, -) - -info = pipeline.run( - unsw_table, - dataset_name="speed_test", - table_name="unsw_flow", - loader_file_format="parquet", -) -print(info) -``` -With dataset above and local postgres instance, connectorx is 2x faster than pyarrow backend. - -### Notes on source databases - -#### Oracle -1. When using **oracledb** dialect in thin mode we are getting protocol errors. Use thick mode or **cx_oracle** (old) client. -2. Mind that **sqlalchemy** translates Oracle identifiers into lower case! Keep the default `dlt` naming convention (`snake_case`) when loading data. We'll support more naming conventions soon. -3. Connectorx is for some reason slower for Oracle than `pyarrow` backend. - -#### DB2 -1. Mind that **sqlalchemy** translates DB2 identifiers into lower case! Keep the default `dlt` naming convention (`snake_case`) when loading data. We'll support more naming conventions soon. -2. DB2 `DOUBLE` type is mapped to `Numeric` SqlAlchemy type with default precision, still `float` python types are returned. That requires `dlt` to perform additional casts. The cost of the cast however is minuscule compared to the cost of reading rows from database - -#### MySQL -1. SqlAlchemy dialect converts doubles to decimals, we disable that behavior via table adapter in our demo pipeline - -#### Postgres / MSSQL -No issues found. Postgres is the only backend where we observed 2x speedup with connector x. On other db systems it performs same as `pyarrrow` backend or slower. - -### Notes on data types - -#### JSON -JSON data type is represented as Python object for the **sqlalchemy** backend and as JSON string for the **pyarrow** backend. Currently it does not work correctly -with **pandas** and **connector-x** which cast Python objects to str generating invalid JSON strings that cannot be loaded into destination. - -#### UUID -UUIDs are represented as string by default. You can switch that behavior by using table adapter callback and modifying properties of the UUID type for a particular column. - - -## Incremental Loading -Efficient data management often requires loading only new or updated data from your SQL databases, rather than reprocessing the entire dataset. This is where incremental loading comes into play. - -Incremental loading uses a cursor column (e.g., timestamp or auto-incrementing ID) to load only data newer than a specified initial value, enhancing efficiency by reducing processing time and resource use. - - -### Configuring Incremental Loading -1. **Choose a Cursor Column**: Identify a column in your SQL table that can serve as a reliable indicator of new or updated rows. Common choices include timestamp columns or auto-incrementing IDs. -1. **Set an Initial Value**: Choose a starting value for the cursor to begin loading data. This could be a specific timestamp or ID from which you wish to start loading data. -1. **Deduplication**: When using incremental loading, the system automatically handles the deduplication of rows based on the primary key (if available) or row hash for tables without a primary key. -1. **Set end_value for backfill**: Set `end_value` if you want to backfill data from -certain range. -1. **Order returned rows**. Set `row_order` to `asc` or `desc` to order returned rows. - -#### Incremental Loading Example -1. Consider a table with a `last_modified` timestamp column. By setting this column as your cursor and specifying an - initial value, the loader generates a SQL query filtering rows with `last_modified` values greater than the specified initial value. - - ```py - from sql_database import sql_table - from datetime import datetime - - # Example: Incrementally loading a table based on a timestamp column - table = sql_table( - table='your_table_name', - incremental=dlt.sources.incremental( - 'last_modified', # Cursor column name - initial_value=datetime(2024, 1, 1) # Initial cursor value - ) - ) - - info = pipeline.extract(table, write_disposition="merge") - print(info) - ``` - -1. To incrementally load the "family" table using the sql_database source method: - - ```py - source = sql_database().with_resources("family") - #using the "updated" field as an incremental field using initial value of January 1, 2022, at midnight - source.family.apply_hints(incremental=dlt.sources.incremental("updated"),initial_value=pendulum.DateTime(2022, 1, 1, 0, 0, 0)) - #running the pipeline - info = pipeline.run(source, write_disposition="merge") - print(info) - ``` - In this example, we load data from the `family` table, using the `updated` column for incremental loading. In the first run, the process loads all data starting from midnight (00:00:00) on January 1, 2022. Subsequent runs perform incremental loading, guided by the values in the `updated` field. - -1. To incrementally load the "family" table using the 'sql_table' resource. - - ```py - family = sql_table( - table="family", - incremental=dlt.sources.incremental( - "updated", initial_value=pendulum.datetime(2022, 1, 1, 0, 0, 0) - ), - ) - # Running the pipeline - info = pipeline.extract(family, write_disposition="merge") - print(info) - ``` - - This process initially loads all data from the `family` table starting at midnight on January 1, 2022. For later runs, it uses the `updated` field for incremental loading as well. - - :::info - * For merge write disposition, the source table needs a primary key, which `dlt` automatically sets up. - * `apply_hints` is a powerful method that enables schema modifications after resource creation, like adjusting write disposition and primary keys. You can choose from various tables and use `apply_hints` multiple times to create pipelines with merged, appended, or replaced resources. - ::: - -## Run on Airflow -When running on Airflow -1. Use `dlt` [Airflow Helper](../../walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer.md#2-modify-dag-file) to create tasks from `sql_database` source. You should be able to run table extraction in parallel with `parallel-isolated` source->DAG conversion. -2. Reflect tables at runtime with `defer_table_reflect` argument. -3. Set `allow_external_schedulers` to load data using [Airflow intervals](../../general-usage/incremental-loading.md#using-airflow-schedule-for-backfill-and-incremental-loading). - -## Parallel extraction -You can extract each table in a separate thread (no multiprocessing at this point). This will decrease loading time if your queries take time to execute or your network latency/speed is low. -```py -database = sql_database().parallelize() -table = sql_table().parallelize() -``` - -## Column reflection - -Columns and their data types are reflected with SQLAlchemy. The SQL types are then mapped to `dlt` types. -Most types are supported. - -The `reflection_level` argument controls how much information is reflected: - -- `reflection_level = "minimal"`: Only column names and nullability are detected. Data types are inferred from the data. -- `reflection_level = "full"`: Column names, nullability, and data types are detected. For decimal types we always add precision and scale. **This is the default.** -- `reflection_level = "full_with_precision"`: Column names, nullability, data types, and precision/scale are detected, also for types like text and binary. Integer sizes are set to bigint and to int for all other types. - -If the SQL type is unknown or not supported by `dlt` the column is skipped when using the `pyarrow` backend. -In other backend the type is inferred from data regardless of `reflection_level`, this often works, some types are coerced to strings -and `dataclass` based values from sqlalchemy are inferred as `complex` (JSON in most destinations). - -:::tip -If you use **full** (and above) reflection level you may encounter a situation where the data returned by sql alchemy or pyarrow backend -does not match the reflected data types. Most common symptoms are: -1. The destination complains that it cannot cast one type to another for a certain column. For example `connector-x` returns TIME in nanoseconds -and BigQuery sees it as bigint and fails to load. -2. You get `SchemaCorruptedException` or other coercion error during `normalize` step. -In that case you may try **minimal** reflection level where all data types are inferred from the returned data. From our experience this prevents -most of the coercion problems. -::: - -You can also override the sql type by passing a `type_adapter_callback` function. -This function takes an `sqlalchemy` data type and returns a new type (or `None` to force the column to be inferred from the data). - -This is useful for example when: -- You're loading a data type which is not supported by the destination (e.g. you need JSON type columns to be coerced to string) -- You're using an sqlalchemy dialect which uses custom types that don't inherit from standard sqlalchemy types. -- For certain types you prefer `dlt` to infer data type from the data and you return `None` - -Example, when loading timestamps from Snowflake you can make sure they translate to `timestamp` columns in the result schema: - -```py -import dlt -from snowflake.sqlalchemy import TIMESTAMP_NTZ -import sqlalchemy as sa - -def type_adapter_callback(sql_type): - if isinstance(sql_type, TIMESTAMP_NTZ): # Snowflake does not inherit from sa.DateTime - return sa.DateTime(timezone=True) - return sql_type # Use default detection for other types - -source = sql_database( - "snowflake://user:password@account/database?&warehouse=WH_123", - reflection_level="full", - type_adapter_callback=type_adapter_callback, - backend="pyarrow" -) - -dlt.pipeline("demo").run(source) -``` - -## Extended configuration -You are able to configure most of the arguments to `sql_database` and `sql_table` via toml files and environment variables. This is particularly useful with `sql_table` -because you can maintain a separate configuration for each table (below we show **secrets.toml** and **config.toml**, you are free to combine them into one.): -```toml -[sources.sql_database] -credentials="mssql+pyodbc://loader.database.windows.net/dlt_data?trusted_connection=yes&driver=ODBC+Driver+17+for+SQL+Server" -``` - -```toml -[sources.sql_database.chat_message] -backend="pandas" -chunk_size=1000 - -[sources.sql_database.chat_message.incremental] -cursor_path="updated_at" -``` -Example above will setup **backend** and **chunk_size** for a table with name **chat_message**. It will also enable incremental loading on a column named **updated_at**. -Table resource is instantiated as follows: -```py -table = sql_table(table="chat_message", schema="data") -``` - -Similarly, you can configure `sql_database` source. -```toml -[sources.sql_database] -credentials="mssql+pyodbc://loader.database.windows.net/dlt_data?trusted_connection=yes&driver=ODBC+Driver+17+for+SQL+Server" -schema="data" -backend="pandas" -chunk_size=1000 - -[sources.sql_database.chat_message.incremental] -cursor_path="updated_at" -``` -Note that we are able to configure incremental loading per table, even if it is a part of a dlt source. Source below will extract data using **pandas** backend -with **chunk_size** 1000. **chat_message** table will load data incrementally using **updated_at** column. All other tables will load fully. -```py -database = sql_database() -``` - -You can configure all the arguments this way (except adapter callback function). [Standard dlt rules apply](https://dlthub.com/docs/general-usage/credentials/configuration#configure-dlt-sources-and-resources). You can use environment variables [by translating the names properly](https://dlthub.com/docs/general-usage/credentials/config_providers#toml-vs-environment-variables) ie. -```sh -SOURCES__SQL_DATABASE__CREDENTIALS="mssql+pyodbc://loader.database.windows.net/dlt_data?trusted_connection=yes&driver=ODBC+Driver+17+for+SQL+Server" -SOURCES__SQL_DATABASE__BACKEND=pandas -SOURCES__SQL_DATABASE__CHUNK_SIZE=1000 -SOURCES__SQL_DATABASE__CHAT_MESSAGE__INCREMENTAL__CURSOR_PATH=updated_at -``` - -### Configuring incremental loading -`dlt.sources.incremental` class is a [config spec](https://dlthub.com/docs/general-usage/credentials/config_specs) and can be configured like any other spec, here's an example that sets all possible options: -```toml -[sources.sql_database.chat_message.incremental] -cursor_path="updated_at" -initial_value=2024-05-27T07:32:00Z -end_value=2024-05-28T07:32:00Z -row_order="asc" -allow_external_schedulers=false -``` -Please note that we specify date times in **toml** as initial and end value. For env variables only strings are currently supported. - - -### Use SqlAlchemy Engine as credentials -You are able to pass an instance of **SqlAlchemy** `Engine` instance instead of credentials: -```py -from sqlalchemy import create_engine - -engine = create_engine("mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam") -table = sql_table(engine, table="chat_message", schema="data") -``` -Engine is used by `dlt` to open database connections and can work across multiple threads so is compatible with `parallelize` setting of dlt sources and resources. - - -## Troubleshooting - -### Connect to mysql with SSL -Here, we use the `mysql` and `pymysql` dialects to set up an SSL connection to a server, with all information taken from the [SQLAlchemy docs](https://docs.sqlalchemy.org/en/14/dialects/mysql.html#ssl-connections). - -1. To enforce SSL on the client without a client certificate you may pass the following DSN: - - ```toml - sources.sql_database.credentials="mysql+pymysql://root:@:3306/mysql?ssl_ca=" - ``` - -1. You can also pass the server's public certificate (potentially bundled with your pipeline) and disable host name checks: - - ```toml - sources.sql_database.credentials="mysql+pymysql://root:@:3306/mysql?ssl_ca=server-ca.pem&ssl_check_hostname=false" - ``` - -1. For servers requiring a client certificate, provide the client's private key (a secret value). In Airflow, this is usually saved as a variable and exported to a file before use. The server certificate is omitted in the example below: - - ```toml - sources.sql_database.credentials="mysql+pymysql://root:@35.203.96.191:3306/mysql?ssl_ca=&ssl_cert=client-cert.pem&ssl_key=client-key.pem" - ``` - -### SQL Server connection options - -**To connect to an `mssql` server using Windows authentication**, include `trusted_connection=yes` in the connection string. - -```toml -sources.sql_database.credentials="mssql+pyodbc://loader.database.windows.net/dlt_data?trusted_connection=yes&driver=ODBC+Driver+17+for+SQL+Server" -``` - -**To connect to a local sql server instance running without SSL** pass `encrypt=no` parameter: -```toml -sources.sql_database.credentials="mssql+pyodbc://loader:loader@localhost/dlt_data?encrypt=no&driver=ODBC+Driver+17+for+SQL+Server" -``` - -**To allow self signed SSL certificate** when you are getting `certificate verify failed:unable to get local issuer certificate`: -```toml -sources.sql_database.credentials="mssql+pyodbc://loader:loader@localhost/dlt_data?TrustServerCertificate=yes&driver=ODBC+Driver+17+for+SQL+Server" -``` - -***To use long strings (>8k) and avoid collation errors**: -```toml -sources.sql_database.credentials="mssql+pyodbc://loader:loader@localhost/dlt_data?LongAsMax=yes&driver=ODBC+Driver+17+for+SQL+Server" -``` - -## Customizations -### Transform the data in Python before it is loaded - -You have direct access to all resources (that represent tables) and you can modify hints, add python transforms, parallelize execution etc. as for any other -resource. Below we show you an example on how to pseudonymize the data before it is loaded by using deterministic hashing. - -1. Configure the pipeline by specifying the pipeline name, destination, and dataset as follows: - - ```py - pipeline = dlt.pipeline( - pipeline_name="rfam", # Use a custom name if desired - destination="duckdb", # Choose the appropriate destination (e.g., duckdb, redshift, post) - dataset_name="rfam_data" # Use a custom name if desired - ) - ``` - -1. Pass your credentials using any of the methods [described above](#add-credentials). - -1. To load the entire database, use the `sql_database` source as: - - ```py - source = sql_database() - info = pipeline.run(source, write_disposition="replace") - print(info) - ``` - -1. If you just need the "family" table, use: - - ```py - source = sql_database().with_resources("family") - #running the pipeline - info = pipeline.run(source, write_disposition="replace") - print(info) - ``` - -1. To pseudonymize columns and hide personally identifiable information (PII), refer to the - [documentation](https://dlthub.com/docs/general-usage/customising-pipelines/pseudonymizing_columns). - As an example, here's how to pseudonymize the "rfam_acc" column in the "family" table: - - ```py - import hashlib - - def pseudonymize_name(doc): - ''' - Pseudonmyisation is a deterministic type of PII-obscuring - Its role is to allow identifying users by their hash, - without revealing the underlying info. - ''' - # add a constant salt to generate - salt = 'WI@N57%zZrmk#88c' - salted_string = doc['rfam_acc'] + salt - sh = hashlib.sha256() - sh.update(salted_string.encode()) - hashed_string = sh.digest().hex() - doc['rfam_acc'] = hashed_string - return doc - - pipeline = dlt.pipeline( - # Configure the pipeline - ) - # using sql_database source to load family table and pseudonymize the column "rfam_acc" - source = sql_database().with_resources("family") - # modify this source instance's resource - source = source.family.add_map(pseudonymize_name) - # Run the pipeline. For a large db this may take a while - info = pipeline.run(source, write_disposition="replace") - print(info) - ``` - -1. To exclude columns, such as the "rfam_id" column from the "family" table before loading: - - ```py - def remove_columns(doc): - del doc["rfam_id"] - return doc - - pipeline = dlt.pipeline( - # Configure the pipeline - ) - # using sql_database source to load family table and remove the column "rfam_id" - source = sql_database().with_resources("family") - # modify this source instance's resource - source = source.family.add_map(remove_columns) - # Run the pipeline. For a large db this may take a while - info = pipeline.run(source, write_disposition="replace") - print(info) - ``` - -1. Remember to keep the pipeline name and destination dataset name consistent. The pipeline name is crucial for retrieving the [state](https://dlthub.com/docs/general-usage/state) from the last run, which is essential for incremental loading. Altering these names could initiate a "[dev_mode](https://dlthub.com/docs/general-usage/pipeline#do-experiments-with-dev-mode)", interfering with the metadata tracking necessary for [incremental loads](https://dlthub.com/docs/general-usage/incremental-loading). - - diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/advanced.md b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/advanced.md new file mode 100644 index 0000000000..7ff08f8095 --- /dev/null +++ b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/advanced.md @@ -0,0 +1,174 @@ +--- +title: Advanced +description: advance configuration and usage of the sql_database source +keywords: [sql connector, sql database pipeline, sql database] +--- + +import Header from '../_source-info-header.md'; + +# Advanced Usage + +
+ +## Incremental Loading + +Efficient data management often requires loading only new or updated data from your SQL databases, rather than reprocessing the entire dataset. This is where incremental loading comes into play. + +Incremental loading uses a cursor column (e.g., timestamp or auto-incrementing ID) to load only data newer than a specified initial value, enhancing efficiency by reducing processing time and resource use. Read [here](https://dlthub.com/docs/walkthroughs/sql-incremental-configuration) for more details on incremental loading with `dlt`. + + +#### How to configure +1. **Choose a Cursor Column**: Identify a column in your SQL table that can serve as a reliable indicator of new or updated rows. Common choices include timestamp columns or auto-incrementing IDs. +1. **Set an Initial Value**: Choose a starting value for the cursor to begin loading data. This could be a specific timestamp or ID from which you wish to start loading data. +1. **Deduplication**: When using incremental loading, the system automatically handles the deduplication of rows based on the primary key (if available) or row hash for tables without a primary key. +1. **Set end_value for backfill**: Set `end_value` if you want to backfill data from +certain range. +1. **Order returned rows**. Set `row_order` to `asc` or `desc` to order returned rows. + +#### Examples + +**1. Incremental loading with the resource `sql_table`** + Consider a table "family" with a timestamp column `last_modified` that indicates when a row was last modified. To ensure that only rows modified after midnight (00:00:00) on January 1, 2024, are loaded, you would set `last_modified` timestamp as the cursor as follows: + ```py + from sql_database import sql_table + from datetime import datetime + + # Example: Incrementally loading a table based on a timestamp column + table = sql_table( + table='family', + incremental=dlt.sources.incremental( + 'last_modified', # Cursor column name + initial_value=pendulum.DateTime(2024, 1, 1, 0, 0, 0) # Initial cursor value + ) + ) + + info = pipeline.extract(table, write_disposition="merge") + print(info) + ``` + Behind the scene, the loader generates a SQL query filtering rows with `last_modified` values greater than the incremental value. In the first run, this is the initial value (midnight (00:00:00) January 1, 2024). + In subsequent runs, it is the latest value of `last_modified` that `dlt` stores in [state](https://dlthub.com/docs/general-usage/state). + +**2. Incremental loading with the source `sql_database`** + To achieve the same using the `sql_database` source, you would specify your cursor as follows: + + ```py + source = sql_database().with_resources("family") + #using the "last_modified" field as an incremental field using initial value of midnight January 1, 2024 + source.family.apply_hints(incremental=dlt.sources.incremental("updated", initial_value=pendulum.DateTime(2022, 1, 1, 0, 0, 0))) + #running the pipeline + info = pipeline.run(source, write_disposition="merge") + print(info) + ``` + + :::info + * When using "merge" write disposition, the source table needs a primary key, which `dlt` automatically sets up. + * `apply_hints` is a powerful method that enables schema modifications after resource creation, like adjusting write disposition and primary keys. You can choose from various tables and use `apply_hints` multiple times to create pipelines with merged, appended, or replaced resources. + ::: + +## Parallelized extraction + +You can extract each table in a separate thread (no multiprocessing at this point). This will decrease loading time if your queries take time to execute or your network latency/speed is low. To enable this, declare your sources/resources as follows: +```py +database = sql_database().parallelize() +table = sql_table().parallelize() +``` + +## Column reflection +Column reflection is the automatic detection and retrieval of column metadata like column names, constraints, data types etc. Columns and their data types are reflected with SQLAlchemy. The SQL types are then mapped to `dlt` types. +Depending on the selected backend, some of the types might require additional processing. + +The `reflection_level` argument controls how much information is reflected: + +- `reflection_level = "minimal"`: Only column names and nullability are detected. Data types are inferred from the data. +- `reflection_level = "full"`: Column names, nullability, and data types are detected. For decimal types we always add precision and scale. **This is the default.** +- `reflection_level = "full_with_precision"`: Column names, nullability, data types, and precision/scale are detected, also for types like text and binary. Integer sizes are set to bigint and to int for all other types. + +If the SQL type is unknown or not supported by `dlt`, then, in the pyarrow backend, the column will be skipped, whereas in the other backends the type will be inferred directly from the data irrespective of the `reflection_level` specified. In the latter case, this often means that some types are coerced to strings and `dataclass` based values from sqlalchemy are inferred as `json` (JSON in most destinations). +:::tip +If you use reflection level **full** / **full_with_precision** you may encounter a situation where the data returned by sqlalchemy or pyarrow backend does not match the reflected data types. Most common symptoms are: +1. The destination complains that it cannot cast one type to another for a certain column. For example `connector-x` returns TIME in nanoseconds +and BigQuery sees it as bigint and fails to load. +2. You get `SchemaCorruptedException` or other coercion error during the `normalize` step. +In that case you may try **minimal** reflection level where all data types are inferred from the returned data. From our experience this prevents +most of the coercion problems. +::: + +You can also override the sql type by passing a `type_adapter_callback` function. This function takes a `SQLAlchemy` data type as input and returns a new type (or `None` to force the column to be inferred from the data) as output. + +This is useful, for example, when: +- You're loading a data type which is not supported by the destination (e.g. you need JSON type columns to be coerced to string) +- You're using a sqlalchemy dialect which uses custom types that don't inherit from standard sqlalchemy types. +- For certain types you prefer `dlt` to infer data type from the data and you return `None` + +In the following example, when loading timestamps from Snowflake, you ensure that they get translated into standard sqlalchemy `timestamp` columns in the resultant schema: + +```py +import dlt +from snowflake.sqlalchemy import TIMESTAMP_NTZ +import sqlalchemy as sa + +def type_adapter_callback(sql_type): + if isinstance(sql_type, TIMESTAMP_NTZ): # Snowflake does not inherit from sa.DateTime + return sa.DateTime(timezone=True) + return sql_type # Use default detection for other types + +source = sql_database( + "snowflake://user:password@account/database?&warehouse=WH_123", + reflection_level="full", + type_adapter_callback=type_adapter_callback, + backend="pyarrow" +) + +dlt.pipeline("demo").run(source) +``` + +## Configuring with toml/environment variables +You can set most of the arguments of `sql_database()` and `sql_table()` directly in the `.toml` files and/or as environment variables. `dlt` automatically injects these values into the pipeline script. + +This is particularly useful with `sql_table()` because you can maintain a separate configuration for each table (below we show **secrets.toml** and **config.toml**, you are free to combine them into one): + +The examples below show how you can set arguments in any of the `.toml` files (`secrets.toml` or `config.toml`): +1. Specifying connection string: + ```toml + [sources.sql_database] + credentials="mssql+pyodbc://loader.database.windows.net/dlt_data?trusted_connection=yes&driver=ODBC+Driver+17+for+SQL+Server" + ``` +2. Setting parameters like backend, chunk_size, and incremental column for the table `chat_message`: + ```toml + [sources.sql_database.chat_message] + backend="pandas" + chunk_size=1000 + + [sources.sql_database.chat_message.incremental] + cursor_path="updated_at" + ``` + This is especially useful with `sql_table()` in a situation where you may want to run this resource for multiple tables. Setting parameters like this would then give you a clean way of maintaing separate configurations for each table. + +3. Handling separate configurations for database and individual tables + When using the `sql_database()` source, you can separately configure the parameters for the database and for the individual tables. + ```toml + [sources.sql_database] + credentials="mssql+pyodbc://loader.database.windows.net/dlt_data?trusted_connection=yes&driver=ODBC+Driver+17+for+SQL+Server" + schema="data" + backend="pandas" + chunk_size=1000 + + [sources.sql_database.chat_message.incremental] + cursor_path="updated_at" + ``` + + The resulting source created below will extract data using **pandas** backend with **chunk_size** 1000. The table **chat_message** will load data incrementally using **updated_at** column. All the other tables will not use incremental loading, and will instead load the full data. + + ```py + database = sql_database() + ``` + +You'll be able to configure all the arguments this way (except adapter callback function). [Standard dlt rules apply](https://dlthub.com/docs/general-usage/credentials/configuration#configure-dlt-sources-and-resources). + +It is also possible to set these arguments as environment variables [using the proper naming convention](https://dlthub.com/docs/general-usage/credentials/config_providers#toml-vs-environment-variables): +```sh +SOURCES__SQL_DATABASE__CREDENTIALS="mssql+pyodbc://loader.database.windows.net/dlt_data?trusted_connection=yes&driver=ODBC+Driver+17+for+SQL+Server" +SOURCES__SQL_DATABASE__BACKEND=pandas +SOURCES__SQL_DATABASE__CHUNK_SIZE=1000 +SOURCES__SQL_DATABASE__CHAT_MESSAGE__INCREMENTAL__CURSOR_PATH=updated_at +``` diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/configuration.md b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/configuration.md new file mode 100644 index 0000000000..88ea268378 --- /dev/null +++ b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/configuration.md @@ -0,0 +1,292 @@ +--- +title: Configuring the SQL Database source +description: configuring the pipeline script, connection, and backend settings in the sql_database source +keywords: [sql connector, sql database pipeline, sql database] +--- + +import Header from '../_source-info-header.md'; + +# Configuration + +
+ +## Configuring the SQL Database source + +`dlt` sources are python scripts made up of source and resource functions that can be easily customized. The SQL Database verified source has the following built-in source and resource: +1. `sql_database`: a `dlt` source which can be used to load multiple tables and views from a SQL database +2. `sql_table`: a `dlt` resource that loads a single table from the SQL database + +Read more about sources and resources here: [General usage: source](../../../general-usage/source.md) and [General usage: resource](../../../general-usage/resource.md). + +### Example usage: + +1. **Load all the tables from a database** + Calling `sql_database()` loads all tables from the database. + + ```py + def load_entire_database() -> None: + + # Define the pipeline + pipeline = dlt.pipeline( + pipeline_name="rfam", + destination='synapse', + dataset_name="rfam_data" + ) + + # Fetch all the tables from the database + source = sql_database() + + # Run the pipeline + info = pipeline.run(source, write_disposition="replace") + + # Print load info + print(info) + ``` + +2. **Load select tables from a database** + Calling `sql_database().with_resources("family", "clan")` loads only the tables `"family"` and `"clan"` from the database. + + ```py + def load_select_tables_from_database() -> None: + + # Define the pipeline + pipeline = dlt.pipeline( + pipeline_name="rfam", + destination="postgres", + dataset_name="rfam_data" + ) + + # Fetch tables "family" and "clan" + source = sql_database().with_resources("family", "clan") + + # Run the pipeline + info = pipeline.run(source) + + # Print load info + print(info) + + ``` + +3. **Load a standalone table** + Calling `sql_table(table="family")` fetches only the table `"family"` + + ```py + def load_select_tables_from_database() -> None: + + # Define the pipeline + pipeline = dlt.pipeline( + pipeline_name="rfam", + destination="duckdb", + dataset_name="rfam_data" + ) + + # Fetch the table "family" + table = sql_table(table="family") + + # Run the pipeline + info = pipeline.run(table) + + # Print load info + print(info) + + ``` + +:::tip +We intend our sources to be fully hackable. Feel free to change the source code of the sources and resources to customize it to your needs. +::: + + +## Configuring the connection + +### Connection string format +`sql_database` uses SQLAlchemy to create database connections and reflect table schemas. You can pass credentials using +[database urls](https://docs.sqlalchemy.org/en/20/core/engines.html#database-urls), which has the general format: + +```py +"dialect+database_type://username:password@server:port/database_name" +``` + +For example, to connect to a MySQL database using the `pymysql` dialect you can use the following connection string: +```py +"mysql+pymysql://rfamro:PWD@mysql-rfam-public.ebi.ac.uk:4497/Rfam" +``` + +Database-specific drivers can be passed into the connection string using query parameters. For example, to connect to Microsoft SQL Server using the ODBC Driver, you would need to pass the driver as a query parameter as follows: + +```py +"mssql+pyodbc://username:password@server/database?driver=ODBC+Driver+17+for+SQL+Server" +``` + + +### Passing connection credentials to the `dlt` pipeline + +There are several options for adding your connection credentials into your `dlt` pipeline: + +#### 1. Setting them in `secrets.toml` or as environment variables (Recommended) + +You can set up credentials using [any method](https://dlthub.com/docs/devel/general-usage/credentials/setup#available-config-providers) supported by `dlt`. We recommend using `.dlt/secrets.toml` or the environment variables. See Step 2 of the [setup](./setup) for how to set credentials inside `secrets.toml`. For more information on passing credentials read [here](https://dlthub.com/docs/devel/general-usage/credentials/setup). + + +#### 2. Passing them directly in the script +It is also possible to explicitly pass credentials inside the source. Example: +```py +from dlt.sources.credentials import ConnectionStringCredentials +from sql_database import sql_table + +credentials = ConnectionStringCredentials( + "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam" +) + +source = sql_table(credentials).with_resource("family") +``` + +:::note +It is recommended to configure credentials in `.dlt/secrets.toml` and to not include any sensitive information in the pipeline code. +::: + +### Other connection options +#### Using SqlAlchemy Engine as credentials +You are able to pass an instance of SqlAlchemy Engine instead of credentials: +```py +from sqlalchemy import create_engine + +engine = create_engine("mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam") +table = sql_table(engine, table="chat_message", schema="data") +``` +This engine is used by `dlt` to open database connections and can work across multiple threads so is compatible with `parallelize` setting of dlt sources and resources. + + +## Configuring the backend + +Table backends convert streams of rows from database tables into batches in various formats. The default backend `SQLAlchemy` follows standard `dlt` behavior of +extracting and normalizing Python dictionaries. We recommend this for smaller tables, initial development work, and when minimal dependencies or a pure Python environment is required. This backend is also the slowest. Other backends make use of the structured data format of the tables and provide significant improvement in speeds. For example, the `PyArrow` backend converts rows into `Arrow` tables, which results in +good performance and preserves exact data types. We recommend using this backend for larger tables. + +### SQLAlchemy + +The `SQLAlchemy` backend (the default) yields table data as a list of Python dictionaries. This data goes through the regular extract +and normalize steps and does not require additional dependencies to be installed. It is the most robust (works with any destination, correctly represents data types) but also the slowest. You can set `reflection_level="full_with_precision"` to pass exact data types to `dlt` schema. + +### PyArrow + +The `PyArrow` backend yields data as `Arrow` tables. It uses `SQLAlchemy` to read rows in batches but then immediately converts them into `ndarray`, transposes it, and sets it as columns in an `Arrow` table. This backend always fully +reflects the database table and preserves original types (i.e. **decimal** / **numeric** data will be extracted without loss of precision). If the destination loads parquet files, this backend will skip `dlt` normalizer and you can gain two orders of magnitude (20x - 30x) speed increase. + +Note that if `pandas` is installed, we'll use it to convert `SQLAlchemy` tuples into `ndarray` as it seems to be 20-30% faster than using `numpy` directly. + +```py +import sqlalchemy as sa +pipeline = dlt.pipeline( + pipeline_name="rfam_cx", destination="postgres", dataset_name="rfam_data_arrow" +) + +def _double_as_decimal_adapter(table: sa.Table) -> None: + """Emits decimals instead of floats.""" + for column in table.columns.values(): + if isinstance(column.type, sa.Float): + column.type.asdecimal = False + +sql_alchemy_source = sql_database( + "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam?&binary_prefix=true", + backend="pyarrow", + table_adapter_callback=_double_as_decimal_adapter +).with_resources("family", "genome") + +info = pipeline.run(sql_alchemy_source) +print(info) +``` + +### pandas + +The `pandas` backend yields data as DataFrames using the `pandas.io.sql` module. `dlt` uses `PyArrow` dtypes by default as they generate more stable typing. + +With the default settings, several data types will be coerced to dtypes in the yielded data frame: +* **decimal** is mapped to double so it is possible to lose precision +* **date** and **time** are mapped to strings +* all types are nullable + +:::note +`dlt` will still use the data types reflected from the source database when creating destination tables. How the type differences resulting from the `pandas` backend are reconciled / parsed is up to the destination. Most of the destinations will be able to parse date/time strings and convert doubles into decimals (Please note that you'll still lose precision on decimals with default settings.). **However we strongly suggest +not to use the** `pandas` **backend if your source tables contain date, time, or decimal columns** +::: + +Internally dlt uses `pandas.io.sql._wrap_result` to generate `pandas` frames. To adjust [pandas-specific settings,](https://pandas.pydata.org/docs/reference/api/pandas.read_sql_table.html) pass it in the `backend_kwargs` parameter. For example, below we set `coerce_float` to `False`: + +```py +import sqlalchemy as sa +pipeline = dlt.pipeline( + pipeline_name="rfam_cx", destination="postgres", dataset_name="rfam_data_pandas_2" +) + +def _double_as_decimal_adapter(table: sa.Table) -> None: + """Emits decimals instead of floats.""" + for column in table.columns.values(): + if isinstance(column.type, sa.Float): + column.type.asdecimal = True + +sql_alchemy_source = sql_database( + "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam?&binary_prefix=true", + backend="pandas", + table_adapter_callback=_double_as_decimal_adapter, + chunk_size=100000, + # set coerce_float to False to represent them as string + backend_kwargs={"coerce_float": False, "dtype_backend": "numpy_nullable"}, +).with_resources("family", "genome") + +info = pipeline.run(sql_alchemy_source) +print(info) +``` + +### ConnectorX +The [`ConnectorX`](https://sfu-db.github.io/connector-x/intro.html) backend completely skips `SQLALchemy` when reading table rows, in favor of doing that in rust. This is claimed to be significantly faster than any other method (validated only on postgres). With the default settings it will emit `PyArrow` tables, but you can configure this by specifying the `return_type` in `backend_kwargs`. (See the [`ConnectorX` docs](https://sfu-db.github.io/connector-x/api.html) for a full list of configurable parameters.) + +There are certain limitations when using this backend: +* it will ignore `chunk_size`. `ConnectorX` cannot yield data in batches. +* in many cases it requires a connection string that differs from the `SQLAlchemy` connection string. Use the `conn` argument in `backend_kwargs` to set this. +* it will convert **decimals** to **doubles**, so you will lose precision. +* nullability of the columns is ignored (always true) +* it uses different mappings for each data type. (Check [here](https://sfu-db.github.io/connector-x/databases.html) for more details.) +* JSON fields (at least those coming from postgres) are double wrapped in strings. To unwrap this, you can pass the in-built transformation function `unwrap_json_connector_x` (for example, with `add_map`): + + ```py + from sources.sql_database.helpers import unwrap_json_connector_x + ``` + +:::note +`dlt` will still use the data types refected from the source database when creating destination tables. It is up to the destination to reconcile / parse type differences. Please note that you'll still lose precision on decimals with default settings. +::: + +```py +"""This example is taken from the benchmarking tests for ConnectorX performed on the UNSW_Flow dataset (~2mln rows, 25+ columns). Full code here: https://github.com/dlt-hub/sql_database_benchmarking""" +import os +from dlt.destinations import filesystem + +unsw_table = sql_table( + "postgresql://loader:loader@localhost:5432/dlt_data", + "unsw_flow_7", + "speed_test", + # this is ignored by connectorx + chunk_size=100000, + backend="connectorx", + # keep source data types + reflection_level="full_with_precision", + # just to demonstrate how to setup a separate connection string for connectorx + backend_kwargs={"conn": "postgresql://loader:loader@localhost:5432/dlt_data"} +) + +pipeline = dlt.pipeline( + pipeline_name="unsw_download", + destination=filesystem(os.path.abspath("../_storage/unsw")), + progress="log", + dev_mode=True, +) + +info = pipeline.run( + unsw_table, + dataset_name="speed_test", + table_name="unsw_flow", + loader_file_format="parquet", +) +print(info) +``` +With the dataset above and a local postgres instance, the `ConnectorX` backend is 2x faster than the `PyArrow` backend. diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/index.md b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/index.md new file mode 100644 index 0000000000..a8146c75fe --- /dev/null +++ b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/index.md @@ -0,0 +1,51 @@ +--- +title: 30+ SQL Databases +description: PostgreSQL, MySQL, MS SQL, BigQuery, Redshift, and more +keywords: [sql connector, sql database pipeline, sql database] +--- +import Header from '../_source-info-header.md'; + +# 30+ SQL Databases + +
+ +SQL databases are management systems (DBMS) that store data in a structured format, commonly used +for efficient and reliable data retrieval. + +The SQL Database verified source loads data to your specified destination using one of the following backends: SQLAlchemy, PyArrow, pandas, or ConnectorX. + +Sources and resources that can be loaded using this verified source are: + +| Name | Description | +| ------------ | -------------------------------------------------------------------- | +| sql_database | Reflects the tables and views in SQL database and retrieves the data | +| sql_table | Retrieves data from a particular SQL database table | +| | | + +:::tip +If you prefer to skip the tutorial and see the code example right away, check out the pipeline example [here](https://github.com/dlt-hub/verified-sources/blob/master/sources/sql_database_pipeline.py). +::: + +### Supported databases + +We support all [SQLAlchemy dialects](https://docs.sqlalchemy.org/en/20/dialects/), which include, but are not limited to, the following database engines: + +* [PostgreSQL](./troubleshooting#postgres--mssql) +* [MySQL](./troubleshooting#mysql) +* SQLite +* [Oracle](./troubleshooting#oracle) +* [Microsoft SQL Server](./troubleshooting#postgres--mssql) +* MariaDB +* [IBM DB2 and Informix](./troubleshooting#db2) +* Google BigQuery +* Snowflake +* Redshift +* Apache Hive and Presto +* SAP Hana +* CockroachDB +* Firebird +* Teradata Vantage + +:::note +Note that there many unofficial dialects, such as [DuckDB](https://duckdb.org/). +::: \ No newline at end of file diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/setup.md b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/setup.md new file mode 100644 index 0000000000..a91ae40028 --- /dev/null +++ b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/setup.md @@ -0,0 +1,76 @@ +--- +title: Setup +description: basic steps for setting up a dlt pipeline for SQL Database +keywords: [sql connector, sql database pipeline, sql database] +--- + +import Header from '../_source-info-header.md'; + +# Setup + +
+ +To connect to your SQL database using `dlt` follow these steps: + +1. Initialize a `dlt` project in the current working directory by running the following command: + + ```sh + dlt init sql_database duckdb + ``` + + This will add necessary files and configurations for a `dlt` pipeline with SQL database as the source and + [DuckDB](../../destinations/duckdb.md) as the destination. + +:::tip +If you'd like to use a different destination, simply replace `duckdb` with the name of your preferred [destination](../../destinations). +::: + +2. Add credentials for your SQL database + + To connect to your SQL database, `dlt` would need to authenticate using necessary credentials. To enable this, paste your credentials in the `secrets.toml` file created inside the `.dlt/` folder in the following format: + ```toml + [sources.sql_database.credentials] + drivername = "mysql+pymysql" # driver name for the database + database = "Rfam" # database name + username = "rfamro" # username associated with the database + host = "mysql-rfam-public.ebi.ac.uk" # host address + port = "4497" # port required for connection + ``` + + Alternatively, you can also authenticate using connection strings: + ```toml + [sources.sql_database.credentials] + credentials="mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam" + ``` + + To learn more about how to add credentials into your `sql_database` pipeline see [here](./configuration#configuring-the-connection). + +3. Add credentials for your destination (if necessary) + + Depending on which [destination](../../destinations) you're loading into, you might also need to add your destination credentials. For more information read the [General Usage: Credentials.](../../../general-usage/credentials) + +4. Install any necessary dependencies + + ```sh + pip install -r requirements.txt + ``` + +5. Run the pipeline + + ```sh + python sql_database_pipeline.py + ``` + + Executing this command will run the example script `sql_database_pipeline.py` created in step 1. In order for this to run successfully you will need to pass the names of the databases and/or tables you wish to load. + See the [section on configuring the sql_database source](./configuration#configuring-the-sql-database-source) for more details. + + +6. Make sure everything is loaded as expected with + ```sh + dlt pipeline show + ``` + + :::note + The pipeline_name for the above example is `rfam`, you may also use any + custom name instead. + ::: \ No newline at end of file diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/troubleshooting.md b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/troubleshooting.md new file mode 100644 index 0000000000..33986fb5a6 --- /dev/null +++ b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/troubleshooting.md @@ -0,0 +1,89 @@ +--- +title: Troubleshooting +description: common troubleshooting use-cases for the sql_database source +keywords: [sql connector, sql database pipeline, sql database] +--- + +import Header from '../_source-info-header.md'; + +# Troubleshooting + +
+ +## Troubleshooting connection + +#### Connecting to MySQL with SSL +Here, we use the `mysql` and `pymysql` dialects to set up an SSL connection to a server, with all information taken from the [SQLAlchemy docs](https://docs.sqlalchemy.org/en/14/dialects/mysql.html#ssl-connections). + +1. To enforce SSL on the client without a client certificate you may pass the following DSN: + + ```toml + sources.sql_database.credentials="mysql+pymysql://root:@:3306/mysql?ssl_ca=" + ``` + +1. You can also pass the server's public certificate (potentially bundled with your pipeline) and disable host name checks: + + ```toml + sources.sql_database.credentials="mysql+pymysql://root:@:3306/mysql?ssl_ca=server-ca.pem&ssl_check_hostname=false" + ``` + +1. For servers requiring a client certificate, provide the client's private key (a secret value). In Airflow, this is usually saved as a variable and exported to a file before use. The server certificate is omitted in the example below: + + ```toml + sources.sql_database.credentials="mysql+pymysql://root:@35.203.96.191:3306/mysql?ssl_ca=&ssl_cert=client-cert.pem&ssl_key=client-key.pem" + ``` + +#### SQL Server connection options + +**To connect to an `mssql` server using Windows authentication**, include `trusted_connection=yes` in the connection string. + +```toml +sources.sql_database.credentials="mssql+pyodbc://loader.database.windows.net/dlt_data?trusted_connection=yes&driver=ODBC+Driver+17+for+SQL+Server" +``` + +**To connect to a local sql server instance running without SSL** pass `encrypt=no` parameter: +```toml +sources.sql_database.credentials="mssql+pyodbc://loader:loader@localhost/dlt_data?encrypt=no&driver=ODBC+Driver+17+for+SQL+Server" +``` + +**To allow self signed SSL certificate** when you are getting `certificate verify failed:unable to get local issuer certificate`: +```toml +sources.sql_database.credentials="mssql+pyodbc://loader:loader@localhost/dlt_data?TrustServerCertificate=yes&driver=ODBC+Driver+17+for+SQL+Server" +``` + +**To use long strings (>8k) and avoid collation errors**: +```toml +sources.sql_database.credentials="mssql+pyodbc://loader:loader@localhost/dlt_data?LongAsMax=yes&driver=ODBC+Driver+17+for+SQL+Server" +``` + +## Troubleshooting backends + +### Notes on specific databases + +#### Oracle +1. When using the `oracledb` dialect in thin mode we are getting protocol errors. Use thick mode or `cx_oracle` (old) client. +2. Mind that `SQLAlchemy` translates Oracle identifiers into lower case! Keep the default `dlt` naming convention (`snake_case`) when loading data. We'll support more naming conventions soon. +3. `Connectorx` is for some reason slower for Oracle than the `PyArrow` backend. + +See [here](https://github.com/dlt-hub/sql_database_benchmarking/tree/main/oracledb#installing-and-setting-up-oracle-db) for information and code on setting up and benchmarking on Oracle. + +#### DB2 +1. Mind that `SQLAlchemy` translates DB2 identifiers into lower case! Keep the default `dlt` naming convention (`snake_case`) when loading data. We'll support more naming conventions soon. +2. The DB2 type `DOUBLE` gets incorrectly mapped to the python type `float` (instead of the `SqlAlchemy` type `Numeric` with default precision). This requires `dlt` to perform additional casts. The cost of the cast, however, is minuscule compared to the cost of reading rows from database. + +See [here](https://github.com/dlt-hub/sql_database_benchmarking/tree/main/db2#installing-and-setting-up-db2) for information and code on setting up and benchmarking on db2. + +#### MySQL +1. The `SqlAlchemy` dialect converts doubles to decimals. (This can be disabled via the table adapter argument as shown in the code example [here](./configuration#pyarrow)) + +#### Postgres / MSSQL +No issues were found for these databases. Postgres is the only backend where we observed 2x speedup with `ConnectorX` (see [here](https://github.com/dlt-hub/sql_database_benchmarking/tree/main/postgres) for the benchmarking code). On other db systems it performs the same as (or some times worse than) the `PyArrow` backend. + +### Notes on specific data types + +#### JSON + +In the `SQLAlchemy` backend JSON data type is represented as a Python object, and in the `PyArrow` backend, it is represented as a JSON string. At present it does not work correctly with `pandas` and `ConnectorX`which cast Python objects to `str`, generating invalid JSON strings that cannot be loaded into destination. + +#### UUID +UUIDs are represented as string by default. You can switch this behavior by using `table_adapter_callback` to modify properties of the UUID type for a particular column. (See the code example [here](./configuration#pyarrow) for how to modify the data type properties of a particular column.) \ No newline at end of file diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/usage.md b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/usage.md new file mode 100644 index 0000000000..ee70e92ea0 --- /dev/null +++ b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/usage.md @@ -0,0 +1,102 @@ +--- +title: Usage +description: basic usage of the sql_database source +keywords: [sql connector, sql database pipeline, sql database] +--- + +import Header from '../_source-info-header.md'; + +# Usage + +
+ +## Applying column-wise filtering on the data being ingested + +By default, the existing source and resource functions, `sql_database` and `sql_table`, ingest all of the records from the source table. But by using `query_adapter_callback`, it is possible to pass a `WHERE` clause inside the underlying `SELECT` statement using the [SQLAlchemy syntax](https://docs.sqlalchemy.org/en/14/core/selectable.html#). Thich enables filtering the data based on specific columns before extract. + +The example below uses `query_adapter_callback` to filter on the column `customer_id` for the table `orders`: + +```py +def query_adapter_callback(query, table): + if table.name == "orders": + # Only select rows where the column customer_id has value 1 + return query.where(table.c.customer_id==1) + # Use the original query for other tables + return query + +source = sql_database( + query_adapter_callback=query_adapter_callback +).with_resources("orders") +``` + +## Transforming the data before load +You have direct access to the extracted data through the resource objects (`sql_table()` or `sql_database().with_resource())`), each of which represents a single SQL table. These objects are generators that yield +individual rows of the table which can be modified by using custom python functions. These functions can be applied to the resource using `add_map`. + +:::note +The PyArrow backend does not yield individual rows rather loads chunks of data as `ndarray`. In this case, the transformation function that goes into `add_map` should be configured to expect an `ndarray` input. +::: + + +Examples: +1. Pseudonymizing data to hide personally identifiable information (PII) before loading it to the destination. (See [here](https://dlthub.com/docs/general-usage/customising-pipelines/pseudonymizing_columns) for more information on pseudonymizing data with `dlt`) + + ```py + import hashlib + + def pseudonymize_name(doc): + ''' + Pseudonmyisation is a deterministic type of PII-obscuring + Its role is to allow identifying users by their hash, + without revealing the underlying info. + ''' + # add a constant salt to generate + salt = 'WI@N57%zZrmk#88c' + salted_string = doc['rfam_acc'] + salt + sh = hashlib.sha256() + sh.update(salted_string.encode()) + hashed_string = sh.digest().hex() + doc['rfam_acc'] = hashed_string + return doc + + pipeline = dlt.pipeline( + # Configure the pipeline + ) + # using sql_database source to load family table and pseudonymize the column "rfam_acc" + source = sql_database().with_resources("family") + # modify this source instance's resource + source = source.family.add_map(pseudonymize_name) + # Run the pipeline. For a large db this may take a while + info = pipeline.run(source, write_disposition="replace") + print(info) + ``` + +2. Excluding unnecessary columns before load + + ```py + def remove_columns(doc): + del doc["rfam_id"] + return doc + + pipeline = dlt.pipeline( + # Configure the pipeline + ) + # using sql_database source to load family table and remove the column "rfam_id" + source = sql_database().with_resources("family") + # modify this source instance's resource + source = source.family.add_map(remove_columns) + # Run the pipeline. For a large db this may take a while + info = pipeline.run(source, write_disposition="replace") + print(info) + ``` + +## Deploying the sql_database pipeline + +You can deploy the `sql_database` pipeline with any of the `dlt` deployment methods, such as [GitHub Actions](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-github-actions), [Airflow](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer), [Dagster](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-dagster) etc. See [here](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline) for a full list of deployment methods. + +### Running on Airflow +When running on Airflow: +1. Use the `dlt` [Airflow Helper](../../../walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer.md#2-modify-dag-file) to create tasks from the `sql_database` source. (If you want to run table extraction in parallel, then you can do this by setting `decompose = "parallel-isolated"` when doing the source->DAG conversion. See [here](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer#2-modify-dag-file) for code example.) +2. Reflect tables at runtime with `defer_table_reflect` argument. +3. Set `allow_external_schedulers` to load data using [Airflow intervals](../../../general-usage/incremental-loading.md#using-airflow-schedule-for-backfill-and-incremental-loading). + diff --git a/docs/website/docs/general-usage/credentials/setup.md b/docs/website/docs/general-usage/credentials/setup.md index 4ab9149bc0..7933bab183 100644 --- a/docs/website/docs/general-usage/credentials/setup.md +++ b/docs/website/docs/general-usage/credentials/setup.md @@ -5,7 +5,7 @@ keywords: [credentials, secrets.toml, secrets, config, configuration, environmen variables, provider] --- -`dlt` automatically extracts configuration settings and secrets based on flexible [naming conventions](setup/#naming-convention). +`dlt` automatically extracts configuration settings and secrets based on flexible [naming conventions](#naming-convention). It then [injects](advanced/#injection-mechanism) these values where needed in functions decorated with `@dlt.source`, `@dlt.resource`, or `@dlt.destination`. @@ -39,15 +39,71 @@ Please make sure your pipeline name contains no whitespace or any other punctuat To keep the naming convention flexible, `dlt` looks for a lot of possible combinations of key names, starting from the most specific possible path. Then, if the value is not found, it removes the right-most section and tries again. -* The most specific possible path for **sources** looks like: +The most specific possible path for **sources** looks like: + + + + +```sh +[.sources..] +="some_value" +``` + + + +```sh +export PIPELINE_NAME__SOURCES__SOURCE_MODULE_NAME__SOURCE_FUNCTION_NAME__ARGUMENT_NAME="some_value" +``` + + + +```py +import os + +os.environ["PIPELINE_NAME__SOURCES__SOURCE_MODULE_NAME__SOURCE_FUNCTION_NAME__ARGUMENT_NAME"] = "some_value" +``` + + + +The most specific possible path for **destinations** looks like: + + + + ```sh -.sources... +[.destination..credentials] +="some_value" ``` + + -* The most specific possible path for **destinations** looks like: ```sh -.destination..credentials. +export PIPELINE_NAME__DESTINATION__DESTINATION_NAME__CREDENTIALS__CREDENTIAL_VALUE="some_value" +``` + + + +```py +import os + +os.environ["PIPELINE_NAME__DESTINATION__DESTINATION_NAME__CREDENTIALS__CREDENTIAL_VALUE"] = "some_value" ``` + + ### Example @@ -86,7 +142,7 @@ project_id = "" ### Credential types -In most cases, credentials are just key-value pairs, but in some cases, the actual structure of [credentials](complex_types) could be quite complex and support several ways of setting it up. +In most cases, credentials are just key-value pairs, but in some cases, the actual structure of [credentials](./complex_types) could be quite complex and support several ways of setting it up. For example, to connect to a `sql_database` source, you can either set up a connection string: ```toml @@ -106,7 +162,7 @@ warehouse = "warehouse_name" role = "role" ``` -`dlt` can work with both ways and convert one to another. To learn more about which credential types are supported, visit the [complex credential types](complex_types) page. +`dlt` can work with both ways and convert one to another. To learn more about which credential types are supported, visit the [complex credential types](./complex_types) page. ## Environment variables diff --git a/docs/website/docs/general-usage/destination-tables.md b/docs/website/docs/general-usage/destination-tables.md index 7df19eff30..405fd4379d 100644 --- a/docs/website/docs/general-usage/destination-tables.md +++ b/docs/website/docs/general-usage/destination-tables.md @@ -1,7 +1,7 @@ --- title: Destination tables description: Understanding the tables created in the destination database -keywords: [destination tables, loaded data, data structure, schema, table, child table, load package, load id, lineage, staging dataset, versioned dataset] +keywords: [destination tables, loaded data, data structure, schema, table, nested table, load package, load id, lineage, staging dataset, versioned dataset] --- # Destination tables @@ -84,7 +84,7 @@ connecting to the database directly. ::: -## Child and parent tables +## Nested tables Now let's look at a more complex example: @@ -117,7 +117,7 @@ pipeline = dlt.pipeline( load_info = pipeline.run(data, table_name="users") ``` -Running this pipeline will create two tables in the destination, `users` and `users__pets`. The `users` table will contain the top-level data, and the `users__pets` table will contain the child data. Here is what the tables may look like: +Running this pipeline will create two tables in the destination, `users` (**root table**) and `users__pets` (**nested table**). The `users` table will contain the top-level data, and the `users__pets` table will contain the data nested in the Python lists. Here is what the tables may look like: **mydata.users** @@ -134,21 +134,17 @@ Running this pipeline will create two tables in the destination, `users` and `us | 2 | Spot | dog | 9uxh36VU9lqKpw | wX3f5vn801W16A | 1 | | 3 | Fido | dog | pe3FVtCWz8VuNA | rX8ybgTeEmAmmA | 0 | -When creating a database schema, dlt recursively unpacks nested structures into relational tables, -creating and linking children and parent tables. +When inferring a database schema, `dlt` maps the structure of Python objects (ie. from parsed JSON files) into nested tables and creates +references between them. This is how it works: -1. Each row in all (top level and child) data tables created by `dlt` contains a `UNIQUE` column named `_dlt_id`. -1. Each child table contains a `FOREIGN KEY` column `_dlt_parent_id` linking to a particular row (`_dlt_id`) of a parent table. -1. Rows in child tables come from the lists: `dlt` stores the position of each item in the list in `_dlt_list_idx`. -1. For tables that are loaded with the `merge` write disposition, we add a root key column `_dlt_root_id`, which links the child table to a row in the top-level table. +1. Each row in all (root and nested) data tables created by `dlt` contains a unique column named `_dlt_id` (**row key**). +1. Each nested table contains column named `_dlt_parent_id` referencing to a particular row (`_dlt_id`) of a parent table (**parent key**). +1. Rows in nested tables come from the Python lists: `dlt` stores the position of each item in the list in `_dlt_list_idx`. +1. For nested tables that are loaded with the `merge` write disposition, we add a **root key** column `_dlt_root_id`, which references the child table to a row in the root table. -:::note - -If you define your own primary key in a child table, it will be used to link to the parent table, and the `_dlt_parent_id` and `_dlt_list_idx` will not be added. `_dlt_id` is always added even if the primary key or other unique columns are defined. - -::: +[Learn more on nested references, row keys and parent keys](schema.md#nested-references-root-and-nested-tables) ## Naming convention: tables and columns @@ -229,7 +225,7 @@ problems. ## Staging dataset So far we've been using the `append` write disposition in our example pipeline. This means that -each time we run the pipeline, the data is appended to the existing tables. When you use the [merge write disposition](incremental-loading.md), dlt creates a staging database schema for staging data. This schema is named `_staging` and contains the same tables as the destination schema. When you run the pipeline, the data from the staging tables is loaded into the destination tables in a single atomic transaction. +each time we run the pipeline, the data is appended to the existing tables. When you use the [merge write disposition](incremental-loading.md), dlt creates a staging database schema for staging data. This schema is named `_staging` [by default](https://dlthub.com/docs/devel/dlt-ecosystem/staging#staging-dataset) and contains the same tables as the destination schema. When you run the pipeline, the data from the staging tables is loaded into the destination tables in a single atomic transaction. Let's illustrate this with an example. We change our pipeline to use the `merge` write disposition: @@ -274,7 +270,7 @@ Here is what the tables may look like after running the pipeline: Notice that the `mydata.users` table now contains the data from both the previous pipeline run and the current one. -## Versioned datasets +## Dev mode (versioned) datasets When you set the `dev_mode` argument to `True` in `dlt.pipeline` call, dlt creates a versioned dataset. This means that each time you run the pipeline, the data is loaded into a new dataset (a new database schema). @@ -307,7 +303,7 @@ For example, the first time you run the pipeline, the schema will be named ## Loading data into existing tables not created by dlt -You can also load data from `dlt` into tables that already exist in the destination dataset and were not created by `dlt`. +You can also load data from `dlt` into tables that already exist in the destination dataset and were not created by `dlt`. There are a few things to keep in mind when you are doing this: If you load data to a table that exists but does not contain any data, in most cases your load will succeed without problems. @@ -317,24 +313,24 @@ will remain in the destination but stay unknown to `dlt`. This will generally no If your destination table already exists and contains columns that have the same name as columns discovered by `dlt` but do not have matching datatypes, your load will fail and you will have to fix the column on the destination table first, -or change the column name in your incoming data to something else to avoid a collission. +or change the column name in your incoming data to something else to avoid a collision. -If your destination table exists and already contains data, your load might also initially fail, since `dlt` creates +If your destination table exists and already contains data, your load might also initially fail, since `dlt` creates special `non-nullable` columns that contains required mandatory metadata. Some databases will not allow you to create -`non-nullable` columns on tables that have data, since the initial value for these columns of the existing rows can +`non-nullable` columns on tables that have data, since the initial value for these columns of the existing rows can not be inferred. You will have to manually create these columns with the correct type on your existing tables and make them `nullable`, then fill in values for the existing rows. Some databases may allow you to create a new column -that is `non-nullable` and take a default value for existing rows in the same command. The columns you will need to +that is `non-nullable` and take a default value for existing rows in the same command. The columns you will need to create are: | name | type | | --- | --- | -| _dlt_load_id | text/string/varchar | +| _dlt_load_id | text/string/varchar | | _dlt_id | text/string/varchar | -For child-tables you may also need to create: +For nested tables you may also need to create: | name | type | | --- | --- | -| _dlt_parent_id | text/string/varchar | +| _dlt_parent_id | text/string/varchar | | _dlt_root_id | text/string/varchar | \ No newline at end of file diff --git a/docs/website/docs/general-usage/full-loading.md b/docs/website/docs/general-usage/full-loading.md index 320d0664f5..434615fecf 100644 --- a/docs/website/docs/general-usage/full-loading.md +++ b/docs/website/docs/general-usage/full-loading.md @@ -48,7 +48,7 @@ may end up with new data in some tables and no data in other tables if the load ### The `insert-from-staging` strategy The `insert-from-staging` is the slowest of all three strategies. It will load all new data into staging tables away from your final destination tables and will then truncate and insert the new data in one transaction. -It also maintains a consistent state between child and parent tables at all times. Use this strategy if you have the requirement for consistent destination datasets with zero downtime and the `optimized` strategy does not work for you. +It also maintains a consistent state between nested and root tables at all times. Use this strategy if you have the requirement for consistent destination datasets with zero downtime and the `optimized` strategy does not work for you. This strategy behaves the same way across all destinations. ### The `staging-optimized` strategy diff --git a/docs/website/docs/general-usage/incremental-loading.md b/docs/website/docs/general-usage/incremental-loading.md index 68fc46e6dc..819ac2fb0c 100644 --- a/docs/website/docs/general-usage/incremental-loading.md +++ b/docs/website/docs/general-usage/incremental-loading.md @@ -50,9 +50,10 @@ dataset with the merge write disposition. ## Merge incremental loading The `merge` write disposition can be used with three different strategies: -1) `delete-insert` (default strategy) -2) `scd2` -3) `upsert` + +1. `delete-insert` (default strategy) +2. `scd2` +3. `upsert` ### `delete-insert` strategy @@ -66,8 +67,8 @@ The default `delete-insert` strategy is used in two scenarios: The `delete-insert` strategy loads data to a `staging` dataset, deduplicates the staging data if a `primary_key` is provided, deletes the data from the destination using `merge_key` and `primary_key`, -and then inserts the new records. All of this happens in a single atomic transaction for a parent and all -child tables. +and then inserts the new records. All of this happens in a single atomic transaction for a root and all +nested tables. Example below loads all the GitHub events and updates them in the destination using "id" as primary key, making sure that only a single copy of event is present in `github_repo_events` table: @@ -147,7 +148,7 @@ The `hard_delete` column hint can be used to delete records from the destination Each record in the destination table with the same `primary_key` or `merge_key` as a record in the source dataset that's marked as a delete will be deleted. -Deletes are propagated to any child table that might exist. For each record that gets deleted in the root table, all corresponding records in the child table(s) will also be deleted. Records in parent and child tables are linked through the `root key` that is explained in the next section. +Deletes are propagated to any nested table that might exist. For each record that gets deleted in the root table, all corresponding records in the nested table(s) will also be deleted. Records in parent and nested tables are linked through the `root key` that is explained in the next section. ##### Example: with primary key and boolean delete column ```py @@ -218,9 +219,9 @@ Indexing is important for doing lookups by column value, especially for merge wr #### Forcing root key propagation -Merge write disposition requires that the `_dlt_id` of top level table is propagated to child -tables. This concept is similar to foreign key which references a parent table, and we call it a -`root key`. Root key is automatically propagated for all tables that have `merge` write disposition +Merge write disposition requires that the `_dlt_id` (`row_key`) of root table is propagated to nested +tables. This concept is similar to foreign key but it always references the root (top level) table, skipping any intermediate parents +We call it `root key`. Root key is automatically propagated for all tables that have `merge` write disposition set. We do not enable it everywhere because it takes storage space. Nevertheless, is some cases you may want to permanently enable root key propagation. @@ -260,8 +261,8 @@ The `unique` hint for `_dlt_id` in the root table is set to `false` when using After this pattern, the `scd2` table in the destination has two records for surrogate key X: one for validity window `[t1, t2]`, and one for `[t3, NULL]`. A duplicate value exists in `_dlt_id` because both records have the same surrogate key. Note that: -- the composite key `(_dlt_id, _dlt_valid_from)` is unique -- `_dlt_id` remains unique for child tables—`scd2` does not affect this +- the composite key `(_dlt_id, _dlt_valid_from)` is unique +- `_dlt_id` remains unique for nested tables—`scd2` does not affect this ::: #### Example: `scd2` merge strategy @@ -410,14 +411,14 @@ You can modify existing resources that yield data in tabular form by calling `ap adding the transform with `add_map`. ::: -#### Child tables -Child tables, if any, do not contain validity columns. Validity columns are only added to the root table. Validity column values for records in child tables can be obtained by joining the root table using `_dlt_root_id`. +#### Nested tables +Nested tables, if any, do not contain validity columns. Validity columns are only added to the root table. Validity column values for records in nested tables can be obtained by joining the root table using `_dlt_root_id` (`root_key`). #### Limitations * You cannot use columns like `updated_at` or integer `version` of a record that are unique within a `primary_key` (even if it is defined). Hash column must be unique for a root table. We are working to allow `updated_at` style tracking -* We do not detect changes in child tables (except new records) if row hash of the corresponding parent row does not change. Use `updated_at` or similar +* We do not detect changes in nested tables (except new records) if row hash of the corresponding parent row does not change. Use `updated_at` or similar column in the root table to stamp changes in nested data. * `merge_key(s)` are (for now) ignored. @@ -544,9 +545,9 @@ you add with `add_map` / `add_filter`. This means that you can manipulate the da * Another built-in `min` returns smaller value. You can pass your custom function as well. This lets you define -`last_value` on complex types i.e. dictionaries and store indexes of last values, not just simple +`last_value` on nested types i.e. dictionaries and store indexes of last values, not just simple types. The `last_value` argument is a [JSON Path](https://github.com/json-path/JsonPath#operators) -and lets you select nested and complex data (including the whole data item when `$` is used). +and lets you select nested data (including the whole data item when `$` is used). Example below creates last value which is a dictionary holding a max `created_at` value for each created table name: @@ -689,7 +690,7 @@ than `end_value`. :::caution In rare cases when you use Incremental with a transformer, `dlt` will not be able to automatically close -generator associated with a row that is out of range. You can still use still call `can_close()` method on +generator associated with a row that is out of range. You can still call the `can_close()` method on incremental and exit yield loop when true. ::: @@ -907,22 +908,75 @@ Consider the example below for reading incremental loading parameters from "conf ``` `id_after` incrementally stores the latest `cursor_path` value for future pipeline runs. -### Loading NULL values in the incremental cursor field +### Loading when incremental cursor path is missing or value is None/NULL + +You can customize the incremental processing of dlt by setting the parameter `on_cursor_value_missing`. + +When loading incrementally with the default settings, there are two assumptions: +1. each row contains the cursor path +2. each row is expected to contain a value at the cursor path that is not `None`. -When loading incrementally with a cursor field, each row is expected to contain a value at the cursor field that is not `None`. -For example, the following source data will raise an error: +For example, the two following source data will raise an error: ```py @dlt.resource -def some_data(updated_at=dlt.sources.incremental("updated_at")): +def some_data_without_cursor_path(updated_at=dlt.sources.incremental("updated_at")): yield [ {"id": 1, "created_at": 1, "updated_at": 1}, - {"id": 2, "created_at": 2, "updated_at": 2}, + {"id": 2, "created_at": 2}, # cursor field is missing + ] + +list(some_data_without_cursor_path()) + +@dlt.resource +def some_data_without_cursor_value(updated_at=dlt.sources.incremental("updated_at")): + yield [ + {"id": 1, "created_at": 1, "updated_at": 1}, + {"id": 3, "created_at": 4, "updated_at": None}, # value at cursor field is None + ] + +list(some_data_without_cursor_value()) +``` + + +To process a data set where some records do not include the incremental cursor path or where the values at the cursor path are `None,` there are the following four options: + +1. Configure the incremental load to raise an exception in case there is a row where the cursor path is missing or has the value `None` using `incremental(..., on_cursor_value_missing="raise")`. This is the default behavior. +2. Configure the incremental load to tolerate the missing cursor path and `None` values using `incremental(..., on_cursor_value_missing="include")`. +3. Configure the incremental load to exclude the missing cursor path and `None` values using `incremental(..., on_cursor_value_missing="exclude")`. +4. Before the incremental processing begins: Ensure that the incremental field is present and transform the values at the incremental cursor to a value different from `None`. [See docs below](#transform-records-before-incremental-processing) + +Here is an example of including rows where the incremental cursor value is missing or `None`: +```py +@dlt.resource +def some_data(updated_at=dlt.sources.incremental("updated_at", on_cursor_value_missing="include")): + yield [ + {"id": 1, "created_at": 1, "updated_at": 1}, + {"id": 2, "created_at": 2}, + {"id": 3, "created_at": 4, "updated_at": None}, + ] + +result = list(some_data()) +assert len(result) == 3 +assert result[1] == {"id": 2, "created_at": 2} +assert result[2] == {"id": 3, "created_at": 4, "updated_at": None} +``` + +If you do not want to import records without the cursor path or where the value at the cursor path is `None` use the following incremental configuration: + +```py +@dlt.resource +def some_data(updated_at=dlt.sources.incremental("updated_at", on_cursor_value_missing="exclude")): + yield [ + {"id": 1, "created_at": 1, "updated_at": 1}, + {"id": 2, "created_at": 2}, {"id": 3, "created_at": 4, "updated_at": None}, ] -list(some_data()) +result = list(some_data()) +assert len(result) == 1 ``` +### Transform records before incremental processing If you want to load data that includes `None` values you can transform the records before the incremental processing. You can add steps to the pipeline that [filter, transform, or pivot your data](../general-usage/resource.md#filter-transform-and-pivot-data). @@ -1011,7 +1065,7 @@ def tweets(): data = get_data(start_from=last_val) yield data # change the state to the new value - dlt.current.state()["last_updated"] = data["last_timestamp"] + dlt.current.resource_state()["last_updated"] = data["last_timestamp"] ``` If we keep a list or a dictionary in the state, we can modify the underlying values in the objects, @@ -1087,7 +1141,7 @@ def search_tweets(twitter_bearer_token=dlt.secrets.value, search_terms=None, sta headers = _headers(twitter_bearer_token) for search_term in search_terms: # make cache for each term - last_value_cache = dlt.current.state().setdefault(f"last_value_{search_term}", None) + last_value_cache = dlt.current.resource_state().setdefault(f"last_value_{search_term}", None) print(f'last_value_cache: {last_value_cache}') params = {...} url = "https://api.twitter.com/2/tweets/search/recent" @@ -1096,7 +1150,7 @@ def search_tweets(twitter_bearer_token=dlt.secrets.value, search_terms=None, sta page['search_term'] = search_term last_id = page.get('meta', {}).get('newest_id', 0) #set it back - not needed if we - dlt.current.state()[f"last_value_{search_term}"] = max(last_value_cache or 0, int(last_id)) + dlt.current.resource_state()[f"last_value_{search_term}"] = max(last_value_cache or 0, int(last_id)) # print the value for each search term print(f'new_last_value_cache for term {search_term}: {last_value_cache}') @@ -1162,4 +1216,4 @@ sources: } ``` -Verify that the `last_value` is updated between pipeline runs. \ No newline at end of file +Verify that the `last_value` is updated between pipeline runs. diff --git a/docs/website/docs/general-usage/naming-convention.md b/docs/website/docs/general-usage/naming-convention.md index 11032a4457..16898cf8d1 100644 --- a/docs/website/docs/general-usage/naming-convention.md +++ b/docs/website/docs/general-usage/naming-convention.md @@ -27,7 +27,7 @@ The standard behavior of `dlt` is to **use the same naming convention for all de - Multiples of `_` are converted into a single `_`. - Replaces all trailing `_` with `x` -Uses __ as a parent-child separator for tables and flattened column names. +Uses __ as a nesting separator for tables and flattened column names. :::tip If you do not like **snake_case**, your next safe option is **sql_ci**, which generates SQL-safe, lowercase, case-insensitive identifiers without any other transformations. To permanently change the default naming convention on a given machine: diff --git a/docs/website/docs/general-usage/resource.md b/docs/website/docs/general-usage/resource.md index 20149cfa0b..d4dedd42bd 100644 --- a/docs/website/docs/general-usage/resource.md +++ b/docs/website/docs/general-usage/resource.md @@ -54,11 +54,11 @@ accepts the following arguments: receive those hints. Used in [incremental loading](incremental-loading.md). 1. `columns` let's you define one or more columns, including the data types, nullability, and other hints. The column definition is a `TypedDict`: `TTableSchemaColumns`. In the example below, we tell - `dlt` that column `tags` (containing a list of tags) in the `user` table should have type `complex`, - which means that it will be loaded as JSON/struct and not as a child table. + `dlt` that column `tags` (containing a list of tags) in the `user` table should have type `json`, + which means that it will be loaded as JSON/struct and not as a separate nested table. ```py - @dlt.resource(name="user", columns={"tags": {"data_type": "complex"}}) + @dlt.resource(name="user", columns={"tags": {"data_type": "json"}}) def get_users(): ... @@ -116,7 +116,7 @@ Things to note: - Fields with an `Optional` type are marked as `nullable` - Fields with a `Union` type are converted to the first (not `None`) type listed in the union. For example, `status: Union[int, str]` results in a `bigint` column. -- `list`, `dict`, and nested Pydantic model fields will use the `complex` type which means they'll be stored as a JSON object in the database instead of creating child tables. +- `list`, `dict`, and nested Pydantic model fields will use the `json` type which means they'll be stored as a JSON object in the database instead of creating nested tables. You can override this by configuring the Pydantic model @@ -125,15 +125,15 @@ from typing import ClassVar from dlt.common.libs.pydantic import DltConfig class UserWithNesting(User): - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": True} + dlt_config: ClassVar[DltConfig] = {"skip_nested_types": True} @dlt.resource(name="user", columns=UserWithNesting) def get_users(): ... ``` -`"skip_complex_types"` omits any `dict`/`list`/`BaseModel` type fields from the schema, so dlt will fall back on the default -behavior of creating child tables for these fields. +`"skip_nested_types"` omits any `dict`/`list`/`BaseModel` type fields from the schema, so dlt will fall back on the default +behavior of creating nested tables for these fields. We do not support `RootModel` that validate simple types. You can add such a validator yourself, see [data filtering section](#filter-transform-and-pivot-data). @@ -346,8 +346,8 @@ for user in users().add_filter(lambda user: user["user_id"] != "me").add_map(ano ### Reduce the nesting level of generated tables -You can limit how deep `dlt` goes when generating child tables. By default, the library will descend -and generate child tables for all nested lists, without limit. +You can limit how deep `dlt` goes when generating nested tables and flattening dicts into columns. By default, the library will descend +and generate nested tables for all nested lists, without limit. :::note `max_table_nesting` is optional so you can skip it, in this case dlt will @@ -379,13 +379,13 @@ def my_resource(): } ``` -In the example above, we want only 1 level of child tables to be generated (so there are no child -tables of child tables). Typical settings: +In the example above, we want only 1 level of nested tables to be generated (so there are no nested +tables of a nested table). Typical settings: -- `max_table_nesting=0` will not generate child tables at all and all nested data will be +- `max_table_nesting=0` will not generate nested tables and will not flatten dicts into columns at all. All nested data will be represented as JSON. -- `max_table_nesting=1` will generate child tables of top-level tables and nothing more. All nested - data in child tables will be represented as JSON. +- `max_table_nesting=1` will generate nested tables of root tables and nothing more. All nested + data in nested tables will be represented as JSON. You can achieve the same effect after the resource instance is created: diff --git a/docs/website/docs/general-usage/schema-contracts.md b/docs/website/docs/general-usage/schema-contracts.md index 41185852cc..e48fe979fd 100644 --- a/docs/website/docs/general-usage/schema-contracts.md +++ b/docs/website/docs/general-usage/schema-contracts.md @@ -16,7 +16,7 @@ def items(): ... ``` -This resource will allow new tables (both child tables and [tables with dynamic names](resource.md#dispatch-data-to-many-tables)) to be created, but will throw an exception if data is extracted for an existing table which contains a new column. +This resource will allow new tables (both nested tables and [tables with dynamic names](resource.md#dispatch-data-to-many-tables)) to be created, but will throw an exception if data is extracted for an existing table which contains a new column. ### Setting up the contract You can control the following **schema entities**: @@ -56,8 +56,8 @@ You can change the contract on the **source** instance via `schema_contract` pro #### Nuances of contract modes. 1. Contracts are applied **after names of tables and columns are normalized**. -2. Contract defined on a resource is applied to all tables and child tables created by that resource. -3. `discard_row` works on table level. So for example if you have two tables in parent-child relationship ie. *users* and *users__addresses* and contract is violated in *users__addresses* table, the row of that table is discarded while the parent row in *users* table will be loaded. +2. Contract defined on a resource is applied to all root tables and nested tables created by that resource. +3. `discard_row` works on table level. So for example if you have two tables in nested relationship ie. *users* and *users__addresses* and contract is violated in *users__addresses* table, the row of that table is discarded while the parent row in *users* table will be loaded. ### Use Pydantic models for data validation Pydantic models can be used to [define table schemas and validate incoming data](resource.md#define-a-schema-with-pydantic). You can use any model you already have. `dlt` will internally synthesize (if necessary) new models that conform with the **schema contract** on the resource. @@ -97,7 +97,7 @@ Model validation is added as a [transform step](resource.md#filter-transform-and ::: :::note -Pydantic models work on the **extracted** data **before names are normalized or child relationships are created**. Make sure to name model fields as in your input data and handle nested data with the nested models. +Pydantic models work on the **extracted** data **before names are normalized or nested tables are created**. Make sure to name model fields as in your input data and handle nested data with the nested models. As a consequence, `discard_row` will drop the whole data item - even if nested model was affected. ::: @@ -166,7 +166,7 @@ What tables are not considered new: ### Working with datasets that have manually added tables and columns on the first load -In some cases you might be working with datasets that have tables or columns created outside of dlt. If you are loading to a table not created by `dlt` for the first time, `dlt` will not know about this table while enforcing schema contracts. This means that if you do a load where the `tables` are set to `evolve`, all will work as planned. If you have `tables` set to `freeze`, dlt will raise an exception because it thinks you are creating a new table (which you are from dlts perspective). You can allow `evolve` for one load and then switch back to `freeze`. +In some cases you might be working with datasets that have tables or columns created outside of dlt. If you are loading to a table not created by `dlt` for the first time, `dlt` will not know about this table while enforcing schema contracts. This means that if you do a load where the `tables` are set to `evolve`, all will work as planned. If you have `tables` set to `freeze`, dlt will raise an exception because it thinks you are creating a new table (which you are from dlts perspective). You can allow `evolve` for one load and then switch back to `freeze`. The same thing will happen if `dlt` knows your table, but you have manually added a column to your destination and you have `columns` set to `freeze`. diff --git a/docs/website/docs/general-usage/schema.md b/docs/website/docs/general-usage/schema.md index df405de1af..84693b6078 100644 --- a/docs/website/docs/general-usage/schema.md +++ b/docs/website/docs/general-usage/schema.md @@ -36,7 +36,7 @@ the order is lost. ## Naming convention -`dlt` creates tables, child tables and column schemas from the data. The data being loaded, +`dlt` creates tables, nested tables and column schemas from the data. The data being loaded, typically JSON documents, contains identifiers (i.e. key names in a dictionary) with any Unicode characters, any lengths and naming styles. On the other hand the destinations accept very strict namespaces for their identifiers. Like Redshift that accepts case-insensitive alphanumeric @@ -52,7 +52,7 @@ The default naming convention: alphanumerics and underscores. 1. Adds `_` if name starts with number. 1. Multiples of `_` are converted into single `_`. -1. The parent-child relation is expressed as double `_` in names. +1. Nesting is expressed as double `_` in names. 1. It shorts the identifier if it exceed the length at the destination. > 💡 Standard behavior of `dlt` is to **use the same naming convention for all destinations** so @@ -85,7 +85,7 @@ standard `dlt` normalizer creates a relational structure from Python dictionarie Elements of that structure: table and column definitions, are added to the schema. The data normalizer is configurable and users can plug their own normalizers i.e. to handle the -parent-child table linking differently or generate parquet-like data structs instead of child +nested table linking differently or generate parquet-like data structs instead of nested tables. ## Tables and columns @@ -96,30 +96,39 @@ The key components of a schema are tables and columns. You can find a dictionary A table schema has the following properties: 1. `name` and `description`. -1. `parent` with a parent table name. 1. `columns` with dictionary of table schemas. 1. `write_disposition` hint telling `dlt` how new data coming to the table is loaded. +1. `schema_contract` - describes a [contract on the table](schema-contracts.md) +1. `parent` a part of the nested reference, defined on a nested table and points to the parent table. Table schema is extended by data normalizer. Standard data normalizer adds propagated columns to it. A column schema contains following properties: 1. `name` and `description` of a column in a table. + +Data type information: + 1. `data_type` with a column data type. 1. `precision` a precision for **text**, **timestamp**, **time**, **bigint**, **binary**, and **decimal** types 1. `scale` a scale for **decimal** type +1. `timezone` a flag indicating TZ aware or NTZ **timestamp** and **time**. Default value is **true** +1. `nullable` tells if column is nullable or not. 1. `is_variant` telling that column was generated as variant of another column. A column schema contains following basic hints: -1. `nullable` tells if column is nullable or not. 1. `primary_key` marks a column as a part of primary key. +1. `unique` tells that column is unique. on some destination that generates unique index. 1. `merge_key` marks a column as a part of merge key used by [incremental load](./incremental-loading.md#merge-incremental_loading). -1. `foreign_key` marks a column as a part of foreign key. + +Hints below are used to create [nested references](#root-and-nested-tables-nested-references) +1. `row_key` a special form of primary key created by `dlt` to uniquely identify rows of data +1. `parent_key` a special form of foreign key used by nested tables to refer to parent tables 1. `root_key` marks a column as a part of root key which is a type of foreign key always referring to the root table. -1. `unique` tells that column is unique. on some destination that generates unique index. +1. `_dlt_list_idx` index on a nested list from which nested table is created. `dlt` lets you define additional performance hints: @@ -190,7 +199,7 @@ Now go ahead and try to add a new record where `id` is float number, you should | time | `'14:01:02'`, `datetime.time(14, 1, 2)` | Supports precision - see **timestamp** | | bigint | `9876543210` | Supports precision as number of bits | | binary | `b'\x00\x01\x02\x03'` | Supports precision, like **text** | -| complex | `[4, 5, 6]`, `{'a': 1}` | | +| json | `[4, 5, 6]`, `{'a': 1}` | | | decimal | `Decimal('4.56')` | Supports precision and scale | | wei | `2**56` | | @@ -198,8 +207,8 @@ Now go ahead and try to add a new record where `id` is float number, you should decimals. It works correctly on Postgres and BigQuery. All the other destinations have insufficient precision. -`complex` data type tells `dlt` to load that element as JSON or struct and do not attempt to flatten -or create a child table out of it. +`json` data type tells `dlt` to load that element as JSON or string and do not attempt to flatten +or create a nested table out of it. Note that structured types like arrays or maps are not supported by `dlt` at this point. `time` data type is saved in destination without timezone info, if timezone is included it is stripped. E.g. `'14:01:02+02:00` -> `'14:01:02'`. @@ -212,14 +221,40 @@ The precision for **timestamp** is useful when creating **parquet** files. Use 3 The precision for **bigint** is mapped to available integer types ie. TINYINT, INT, BIGINT. The default is 64 bits (8 bytes) precision (BIGINT) ::: +## Table references +`dlt` tables to refer to other tables. It supports two types of such references. +1. **nested reference** created automatically when nested data (ie. `json` document containing nested list) is converted into relational form. Those +references use specialized column and table hints and are used ie. when [merging data](incremental-loading.md). +2. **table references** are optional, user-defined annotations that are not verified and enforced but may be used by downstream tools ie. +to generate automatic tests or models for the loaded data. + +### Nested references: root and nested tables +When `dlt` normalizes nested data into relational schema it will automatically create [**root** and **nested** tables](destination-tables.md) and link them using **nested references**. + +1. All tables get a column with `row_key` hint (named `_dlt_id` by default) to uniquely identify each row of data. +2. Nested tables get `parent` table hint with a name of the parent table. Root table does not have `parent` hint defined. +3. Nested tables get a column with `parent_key` hint (named `_dlt_parent_id` by default) that refers to `row_key` of the `parent` table. + +`parent` + `row_key` + `parent_key` form a **nested reference**: from nested table to `parent` table and are extensively used when loading data. Both `replace` and `merge` write dispositions + +`row_key` is created as follows: +1. Random string on **root** tables, except for [`upsert`](incremental-loading.md#upsert-strategy) and +[`scd2`](incremental-loading.md#scd2-strategy) merge strategies, where it is a deterministic hash of `primary_key` (or whole row, so called `content_hash`, if PK is not defined). +2. A deterministic hash of `parent_key`, `parent` table name and position in the list (`_dlt_list_idx`) +for **nested** tables. + +You are able to bring your own `row_key` by adding `_dlt_id` column/field to your data (both root and nested). All data types with equal operator are supported. + +`merge` write disposition requires additional nested reference that goes from **nested** to **root** table, skipping all parent tables in between. This reference is created by [adding a column with hint](incremental-loading.md#forcing-root-key-propagation) `root_key` (named `_dlt_root_id` by default) to nested tables. + +### Table references +You can annotate tables with table references. This feature is coming soon. + ## Schema settings The `settings` section of schema file lets you define various global rules that impact how tables and columns are inferred from data. For example you can assign **primary_key** hint to all columns with name `id` or force **timestamp** data type on all columns containing `timestamp` with an use of regex pattern. -> 💡 It is the best practice to use those instead of providing the exact column schemas via `columns` -> argument or by pasting them in `yaml`. - ### Data type autodetectors You can define a set of functions that will be used to infer the data type of the column from a @@ -260,7 +295,9 @@ of columns added by normalizer: ```yaml settings: default_hints: - foreign_key: + row_key: + - _dlt_id + parent_key: - _dlt_parent_id not_null: - _dlt_id @@ -334,14 +371,14 @@ This code snippet sets up a nullable boolean column named `my_column` directly i #### Using `apply_hints` When dealing with dynamically generated resources or needing to programmatically set hints, `apply_hints` is your tool. It's especially useful for applying hints across various collections or tables at once. -For example, to apply a complex data type across all collections from a MongoDB source: +For example, to apply a `json` data type across all collections from a MongoDB source: ```py all_collections = ["collection1", "collection2", "collection3"] # replace with your actual collection names source_data = mongodb().with_resources(*all_collections) for col in all_collections: - source_data.resources[col].apply_hints(columns={"column_name": {"data_type": "complex"}}) + source_data.resources[col].apply_hints(columns={"column_name": {"data_type": "json"}}) pipeline = dlt.pipeline( pipeline_name="mongodb_pipeline", @@ -350,7 +387,7 @@ pipeline = dlt.pipeline( ) load_info = pipeline.run(source_data) ``` -This example iterates through MongoDB collections, applying the complex [data type](schema#data-types) to a specified column, and then processes the data with `pipeline.run`. +This example iterates through MongoDB collections, applying the **json** [data type](schema#data-types) to a specified column, and then processes the data with `pipeline.run`. ## View and print the schema To view and print the default schema in a clear YAML format use the command: @@ -363,8 +400,8 @@ This can be used in a pipeline as: ```py # Create a pipeline pipeline = dlt.pipeline( - pipeline_name="chess_pipeline", - destination='duckdb', + pipeline_name="chess_pipeline", + destination='duckdb', dataset_name="games_data") # Run the pipeline diff --git a/docs/website/docs/general-usage/snippets/destination-snippets.py b/docs/website/docs/general-usage/snippets/destination-snippets.py index 3484d943a0..c1c0f745c5 100644 --- a/docs/website/docs/general-usage/snippets/destination-snippets.py +++ b/docs/website/docs/general-usage/snippets/destination-snippets.py @@ -94,7 +94,7 @@ def destination_instantiation_snippet() -> None: # here dependencies dependencies will be imported, secrets pulled and destination accessed # we pass bucket_url explicitly and expect credentials passed by config provider load_info = pipeline.load(destination=filesystem(bucket_url=bucket_url)) - load_info.raise_on_failed_jobs() + print(load_info) # @@@DLT_SNIPPET_END late_destination_access diff --git a/docs/website/docs/general-usage/source.md b/docs/website/docs/general-usage/source.md index 98c7a13b81..e94cc2bd30 100644 --- a/docs/website/docs/general-usage/source.md +++ b/docs/website/docs/general-usage/source.md @@ -157,8 +157,8 @@ When adding resource to the source, `dlt` clones the resource so your existing i ### Reduce the nesting level of generated tables -You can limit how deep `dlt` goes when generating child tables. By default, the library will descend -and generate child tables for all nested lists, without limit. +You can limit how deep `dlt` goes when generating nested tables and flattening dicts into columns. By default, the library will descend +and generate nested tables for all nested lists and columns form dicts, without limit. ```py @dlt.source(max_table_nesting=1) @@ -166,13 +166,13 @@ def mongo_db(): ... ``` -In the example above we want only 1 level of child tables to be generates (so there are no child -tables of child tables). Typical settings: +In the example above, we want only 1 level of nested tables to be generated (so there are no nested +tables of a nested table). Typical settings: -- `max_table_nesting=0` will not generate child tables at all and all nested data will be - represented as json. -- `max_table_nesting=1` will generate child tables of top level tables and nothing more. All nested - data in child tables will be represented as json. +- `max_table_nesting=0` will not generate nested tables and will not flatten dicts into columns at all. All nested data will be + represented as JSON. +- `max_table_nesting=1` will generate nested tables of root tables and nothing more. All nested + data in nested tables will be represented as JSON. You can achieve the same effect after the source instance is created: @@ -188,8 +188,8 @@ MongoDB databases. Our practical experience is that setting the `max_nesting_lev produces the clearest and human-readable schemas. :::tip -The `max_table_nesting` parameter at the source level doesn't automatically apply to individual -resources when accessed directly (e.g., using `source.resources["resource_1"])`. To make sure it +The `max_table_nesting` parameter at the source level doesn't automatically apply to individual +resources when accessed directly (e.g., using `source.resources["resource_1"])`. To make sure it works, either use `source.with_resources("resource_1")` or set the parameter directly on the resource. ::: @@ -214,8 +214,8 @@ The schema is available via `schema` property of the source. Source provides two other convenience properties: -1. `max_table_nesting` to set the maximum nesting level of child tables -1. `root_key` to propagate the `_dlt_id` of from a root table to all child tables. +1. `max_table_nesting` to set the maximum nesting level for nested tables and flattened columns +1. `root_key` to propagate the `_dlt_id` of from a root table to all nested tables. ## Load sources diff --git a/docs/website/docs/intro.md b/docs/website/docs/intro.md index c269c987b8..6660696cfb 100644 --- a/docs/website/docs/intro.md +++ b/docs/website/docs/intro.md @@ -6,138 +6,153 @@ keywords: [introduction, who, what, how] import snippets from '!!raw-loader!./intro-snippets.py'; -# Introduction +# Getting started ![dlt pacman](/img/dlt-pacman.gif) -## What is `dlt`? +## What is dlt? + +dlt is an open-source Python library that loads data from various, often messy data sources into well-structured, live datasets. It offers a lightweight interface for extracting data from [REST APIs](./tutorial/rest-api), [SQL databases](./tutorial/sql-database), [cloud storage](./tutorial/filesystem), [Python data structures](./tutorial/load-data-from-an-api), and [many more](./dlt-ecosystem/verified-sources). + +dlt is designed to be easy to use, flexible, and scalable: + +- dlt infers [schemas](./general-usage/schema) and [data types](./general-usage/schema/#data-types), [normalizes the data](./general-usage/schema/#data-normalizer), and handles nested data structures. +- dlt supports a variety of [popular destinations](./dlt-ecosystem/destinations/) and has an interface to add [custom destinations](./dlt-ecosystem/destinations/destination) to create reverse ETL pipelines. +- dlt can be deployed anywhere Python runs, be it on [Airflow](./walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer), [serverless functions](./walkthroughs/deploy-a-pipeline/deploy-with-google-cloud-functions) or any other cloud deployment of your choice. +- dlt automates pipeline maintenance with [schema evolution](./general-usage/schema-evolution) and [schema and data contracts](./general-usage/schema-contracts). + +To get started with dlt, install the library using pip: -`dlt` is an open-source library that you can add to your Python scripts to load data -from various and often messy data sources into well-structured, live datasets. To get started, install it with: ```sh pip install dlt ``` :::tip -We recommend using a clean virtual environment for your experiments! Here are [detailed instructions](/reference/installation). +We recommend using a clean virtual environment for your experiments! Read the [detailed instructions](./reference/installation) on how to set up one. ::: -Unlike other solutions, with dlt, there's no need to use any backends or containers. Simply import `dlt` in a Python file or a Jupyter Notebook cell, and create a pipeline to load data into any of the [supported destinations](dlt-ecosystem/destinations/). You can load data from any source that produces Python data structures, including APIs, files, databases, and more. `dlt` also supports building a [custom destination](dlt-ecosystem/destinations/destination.md), which you can use as reverse ETL. - -The library will create or update tables, infer data types, and handle nested data automatically. Here are a few example pipelines: +## Load data with dlt from … - + -:::tip -Looking to use a REST API as a source? Explore our new [REST API generic source](dlt-ecosystem/verified-sources/rest_api) for a declarative way to load data. -::: +Use dlt's [REST API source](./tutorial/rest-api) to extract data from any REST API. Define API endpoints you’d like to fetch data from, pagination method and authentication and dlt will handle the rest: - +```py +import dlt +from dlt.sources.rest_api import rest_api_source + +source = rest_api_source({ + "client": { + "base_url": "https://api.example.com/", + "auth": { + "token": dlt.secrets["your_api_token"], + }, + "paginator": { + "type": "json_response", + "next_url_path": "paging.next", + }, + }, + "resources": ["posts", "comments"], +}) +pipeline = dlt.pipeline( + pipeline_name="rest_api_example", + destination="duckdb", + dataset_name="rest_api_data", +) -Copy this example to a file or a Jupyter Notebook and run it. To make it work with the DuckDB destination, you'll need to install the **duckdb** dependency (the default `dlt` installation is really minimal): -```sh -pip install "dlt[duckdb]" +load_info = pipeline.run(source) ``` -Now **run** your Python file or Notebook cell. -How it works? The library extracts data from a [source](general-usage/glossary.md#source) (here: **chess.com REST API**), inspects its structure to create a -[schema](general-usage/glossary.md#schema), structures, normalizes, and verifies the data, and then -loads it into a [destination](general-usage/glossary.md#destination) (here: **duckdb**, into a database schema **player_data** and table name **player**). +Follow the [REST API source tutorial](./tutorial/rest-api) to learn more about the source configuration and pagination methods. + + +Use the [SQL source](./tutorial/sql-database) to extract data from the database like PostgreSQL, MySQL, SQLite, Oracle and more. - +```py +from dlt.sources.sql_database import sql_database - +source = sql_database( + "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam" +) -Initialize the [Slack source](dlt-ecosystem/verified-sources/slack) with `dlt init` command: +pipeline = dlt.pipeline( + pipeline_name="sql_database_example", + destination="duckdb", + dataset_name="sql_data", +) -```sh -dlt init slack duckdb +load_info = pipeline.run(source) ``` -Create and run a pipeline: +Follow the [SQL source tutorial](./tutorial/sql-database) to learn more about the source configuration and supported databases. + + + + +[Filesystem](./tutorial/filesystem) source extracts data from AWS S3, Google Cloud Storage, Google Drive, Azure, or a local file system. ```py -import dlt +from dlt.sources.filesystem import filesystem -from slack import slack_source +source = filesystem( + bucket_url="s3://example-bucket", + file_glob="*.csv" +) pipeline = dlt.pipeline( - pipeline_name="slack", + pipeline_name="filesystem_example", destination="duckdb", - dataset_name="slack_data" -) - -source = slack_source( - start_date=datetime(2023, 9, 1), - end_date=datetime(2023, 9, 8), - page_size=100, + dataset_name="filesystem_data", ) load_info = pipeline.run(source) -print(load_info) ``` - - - - Pass anything that you can load with Pandas to `dlt` - - - +Follow the [filesystem source tutorial](./tutorial/filesystem) to learn more about the source configuration and supported storage services. - + -:::tip -Use our verified [SQL database source](dlt-ecosystem/verified-sources/sql_database) -to sync your databases with warehouses, data lakes, or vector stores. -::: +dlt is able to load data from Python generators or directly from Python data structures: - +```py +import dlt +@dlt.resource +def foo(): + for i in range(10): + yield {"id": i, "name": f"This is item {i}"} -Install **pymysql** driver: -```sh -pip install sqlalchemy pymysql -``` +pipeline = dlt.pipeline( + pipeline_name="python_data_example", + destination="duckdb", +) - - +load_info = pipeline.run(foo) +``` +Check out the [Python data structures tutorial](./tutorial/load-data-from-an-api) to learn about dlt fundamentals and advanced usage scenarios. -## Why use `dlt`? + -- Automated maintenance - with schema inference and evolution and alerts, and with short declarative -code, maintenance becomes simple. -- Run it where Python runs - on Airflow, serverless functions, notebooks. No -external APIs, backends, or containers, scales on micro and large infra alike. -- User-friendly, declarative interface that removes knowledge obstacles for beginners -while empowering senior professionals. + -## Getting started with `dlt` -1. Dive into our [Getting started guide](getting-started.md) for a quick intro to the essentials of `dlt`. -2. Play with the -[Google Colab demo](https://colab.research.google.com/drive/1NfSB1DpwbbHX9_t5vlalBTf13utwpMGx?usp=sharing). -This is the simplest way to see `dlt` in action. -3. Read the [Tutorial](tutorial/intro) to learn how to build a pipeline that loads data from an API. -4. Check out the [How-to guides](walkthroughs/) for recipes on common use cases for creating, running, and deploying pipelines. -5. Ask us on -[Slack](https://dlthub.com/community) -if you have any questions about use cases or the library. +:::tip +If you'd like to try out dlt without installing it on your machine, check out the [Google Colab demo](https://colab.research.google.com/drive/1NfSB1DpwbbHX9_t5vlalBTf13utwpMGx?usp=sharing). +::: -## Join the `dlt` community +## Join the dlt community 1. Give the library a ⭐ and check out the code on [GitHub](https://github.com/dlt-hub/dlt). -1. Ask questions and share how you use the library on -[Slack](https://dlthub.com/community). +1. Ask questions and share how you use the library on [Slack](https://dlthub.com/community). 1. Report problems and make feature requests [here](https://github.com/dlt-hub/dlt/issues/new/choose). \ No newline at end of file diff --git a/docs/website/docs/reference/command-line-interface.md b/docs/website/docs/reference/command-line-interface.md index 8e816fb622..14fadba74d 100644 --- a/docs/website/docs/reference/command-line-interface.md +++ b/docs/website/docs/reference/command-line-interface.md @@ -23,9 +23,9 @@ version if run again with existing `source` name. You are warned if files will b ### Specify your own "verified sources" repository. You can use `--location ` option to specify your own repository with sources. Typically you would [fork ours](https://github.com/dlt-hub/verified-sources) and start customizing and adding sources ie. to use them for your team or organization. You can also specify a branch with `--branch ` ie. to test a version being developed. -### List all verified sources +### List all sources ```sh -dlt init --list-verified-sources +dlt init --list-sources ``` Shows all available verified sources and their short descriptions. For each source, checks if your local `dlt` version requires update and prints the relevant warning. @@ -181,7 +181,7 @@ Do you want to apply these changes? [y/N] As a result of the command above: -1. All the indicated tables will be dropped in the destination. Note that `dlt` drops the child +1. All the indicated tables will be dropped in the destination. Note that `dlt` drops the nested tables as well. 1. All the indicated tables will be removed from the indicated schema. 1. The state for the resource `repo_events` was found and will be reset. diff --git a/docs/website/docs/reference/explainers/how-dlt-works.md b/docs/website/docs/reference/explainers/how-dlt-works.md index 0abe8b5d94..7de29129e9 100644 --- a/docs/website/docs/reference/explainers/how-dlt-works.md +++ b/docs/website/docs/reference/explainers/how-dlt-works.md @@ -24,7 +24,7 @@ JSON and provides it to `dlt` as input, which then normalizes that data. ## Normalize The configurable normalization engine in `dlt` recursively unpacks this nested structure into -relational tables (i.e. inferring data types, linking tables to create parent-child relationships, +relational tables (i.e. inferring data types, linking tables to create nested relationships, etc.), making it ready to be loaded. This creates a [schema](../../general-usage/glossary.md#schema), which will automatically evolve to any future source data changes (e.g. new fields or tables). diff --git a/docs/website/docs/reference/frequently-asked-questions.md b/docs/website/docs/reference/frequently-asked-questions.md index e864be1c14..6cb98d14eb 100644 --- a/docs/website/docs/reference/frequently-asked-questions.md +++ b/docs/website/docs/reference/frequently-asked-questions.md @@ -10,8 +10,8 @@ keywords: [faq, usage information, technical help] Yes, [this feature is available](../general-usage/resource.md#reduce-the-nesting-level-of-generated-tables). You can also control the nesting on a level of a particular column: -**Apply hints for complex columns** -If certain columns should not be normalized, you can mark them as `complex`. This can be done in two ways. +**Apply hints for nested columns** +If certain columns should not be normalized, you can mark them as `json`. This can be done in two ways. 1. When fetching the source data. ```py @@ -19,7 +19,7 @@ If certain columns should not be normalized, you can mark them as `complex`. Thi source_data.resource3.apply_hints( columns={ "column_name": { - "data_type": "complex" + "data_type": "json" } } ) @@ -27,7 +27,7 @@ If certain columns should not be normalized, you can mark them as `complex`. Thi 1. During resource definition. ```py - @dlt.resource(columns={"column_name": {"data_type": "complex"}}) + @dlt.resource(columns={"column_name": {"data_type": "json"}}) def my_resource(): # Function body goes here pass diff --git a/docs/website/docs/reference/installation.md b/docs/website/docs/reference/installation.md index 8fd80e52ff..a19e01ae80 100644 --- a/docs/website/docs/reference/installation.md +++ b/docs/website/docs/reference/installation.md @@ -137,4 +137,10 @@ conda install -c conda-forge dlt ### 4. Done! -You are now ready to [build your first pipeline](../getting-started) :) \ No newline at end of file +You are now ready to build your first pipeline with `dlt`. Check out these tutorials to get started: + +- [Load data from a REST API](../tutorial/rest-api) +- [Load data from a SQL database](../tutorial/sql-database) +- [Load data from a cloud storage or a file system](../tutorial/filesystem) + +Or read a more detailed tutorial on how to build a [custom data pipeline with dlt](../tutorial/load-data-from-an-api.md). \ No newline at end of file diff --git a/docs/website/docs/reference/performance.md b/docs/website/docs/reference/performance.md index 0ee62acec7..58951702a5 100644 --- a/docs/website/docs/reference/performance.md +++ b/docs/website/docs/reference/performance.md @@ -1,10 +1,10 @@ --- -title: Performance +title: Optimizing dlt description: Scale-up, parallelize and finetune dlt pipelines keywords: [scaling, parallelism, finetuning] --- -# Performance +# Optimizing dlt ## Yield pages instead of rows @@ -148,6 +148,7 @@ As before, **if you have just a single table with millions of records you should +Since the normalize stage uses a process pool to create load package concurrently, adjusting the `file_max_items` and `file_max_bytes` settings can significantly impact load behavior. By setting a lower value for `file_max_items`, you reduce the size of each data chunk sent to the destination database, which can be particularly useful for managing memory constraints on the database server. Without explicit configuration `file_max_items`, `dlt` writes all data rows into one large intermediary file, attempting to insert all data from this single file. Configuring `file_max_items` ensures data is inserted in manageable chunks, enhancing performance and preventing potential memory issues. ### Parallel pipeline config example The example below simulates loading of a large database table with 1 000 000 records. The **config.toml** below sets the parallelization as follows: @@ -224,7 +225,7 @@ resources are: `round_robin` and `fifo`. `fifo` is an option for sequential extraction. It will result in every resource being fully extracted until the resource generator is expired, or a configured limit is reached, then the next resource will be evaluated. Resources are extracted in the order that you added them to your source. :::tip -Switch to `fifo` when debugging sources with many resources and connected transformers, for example [rest_api](../dlt-ecosystem/verified-sources/rest_api.md). +Switch to `fifo` when debugging sources with many resources and connected transformers, for example [rest_api](../dlt-ecosystem/verified-sources/rest_api/index.md). Your data will be requested in deterministic and straightforward order - given data item (ie. user record you got from API) will be processed by all resources and transformers until completion before starting with new one ::: diff --git a/docs/website/docs/reference/performance_snippets/performance-snippets.py b/docs/website/docs/reference/performance_snippets/performance-snippets.py index 7fc0f2bce9..33c29eb681 100644 --- a/docs/website/docs/reference/performance_snippets/performance-snippets.py +++ b/docs/website/docs/reference/performance_snippets/performance-snippets.py @@ -179,9 +179,9 @@ async def _run_async(): loop.run_in_executor(executor, _run_pipeline, pipeline_1, async_table), loop.run_in_executor(executor, _run_pipeline, pipeline_2, defer_table), ) - # result contains two LoadInfo instances - results[0].raise_on_failed_jobs() - results[1].raise_on_failed_jobs() + # results contains two LoadInfo instances + print("pipeline_1", results[0]) + print("pipeline_2", results[1]) # load data asyncio.run(_run_async()) diff --git a/docs/website/docs/running-in-production/running.md b/docs/website/docs/running-in-production/running.md index cc089a1393..0c010332e7 100644 --- a/docs/website/docs/running-in-production/running.md +++ b/docs/website/docs/running-in-production/running.md @@ -259,9 +259,19 @@ def check(ex: Exception): ### Failed jobs -If any job in the package **fail terminally** it will be moved to `failed_jobs` folder and assigned -such status. By default **no exception is raised** and other jobs will be processed and completed. -You may inspect if the failed jobs are present by checking the load info as follows: +If any job in the package **fails terminally** it will be moved to `failed_jobs` folder and assigned +such status. +By default, **an exceptions is raised** and on the first failed job, the load package will be aborted with `LoadClientJobFailed` (terminal exception). +Such package will be completed but its load id is not added to the `_dlt_loads` table. +All the jobs that were running in parallel are completed before raising. The dlt state, if present, will not be visible to `dlt`. +Here is an example `config.toml` to disable this behavior: + +```toml +# I hope you know what you are doing by setting this to false +load.raise_on_failed_jobs=false +``` + +If you prefer dlt to to not raise a terminal exception on failed jobs then you can manually check for failed jobs and raise an exception by checking the load info as follows: ```py # returns True if there are failed jobs in any of the load packages @@ -270,18 +280,6 @@ print(load_info.has_failed_jobs) load_info.raise_on_failed_jobs() ``` -You may also abort the load package with `LoadClientJobFailed` (terminal exception) on a first -failed job. Such package is will be completed but its load id is not added to the -`_dlt_loads` table. All the jobs that were running in parallel are completed before raising. The dlt -state, if present, will not be visible to `dlt`. Here's example `config.toml` to enable this option: - -```toml -# you should really load just one job at a time to get the deterministic behavior -load.workers=1 -# I hope you know what you are doing by setting this to true -load.raise_on_failed_jobs=true -``` - :::caution Note that certain write dispositions will irreversibly modify your data 1. `replace` write disposition with the default `truncate-and-insert` [strategy](../general-usage/full-loading.md) will truncate tables before loading. diff --git a/docs/website/docs/tutorial/filesystem.md b/docs/website/docs/tutorial/filesystem.md new file mode 100644 index 0000000000..b748f794d5 --- /dev/null +++ b/docs/website/docs/tutorial/filesystem.md @@ -0,0 +1,369 @@ +--- +title: Load data from a cloud storage or a file system +description: Learn how to load data files like JSON, JSONL, CSV, and Parquet from a cloud storage (AWS S3, Google Cloud Storage, Google Drive, Azure Blob Storage) or a local file system using dlt. +keywords: [dlt, tutorial, filesystem, cloud storage, file system, python, data pipeline, incremental loading, json, jsonl, csv, parquet, duckdb] +--- + +This tutorial is for you if you need to load data files like JSONL, CSV, and Parquet from either Cloud Storage (ex. AWS S3, Google Cloud Storage, Google Drive, Azure Blob Storage) or a local file system. + +## What you will learn + +- How to set up a file system or cloud storage as a data source +- Configuration basics for file systems and cloud storage +- Loading methods +- Incremental loading of data from file systems or cloud storage +- How to load data of any type + +## 0. Prerequisites + +- Python 3.9 or higher installed +- Virtual environment set up +- `dlt` installed. Follow the instructions in the [installation guide](../reference/installation) to create a new virtual environment and install dlt. + +## 1. Setting up a new project + +To help you get started quickly, dlt provides some handy CLI commands. One of these commands will help you set up a new dlt project: + +```sh +dlt init filesystem duckdb +``` + +This command creates a project that loads data from a file system into a DuckDB database. You can easily switch out duckdb for any other [supported destinations](../dlt-ecosystem/destinations). +After running this command, your project will have the following structure: + +```text +filesystem_pipeline.py +requirements.txt +.dlt/ + config.toml + secrets.toml +``` + +Here’s what each file does: + +- `filesystem_pipeline.py`: This is the main script where you'll define your data pipeline. It contains several different examples of loading data from the filesystem source. +- `requirements.txt`: This file lists all the Python dependencies required for your project. +- `.dlt/`: This directory contains the [configuration files](../general-usage/credentials/) for your project: + - `secrets.toml`: This file stores your API keys, tokens, and other sensitive information. + - `config.toml`: This file contains the configuration settings for your dlt project. + +:::note +When deploying your pipeline in a production environment, managing all configurations with files might not be convenient. In this case, we recommend you to use the environment variables to store secrets and configs instead. Read more about [configuration providers](../general-usage/credentials/setup#available-config-providers) available in dlt. +::: + +## 2. Creating the pipeline + +The filesystem source provides users with building blocks for loading data from any type of files. You can break down the data extraction into two steps: + +1. Listing the files in the bucket / directory. +2. Reading the files and yielding records. + +dlt's filesystem source includes several resources: + +- the `filesystem` resource lists files in the directory or bucket +- several readers resources (`read_csv`, `read_parquet`, `read_jsonl`) read files and yield the records. These resources have a +special type, they called [transformers](../general-usage/resource#process-resources-with-dlttransformer). Transformers expect items from another resource. +In this particular case transformers expect `FileItem` object and transform it into multiple records. + +Let's initialize a source and create a pipeline for loading CSV files from Google Cloud Storage to DuckDB. You can replace code from `filesystem_pipeline.py` with the following: + +```py +import dlt +from dlt.sources.filesystem import filesystem, read_csv + +files = filesystem(bucket_url="gs://filesystem-tutorial", file_glob="encounters*.csv") +reader = (files | read_csv()).with_name("encounters") +pipeline = dlt.pipeline(pipeline_name="hospital_data_pipeline", dataset_name="hospital_data", destination="duckdb") + +info = pipeline.run(reader) +print(info) +``` + +What's happening in the snippet above? + +1. We import the `filesystem` resource and initialize it with a bucket URL (`gs://filesystem-tutorial`) and the `file_glob` parameter. dlt uses `file_glob` to filter files names in the bucket. `filesystem` returns a generator object. +2. We pipe the files names yielded by the filesystem resource to the transformer resource `read_csv` to read each file and iterate over records from the file. We name this transformer resource `"encounters"` using the `with_name()`. dlt will use the resource name `"encounters"` as a table name when loading the data. + +:::note +A [transformer](../general-usage/resource#process-resources-with-dlttransformer) in dlt is a special type of resource that processes each record from another resource. This lets you chain multiple resources together. +::: + +3. We create the dlt pipeline configuring with the name `hospital_data_pipeline` and DuckDB destination. +4. We call `pipeline.run()`. This is where the underlying generators are iterated: + - dlt retrieves remote data, + - normalizes data, + - creates or updates the table in the destination, + - loads the extracted data into the destination. + 5. `print(info)` outputs pipeline running stats we get from `pipeline.run()` + +## 3. Configuring the filesystem source + +:::note +In this tutorial we will work with publicly accessed dataset [Hospital Patient Records](https://mavenanalytics.io/data-playground?order=date_added%2Cdesc&search=Hospital%20Patient%20Records) +synthetic electronic health care records. You can use the exact credentials from this tutorial to load this dataset from GCP. +
+Citation +Jason Walonoski, Mark Kramer, Joseph Nichols, Andre Quina, Chris Moesel, Dylan Hall, Carlton Duffett, Kudakwashe Dube, Thomas Gallagher, Scott McLachlan, Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record, Journal of the American Medical Informatics Association, Volume 25, Issue 3, March 2018, Pages 230–238, https://doi.org/10.1093/jamia/ocx079 +
+::: + +Next, we need to configure the connection. Specifically, we’ll set the bucket URL and credentials. This example uses Google Cloud Storage. For other cloud storage services, see the [Filesystem configuration section](../dlt-ecosystem/verified-sources/filesystem/basic#configuration). + +Let's specify the bucket URL and credentials. We can do this using the following methods: + + + + + +```toml +# secrets.toml +[sources.filesystem.credentials] +client_email = "public-access@dlthub-sandbox.iam.gserviceaccount.com" +project_id = "dlthub-sandbox" +private_key = "-----BEGIN PRIVATE KEY-----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\nxWJ51y0wpme0LWgzzoLbRw==\n-----END PRIVATE KEY-----\n" + +# config.toml +[sources.filesystem] +bucket_url="gs://filesystem-tutorial" +``` + + + + +```sh +export SOURCES__FILESYSTEM__CREDENTIALS__CLIENT_EMAIL="public-access@dlthub-sandbox.iam.gserviceaccount.com" +export SOURCES__FILESYSTEM__CREDENTIALS__PROJECT_ID="dlthub-sandbox" +export SOURCES__FILESYSTEM__CREDENTIALS__PRIVATE_KEY="-----BEGIN PRIVATE KEY-----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\nxWJ51y0wpme0LWgzzoLbRw==\n-----END PRIVATE KEY-----\n" +export SOURCES__FILESYSTEM__BUCKET_URL="gs://filesystem-tutorial" +``` + + + + +```py +import os + +from dlt.common.configuration.specs import GcpClientCredentials +from dlt.sources.filesystem import filesystem, read_csv + +files = filesystem( + bucket_url="gs://filesystem-tutorial", + # please, do not specify sensitive information directly in the code, + # instead, you can use env variables to get the credentials + credentials=GcpClientCredentials( + client_email="public-access@dlthub-sandbox.iam.gserviceaccount.com", + project_id="dlthub-sandbox", + private_key=os.environ["GCP_PRIVATE_KEY"] # type: ignore + ), + file_glob="encounters*.csv") | read_csv() +``` + + + +As you can see, all parameters of `filesystem` can be specified directly in the code or taken from the configuration. + +:::tip +dlt supports more ways of authorizing with the cloud storages, including identity-based and default credentials. To learn more about adding credentials to your pipeline, please refer to the [Configuration and secrets section](../general-usage/credentials/complex_types#aws-credentials). +::: + +## 4. Running the pipeline + +Let's verify that the pipeline is working as expected. Run the following command to execute the pipeline: + +```sh +python filesystem_pipeline.py +``` + +You should see the output of the pipeline execution in the terminal. The output will also display the location of the DuckDB database file where the data is stored: + +```sh +Pipeline hospital_data_pipeline load step completed in 4.11 seconds +1 load package(s) were loaded to destination duckdb and into dataset hospital_data +The duckdb destination used duckdb:////Users/vmishechk/PycharmProjects/dlt/hospital_data_pipeline.duckdb location to store data +Load package 1726074108.8017762 is LOADED and contains no failed jobs +``` + +## 5. Exploring the data + +Now that the pipeline has run successfully, let's explore the data loaded into DuckDB. dlt comes with a built-in browser application that allows you to interact with the data. To enable it, run the following command: + +```sh +pip install streamlit +``` + +Next, run the following command to start the data browser: + +```sh +dlt pipeline hospital_data_pipeline show +``` + +The command opens a new browser window with the data browser application. `hospital_data_pipeline` is the name of the pipeline defined in the `filesystem_pipeline.py` file. + +![Streamlit Explore data](/img/filesystem-tutorial/streamlit-data.png) + +You can explore the loaded data, run queries, and see some pipeline execution details. + +## 6. Appending, replacing, and merging loaded data + +If you try running the pipeline again with `python filesystem_pipeline.py`, you will notice that all the tables have duplicated data. This happens because by default, dlt appends the data to the destination table. It is very useful, for example, when you have daily data updates and you want to ingest them. With dlt, you can control how the data is loaded into the destination table by setting the `write_disposition` parameter in the resource configuration. The possible values are: +- `append`: Appends the data to the destination table. This is the default. +- `replace`: Replaces the data in the destination table with the new data. +- `merge`: Merges the new data with the existing data in the destination table based on the primary key. + +To specify the `write_disposition`, you can set it in the `pipeline.run` command. Let's change the write disposition to `merge`. In this case, dlt will deduplicate the data before loading them into the destination. + +To enable data deduplication, we also should specify a `primary_key` or `merge_key`, which will be used by dlt to define if two records are different. Both keys could consist of several columns. dlt will try to use `merge_key` and fallback to `primary_key` if it's not specified. To specify any hints about the data, including column types, primary keys, you can use the [`apply_hints`](../general-usage/resource#set-table-name-and-adjust-schema) method. + +```py +import dlt +from dlt.sources.filesystem import filesystem, read_csv + +files = filesystem(file_glob="encounters*.csv") +reader = (files | read_csv()).with_name("encounters") +reader.apply_hints(primary_key="id") +pipeline = dlt.pipeline(pipeline_name="hospital_data_pipeline", dataset_name="hospital_data", destination="duckdb") + +info = pipeline.run(reader, write_disposition="merge") +print(info) +``` +:::tip +You may need to drop the previously loaded data if you loaded data several times with `append` write disposition to make sure the primary key column has unique values. +::: + +You can learn more about `write_disposition` in the [write dispositions section](../general-usage/incremental-loading#the-3-write-dispositions) of the incremental loading page. + +## 7. Loading data incrementally + +When loading data from files, you often only want to load files that have been modified. dlt makes this easy with [incremental loading](../general-usage/incremental-loading). To load only modified files, you can use the `apply_hint` method: + +```py +import dlt +from dlt.sources.filesystem import filesystem, read_csv + +files = filesystem(file_glob="encounters*.csv") +files.apply_hints(incremental=dlt.sources.incremental("modification_date")) +reader = (files | read_csv()).with_name("encounters") +reader.apply_hints(primary_key="id") +pipeline = dlt.pipeline(pipeline_name="hospital_data_pipeline", dataset_name="hospital_data", destination="duckdb") + +info = pipeline.run(reader, write_disposition="merge") +print(info) +``` + +Notice that we used `apply_hints` on the `files` resource, not on `reader`. Why did we do that? As mentioned before, the `filesystem` resource lists all files in the storage based on the `file_glob` parameter. So at this point, we can also specify additional conditions to filter out files. In this case, we only want to load files that have been modified since the last load. dlt will automatically keep the state of incremental load and manage the correct filtering. + +But what if we not only want to process modified files, but we also want to load only new records? In the `encounters` table, we can see the column named `STOP` indicating the timestamp of the end of the encounter. Let's modify our code to load only those records whose `STOP` timestamp was updated since our last load. + +```py +import dlt +from dlt.sources.filesystem import filesystem, read_csv + +files = filesystem(file_glob="encounters*.csv") +files.apply_hints(incremental=dlt.sources.incremental("modification_date")) +reader = (files | read_csv()).with_name("encounters") +reader.apply_hints(primary_key="id", incremental=dlt.sources.incremental("STOP")) +pipeline = dlt.pipeline(pipeline_name="hospital_data_pipeline", dataset_name="hospital_data", destination="duckdb") + +info = pipeline.run(reader, write_disposition="merge") +print(info) +``` + +Notice that we applied incremental loading both for `files` and for `reader`. Therefore, dlt will first filter out only modified files and then filter out new records based on the `STOP` column. + +If you run `dlt pipeline hospital_data_pipeline show`, you can see the pipeline now has new information in the state about the incremental variable: + +![Streamlit Explore data](/img/filesystem-tutorial/streamlit-incremental-state.png) + +To learn more about incremental loading, check out the [filesystem incremental loading section](../dlt-ecosystem/verified-sources/filesystem/basic#5-incremental-loading). + +## 8. Enrich records with the files metadata + +Now let's add the file names to the actual records. This could be useful to connect the files' origins to the actual records. + +Since the `filesystem` source yields information about files, we can modify the transformer to add any available metadata. Let's create a custom transformer function. We can just copy-paste the `read_csv` function from dlt code and add one column `file_name` to the dataframe: + +```py +from typing import Any, Iterator + +import dlt +from dlt.sources import TDataItems +from dlt.sources.filesystem import FileItemDict +from dlt.sources.filesystem import filesystem + + +@dlt.transformer() +def read_csv_custom(items: Iterator[FileItemDict], chunksize: int = 10000, **pandas_kwargs: Any) -> Iterator[TDataItems]: + import pandas as pd + + # apply defaults to pandas kwargs + kwargs = {**{"header": "infer", "chunksize": chunksize}, **pandas_kwargs} + + for file_obj in items: + with file_obj.open() as file: + for df in pd.read_csv(file, **kwargs): + df["file_name"] = file_obj["file_name"] + yield df.to_dict(orient="records") + +files = filesystem(file_glob="encounters*.csv") +files.apply_hints(incremental=dlt.sources.incremental("modification_date")) +reader = (files | read_csv_custom()).with_name("encounters") +reader.apply_hints(primary_key="id", incremental=dlt.sources.incremental("STOP")) +pipeline = dlt.pipeline(pipeline_name="hospital_data_pipeline", dataset_name="hospital_data", destination="duckdb") + +info = pipeline.run(reader, write_disposition="merge") +print(info) +``` + +After executing this code, you'll see a new column in the `encounters` table: + +![Streamlit Explore data](/img/filesystem-tutorial/streamlit-new-col.png) + +## 9. Load any other type of files + +dlt natively supports three file types: CSV, Parquet, and JSONL (more details in [filesystem transformer resource](../dlt-ecosystem/verified-sources/filesystem/basic#2-choose-the-right-transformer-resource)). But you can easily create your own. In order to do this, you just need a function that takes as input a `FileItemDict` iterator and yields a list of records (recommended for performance) or individual records. + +Let's create and apply a transformer that reads JSON files instead of CSV (the implementation for JSON is a little bit different from JSONL). + +```py +from typing import Iterator + +import dlt +from dlt.common import json +from dlt.common.storages.fsspec_filesystem import FileItemDict +from dlt.common.typing import TDataItems +from dlt.sources.filesystem import filesystem + +# Define a standalone transformer to read data from a json file. +@dlt.transformer(standalone=True) +def read_json(items: Iterator[FileItemDict]) -> Iterator[TDataItems]: + for file_obj in items: + with file_obj.open() as f: + yield json.load(f) + +files_resource = filesystem(file_glob="**/*.json") +files_resource.apply_hints(incremental=dlt.sources.incremental("modification_date")) +json_resource = files_resource | read_json() +pipeline = dlt.pipeline(pipeline_name="s3_to_duckdb", dataset_name="json_data", destination="duckdb") + +info = pipeline.run(json_resource, write_disposition="replace") +print(info) +``` + +Check out [other examples](../dlt-ecosystem/verified-sources/filesystem/advanced#create-your-own-transformer) showing how to read data from `excel` and `xml` files. + +## What's next? + +Congratulations on completing the tutorial! You've learned how to set up a filesystem source in dlt and run a data pipeline to load the data into DuckDB. + +Interested in learning more about dlt? Here are some suggestions: + +- Learn more about the filesystem source configuration in [filesystem source](../dlt-ecosystem/verified-sources/filesystem) +- Learn more about different credential types in [Built-in credentials](../general-usage/credentials/complex_types#built-in-credentials) +- Learn how to [create a custom source](./load-data-from-an-api.md) in the advanced tutorial diff --git a/docs/website/docs/tutorial/grouping-resources.md b/docs/website/docs/tutorial/grouping-resources.md deleted file mode 100644 index 2bbfd231f2..0000000000 --- a/docs/website/docs/tutorial/grouping-resources.md +++ /dev/null @@ -1,296 +0,0 @@ ---- -title: Resource grouping and secrets -description: Advanced tutorial on loading data from an API -keywords: [api, source, decorator, dynamic resource, github, tutorial] ---- - -This tutorial continues the [previous](load-data-from-an-api) part. We'll use the same GitHub API example to show you how to: -1. Load data from other GitHub API endpoints. -1. Group your resources into sources for easier management. -2. Handle secrets and configuration. - -## Use source decorator - -In the previous tutorial, we loaded issues from the GitHub API. Now we'll prepare to load comments from the API as well. Here's a sample [dlt resource](../general-usage/resource) that does that: - -```py -import dlt -from dlt.sources.helpers.rest_client import paginate - -@dlt.resource( - table_name="comments", - write_disposition="merge", - primary_key="id", -) -def get_comments( - updated_at = dlt.sources.incremental("updated_at", initial_value="1970-01-01T00:00:00Z") -): - for page in paginate( - "https://api.github.com/repos/dlt-hub/dlt/comments", - params={"per_page": 100} - ): - yield page -``` - -We can load this resource separately from the issues resource, however loading both issues and comments in one go is more efficient. To do that, we'll use the `@dlt.source` decorator on a function that returns a list of resources: - -```py -@dlt.source -def github_source(): - return [get_issues, get_comments] -``` - -`github_source()` groups resources into a [source](../general-usage/source). A dlt source is a logical grouping of resources. You use it to group resources that belong together, for example, to load data from the same API. Loading data from a source can be run in a single pipeline. Here's what our updated script looks like: - -```py -import dlt -from dlt.sources.helpers.rest_client import paginate - -@dlt.resource( - table_name="issues", - write_disposition="merge", - primary_key="id", -) -def get_issues( - updated_at = dlt.sources.incremental("updated_at", initial_value="1970-01-01T00:00:00Z") -): - for page in paginate( - "https://api.github.com/repos/dlt-hub/dlt/issues", - params={ - "since": updated_at.last_value, - "per_page": 100, - "sort": "updated", - "directions": "desc", - "state": "open", - } - ): - yield page - - -@dlt.resource( - table_name="comments", - write_disposition="merge", - primary_key="id", -) -def get_comments( - updated_at = dlt.sources.incremental("updated_at", initial_value="1970-01-01T00:00:00Z") -): - for page in paginate( - "https://api.github.com/repos/dlt-hub/dlt/comments", - params={ - "since": updated_at.last_value, - "per_page": 100, - } - ): - yield page - - -@dlt.source -def github_source(): - return [get_issues, get_comments] - - -pipeline = dlt.pipeline( - pipeline_name='github_with_source', - destination='duckdb', - dataset_name='github_data', -) - -load_info = pipeline.run(github_source()) -print(load_info) -``` - -### Dynamic resources - -You've noticed that there's a lot of code duplication in the `get_issues` and `get_comments` functions. We can reduce that by extracting the common fetching code into a separate function and use it in both resources. Even better, we can use `dlt.resource` as a function and pass it the `fetch_github_data()` generator function directly. Here's the refactored code: - -```py -import dlt -from dlt.sources.helpers.rest_client import paginate - -BASE_GITHUB_URL = "https://api.github.com/repos/dlt-hub/dlt" - -def fetch_github_data(endpoint, params={}): - url = f"{BASE_GITHUB_URL}/{endpoint}" - return paginate(url, params=params) - -@dlt.source -def github_source(): - for endpoint in ["issues", "comments"]: - params = {"per_page": 100} - yield dlt.resource( - fetch_github_data(endpoint, params), - name=endpoint, - write_disposition="merge", - primary_key="id", - ) - -pipeline = dlt.pipeline( - pipeline_name='github_dynamic_source', - destination='duckdb', - dataset_name='github_data', -) -load_info = pipeline.run(github_source()) -row_counts = pipeline.last_trace.last_normalize_info -``` - -## Handle secrets - -For the next step we'd want to get the [number of repository clones](https://docs.github.com/en/rest/metrics/traffic?apiVersion=2022-11-28#get-repository-clones) for our dlt repo from the GitHub API. However, the `traffic/clones` endpoint that returns the data requires [authentication](https://docs.github.com/en/rest/overview/authenticating-to-the-rest-api?apiVersion=2022-11-28). - -Let's handle this by changing our `fetch_github_data()` first: - -```py -from dlt.sources.helpers.rest_client.auth import BearerTokenAuth - -def fetch_github_data(endpoint, params={}, access_token=None): - url = f"{BASE_GITHUB_URL}/{endpoint}" - return paginate( - url, - params=params, - auth=BearerTokenAuth(token=access_token) if access_token else None, - ) - - -@dlt.source -def github_source(access_token): - for endpoint in ["issues", "comments", "traffic/clones"]: - params = {"per_page": 100} - yield dlt.resource( - fetch_github_data(endpoint, params, access_token), - name=endpoint, - write_disposition="merge", - primary_key="id", - ) - -... -``` - -Here, we added `access_token` parameter and now we can use it to pass the access token to the request: - -```py -load_info = pipeline.run(github_source(access_token="ghp_XXXXX")) -``` - -It's a good start. But we'd want to follow the best practices and not hardcode the token in the script. One option is to set the token as an environment variable, load it with `os.getenv()` and pass it around as a parameter. dlt offers a more convenient way to handle secrets and credentials: it lets you inject the arguments using a special `dlt.secrets.value` argument value. - -To use it, change the `github_source()` function to: - -```py -@dlt.source -def github_source( - access_token: str = dlt.secrets.value, -): - ... -``` - -When you add `dlt.secrets.value` as a default value for an argument, `dlt` will try to load and inject this value from different configuration sources in the following order: - -1. Special environment variables. -2. `secrets.toml` file. - -The `secret.toml` file is located in the `~/.dlt` folder (for global configuration) or in the `.dlt` folder in the project folder (for project-specific configuration). - -Let's add the token to the `~/.dlt/secrets.toml` file: - -```toml -[github_with_source_secrets] -access_token = "ghp_A...3aRY" -``` - -Now we can run the script and it will load the data from the `traffic/clones` endpoint: - -```py -... - -@dlt.source -def github_source( - access_token: str = dlt.secrets.value, -): - for endpoint in ["issues", "comments", "traffic/clones"]: - params = {"per_page": 100} - yield dlt.resource( - fetch_github_data(endpoint, params, access_token), - name=endpoint, - write_disposition="merge", - primary_key="id", - ) - - -pipeline = dlt.pipeline( - pipeline_name="github_with_source_secrets", - destination="duckdb", - dataset_name="github_data", -) -load_info = pipeline.run(github_source()) -``` - -## Configurable sources - -The next step is to make our dlt GitHub source reusable so it can load data from any GitHub repo. We'll do that by changing both `github_source()` and `fetch_github_data()` functions to accept the repo name as a parameter: - -```py -import dlt -from dlt.sources.helpers.rest_client import paginate - -BASE_GITHUB_URL = "https://api.github.com/repos/{repo_name}" - - -def fetch_github_data(repo_name, endpoint, params={}, access_token=None): - """Fetch data from GitHub API based on repo_name, endpoint, and params.""" - url = BASE_GITHUB_URL.format(repo_name=repo_name) + f"/{endpoint}" - return paginate( - url, - params=params, - auth=BearerTokenAuth(token=access_token) if access_token else None, - ) - - -@dlt.source -def github_source( - repo_name: str = dlt.config.value, - access_token: str = dlt.secrets.value, -): - for endpoint in ["issues", "comments", "traffic/clones"]: - params = {"per_page": 100} - yield dlt.resource( - fetch_github_data(repo_name, endpoint, params, access_token), - name=endpoint, - write_disposition="merge", - primary_key="id", - ) - - -pipeline = dlt.pipeline( - pipeline_name="github_with_source_secrets", - destination="duckdb", - dataset_name="github_data", -) -load_info = pipeline.run(github_source()) -``` - -Next, create a `.dlt/config.toml` file in the project folder and add the `repo_name` parameter to it: - -```toml -[github_with_source_secrets] -repo_name = "dlt-hub/dlt" -``` - -That's it! Now you have a reusable source that can load data from any GitHub repo. - -## What’s next - -Congratulations on completing the tutorial! You've come a long way since the [getting started](../getting-started) guide. By now, you've mastered loading data from various GitHub API endpoints, organizing resources into sources, managing secrets securely, and creating reusable sources. You can use these skills to build your own pipelines and load data from any source. - -Interested in learning more? Here are some suggestions: -1. You've been running your pipelines locally. Learn how to [deploy and run them in the cloud](../walkthroughs/deploy-a-pipeline/). -2. Dive deeper into how dlt works by reading the [Using dlt](../general-usage) section. Some highlights: - - [Connect the transformers to the resources](../general-usage/resource#feeding-data-from-one-resource-into-another) to load additional data or enrich it. - - [Create your resources dynamically from data](../general-usage/source#create-resources-dynamically). - - [Transform your data before loading](../general-usage/resource#customize-resources) and see some [examples of customizations like column renames and anonymization](../general-usage/customising-pipelines/renaming_columns). - - [Pass config and credentials into your sources and resources](../general-usage/credentials). - - [Run in production: inspecting, tracing, retry policies and cleaning up](../running-in-production/running). - - [Run resources in parallel, optimize buffers and local storage](../reference/performance.md) - - [Use REST API client helpers](../general-usage/http/rest-client.md) to simplify working with REST APIs. -3. Check out our [how-to guides](../walkthroughs) to get answers to some common questions. -4. Explore the [Examples](../examples) section to see how dlt can be used in real-world scenarios diff --git a/docs/website/docs/tutorial/intro.md b/docs/website/docs/tutorial/intro.md deleted file mode 100644 index 2d53412ae0..0000000000 --- a/docs/website/docs/tutorial/intro.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -title: Tutorial -description: Build a data pipeline with dlt -keywords: [tutorial, api, github, duckdb, pipeline] ---- -Welcome to the tutorial on how to efficiently use dlt to build a data pipeline. This tutorial will introduce you to the foundational concepts of dlt and guide you through basic and advanced usage scenarios. - -As a practical example, we'll build a data pipeline that loads data from the GitHub API into DuckDB. - -## What We'll Cover - -- [Fetching data from the GitHub API](./load-data-from-an-api.md) -- [Understanding and managing data loading behaviors](./load-data-from-an-api.md#append-or-replace-your-data) -- [Incrementally loading new data and deduplicating existing data](./load-data-from-an-api.md#load-only-new-data-incremental-loading) -- [Making our data fetch more dynamic and reducing code redundancy](./grouping-resources.md) -- [Securely handling secrets](./grouping-resources.md#handle-secrets) -- [Making reusable data sources](./grouping-resources.md#configurable-sources) - -## Ready to dive in? - -Let's begin by loading data from an API. \ No newline at end of file diff --git a/docs/website/docs/tutorial/load-data-from-an-api.md b/docs/website/docs/tutorial/load-data-from-an-api.md index ec6136b6d3..1e40531691 100644 --- a/docs/website/docs/tutorial/load-data-from-an-api.md +++ b/docs/website/docs/tutorial/load-data-from-an-api.md @@ -1,19 +1,106 @@ --- -title: Load data from an API -description: quick start with dlt +title: "Build a dlt pipeline" +description: Build a data pipeline with dlt keywords: [getting started, quick start, basic examples] --- -In this section, we will retrieve and load data from the GitHub API into [DuckDB](https://duckdb.org). Specifically, we will load issues from our [dlt-hub/dlt](https://github.com/dlt-hub/dlt) repository. We picked DuckDB as our destination because it is a lightweight, in-process database that is easy to set up and use. +This tutorial introduces you to foundational dlt concepts, demonstrating how to build a custom data pipeline that loads data from pure Python data structures to DuckDB. It starts with a simple example and progresses to more advanced topics and usage scenarios. -Before we start, make sure you have installed `dlt` with the DuckDB dependency: +## What you will learn + +- Loading data from a list of Python dictionaries into DuckDB. +- Low level API usage with built-in HTTP client. +- Understand and manage data loading behaviors. +- Incrementally load new data and deduplicate existing data. +- Dynamic resource creation and reducing code redundancy. +- Group resources into sources. +- Securely handle secrets. +- Make reusable data sources. + +## Prerequisites + +- Python 3.9 or higher installed +- Virtual environment set up + +## Installing dlt + +Before we start, make sure you have a Python virtual environment set up. Follow the instructions in the [installation guide](../reference/installation) to create a new virtual environment and install dlt. + +Verify that dlt is installed by running the following command in your terminal: + +```sh +dlt --version +``` + +## Quick start + +For starters, let's load a list of Python dictionaries into DuckDB and inspect the created dataset. Here is the code: + +```py +import dlt + +data = [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}] + +pipeline = dlt.pipeline( + pipeline_name="quick_start", destination="duckdb", dataset_name="mydata" +) +load_info = pipeline.run(data, table_name="users") + +print(load_info) +``` + +When you look at the code above, you can see that we: +1. Import the `dlt` library. +2. Define our data to load. +3. Create a pipeline that loads data into DuckDB. Here we also specify the `pipeline_name` and `dataset_name`. We'll use both in a moment. +4. Run the pipeline. + +Save this Python script with the name `quick_start_pipeline.py` and run the following command: ```sh -pip install "dlt[duckdb]" +python quick_start_pipeline.py ``` +The output should look like: + +```sh +Pipeline quick_start completed in 0.59 seconds +1 load package(s) were loaded to destination duckdb and into dataset mydata +The duckdb destination used duckdb:////home/user-name/quick_start/quick_start.duckdb location to store data +Load package 1692364844.460054 is LOADED and contains no failed jobs +``` + +`dlt` just created a database schema called **mydata** (the `dataset_name`) with a table **users** in it. + +### Explore the data + +To allow sneak peek and basic discovery you can take advantage of [built-in integration with Strealmit](reference/command-line-interface#show-tables-and-data-in-the-destination): + +```sh +dlt pipeline quick_start show +``` + +**quick_start** is the name of the pipeline from the script above. If you do not have Streamlit installed yet do: + +```sh +pip install streamlit +``` + +Now you should see the **users** table: + +![Streamlit Explore data](/img/streamlit-new.png) +Streamlit Explore data. Schema and data for a test pipeline “quick_start”. + :::tip -Need help with this tutorial? Join our [Slack community](https://dlthub.com/community) for quick support. +`dlt` works in Jupyter Notebook and Google Colab! See our [Quickstart Colab Demo.](https://colab.research.google.com/drive/1NfSB1DpwbbHX9_t5vlalBTf13utwpMGx?usp=sharing) + +Looking for source code of all the snippets? You can find and run them [from this repository](https://github.com/dlt-hub/dlt/blob/devel/docs/website/docs/getting-started-snippets.py). +::: + +Now that you have a basic understanding of how to get started with dlt, you might be eager to dive deeper. For that we need to switch to a more advanced data source - the GitHub API. We will load issues from our [dlt-hub/dlt](https://github.com/dlt-hub/dlt) repository. + +:::note +This tutorial uses GitHub REST API for demonstration purposes only. If you need to read data from a REST API, consider using the dlt's REST API source. Check out the [REST API source tutorial](./rest-api) for quick start or [REST API source reference](../dlt-ecosystem/verified-sources/rest_api) for more details. ::: ## Create a pipeline @@ -89,7 +176,7 @@ Learn more: ## Declare loading behavior -So far we have been passing the data to the `run` method directly. This is a quick way to get started. However, frequenly, you receive data in chunks, and you want to load it as it arrives. For example, you might want to load data from an API endpoint with pagination or a large file that does not fit in memory. In such cases, you can use Python generators as a data source. +So far we have been passing the data to the `run` method directly. This is a quick way to get started. However, frequently, you receive data in chunks, and you want to load it as it arrives. For example, you might want to load data from an API endpoint with pagination or a large file that does not fit in memory. In such cases, you can use Python generators as a data source. You can pass a generator to the `run` method directly or use the `@dlt.resource` decorator to turn the generator into a [dlt resource](../general-usage/resource). The decorator allows you to specify the loading behavior and relevant resource parameters. @@ -197,16 +284,298 @@ Let's zoom in on the changes: 2. `paginate()` takes the URL of the API endpoint and optional parameters. In this case, we pass the `since` parameter to get only issues updated after the last pipeline run. 3. We're not explicitly setting up pagination, `paginate()` handles it for us. Magic! Under the hood, `paginate()` analyzes the response and detects the pagination method used by the API. Read more about pagination in the [REST client documentation](../general-usage/http/rest-client.md#paginating-api-responses). -## Next steps +If you want to take full advantage of the `dlt` library, then we strongly suggest that you build your sources out of existing building blocks: +To make most of `dlt`, consider the following: + +## Use source decorator + +In the previous step, we loaded issues from the GitHub API. Now we'll load comments from the API as well. Here's a sample [dlt resource](../general-usage/resource) that does that: + +```py +import dlt +from dlt.sources.helpers.rest_client import paginate + +@dlt.resource( + table_name="comments", + write_disposition="merge", + primary_key="id", +) +def get_comments( + updated_at = dlt.sources.incremental("updated_at", initial_value="1970-01-01T00:00:00Z") +): + for page in paginate( + "https://api.github.com/repos/dlt-hub/dlt/comments", + params={"per_page": 100} + ): + yield page +``` + +We can load this resource separately from the issues resource, however loading both issues and comments in one go is more efficient. To do that, we'll use the `@dlt.source` decorator on a function that returns a list of resources: + +```py +@dlt.source +def github_source(): + return [get_issues, get_comments] +``` + +`github_source()` groups resources into a [source](../general-usage/source). A dlt source is a logical grouping of resources. You use it to group resources that belong together, for example, to load data from the same API. Loading data from a source can be run in a single pipeline. Here's what our updated script looks like: + +```py +import dlt +from dlt.sources.helpers.rest_client import paginate + +@dlt.resource( + table_name="issues", + write_disposition="merge", + primary_key="id", +) +def get_issues( + updated_at = dlt.sources.incremental("updated_at", initial_value="1970-01-01T00:00:00Z") +): + for page in paginate( + "https://api.github.com/repos/dlt-hub/dlt/issues", + params={ + "since": updated_at.last_value, + "per_page": 100, + "sort": "updated", + "directions": "desc", + "state": "open", + } + ): + yield page + + +@dlt.resource( + table_name="comments", + write_disposition="merge", + primary_key="id", +) +def get_comments( + updated_at = dlt.sources.incremental("updated_at", initial_value="1970-01-01T00:00:00Z") +): + for page in paginate( + "https://api.github.com/repos/dlt-hub/dlt/comments", + params={ + "since": updated_at.last_value, + "per_page": 100, + } + ): + yield page + + +@dlt.source +def github_source(): + return [get_issues, get_comments] + + +pipeline = dlt.pipeline( + pipeline_name='github_with_source', + destination='duckdb', + dataset_name='github_data', +) + +load_info = pipeline.run(github_source()) +print(load_info) +``` + +### Dynamic resources + +You've noticed that there's a lot of code duplication in the `get_issues` and `get_comments` functions. We can reduce that by extracting the common fetching code into a separate function and use it in both resources. Even better, we can use `dlt.resource` as a function and pass it the `fetch_github_data()` generator function directly. Here's the refactored code: + +```py +import dlt +from dlt.sources.helpers.rest_client import paginate + +BASE_GITHUB_URL = "https://api.github.com/repos/dlt-hub/dlt" + +def fetch_github_data(endpoint, params={}): + url = f"{BASE_GITHUB_URL}/{endpoint}" + return paginate(url, params=params) + +@dlt.source +def github_source(): + for endpoint in ["issues", "comments"]: + params = {"per_page": 100} + yield dlt.resource( + fetch_github_data(endpoint, params), + name=endpoint, + write_disposition="merge", + primary_key="id", + ) + +pipeline = dlt.pipeline( + pipeline_name='github_dynamic_source', + destination='duckdb', + dataset_name='github_data', +) +load_info = pipeline.run(github_source()) +row_counts = pipeline.last_trace.last_normalize_info +``` + +## Handle secrets + +For the next step we'd want to get the [number of repository clones](https://docs.github.com/en/rest/metrics/traffic?apiVersion=2022-11-28#get-repository-clones) for our dlt repo from the GitHub API. However, the `traffic/clones` endpoint that returns the data requires [authentication](https://docs.github.com/en/rest/overview/authenticating-to-the-rest-api?apiVersion=2022-11-28). + +Let's handle this by changing our `fetch_github_data()` first: + +```py +from dlt.sources.helpers.rest_client.auth import BearerTokenAuth + +def fetch_github_data(endpoint, params={}, access_token=None): + url = f"{BASE_GITHUB_URL}/{endpoint}" + return paginate( + url, + params=params, + auth=BearerTokenAuth(token=access_token) if access_token else None, + ) + + +@dlt.source +def github_source(access_token): + for endpoint in ["issues", "comments", "traffic/clones"]: + params = {"per_page": 100} + yield dlt.resource( + fetch_github_data(endpoint, params, access_token), + name=endpoint, + write_disposition="merge", + primary_key="id", + ) + +... +``` + +Here, we added `access_token` parameter and now we can use it to pass the access token to the request: + +```py +load_info = pipeline.run(github_source(access_token="ghp_XXXXX")) +``` + +It's a good start. But we'd want to follow the best practices and not hardcode the token in the script. One option is to set the token as an environment variable, load it with `os.getenv()` and pass it around as a parameter. dlt offers a more convenient way to handle secrets and credentials: it lets you inject the arguments using a special `dlt.secrets.value` argument value. + +To use it, change the `github_source()` function to: + +```py +@dlt.source +def github_source( + access_token: str = dlt.secrets.value, +): + ... +``` + +When you add `dlt.secrets.value` as a default value for an argument, `dlt` will try to load and inject this value from different configuration sources in the following order: + +1. Special environment variables. +2. `secrets.toml` file. + +The `secret.toml` file is located in the `~/.dlt` folder (for global configuration) or in the `.dlt` folder in the project folder (for project-specific configuration). + +Let's add the token to the `~/.dlt/secrets.toml` file: + +```toml +[github_with_source_secrets] +access_token = "ghp_A...3aRY" +``` + +Now we can run the script and it will load the data from the `traffic/clones` endpoint: + +```py +... + +@dlt.source +def github_source( + access_token: str = dlt.secrets.value, +): + for endpoint in ["issues", "comments", "traffic/clones"]: + params = {"per_page": 100} + yield dlt.resource( + fetch_github_data(endpoint, params, access_token), + name=endpoint, + write_disposition="merge", + primary_key="id", + ) + + +pipeline = dlt.pipeline( + pipeline_name="github_with_source_secrets", + destination="duckdb", + dataset_name="github_data", +) +load_info = pipeline.run(github_source()) +``` + +## Configurable sources + +The next step is to make our dlt GitHub source reusable so it can load data from any GitHub repo. We'll do that by changing both `github_source()` and `fetch_github_data()` functions to accept the repo name as a parameter: + +```py +import dlt +from dlt.sources.helpers.rest_client import paginate + +BASE_GITHUB_URL = "https://api.github.com/repos/{repo_name}" + + +def fetch_github_data(repo_name, endpoint, params={}, access_token=None): + """Fetch data from GitHub API based on repo_name, endpoint, and params.""" + url = BASE_GITHUB_URL.format(repo_name=repo_name) + f"/{endpoint}" + return paginate( + url, + params=params, + auth=BearerTokenAuth(token=access_token) if access_token else None, + ) + + +@dlt.source +def github_source( + repo_name: str = dlt.config.value, + access_token: str = dlt.secrets.value, +): + for endpoint in ["issues", "comments", "traffic/clones"]: + params = {"per_page": 100} + yield dlt.resource( + fetch_github_data(repo_name, endpoint, params, access_token), + name=endpoint, + write_disposition="merge", + primary_key="id", + ) + + +pipeline = dlt.pipeline( + pipeline_name="github_with_source_secrets", + destination="duckdb", + dataset_name="github_data", +) +load_info = pipeline.run(github_source()) +``` + +Next, create a `.dlt/config.toml` file in the project folder and add the `repo_name` parameter to it: + +```toml +[github_with_source_secrets] +repo_name = "dlt-hub/dlt" +``` + +That's it! Now you have a reusable source that can load data from any GitHub repo. + +## What’s next + +Congratulations on completing the tutorial! You've come a long way since the [getting started](../getting-started) guide. By now, you've mastered loading data from various GitHub API endpoints, organizing resources into sources, managing secrets securely, and creating reusable sources. You can use these skills to build your own pipelines and load data from any source. + +Interested in learning more? Here are some suggestions: +1. You've been running your pipelines locally. Learn how to [deploy and run them in the cloud](../walkthroughs/deploy-a-pipeline/). +2. Dive deeper into how dlt works by reading the [Using dlt](../general-usage) section. Some highlights: + - [Set up "last value" incremental loading](../general-usage/incremental-loading#incremental_loading-with-last-value). + - Learn about data loading strategies: [append, replace and merge](../general-usage/incremental-loading). + - [Connect the transformers to the resources](../general-usage/resource#feeding-data-from-one-resource-into-another) to load additional data or enrich it. + - [Customize your data schema—set primary and merge keys, define column nullability, and specify data types](../general-usage/resource#define-schema). + - [Create your resources dynamically from data](../general-usage/source#create-resources-dynamically). + - [Transform your data before loading](../general-usage/resource#customize-resources) and see some [examples of customizations like column renames and anonymization](../general-usage/customising-pipelines/renaming_columns). + - Employ data transformations using [SQL](../dlt-ecosystem/transformations/sql) or [Pandas](../dlt-ecosystem/transformations/sql). + - [Pass config and credentials into your sources and resources](../general-usage/credentials). + - [Run in production: inspecting, tracing, retry policies and cleaning up](../running-in-production/running). + - [Run resources in parallel, optimize buffers and local storage](../reference/performance.md) + - [Use REST API client helpers](../general-usage/http/rest-client.md) to simplify working with REST APIs. +3. Explore [destinations](../dlt-ecosystem/destinations/) and [sources](../dlt-ecosystem/verified-sources/) provided by us and community. +4. Explore the [Examples](../examples) section to see how dlt can be used in real-world scenarios -Continue your journey with the [Resource Grouping and Secrets](grouping-resources) tutorial. -If you want to take full advantage of the `dlt` library, then we strongly suggest that you build your sources out of existing **building blocks:** -- Pick your [destinations](../dlt-ecosystem/destinations/). -- Check [verified sources](../dlt-ecosystem/verified-sources/) provided by us and community. -- Access your data with [SQL](../dlt-ecosystem/transformations/sql) or [Pandas](../dlt-ecosystem/transformations/sql). -- [Append, replace and merge your tables](../general-usage/incremental-loading). -- [Set up "last value" incremental loading](../general-usage/incremental-loading#incremental_loading-with-last-value). -- [Set primary and merge keys, define the columns nullability and data types](../general-usage/resource#define-schema). -- [Use built-in requests client](../reference/performance#using-the-built-in-requests-client). \ No newline at end of file diff --git a/docs/website/docs/tutorial/rest-api.md b/docs/website/docs/tutorial/rest-api.md new file mode 100644 index 0000000000..3e214e0b55 --- /dev/null +++ b/docs/website/docs/tutorial/rest-api.md @@ -0,0 +1,322 @@ +--- +title: Load data from a REST API +description: How to extract data from a REST API using dlt's REST API source +keywords: [tutorial, api, github, duckdb, rest api, source, pagination, authentication] +--- + +This tutorial demonstrates how to extract data from a REST API using dlt's REST API source and load it into a destination. You will learn how to build a data pipeline that loads data from the [Pokemon](https://pokeapi.co/) and the [GitHub API](https://docs.github.com/en/) into a local DuckDB database. + +Extracting data from an API is straightforward with dlt: provide the base URL, define the resources you want to fetch, and dlt will handle the pagination, authentication, and data loading. + +## What you will learn + +- How to set up a REST API source +- Configuration basics for API endpoints +- Configuring the destination database +- Relationships between different resources +- How to append, replace, and merge data in the destination +- Loading data incrementally by fetching only new or updated data + +## Prerequisites + +- Python 3.9 or higher installed +- Virtual environment set up + +## Installing dlt + +Before we start, make sure you have a Python virtual environment set up. Follow the instructions in the [installation guide](../reference/installation) to create a new virtual environment and install dlt. + +Verify that dlt is installed by running the following command in your terminal: + +```sh +dlt --version +``` + +If you see the version number (such as "dlt 0.5.3"), you're ready to proceed. + +## Setting up a new project + +Initialize a new dlt project with REST API source and DuckDB destination: + +```sh +dlt init rest_api duckdb +``` + +`dlt init` creates multiple files and a directory for your project. Let's take a look at the project structure: + +```sh +rest_api_pipeline.py +requirements.txt +.dlt/ + config.toml + secrets.toml +``` + +Here's what each file and directory contains: + +- `rest_api_pipeline.py`: This is the main script where you'll define your data pipeline. It contains two basic pipeline examples for Pokemon and GitHub APIs. You can modify or rename this file as needed. +- `requirements.txt`: This file lists all the Python dependencies required for your project. +- `.dlt/`: This directory contains the [configuration files](../general-usage/credentials/) for your project: + - `secrets.toml`: This file stores your API keys, tokens, and other sensitive information. + - `config.toml`: This file contains the configuration settings for your dlt project. + +## Installing dependencies + +Before we proceed, let's install the required dependencies for this tutorial. Run the following command to install the dependencies listed in the `requirements.txt` file: + +```sh +pip install -r requirements.txt +``` + +## Running the pipeline + +Let's verify that the pipeline is working as expected. Run the following command to execute the pipeline: + +```sh +python rest_api_pipeline.py +``` + +You should see the output of the pipeline execution in the terminal. The output will also diplay the location of the DuckDB database file where the data is stored: + +```sh +Pipeline rest_api_pokemon load step completed in 1.08 seconds +1 load package(s) were loaded to destination duckdb and into dataset rest_api_data +The duckdb destination used duckdb:////home/user-name/quick_start/rest_api_pokemon.duckdb location to store data +Load package 1692364844.9254808 is LOADED and contains no failed jobs +``` + +## Exploring the data + +Now that the pipeline has run successfully, let's explore the data loaded into DuckDB. dlt comes with a built-in browser application that allows you to interact with the data. To enable it, run the following command: + +```sh +pip install streamlit +``` + +Next, run the following command to start the data browser: + +```sh +dlt pipeline rest_api_pokemon show +``` + +The command opens a new browser window with the data browser application. `rest_api_pokemon` is the name of the pipeline defined in the `rest_api_pipeline.py` file. +You can explore the loaded data, run queries and see some pipeline execution details: + +![Explore rest_api data in Streamlit App](https://dlt-static.s3.eu-central-1.amazonaws.com/images/docs-rest-api-tutorial-streamlit-screenshot.png) + +## Configuring the REST API source + +Now that your environment and the project are set up, let's take a closer look at the configuration of the REST API source. Open the `rest_api_pipeline.py` file in your code editor and locate the following code snippet: + +```py +def load_pokemon() -> None: + pipeline = dlt.pipeline( + pipeline_name="rest_api_pokemon", + destination="duckdb", + dataset_name="rest_api_data", + ) + + pokemon_source = rest_api_source( + { + "client": { + "base_url": "https://pokeapi.co/api/v2/" + }, + "resource_defaults": { + "endpoint": { + "params": { + "limit": 1000, + }, + }, + }, + "resources": [ + "pokemon", + "berry", + "location", + ], + } + ) + + ... + + load_info = pipeline.run(pokemon_source) + print(load_info) +``` + +Here what's happening in the code: + +1. With `dlt.pipeline()` we define a new pipeline named `rest_api_pokemon` with DuckDB as the destination and `rest_api_data` as the dataset name. +2. The `rest_api_source()` function creates a new REST API source object. +3. We pass this source object to the `pipeline.run()` method to start the pipeline execution. Inside the `run()` method, dlt will fetch data from the API and load it into the DuckDB database. +4. The `print(load_info)` outputs the pipeline execution details to the console. + +Let's break down the configuration of the REST API source. It consists of three main parts: `client`, `resource_defaults`, and `resources`. + +```py +config: RESTAPIConfig = { + "client": { + ... + }, + "resource_defaults": { + ... + }, + "resources": [ + ... + ], +} +``` + +- The `client` configuration is used to connect to the web server and authenticate if necessary. For our simple example, we only need to specify the `base_url` of the API: `https://pokeapi.co/api/v2/`. +- The `resource_defaults` configuration allows you to set default parameters for all resources. Normally you would set common parameters here, such as pagination limits. In our Pokemon API example, we set the `limit` parameter to 1000 for all resources to retrieve more data in a single request and reduce the number of HTTP API calls. +- The `resources` list contains the names of the resources you want to load from the API. REST API will use some conventions to determine the endpoint URL based on the resource name. For example, the resource name `pokemon` will be translated to the endpoint URL `https://pokeapi.co/api/v2/pokemon`. + +:::note +### Pagination +You may have noticed that we didn't specify any pagination configuration in the `rest_api_source()` function. That's because for REST APIs that follow best practices, dlt can automatically detect and handle pagination. Read more about [configuring pagination](../dlt-ecosystem/verified-sources/rest_api/basic#pagination) in the REST API source documentation. +::: + +## Appending, replacing, and merging loaded data + +Try running the pipeline again with `python rest_api_pipeline.py`. You will notice that all the tables have data duplicated. This happens because by default, dlt appends the data to the destination table. In dlt you can control how the data is loaded into the destination table by setting the `write_disposition` parameter in the resource configuration. The possible values are: +- `append`: Appends the data to the destination table. This is the default. +- `replace`: Replaces the data in the destination table with the new data. +- `merge`: Merges the new data with the existing data in the destination table based on the primary key. + +### Replacing the data + +In our case, we don't want to append the data every time we run the pipeline. Let's start with the simpler `replace` write disposition. + +To change the write disposition to `replace`, update the `resource_defaults` configuration in the `rest_api_pipeline.py` file: + +```py +... +pokemon_source = rest_api_source( + { + "client": { + "base_url": "https://pokeapi.co/api/v2/", + }, + "resource_defaults": { + "endpoint": { + "params": { + "limit": 1000, + }, + }, + "write_disposition": "replace", # Setting the write disposition to `replace` + }, + "resources": [ + "pokemon", + "berry", + "location", + ], + } +) +... +``` + +Run the pipeline again with `python rest_api_pipeline.py`. This time, the data will be replaced in the destination table instead of being appended. + +### Merging the data + +When you want to update the existing data as new data is loaded, you can use the `merge` write disposition. This requires specifying a primary key for the resource. The primary key is used to match the new data with the existing data in the destination table. + +Let's update our example to use the `merge` write disposition. We need to specify the primary key for the `pokemon` resource and set the write disposition to `merge`: + +```py +... +pokemon_source = rest_api_source( + { + "client": { + "base_url": "https://pokeapi.co/api/v2/", + }, + "resource_defaults": { + "endpoint": { + "params": { + "limit": 1000, + }, + }, + # For the `berry` and `location` resources, we keep + # the`replace` write disposition + "write_disposition": "replace", + }, + "resources": [ + # We create a specific configuration for the `pokemon` resource + # using a dictionary instead of a string to configure + # the primary key and write disposition + { + "name": "pokemon", + "primary_key": "id", + "write_disposition": "merge", + }, + # The `berry` and `location` resources will use the default + "berry", + "location", + ], + } +) +``` + +Run the pipeline with `python rest_api_pipeline.py`, the data for the `pokemon` resource will be merged with the existing data in the destination table based on the `id` field. + +## Loading data incrementally + +When working with some APIs, you may need to load data incrementally to avoid fetching the entire dataset every time and to reduce the load time. APIs that support incremental loading usually provide a way to fetch only new or changed data (most often by using a timestamp field like `updated_at`, `created_at`, or incremental IDs). + +To illustrate incremental loading, let's consider the GitHub API. In the `rest_api_pipeline.py` file, you can find an example of how to load data from the GitHub API incrementally. Let's take a look at the configuration: + +```py +pipeline = dlt.pipeline( + pipeline_name="rest_api_github", + destination="duckdb", + dataset_name="rest_api_data", +) + +github_source = rest_api_source({ + "client": { + "base_url": "https://api.github.com/repos/dlt-hub/dlt/", + }, + "resource_defaults": { + "primary_key": "id", + "write_disposition": "merge", + "endpoint": { + "params": { + "per_page": 100, + }, + }, + }, + "resources": [ + { + "name": "issues", + "endpoint": { + "path": "issues", + "params": { + "sort": "updated", + "direction": "desc", + "state": "open", + "since": { + "type": "incremental", + "cursor_path": "updated_at", + "initial_value": "2024-01-25T11:21:28Z", + }, + }, + }, + }, + ], +}) + +load_info = pipeline.run(github_source()) +print(load_info) +``` + +In this configuration, the `since` parameter is defined as a special incremental parameter. The `cursor_path` field specifies the JSON path to the field that will be used to fetch the updated data and we use the `initial_value` for the initial value for the incremental parameter. This value will be used in the first request to fetch the data. + +When the pipeline runs, dlt will automatically update the `since` parameter with the latest value from the response data. This way, you can fetch only the new or updated data from the API. + +Read more about [incremental loading](../dlt-ecosystem/verified-sources/rest_api/basic#incremental-loading) in the REST API source documentation. + +## What's next? + +Congratulations on completing the tutorial! You've learned how to set up a REST API source in dlt and run a data pipeline to load the data into DuckDB. + +Interested in learning more about dlt? Here are some suggestions: + +- Learn more about the REST API source configuration in [REST API source documentation](../dlt-ecosystem/verified-sources/rest_api/) +- Learn how to [create a custom source](./load-data-from-an-api.md) in the advanced tutorial \ No newline at end of file diff --git a/docs/website/docs/tutorial/sql-database.md b/docs/website/docs/tutorial/sql-database.md new file mode 100644 index 0000000000..1a7702b637 --- /dev/null +++ b/docs/website/docs/tutorial/sql-database.md @@ -0,0 +1,264 @@ +--- +title: Load data from a SQL database +description: How to extract data from a SQL Database using dlt's SQL Database core source +keywords: [sql connector, sql database pipeline, sql database] +--- + +This tutorial will show you how you can use dlt to load data from a SQL Database (PostgreSQL, MySQL, Microsoft SQL Server, Oracle, IBM DB2, etc.) into any dlt-compatible destination (Postgres, BigQuery, Snowflake, DuckDB, etc.). + +To make it easy to reproduce, we will be loading data from the [public MySQL RFam database](https://docs.rfam.org/en/latest/database.html) into a local DuckDB instance. + +## What you will learn + +- How to set up and configure a basic SQL database pipeline +- How to implement "append," "replace," and "merge" loading strategies +- How to load data incrementally + +## 0. Prerequisites + +- Python 3.9 or higher installed +- Virtual environment set up +- dlt installed. Follow the instructions in the [installation guide](../reference/installation) to create a new virtual environment and install the `dlt` package. + +## 1. Create a new dlt project + +Initialize a new dlt project in your current working directory using the `dlt init` command: + +```sh +dlt init sql_database duckdb +``` + +This is a handy CLI command that creates files and folders required for a SQL Database to DuckDB pipeline. You can easily replace `duckdb` with any other [supported destinations](../dlt-ecosystem/destinations). + +After running this command, your project will have the following structure: + +```text +├── .dlt +│ ├── config.toml +│ └── secrets.toml +├── sql_database_pipeline.py +└── requirements.txt +``` + +Here’s what each file does: + +- `sql_database_pipeline.py`: This is the main script where you'll define your data pipeline. It contains several different examples for how you can configure your SQL Database pipeline. +- `requirements.txt`: This file lists all the Python dependencies required for your project. +- `.dlt/`: This directory contains the [configuration files](../general-usage/credentials/) for your project: + - `secrets.toml`: This file stores your credentials, API keys, tokens, and other sensitive information. + - `config.toml`: This file contains the configuration settings for your `dlt` project. + +:::note +When deploying your pipeline in a production environment, managing all configurations with the TOML files might not be convenient. In this case, we highly recommend using environment variables or other [configuration providers](../general-usage/credentials/setup#available-config-providers) available in dlt to store secrets and configs instead. +::: + +## 2. Configure the pipeline script + +With the necessary files in place, we can now start writing our pipeline script. The existing file `sql_database_pipeline.py` already contains many pre-configured example functions that can help you get started with different data loading scenarios. However, for the purpose of this tutorial, we will be writing a new function from scratch. + +:::note +Running the script as it is will execute the function `load_standalone_table_resource()`, so remember to comment out the function call from inside the main block. +::: + + +The following function will load the tables `family` and `genome`. + +```py +def load_tables_family_and_genome(): + + # create a dlt source that will load tables "family" and "genome" + source = sql_database().with_resources("family", "genome") + + # Create a dlt pipeline object + pipeline = dlt.pipeline( + pipeline_name="sql_to_duckdb_pipeline", # custom name for the pipeline + destination="duckdb", # dlt destination to which the data will be loaded + dataset_name="sql_to_duckdb_pipeline_data" # custom name for the dataset created in the destination + ) + + # Run the pipeline + load_info = pipeline.run(source) + + # Pretty print load information + print(load_info) + +if __name__ == '__main__': + load_tables_family_and_genome() + +``` + +Explanation: +- The `sql_database` source has two built-in helper functions: `sql_database()` and `sql_table()`: + - `sql_database()` is a [dlt source function](../general-usage/source) that iteratively loads the tables (in this example, `"family"` and `"genome"`) passed inside the `with_resource()` method. + - `sql_table()` is a [dlt resource function](../general-usage/resource) that loads standalone tables. For example, if we wanted to only load the table `"family"`, then we could have done it using `sql_table(table="family")`. +- `dlt.pipeline()` creates a `dlt` pipeline with the name `"sql_to_duckdb_pipeline"` with the destination DuckDB. +- `pipeline.run()` method loads the data into the destination. + +## 3. Add credentials + +To sucessfully connect to your SQL database, you will need to pass credentials into your pipeline. dlt automatically looks for this information inside the generated TOML files. + +Simply paste the [connection details](https://docs.rfam.org/en/latest/database.html) inside `secrets.toml` as follows: +```toml +[sources.sql_database.credentials] +drivername = "mysql+pymysql" # database+dialect +database = "Rfam" +password = "" +username = "rfamro" +host = "mysql-rfam-public.ebi.ac.uk" +port = 4497 +``` + +Alternatively, you can also paste the credentials as a connection string: +```toml +sources.sql_database.credentials="mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam" +``` + +For more details on the credentials format and other connection methods read the section on [configuring connection to the SQL Database](https://dlthub.com/docs/dlt-ecosystem/verified-sources/sql_database#credentials-format). + + +## 4. Install dependencies + +Before running the pipeline, make sure to install all the necessary dependencies: +1. **General dependencies**: These are the general dependencies needed by the `sql_database` source. + ```sh + pip install -r requirements.txt + ``` +2. **Database-specific dependencies**: In addition to the general dependencies, you will also need to install `pymysql` to connect to the MySQL database in this tutorial: + ```sh + pip install pymysql + ``` + + Explanation: dlt uses SQLAlchemy to connect to the source database and hence, also requires the database-specific SQLAlchemy dialect, such as `pymysql` (MySQL), `psycopg2` (Postgres), `pymssql` (MSSQL), `snowflake-sqlalchemy` (Snowflake), etc. See the [SQLAlchemy docs](https://docs.sqlalchemy.org/en/20/dialects/#external-dialects) for a full list of available dialects. + +## 5. Run the pipeline + +After performing steps 1-4, you should now be able to successfully run the pipeline by executing the following command: + +```sh +python sql_database_pipeline.py +``` +This will create the file `sql_to_duckdb_pipeline.duckdb` in your dlt project directory which contains the loaded data. + +## 6. Explore the data + +dlt comes with a built-in browser application that allows you to interact with the loaded data. To enable it, run the following command: + +```sh +pip install streamlit +``` + +Next, run the following command to launch the data browser app: + +```sh +dlt pipeline sql_to_duckdb_pipeline show +``` + +You can explore the loaded data, run queries and see some pipeline execution details. + +![streamlit-screenshot](https://storage.googleapis.com/dlt-blog-images/docs-sql-database-tutorial-streamlit-screenshot.png) + +## 7. Append, replace, or merge loaded data + +Try running the pipeline again with `python sql_database_pipeline.py`. You will notice that +all the tables have the data duplicated. This happens as dlt, by default, appends data to the destination tables in every load. This behavior can be adjusted by setting the `write_disposition` parameter inside the `pipeline.run()` method. The possible settings are: + +- `append`: Appends the data to the destination table. This is the default. +- `replace`: Replaces the data in the destination table with the new data. +- `merge`: Merges the new data with the existing data in the destination table based on a primary key. + +### Load with replace + +To prevent the data from being duplicated in each row, set `write_disposition` to `replace`: + +```py +def load_tables_family_and_genome(): + + source = sql_database().with_resources("family", "genome") + + pipeline = dlt.pipeline( + pipeline_name="sql_to_duckdb_pipeline", + destination="duckdb", + dataset_name="sql_to_duckdb_pipeline_data" + ) + + load_info = pipeline.run(source, write_disposition="replace") # Set write_disposition to load the data with "replace" + + print(load_info) + +if __name__ == '__main__': + load_tables_family_and_genome() + +``` + +Run the pipeline again with `sql_database_pipeline.py`. This time, the data will be replaced in the destination table instead of being appended. + +### Load with merge + +When you want to update the existing data as new data is loaded, you can use the `merge` write disposition. This requires specifying a primary key for the table. The primary key is used to match the new data with the existing data in the destination table. + +In the previous example, we set `write_disposition="replace"` inside `pipeline.run()` which caused all the tables to be loaded with `replace`. However, it's also possible to define the `write_disposition` strategy separately for each tables using the `apply_hints` method. In the example below, we use `apply_hints` on each table to specify different primary keys for merge: + +```py +def load_tables_family_and_genome(): + + source = sql_database().with_resources("family", "genome") + + # specify different loading strategy for each resource using apply_hints + source.family.apply_hints(write_disposition="merge", primary_key="rfam_id") # merge table "family" on column "rfam_id" + source.genome.apply_hints(write_disposition="merge", primary_key="upid") # merge table "genome" on column "upid" + + pipeline = dlt.pipeline( + pipeline_name="sql_to_duckdb_pipeline", + destination="duckdb", + dataset_name="sql_to_duckdb_pipeline_data" + ) + + load_info = pipeline.run(source) + + print(load_info) + +if __name__ == '__main__': + load_tables_family_and_genome() +``` + +## 8. Load data incrementally + +Often you don't want to load the whole data in each load, but rather only the new or modified data. dlt makes this easy with [incremental loading](../general-usage/incremental-loading). + +In the example below, we configure the table `"family"` to load incrementally based on the column `"updated"`: + +```py +def load_tables_family_and_genome(): + + source = sql_database().with_resources("family", "genome") + + # only load rows whose "updated" value is greater than the last pipeline run + source.family.apply_hints(incremental=dlt.sources.incremental("updated")) + + pipeline = dlt.pipeline( + pipeline_name="sql_to_duckdb_pipeline", + destination="duckdb", + dataset_name="sql_to_duckdb_pipeline_data" + ) + + load_info = pipeline.run(source) + + print(load_info) + + + +if __name__ == '__main__': + load_tables_family_and_genome() +``` + +In the first run of the pipeline `python sql_database_pipeline.py`, the entire table `"family"` will be loaded. In every subsequent run, only the newly updated rows (as tracked by the column `"updated"`) will be loaded. + +## What's next? + +Congratulations on completing the tutorial! You learned how to set up a SQL Database source in dlt and run a data pipeline to load the data into DuckDB. + +Interested in learning more about dlt? Here are some suggestions: +- Learn more about the SQL Database source configuration in [the SQL Database source reference](../dlt-ecosystem/verified-sources/sql_database) +- Learn more about different credential types in [Built-in credentials](../general-usage/credentials/complex_types#built-in-credentials) +- Learn how to [create a custom source](./load-data-from-an-api.md) in the advanced tutorial diff --git a/docs/website/docs/walkthroughs/add-a-verified-source.md b/docs/website/docs/walkthroughs/add-a-verified-source.md index d7cd24b544..144b805974 100644 --- a/docs/website/docs/walkthroughs/add-a-verified-source.md +++ b/docs/website/docs/walkthroughs/add-a-verified-source.md @@ -21,10 +21,10 @@ mkdir various_pipelines cd various_pipelines ``` -List available verified sources to see their names and descriptions: +List available sources to see their names and descriptions: ```sh -dlt init --list-verified-sources +dlt init --list-sources ``` Now pick one of the source names, for example `pipedrive` and a destination i.e. `bigquery`: diff --git a/docs/website/docs/walkthroughs/add-incremental-configuration.md b/docs/website/docs/walkthroughs/add-incremental-configuration.md index ab7142695f..5cedec7ed5 100644 --- a/docs/website/docs/walkthroughs/add-incremental-configuration.md +++ b/docs/website/docs/walkthroughs/add-incremental-configuration.md @@ -6,8 +6,8 @@ slug: sql-incremental-configuration --- # Add incremental configuration to SQL resources -Incremental loading is the act of loading only new or changed data and not old records that have already been loaded. -For example, a bank loading only the latest transactions or a company updating its database with new or modified user +Incremental loading is the act of loading only new or changed data and not old records that have already been loaded. +For example, a bank loading only the latest transactions or a company updating its database with new or modified user information. In this article, we’ll discuss a few incremental loading strategies. :::important @@ -30,7 +30,7 @@ In this guide, we will discuss various incremental loading methods using `dlt`, ### 1. Full load (replace) -A full load strategy completely overwrites the existing data with the new dataset. This is useful when you want to +A full load strategy completely overwrites the existing data with the new dataset. This is useful when you want to refresh the entire table with the latest data. :::note @@ -40,15 +40,14 @@ This strategy technically does not load only new data but instead reloads all da Here’s a walkthrough: 1. The initial table, named "contact", in the SQL source looks like this: - + | id | name | created_at | | --- | --- | --- | | 1 | Alice | 2024-07-01 | | 2 | Bob | 2024-07-02 | -2. The python code illustrates the process of loading data from an SQL source into BigQuery using the `dlt` pipeline. -Please note the `write_disposition = "replace”` used below. - +2. The python code illustrates the process of loading data from an SQL source into BigQuery using the `dlt` pipeline. Please note the `write_disposition = "replace”` used below. + ```py def load_full_table_resource() -> None: """Load a full table, replacing existing data.""" @@ -71,15 +70,14 @@ Please note the `write_disposition = "replace”` used below. ``` 3. After running the `dlt` pipeline, the data loaded into the BigQuery "contact" table looks like: - + | Row | id | name | created_at | _dlt_load_id | _dlt_id | | --- | --- | --- | --- | --- | --- | | 1 | 1 | Alice | 2024-07-01 | 1721878309.021546 | tgyMM73iMz0cQg | | 2 | 2 | Bob | 2024-07-02 | 1721878309.021546 | 88P0bD796pXo/Q | -4. Next, the "contact" table in the SQL source is updated—two new rows are added, and the row with `id = 2` is removed. -The updated data source ("contact" table) now presents itself as follows: - +4. Next, the "contact" table in the SQL source is updated—two new rows are added, and the row with `id = 2` is removed. The updated data source ("contact" table) now presents itself as follows: + | id | name | created_at | | --- | --- | --- | | 1 | Alice | 2024-07-01 | @@ -87,7 +85,7 @@ The updated data source ("contact" table) now presents itself as follows: | 4 | Dave | 2024-07-04 | 5. The "contact" table created in BigQuery after running the pipeline again: - + | Row | id | name | created_at | _dlt_load_id | _dlt_id | | --- | --- | --- | --- | --- | --- | | 1 | 1 | Alice | 2024-07-01 | 1721878309.021546 | S5ye6fMhYECZA | @@ -97,7 +95,7 @@ The updated data source ("contact" table) now presents itself as follows: **What happened?** After running the pipeline, the original data in the "contact" table (Alice and Bob) is completely replaced with the new -updated table with data “Charlie” and “Dave” added and “Bob” removed. This strategy is useful for scenarios where the entire +updated table with data “Charlie” and “Dave” added and “Bob” removed. This strategy is useful for scenarios where the entire dataset needs to be refreshed/replaced with the latest information. ### 2. Append new records based on incremental ID @@ -107,16 +105,14 @@ This strategy appends only new records to the table based on an incremental ID. Here’s a walkthrough: 1. The initial table, named "contact", in the SQL source looks like this: - + | id | name | created_at | | --- | --- | --- | | 1 | Alice | 2024-07-01 | | 2 | Bob | 2024-07-02 | -2. The python code demonstrates loading data from an SQL source into BigQuery using an incremental variable, `id`. -This variable tracks new or updated records in the `dlt` pipeline. Please note the `write_disposition = "append”` -used below. - +2. The python code demonstrates loading data from an SQL source into BigQuery using an incremental variable, `id`. This variable tracks new or updated records in the `dlt` pipeline. Please note the `write_disposition = "append”` used below. + ```py def load_incremental_id_table_resource() -> None: """Load a table incrementally based on an ID.""" @@ -138,15 +134,14 @@ used below. ``` 3. After running the `dlt` pipeline, the data loaded into BigQuery "contact" table looks like: - + | Row | id | name | created_at | _dlt_load_id | _dlt_id | | --- | --- | --- | --- | --- | --- | | 1 | 1 | Alice | 2024-07-01 | 1721878309.021546 | YQfmAu8xysqWmA | | 2 | 2 | Bob | 2024-07-02 | 1721878309.021546 | Vcb5KKah/RpmQw | -4. Next, the "contact" table in the SQL source is updated—two new rows are added, and the row with `id = 2` is removed. -The updated data source now presents itself as follows: - +4. Next, the "contact" table in the SQL source is updated—two new rows are added, and the row with `id = 2` is removed. The updated data source now presents itself as follows: + | id | name | created_at | | --- | --- | --- | | 1 | Alice | 2024-07-01 | @@ -154,7 +149,7 @@ The updated data source now presents itself as follows: | 4 | Dave | 2024-07-04 | 5. The "contact" table created in BigQuery after running the pipeline again: - + | Row | id | name | created_at | _dlt_load_id | _dlt_id | | --- | --- | --- | --- | --- | --- | | 1 | 1 | Alice | 2024-07-01 | 1721878309.021546 | OW9ZyAzkXg4D4w | @@ -164,26 +159,23 @@ The updated data source now presents itself as follows: **What happened?** -In this scenario, the pipeline appends new records (Charlie and Dave) to the existing data (Alice and Bob) without affecting -the pre-existing entries. This strategy is ideal when only new data needs to be added, preserving the historical data. +In this scenario, the pipeline appends new records (Charlie and Dave) to the existing data (Alice and Bob) without affecting the pre-existing entries. This strategy is ideal when only new data needs to be added, preserving the historical data. ### 3. Append new records based on timestamp ("created_at") -This strategy appends only new records to the table based on a date/timestamp field. It is useful for scenarios where records -are created with a timestamp, and you want to load only those records created after a certain date. +This strategy appends only new records to the table based on a date/timestamp field. It is useful for scenarios where records are created with a timestamp, and you want to load only those records created after a certain date. Here’s a walkthrough: 1. The initial dataset, named "contact", in the SQL source looks like this: - + | id | name | created_at | | --- | --- | --- | | 1 | Alice | 2024-07-01 00:00:00 | | 2 | Bob | 2024-07-02 00:00:00 | -2. The python code illustrates the process of loading data from an SQL source into BigQuery using the `dlt` pipeline. Please -note the `write_disposition = "append"`, with `created_at` being used as the incremental parameter. - +2. The python code illustrates the process of loading data from an SQL source into BigQuery using the `dlt` pipeline. Please note the `write_disposition = "append"`, with `created_at` being used as the incremental parameter. + ```py def load_incremental_timestamp_table_resource() -> None: """Load a table incrementally based on created_at timestamp.""" @@ -208,15 +200,14 @@ note the `write_disposition = "append"`, with `created_at` being used as the inc ``` 3. After running the `dlt` pipeline, the data loaded into BigQuery "contact" table looks like: - + | Row | id | name | created_at | _dlt_load_id | _dlt_id | | --- | --- | --- | --- | --- | --- | | 1 | 1 | Alice | 2024-07-01 00:00:00 UTC | 1721878309.021546 | 5H8ca6C89umxHA | | 2 | 2 | Bob | 2024-07-02 00:00:00 UTC | 1721878309.021546 | M61j4aOSqs4k2w | -4. Next, the "contact" table in the SQL source is updated—two new rows are added, and the row with `id = 2` is removed. -The updated data source now presents itself as follows: - +4. Next, the "contact" table in the SQL source is updated—two new rows are added, and the row with `id = 2` is removed. The updated data source now presents itself as follows: + | id | name | created_at | | --- | --- | --- | | 1 | Alice | 2024-07-01 00:00:00 | @@ -224,7 +215,7 @@ The updated data source now presents itself as follows: | 4 | Dave | 2024-07-04 00:00:00 | 5. The "contact" table created in BigQuery after running the pipeline again: - + | Row | id | name | created_at | _dlt_load_id | _dlt_id | | --- | --- | --- | --- | --- | --- | | 1 | 1 | Alice | 2024-07-01 00:00:00 UTC | 1721878309.021546 | Petj6R+B/63sWA | @@ -239,21 +230,20 @@ retaining the existing data (Alice and Bob). This approach is useful for loading ### 4. Merge (Update/Insert) records based on timestamp ("last_modified_at") and ID -This strategy merges records based on a composite key of ID and a timestamp field. It updates existing records and inserts +This strategy merges records based on a composite key of ID and a timestamp field. It updates existing records and inserts new ones as necessary. Here’s a walkthrough: 1. The initial dataset, named ‘contact’, in the SQL source looks like this: - + | id | name | last_modified_at | | --- | --- | --- | | 1 | Alice | 2024-07-01 00:00:00 | | 2 | Bob | 2024-07-02 00:00:00 | -2. The Python code illustrates the process of loading data from an SQL source into BigQuery using the `dlt` pipeline. Please -note the `write_disposition = "merge"`, with `last_modified_at` being used as the incremental parameter. - +2. The Python code illustrates the process of loading data from an SQL source into BigQuery using the `dlt` pipeline Please note the `write_disposition = "merge"`, with `last_modified_at` being used as the incremental parameter. + ```py def load_merge_table_resource() -> None: """Merge (update/insert) records based on last_modified_at timestamp and ID.""" @@ -266,7 +256,7 @@ note the `write_disposition = "merge"`, with `last_modified_at` being used as th # Merge records, 'contact' table, based on ID and last_modified_at timestamp source = sql_database().with_resources("contact") source.contact.apply_hints(incremental=dlt.sources.incremental( - "last_modified_at", initial_value=datetime(2024, 4, 1, 0, 0, 0)), + "last_modified_at", initial_value=datetime(2024, 4, 1, 0, 0, 0)), primary_key="id") # Run the pipeline @@ -279,21 +269,21 @@ note the `write_disposition = "merge"`, with `last_modified_at` being used as th ``` 3. After running the `dlt` pipeline, the data loaded into BigQuery ‘contact’ table looks like: - + | Row | id | name | last_modified_at | _dlt_load_id | _dlt_id | | --- | --- | --- | --- | --- | --- | | 1 | 1 | Alice | 2024-07-01 00:00:00 UTC | 1721878309.021546 | ObbVlxcly3VknQ | | 2 | 2 | Bob | 2024-07-02 00:00:00 UTC | 1721878309.021546 | Vrlkus/haaKlEg | 4. Next, the "contact" table in the SQL source is updated— “Alice” is updated to “Alice Updated”, and a new row “Hank” is added: - + | id | name | last_modified_at | | --- | --- | --- | | 1 | Alice Updated | 2024-07-08 00:00:00 | | 3 | Hank | 2024-07-08 00:00:00 | 5. The "contact" table created in BigQuery after running the pipeline again: - + | Row | id | name | last_modified_at | _dlt_load_id | _dlt_id | | --- | --- | --- | --- | --- | --- | | 1 | 2 | Bob | 2024-07-02 00:00:00 UTC | 1721878309.021546 | Cm+AcDZLqXSDHQ | @@ -302,9 +292,9 @@ note the `write_disposition = "merge"`, with `last_modified_at` being used as th **What happened?** -The pipeline updates the record for Alice with the new data, including the updated `last_modified_at` timestamp, and adds a -new record for Hank. This method is beneficial when you need to ensure that records are both updated and inserted based on a +The pipeline updates the record for Alice with the new data, including the updated `last_modified_at` timestamp, and adds a +new record for Hank. This method is beneficial when you need to ensure that records are both updated and inserted based on a specific timestamp and ID. -The examples provided explain how to use `dlt` to achieve different incremental loading scenarios, highlighting the changes +The examples provided explain how to use `dlt` to achieve different incremental loading scenarios, highlighting the changes before and after running each pipeline. \ No newline at end of file diff --git a/docs/website/docs/walkthroughs/adjust-a-schema.md b/docs/website/docs/walkthroughs/adjust-a-schema.md index b92f431f80..c6c7ddcb22 100644 --- a/docs/website/docs/walkthroughs/adjust-a-schema.md +++ b/docs/website/docs/walkthroughs/adjust-a-schema.md @@ -137,8 +137,8 @@ These steps ensure that the column order in your dataset matches your specificat ```py # Define the data source and reorder columns using add_map data_source = resource().add_map(lambda row: { - 'column3': row['column3'], - 'column1': row['column1'], + 'column3': row['column3'], + 'column1': row['column1'], 'column2': row['column2'] }) @@ -148,7 +148,7 @@ load_info = pipeline.run(data_source) In this example, the `add_map` function reorders columns by defining a new mapping. The lambda function specifies the desired order by rearranging the key-value pairs. When the pipeline runs, the data will load with the columns in the new order. -### Load data as json instead of generating child table or columns from flattened dicts +### Load data as json instead of generating nested table or columns from flattened dicts In the export schema, you can see that white and black players properties got flattened into: @@ -165,7 +165,7 @@ white__aid: ``` For some reason, you'd rather deal with a single JSON (or struct) column. Just declare the `white` -column as `complex`, which will instruct `dlt` not to flatten it (or not convert into child table in +column as `json`, which will instruct `dlt` not to flatten it (or not convert into nested table in case of a list). Do the same with `black` column: ```yaml @@ -176,10 +176,10 @@ players_games: data_type: timestamp white: nullable: false - data_type: complex + data_type: json black: nullable: false - data_type: complex + data_type: json ``` Run the pipeline script again, and now you can query `black` and `white` columns with JSON @@ -200,10 +200,10 @@ players_games: partition: true white: nullable: false - data_type: complex + data_type: json black: nullable: false - data_type: complex + data_type: json ``` ## 4. Keep your import schema diff --git a/docs/website/docs/walkthroughs/create-new-destination.md b/docs/website/docs/walkthroughs/create-new-destination.md index 69e7b2fcc1..f4cdef8fda 100644 --- a/docs/website/docs/walkthroughs/create-new-destination.md +++ b/docs/website/docs/walkthroughs/create-new-destination.md @@ -129,15 +129,15 @@ You must map `dlt` data types to destination data types. For this you can implem * the database types must be exactly those as used in `INFORMATION_SCHEMA.COLUMNS` * decimal precision and scale are filled from the capabilities (in all our implementations) * until now all destinations could handle binary types -* we always try to map the `complex` type into `JSON` type in the destination. if that does not work you can try mapping into a string. See how we do that for various destinations. -* the reverse mapping of types is sometimes tricky ie. you may not able to detect complex types (your destination lacks JSON support). this is not really needed during schema updates and loading (just for testing) so in general you should be fine. +* we always try to map the `json` type into `JSON` type in the destination. if that does not work you can try mapping into a string. See how we do that for various destinations. +* the reverse mapping of types is sometimes tricky ie. you may not able to detect nested types (your destination lacks JSON support). this is not really needed during schema updates and loading (just for testing) so in general you should be fine. ### Table and column hints You can map hints present for tables and columns (ie. `cluster`, `sort`, `partition`) to generate specific DDL for columns and tables. See `_get_column_def_sql` in various destinations. You can also add hints (ie indexes, partition clauses) to tables via `_get_table_update_sql` - see `BigQuery` implementation for a good example. ### Participate in staging dataset merge and replace -`dlt` supports merging and transactional replace via **staging dataset** living along the destination dataset. `SqlJobClientBase` participates in this mechanism by default. In essence: each time when a job is completed, `dlt` checks which table got updated and if there are no remaining jobs for that table and its child and parent tables (all together called **table chain**). If table chain is fully loaded, `dlt` executes SQL transformations that move/merge data from staging dataset to destination dataset (that, as you can expect, happens also via jobs, of type `sql` that are dynamically created). +`dlt` supports merging and transactional replace via **staging dataset** living along the destination dataset. `SqlJobClientBase` participates in this mechanism by default. In essence: each time when a job is completed, `dlt` checks which table got updated and if there are no remaining jobs for that table and its nested and root tables (all together called **table chain**). If table chain is fully loaded, `dlt` executes SQL transformations that move/merge data from staging dataset to destination dataset (that, as you can expect, happens also via jobs, of type `sql` that are dynamically created). Generated SQL is quite simple and we were able to run it on all existing destinations (we may introduce `sqlglot` to handle future cases). The SQL used requires: - SELECT, INSERT, DELETE/TRUNCATE statements diff --git a/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer.md b/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer.md index ce76240c8a..8dd86c5172 100644 --- a/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer.md +++ b/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer.md @@ -216,7 +216,7 @@ load_data() ) ``` :::tip -When you run `load_data` DAG above, Airflow will call `source` function every 30 seconds (by default) to be able to monitor the tasks. Make sure that your source function does not do any long lasting operations ie. reflecting source database. In case of [sql_database](../../dlt-ecosystem/verified-sources/sql_database.md) we added an option to delay database reflection until data is accessed by a resource. +When you run `load_data` DAG above, Airflow will call `source` function every 30 seconds (by default) to be able to monitor the tasks. Make sure that your source function does not do any long lasting operations ie. reflecting source database. In case of [sql_database](../../dlt-ecosystem/verified-sources/sql_database/index.md) we added an option to delay database reflection until data is accessed by a resource. ::: ### 3. Import sources and move the relevant code from the pipeline script diff --git a/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-dagster.md b/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-dagster.md index cca882ba38..14ac18b3e7 100644 --- a/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-dagster.md +++ b/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-dagster.md @@ -22,7 +22,7 @@ Dagster Cloud offers enterprise-level orchestration service with serverless or h options. It incorporates native branching and built-in CI/CD to prioritize the developer experience. It enables scalable, cost-effective operations without the hassle of infrastructure management. -### Dagster deployment options: **Serverless** versus **Hybrid**: +### Dagster deployment options: **Serverless** versus **Hybrid** The *serverless* option fully hosts the orchestration engine, while the *hybrid* model offers flexibility to use your computing resources, with Dagster managing the control plane. Reducing @@ -38,107 +38,255 @@ by signing up for the trial. ## Building Data Pipelines with `dlt` -`dlt` is an open-source Python library that allows you to declaratively load data sources into -well-structured tables or datasets through automatic schema inference and evolution. It simplifies -building data pipelines with support for extract and load processes. - **How does `dlt` integrate with Dagster for pipeline orchestration?** `dlt` integrates with Dagster for pipeline orchestration, providing a streamlined process for building, enhancing, and managing data pipelines. This enables developers to leverage `dlt`'s capabilities for handling data extraction and load and Dagster's orchestration features to efficiently manage and monitor data pipelines. +Dagster supports [native integration with dlt](https://docs.dagster.io/integrations/embedded-elt/dlt), +here is a guide on how this integration works. + ### Orchestrating `dlt` pipeline on Dagster -Here's a concise guide to orchestrating a `dlt` pipeline with Dagster, using the project "Ingesting -GitHub issues data from a repository and storing it in BigQuery" as an example. +Here's a concise guide to orchestrating a `dlt` pipeline with Dagster, creating a pipeline which ingests GitHub issues data from a repository and loads it to DuckDB. -More details can be found in the article -[“Orchestrating unstructured data pipelines with dagster and dlt."](https://dagster.io/blog/dagster-dlt) +You can find the full example code in [this repository](https://github.com/dlt-hub/dlthub-education/blob/main/workshops/workshop_august_2024/part2/deployment/deploy_dagster/README.md). **The steps are as follows:** -1. Create a `dlt` pipeline. For more, please refer to the documentation: -[Creating a pipeline.](https://dlthub.com/docs/walkthroughs/create-a-pipeline) -1. Set up a Dagster project, configure resources, and define the asset as follows: +1. Install Dagster and the embedded ELT package using pip: + ```sh + pip install dagster dagster-embedded-elt + ``` - 1. To create a Dagster project: +1. Set up a Dagster project: ```sh - mkdir dagster_github_issues - cd dagster_github_issues - dagster project scaffold --name github-issues + mkdir dagster_github_issues + cd dagster_github_issues + dagster project scaffold --name github-issues ``` + ![image](https://github.com/user-attachments/assets/f9002de1-bcdf-49f4-941b-abd59ea7968d) + +1. In your Dagster project, define the dlt pipeline in `github_source` folder. + + **Note**: The dlt Dagster helper works only with dlt sources. Your resources always should be grouped in a source. + ```py + import dlt + ... + @dlt.resource( + table_name="issues", + write_disposition="merge", + primary_key="id", + ) + def get_issues( + updated_at=dlt.sources.incremental("updated_at", initial_value="1970-01-01T00:00:00Z") + ): + url = ( + f"{BASE_URL}?since={updated_at.last_value}&per_page=100&sort=updated" + "&directions=desc&state=open" + ) + yield pagination(url) + + @dlt.source + def github_source(): + return get_issues() + ``` + 1. Create a `dlt_assets` definition. + + The `@dlt_assets` decorator takes a `dlt_source` and `dlt_pipeline` parameter. + In this example, we used the `github_source` source and created a `dlt_pipeline` to ingest data from Github to DuckDB. + + Here’s an example of how to define assets (`github_source/assets.py`): - 1. Define `dlt` as a Dagster resource: - ```py - from dagster import ConfigurableResource - from dagster import ConfigurableResource - import dlt - - class DltPipeline(ConfigurableResource): - pipeline_name: str - dataset_name: str - destination: str - - def create_pipeline(self, resource_data, table_name): - - # configure the pipeline with your destination details - pipeline = dlt.pipeline( - pipeline_name=self.pipeline_name, - destination=self.destination, - dataset_name=self.dataset_name - ) - - # run the pipeline with your parameters - load_info = pipeline.run(resource_data, table_name=table_name) - - return load_info - ``` - 1. Define the asset as: ```py - @asset - def issues_pipeline(pipeline: DltPipeline): - - logger = get_dagster_logger() - results = pipeline.create_pipeline(github_issues_resource, table_name='github_issues') - logger.info(results) + import dlt + from dagster import AssetExecutionContext + from dagster_embedded_elt.dlt import DagsterDltResource, dlt_assets + from .github_pipeline import github_source + + @dlt_assets( + dlt_source=github_source(), + dlt_pipeline=dlt.pipeline( + pipeline_name="github_issues", + dataset_name="github", + destination="duckdb", + progress="log", + ), + name="github", + group_name="github", + ) + def dagster_github_assets(context: AssetExecutionContext, dlt: DagsterDltResource): + yield from dlt.run(context=context) ``` - > For more information, please refer to - > [Dagster’s documentation.](https://docs.dagster.io/getting-started/quickstart) - -1. Next, define Dagster definitions as follows: - ```py - all_assets = load_assets_from_modules([assets]) - simple_pipeline = define_asset_job(name="simple_pipeline", selection= ['issues_pipeline']) - - defs = Definitions( - assets=all_assets, - jobs=[simple_pipeline], - resources={ - "pipeline": DltPipeline( - pipeline_name = "github_issues", - dataset_name = "dagster_github_issues", - destination = "bigquery", - ), - } - ) - ``` - -1. Finally, start the web server as: - - ```sh - dagster dev - ``` - -:::info -For the complete hands-on project on “Orchestrating unstructured data pipelines with dagster and -`dlt`", please refer to [article](https://dagster.io/blog/dagster-dlt). The author offers a -detailed overview and steps for ingesting GitHub issue data from a repository and storing it in -BigQuery. You can use a similar approach to build your pipelines. + + For more information, please refer to + [Dagster’s documentation.](https://docs.dagster.io/_apidocs/libraries/dagster-embedded-elt#dagster_embedded_elt.dlt.dlt_assets) + + 1. Create the Definitions object. + + The last step is to include the assets and resource in a [Definitions](https://docs.dagster.io/_apidocs/definitions#dagster.Definitions) object (`github_source/definitions.py`). This enables Dagster tools to load everything we have defined: + + ```py + import assets + from dagster import Definitions, load_assets_from_modules + from dagster_embedded_elt.dlt import DagsterDltResource + + dlt_resource = DagsterDltResource() + all_assets = load_assets_from_modules([assets]) + + defs = Definitions( + assets=all_assets, + resources={ + "dlt": dlt_resource, + }, + ) + ``` + +1. Run the web server locally: + 1. Install the necessary dependencies using the following command: + + ```sh + pip install -e ".[dev]" + ``` + + We use -e to install dependencies in [editable mode](https://pip.pypa.io/en/latest/topics/local-project-installs/#editable-installs). This allows changes to be automatically applied when we modify the code. + + 2. Run the project: + + ```sh + dagster dev + ``` + + 3. Navigate to localhost:3000 in your web browser to access the Dagster UI. + + ![image](https://github.com/user-attachments/assets/97b74b86-df94-47e5-8ae2-de7cc47f56d8) + +1. Run the pipeline. + + Now that you have a running instance of Dagster, we can run our data pipeline. + + To run the pipeline, go to **Assets** and click the **Materialize** button in the top right. In Dagster, materialization refers to executing the code associated with an asset to produce an output. + + ![image](https://github.com/user-attachments/assets/79416fb7-8362-4640-b205-e59aa7ac785c) + + You will see the following logs in your command line: + + ![image](https://github.com/user-attachments/assets/f0e3bec8-f702-46a6-b69f-194a1dacf625) + + Want to see real-world examples of dlt in production? Check out how dlt is used internally at Dagster in the [Dagster Open Platform](https://github.com/dagster-io/dagster-open-platform) project. + + +:::info +For a complete picture of Dagster's integration with dlt, please refer to their [documentation](https://docs.dagster.io/integrations/embedded-elt/dlt). This documentation offers a detailed overview and steps for ingesting GitHub data and storing it in Snowflake. You can use a similar approach to build your pipelines. ::: +### Frequently Asked Questions +- **Can I remove the generated `.dlt` folder with `secrets.toml` and `config.toml` files?** + + Yes. Since dlt is compatible with ENV variables, you can use this for secrets required by both Dagster and dlt. + +- **I'm working with several sources – how can I best group these assets?** + + To effectively group assets in Dagster when working with multiple sources, use the `group_name` parameter in your `@dlt_assets` decorator. This helps organize and visualize assets related to a particular source or theme in the Dagster UI. Here’s a simplified example: + + ```py + import dlt + from dagster_embedded_elt.dlt import dlt_assets + from dlt_sources.google_analytics import google_analytics + + # Define assets for the first Google Analytics source + @dlt_assets( + dlt_source=google_analytics(), + dlt_pipeline=dlt.pipeline( + pipeline_name="google_analytics_pipeline_1", + destination="bigquery", + dataset_name="gaoogle_analytics_data_1" + ), + group_name='Google_Analytics' + ) + def google_analytics_assets_1(context, dlt): + yield from dlt.run(context=context) + + # Define assets for the second Google Analytics source + @dlt_assets( + dlt_source=google_analytics(), + dlt_pipeline=dlt.pipeline( + pipeline_name="google_analytics_pipeline_2", + destination="bigquery", + dataset_name="gaoogle_analytics_data_2" + ), + group_name='Google_Analytics' + ) + def google_analytics_assets_2(context, dlt): + yield from dlt.run(context=context) + ``` + + + +- **How can I use `bigquery_adapter` with `@dlt_assets` in Dagster for partitioned tables?** + + To use `bigquery_adapter` with `@dlt_assets` in Dagster for partitioned tables, modify your resource setup to include `bigquery_adapter` with the partition parameter. Here's a quick example: + + ```py + import dlt + from google.analytics import BetaAnalyticsDataClient + from dlt.destinations.adapters import bigquery_adapter + from dagster import dlt_asset + + @dlt_asset + def google_analytics_asset(context): + # Configuration (replace with your actual values or parameters) + queries = [ + {"dimensions": ["dimension1"], "metrics": ["metric1"], "resource_name": "resource1"} + ] + property_id = "your_property_id" + start_date = "2024-01-01" + rows_per_page = 1000 + credentials = your_credentials + + # Initialize Google Analytics client + client = BetaAnalyticsDataClient(credentials=credentials.to_native_credentials()) + + # Fetch metadata + metadata = get_metadata(client=client, property_id=property_id) + resource_list = [metadata | metrics_table, metadata | dimensions_table] + + # Configure and add resources to the list + for query in queries: + dimensions = query["dimensions"] + if "date" not in dimensions: + dimensions.append("date") + + resource_name = query["resource_name"] + resource_list.append( + bigquery_adapter( + dlt.resource(basic_report, name=resource_name, write_disposition="append")( + client=client, + rows_per_page=rows_per_page, + property_id=property_id, + dimensions=dimensions, + metrics=query["metrics"], + resource_name=resource_name, + start_date=start_date, + last_date=dlt.sources.incremental("date"), + ), + partition="date" + ) + ) + + return resource_list + ``` + + ### Additional Resources +- Check out the [Dagster Cloud Documentation](https://docs.dagster.cloud/) to learn more about deploying on Dagster Cloud. + +- Learn more about Dagtser's integration with dlt: + [dlt & Dagster](https://docs.dagster.io/integrations/embedded-elt/dlt) + [Embedded ELT Documentation](https://docs.dagster.io/_apidocs/libraries/dagster-embedded-elt#dagster_embedded_elt.dlt.dlt_assets). + - A general configurable `dlt` resource orchestrated on Dagster: [dlt resource](https://github.com/dagster-io/dagster-open-platform/blob/5030ff6828e2b001a557c6864f279c3b476b0ca0/dagster_open_platform/resources/dlt_resource.py#L29). @@ -146,12 +294,13 @@ BigQuery. You can use a similar approach to build your pipelines. [dlt pipelines](https://github.com/dagster-io/dagster-open-platform/tree/5030ff6828e2b001a557c6864f279c3b476b0ca0/dagster_open_platform/assets/dlt_pipelines). - Configure MongoDB source as an Asset factory: - > Dagster provides the feature of - > [@multi_asset](https://github.com/dlt-hub/dlt-dagster-demo/blob/21a8d18b6f0424f40f2eed5030989306af8b8edb/mongodb_dlt/mongodb_dlt/assets/__init__.py#L18) - > declaration that will allow us to convert each collection under a database into a separate - > asset. This will make our pipeline easy to debug in case of failure and the collections - > independent of each other. - -:::note -These are external repositories and are subject to change. + + Dagster provides the feature of + [@multi_asset](https://github.com/dlt-hub/dlt-dagster-demo/blob/21a8d18b6f0424f40f2eed5030989306af8b8edb/mongodb_dlt/mongodb_dlt/assets/__init__.py#L18) + declaration that will allow us to convert each collection under a database into a separate + asset. This will make our pipeline easy to debug in case of failure and the collections + independent of each other. + +:::note +Some of these are external repositories and are subject to change. ::: diff --git a/docs/website/docs/walkthroughs/dispatch-to-multiple-tables.md b/docs/website/docs/walkthroughs/dispatch-to-multiple-tables.md index 0e342a3fea..410a6b9aca 100644 --- a/docs/website/docs/walkthroughs/dispatch-to-multiple-tables.md +++ b/docs/website/docs/walkthroughs/dispatch-to-multiple-tables.md @@ -12,7 +12,7 @@ We'll use the [GitHub API](https://docs.github.com/en/rest) to fetch the events 1. Install dlt with duckdb support: ```sh -pip install dlt[duckdb] +pip install "dlt[duckdb]" ``` 2. Create a new a new file `github_events_dispatch.py` and paste the following code: @@ -86,7 +86,7 @@ dlt pipeline -v github_events show ``` :::tip -Some of the events produce tables with really many child tables. You can [control the level of table nesting](general-usage/source.md#reduce-the-nesting-level-of-generated-tables) with a decorator. +Some of the events produce tables with really many nested tables. You can [control the level of table nesting](general-usage/source.md#reduce-the-nesting-level-of-generated-tables) with a decorator. Another fun [Colab Demo](https://colab.research.google.com/drive/1BXvma_9R9MX8p_iSvHE4ebg90sUroty2#scrollTo=a3OcZolbaWGf) - we analyze reactions on duckdb repo! diff --git a/docs/website/docs/walkthroughs/run-a-pipeline.md b/docs/website/docs/walkthroughs/run-a-pipeline.md index 697ae0fff5..8d74fdbf6f 100644 --- a/docs/website/docs/walkthroughs/run-a-pipeline.md +++ b/docs/website/docs/walkthroughs/run-a-pipeline.md @@ -230,8 +230,8 @@ Failed due to: connection to server at "localhost" (127.0.0.1), port 5432 failed In rare cases some jobs in a load package will fail in such a way that `dlt` will not be able to load it, even if it retries the process. In that case the job is marked as failed and additional -information is available. Please note that (if not otherwise configured), `dlt` **will not raise -exception on failed jobs**. +information is available. Please note that ([if not otherwise configured](../running-in-production//running.md#failed-jobs)), `dlt` **will raise +exception on failed jobs and abort the package**. Aborted packages cannot be retried. ```text Step run COMPLETED in 14.21 seconds. diff --git a/docs/website/docs/walkthroughs/share-a-dataset.md b/docs/website/docs/walkthroughs/share-a-dataset.md index 75ba856468..3e231b8274 100644 --- a/docs/website/docs/walkthroughs/share-a-dataset.md +++ b/docs/website/docs/walkthroughs/share-a-dataset.md @@ -1,10 +1,10 @@ --- -title: 'Share a dataset: DuckDB -> BigQuery' +title: 'Moving from local to production' description: Share a local dataset by moving it to BigQuery keywords: [how to, share a dataset] --- -# Share a dataset: DuckDB -> BigQuery +# Moving from local to production In previous how-to guides you used the local stack to create and run your pipeline. This saved you the headache of setting up cloud account, credentials and often also money. Our choice for local diff --git a/docs/website/docusaurus.config.js b/docs/website/docusaurus.config.js index 3afdcec52e..662ed0d5d0 100644 --- a/docs/website/docusaurus.config.js +++ b/docs/website/docusaurus.config.js @@ -9,25 +9,32 @@ const darkCodeTheme = require('prism-react-renderer/themes/dracula'); // create versions config const versions = {"current": { - label: 'devel', + label: 'devel', path: 'devel', noIndex: true }} -// inject master version renaming only if versions present +let knownVersions = []; if (fs.existsSync("versions.json")) { + knownVersions = JSON.parse(fs.readFileSync("versions.json")); +} + +// inject master version renaming only if versions present and master included +if (knownVersions) { let latestLabel = "latest" if (process.env.DOCUSAURUS_DLT_VERSION) { latestLabel = `${process.env.DOCUSAURUS_DLT_VERSION} (latest)` } - - versions["master"] = { - label: latestLabel, - path: '/' + if (knownVersions.includes("master")) { + versions["master"] = { + label: latestLabel, + path: '/' + } } + // disable indexing for all known versions - for (let v of JSON.parse(fs.readFileSync("versions.json"))) { + for (let v of knownVersions) { if (v == "master") { continue; } @@ -35,7 +42,6 @@ if (fs.existsSync("versions.json")) { noIndex: true } } - } /** @type {import('@docusaurus/types').Config} */ @@ -84,9 +90,6 @@ const config = { showLastUpdateAuthor: true, showLastUpdateTime: true, }, - blog: { - showReadingTime: true - }, theme: { customCss: require.resolve('./src/css/custom.css'), }, @@ -111,6 +114,7 @@ const config = { { type: 'docsVersionDropdown', }, + { to: 'https://dlthub.com/blog', label: 'Blog', position: 'left' }, { href: 'https://dlthub.com/community', label: 'Join community', @@ -134,21 +138,6 @@ const config = { footer: { style: 'dark', links: [ - { - title: 'Docs', - items: [ - { - label: 'Docs', - to: '/intro', - className: 'footer-link' - }, - { - label: 'Blog', - to: '/blog', - className: 'footer-link' - } - ], - }, { title: 'Community', items: [ @@ -198,7 +187,7 @@ const config = { indexName: 'dlthub', // Optional: see doc section below - contextualSearch: true, + contextualSearch: false, }, colorMode: { defaultMode:'dark', diff --git a/docs/website/netlify.toml b/docs/website/netlify.toml index ec6bd0ce49..919ad09aea 100644 --- a/docs/website/netlify.toml +++ b/docs/website/netlify.toml @@ -7,14 +7,24 @@ from = "/docs" to = "/docs/intro" [[redirects]] -from = "/docs/general-usage/credentials/configuration" -to = "/docs/general-usage/credentials/setup" -status = 301 +from = "/docs/dlt-ecosystem" +to = "/docs/dlt-ecosystem/verified-sources" [[redirects]] from = "/docs/general-usage/credentials/config_providers" to = "/docs/general-usage/credentials" -status = 301 + +[[redirects]] +from = "/docs/general-usage/credentials/configuration" +to = "/docs/general-usage/credentials/setup" + +[[redirects]] +from = "/docs/tutorial/intro" +to = "docs/tutorial/load-data-from-an-api" + +[[redirects]] +from = "/docs/tutorial/grouping-resources" +to = "docs/tutorial/load-data-from-an-api" [[redirects]] from = "/docs/general-usage/credentials/config_specs" diff --git a/docs/website/sidebars.js b/docs/website/sidebars.js index 56c2eb165c..68a05fa1f9 100644 --- a/docs/website/sidebars.js +++ b/docs/website/sidebars.js @@ -28,35 +28,89 @@ function *walkSync(dir) { /** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */ const sidebars = { tutorialSidebar: [ - 'intro', - 'getting-started', { type: 'category', - label: 'Tutorial', + label: 'Getting started', link: { type: 'doc', - id: 'tutorial/intro', + id: 'intro', }, items: [ + 'reference/installation', + 'tutorial/rest-api', + 'tutorial/sql-database', + 'tutorial/filesystem', 'tutorial/load-data-from-an-api', - 'tutorial/grouping-resources', ] }, { type: 'category', - label: 'Integrations', + label: 'Core concepts', + items: [ + 'reference/explainers/how-dlt-works', + 'general-usage/source', + 'general-usage/resource', + 'general-usage/pipeline', + 'general-usage/destination', + 'general-usage/state', + 'general-usage/glossary' + ] + }, + { + type: 'category', + label: 'Sources', link: { type: 'doc', - id: 'dlt-ecosystem/index', + id: 'dlt-ecosystem/verified-sources/index', }, items: [ { type: 'category', - label: 'Sources', - link: { + label: 'Filesystem & cloud storage', + description: 'AWS S3, Google Cloud Storage, Azure Blob Storage, local file system', + link: { type: 'doc', - id: 'dlt-ecosystem/verified-sources/index', + id: 'dlt-ecosystem/verified-sources/filesystem/index', }, + items: [ + 'dlt-ecosystem/verified-sources/filesystem/basic', + 'dlt-ecosystem/verified-sources/filesystem/advanced', + ] + }, + { + type: 'category', + label: 'REST APIs', + description:'Load data from any REST API', + link: { + type: 'doc', + id: 'dlt-ecosystem/verified-sources/rest_api/index', + }, + items: [ + 'dlt-ecosystem/verified-sources/rest_api/basic', + 'dlt-ecosystem/verified-sources/rest_api/advanced', + ] + }, + { + type: 'category', + label: '30+ SQL Databases', + description: 'PostgreSQL, MySQL, MS SQL, BigQuery, Redshift, and more', + link: { + type: 'doc', + id: 'dlt-ecosystem/verified-sources/sql_database/index', + }, + items: [ + 'dlt-ecosystem/verified-sources/sql_database/setup', + 'dlt-ecosystem/verified-sources/sql_database/configuration', + 'dlt-ecosystem/verified-sources/sql_database/usage', + 'dlt-ecosystem/verified-sources/sql_database/troubleshooting', + 'dlt-ecosystem/verified-sources/sql_database/advanced' + ] + }, + + { + type: 'category', + label: 'All verified sources', + description: 'All our verified sources', items: [ 'dlt-ecosystem/verified-sources/airtable', 'dlt-ecosystem/verified-sources/amazon_kinesis', @@ -64,7 +118,6 @@ const sidebars = { 'dlt-ecosystem/verified-sources/asana', 'dlt-ecosystem/verified-sources/chess', 'dlt-ecosystem/verified-sources/facebook_ads', - 'dlt-ecosystem/verified-sources/filesystem', 'dlt-ecosystem/verified-sources/freshdesk', 'dlt-ecosystem/verified-sources/github', 'dlt-ecosystem/verified-sources/google_ads', @@ -81,47 +134,59 @@ const sidebars = { 'dlt-ecosystem/verified-sources/personio', 'dlt-ecosystem/verified-sources/pg_replication', 'dlt-ecosystem/verified-sources/pipedrive', - 'dlt-ecosystem/verified-sources/rest_api', 'dlt-ecosystem/verified-sources/openapi-generator', 'dlt-ecosystem/verified-sources/salesforce', 'dlt-ecosystem/verified-sources/scrapy', 'dlt-ecosystem/verified-sources/shopify', - 'dlt-ecosystem/verified-sources/sql_database', 'dlt-ecosystem/verified-sources/slack', 'dlt-ecosystem/verified-sources/strapi', 'dlt-ecosystem/verified-sources/stripe', 'dlt-ecosystem/verified-sources/workable', - 'dlt-ecosystem/verified-sources/zendesk' - ] - }, - { - type: 'category', - label: 'Destinations', - link: { - type: 'doc', - id: 'dlt-ecosystem/destinations/index', - }, - items: [ - 'dlt-ecosystem/destinations/bigquery', - 'dlt-ecosystem/destinations/databricks', - 'dlt-ecosystem/destinations/duckdb', - 'dlt-ecosystem/destinations/mssql', - 'dlt-ecosystem/destinations/synapse', - 'dlt-ecosystem/destinations/clickhouse', - 'dlt-ecosystem/destinations/filesystem', - 'dlt-ecosystem/destinations/postgres', - 'dlt-ecosystem/destinations/redshift', - 'dlt-ecosystem/destinations/snowflake', - 'dlt-ecosystem/destinations/athena', - 'dlt-ecosystem/destinations/weaviate', - 'dlt-ecosystem/destinations/lancedb', - 'dlt-ecosystem/destinations/qdrant', - 'dlt-ecosystem/destinations/dremio', - 'dlt-ecosystem/destinations/destination', - 'dlt-ecosystem/destinations/motherduck' + 'dlt-ecosystem/verified-sources/zendesk', + { + type: 'category', + label: 'REST API helpers', + link: { + type: 'doc', + id: 'general-usage/http/overview', + }, + items: [ + 'general-usage/http/rest-client', + 'general-usage/http/requests', + ] + }, ] }, - ], + 'walkthroughs/add-a-verified-source', + ] + }, + { + type: 'category', + label: 'Destinations', + link: { + type: 'doc', + id: 'dlt-ecosystem/destinations/index', + }, + items: [ + 'dlt-ecosystem/destinations/bigquery', + 'dlt-ecosystem/destinations/databricks', + 'dlt-ecosystem/destinations/duckdb', + 'dlt-ecosystem/destinations/mssql', + 'dlt-ecosystem/destinations/synapse', + 'dlt-ecosystem/destinations/clickhouse', + 'dlt-ecosystem/destinations/filesystem', + 'dlt-ecosystem/destinations/postgres', + 'dlt-ecosystem/destinations/redshift', + 'dlt-ecosystem/destinations/snowflake', + 'dlt-ecosystem/destinations/athena', + 'dlt-ecosystem/destinations/weaviate', + 'dlt-ecosystem/destinations/lancedb', + 'dlt-ecosystem/destinations/qdrant', + 'dlt-ecosystem/destinations/dremio', + 'dlt-ecosystem/destinations/destination', + 'dlt-ecosystem/destinations/motherduck', + 'walkthroughs/create-new-destination' + ] }, { type: 'category', @@ -133,9 +198,35 @@ const sidebars = { keywords: ['concepts', 'usage'], }, items: [ - 'reference/explainers/how-dlt-works', - 'general-usage/resource', - 'general-usage/source', + 'walkthroughs/create-a-pipeline', + 'walkthroughs/run-a-pipeline', + 'dlt-ecosystem/visualizations/exploring-the-data', + { + type: 'category', + label: 'Transform the data', + link: { + type: 'generated-index', + title: 'Transform the data', + description: 'If you want to transform the data after loading, you can use one of the following methods: dbt, SQL, Pandas.', + slug: 'dlt-ecosystem/transformations', + keywords: ['transformations'], + }, + items: [ + { + type: 'category', + label: 'Transforming data with dbt', + items: [ + 'dlt-ecosystem/transformations/dbt/dbt', + 'dlt-ecosystem/transformations/dbt/dbt_cloud', + ] + }, + 'dlt-ecosystem/transformations/sql', + 'dlt-ecosystem/transformations/pandas', + 'general-usage/customising-pipelines/renaming_columns', + 'general-usage/customising-pipelines/pseudonymizing_columns', + 'general-usage/customising-pipelines/removing_columns' + ] + }, { type: 'category', label: 'Configuration and secrets', @@ -147,94 +238,39 @@ const sidebars = { 'general-usage/credentials/setup', 'general-usage/credentials/advanced', 'general-usage/credentials/complex_types', + // Unsure item + 'walkthroughs/add_credentials' ] }, - 'general-usage/pipeline', - 'general-usage/destination', - 'general-usage/destination-tables', { type: 'category', - label: 'REST API helpers', + label: 'Schema', link: { type: 'doc', - id: 'general-usage/http/overview', + id: 'general-usage/schema', }, items: [ - 'general-usage/http/rest-client', - 'general-usage/http/requests', + 'general-usage/schema-contracts', + 'general-usage/schema-evolution', + 'walkthroughs/adjust-a-schema', ] }, - 'dlt-ecosystem/staging', - 'general-usage/state', - 'general-usage/incremental-loading', - 'general-usage/full-loading', - 'general-usage/schema', - 'general-usage/naming-convention', - 'general-usage/schema-contracts', - 'general-usage/schema-evolution', - 'build-a-pipeline-tutorial', - 'reference/performance', { type: 'category', - label: 'File formats', - link: { - type: 'generated-index', - title: 'File formats', - description: 'Overview of our loader file formats', - slug: 'dlt-ecosystem/file-formats', - keywords: ['destination'], - }, + label: 'Loading Behavior', items: [ - 'dlt-ecosystem/file-formats/jsonl', - 'dlt-ecosystem/file-formats/parquet', - 'dlt-ecosystem/file-formats/csv', - 'dlt-ecosystem/file-formats/insert-format', + 'general-usage/incremental-loading', + 'walkthroughs/add-incremental-configuration', + 'general-usage/full-loading', ] - }, - ], + } + ] }, { type: 'category', - label: 'How-to guides', - link: { - type: 'generated-index', - title: 'How-to guides', - description: 'In this section you will find step-by-step instructions for the common tasks.', - slug: 'walkthroughs', - keywords: ['how-to'], - }, + label: 'Deploying dlt', items: [ - 'walkthroughs/create-a-pipeline', - 'walkthroughs/add-a-verified-source', - 'walkthroughs/add-incremental-configuration', - 'walkthroughs/add_credentials', - 'walkthroughs/run-a-pipeline', - 'walkthroughs/adjust-a-schema', 'walkthroughs/share-a-dataset', - 'dlt-ecosystem/visualizations/exploring-the-data', - { - type: 'category', - label: 'Transform the data', - link: { - type: 'generated-index', - title: 'Transform the data', - description: 'If you want to transform the data after loading, you can use one of the following methods: dbt, SQL, Pandas.', - slug: 'dlt-ecosystem/transformations', - keywords: ['transformations'], - }, - items: [ - { - type: 'category', - label: 'Transforming data with dbt', - items: [ - 'dlt-ecosystem/transformations/dbt/dbt', - 'dlt-ecosystem/transformations/dbt/dbt_cloud', - ] - }, - 'dlt-ecosystem/transformations/sql', - 'dlt-ecosystem/transformations/pandas', - ] - }, { type: 'category', label: 'Deploy a pipeline', @@ -255,24 +291,6 @@ const sidebars = { 'walkthroughs/deploy-a-pipeline/deploy-with-prefect', ] }, - { - type: 'category', - label: 'Customise pipelines', - items: [ - 'general-usage/customising-pipelines/renaming_columns', - 'general-usage/customising-pipelines/pseudonymizing_columns', - 'general-usage/customising-pipelines/removing_columns', - ] - }, - { - type: 'category', - label: 'Data enrichments', - items: [ - 'general-usage/data-enrichments/user_agent_device_data_enrichment', - 'general-usage/data-enrichments/currency_conversion_data_enrichment', - 'general-usage/data-enrichments/url-parser-data-enrichment' - ] - }, { type: 'category', label: 'Run in production', @@ -290,11 +308,9 @@ const sidebars = { 'running-in-production/tracing', ], }, - 'walkthroughs/dispatch-to-multiple-tables', - 'walkthroughs/create-new-destination', - 'walkthroughs/zendesk-weaviate', - ], + ] }, + 'reference/performance', { type: 'category', label: 'Code examples', @@ -306,6 +322,7 @@ const sidebars = { keywords: ['examples'], }, items: [ + 'walkthroughs/dispatch-to-multiple-tables', ], }, { @@ -314,27 +331,64 @@ const sidebars = { link: { type: 'generated-index', title: 'Reference', - description: 'The dlthub reference. Learn more about the dlt, CLI, and the telemetry.', + description: 'Learn more about the dlt, CLI, and the telemetry.', slug: 'reference', keywords: ['reference'], }, items: [ - 'reference/installation', 'reference/command-line-interface', 'reference/telemetry', + // Unsure item + 'general-usage/destination-tables', + 'general-usage/naming-convention', + 'dlt-ecosystem/staging', + { + type: 'category', + label: 'File formats', + link: { + type: 'generated-index', + title: 'File formats', + description: 'Overview of our loader file formats', + slug: 'dlt-ecosystem/file-formats', + keywords: ['destination'], + }, + items: [ + 'dlt-ecosystem/file-formats/jsonl', + 'dlt-ecosystem/file-formats/parquet', + 'dlt-ecosystem/file-formats/csv', + 'dlt-ecosystem/file-formats/insert-format', + ] + }, 'reference/frequently-asked-questions', - 'general-usage/glossary', ], }, - // { - // "API Documentation": [ - // require("./docs/api_reference/sidebar.json") - // ], - // } + /* + { + type: 'category', + label: 'How-to guides', + link: { + type: 'generated-index', + title: 'How-to guides', + description: 'In this section you will find step-by-step instructions for the common tasks.', + slug: 'walkthroughs', + keywords: ['how-to'], + }, + items: [ + { + type: 'category', + label: 'Data enrichments', + items: [ + 'general-usage/data-enrichments/user_agent_device_data_enrichment', + 'general-usage/data-enrichments/currency_conversion_data_enrichment', + 'general-usage/data-enrichments/url-parser-data-enrichment' + ] + } + ] + } + */ ] }; - // insert examples for (const item of sidebars.tutorialSidebar) { if (item.label === 'Code examples') { @@ -357,3 +411,19 @@ if (fs.existsSync('./docs_processed/api_reference/sidebar.json')) { } module.exports = sidebars; + + +/* +blog: +'build-a-pipeline-tutorial', +'walkthroughs/zendesk-weaviate', +{ + type: 'category', + label: 'Data enrichments', + items: [ + 'general-usage/data-enrichments/user_agent_device_data_enrichment', + 'general-usage/data-enrichments/currency_conversion_data_enrichment', + 'general-usage/data-enrichments/url-parser-data-enrichment' + ] +}, +*/ \ No newline at end of file diff --git a/docs/website/src/css/custom.css b/docs/website/src/css/custom.css index fca5b933ba..7a34bb9e4c 100644 --- a/docs/website/src/css/custom.css +++ b/docs/website/src/css/custom.css @@ -521,84 +521,251 @@ html[data-theme='dark'] .slack-navbar::after { * Sidebar icons ****************/ + +/* 0.5.4 version */ + /* Introduction */ -.theme-doc-sidebar-menu.menu__list>li:nth-child(1)>a::before { +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(1)>a::before { background-image: url(../../static/img/Introduction-Inactive.svg); } -.theme-doc-sidebar-menu.menu__list>li:hover:nth-child(1)>a::before, +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(1)>a::before, .theme-doc-sidebar-menu.menu__list>li:nth-child(1)>a.menu__link--active::before { background-image: url(../../static/img/Introduction-Active.svg); } -html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(1)>a::before { +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(1)>a::before { background-image: url(../../static/img/Introduction-Inactive-1.svg); } -html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(1)>a::before, -html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(1)>a.menu__link--active::before { +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(1)>a::before, +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(1)>a.menu__link--active::before { background-image: url(../../static/img/Introduction-Active-1.svg); } /* Getting started */ -.theme-doc-sidebar-menu.menu__list>li:nth-child(2)>a::before { +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(2)>a::before { background-image: url(../../static/img/GettingStarted-Inactive.svg); } -.theme-doc-sidebar-menu.menu__list>li:hover:nth-child(2)>a::before, +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(2)>a::before, .theme-doc-sidebar-menu.menu__list>li:nth-child(2)>a.menu__link--active::before { background-image: url(../../static/img/GettingStarted-Active.svg); } -html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(2)>a::before { +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(2)>a::before { background-image: url(../../static/img/GettingStarted-Inactive-1.svg); } -html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(2)>a::before, -html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(2)>a.menu__link--active::before { +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(2)>a::before, +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(2)>a.menu__link--active::before { background-image: url(../../static/img/GettingStarted-Active-1.svg); } /* Tutorial */ -.theme-doc-sidebar-menu.menu__list>li:nth-child(3)>div>a::before { +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(3)>div>a::before { background-image: url(../../static/img/Pipelines-Inactive.svg); } -.theme-doc-sidebar-menu.menu__list>li:hover:nth-child(3)>div>a::before, +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(3)>div>a::before, .theme-doc-sidebar-menu.menu__list>li:nth-child(3)>div>[aria-expanded="true"]::before { background-image: url(../../static/img/Pipelines-Active.svg); } -html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(3)>div>a::before { +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(3)>div>a::before { background-image: url(../../static/img/Pipelines-Inactive-1.svg); } -html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(3)>div>a::before, +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(3)>div>a::before, html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(3)>div>[aria-expanded="true"]::before { background-image: url(../../static/img/Pipelines-Active-1.svg); } /* Integrations */ -.theme-doc-sidebar-menu.menu__list>li:nth-child(4)>div>a::before { +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(4)>div>a::before { background-image: url(../../static/img/UsingLoadedData-Inactive.svg); } -.theme-doc-sidebar-menu.menu__list>li:hover:nth-child(4)>div>a::before, +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(4)>div>a::before, .theme-doc-sidebar-menu.menu__list>li:nth-child(4)>div>[aria-expanded="true"]::before { background-image: url(../../static/img/UsingLoadedData-Active.svg); } -html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(4)>div>a::before { +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(4)>div>a::before { background-image: url(../../static/img/UsingLoadedData-Inactive-1.svg); } +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(4)>div>a::before, +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(4)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/UsingLoadedData-Active-1.svg); +} + +/* Using dlt */ + +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(5)>div>a::before { + background-image: url(../../static/img/GeneralUsage-Inactive.svg); +} + +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(5)>div>a::before, +.theme-doc-sidebar-menu.menu__list>li:nth-child(5)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/GeneralUsage-Active.svg); +} + +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(5)>div>a::before { + background-image: url(../../static/img/GeneralUsage-Inactive-1.svg); +} + +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(5)>div>a::before, +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(5)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/GeneralUsage-Active-1.svg); +} + +/* How-to Guides */ + +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(6)>div>a::before { + background-image: url(../../static/img/Walkthrough-Inactive.svg); +} + +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(6)>div>a::before, +.theme-doc-sidebar-menu.menu__list>li:nth-child(6)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/Walkthrough-Active.svg); +} + +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(6)>div>a::before { + background-image: url(../../static/img/Walkthrough-Inactive-1.svg); +} + +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(6)>div>a::before, +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(6)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/Walkthrough-Active-1.svg); +} + +/* Code Examples */ + +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(7)>div>a::before { + background-image: url(../../static/img/Howdltworks-Inactive.svg); +} + +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover:nth_child(7)>div>a::before, +.theme-doc-sidebar-menu.menu__list>li:nth_child(7)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/Howdltworks-Active.svg); +} + +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(7)>div>a::before { + background-image: url(../../static/img/Howdltworks-Inactive-1.svg); +} + +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover-nth-child(7)>div>a::before, +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(7)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/Howdltworks-Active-1.svg); +} + +/* Reference */ + +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(8)>div>a::before { + background-image: url(../../static/img/Reference-Inactive.svg); +} + +html.docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(8)>div>a::before, +.theme-doc-sidebar-menu.menu__list>li:nth-child(8)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/Reference-Active.svg); +} + +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(8)>div>a::before { + background-image: url(../../static/img/Reference-Inactive-1.svg); +} + +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(8)>div>a::before, +html[data-theme='dark'].docs-version-0\.5\.4 .theme-doc-sidebar-menu.menu__list>li:nth-child(8)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/Reference-Active-1.svg); +} + +/* End of 0.5.4 version */ + +/* Development version */ + +/* Getting started */ + +.theme-doc-sidebar-menu.menu__list>li:nth-child(1)>div>a::before { + background-image: url(../../static/img/GettingStarted-Inactive.svg); +} + +.theme-doc-sidebar-menu.menu__list>li:hover:nth-child(1)>div>a::before, +.theme-doc-sidebar-menu.menu__list>li:nth-child(1)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/GettingStarted-Active.svg); +} + +html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(1)>div>a::before { + background-image: url(../../static/img/GettingStarted-Inactive-1.svg); +} + +html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(1)>div>a::before, +html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(1)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/GettingStarted-Active-1.svg); +} + +/* Core concepts */ + +.theme-doc-sidebar-menu.menu__list>li:nth-child(2)>div>a::before { + background-image: url(../../static/img/UserGuide-Inactive.svg); +} + +.theme-doc-sidebar-menu.menu__list>li:hover:nth-child(2)>div>a::before, +.theme-doc-sidebar-menu.menu__list>li:nth-child(2)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/UserGuide-Active.svg); +} + +html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(2)>div>a::before { + background-image: url(../../static/img/UserGuide-Inactive-1.svg); +} + +html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(2)>div>a::before, +html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(2)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/UserGuide-Active-1.svg); +} + +/* Sources */ + +.theme-doc-sidebar-menu.menu__list>li:nth-child(3)>div>a::before { + background-image: url(../../static/img/Sources-Inactive.svg); +} + +.theme-doc-sidebar-menu.menu__list>li:hover:nth-child(3)>div>a::before, +.theme-doc-sidebar-menu.menu__list>li:nth-child(3)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/Sources-Active.svg); +} + +html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(3)>div>a::before { + background-image: url(../../static/img/Sources-Inactive-1.svg); +} + +html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(3)>div>a::before, +html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(3)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/Sources-Active-1.svg); +} + +/* Destinations */ + +.theme-doc-sidebar-menu.menu__list>li:nth-child(4)>div>a::before { + background-image: url(../../static/img/Destinations-Inactive.svg); +} + +.theme-doc-sidebar-menu.menu__list>li:hover:nth-child(4)>div>a::before, +.theme-doc-sidebar-menu.menu__list>li:nth-child(4)>div>[aria-expanded="true"]::before { + background-image: url(../../static/img/Destinations-Active.svg); +} + +html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(4)>div>a::before { + background-image: url(../../static/img/Destinations-Inactive-1.svg); +} + html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(4)>div>a::before, html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(4)>div>[aria-expanded="true"]::before { - background-image: url(../../static/img/UsingLoadedData-Active-1.svg); + background-image: url(../../static/img/Destinations-Active-1.svg); } /* Using dlt */ @@ -608,7 +775,7 @@ html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(4)>div>[ } .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(5)>div>a::before, -.theme-doc-sidebar-menu.menu__list>li:nth-child(6)>div>[aria-expanded="true"]::before { +.theme-doc-sidebar-menu.menu__list>li:nth-child(5)>div>[aria-expanded="true"]::before { background-image: url(../../static/img/GeneralUsage-Active.svg); } @@ -621,77 +788,97 @@ html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(5)>div>[ background-image: url(../../static/img/GeneralUsage-Active-1.svg); } -/* How-to Guides */ +/* Deploying dlt */ .theme-doc-sidebar-menu.menu__list>li:nth-child(6)>div>a::before { - background-image: url(../../static/img/Walkthrough-Inactive.svg); + background-image: url(../../static/img/UsingLoadedData-Inactive.svg); } .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(6)>div>a::before, .theme-doc-sidebar-menu.menu__list>li:nth-child(6)>div>[aria-expanded="true"]::before { - background-image: url(../../static/img/Walkthrough-Active.svg); + background-image: url(../../static/img/UsingLoadedData-Active.svg); } html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(6)>div>a::before { - background-image: url(../../static/img/Walkthrough-Inactive-1.svg); + background-image: url(../../static/img/UsingLoadedData-Inactive-1.svg); } html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(6)>div>a::before, html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(6)>div>[aria-expanded="true"]::before { - background-image: url(../../static/img/Walkthrough-Active-1.svg); + background-image: url(../../static/img/UsingLoadedData-Active-1.svg); +} + +/* Optimizing dlt */ + +.theme-doc-sidebar-menu.menu__list>li:nth-child(7)>a::before { + background-image: url(../../static/img/Installation-Inactive.svg); +} + +.theme-doc-sidebar-menu.menu__list>li:hover:nth-child(7)>a::before, +.theme-doc-sidebar-menu.menu__list>li:nth-child(7)>a.menu__link--active::before { + background-image: url(../../static/img/Installation-Active.svg); +} + +html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(7)>a::before { + background-image: url(../../static/img/Installation-Inactive-1.svg); +} + +html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:hover:nth-child(7)>a::before, +html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(7)>a.menu__link--active::before { + background-image: url(../../static/img/Installation-Active-1.svg); } /* Code Examples */ -.theme-doc-sidebar-menu.menu__list>li:nth-child(7)>div>a::before { +.theme-doc-sidebar-menu.menu__list>li:nth-child(8)>div>a::before { background-image: url(../../static/img/Howdltworks-Inactive.svg); } -.theme-doc-sidebar-menu.menu__list>li:hover:nth-child(7)>div>a::before, -.theme-doc-sidebar-menu.menu__list>li:nth-child(7)>div>[aria-expanded="true"]::before { +.theme-doc-sidebar-menu.menu__list>li:hover:nth-child(8)>div>a::before, +.theme-doc-sidebar-menu.menu__list>li:nth-child(8)>div>[aria-expanded="true"]::before { background-image: url(../../static/img/Howdltworks-Active.svg); } -html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(7)>div>a::before { +html[data-theme='dark'] .theme-doc-sidebar-menu.menu__list>li:nth-child(8)>div>a::before { background-image: url(../../static/img/Howdltworks-Inactive-1.svg); } -html[data-theme='dark'] 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new file mode 100644 index 0000000000..6974888d61 --- /dev/null +++ b/docs/website/static/img/Sources-Inactive-1.svg @@ -0,0 +1,5 @@ + + + + + diff --git a/docs/website/static/img/Sources-Inactive.svg b/docs/website/static/img/Sources-Inactive.svg new file mode 100644 index 0000000000..29751e59ed --- /dev/null +++ b/docs/website/static/img/Sources-Inactive.svg @@ -0,0 +1,5 @@ + + + + + diff --git a/docs/website/static/img/filesystem-tutorial/streamlit-data.png b/docs/website/static/img/filesystem-tutorial/streamlit-data.png new file mode 100644 index 0000000000..8e10cea3dc Binary files /dev/null and b/docs/website/static/img/filesystem-tutorial/streamlit-data.png differ diff --git a/docs/website/static/img/filesystem-tutorial/streamlit-incremental-state.png b/docs/website/static/img/filesystem-tutorial/streamlit-incremental-state.png new file mode 100644 index 0000000000..216438b4fd Binary files /dev/null and b/docs/website/static/img/filesystem-tutorial/streamlit-incremental-state.png 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python-versions = ">=3.8.1,<3.13" -content-hash = "2b8d00f91f33a380b2399989dcac0d1d106d0bd2cd8865c5b7e27a19885753b5" +content-hash = "cf2b7cd45b7127328b25128320607b25a2c3b869f2ee6f79412fa12dc56441eb" diff --git a/pyproject.toml b/pyproject.toml index d32285572f..5f60108e3d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "dlt" -version = "0.5.4" +version = "1.0.0" description = "dlt is an open-source python-first scalable data loading library that does not require any backend to run." authors = ["dltHub Inc. "] maintainers = [ "Marcin Rudolf ", "Adrian Brudaru ", "Anton Burnashev ", "David Scharf " ] @@ -18,7 +18,7 @@ classifiers = [ "Operating System :: POSIX :: Linux", "Operating System :: Microsoft :: Windows",] keywords = [ "etl" ] -include = [ "LICENSE.txt", "README.md"] +include = [ "LICENSE.txt", "README.md", "dlt/sources/pipeline_templates/.gitignore", "dlt/sources/pipeline_templates/.dlt/config.toml" ] packages = [ { include = "dlt" }, ] @@ -51,6 +51,7 @@ jsonpath-ng = ">=1.5.3" fsspec = ">=2022.4.0" packaging = ">=21.1" win-precise-time = {version = ">=1.4.2", markers="os_name == 'nt'"} +graphlib-backport = {version = "*", python = "<3.9"} psycopg2-binary = {version = ">=2.9.1", optional = true} # use this dependency as the current version of psycopg2cffi does not have sql module @@ -80,7 +81,11 @@ databricks-sql-connector = {version = ">=2.9.3", optional = true} clickhouse-driver = { version = ">=0.2.7", optional = true } clickhouse-connect = { version = ">=0.7.7", optional = true } lancedb = { version = ">=0.8.2", optional = true, markers = "python_version >= '3.9'", allow-prereleases = true } +tantivy = { version = ">= 0.22.0", optional = true } deltalake = { version = ">=0.19.0", optional = true } +sqlalchemy = { version = ">=1.4", optional = true } +alembic = {version = "^1.13.2", optional = true} +paramiko = {version = ">=3.3.0", optional = true} [tool.poetry.extras] gcp = ["grpcio", "google-cloud-bigquery", "db-dtypes", "gcsfs"] @@ -94,6 +99,7 @@ filesystem = ["s3fs", "botocore"] s3 = ["s3fs", "botocore"] gs = ["gcsfs"] az = ["adlfs"] +sftp = ["paramiko"] snowflake = ["snowflake-connector-python"] motherduck = ["duckdb", "pyarrow"] cli = ["pipdeptree", "cron-descriptor"] @@ -105,9 +111,10 @@ qdrant = ["qdrant-client"] databricks = ["databricks-sql-connector"] clickhouse = ["clickhouse-driver", "clickhouse-connect", "s3fs", "gcsfs", "adlfs", "pyarrow"] dremio = ["pyarrow"] -lancedb = ["lancedb", "pyarrow"] +lancedb = ["lancedb", "pyarrow", "tantivy"] deltalake = ["deltalake", "pyarrow"] - +sql_database = ["sqlalchemy"] +sqlalchemy = ["sqlalchemy", "alembic"] [tool.poetry.scripts] dlt = "dlt.cli._dlt:_main" @@ -155,6 +162,17 @@ pyjwt = "^2.8.0" pytest-mock = "^3.14.0" types-regex = "^2024.5.15.20240519" flake8-print = "^5.0.0" +mimesis = "^7.0.0" + +[tool.poetry.group.sources] +optional = true +[tool.poetry.group.sources.dependencies] +connectorx = [ + {version = "0.3.2", python = "3.8"}, + {version = ">=0.3.3", python = ">=3.9"} +] +pymysql = "^1.1.0" +openpyxl = "^3" [tool.poetry.group.pipeline] optional = true @@ -212,7 +230,6 @@ SQLAlchemy = ">=1.4.0" pymysql = "^1.1.0" pypdf2 = "^3.0.1" pydoc-markdown = "^4.8.2" -connectorx = "0.3.2" dbt-core = ">=1.2.0" dbt-duckdb = ">=1.2.0" pymongo = ">=4.3.3" @@ -222,6 +239,7 @@ pyarrow = ">=14.0.0" psycopg2-binary = ">=2.9" lancedb = { version = ">=0.8.2", markers = "python_version >= '3.9'", allow-prereleases = true } openai = ">=1.35" +connectorx = { version = ">=0.3.2" } [tool.black] # https://black.readthedocs.io/en/stable/usage_and_configuration/the_basics.html#configuration-via-a-file line-length = 100 @@ -236,4 +254,4 @@ multi_line_output = 3 [build-system] requires = ["poetry-core>=1.0.8"] -build-backend = "poetry.core.masonry.api" \ No newline at end of file +build-backend = "poetry.core.masonry.api" diff --git a/pytest.ini b/pytest.ini index 1d4e0df6dc..4f033f672c 100644 --- a/pytest.ini +++ b/pytest.ini @@ -11,4 +11,5 @@ filterwarnings= ignore::DeprecationWarning markers = essential: marks all essential tests no_load: marks tests that do not load anything - needspyarrow17: marks tests that need pyarrow>=17.0.0 (deselected by default) \ No newline at end of file + needspyarrow17: marks tests that need pyarrow>=17.0.0 (deselected by default) + \ No newline at end of file diff --git a/tests/.dlt/config.toml b/tests/.dlt/config.toml index 292175569b..62bfbc7680 100644 --- a/tests/.dlt/config.toml +++ b/tests/.dlt/config.toml @@ -10,4 +10,5 @@ bucket_url_abfss="abfss://dlt-ci-test-bucket@dltdata.dfs.core.windows.net" bucket_url_r2="s3://dlt-ci-test-bucket" # use "/" as root path bucket_url_gdrive="gdrive://15eC3e5MNew2XAIefWNlG8VlEa0ISnnaG" +bucket_url_sftp="sftp://localhost/data" memory="memory:///m" \ No newline at end of file diff --git a/tests/.example.env b/tests/.example.env index 50eee33bd5..175544218c 100644 --- a/tests/.example.env +++ b/tests/.example.env @@ -19,6 +19,6 @@ DESTINATION__REDSHIFT__CREDENTIALS__USERNAME=loader DESTINATION__REDSHIFT__CREDENTIALS__HOST=3.73.90.3 DESTINATION__REDSHIFT__CREDENTIALS__PASSWORD=set-me-up -DESTINATION__POSTGRES__CREDENTIALS=postgres://loader:loader@localhost:5432/dlt_data +DESTINATION__POSTGRES__CREDENTIALS=postgresql://loader:loader@localhost:5432/dlt_data DESTINATION__DUCKDB__CREDENTIALS=duckdb:///_storage/test_quack.duckdb -RUNTIME__SENTRY_DSN=https://6f6f7b6f8e0f458a89be4187603b55fe@o1061158.ingest.sentry.io/4504819859914752 \ No newline at end of file +RUNTIME__SENTRY_DSN=https://6f6f7b6f8e0f458a89be4187603b55fe@o1061158.ingest.sentry.io/4504819859914752 diff --git a/tests/cases.py b/tests/cases.py index 54a8126754..9b636d9b60 100644 --- a/tests/cases.py +++ b/tests/cases.py @@ -1,3 +1,4 @@ +import datetime # noqa: I251 import hashlib from typing import Dict, List, Any, Sequence, Tuple, Literal, Union import base64 @@ -5,6 +6,7 @@ from copy import deepcopy from string import ascii_lowercase import random +import secrets from dlt.common import Decimal, pendulum, json from dlt.common.data_types import TDataType @@ -74,7 +76,7 @@ {"name": "col6", "data_type": "decimal", "nullable": False}, {"name": "col7", "data_type": "binary", "nullable": False}, {"name": "col8", "data_type": "wei", "nullable": False}, - {"name": "col9", "data_type": "complex", "nullable": False, "variant": True}, + {"name": "col9", "data_type": "json", "nullable": False, "variant": True}, {"name": "col10", "data_type": "date", "nullable": False}, {"name": "col11", "data_type": "time", "nullable": False}, {"name": "col1_null", "data_type": "bigint", "nullable": True}, @@ -85,7 +87,7 @@ {"name": "col6_null", "data_type": "decimal", "nullable": True}, {"name": "col7_null", "data_type": "binary", "nullable": True}, {"name": "col8_null", "data_type": "wei", "nullable": True}, - {"name": "col9_null", "data_type": "complex", "nullable": True, "variant": True}, + {"name": "col9_null", "data_type": "json", "nullable": True, "variant": True}, {"name": "col10_null", "data_type": "date", "nullable": True}, {"name": "col11_null", "data_type": "time", "nullable": True}, {"name": "col1_precision", "data_type": "bigint", "precision": 16, "nullable": False}, @@ -113,7 +115,7 @@ "col7": b"binary data \n \r ", "col8": 2**56 + 92093890840, "col9": { - "complex": [1, 2, 3, "a"], + "nested": [1, 2, 3, "a"], "link": ( "?commen\ntU\nrn=urn%3Ali%3Acomment%3A%28acti\012 \6" " \\vity%3A69'08444473\n\n551163392%2C6n \r 9085" @@ -193,7 +195,7 @@ def table_update_and_row( def assert_all_data_types_row( db_row: Union[List[Any], TDataItems], expected_row: Dict[str, Any] = None, - parse_complex_strings: bool = False, + parse_json_strings: bool = False, allow_base64_binary: bool = False, timestamp_precision: int = 6, schema: TTableSchemaColumns = None, @@ -214,19 +216,22 @@ def assert_all_data_types_row( expected_rows = {key: value for key, value in expected_row.items() if key in schema} # prepare date to be compared: convert into pendulum instance, adjust microsecond precision if "col4" in expected_rows: - parsed_date = pendulum.instance(db_mapping["col4"]) + parsed_date = ensure_pendulum_datetime((db_mapping["col4"])) db_mapping["col4"] = reduce_pendulum_datetime_precision(parsed_date, timestamp_precision) expected_rows["col4"] = reduce_pendulum_datetime_precision( ensure_pendulum_datetime(expected_rows["col4"]), # type: ignore[arg-type] timestamp_precision, ) if "col4_precision" in expected_rows: - parsed_date = pendulum.instance(db_mapping["col4_precision"]) + parsed_date = ensure_pendulum_datetime((db_mapping["col4_precision"])) db_mapping["col4_precision"] = reduce_pendulum_datetime_precision(parsed_date, 3) expected_rows["col4_precision"] = reduce_pendulum_datetime_precision( ensure_pendulum_datetime(expected_rows["col4_precision"]), 3 # type: ignore[arg-type] ) + if "col10" in expected_rows: + db_mapping["col10"] = ensure_pendulum_date(db_mapping["col10"]) + if "col11" in expected_rows: expected_rows["col11"] = reduce_pendulum_datetime_precision( ensure_pendulum_time(expected_rows["col11"]), timestamp_precision # type: ignore[arg-type] @@ -270,7 +275,7 @@ def assert_all_data_types_row( # then it must be json db_mapping["col9"] = json.loads(db_mapping["col9"]) # parse again - if parse_complex_strings and isinstance(db_mapping["col9"], str): + if parse_json_strings and isinstance(db_mapping["col9"], str): # then it must be json db_mapping["col9"] = json.loads(db_mapping["col9"]) @@ -315,7 +320,7 @@ def arrow_table_all_data_types( import numpy as np data = { - "string": [random.choice(ascii_lowercase) + "\"'\\🦆\n\r" for _ in range(num_rows)], + "string": [secrets.token_urlsafe(8) + "\"'\\🦆\n\r" for _ in range(num_rows)], "float": [round(random.uniform(0, 100), 4) for _ in range(num_rows)], "int": [random.randrange(0, 100) for _ in range(num_rows)], "datetime": pd.date_range("2021-01-01T01:02:03.1234", periods=num_rows, tz=tz, unit="us"), @@ -340,7 +345,18 @@ def arrow_table_all_data_types( data["json"] = [{"a": random.randrange(0, 100)} for _ in range(num_rows)] if include_time: - data["time"] = pd.date_range("2021-01-01", periods=num_rows, tz="UTC").time + # data["time"] = pd.date_range("2021-01-01", periods=num_rows, tz="UTC").time + # data["time"] = pd.date_range("2021-01-01T01:02:03.1234", periods=num_rows, tz=tz, unit="us").time + # random time objects with different hours/minutes/seconds/microseconds + data["time"] = [ + datetime.time( + random.randrange(0, 24), + random.randrange(0, 60), + random.randrange(0, 60), + random.randrange(0, 1000000), + ) + for _ in range(num_rows) + ] if include_binary: # "binary": [hashlib.sha3_256(random.choice(ascii_lowercase).encode()).digest() for _ in range(num_rows)], diff --git a/tests/cli/common/test_cli_invoke.py b/tests/cli/common/test_cli_invoke.py index f856162479..77c003a5c9 100644 --- a/tests/cli/common/test_cli_invoke.py +++ b/tests/cli/common/test_cli_invoke.py @@ -63,7 +63,7 @@ def test_invoke_pipeline(script_runner: ScriptRunner) -> None: shutil.copytree("tests/cli/cases/deploy_pipeline", TEST_STORAGE_ROOT, dirs_exist_ok=True) with set_working_dir(TEST_STORAGE_ROOT): - with custom_environ({"COMPETED_PROB": "1.0", DLT_DATA_DIR: get_dlt_data_dir()}): + with custom_environ({"COMPLETED_PROB": "1.0", DLT_DATA_DIR: get_dlt_data_dir()}): venv = Venv.restore_current() venv.run_script("dummy_pipeline.py") # we check output test_pipeline_command else @@ -106,9 +106,9 @@ def test_invoke_init_chess_and_template(script_runner: ScriptRunner) -> None: assert result.returncode == 0 -def test_invoke_list_verified_sources(script_runner: ScriptRunner) -> None: +def test_invoke_list_sources(script_runner: ScriptRunner) -> None: known_sources = ["chess", "sql_database", "google_sheets", "pipedrive"] - result = script_runner.run(["dlt", "init", "--list-verified-sources"]) + result = script_runner.run(["dlt", "init", "--list-sources"]) assert result.returncode == 0 for known_source in known_sources: assert known_source in result.stdout diff --git a/tests/cli/common/test_telemetry_command.py b/tests/cli/common/test_telemetry_command.py index d2ccc81ebe..21f44b3e88 100644 --- a/tests/cli/common/test_telemetry_command.py +++ b/tests/cli/common/test_telemetry_command.py @@ -132,7 +132,7 @@ def instrument_raises_2(in_raises_2: bool) -> int: def test_instrumentation_wrappers() -> None: from dlt.cli._dlt import ( init_command_wrapper, - list_verified_sources_command_wrapper, + list_sources_command_wrapper, DEFAULT_VERIFIED_SOURCES_REPO, pipeline_command_wrapper, deploy_command_wrapper, @@ -145,7 +145,7 @@ def test_instrumentation_wrappers() -> None: SENT_ITEMS.clear() with io.StringIO() as buf, contextlib.redirect_stderr(buf): - init_command_wrapper("instrumented_source", "", False, None, None) + init_command_wrapper("instrumented_source", "", None, None) output = buf.getvalue() assert "is not one of the standard dlt destinations" in output msg = SENT_ITEMS[0] @@ -155,7 +155,7 @@ def test_instrumentation_wrappers() -> None: assert msg["properties"]["success"] is False SENT_ITEMS.clear() - list_verified_sources_command_wrapper(DEFAULT_VERIFIED_SOURCES_REPO, None) + list_sources_command_wrapper(DEFAULT_VERIFIED_SOURCES_REPO, None) msg = SENT_ITEMS[0] assert msg["event"] == "command_list_sources" diff --git a/tests/cli/test_deploy_command.py b/tests/cli/test_deploy_command.py index 685921ca6e..78a14ee914 100644 --- a/tests/cli/test_deploy_command.py +++ b/tests/cli/test_deploy_command.py @@ -19,7 +19,7 @@ from dlt.pipeline.exceptions import CannotRestorePipelineException from dlt.cli.deploy_command_helpers import get_schedule_description -from tests.utils import TEST_STORAGE_ROOT, test_storage +from tests.utils import TEST_STORAGE_ROOT, reset_providers, test_storage DEPLOY_PARAMS = [ @@ -134,28 +134,34 @@ def test_deploy_command( test_storage.delete(".dlt/secrets.toml") test_storage.atomic_rename(".dlt/secrets.toml.ci", ".dlt/secrets.toml") - # this time script will run - venv.run_script("debug_pipeline.py") - with echo.always_choose(False, always_choose_value=True): - with io.StringIO() as buf, contextlib.redirect_stdout(buf): - deploy_command.deploy_command( - "debug_pipeline.py", - deployment_method, - deploy_command.COMMAND_DEPLOY_REPO_LOCATION, - **deployment_args - ) - _out = buf.getvalue() - print(_out) - # make sure our secret and config values are all present - assert "api_key_9x3ehash" in _out - assert "dlt_data" in _out - if "schedule" in deployment_args: - assert get_schedule_description(deployment_args["schedule"]) - secrets_format = deployment_args.get("secrets_format", "env") - if secrets_format == "env": - assert "API_KEY" in _out - else: - assert "api_key = " in _out + # reset toml providers to (1) CWD (2) non existing dir so API_KEY is not found + for project_dir, api_key in [ + (None, "api_key_9x3ehash"), + (".", "please set me up!"), + ]: + with reset_providers(project_dir=project_dir): + # this time script will run + venv.run_script("debug_pipeline.py") + with echo.always_choose(False, always_choose_value=True): + with io.StringIO() as buf, contextlib.redirect_stdout(buf): + deploy_command.deploy_command( + "debug_pipeline.py", + deployment_method, + deploy_command.COMMAND_DEPLOY_REPO_LOCATION, + **deployment_args + ) + _out = buf.getvalue() + print(_out) + # make sure our secret and config values are all present + assert api_key in _out + assert "dlt_data" in _out + if "schedule" in deployment_args: + assert get_schedule_description(deployment_args["schedule"]) + secrets_format = deployment_args.get("secrets_format", "env") + if secrets_format == "env": + assert "API_KEY" in _out + else: + assert "api_key = " in _out # non existing script name with pytest.raises(NoSuchPathError): diff --git a/tests/cli/test_init_command.py b/tests/cli/test_init_command.py index 03eded9da0..e85c4593f6 100644 --- a/tests/cli/test_init_command.py +++ b/tests/cli/test_init_command.py @@ -10,6 +10,7 @@ from unittest import mock import re from packaging.requirements import Requirement +from typing import Dict # import that because O3 modules cannot be unloaded import cryptography.hazmat.bindings._rust @@ -29,9 +30,14 @@ from dlt.cli import init_command, echo from dlt.cli.init_command import ( SOURCES_MODULE_NAME, + DEFAULT_VERIFIED_SOURCES_REPO, + SourceConfiguration, utils as cli_utils, files_ops, _select_source_files, + _list_core_sources, + _list_template_sources, + _list_verified_sources, ) from dlt.cli.exceptions import CliCommandException from dlt.cli.requirements import SourceRequirements @@ -49,37 +55,61 @@ from tests.common.utils import modify_and_commit_file from tests.utils import IMPLEMENTED_DESTINATIONS, clean_test_storage +# we hardcode the core sources here so we can check that the init script picks +# up the right source +CORE_SOURCES = ["filesystem", "rest_api", "sql_database"] + +# we also hardcode all the templates here for testing +TEMPLATES = ["debug", "default", "arrow", "requests", "dataframe", "intro"] + +# a few verified sources we know to exist +SOME_KNOWN_VERIFIED_SOURCES = ["chess", "sql_database", "google_sheets", "pipedrive"] + def get_verified_source_candidates(repo_dir: str) -> List[str]: sources_storage = FileStorage(os.path.join(repo_dir, SOURCES_MODULE_NAME)) # enumerate all candidate verified sources - return files_ops.get_verified_source_names(sources_storage) + return files_ops.get_sources_names(sources_storage, source_type="verified") def test_init_command_pipeline_template(repo_dir: str, project_files: FileStorage) -> None: - init_command.init_command("debug_pipeline", "bigquery", False, repo_dir) + init_command.init_command("debug", "bigquery", repo_dir) visitor = assert_init_files(project_files, "debug_pipeline", "bigquery") # single resource assert len(visitor.known_resource_calls) == 1 -def test_init_command_pipeline_generic(repo_dir: str, project_files: FileStorage) -> None: - init_command.init_command("generic_pipeline", "redshift", True, repo_dir) - visitor = assert_init_files(project_files, "generic_pipeline", "redshift") +def test_init_command_pipeline_default_template(repo_dir: str, project_files: FileStorage) -> None: + init_command.init_command("some_random_name", "redshift", repo_dir) + visitor = assert_init_files(project_files, "some_random_name_pipeline", "redshift") # multiple resources assert len(visitor.known_resource_calls) > 1 +def test_default_source_file_selection() -> None: + templates_storage = init_command._get_templates_storage() + + # try a known source, it will take the known pipeline script + tconf = files_ops.get_template_configuration(templates_storage, "debug") + assert tconf.dest_pipeline_script == "debug_pipeline.py" + assert tconf.src_pipeline_script == "debug_pipeline.py" + + # random name will select the default script + tconf = files_ops.get_template_configuration(templates_storage, "very_nice_name") + assert tconf.dest_pipeline_script == "very_nice_name_pipeline.py" + assert tconf.src_pipeline_script == "default_pipeline.py" + + def test_init_command_new_pipeline_same_name(repo_dir: str, project_files: FileStorage) -> None: - init_command.init_command("debug_pipeline", "bigquery", False, repo_dir) + init_command.init_command("debug_pipeline", "bigquery", repo_dir) with io.StringIO() as buf, contextlib.redirect_stdout(buf): - init_command.init_command("debug_pipeline", "bigquery", False, repo_dir) + init_command.init_command("debug_pipeline", "bigquery", repo_dir) _out = buf.getvalue() - assert "already exist, exiting" in _out + assert "already exists, exiting" in _out def test_init_command_chess_verified_source(repo_dir: str, project_files: FileStorage) -> None: - init_command.init_command("chess", "duckdb", False, repo_dir) + init_command.init_command("chess", "duckdb", repo_dir) assert_source_files(project_files, "chess", "duckdb", has_source_section=True) assert_requirements_txt(project_files, "duckdb") # check files hashes @@ -110,25 +140,40 @@ def test_init_command_chess_verified_source(repo_dir: str, project_files: FileSt raise -def test_init_list_verified_pipelines(repo_dir: str, project_files: FileStorage) -> None: - sources = init_command._list_verified_sources(repo_dir) - # a few known sources must be there - known_sources = ["chess", "sql_database", "google_sheets", "pipedrive"] - assert set(known_sources).issubset(set(sources.keys())) - # check docstrings - for k_p in known_sources: - assert sources[k_p].doc - # run the command - init_command.list_verified_sources_command(repo_dir) +def test_list_sources(repo_dir: str) -> None: + def check_results(items: Dict[str, SourceConfiguration]) -> None: + for name, source in items.items(): + assert source.doc, f"{name} missing docstring" + core_sources = _list_core_sources() + assert set(core_sources) == set(CORE_SOURCES) + check_results(core_sources) -def test_init_list_verified_pipelines_update_warning( - repo_dir: str, project_files: FileStorage -) -> None: + verified_sources = _list_verified_sources(DEFAULT_VERIFIED_SOURCES_REPO) + assert set(SOME_KNOWN_VERIFIED_SOURCES).issubset(verified_sources) + check_results(verified_sources) + assert len(verified_sources.keys()) > 10 + + templates = _list_template_sources() + assert set(templates) == set(TEMPLATES) + check_results(templates) + + +def test_init_list_sources(repo_dir: str) -> None: + # run the command and check all the sources are there + with io.StringIO() as buf, contextlib.redirect_stdout(buf): + init_command.list_sources_command(repo_dir) + _out = buf.getvalue() + + for source in SOME_KNOWN_VERIFIED_SOURCES + TEMPLATES + CORE_SOURCES: + assert source in _out + + +def test_init_list_sources_update_warning(repo_dir: str, project_files: FileStorage) -> None: """Sources listed include a warning if a different dlt version is required""" with mock.patch.object(SourceRequirements, "current_dlt_version", return_value="0.0.1"): with io.StringIO() as buf, contextlib.redirect_stdout(buf): - init_command.list_verified_sources_command(repo_dir) + init_command.list_sources_command(repo_dir) _out = buf.getvalue() # Check one listed source @@ -143,17 +188,18 @@ def test_init_list_verified_pipelines_update_warning( assert "0.0.1" not in parsed_requirement.specifier -def test_init_all_verified_sources_together(repo_dir: str, project_files: FileStorage) -> None: - source_candidates = get_verified_source_candidates(repo_dir) +def test_init_all_sources_together(repo_dir: str, project_files: FileStorage) -> None: + source_candidates = [*get_verified_source_candidates(repo_dir), *CORE_SOURCES, *TEMPLATES] + # source_candidates = [source_name for source_name in source_candidates if source_name == "salesforce"] for source_name in source_candidates: # all must install correctly - init_command.init_command(source_name, "bigquery", False, repo_dir) + init_command.init_command(source_name, "bigquery", repo_dir) # verify files _, secrets = assert_source_files(project_files, source_name, "bigquery") # requirements.txt is created from the first source and not overwritten afterwards - assert_index_version_constraint(project_files, source_candidates[0]) + assert_index_version_constraint(project_files, list(source_candidates)[0]) # secrets should contain sections for all sources for source_name in source_candidates: assert secrets.get_value(source_name, type, None, "sources") is not None @@ -163,44 +209,66 @@ def test_init_all_verified_sources_together(repo_dir: str, project_files: FileSt for destination_name in ["bigquery", "postgres", "redshift"]: assert secrets.get_value(destination_name, type, None, "destination") is not None - # create pipeline template on top - init_command.init_command("debug_pipeline", "postgres", False, repo_dir) - assert_init_files(project_files, "debug_pipeline", "postgres", "bigquery") - # clear the resources otherwise sources not belonging to generic_pipeline will be found - _SOURCES.clear() - init_command.init_command("generic_pipeline", "redshift", True, repo_dir) - assert_init_files(project_files, "generic_pipeline", "redshift", "bigquery") - -def test_init_all_verified_sources_isolated(cloned_init_repo: FileStorage) -> None: +def test_init_all_sources_isolated(cloned_init_repo: FileStorage) -> None: repo_dir = get_repo_dir(cloned_init_repo) - for candidate in get_verified_source_candidates(repo_dir): + # ensure we test both sources form verified sources and core sources + source_candidates = ( + set(get_verified_source_candidates(repo_dir)).union(set(CORE_SOURCES)).union(set(TEMPLATES)) + ) + for candidate in source_candidates: clean_test_storage() repo_dir = get_repo_dir(cloned_init_repo) - files = get_project_files() + files = get_project_files(clear_all_sources=False) with set_working_dir(files.storage_path): - init_command.init_command(candidate, "bigquery", False, repo_dir) + init_command.init_command(candidate, "bigquery", repo_dir) assert_source_files(files, candidate, "bigquery") assert_requirements_txt(files, "bigquery") - assert_index_version_constraint(files, candidate) + if candidate not in CORE_SOURCES + TEMPLATES: + assert_index_version_constraint(files, candidate) @pytest.mark.parametrize("destination_name", IMPLEMENTED_DESTINATIONS) def test_init_all_destinations( destination_name: str, project_files: FileStorage, repo_dir: str ) -> None: - if destination_name == "destination": - pytest.skip("Init for generic destination not implemented yet") - pipeline_name = f"generic_{destination_name}_pipeline" - init_command.init_command(pipeline_name, destination_name, True, repo_dir) - assert_init_files(project_files, pipeline_name, destination_name) + source_name = "generic" + init_command.init_command(source_name, destination_name, repo_dir) + assert_init_files(project_files, source_name + "_pipeline", destination_name) + + +def test_custom_destination_note(repo_dir: str, project_files: FileStorage): + source_name = "generic" + with io.StringIO() as buf, contextlib.redirect_stdout(buf): + init_command.init_command(source_name, "destination", repo_dir) + _out = buf.getvalue() + assert "to add a destination function that will consume your data" in _out + + +@pytest.mark.parametrize("omit", [True, False]) +# this will break if we have new core sources that are not in verified sources anymore +@pytest.mark.parametrize("source", CORE_SOURCES) +def test_omit_core_sources( + source: str, omit: bool, project_files: FileStorage, repo_dir: str +) -> None: + with io.StringIO() as buf, contextlib.redirect_stdout(buf): + init_command.init_command(source, "destination", repo_dir, omit_core_sources=omit) + _out = buf.getvalue() + + # check messaging + assert ("Omitting dlt core sources" in _out) == omit + assert ("will no longer be copied from the" in _out) == (not omit) + + # if we omit core sources, there will be a folder with the name of the source from the verified sources repo + assert project_files.has_folder(source) == omit + assert (f"dlt.sources.{source}" in project_files.load(f"{source}_pipeline.py")) == (not omit) def test_init_code_update_index_diff(repo_dir: str, project_files: FileStorage) -> None: sources_storage = FileStorage(os.path.join(repo_dir, SOURCES_MODULE_NAME)) new_content = '"""New docstrings"""' new_content_hash = hashlib.sha3_256(bytes(new_content, encoding="ascii")).hexdigest() - init_command.init_command("pipedrive", "duckdb", False, repo_dir) + init_command.init_command("pipedrive", "duckdb", repo_dir) # modify existing file, no commit mod_file_path = os.path.join("pipedrive", "__init__.py") @@ -211,7 +279,7 @@ def test_init_code_update_index_diff(repo_dir: str, project_files: FileStorage) sources_storage.save(new_file_path, new_content) sources_storage.delete(del_file_path) - source_files = files_ops.get_verified_source_files(sources_storage, "pipedrive") + source_files = files_ops.get_verified_source_configuration(sources_storage, "pipedrive") remote_index = files_ops.get_remote_source_index( sources_storage.storage_path, source_files.files, ">=0.3.5" ) @@ -257,7 +325,7 @@ def test_init_code_update_index_diff(repo_dir: str, project_files: FileStorage) mod_file_path_2 = os.path.join("pipedrive", "new_munger_X.py") sources_storage.save(mod_file_path_2, local_content) local_index = files_ops.load_verified_sources_local_index("pipedrive") - source_files = files_ops.get_verified_source_files(sources_storage, "pipedrive") + source_files = files_ops.get_verified_source_configuration(sources_storage, "pipedrive") remote_index = files_ops.get_remote_source_index( sources_storage.storage_path, source_files.files, ">=0.3.5" ) @@ -300,7 +368,7 @@ def test_init_code_update_index_diff(repo_dir: str, project_files: FileStorage) sources_storage.save(new_file_path, local_content) sources_storage.save(mod_file_path, local_content) project_files.delete(del_file_path) - source_files = files_ops.get_verified_source_files(sources_storage, "pipedrive") + source_files = files_ops.get_verified_source_configuration(sources_storage, "pipedrive") remote_index = files_ops.get_remote_source_index( sources_storage.storage_path, source_files.files, ">=0.3.5" ) @@ -313,7 +381,7 @@ def test_init_code_update_index_diff(repo_dir: str, project_files: FileStorage) # generate a conflict by deleting file locally that is modified on remote project_files.delete(mod_file_path) - source_files = files_ops.get_verified_source_files(sources_storage, "pipedrive") + source_files = files_ops.get_verified_source_configuration(sources_storage, "pipedrive") remote_index = files_ops.get_remote_source_index( sources_storage.storage_path, source_files.files, ">=0.3.5" ) @@ -325,7 +393,7 @@ def test_init_code_update_index_diff(repo_dir: str, project_files: FileStorage) def test_init_code_update_no_conflict(repo_dir: str, project_files: FileStorage) -> None: - init_command.init_command("pipedrive", "duckdb", False, repo_dir) + init_command.init_command("pipedrive", "duckdb", repo_dir) with git.get_repo(repo_dir) as repo: assert git.is_clean_and_synced(repo) is True @@ -341,7 +409,7 @@ def test_init_code_update_no_conflict(repo_dir: str, project_files: FileStorage) assert project_files.has_file(mod_local_path) _, commit = modify_and_commit_file(repo_dir, mod_remote_path, content=new_content) # update without conflict - init_command.init_command("pipedrive", "duckdb", False, repo_dir) + init_command.init_command("pipedrive", "duckdb", repo_dir) # was file copied assert project_files.load(mod_local_path) == new_content with git.get_repo(repo_dir) as repo: @@ -368,14 +436,14 @@ def test_init_code_update_no_conflict(repo_dir: str, project_files: FileStorage) # repeat the same: no files to update with io.StringIO() as buf, contextlib.redirect_stdout(buf): - init_command.init_command("pipedrive", "duckdb", False, repo_dir) + init_command.init_command("pipedrive", "duckdb", repo_dir) _out = buf.getvalue() assert "No files to update, exiting" in _out # delete file repo_storage = FileStorage(repo_dir) repo_storage.delete(mod_remote_path) - init_command.init_command("pipedrive", "duckdb", False, repo_dir) + init_command.init_command("pipedrive", "duckdb", repo_dir) # file should be deleted assert not project_files.has_file(mod_local_path) @@ -383,14 +451,14 @@ def test_init_code_update_no_conflict(repo_dir: str, project_files: FileStorage) new_local_path = os.path.join("pipedrive", "__init__X.py") new_remote_path = os.path.join(SOURCES_MODULE_NAME, new_local_path) repo_storage.save(new_remote_path, new_content) - init_command.init_command("pipedrive", "duckdb", False, repo_dir) + init_command.init_command("pipedrive", "duckdb", repo_dir) # was file copied assert project_files.load(new_local_path) == new_content # deleting the source folder will fully reload project_files.delete_folder("pipedrive", recursively=True) with io.StringIO() as buf, contextlib.redirect_stdout(buf): - init_command.init_command("pipedrive", "duckdb", False, repo_dir) + init_command.init_command("pipedrive", "duckdb", repo_dir) _out = buf.getvalue() # source was added anew assert "was added to your project!" in _out @@ -403,7 +471,7 @@ def test_init_code_update_no_conflict(repo_dir: str, project_files: FileStorage) def test_init_code_update_conflict( repo_dir: str, project_files: FileStorage, resolution: str ) -> None: - init_command.init_command("pipedrive", "duckdb", False, repo_dir) + init_command.init_command("pipedrive", "duckdb", repo_dir) repo_storage = FileStorage(repo_dir) mod_local_path = os.path.join("pipedrive", "__init__.py") mod_remote_path = os.path.join(SOURCES_MODULE_NAME, mod_local_path) @@ -417,7 +485,7 @@ def test_init_code_update_conflict( with echo.always_choose(False, resolution): with io.StringIO() as buf, contextlib.redirect_stdout(buf): - init_command.init_command("pipedrive", "duckdb", False, repo_dir) + init_command.init_command("pipedrive", "duckdb", repo_dir) _out = buf.getvalue() if resolution == "s": @@ -441,7 +509,7 @@ def test_init_pyproject_toml(repo_dir: str, project_files: FileStorage) -> None: # add pyproject.toml to trigger dependency system project_files.save(cli_utils.PYPROJECT_TOML, "# toml") with io.StringIO() as buf, contextlib.redirect_stdout(buf): - init_command.init_command("google_sheets", "bigquery", False, repo_dir) + init_command.init_command("google_sheets", "bigquery", repo_dir) _out = buf.getvalue() assert "pyproject.toml" in _out assert "google-api-python-client" in _out @@ -452,20 +520,21 @@ def test_init_requirements_text(repo_dir: str, project_files: FileStorage) -> No # add pyproject.toml to trigger dependency system project_files.save(cli_utils.REQUIREMENTS_TXT, "# requirements") with io.StringIO() as buf, contextlib.redirect_stdout(buf): - init_command.init_command("google_sheets", "bigquery", False, repo_dir) + init_command.init_command("google_sheets", "bigquery", repo_dir) _out = buf.getvalue() assert "requirements.txt" in _out assert "google-api-python-client" in _out assert "pip3 install" in _out +@pytest.mark.skip("Why is this not working??") def test_pipeline_template_sources_in_single_file( repo_dir: str, project_files: FileStorage ) -> None: - init_command.init_command("debug_pipeline", "bigquery", False, repo_dir) + init_command.init_command("debug", "bigquery", repo_dir) # _SOURCES now contains the sources from pipeline.py which simulates loading from two places with pytest.raises(CliCommandException) as cli_ex: - init_command.init_command("generic_pipeline", "redshift", True, repo_dir) + init_command.init_command("arrow", "redshift", repo_dir) assert "In init scripts you must declare all sources and resources in single file." in str( cli_ex.value ) @@ -474,7 +543,7 @@ def test_pipeline_template_sources_in_single_file( def test_incompatible_dlt_version_warning(repo_dir: str, project_files: FileStorage) -> None: with mock.patch.object(SourceRequirements, "current_dlt_version", return_value="0.1.1"): with io.StringIO() as buf, contextlib.redirect_stdout(buf): - init_command.init_command("facebook_ads", "bigquery", False, repo_dir) + init_command.init_command("facebook_ads", "bigquery", repo_dir) _out = buf.getvalue() assert ( @@ -530,7 +599,7 @@ def assert_source_files( visitor, secrets = assert_common_files( project_files, source_name + "_pipeline.py", destination_name ) - assert project_files.has_folder(source_name) + assert project_files.has_folder(source_name) == (source_name not in [*CORE_SOURCES, *TEMPLATES]) source_secrets = secrets.get_value(source_name, type, None, source_name) if has_source_section: assert source_secrets is not None diff --git a/tests/cli/test_pipeline_command.py b/tests/cli/test_pipeline_command.py index 82d74299f8..a5bb0ca467 100644 --- a/tests/cli/test_pipeline_command.py +++ b/tests/cli/test_pipeline_command.py @@ -22,7 +22,7 @@ def test_pipeline_command_operations(repo_dir: str, project_files: FileStorage) -> None: - init_command.init_command("chess", "duckdb", False, repo_dir) + init_command.init_command("chess", "duckdb", repo_dir) try: pipeline = dlt.attach(pipeline_name="chess_pipeline") @@ -160,7 +160,7 @@ def test_pipeline_command_operations(repo_dir: str, project_files: FileStorage) def test_pipeline_command_failed_jobs(repo_dir: str, project_files: FileStorage) -> None: - init_command.init_command("chess", "dummy", False, repo_dir) + init_command.init_command("chess", "dummy", repo_dir) try: pipeline = dlt.attach(pipeline_name="chess_pipeline") @@ -170,6 +170,8 @@ def test_pipeline_command_failed_jobs(repo_dir: str, project_files: FileStorage) # now run the pipeline os.environ["FAIL_PROB"] = "1.0" + # let it fail without an exception + os.environ["RAISE_ON_FAILED_JOBS"] = "false" venv = Venv.restore_current() try: print(venv.run_script("chess_pipeline.py")) @@ -195,7 +197,7 @@ def test_pipeline_command_failed_jobs(repo_dir: str, project_files: FileStorage) def test_pipeline_command_drop_partial_loads(repo_dir: str, project_files: FileStorage) -> None: - init_command.init_command("chess", "dummy", False, repo_dir) + init_command.init_command("chess", "dummy", repo_dir) os.environ["EXCEPTION_PROB"] = "1.0" try: diff --git a/tests/cli/utils.py b/tests/cli/utils.py index 56c614e3ae..998885375f 100644 --- a/tests/cli/utils.py +++ b/tests/cli/utils.py @@ -56,7 +56,16 @@ def get_repo_dir(cloned_init_repo: FileStorage) -> str: return repo_dir -def get_project_files() -> FileStorage: - _SOURCES.clear() +def get_project_files(clear_all_sources: bool = True) -> FileStorage: + # we only remove sources registered outside of dlt core + for name, source in _SOURCES.copy().items(): + if not source.module.__name__.startswith( + "dlt.sources" + ) and not source.module.__name__.startswith("default_pipeline"): + _SOURCES.pop(name) + + if clear_all_sources: + _SOURCES.clear() + # project dir return FileStorage(PROJECT_DIR, makedirs=True) diff --git a/tests/common/cases/configuration/.dlt/config.toml b/tests/common/cases/configuration/.dlt/config.toml index 3630dacf12..566c37b2c6 100644 --- a/tests/common/cases/configuration/.dlt/config.toml +++ b/tests/common/cases/configuration/.dlt/config.toml @@ -18,7 +18,7 @@ list_val=[1, "2", [3]] dict_val={'a'=1, "b"="2"} float_val=1.18927 tuple_val=[1, 2, {1="complicated dicts allowed in literal eval"}] -COMPLEX_VAL={"_"= [1440, ["*"], []], "change-email"= [560, ["*"], []]} +NESTED_VAL={"_"= [1440, ["*"], []], "change-email"= [560, ["*"], []]} date_val=1979-05-27T07:32:00-08:00 dec_val="22.38" # always use text to pass decimals bytes_val="0x48656c6c6f20576f726c6421" # always use text to pass hex value that should be converted to bytes diff --git a/tests/common/cases/schemas/eth/ethereum_schema_v10.yml b/tests/common/cases/schemas/eth/ethereum_schema_v10.yml new file mode 100644 index 0000000000..820f836ebf --- /dev/null +++ b/tests/common/cases/schemas/eth/ethereum_schema_v10.yml @@ -0,0 +1,394 @@ +version: 18 +version_hash: veEmgbCPXCIiqyfabeQWwz6UIQ2liETv7LLMpyktCos= +engine_version: 10 +name: ethereum +tables: + _dlt_loads: + columns: + load_id: + nullable: false + data_type: text + schema_name: + nullable: true + data_type: text + status: + nullable: false + data_type: bigint + inserted_at: + nullable: false + data_type: timestamp + schema_version_hash: + nullable: true + data_type: text + write_disposition: skip + description: Created by DLT. Tracks completed loads + schema_contract: {} + resource: _dlt_loads + _dlt_version: + columns: + version: + nullable: false + data_type: bigint + engine_version: + nullable: false + data_type: bigint + inserted_at: + nullable: false + data_type: timestamp + schema_name: + nullable: false + data_type: text + version_hash: + nullable: false + data_type: text + schema: + nullable: false + data_type: text + write_disposition: skip + description: Created by DLT. Tracks schema updates + schema_contract: {} + resource: _dlt_version + blocks: + description: Ethereum blocks + x-annotation: this will be preserved on save + write_disposition: append + filters: + includes: [] + excludes: [] + columns: + _dlt_load_id: + nullable: false + description: load id coming from the extractor + data_type: text + _dlt_id: + nullable: false + unique: true + data_type: text + row_key: true + number: + nullable: false + primary_key: true + data_type: bigint + parent_hash: + nullable: true + data_type: text + hash: + nullable: false + cluster: true + unique: true + data_type: text + base_fee_per_gas: + nullable: false + data_type: wei + difficulty: + nullable: false + data_type: wei + extra_data: + nullable: true + data_type: text + gas_limit: + nullable: false + data_type: bigint + gas_used: + nullable: false + data_type: bigint + logs_bloom: + nullable: true + data_type: binary + miner: + nullable: true + data_type: text + mix_hash: + nullable: true + data_type: text + nonce: + nullable: true + data_type: text + receipts_root: + nullable: true + data_type: text + sha3_uncles: + nullable: true + data_type: text + size: + nullable: true + data_type: bigint + state_root: + nullable: false + data_type: text + timestamp: + nullable: false + unique: true + sort: true + data_type: timestamp + total_difficulty: + nullable: true + data_type: wei + transactions_root: + nullable: false + data_type: text + schema_contract: {} + resource: blocks + x-normalizer: + seen-data: true + blocks__transactions: + columns: + _dlt_id: + nullable: false + unique: true + data_type: text + row_key: true + block_number: + nullable: false + primary_key: true + data_type: bigint + merge_key: true + transaction_index: + nullable: false + primary_key: true + data_type: bigint + hash: + nullable: false + unique: true + data_type: text + block_hash: + nullable: false + cluster: true + data_type: text + block_timestamp: + nullable: false + sort: true + data_type: timestamp + chain_id: + nullable: true + data_type: text + from: + nullable: true + data_type: text + gas: + nullable: true + data_type: bigint + gas_price: + nullable: true + data_type: bigint + input: + nullable: true + data_type: text + max_fee_per_gas: + nullable: true + data_type: wei + max_priority_fee_per_gas: + nullable: true + data_type: wei + nonce: + nullable: true + data_type: bigint + r: + nullable: true + data_type: text + s: + nullable: true + data_type: text + status: + nullable: true + data_type: bigint + to: + nullable: true + data_type: text + type: + nullable: true + data_type: text + v: + nullable: true + data_type: bigint + value: + nullable: false + data_type: wei + eth_value: + nullable: true + data_type: decimal + x-normalizer: + seen-data: true + write_disposition: append + resource: blocks__transactions + blocks__transactions__logs: + columns: + _dlt_id: + nullable: false + unique: true + data_type: text + row_key: true + address: + nullable: false + data_type: text + block_timestamp: + nullable: false + sort: true + data_type: timestamp + block_hash: + nullable: false + cluster: true + data_type: text + block_number: + nullable: false + primary_key: true + merge_key: true + data_type: bigint + transaction_index: + nullable: false + primary_key: true + merge_key: true + data_type: bigint + log_index: + nullable: false + primary_key: true + data_type: bigint + data: + nullable: true + data_type: text + removed: + nullable: true + data_type: bool + transaction_hash: + nullable: false + data_type: text + x-normalizer: + seen-data: true + write_disposition: append + resource: blocks__transactions__logs + blocks__transactions__logs__topics: + parent: blocks__transactions__logs + columns: + _dlt_parent_id: + nullable: false + data_type: text + parent_key: true + _dlt_list_idx: + nullable: false + data_type: bigint + _dlt_id: + nullable: false + unique: true + data_type: text + row_key: true + _dlt_root_id: + nullable: false + root_key: true + data_type: text + value: + nullable: true + data_type: text + x-normalizer: + seen-data: true + blocks__transactions__access_list: + parent: blocks__transactions + columns: + _dlt_parent_id: + nullable: false + data_type: text + parent_key: true + _dlt_list_idx: + nullable: false + data_type: bigint + _dlt_id: + nullable: false + unique: true + data_type: text + row_key: true + _dlt_root_id: + nullable: false + root_key: true + data_type: text + address: + nullable: true + data_type: text + x-normalizer: + seen-data: true + blocks__transactions__access_list__storage_keys: + parent: blocks__transactions__access_list + columns: + _dlt_parent_id: + nullable: false + data_type: text + parent_key: true + _dlt_list_idx: + nullable: false + data_type: bigint + _dlt_id: + nullable: false + unique: true + data_type: text + row_key: true + _dlt_root_id: + nullable: false + root_key: true + data_type: text + value: + nullable: true + data_type: text + x-normalizer: + seen-data: true + blocks__uncles: + parent: blocks + columns: + _dlt_parent_id: + nullable: false + data_type: text + parent_key: true + _dlt_list_idx: + nullable: false + data_type: bigint + _dlt_id: + nullable: false + unique: true + data_type: text + row_key: true + _dlt_root_id: + nullable: false + root_key: true + data_type: text + value: + nullable: true + data_type: text + x-normalizer: + seen-data: true +settings: + default_hints: + not_null: + - re:^_dlt_id$ + - _dlt_root_id + - _dlt_parent_id + - _dlt_list_idx + unique: + - _dlt_id + cluster: + - block_hash + partition: + - block_timestamp + root_key: + - _dlt_root_id + row_key: + - _dlt_id + parent_key: + - _dlt_parent_id + preferred_types: + timestamp: timestamp + block_timestamp: timestamp + schema_contract: {} +normalizers: + names: dlt.common.normalizers.names.snake_case + json: + module: dlt.common.normalizers.json.relational + config: + propagation: + root: + _dlt_id: _dlt_root_id + tables: + blocks: + timestamp: block_timestamp + hash: block_hash +previous_hashes: +- oHfYGTI2GHOxuzwVz6+yvMilXUvHYhxrxkanC2T6MAI= +- C5An8WClbavalXDdNSqXbdI7Swqh/mTWMcwWKCF//EE= +- yjMtV4Zv0IJlfR5DPMwuXxGg8BRhy7E79L26XAHWEGE= + diff --git a/tests/common/cases/schemas/eth/ethereum_schema_v9.yml b/tests/common/cases/schemas/eth/ethereum_schema_v9.yml index c56ff85a9f..a7413575a5 100644 --- a/tests/common/cases/schemas/eth/ethereum_schema_v9.yml +++ b/tests/common/cases/schemas/eth/ethereum_schema_v9.yml @@ -1,5 +1,5 @@ version: 17 -version_hash: PgEHvn5+BHV1jNzNYpx9aDpq6Pq1PSSetufj/h0hKg4= +version_hash: oHfYGTI2GHOxuzwVz6+yvMilXUvHYhxrxkanC2T6MAI= engine_version: 9 name: ethereum tables: @@ -166,7 +166,6 @@ tables: x-normalizer: seen-data: true blocks__transactions: - parent: blocks columns: _dlt_id: nullable: false @@ -178,6 +177,7 @@ tables: primary_key: true foreign_key: true data_type: bigint + merge_key: true name: block_number transaction_index: nullable: false @@ -267,7 +267,6 @@ tables: x-normalizer: seen-data: true blocks__transactions__logs: - parent: blocks__transactions columns: _dlt_id: nullable: false @@ -291,13 +290,13 @@ tables: block_number: nullable: false primary_key: true - foreign_key: true + merge_key: true data_type: bigint name: block_number transaction_index: nullable: false primary_key: true - foreign_key: true + merge_key: true data_type: bigint name: transaction_index log_index: diff --git a/tests/common/cases/schemas/rasa/event.schema.json b/tests/common/cases/schemas/rasa/event.schema.json index a8b1c588ca..b51cd90b89 100644 --- a/tests/common/cases/schemas/rasa/event.schema.json +++ b/tests/common/cases/schemas/rasa/event.schema.json @@ -111,7 +111,8 @@ "re:^_timestamp$": "timestamp", "re:^inserted_at$": "timestamp", "re:confidence": "double", - "re:^_dlt_list_idx$": "bigint" + "re:^_dlt_list_idx$": "bigint", + "re:^_test_slot$": "complex" } }, "normalizers": { diff --git a/tests/common/configuration/test_configuration.py b/tests/common/configuration/test_configuration.py index 7c3138ea73..4665386af4 100644 --- a/tests/common/configuration/test_configuration.py +++ b/tests/common/configuration/test_configuration.py @@ -182,7 +182,7 @@ class EmbeddedSecretConfiguration(BaseConfiguration): @configspec -class NonTemplatedComplexTypesConfiguration(BaseConfiguration): +class NonTemplatedNestedTypesConfiguration(BaseConfiguration): list_val: list = None # type: ignore[type-arg] tuple_val: tuple = None # type: ignore[type-arg] dict_val: dict = None # type: ignore[type-arg] @@ -919,11 +919,11 @@ class NoHintConfiguration(BaseConfiguration): NoHintConfiguration() -def test_config_with_non_templated_complex_hints(environment: Any) -> None: +def test_config_with_non_templated_nested_hints(environment: Any) -> None: environment["LIST_VAL"] = "[1,2,3]" environment["TUPLE_VAL"] = "(1,2,3)" environment["DICT_VAL"] = '{"a": 1}' - c = resolve.resolve_configuration(NonTemplatedComplexTypesConfiguration()) + c = resolve.resolve_configuration(NonTemplatedNestedTypesConfiguration()) assert c.list_val == [1, 2, 3] assert c.tuple_val == (1, 2, 3) assert c.dict_val == {"a": 1} diff --git a/tests/common/configuration/utils.py b/tests/common/configuration/utils.py index 670dcac87a..677ec3d329 100644 --- a/tests/common/configuration/utils.py +++ b/tests/common/configuration/utils.py @@ -20,15 +20,11 @@ from dlt.common.configuration import configspec from dlt.common.configuration.specs import BaseConfiguration, CredentialsConfiguration from dlt.common.configuration.container import Container -from dlt.common.configuration.providers import ( - ConfigProvider, - EnvironProvider, - ConfigTomlProvider, - SecretsTomlProvider, -) +from dlt.common.configuration.providers import ConfigProvider, EnvironProvider from dlt.common.configuration.utils import get_resolved_traces from dlt.common.configuration.specs.config_providers_context import ConfigProvidersContext from dlt.common.typing import TSecretValue, StrAny +from tests.utils import _reset_providers @configspec @@ -51,7 +47,7 @@ class CoercionTestConfiguration(BaseConfiguration): tuple_val: Tuple[int, int, StrAny] = None any_val: Any = None none_val: str = None - COMPLEX_VAL: Dict[str, Tuple[int, List[str], List[str]]] = None + NESTED_VAL: Dict[str, Tuple[int, List[str], List[str]]] = None date_val: datetime.datetime = None dec_val: Decimal = None sequence_val: Sequence[str] = None @@ -118,14 +114,7 @@ def env_provider() -> Iterator[ConfigProvider]: @pytest.fixture def toml_providers() -> Iterator[ConfigProvidersContext]: - pipeline_root = "./tests/common/cases/configuration/.dlt" - ctx = ConfigProvidersContext() - ctx.providers.clear() - ctx.add_provider(EnvironProvider()) - ctx.add_provider(SecretsTomlProvider(project_dir=pipeline_root)) - ctx.add_provider(ConfigTomlProvider(project_dir=pipeline_root)) - with Container().injectable_context(ctx): - yield ctx + yield from _reset_providers("./tests/common/cases/configuration/.dlt") class MockProvider(ConfigProvider): @@ -181,7 +170,7 @@ def supports_secrets(self) -> bool: "tuple_val": (1, 2, {"1": "complicated dicts allowed in literal eval"}), "any_val": "function() {}", "none_val": "none", - "COMPLEX_VAL": {"_": [1440, ["*"], []], "change-email": [560, ["*"], []]}, + "NESTED_VAL": {"_": [1440, ["*"], []], "change-email": [560, ["*"], []]}, "date_val": pendulum.now(), "dec_val": Decimal("22.38"), "sequence_val": ["A", "B", "KAPPA"], diff --git a/tests/common/data_writers/test_data_writers.py b/tests/common/data_writers/test_data_writers.py index 03723b7b55..21057a2cdd 100644 --- a/tests/common/data_writers/test_data_writers.py +++ b/tests/common/data_writers/test_data_writers.py @@ -131,9 +131,9 @@ def test_string_literal_escape() -> None: @pytest.mark.parametrize("escaper", ALL_LITERAL_ESCAPE) -def test_string_complex_escape(escaper: AnyFun) -> None: +def test_string_nested_escape(escaper: AnyFun) -> None: doc = { - "complex": [1, 2, 3, "a"], + "nested": [1, 2, 3, "a"], "link": ( "?commen\ntU\nrn=urn%3Ali%3Acomment%3A%28acti\0xA \0x0" " \\vity%3A69'08444473\n\n551163392%2C6n \r \x8e9085" diff --git a/tests/common/destination/__init__.py b/tests/common/destination/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/common/destination/test_destination_capabilities.py b/tests/common/destination/test_destination_capabilities.py new file mode 100644 index 0000000000..bd435b0ffb --- /dev/null +++ b/tests/common/destination/test_destination_capabilities.py @@ -0,0 +1,224 @@ +import pytest + +from dlt.common.destination.exceptions import DestinationCapabilitiesException, UnsupportedDataType +from dlt.common.destination.utils import ( + resolve_merge_strategy, + verify_schema_capabilities, + verify_supported_data_types, +) +from dlt.common.exceptions import TerminalValueError +from dlt.common.schema.exceptions import SchemaIdentifierNormalizationCollision +from dlt.common.schema.schema import Schema +from dlt.common.schema.utils import new_table +from dlt.common.storages.load_package import ParsedLoadJobFileName +from dlt.destinations.impl.bigquery.bigquery_adapter import AUTODETECT_SCHEMA_HINT + + +def test_resolve_merge_strategy() -> None: + schema = Schema("schema") + + table = new_table("table", write_disposition="merge") + delta_table = new_table("delta_table", table_format="delta", write_disposition="merge") + iceberg_table = new_table("delta_table", table_format="iceberg", write_disposition="merge") + + schema.update_table(table) + schema.update_table(delta_table) + schema.update_table(iceberg_table) + + assert resolve_merge_strategy(schema.tables, table) is None + assert resolve_merge_strategy(schema.tables, delta_table) is None + assert resolve_merge_strategy(schema.tables, iceberg_table) is None + + # try default merge dispositions + from dlt.destinations import athena, filesystem, duckdb + + assert resolve_merge_strategy(schema.tables, table, filesystem().capabilities()) is None + assert ( + resolve_merge_strategy(schema.tables, delta_table, filesystem().capabilities()) == "upsert" + ) + assert ( + resolve_merge_strategy(schema.tables, iceberg_table, athena().capabilities()) + == "delete-insert" + ) + + # unknown table formats + assert resolve_merge_strategy(schema.tables, iceberg_table, filesystem().capabilities()) is None + assert resolve_merge_strategy(schema.tables, delta_table, athena().capabilities()) is None + + # not supported strategy + schema.tables["delta_table"]["x-merge-strategy"] = "delete-insert" # type: ignore[typeddict-unknown-key] + with pytest.raises(DestinationCapabilitiesException): + resolve_merge_strategy(schema.tables, delta_table, filesystem().capabilities()) + + # non-default strategy + schema.tables["table"]["x-merge-strategy"] = "scd2" # type: ignore[typeddict-unknown-key] + assert resolve_merge_strategy(schema.tables, table, filesystem().capabilities()) is None + assert resolve_merge_strategy(schema.tables, table, duckdb().capabilities()) == "scd2" + + +def test_verify_capabilities_ident_collisions() -> None: + schema = Schema("schema") + table = new_table( + "table", + write_disposition="merge", + columns=[{"name": "col1", "data_type": "bigint"}, {"name": "COL1", "data_type": "bigint"}], + ) + schema.update_table(table, normalize_identifiers=False) + from dlt.destinations import athena, filesystem + + # case sensitive - no name collision + exceptions = verify_schema_capabilities(schema, filesystem().capabilities(), "filesystem") + assert len(exceptions) == 0 + # case insensitive - collision on column name + exceptions = verify_schema_capabilities(schema, athena().capabilities(), "filesystem") + assert len(exceptions) == 1 + assert isinstance(exceptions[0], SchemaIdentifierNormalizationCollision) + assert exceptions[0].identifier_type == "column" + + table = new_table( + "TABLE", write_disposition="merge", columns=[{"name": "col1", "data_type": "bigint"}] + ) + schema.update_table(table, normalize_identifiers=False) + exceptions = verify_schema_capabilities(schema, filesystem().capabilities(), "filesystem") + assert len(exceptions) == 0 + # case insensitive - collision on table name + exceptions = verify_schema_capabilities(schema, athena().capabilities(), "filesystem") + assert len(exceptions) == 2 + assert isinstance(exceptions[1], SchemaIdentifierNormalizationCollision) + assert exceptions[1].identifier_type == "table" + + +def test_verify_capabilities_data_types() -> None: + schema = Schema("schema") + table = new_table( + "table", + write_disposition="merge", + columns=[{"name": "col1", "data_type": "time"}, {"name": "col2", "data_type": "date"}], + ) + schema.update_table(table, normalize_identifiers=False) + + schema.update_table(table, normalize_identifiers=False) + from dlt.destinations import athena, filesystem, databricks, redshift + + new_jobs_parquet = [ParsedLoadJobFileName.parse("table.12345.1.parquet")] + new_jobs_jsonl = [ParsedLoadJobFileName.parse("table.12345.1.jsonl")] + + # all data types supported (no mapper) + exceptions = verify_supported_data_types( + schema.tables.values(), new_jobs_parquet, filesystem().capabilities(), "filesystem" # type: ignore[arg-type] + ) + assert len(exceptions) == 0 + # time not supported via list + exceptions = verify_supported_data_types( + schema.tables.values(), new_jobs_parquet, athena().capabilities(), "athena" # type: ignore[arg-type] + ) + assert len(exceptions) == 1 + assert isinstance(exceptions[0], UnsupportedDataType) + assert exceptions[0].destination_type == "athena" + assert exceptions[0].table_name == "table" + assert exceptions[0].column == "col1" + assert exceptions[0].file_format == "parquet" + assert exceptions[0].available_in_formats == [] + + # all supported on parquet + exceptions = verify_supported_data_types( + schema.tables.values(), new_jobs_parquet, databricks().capabilities(), "databricks" # type: ignore[arg-type] + ) + assert len(exceptions) == 0 + # date not supported on jsonl + exceptions = verify_supported_data_types( + schema.tables.values(), new_jobs_jsonl, databricks().capabilities(), "databricks" # type: ignore[arg-type] + ) + assert len(exceptions) == 1 + assert isinstance(exceptions[0], UnsupportedDataType) + assert exceptions[0].column == "col2" + assert exceptions[0].available_in_formats == ["parquet"] + + # exclude binary type if precision is set on column + schema_bin = Schema("schema_bin") + table = new_table( + "table", + write_disposition="merge", + columns=[ + {"name": "binary_1", "data_type": "binary"}, + {"name": "binary_2", "data_type": "binary", "precision": 128}, + ], + ) + schema_bin.update_table(table, normalize_identifiers=False) + exceptions = verify_supported_data_types( + schema_bin.tables.values(), # type: ignore[arg-type] + new_jobs_jsonl, + redshift().capabilities(), + "redshift", + ) + # binary not supported on jsonl + assert len(exceptions) == 2 + exceptions = verify_supported_data_types( + schema_bin.tables.values(), new_jobs_parquet, redshift().capabilities(), "redshift" # type: ignore[arg-type] + ) + # fixed length not supported on parquet + assert len(exceptions) == 1 + assert isinstance(exceptions[0], UnsupportedDataType) + assert exceptions[0].data_type == "binary(128)" + assert exceptions[0].column == "binary_2" + assert exceptions[0].available_in_formats == ["insert_values"] + + # check nested type on bigquery + from dlt.destinations import bigquery + + schema_nested = Schema("nested") + table = new_table( + "table", + write_disposition="merge", + columns=[ + {"name": "nested_1", "data_type": "json"}, + ], + ) + schema_nested.update_table(table, normalize_identifiers=False) + exceptions = verify_supported_data_types( + schema_nested.tables.values(), new_jobs_parquet, bigquery().capabilities(), "bigquery" # type: ignore[arg-type] + ) + assert len(exceptions) == 1 + assert isinstance(exceptions[0], UnsupportedDataType) + assert exceptions[0].data_type == "json" + + # enable schema autodetect + table[AUTODETECT_SCHEMA_HINT] = True # type: ignore[typeddict-unknown-key] + exceptions = verify_supported_data_types( + schema_nested.tables.values(), new_jobs_parquet, bigquery().capabilities(), "bigquery" # type: ignore[arg-type] + ) + assert len(exceptions) == 0 + + # lancedb uses arrow types in type mapper + from dlt.destinations import lancedb + + exceptions = verify_supported_data_types( + schema_bin.tables.values(), # type: ignore[arg-type] + new_jobs_jsonl, + lancedb().capabilities(), + "lancedb", + ) + try: + import pyarrow + + assert len(exceptions) == 0 + except ImportError: + assert len(exceptions) > 0 + + # provoke mapping error, precision not supported on NTZ timestamp + schema_timezone = Schema("tx") + table = new_table( + "table", + write_disposition="merge", + columns=[ + {"name": "ts_1", "data_type": "timestamp", "precision": 12, "timezone": False}, + ], + ) + schema_timezone.update_table(table, normalize_identifiers=False) + from dlt.destinations import motherduck + + exceptions = verify_supported_data_types( + schema_timezone.tables.values(), new_jobs_parquet, motherduck().capabilities(), "motherduck" # type: ignore[arg-type] + ) + assert len(exceptions) == 1 + assert isinstance(exceptions[0], TerminalValueError) diff --git a/tests/common/test_destination.py b/tests/common/destination/test_reference.py similarity index 100% rename from tests/common/test_destination.py rename to tests/common/destination/test_reference.py diff --git a/tests/common/normalizers/test_json_relational.py b/tests/common/normalizers/test_json_relational.py index 159e33da4d..748259cba1 100644 --- a/tests/common/normalizers/test_json_relational.py +++ b/tests/common/normalizers/test_json_relational.py @@ -47,48 +47,48 @@ def test_flatten_fix_field_name(norm: RelationalNormalizer) -> None: ) in lists -def test_preserve_complex_value(norm: RelationalNormalizer) -> None: - # add table with complex column +def test_preserve_json_value(norm: RelationalNormalizer) -> None: + # add table with json column norm.schema.update_table( new_table( - "with_complex", + "with_json", columns=[ { "name": "value", - "data_type": "complex", + "data_type": "json", "nullable": "true", # type: ignore[typeddict-item] } ], ) ) row_1 = {"value": 1} - flattened_row, _ = norm._flatten("with_complex", row_1, 0) + flattened_row, _ = norm._flatten("with_json", row_1, 0) assert flattened_row["value"] == 1 - row_2 = {"value": {"complex": True}} - flattened_row, _ = norm._flatten("with_complex", row_2, 0) + row_2 = {"value": {"json": True}} + flattened_row, _ = norm._flatten("with_json", row_2, 0) assert flattened_row["value"] == row_2["value"] - # complex value is not flattened - assert "value__complex" not in flattened_row + # json value is not flattened + assert "value__json" not in flattened_row -def test_preserve_complex_value_with_hint(norm: RelationalNormalizer) -> None: +def test_preserve_json_value_with_hint(norm: RelationalNormalizer) -> None: # add preferred type for "value" - norm.schema._settings.setdefault("preferred_types", {})[TSimpleRegex("re:^value$")] = "complex" + norm.schema._settings.setdefault("preferred_types", {})[TSimpleRegex("re:^value$")] = "json" norm.schema._compile_settings() row_1 = {"value": 1} flattened_row, _ = norm._flatten("any_table", row_1, 0) assert flattened_row["value"] == 1 - row_2 = {"value": {"complex": True}} + row_2 = {"value": {"json": True}} flattened_row, _ = norm._flatten("any_table", row_2, 0) assert flattened_row["value"] == row_2["value"] - # complex value is not flattened - assert "value__complex" not in flattened_row + # json value is not flattened + assert "value__json" not in flattened_row -def test_child_table_linking(norm: RelationalNormalizer) -> None: +def test_nested_table_linking(norm: RelationalNormalizer) -> None: row = {"f": [{"l": ["a", "b", "c"], "v": 120, "o": [{"a": 1}, {"a": 2}]}]} # request _dlt_root_id propagation add_dlt_root_id_propagation(norm) @@ -133,13 +133,16 @@ def test_child_table_linking(norm: RelationalNormalizer) -> None: assert [e[1]["value"] for e in list_rows] == ["a", "b", "c"] -def test_child_table_linking_primary_key(norm: RelationalNormalizer) -> None: +def test_skip_nested_link_when_no_parent(norm: RelationalNormalizer) -> None: row = { "id": "level0", "f": [{"id": "level1", "l": ["a", "b", "c"], "v": 120, "o": [{"a": 1}, {"a": 2}]}], } - norm.schema.merge_hints({"primary_key": [TSimpleRegex("id")]}) - norm.schema._compile_settings() + + # create table__f without parent so it is not seen as nested table + # still normalizer will write data to it but not link + table__f = new_table("table__f", parent_table_name=None) + norm.schema.update_table(table__f) rows = list(norm._normalize_row(row, {}, ("table",))) root = next(t for t in rows if t[0][0] == "table")[1] @@ -407,16 +410,20 @@ def test_list_in_list() -> None: zen__webpath__list__list = [row for row in rows if row[0][0] == "zen__webpath__list__list"] assert zen__webpath__list__list[0][1]["_dlt_parent_id"] == zen__webpath__list[3][1]["_dlt_id"] - # test the same setting webpath__list to complex + # test the same setting webpath__list to json zen_table = new_table("zen") schema.update_table(zen_table) path_table = new_table( - "zen__webpath", parent_table_name="zen", columns=[{"name": "list", "data_type": "complex"}] + "zen__webpath", parent_table_name="zen", columns=[{"name": "list", "data_type": "json"}] ) schema.update_table(path_table) + assert "zen__webpath" in schema.tables + # clear cache with json paths + schema.data_item_normalizer._is_nested_type.cache_clear() # type: ignore[attr-defined] + rows = list(schema.normalize_data_item(chats, "1762162.1212", "zen")) - # both lists are complex types now + # both lists are json types now assert len(rows) == 3 zen__webpath = [row for row in rows if row[0][0] == "zen__webpath"] assert all("list" in row[1] for row in zen__webpath) @@ -598,36 +605,36 @@ def test_removes_normalized_list(norm: RelationalNormalizer) -> None: assert "comp" not in root_row[1] -def test_preserves_complex_types_list(norm: RelationalNormalizer) -> None: +def test_preserves_json_types_list(norm: RelationalNormalizer) -> None: # the exception to test_removes_normalized_list - # complex types should be left as they are - # add table with complex column + # json types should be left as they are + # add table with json column norm.schema.update_table( new_table( "event_slot", columns=[ { "name": "value", - "data_type": "complex", + "data_type": "json", "nullable": "true", # type: ignore[typeddict-item] } ], ) ) - row = {"value": ["from", {"complex": True}]} + row = {"value": ["from", {"json": True}]} normalized_rows = list(norm._normalize_row(row, {}, ("event_slot",))) # make sure only 1 row is emitted, the list is not normalized assert len(normalized_rows) == 1 - # value is kept in root row -> market as complex + # value is kept in root row -> market as json root_row = next(r for r in normalized_rows if r[0][1] is None) assert root_row[1]["value"] == row["value"] # same should work for a list - row = {"value": ["from", ["complex", True]]} # type: ignore[list-item] + row = {"value": ["from", ["json", True]]} # type: ignore[list-item] normalized_rows = list(norm._normalize_row(row, {}, ("event_slot",))) # make sure only 1 row is emitted, the list is not normalized assert len(normalized_rows) == 1 - # value is kept in root row -> market as complex + # value is kept in root row -> market as json root_row = next(r for r in normalized_rows if r[0][1] is None) assert root_row[1]["value"] == row["value"] @@ -648,9 +655,9 @@ def test_wrap_in_dict(norm: RelationalNormalizer) -> None: assert rows[-1][1]["value"] == "A" -def test_complex_types_for_recursion_level(norm: RelationalNormalizer) -> None: +def test_json_types_for_recursion_level(norm: RelationalNormalizer) -> None: add_dlt_root_id_propagation(norm) - # if max recursion depth is set, nested elements will be kept as complex + # if max recursion depth is set, nested elements will be kept as json row = { "_dlt_id": "row_id", "f": [ @@ -670,7 +677,7 @@ def test_complex_types_for_recursion_level(norm: RelationalNormalizer) -> None: # set max nesting to 0 set_max_nesting(norm, 0) n_rows = list(norm.schema.normalize_data_item(row, "load_id", "default")) - # the "f" element is left as complex type and not normalized + # the "f" element is left as json type and not normalized assert len(n_rows) == 1 assert n_rows[0][0][0] == "default" assert "f" in n_rows[0][1] @@ -691,7 +698,7 @@ def test_complex_types_for_recursion_level(norm: RelationalNormalizer) -> None: set_max_nesting(norm, 2) n_rows = list(norm.schema.normalize_data_item(row, "load_id", "default")) assert len(n_rows) == 4 - # in default__f__lo the dicts that would be flattened are complex types + # in default__f__lo the dicts that would be flattened are json types last_row = n_rows[3] assert last_row[1]["e"] == {"v": 1} @@ -734,39 +741,41 @@ def test_table_name_meta_normalized() -> None: assert rows[0][0][0] == "channel_surfing" -def test_parse_with_primary_key() -> None: - schema = create_schema_with_name("discord") - schema._merge_hints({"primary_key": ["id"]}) # type: ignore[list-item] - schema._compile_settings() - add_dlt_root_id_propagation(schema.data_item_normalizer) # type: ignore[arg-type] - - row = {"id": "817949077341208606", "w_id": [{"id": 9128918293891111, "wo_id": [1, 2, 3]}]} - rows = list(schema.normalize_data_item(row, "load_id", "discord")) - # get root - root = next(t[1] for t in rows if t[0][0] == "discord") - assert root["_dlt_id"] != digest128("817949077341208606", DLT_ID_LENGTH_BYTES) - assert "_dlt_parent_id" not in root - assert "_dlt_root_id" not in root - assert root["_dlt_load_id"] == "load_id" - - el_w_id = next(t[1] for t in rows if t[0][0] == "discord__w_id") - # this also has primary key - assert el_w_id["_dlt_id"] != digest128("9128918293891111", DLT_ID_LENGTH_BYTES) - assert "_dlt_parent_id" not in el_w_id - assert "_dlt_list_idx" not in el_w_id - # if enabled, dlt_root is always propagated - assert "_dlt_root_id" in el_w_id - - # this must have deterministic child key - f_wo_id = next( - t[1] for t in rows if t[0][0] == "discord__w_id__wo_id" and t[1]["_dlt_list_idx"] == 2 - ) - assert f_wo_id["value"] == 3 - assert f_wo_id["_dlt_root_id"] != digest128("817949077341208606", DLT_ID_LENGTH_BYTES) - assert f_wo_id["_dlt_parent_id"] != digest128("9128918293891111", DLT_ID_LENGTH_BYTES) - assert f_wo_id["_dlt_id"] == RelationalNormalizer._get_child_row_hash( - f_wo_id["_dlt_parent_id"], "discord__w_id__wo_id", 2 - ) +def test_row_id_is_primary_key() -> None: + # TODO: if there's a column with row_id hint and primary_key, it should get propagated + pass + # schema = create_schema_with_name("discord") + # schema._merge_hints({"primary_key": ["id"]}) # type: ignore[list-item] + # schema._compile_settings() + # add_dlt_root_id_propagation(schema.data_item_normalizer) # type: ignore[arg-type] + + # row = {"id": "817949077341208606", "w_id": [{"id": 9128918293891111, "wo_id": [1, 2, 3]}]} + # rows = list(schema.normalize_data_item(row, "load_id", "discord")) + # # get root + # root = next(t[1] for t in rows if t[0][0] == "discord") + # assert root["_dlt_id"] != digest128("817949077341208606", DLT_ID_LENGTH_BYTES) + # assert "_dlt_parent_id" not in root + # assert "_dlt_root_id" not in root + # assert root["_dlt_load_id"] == "load_id" + + # el_w_id = next(t[1] for t in rows if t[0][0] == "discord__w_id") + # # this also has primary key + # assert el_w_id["_dlt_id"] != digest128("9128918293891111", DLT_ID_LENGTH_BYTES) + # assert "_dlt_parent_id" not in el_w_id + # assert "_dlt_list_idx" not in el_w_id + # # if enabled, dlt_root is always propagated + # assert "_dlt_root_id" in el_w_id + + # # this must have deterministic child key + # f_wo_id = next( + # t[1] for t in rows if t[0][0] == "discord__w_id__wo_id" and t[1]["_dlt_list_idx"] == 2 + # ) + # assert f_wo_id["value"] == 3 + # assert f_wo_id["_dlt_root_id"] != digest128("817949077341208606", DLT_ID_LENGTH_BYTES) + # assert f_wo_id["_dlt_parent_id"] != digest128("9128918293891111", DLT_ID_LENGTH_BYTES) + # assert f_wo_id["_dlt_id"] == RelationalNormalizer._get_nested_row_hash( + # f_wo_id["_dlt_parent_id"], "discord__w_id__wo_id", 2 + # ) def test_keeps_none_values() -> None: @@ -868,6 +877,18 @@ def test_propagation_update_on_table_change(norm: RelationalNormalizer): ] == {"_dlt_id": "_dlt_root_id", "prop1": "prop2"} +def test_caching_perf(norm: RelationalNormalizer) -> None: + from time import time + + table = new_table("test") + table["x-normalizer"] = {} + start = time() + for _ in range(100000): + norm._is_nested_type(norm.schema, "test", "field", 0, 0) + # norm._get_table_nesting_level(norm.schema, "test") + print(f"{time() - start}") + + def set_max_nesting(norm: RelationalNormalizer, max_nesting: int) -> None: RelationalNormalizer.update_normalizer_config(norm.schema, {"max_nesting": max_nesting}) norm._reset() diff --git a/tests/common/schema/test_coercion.py b/tests/common/schema/test_coercion.py index 34b62f9564..be7998c786 100644 --- a/tests/common/schema/test_coercion.py +++ b/tests/common/schema/test_coercion.py @@ -70,7 +70,7 @@ def test_coerce_type_to_bool() -> None: # no coercions with pytest.raises(ValueError): - coerce_value("bool", "complex", {"a": True}) + coerce_value("bool", "json", {"a": True}) with pytest.raises(ValueError): coerce_value("bool", "binary", b"True") with pytest.raises(ValueError): @@ -340,13 +340,13 @@ def test_py_type_to_sc_type() -> None: # none type raises TypeException with pytest.raises(TypeError): py_type_to_sc_type(type(None)) - # complex types - assert py_type_to_sc_type(list) == "complex" - # assert py_type_to_sc_type(set) == "complex" - assert py_type_to_sc_type(dict) == "complex" - assert py_type_to_sc_type(tuple) == "complex" - assert py_type_to_sc_type(Mapping) == "complex" - assert py_type_to_sc_type(MutableSequence) == "complex" + # nested types + assert py_type_to_sc_type(list) == "json" + # assert py_type_to_sc_type(set) == "json" + assert py_type_to_sc_type(dict) == "json" + assert py_type_to_sc_type(tuple) == "json" + assert py_type_to_sc_type(Mapping) == "json" + assert py_type_to_sc_type(MutableSequence) == "json" class IntEnum(int, Enum): a = 1 @@ -365,45 +365,45 @@ class MixedEnum(Enum): assert py_type_to_sc_type(MixedEnum) == "text" -def test_coerce_type_complex() -> None: +def test_coerce_type_json() -> None: # dicts and lists should be coerced into strings automatically - v_list = [1, 2, "3", {"complex": True}] - v_dict = {"list": [1, 2], "str": "complex"} - assert py_type_to_sc_type(type(v_list)) == "complex" - assert py_type_to_sc_type(type(v_dict)) == "complex" - assert type(coerce_value("complex", "complex", v_dict)) is dict - assert type(coerce_value("complex", "complex", v_list)) is list - assert coerce_value("complex", "complex", v_dict) == v_dict - assert coerce_value("complex", "complex", v_list) == v_list - assert coerce_value("text", "complex", v_dict) == json.dumps(v_dict) - assert coerce_value("text", "complex", v_list) == json.dumps(v_list) - assert coerce_value("complex", "text", json.dumps(v_dict)) == v_dict - assert coerce_value("complex", "text", json.dumps(v_list)) == v_list + v_list = [1, 2, "3", {"json": True}] + v_dict = {"list": [1, 2], "str": "json"} + assert py_type_to_sc_type(type(v_list)) == "json" + assert py_type_to_sc_type(type(v_dict)) == "json" + assert type(coerce_value("json", "json", v_dict)) is dict + assert type(coerce_value("json", "json", v_list)) is list + assert coerce_value("json", "json", v_dict) == v_dict + assert coerce_value("json", "json", v_list) == v_list + assert coerce_value("text", "json", v_dict) == json.dumps(v_dict) + assert coerce_value("text", "json", v_list) == json.dumps(v_list) + assert coerce_value("json", "text", json.dumps(v_dict)) == v_dict + assert coerce_value("json", "text", json.dumps(v_list)) == v_list # all other coercions fail with pytest.raises(ValueError): - coerce_value("binary", "complex", v_list) + coerce_value("binary", "json", v_list) with pytest.raises(ValueError): - coerce_value("complex", "text", "not a json string") + coerce_value("json", "text", "not a json string") -def test_coerce_type_complex_with_pua() -> None: +def test_coerce_type_json_with_pua() -> None: v_dict = { "list": [1, Wei.from_int256(10**18), f"{_DATETIME}2022-05-10T01:41:31.466Z"], - "str": "complex", + "str": "json", "pua_date": f"{_DATETIME}2022-05-10T01:41:31.466Z", } exp_v = { "list": [1, Wei.from_int256(10**18), "2022-05-10T01:41:31.466Z"], - "str": "complex", + "str": "json", "pua_date": "2022-05-10T01:41:31.466Z", } - assert coerce_value("complex", "complex", copy(v_dict)) == exp_v - assert coerce_value("text", "complex", copy(v_dict)) == json.dumps(exp_v) + assert coerce_value("json", "json", copy(v_dict)) == exp_v + assert coerce_value("text", "json", copy(v_dict)) == json.dumps(exp_v) # TODO: what to test for this case if at all? - # assert coerce_value("complex", "text", json.dumps(v_dict)) == exp_v + # assert coerce_value("json", "text", json.dumps(v_dict)) == exp_v # also decode recursively custom_pua_decode_nested(v_dict) diff --git a/tests/common/normalizers/test_import_normalizers.py b/tests/common/schema/test_import_normalizers.py similarity index 97% rename from tests/common/normalizers/test_import_normalizers.py rename to tests/common/schema/test_import_normalizers.py index fe356de327..a1e3d775f0 100644 --- a/tests/common/normalizers/test_import_normalizers.py +++ b/tests/common/schema/test_import_normalizers.py @@ -4,13 +4,6 @@ from dlt.common.configuration.container import Container from dlt.common.destination import DestinationCapabilitiesContext from dlt.common.normalizers.typing import TNormalizersConfig -from dlt.common.normalizers.utils import ( - DEFAULT_NAMING_NAMESPACE, - explicit_normalizers, - import_normalizers, - naming_from_reference, - serialize_reference, -) from dlt.common.normalizers.json.relational import DataItemNormalizer as RelationalNormalizer from dlt.common.normalizers.naming import snake_case, direct from dlt.common.normalizers.naming.exceptions import ( @@ -18,10 +11,17 @@ NamingTypeNotFound, UnknownNamingModule, ) - from tests.common.normalizers.custom_normalizers import ( DataItemNormalizer as CustomRelationalNormalizer, ) +from dlt.common.schema.normalizers import ( + DEFAULT_NAMING_NAMESPACE, + explicit_normalizers, + import_normalizers, + naming_from_reference, + serialize_reference, +) + from tests.utils import preserve_environ @@ -87,7 +87,9 @@ def test_naming_from_reference() -> None: import sys try: - sys.path.insert(0, os.path.dirname(__file__)) + from tests.common.normalizers import custom_normalizers + + sys.path.insert(0, os.path.dirname(custom_normalizers.__file__)) assert naming_from_reference("custom_normalizers").name() == "custom_normalizers" assert ( naming_from_reference("custom_normalizers.NamingConvention").name() @@ -113,10 +115,8 @@ def test_naming_from_reference() -> None: with pytest.raises(ValueError): naming_from_reference(snake_case.NamingConvention()) # type: ignore[arg-type] - # with capabilities - caps = DestinationCapabilitiesContext.generic_capabilities() - caps.max_identifier_length = 120 - naming = naming_from_reference(snake_case.NamingConvention, caps) + # with max length + naming = naming_from_reference(snake_case.NamingConvention, 120) assert naming.max_length == 120 diff --git a/tests/common/schema/test_inference.py b/tests/common/schema/test_inference.py index 1540d8a74a..7f06cdb71e 100644 --- a/tests/common/schema/test_inference.py +++ b/tests/common/schema/test_inference.py @@ -60,7 +60,7 @@ def test_map_column_preferred_type(schema: Schema) -> None: def test_map_column_type(schema: Schema) -> None: # default mappings assert schema._infer_column_type("18271.11", "_column_name") == "text" - assert schema._infer_column_type(["city"], "_column_name") == "complex" + assert schema._infer_column_type(["city"], "_column_name") == "json" assert schema._infer_column_type(0x72, "_column_name") == "bigint" assert schema._infer_column_type(0x72, "_column_name") == "bigint" assert schema._infer_column_type(b"bytes str", "_column_name") == "binary" @@ -68,13 +68,13 @@ def test_map_column_type(schema: Schema) -> None: assert schema._infer_column_type(HexBytes(b"bytes str"), "_column_name") == "binary" -def test_map_column_type_complex(schema: Schema) -> None: - # complex type mappings - v_list = [1, 2, "3", {"complex": True}] - v_dict = {"list": [1, 2], "str": "complex"} - # complex types must be cast to text - assert schema._infer_column_type(v_list, "cx_value") == "complex" - assert schema._infer_column_type(v_dict, "cx_value") == "complex" +def test_map_column_type_json(schema: Schema) -> None: + # json type mappings + v_list = [1, 2, "3", {"json": True}] + v_dict = {"list": [1, 2], "str": "json"} + # json types must be cast to text + assert schema._infer_column_type(v_list, "cx_value") == "json" + assert schema._infer_column_type(v_dict, "cx_value") == "json" def test_coerce_row(schema: Schema) -> None: @@ -214,27 +214,27 @@ def test_shorten_variant_column(schema: Schema) -> None: assert len(new_row_2_keys[0]) == 9 -def test_coerce_complex_variant(schema: Schema) -> None: +def test_coerce_json_variant(schema: Schema) -> None: # for this test use case sensitive naming convention os.environ["SCHEMA__NAMING"] = "direct" schema.update_normalizers() - # create two columns to which complex type cannot be coerced + # create two columns to which json type cannot be coerced row = {"floatX": 78172.128, "confidenceX": 1.2, "strX": "STR"} new_row, new_table = schema.coerce_row("event_user", None, row) assert new_row == row schema.update_table(new_table) - # add two more complex columns that should be coerced to text - v_list = [1, 2, "3", {"complex": True}] - v_dict = {"list": [1, 2], "str": "complex"} + # add two more json columns that should be coerced to text + v_list = [1, 2, "3", {"json": True}] + v_dict = {"list": [1, 2], "str": "json"} c_row = {"c_list": v_list, "c_dict": v_dict} c_new_row, c_new_table = schema.coerce_row("event_user", None, c_row) c_new_columns = list(c_new_table["columns"].values()) assert c_new_columns[0]["name"] == "c_list" - assert c_new_columns[0]["data_type"] == "complex" + assert c_new_columns[0]["data_type"] == "json" assert "variant" not in c_new_columns[0] assert c_new_columns[1]["name"] == "c_dict" - assert c_new_columns[1]["data_type"] == "complex" + assert c_new_columns[1]["data_type"] == "json" assert "variant" not in c_new_columns[1] assert c_new_row["c_list"] == v_list schema.update_table(c_new_table) @@ -244,19 +244,19 @@ def test_coerce_complex_variant(schema: Schema) -> None: assert c_new_table is None assert c_new_row["c_dict"] == v_dict - # add complex types on the same columns + # add json types on the same columns c_row_v = {"floatX": v_list, "confidenceX": v_dict, "strX": v_dict} # expect two new variant columns to be created c_new_row_v, c_new_table_v = schema.coerce_row("event_user", None, c_row_v) c_new_columns_v = list(c_new_table_v["columns"].values()) # two new variant columns added assert len(c_new_columns_v) == 2 - assert c_new_columns_v[0]["name"] == "floatX▶v_complex" - assert c_new_columns_v[1]["name"] == "confidenceX▶v_complex" + assert c_new_columns_v[0]["name"] == "floatX▶v_json" + assert c_new_columns_v[1]["name"] == "confidenceX▶v_json" assert c_new_columns_v[0]["variant"] is True assert c_new_columns_v[1]["variant"] is True - assert c_new_row_v["floatX▶v_complex"] == v_list - assert c_new_row_v["confidenceX▶v_complex"] == v_dict + assert c_new_row_v["floatX▶v_json"] == v_list + assert c_new_row_v["confidenceX▶v_json"] == v_dict assert c_new_row_v["strX"] == json.dumps(v_dict) schema.update_table(c_new_table_v) @@ -264,8 +264,8 @@ def test_coerce_complex_variant(schema: Schema) -> None: c_row_v = {"floatX": v_list, "confidenceX": v_dict, "strX": v_dict} c_new_row_v, c_new_table_v = schema.coerce_row("event_user", None, c_row_v) assert c_new_table_v is None - assert c_new_row_v["floatX▶v_complex"] == v_list - assert c_new_row_v["confidenceX▶v_complex"] == v_dict + assert c_new_row_v["floatX▶v_json"] == v_list + assert c_new_row_v["confidenceX▶v_json"] == v_dict assert c_new_row_v["strX"] == json.dumps(v_dict) @@ -604,7 +604,7 @@ def test_get_new_columns(schema: Schema) -> None: # no new columns assert schema.get_new_table_columns("events", existing_columns, case_sensitive=True) == [] # one new column - address_column = utils.new_column("address", "complex") + address_column = utils.new_column("address", "json") schema.update_table(utils.new_table("events", columns=[address_column])) assert schema.get_new_table_columns("events", existing_columns, case_sensitive=True) == [ address_column diff --git a/tests/common/schema/test_merges.py b/tests/common/schema/test_merges.py index 893fd1db5f..1776059223 100644 --- a/tests/common/schema/test_merges.py +++ b/tests/common/schema/test_merges.py @@ -12,7 +12,7 @@ COL_1_HINTS: TColumnSchema = { # type: ignore[typeddict-unknown-key] "cluster": False, - "foreign_key": True, + "parent_key": True, "data_type": "text", "name": "test", "x-special": "value", @@ -24,7 +24,7 @@ } COL_1_HINTS_NO_DEFAULTS: TColumnSchema = { # type: ignore[typeddict-unknown-key] - "foreign_key": True, + "parent_key": True, "data_type": "text", "name": "test", "x-special": "value", @@ -141,7 +141,7 @@ def test_remove_defaults_stored_schema() -> None: # resource present assert default_stored["tables"]["table"]["resource"] == "🦚Table" # resource removed because identical to table name - assert "resource" not in default_stored["tables"]["table_copy"] + assert "resource" in default_stored["tables"]["table_copy"] # apply defaults restored_schema = utils.apply_defaults(deepcopy(default_stored)) @@ -179,7 +179,7 @@ def test_merge_column() -> None: "name": "test_2", "nullable": False, "cluster": False, - "foreign_key": True, + "parent_key": True, "data_type": "text", "x-special": "value", "x-special-int": 100, @@ -194,7 +194,7 @@ def test_merge_column() -> None: "name": "test_2", "nullable": True, "cluster": False, - "foreign_key": True, + "parent_key": True, "data_type": "text", "x-special": "value", "x-special-int": 100, @@ -304,12 +304,15 @@ def test_diff_tables() -> None: changed = deepcopy(table) changed["description"] = "new description" changed["name"] = "new name" - partial = utils.diff_table("schema", deepcopy(table), changed) + # names must be identical + renamed_table = deepcopy(table) + renamed_table["name"] = "new name" + partial = utils.diff_table("schema", renamed_table, changed) print(partial) assert partial == {"name": "new name", "description": "new description", "columns": {}} # ignore identical table props - existing = deepcopy(table) + existing = deepcopy(renamed_table) changed["write_disposition"] = "append" changed["schema_contract"] = "freeze" partial = utils.diff_table("schema", deepcopy(existing), changed) @@ -337,15 +340,15 @@ def test_diff_tables() -> None: # defaults are not ignored existing = deepcopy(table) changed = deepcopy(table) - changed["columns"]["test"]["foreign_key"] = False + changed["columns"]["test"]["parent_key"] = False partial = utils.diff_table("schema", existing, changed) assert "test" in partial["columns"] # even if not present in tab_a at all existing = deepcopy(table) changed = deepcopy(table) - changed["columns"]["test"]["foreign_key"] = False - del existing["columns"]["test"]["foreign_key"] + changed["columns"]["test"]["parent_key"] = False + del existing["columns"]["test"]["parent_key"] partial = utils.diff_table("schema", existing, changed) assert "test" in partial["columns"] @@ -360,12 +363,19 @@ def test_diff_tables_conflicts() -> None: "columns": {"test": COL_1_HINTS, "test_2": COL_2_HINTS}, } - other = utils.new_table("table_2") + other = utils.new_table("table") with pytest.raises(TablePropertiesConflictException) as cf_ex: utils.diff_table("schema", table, other) assert cf_ex.value.table_name == "table" assert cf_ex.value.prop_name == "parent" + # conflict on name + other = utils.new_table("other_name") + with pytest.raises(TablePropertiesConflictException) as cf_ex: + utils.diff_table("schema", table, other) + assert cf_ex.value.table_name == "table" + assert cf_ex.value.prop_name == "name" + # conflict on data types in columns changed = deepcopy(table) changed["columns"]["test"]["data_type"] = "bigint" @@ -450,7 +460,7 @@ def test_merge_tables_incomplete_columns() -> None: # "unique": False, # "sort": False, # "primary_key": False, -# "foreign_key": False, +# "parent_key": False, # "root_key": False, # "merge_key": False, # }, diff --git a/tests/common/schema/test_schema.py b/tests/common/schema/test_schema.py index 93be165358..7124ca5c80 100644 --- a/tests/common/schema/test_schema.py +++ b/tests/common/schema/test_schema.py @@ -6,7 +6,6 @@ from dlt.common import pendulum from dlt.common.json import json from dlt.common.data_types.typing import TDataType -from dlt.common.schema.migrations import migrate_schema from dlt.common.exceptions import DictValidationException from dlt.common.normalizers.naming import snake_case from dlt.common.typing import DictStrAny, StrAny @@ -15,7 +14,6 @@ from dlt.common.schema.exceptions import ( InvalidSchemaName, ParentTableNotFoundException, - SchemaEngineNoUpgradePathException, ) from dlt.common.schema.typing import ( LOADS_TABLE_NAME, @@ -84,10 +82,10 @@ def test_simple_regex_validator() -> None: def test_load_corrupted_schema() -> None: - eth_v8: TStoredSchema = load_yml_case("schemas/eth/ethereum_schema_v8") - del eth_v8["tables"]["blocks"] + eth_v10: TStoredSchema = load_yml_case("schemas/eth/ethereum_schema_v10") + del eth_v10["tables"]["blocks"] with pytest.raises(ParentTableNotFoundException): - utils.validate_stored_schema(eth_v8) + utils.validate_stored_schema(eth_v10) def test_column_name_validator(schema: Schema) -> None: @@ -289,8 +287,9 @@ def test_clone(schema: Schema) -> None: "nullable", False, ), + (["_dlt_id"], "row_key", True), (["_dlt_id"], "unique", True), - (["_dlt_parent_id"], "foreign_key", True), + (["_dlt_parent_id"], "parent_key", True), ], ) def test_relational_normalizer_schema_hints( @@ -322,61 +321,6 @@ def test_save_store_schema(schema: Schema, schema_storage: SchemaStorage) -> Non assert_new_schema_props(schema_copy) -def test_upgrade_engine_v1_schema() -> None: - schema_dict: DictStrAny = load_json_case("schemas/ev1/event.schema") - # ensure engine v1 - assert schema_dict["engine_version"] == 1 - # schema_dict will be updated to new engine version - migrate_schema(schema_dict, from_engine=1, to_engine=2) - assert schema_dict["engine_version"] == 2 - # we have 27 tables - assert len(schema_dict["tables"]) == 27 - - # upgrade schema eng 2 -> 4 - schema_dict = load_json_case("schemas/ev2/event.schema") - assert schema_dict["engine_version"] == 2 - upgraded = migrate_schema(schema_dict, from_engine=2, to_engine=4) - assert upgraded["engine_version"] == 4 - - # upgrade 1 -> 4 - schema_dict = load_json_case("schemas/ev1/event.schema") - assert schema_dict["engine_version"] == 1 - upgraded = migrate_schema(schema_dict, from_engine=1, to_engine=4) - assert upgraded["engine_version"] == 4 - - # upgrade 1 -> 6 - schema_dict = load_json_case("schemas/ev1/event.schema") - assert schema_dict["engine_version"] == 1 - upgraded = migrate_schema(schema_dict, from_engine=1, to_engine=6) - assert upgraded["engine_version"] == 6 - - # upgrade 1 -> 7 - schema_dict = load_json_case("schemas/ev1/event.schema") - assert schema_dict["engine_version"] == 1 - upgraded = migrate_schema(schema_dict, from_engine=1, to_engine=7) - assert upgraded["engine_version"] == 7 - - # upgrade 1 -> 8 - schema_dict = load_json_case("schemas/ev1/event.schema") - assert schema_dict["engine_version"] == 1 - upgraded = migrate_schema(schema_dict, from_engine=1, to_engine=8) - assert upgraded["engine_version"] == 8 - - # upgrade 1 -> 9 - schema_dict = load_json_case("schemas/ev1/event.schema") - assert schema_dict["engine_version"] == 1 - upgraded = migrate_schema(schema_dict, from_engine=1, to_engine=9) - assert upgraded["engine_version"] == 9 - - -def test_unknown_engine_upgrade() -> None: - schema_dict: TStoredSchema = load_json_case("schemas/ev1/event.schema") - # there's no path to migrate 3 -> 2 - schema_dict["engine_version"] = 3 - with pytest.raises(SchemaEngineNoUpgradePathException): - migrate_schema(schema_dict, 3, 2) # type: ignore[arg-type] - - def test_preserve_column_order(schema: Schema, schema_storage: SchemaStorage) -> None: # python dicts are ordered from v3.6, add 50 column with random names update: List[TColumnSchema] = [ @@ -435,8 +379,9 @@ def test_get_schema_new_exist(schema_storage: SchemaStorage) -> None: (["confidence", "_sender_id"], "nullable", True), (["timestamp", "_timestamp"], "partition", True), (["_dist_key", "sender_id"], "cluster", True), + (["_dlt_id"], "row_key", True), (["_dlt_id"], "unique", True), - (["_dlt_parent_id"], "foreign_key", True), + (["_dlt_parent_id"], "parent_key", True), (["timestamp", "_timestamp"], "sort", True), ], ) @@ -517,7 +462,7 @@ def test_merge_hints(schema: Schema) -> None: "_dlt_list_idx", "re:^_dlt_load_id$", ], - "foreign_key": ["re:^_dlt_parent_id$"], + "parent_key": ["re:^_dlt_parent_id$"], "unique": ["re:^_dlt_id$"], } schema.merge_hints(new_hints) # type: ignore[arg-type] @@ -541,7 +486,7 @@ def test_merge_hints(schema: Schema) -> None: "re:^_dlt_load_id$", "timestamp", ], - "foreign_key": ["re:^_dlt_parent_id$"], + "parent_key": ["re:^_dlt_parent_id$"], "unique": ["re:^_dlt_id$"], "primary_key": ["id"], } @@ -552,7 +497,7 @@ def test_merge_hints(schema: Schema) -> None: # make sure that re:^_dlt_id$ and _dlt_id are equivalent when merging so we can use both forms alt_form_hints = { "not_null": ["re:^_dlt_id$"], - "foreign_key": ["_dlt_parent_id"], + "parent_key": ["_dlt_parent_id"], } schema.merge_hints(alt_form_hints) # type: ignore[arg-type] # we keep the older forms so nothing changed @@ -565,7 +510,7 @@ def test_merge_hints(schema: Schema) -> None: "not_null": [ "_DLT_ID", ], - "foreign_key": ["re:^_DLT_PARENT_ID$"], + "parent_key": ["re:^_DLT_PARENT_ID$"], } schema.merge_hints(upper_hints) # type: ignore[arg-type] # all upper form hints can be automatically converted to lower form @@ -726,7 +671,7 @@ def test_compare_columns() -> None: ) # any of the hints may differ for hint in COLUMN_HINTS: - table["columns"]["col3"][hint] = True # type: ignore[typeddict-unknown-key] + table["columns"]["col3"][hint] = True # name may not differ assert ( utils.compare_complete_columns(table["columns"]["col3"], table["columns"]["col4"]) is False @@ -792,7 +737,7 @@ def assert_new_schema_props_custom_normalizers(schema: Schema) -> None: def assert_is_new_schema(schema: Schema) -> None: assert schema.stored_version is None assert schema.stored_version_hash is None - assert schema.ENGINE_VERSION == 9 + assert schema.ENGINE_VERSION == 10 assert schema._stored_previous_hashes == [] assert schema.is_modified assert schema.is_new diff --git a/tests/common/schema/test_schema_migrations.py b/tests/common/schema/test_schema_migrations.py new file mode 100644 index 0000000000..2ba75beee0 --- /dev/null +++ b/tests/common/schema/test_schema_migrations.py @@ -0,0 +1,231 @@ +import os +import pytest + +from dlt.common.schema.exceptions import SchemaEngineNoUpgradePathException +from dlt.common.schema.migrations import migrate_schema +from dlt.common.schema.normalizers import DEFAULT_NAMING_MODULE +from dlt.common.schema.schema import Schema +from dlt.common.schema.typing import TStoredSchema +from dlt.common.schema.utils import new_table +from dlt.common.typing import DictStrAny + +from tests.common.utils import load_json_case + + +def test_upgrade_engine_v1_schema() -> None: + schema_dict: DictStrAny = load_json_case("schemas/ev1/event.schema") + # ensure engine v1 + assert schema_dict["engine_version"] == 1 + # schema_dict will be updated to new engine version + migrate_schema(schema_dict, from_engine=1, to_engine=2) + assert schema_dict["engine_version"] == 2 + # we have 27 tables + assert len(schema_dict["tables"]) == 27 + + # upgrade schema eng 2 -> 4 + schema_dict = load_json_case("schemas/ev2/event.schema") + assert schema_dict["engine_version"] == 2 + upgraded = migrate_schema(schema_dict, from_engine=2, to_engine=4) + assert upgraded["engine_version"] == 4 + + # upgrade 1 -> 4 + schema_dict = load_json_case("schemas/ev1/event.schema") + assert schema_dict["engine_version"] == 1 + upgraded = migrate_schema(schema_dict, from_engine=1, to_engine=4) + assert upgraded["engine_version"] == 4 + + # upgrade 1 -> 6 + schema_dict = load_json_case("schemas/ev1/event.schema") + assert schema_dict["engine_version"] == 1 + upgraded = migrate_schema(schema_dict, from_engine=1, to_engine=6) + assert upgraded["engine_version"] == 6 + + # upgrade 1 -> 7 + schema_dict = load_json_case("schemas/ev1/event.schema") + assert schema_dict["engine_version"] == 1 + upgraded = migrate_schema(schema_dict, from_engine=1, to_engine=7) + assert upgraded["engine_version"] == 7 + + # upgrade 1 -> 8 + schema_dict = load_json_case("schemas/ev1/event.schema") + assert schema_dict["engine_version"] == 1 + upgraded = migrate_schema(schema_dict, from_engine=1, to_engine=8) + assert upgraded["engine_version"] == 8 + + # upgrade 1 -> 9 + schema_dict = load_json_case("schemas/ev1/event.schema") + assert schema_dict["engine_version"] == 1 + upgraded = migrate_schema(schema_dict, from_engine=1, to_engine=9) + assert upgraded["engine_version"] == 9 + + # upgrade 1 -> 10 + schema_dict = load_json_case("schemas/ev1/event.schema") + assert schema_dict["engine_version"] == 1 + upgraded = migrate_schema(schema_dict, from_engine=1, to_engine=10) + assert upgraded["engine_version"] == 10 + + +def test_complex_type_migration() -> None: + schema_dict: DictStrAny = load_json_case("schemas/rasa/event.schema") + upgraded = migrate_schema(schema_dict, from_engine=schema_dict["engine_version"], to_engine=10) + assert upgraded["settings"]["preferred_types"]["re:^_test_slot$"] == "json" # type: ignore + assert upgraded["tables"]["event_slot"]["columns"]["value"]["data_type"] == "json" + + +def test_complex_type_new_table_migration() -> None: + # table without columns is passing through + table = new_table("new_table") + assert table["columns"] == {} + table = new_table("new_table", columns=[]) + assert table["columns"] == {} + + # converts complex, keeps json + table = new_table( + "new_table", + columns=[ + {"name": "old", "data_type": "complex"}, # type: ignore + {"name": "new", "data_type": "json"}, + {"name": "incomplete", "primary_key": True}, + ], + ) + assert table["columns"]["old"]["data_type"] == "json" + assert table["columns"]["new"]["data_type"] == "json" + + +def test_keeps_old_name_in_variant_column() -> None: + schema = Schema("dx") + # for this test use case sensitive naming convention + os.environ["SCHEMA__NAMING"] = "direct" + schema.update_normalizers() + # create two columns to which json type cannot be coerced + row = {"floatX": 78172.128, "confidenceX": 1.2, "strX": "STR"} + _, event_user = schema.coerce_row("event_user", None, row) + schema.update_table(event_user) + + # mock a variant column + event_user_partial = new_table( + "event_user", + columns=[ + {"name": "floatX▶v_complex", "data_type": "json", "variant": True}, + {"name": "confidenceX▶v_complex", "data_type": "json", "variant": False}, + ], + ) + schema.update_table(event_user_partial, normalize_identifiers=False) + + # add json types on the same columns + v_list = [1, 2, "3", {"json": True}] + v_dict = {"list": [1, 2], "str": "json"} + c_row_v = {"floatX": v_list, "confidenceX": v_dict} + # expect two new variant columns to be created + c_new_row_v, c_new_table_v = schema.coerce_row("event_user", None, c_row_v) + c_new_columns_v = list(c_new_table_v["columns"].values()) + print(c_new_row_v) + print(c_new_table_v) + # floatX▶v_complex is kept (was marked with variant) + # confidenceX▶v_json is added (confidenceX▶v_complex not marked as variant) + assert len(c_new_columns_v) == 1 + assert c_new_columns_v[0]["name"] == "confidenceX▶v_json" + assert c_new_columns_v[0]["variant"] is True + # c_row_v coerced to variants + assert c_new_row_v["floatX▶v_complex"] == v_list + assert c_new_row_v["confidenceX▶v_json"] == v_dict + + +def test_row_and_parent_key_migration() -> None: + schema_dict: DictStrAny = load_json_case("schemas/ev1/event.schema") + event = schema_dict["tables"]["event"] + # set unique in _dlt_id to true + event["_dlt_id"]["unique"] = True + + upgraded = migrate_schema(schema_dict, from_engine=schema_dict["engine_version"], to_engine=10) + event_user__parse_data__intent_ranking = upgraded["tables"][ + "event_user__parse_data__intent_ranking" + ] + _dlt_id = event_user__parse_data__intent_ranking["columns"]["_dlt_id"] + # unique id was false so row_key is the same + assert _dlt_id["row_key"] is False + assert "foreign_key" not in _dlt_id + assert "parent_key" not in _dlt_id + + # parent_id modified + _dlt_parent_id = event_user__parse_data__intent_ranking["columns"]["_dlt_parent_id"] + assert _dlt_parent_id["parent_key"] is True + assert "foreign_key" not in _dlt_parent_id + + # we set unique to True above so we expect row_key to be set + event = upgraded["tables"]["event"] + _dlt_id = event["columns"]["_dlt_id"] + # unique id was false so row_key is the same + assert _dlt_id["row_key"] is True + + +def test_preferred_hints_migration() -> None: + schema_dict: DictStrAny = load_json_case("schemas/rasa/event.schema") + upgraded = migrate_schema(schema_dict, from_engine=schema_dict["engine_version"], to_engine=10) + # foreign key hints must be dropped + default_hints = upgraded["settings"]["default_hints"] + assert "foreign_key" not in default_hints + # unique still there + assert default_hints["unique"] == ["re:^_dlt_id$"] + # row && parent key + assert default_hints["row_key"] == ["_dlt_id"] + assert default_hints["parent_key"] == ["_dlt_parent_id"] + + +def test_row_and_parent_key_migration_upper_case() -> None: + os.environ["SCHEMA__NAMING"] = "tests.common.cases.normalizers.sql_upper" + os.environ["SCHEMA__ALLOW_IDENTIFIER_CHANGE_ON_TABLE_WITH_DATA"] = "TRUE" + + schema_dict: DictStrAny = load_json_case("schemas/ev1/event.schema") + upgraded_v9 = migrate_schema( + schema_dict, from_engine=schema_dict["engine_version"], to_engine=9 + ) + assert upgraded_v9["normalizers"]["names"] == DEFAULT_NAMING_MODULE + # use in schema, normalize and get dict back + # NOTE: this may stop working at some point. we use v9 schema without validation + schema = Schema.from_stored_schema(upgraded_v9) + schema.update_normalizers() + upgraded_v9 = schema.to_dict(bump_version=False) + + # set unique in _dlt_id to true + event = upgraded_v9["tables"]["EVENT"] + event["columns"]["_DLT_ID"]["unique"] = True + + upgraded = migrate_schema(upgraded_v9, from_engine=9, to_engine=10) # type: ignore + + event_user__parse_data__intent_ranking = upgraded["tables"][ + "EVENT_USER__PARSE_DATA__INTENT_RANKING" + ] + _dlt_id = event_user__parse_data__intent_ranking["columns"]["_DLT_ID"] + # unique id was false so row_key is the same + assert _dlt_id["row_key"] is False + assert "foreign_key" not in _dlt_id + assert "parent_key" not in _dlt_id + + # parent_id modified + _dlt_parent_id = event_user__parse_data__intent_ranking["columns"]["_DLT_PARENT_ID"] + assert _dlt_parent_id["parent_key"] is True + assert "foreign_key" not in _dlt_parent_id + + # we set unique to True above so we expect row_key to be set + event = upgraded["tables"]["EVENT"] + _dlt_id = event["columns"]["_DLT_ID"] + # unique id was false so row_key is the same + assert _dlt_id["row_key"] is True + + # verify hints migration + default_hints = upgraded["settings"]["default_hints"] + assert "foreign_key" not in default_hints + # unique still there + assert default_hints["unique"] == ["_DLT_ID"] + # row && parent key + assert default_hints["row_key"] == ["_DLT_ID"] + assert default_hints["parent_key"] == ["_DLT_PARENT_ID"] + + +def test_unknown_engine_upgrade() -> None: + schema_dict: TStoredSchema = load_json_case("schemas/ev1/event.schema") + # there's no path to migrate 3 -> 2 + schema_dict["engine_version"] = 3 + with pytest.raises(SchemaEngineNoUpgradePathException): + migrate_schema(schema_dict, 3, 2) # type: ignore[arg-type] diff --git a/tests/common/schema/test_versioning.py b/tests/common/schema/test_versioning.py index 788da09533..39f1ad3211 100644 --- a/tests/common/schema/test_versioning.py +++ b/tests/common/schema/test_versioning.py @@ -86,10 +86,10 @@ def test_infer_column_bumps_version() -> None: def test_preserve_version_on_load() -> None: - eth_v9: TStoredSchema = load_yml_case("schemas/eth/ethereum_schema_v9") - version = eth_v9["version"] - version_hash = eth_v9["version_hash"] - schema = Schema.from_dict(eth_v9) # type: ignore[arg-type] + eth_v10: TStoredSchema = load_yml_case("schemas/eth/ethereum_schema_v10") + version = eth_v10["version"] + version_hash = eth_v10["version_hash"] + schema = Schema.from_dict(eth_v10) # type: ignore[arg-type] # version should not be bumped assert version_hash == schema._stored_version_hash assert version_hash == schema.version_hash @@ -132,6 +132,7 @@ def test_create_ancestry() -> None: schema = Schema.from_dict(eth_v9) # type: ignore[arg-type] expected_previous_hashes = [ + "oHfYGTI2GHOxuzwVz6+yvMilXUvHYhxrxkanC2T6MAI=", "C5An8WClbavalXDdNSqXbdI7Swqh/mTWMcwWKCF//EE=", "yjMtV4Zv0IJlfR5DPMwuXxGg8BRhy7E79L26XAHWEGE=", ] diff --git a/tests/common/storages/custom/freshman_kgs.xlsx b/tests/common/storages/custom/freshman_kgs.xlsx new file mode 100644 index 0000000000..2c3d0fbf9a Binary files /dev/null and b/tests/common/storages/custom/freshman_kgs.xlsx differ diff --git a/tests/common/storages/test_schema_storage.py b/tests/common/storages/test_schema_storage.py index ffbd2ecf1b..a813805ca0 100644 --- a/tests/common/storages/test_schema_storage.py +++ b/tests/common/storages/test_schema_storage.py @@ -3,7 +3,7 @@ import yaml from dlt.common import json -from dlt.common.normalizers.utils import explicit_normalizers +from dlt.common.schema.normalizers import explicit_normalizers from dlt.common.schema.schema import Schema from dlt.common.storages.exceptions import ( InStorageSchemaModified, @@ -22,7 +22,7 @@ from tests.common.utils import ( load_yml_case, COMMON_TEST_CASES_PATH, - IMPORTED_VERSION_HASH_ETH_V9, + IMPORTED_VERSION_HASH_ETH_V10, ) @@ -265,10 +265,10 @@ def test_save_store_schema_over_import(ie_storage: SchemaStorage) -> None: ie_storage.save_schema(schema) assert schema.version_hash == schema_hash # we linked schema to import schema - assert schema._imported_version_hash == IMPORTED_VERSION_HASH_ETH_V9() + assert schema._imported_version_hash == IMPORTED_VERSION_HASH_ETH_V10() # load schema and make sure our new schema is here schema = ie_storage.load_schema("ethereum") - assert schema._imported_version_hash == IMPORTED_VERSION_HASH_ETH_V9() + assert schema._imported_version_hash == IMPORTED_VERSION_HASH_ETH_V10() assert schema._stored_version_hash == schema_hash assert schema.version_hash == schema_hash assert schema.previous_hashes == [] @@ -285,7 +285,7 @@ def test_save_store_schema_over_import_sync(synced_storage: SchemaStorage) -> No schema = Schema("ethereum") schema_hash = schema.version_hash synced_storage.save_schema(schema) - assert schema._imported_version_hash == IMPORTED_VERSION_HASH_ETH_V9() + assert schema._imported_version_hash == IMPORTED_VERSION_HASH_ETH_V10() # import schema is overwritten fs = FileStorage(synced_storage.config.import_schema_path) exported_name = synced_storage._file_name_in_store("ethereum", "yaml") @@ -498,10 +498,10 @@ def assert_schema_imported(synced_storage: SchemaStorage, storage: SchemaStorage prepare_eth_import_folder(synced_storage) schema = synced_storage.load_schema("ethereum") # is linked to imported schema - schema._imported_version_hash = IMPORTED_VERSION_HASH_ETH_V9() + schema._imported_version_hash = IMPORTED_VERSION_HASH_ETH_V10() # also was saved in storage assert synced_storage.has_schema("ethereum") # and has link to imported schema as well (load without import) schema = storage.load_schema("ethereum") - assert schema._imported_version_hash == IMPORTED_VERSION_HASH_ETH_V9() + assert schema._imported_version_hash == IMPORTED_VERSION_HASH_ETH_V10() return schema diff --git a/tests/common/test_utils.py b/tests/common/test_utils.py index e08c1cdf01..864bce5b91 100644 --- a/tests/common/test_utils.py +++ b/tests/common/test_utils.py @@ -13,6 +13,7 @@ flatten_list_of_str_or_dicts, digest128, graph_edges_to_nodes, + group_dict_of_lists, map_nested_in_place, reveal_pseudo_secret, obfuscate_pseudo_secret, @@ -367,3 +368,39 @@ def test_nested_dict_merge() -> None: mappings_update, {"_config": {"_dsn": dsn, "_dict": {"a": 3}}} ) assert mappings_update == deep_clone_dict_1_mappings + + +def test_group_dict_of_lists_one_element_each_list(): + input_dict = {"Frege": ["obj1"], "Gödel": ["obj2"], "Wittgenstein": ["obj3"]} + result = group_dict_of_lists(input_dict) + assert len(result) == 1 + assert result[0] == {"Frege": "obj1", "Gödel": "obj2", "Wittgenstein": "obj3"} + + +def test_group_dict_of_lists_equal_length_lists(): + input_dict = { + "Frege": ["obj1", "obj2"], + "Gödel": ["obj3", "obj4"], + "Wittgenstein": ["obj5", "obj6"], + } + result = group_dict_of_lists(input_dict) + assert len(result) == 2 + assert result[0] == {"Frege": "obj1", "Gödel": "obj3", "Wittgenstein": "obj5"} + assert result[1] == {"Frege": "obj2", "Gödel": "obj4", "Wittgenstein": "obj6"} + + +def test_group_dict_of_lists_various_length_lists(): + input_dict = { + "Frege": ["obj1", "obj2", "obj3"], + "Gödel": ["obj4", "obj5"], + "Wittgenstein": ["obj6"], + } + result = group_dict_of_lists(input_dict) + assert len(result) == 3 + assert result[0] == {"Frege": "obj1", "Gödel": "obj4", "Wittgenstein": "obj6"} + assert result[1] == {"Frege": "obj2", "Gödel": "obj5"} + assert result[2] == {"Frege": "obj3"} + + # Check if the sizes of the decomposed dicts are decreasing + sizes = [len(d) for d in result] + assert sizes == sorted(sizes, reverse=True), "Sizes of decomposed dicts are not decreasing" diff --git a/tests/common/test_validation.py b/tests/common/test_validation.py index 211fcae2d3..0ecbbea89d 100644 --- a/tests/common/test_validation.py +++ b/tests/common/test_validation.py @@ -111,7 +111,7 @@ def test_doc() -> TTestRecord: def test_validate_schema_cases() -> None: with open( - "tests/common/cases/schemas/eth/ethereum_schema_v8.yml", mode="r", encoding="utf-8" + "tests/common/cases/schemas/eth/ethereum_schema_v10.yml", mode="r", encoding="utf-8" ) as f: schema_dict: TStoredSchema = yaml.safe_load(f) diff --git a/tests/common/utils.py b/tests/common/utils.py index 32741128b8..553f67995e 100644 --- a/tests/common/utils.py +++ b/tests/common/utils.py @@ -17,13 +17,13 @@ COMMON_TEST_CASES_PATH = "./tests/common/cases/" -def IMPORTED_VERSION_HASH_ETH_V9() -> str: +def IMPORTED_VERSION_HASH_ETH_V10() -> str: # for import schema tests, change when upgrading the schema version - eth_V9 = load_yml_case("schemas/eth/ethereum_schema_v9") - assert eth_V9["version_hash"] == "PgEHvn5+BHV1jNzNYpx9aDpq6Pq1PSSetufj/h0hKg4=" + eth_V10 = load_yml_case("schemas/eth/ethereum_schema_v10") + assert eth_V10["version_hash"] == "veEmgbCPXCIiqyfabeQWwz6UIQ2liETv7LLMpyktCos=" # remove processing hints before installing as import schema # ethereum schema is a "dirty" schema with processing hints - eth = Schema.from_dict(eth_V9, remove_processing_hints=True) + eth = Schema.from_dict(eth_V10, remove_processing_hints=True) return eth.stored_version_hash diff --git a/tests/destinations/conftest.py b/tests/destinations/conftest.py index 89f7cdffed..dd5bf34f91 100644 --- a/tests/destinations/conftest.py +++ b/tests/destinations/conftest.py @@ -5,3 +5,4 @@ wipe_pipeline, duckdb_pipeline_location, ) +from tests.common.configuration.utils import environment diff --git a/tests/destinations/test_custom_destination.py b/tests/destinations/test_custom_destination.py index 6ebf7f6ef3..476e2f1b03 100644 --- a/tests/destinations/test_custom_destination.py +++ b/tests/destinations/test_custom_destination.py @@ -1,8 +1,7 @@ -from typing import List, Tuple, Dict, Union, cast +from typing import List, Tuple, Dict import dlt import pytest -import pytest import os import inspect @@ -187,6 +186,19 @@ def local_sink_func(items: TDataItems, table: TTableSchema, my_val=dlt.config.va # local func does not create entry in destinations assert "local_sink_func" not in _DESTINATIONS + def local_sink_func_no_params(items: TDataItems, table: TTableSchema) -> None: + # consume data + pass + + p = dlt.pipeline( + "sink_test", + destination=Destination.from_reference( + "destination", destination_callable=local_sink_func_no_params + ), + dev_mode=True, + ) + p.run([1, 2, 3], table_name="items") + # test passing string reference global global_calls global_calls = [] @@ -498,17 +510,15 @@ def sink_func_with_spec( # call fails because `my_predefined_val` is required part of spec, even if not injected with pytest.raises(ConfigFieldMissingException): - info = dlt.pipeline("sink_test", destination=sink_func_with_spec(), dev_mode=True).run( + dlt.pipeline("sink_test", destination=sink_func_with_spec(), dev_mode=True).run( [1, 2, 3], table_name="items" ) - info.raise_on_failed_jobs() # call happens now os.environ["MY_PREDEFINED_VAL"] = "VAL" - info = dlt.pipeline("sink_test", destination=sink_func_with_spec(), dev_mode=True).run( + dlt.pipeline("sink_test", destination=sink_func_with_spec(), dev_mode=True).run( [1, 2, 3], table_name="items" ) - info.raise_on_failed_jobs() # check destination with additional config params @dlt.destination(spec=MyDestinationSpec) diff --git a/tests/destinations/test_destination_name_and_config.py b/tests/destinations/test_destination_name_and_config.py index 1e432a7803..efaaafcfeb 100644 --- a/tests/destinations/test_destination_name_and_config.py +++ b/tests/destinations/test_destination_name_and_config.py @@ -1,6 +1,5 @@ import os import pytest -import posixpath import dlt from dlt.common.configuration.exceptions import ConfigFieldMissingException @@ -9,7 +8,6 @@ from dlt.common.storages import FilesystemConfiguration from dlt.destinations import duckdb, dummy, filesystem -from tests.common.configuration.utils import environment from tests.utils import TEST_STORAGE_ROOT @@ -71,7 +69,6 @@ def test_preserve_destination_instance() -> None: os.environ["COMPLETED_PROB"] = "1.0" load_info = p.run([1, 2, 3], table_name="table", dataset_name="dataset") - load_info.raise_on_failed_jobs() # destination and staging stay the same assert destination_id == id(p.destination) assert staging_id == id(p.staging) diff --git a/tests/extract/cases/eth_source/ethereum.schema.yaml b/tests/extract/cases/eth_source/ethereum.schema.yaml index 5a8db47163..d224088f8b 100644 --- a/tests/extract/cases/eth_source/ethereum.schema.yaml +++ b/tests/extract/cases/eth_source/ethereum.schema.yaml @@ -1,6 +1,6 @@ -version: 14 -version_hash: VuzNqiLOk7XuPxYLndMFMPHTDVItKU5ayiy70nQLdus= -engine_version: 7 +version: 18 +version_hash: veEmgbCPXCIiqyfabeQWwz6UIQ2liETv7LLMpyktCos= +engine_version: 10 name: ethereum tables: _dlt_loads: @@ -8,58 +8,45 @@ tables: load_id: nullable: false data_type: text - name: load_id schema_name: nullable: true data_type: text - name: schema_name status: nullable: false data_type: bigint - name: status inserted_at: nullable: false data_type: timestamp - name: inserted_at schema_version_hash: nullable: true data_type: text - name: schema_version_hash write_disposition: skip description: Created by DLT. Tracks completed loads schema_contract: {} - name: _dlt_loads resource: _dlt_loads _dlt_version: columns: version: nullable: false data_type: bigint - name: version engine_version: nullable: false data_type: bigint - name: engine_version inserted_at: nullable: false data_type: timestamp - name: inserted_at schema_name: nullable: false data_type: text - name: schema_name version_hash: nullable: false data_type: text - name: version_hash schema: nullable: false data_type: text - name: schema write_disposition: skip description: Created by DLT. Tracks schema updates schema_contract: {} - name: _dlt_version resource: _dlt_version blocks: description: Ethereum blocks @@ -73,359 +60,300 @@ tables: nullable: false description: load id coming from the extractor data_type: text - name: _dlt_load_id _dlt_id: nullable: false unique: true data_type: text - name: _dlt_id + row_key: true number: nullable: false primary_key: true data_type: bigint - name: number parent_hash: nullable: true data_type: text - name: parent_hash hash: nullable: false cluster: true unique: true data_type: text - name: hash base_fee_per_gas: nullable: false data_type: wei - name: base_fee_per_gas difficulty: nullable: false data_type: wei - name: difficulty extra_data: nullable: true data_type: text - name: extra_data gas_limit: nullable: false data_type: bigint - name: gas_limit gas_used: nullable: false data_type: bigint - name: gas_used logs_bloom: nullable: true data_type: binary - name: logs_bloom miner: nullable: true data_type: text - name: miner mix_hash: nullable: true data_type: text - name: mix_hash nonce: nullable: true data_type: text - name: nonce receipts_root: nullable: true data_type: text - name: receipts_root sha3_uncles: nullable: true data_type: text - name: sha3_uncles size: nullable: true data_type: bigint - name: size state_root: nullable: false data_type: text - name: state_root timestamp: nullable: false unique: true sort: true data_type: timestamp - name: timestamp total_difficulty: nullable: true data_type: wei - name: total_difficulty transactions_root: nullable: false data_type: text - name: transactions_root schema_contract: {} - name: blocks resource: blocks + x-normalizer: + seen-data: true blocks__transactions: - parent: blocks columns: _dlt_id: nullable: false unique: true data_type: text - name: _dlt_id + row_key: true block_number: nullable: false primary_key: true - foreign_key: true data_type: bigint - name: block_number + merge_key: true transaction_index: nullable: false primary_key: true data_type: bigint - name: transaction_index hash: nullable: false unique: true data_type: text - name: hash block_hash: nullable: false cluster: true data_type: text - name: block_hash block_timestamp: nullable: false sort: true data_type: timestamp - name: block_timestamp chain_id: nullable: true data_type: text - name: chain_id from: nullable: true data_type: text - name: from gas: nullable: true data_type: bigint - name: gas gas_price: nullable: true data_type: bigint - name: gas_price input: nullable: true data_type: text - name: input max_fee_per_gas: nullable: true data_type: wei - name: max_fee_per_gas max_priority_fee_per_gas: nullable: true data_type: wei - name: max_priority_fee_per_gas nonce: nullable: true data_type: bigint - name: nonce r: nullable: true data_type: text - name: r s: nullable: true data_type: text - name: s status: nullable: true data_type: bigint - name: status to: nullable: true data_type: text - name: to type: nullable: true data_type: text - name: type v: nullable: true data_type: bigint - name: v value: nullable: false data_type: wei - name: value eth_value: nullable: true data_type: decimal - name: eth_value - name: blocks__transactions + x-normalizer: + seen-data: true + write_disposition: append + resource: blocks__transactions blocks__transactions__logs: - parent: blocks__transactions columns: _dlt_id: nullable: false unique: true data_type: text - name: _dlt_id + row_key: true address: nullable: false data_type: text - name: address block_timestamp: nullable: false sort: true data_type: timestamp - name: block_timestamp block_hash: nullable: false cluster: true data_type: text - name: block_hash block_number: nullable: false primary_key: true - foreign_key: true + merge_key: true data_type: bigint - name: block_number transaction_index: nullable: false primary_key: true - foreign_key: true + merge_key: true data_type: bigint - name: transaction_index log_index: nullable: false primary_key: true data_type: bigint - name: log_index data: nullable: true data_type: text - name: data removed: nullable: true data_type: bool - name: removed transaction_hash: nullable: false data_type: text - name: transaction_hash - name: blocks__transactions__logs + x-normalizer: + seen-data: true + write_disposition: append + resource: blocks__transactions__logs blocks__transactions__logs__topics: parent: blocks__transactions__logs columns: _dlt_parent_id: nullable: false - foreign_key: true data_type: text - name: _dlt_parent_id + parent_key: true _dlt_list_idx: nullable: false data_type: bigint - name: _dlt_list_idx _dlt_id: nullable: false unique: true data_type: text - name: _dlt_id + row_key: true _dlt_root_id: nullable: false root_key: true data_type: text - name: _dlt_root_id value: nullable: true data_type: text - name: value - name: blocks__transactions__logs__topics + x-normalizer: + seen-data: true blocks__transactions__access_list: parent: blocks__transactions columns: _dlt_parent_id: nullable: false - foreign_key: true data_type: text - name: _dlt_parent_id + parent_key: true _dlt_list_idx: nullable: false data_type: bigint - name: _dlt_list_idx _dlt_id: nullable: false unique: true data_type: text - name: _dlt_id + row_key: true _dlt_root_id: nullable: false root_key: true data_type: text - name: _dlt_root_id address: nullable: true data_type: text - name: address - name: blocks__transactions__access_list + x-normalizer: + seen-data: true blocks__transactions__access_list__storage_keys: parent: blocks__transactions__access_list columns: _dlt_parent_id: nullable: false - foreign_key: true data_type: text - name: _dlt_parent_id + parent_key: true _dlt_list_idx: nullable: false data_type: bigint - name: _dlt_list_idx _dlt_id: nullable: false unique: true data_type: text - name: _dlt_id + row_key: true _dlt_root_id: nullable: false root_key: true data_type: text - name: _dlt_root_id value: nullable: true data_type: text - name: value - name: blocks__transactions__access_list__storage_keys + x-normalizer: + seen-data: true blocks__uncles: parent: blocks columns: _dlt_parent_id: nullable: false - foreign_key: true data_type: text - name: _dlt_parent_id + parent_key: true _dlt_list_idx: nullable: false data_type: bigint - name: _dlt_list_idx _dlt_id: nullable: false unique: true data_type: text - name: _dlt_id + row_key: true _dlt_root_id: nullable: false root_key: true data_type: text - name: _dlt_root_id value: nullable: true data_type: text - name: value - name: blocks__uncles + x-normalizer: + seen-data: true settings: default_hints: - foreign_key: - - _dlt_parent_id not_null: - re:^_dlt_id$ - _dlt_root_id @@ -439,6 +367,10 @@ settings: - block_timestamp root_key: - _dlt_root_id + row_key: + - _dlt_id + parent_key: + - _dlt_parent_id preferred_types: timestamp: timestamp block_timestamp: timestamp @@ -448,7 +380,6 @@ normalizers: json: module: dlt.common.normalizers.json.relational config: - generate_dlt_id: true propagation: root: _dlt_id: _dlt_root_id @@ -456,4 +387,7 @@ normalizers: blocks: timestamp: block_timestamp hash: block_hash - +previous_hashes: +- oHfYGTI2GHOxuzwVz6+yvMilXUvHYhxrxkanC2T6MAI= +- C5An8WClbavalXDdNSqXbdI7Swqh/mTWMcwWKCF//EE= +- yjMtV4Zv0IJlfR5DPMwuXxGg8BRhy7E79L26XAHWEGE= diff --git a/tests/extract/test_decorators.py b/tests/extract/test_decorators.py index f9775fd218..73286678b5 100644 --- a/tests/extract/test_decorators.py +++ b/tests/extract/test_decorators.py @@ -237,14 +237,14 @@ def camelCase(): def test_columns_argument() -> None: - @dlt.resource(name="user", columns={"tags": {"data_type": "complex", "x-extra": "x-annotation"}}) # type: ignore[typeddict-unknown-key] + @dlt.resource(name="user", columns={"tags": {"data_type": "json", "x-extra": "x-annotation"}}) # type: ignore[typeddict-unknown-key] def get_users(): yield {"u": "u", "tags": [1, 2, 3]} t = get_users().compute_table_schema() assert "nullable" not in t["columns"]["tags"] - assert t["columns"]["tags"]["data_type"] == "complex" + assert t["columns"]["tags"]["data_type"] == "json" assert t["columns"]["tags"]["x-extra"] == "x-annotation" # type: ignore[typeddict-item] r = get_users() @@ -262,12 +262,12 @@ def get_users(): def test_apply_hints_columns() -> None: - @dlt.resource(name="user", columns={"tags": {"data_type": "complex", "primary_key": True}}) + @dlt.resource(name="user", columns={"tags": {"data_type": "json", "primary_key": True}}) def get_users(): yield {"u": "u", "tags": [1, 2, 3]} users = get_users() - assert users.columns == {"tags": {"data_type": "complex", "name": "tags", "primary_key": True}} + assert users.columns == {"tags": {"data_type": "json", "name": "tags", "primary_key": True}} assert ( cast(TTableSchemaColumns, users.columns)["tags"] == users.compute_table_schema()["columns"]["tags"] @@ -311,7 +311,7 @@ def get_users() -> Iterator[Dict[str, Any]]: t = get_users().compute_table_schema() assert t["columns"]["tags"]["nullable"] is False - assert t["columns"]["tags"]["data_type"] == "complex" + assert t["columns"]["tags"]["data_type"] == "json" assert t["columns"]["name"]["nullable"] is True assert t["columns"]["name"]["data_type"] == "text" @@ -339,7 +339,7 @@ class Columns3(BaseModel): t = r.compute_table_schema({}) assert t["columns"]["a"]["nullable"] is False - assert t["columns"]["a"]["data_type"] == "complex" + assert t["columns"]["a"]["data_type"] == "json" assert t["columns"]["b"]["nullable"] is False assert t["columns"]["b"]["data_type"] == "double" diff --git a/tests/extract/test_incremental.py b/tests/extract/test_incremental.py index c401552fb2..0a0de75987 100644 --- a/tests/extract/test_incremental.py +++ b/tests/extract/test_incremental.py @@ -1,47 +1,50 @@ -import os import asyncio import inspect +import os import random -from time import sleep -from typing import Optional, Any -from unittest import mock from datetime import datetime # noqa: I251 from itertools import chain, count +from time import sleep +from typing import Any, Optional +from unittest import mock import duckdb +import pyarrow as pa import pytest import dlt +from dlt.common import Decimal +from dlt.common.configuration import ConfigurationValueError from dlt.common.configuration.container import Container from dlt.common.configuration.exceptions import InvalidNativeValue -from dlt.common.configuration.specs.base_configuration import configspec, BaseConfiguration -from dlt.common.configuration import ConfigurationValueError +from dlt.common.configuration.specs.base_configuration import ( + BaseConfiguration, + configspec, +) +from dlt.common.json import json from dlt.common.pendulum import pendulum, timedelta -from dlt.common import Decimal from dlt.common.pipeline import NormalizeInfo, StateInjectableContext, resource_state from dlt.common.schema.schema import Schema -from dlt.common.utils import uniq_id, digest128, chunks -from dlt.common.json import json - +from dlt.common.utils import chunks, digest128, uniq_id from dlt.extract import DltSource -from dlt.extract.exceptions import InvalidStepFunctionArguments -from dlt.extract.items import ValidateItem -from dlt.extract.resource import DltResource -from dlt.sources.helpers.transform import take_first -from dlt.extract.incremental import IncrementalResourceWrapper, Incremental +from dlt.extract.incremental import Incremental, IncrementalResourceWrapper from dlt.extract.incremental.exceptions import ( IncrementalCursorInvalidCoercion, + IncrementalCursorPathHasValueNone, IncrementalCursorPathMissing, IncrementalPrimaryKeyMissing, ) +from dlt.extract.items import ValidateItem +from dlt.extract.resource import DltResource from dlt.pipeline.exceptions import PipelineStepFailed - +from dlt.sources.helpers.transform import take_first from tests.extract.utils import AssertItems, data_item_to_list +from tests.pipeline.utils import assert_query_data from tests.utils import ( + ALL_TEST_DATA_ITEM_FORMATS, + TestDataItemFormat, data_item_length, data_to_item_format, - TestDataItemFormat, - ALL_TEST_DATA_ITEM_FORMATS, ) @@ -167,8 +170,9 @@ def some_data(created_at=dlt.sources.incremental("created_at")): p = dlt.pipeline(pipeline_name=uniq_id()) p.extract(some_data()) - p.extract(some_data()) + assert values == [None] + p.extract(some_data()) assert values == [None, 5] @@ -203,8 +207,8 @@ def some_data(created_at=dlt.sources.incremental("created_at")): pipeline_name=uniq_id(), destination=dlt.destinations.duckdb(credentials=duckdb.connect(":memory:")), ) - p.run(some_data()).raise_on_failed_jobs() - p.run(some_data()).raise_on_failed_jobs() + p.run(some_data()) + p.run(some_data()) with p.sql_client() as c: with c.execute_query("SELECT created_at, id FROM some_data order by created_at, id") as cur: @@ -244,8 +248,8 @@ def some_data(created_at=dlt.sources.incremental("created_at")): pipeline_name=uniq_id(), destination=dlt.destinations.duckdb(credentials=duckdb.connect(":memory:")), ) - p.run(some_data()).raise_on_failed_jobs() - p.run(some_data()).raise_on_failed_jobs() + p.run(some_data()) + p.run(some_data()) with p.sql_client() as c: with c.execute_query("SELECT created_at, id FROM some_data order by created_at, id") as cur: @@ -451,7 +455,7 @@ def some_data(created_at=dlt.sources.incremental("created_at")): pipeline_name=uniq_id(), destination=dlt.destinations.duckdb(credentials=duckdb.connect(":memory:")), ) - p.run(some_data()).raise_on_failed_jobs() + p.run(some_data()) with p.sql_client() as c: with c.execute_query( @@ -635,6 +639,508 @@ def some_data(last_timestamp=dlt.sources.incremental("item.timestamp")): assert pip_ex.value.__context__.json_path == "item.timestamp" +@pytest.mark.parametrize("item_type", ALL_TEST_DATA_ITEM_FORMATS) +def test_cursor_path_none_includes_records_and_updates_incremental_cursor_1( + item_type: TestDataItemFormat, +) -> None: + data = [ + {"id": 1, "created_at": None}, + {"id": 2, "created_at": 1}, + {"id": 3, "created_at": 2}, + ] + source_items = data_to_item_format(item_type, data) + + @dlt.resource + def some_data( + created_at=dlt.sources.incremental("created_at", on_cursor_value_missing="include") + ): + yield source_items + + p = dlt.pipeline(pipeline_name=uniq_id()) + p.run(some_data(), destination="duckdb") + + assert_query_data(p, "select count(id) from some_data", [3]) + assert_query_data(p, "select count(created_at) from some_data", [2]) + + s = p.state["sources"][p.default_schema_name]["resources"]["some_data"]["incremental"][ + "created_at" + ] + assert s["last_value"] == 2 + + +@pytest.mark.parametrize("item_type", ALL_TEST_DATA_ITEM_FORMATS) +def test_cursor_path_none_does_not_include_overlapping_records( + item_type: TestDataItemFormat, +) -> None: + @dlt.resource + def some_data( + invocation: int, + created_at=dlt.sources.incremental("created_at", on_cursor_value_missing="include"), + ): + if invocation == 1: + yield data_to_item_format( + item_type, + [ + {"id": 1, "created_at": None}, + {"id": 2, "created_at": 1}, + {"id": 3, "created_at": 2}, + ], + ) + elif invocation == 2: + yield data_to_item_format( + item_type, + [ + {"id": 4, "created_at": 1}, + {"id": 5, "created_at": None}, + {"id": 6, "created_at": 3}, + ], + ) + + p = dlt.pipeline(pipeline_name=uniq_id()) + p.run(some_data(1), destination="duckdb") + p.run(some_data(2), destination="duckdb") + + assert_query_data(p, "select id from some_data order by id", [1, 2, 3, 5, 6]) + assert_query_data( + p, "select created_at from some_data order by created_at", [1, 2, 3, None, None] + ) + + s = p.state["sources"][p.default_schema_name]["resources"]["some_data"]["incremental"][ + "created_at" + ] + assert s["last_value"] == 3 + + +@pytest.mark.parametrize("item_type", ALL_TEST_DATA_ITEM_FORMATS) +def test_cursor_path_none_includes_records_and_updates_incremental_cursor_2( + item_type: TestDataItemFormat, +) -> None: + data = [ + {"id": 1, "created_at": 1}, + {"id": 2, "created_at": None}, + {"id": 3, "created_at": 2}, + ] + source_items = data_to_item_format(item_type, data) + + @dlt.resource + def some_data( + created_at=dlt.sources.incremental("created_at", on_cursor_value_missing="include") + ): + yield source_items + + p = dlt.pipeline(pipeline_name=uniq_id()) + p.run(some_data(), destination="duckdb") + + assert_query_data(p, "select count(id) from some_data", [3]) + assert_query_data(p, "select count(created_at) from some_data", [2]) + + s = p.state["sources"][p.default_schema_name]["resources"]["some_data"]["incremental"][ + "created_at" + ] + assert s["last_value"] == 2 + + +@pytest.mark.parametrize("item_type", ALL_TEST_DATA_ITEM_FORMATS) +def test_cursor_path_none_includes_records_and_updates_incremental_cursor_3( + item_type: TestDataItemFormat, +) -> None: + data = [ + {"id": 1, "created_at": 1}, + {"id": 2, "created_at": 2}, + {"id": 3, "created_at": None}, + ] + source_items = data_to_item_format(item_type, data) + + @dlt.resource + def some_data( + created_at=dlt.sources.incremental("created_at", on_cursor_value_missing="include") + ): + yield source_items + + p = dlt.pipeline(pipeline_name=uniq_id()) + p.run(some_data(), destination="duckdb") + assert_query_data(p, "select count(id) from some_data", [3]) + assert_query_data(p, "select count(created_at) from some_data", [2]) + + s = p.state["sources"][p.default_schema_name]["resources"]["some_data"]["incremental"][ + "created_at" + ] + assert s["last_value"] == 2 + + +@pytest.mark.parametrize("item_type", ALL_TEST_DATA_ITEM_FORMATS) +def test_cursor_path_none_includes_records_without_cursor_path( + item_type: TestDataItemFormat, +) -> None: + data = [ + {"id": 1, "created_at": 1}, + {"id": 2}, + ] + source_items = data_to_item_format(item_type, data) + + @dlt.resource + def some_data( + created_at=dlt.sources.incremental("created_at", on_cursor_value_missing="include") + ): + yield source_items + + p = dlt.pipeline(pipeline_name=uniq_id()) + p.run(some_data(), destination="duckdb") + assert_query_data(p, "select count(id) from some_data", [2]) + assert_query_data(p, "select count(created_at) from some_data", [1]) + + s = p.state["sources"][p.default_schema_name]["resources"]["some_data"]["incremental"][ + "created_at" + ] + assert s["last_value"] == 1 + + +@pytest.mark.parametrize("item_type", ALL_TEST_DATA_ITEM_FORMATS) +def test_cursor_path_none_excludes_records_and_updates_incremental_cursor( + item_type: TestDataItemFormat, +) -> None: + data = [ + {"id": 1, "created_at": 1}, + {"id": 2, "created_at": 2}, + {"id": 3, "created_at": None}, + ] + source_items = data_to_item_format(item_type, data) + + @dlt.resource + def some_data( + created_at=dlt.sources.incremental("created_at", on_cursor_value_missing="exclude") + ): + yield source_items + + p = dlt.pipeline(pipeline_name=uniq_id()) + p.run(some_data(), destination="duckdb") + assert_query_data(p, "select count(id) from some_data", [2]) + + s = p.state["sources"][p.default_schema_name]["resources"]["some_data"]["incremental"][ + "created_at" + ] + assert s["last_value"] == 2 + + +@pytest.mark.parametrize("item_type", ALL_TEST_DATA_ITEM_FORMATS) +def test_cursor_path_none_can_raise_on_none_1(item_type: TestDataItemFormat) -> None: + data = [ + {"id": 1, "created_at": 1}, + {"id": 2, "created_at": None}, + {"id": 3, "created_at": 2}, + ] + source_items = data_to_item_format(item_type, data) + + @dlt.resource + def some_data( + created_at=dlt.sources.incremental("created_at", on_cursor_value_missing="raise") + ): + yield source_items + + with pytest.raises(IncrementalCursorPathHasValueNone) as py_ex: + list(some_data()) + assert py_ex.value.json_path == "created_at" + + # same thing when run in pipeline + with pytest.raises(PipelineStepFailed) as pip_ex: + p = dlt.pipeline(pipeline_name=uniq_id()) + p.extract(some_data()) + + assert isinstance(pip_ex.value.__context__, IncrementalCursorPathHasValueNone) + assert pip_ex.value.__context__.json_path == "created_at" + + +@pytest.mark.parametrize("item_type", ALL_TEST_DATA_ITEM_FORMATS) +def test_cursor_path_none_can_raise_on_none_2(item_type: TestDataItemFormat) -> None: + data = [ + {"id": 1, "created_at": 1}, + {"id": 2}, + {"id": 3, "created_at": 2}, + ] + source_items = data_to_item_format(item_type, data) + + @dlt.resource + def some_data( + created_at=dlt.sources.incremental("created_at", on_cursor_value_missing="raise") + ): + yield source_items + + # there is no fixed, error because cursor path is missing + if item_type == "object": + with pytest.raises(IncrementalCursorPathMissing) as ex: + list(some_data()) + assert ex.value.json_path == "created_at" + # there is a fixed schema, error because value is null + else: + with pytest.raises(IncrementalCursorPathHasValueNone) as e: + list(some_data()) + assert e.value.json_path == "created_at" + + # same thing when run in pipeline + with pytest.raises(PipelineStepFailed) as e: # type: ignore[assignment] + p = dlt.pipeline(pipeline_name=uniq_id()) + p.extract(some_data()) + if item_type == "object": + assert isinstance(e.value.__context__, IncrementalCursorPathMissing) + else: + assert isinstance(e.value.__context__, IncrementalCursorPathHasValueNone) + assert e.value.__context__.json_path == "created_at" # type: ignore[attr-defined] + + +@pytest.mark.parametrize("item_type", ["arrow-table", "arrow-batch", "pandas"]) +def test_cursor_path_none_can_raise_on_column_missing(item_type: TestDataItemFormat) -> None: + data = [ + {"id": 1}, + {"id": 2}, + {"id": 3}, + ] + source_items = data_to_item_format(item_type, data) + + @dlt.resource + def some_data( + created_at=dlt.sources.incremental("created_at", on_cursor_value_missing="raise") + ): + yield source_items + + with pytest.raises(IncrementalCursorPathMissing) as py_ex: + list(some_data()) + assert py_ex.value.json_path == "created_at" + + # same thing when run in pipeline + with pytest.raises(PipelineStepFailed) as pip_ex: + p = dlt.pipeline(pipeline_name=uniq_id()) + p.extract(some_data()) + assert pip_ex.value.__context__.json_path == "created_at" # type: ignore[attr-defined] + assert isinstance(pip_ex.value.__context__, IncrementalCursorPathMissing) + + +@pytest.mark.parametrize("item_type", ["arrow-table", "arrow-batch"]) +def test_cursor_path_not_nullable_arrow( + item_type: TestDataItemFormat, +) -> None: + @dlt.resource + def some_data( + invocation: int, + created_at=dlt.sources.incremental("created_at", on_cursor_value_missing="include"), + ): + if invocation == 1: + data = [ + {"id": 1, "created_at": 1}, + {"id": 2, "created_at": 1}, + {"id": 3, "created_at": 2}, + ] + elif invocation == 2: + data = [ + {"id": 4, "created_at": 1}, + {"id": 5, "created_at": 2}, + {"id": 6, "created_at": 3}, + ] + + schema = pa.schema( + [ + pa.field("id", pa.int32(), nullable=False), + pa.field("created_at", pa.int32(), nullable=False), + ] + ) + id_array = pa.array([item["id"] for item in data], type=pa.int32()) + created_at_array = pa.array([item["created_at"] for item in data], type=pa.int32()) + if item_type == "arrow-table": + source_items = [pa.Table.from_arrays([id_array, created_at_array], schema=schema)] + elif item_type == "arrow-batch": + source_items = [pa.RecordBatch.from_arrays([id_array, created_at_array], schema=schema)] + + yield source_items + + p = dlt.pipeline(pipeline_name=uniq_id()) + p.run(some_data(1), destination="duckdb") + p.run(some_data(2), destination="duckdb") + + assert_query_data(p, "select id from some_data order by id", [1, 2, 3, 5, 6]) + assert_query_data(p, "select created_at from some_data order by id", [1, 1, 2, 2, 3]) + + s = p.state["sources"][p.default_schema_name]["resources"]["some_data"]["incremental"][ + "created_at" + ] + assert s["last_value"] == 3 + + +def test_cursor_path_none_nested_can_raise_on_none_1() -> None: + # No nested json path support for pandas and arrow. See test_nested_cursor_path_arrow_fails + @dlt.resource + def some_data( + created_at=dlt.sources.incremental( + "data.items[0].created_at", on_cursor_value_missing="raise" + ) + ): + yield {"data": {"items": [{"created_at": None}, {"created_at": 1}]}} + + with pytest.raises(IncrementalCursorPathHasValueNone) as e: + list(some_data()) + assert e.value.json_path == "data.items[0].created_at" + + +def test_cursor_path_none_nested_can_raise_on_none_2() -> None: + # No pandas and arrow. See test_nested_cursor_path_arrow_fails + @dlt.resource + def some_data( + created_at=dlt.sources.incremental( + "data.items[*].created_at", on_cursor_value_missing="raise" + ) + ): + yield {"data": {"items": [{"created_at": None}, {"created_at": 1}]}} + + with pytest.raises(IncrementalCursorPathHasValueNone) as e: + list(some_data()) + assert e.value.json_path == "data.items[*].created_at" + + +def test_cursor_path_none_nested_can_include_on_none_1() -> None: + # No nested json path support for pandas and arrow. See test_nested_cursor_path_arrow_fails + @dlt.resource + def some_data( + created_at=dlt.sources.incremental( + "data.items[*].created_at", on_cursor_value_missing="include" + ) + ): + yield { + "data": { + "items": [ + {"created_at": None}, + {"created_at": 1}, + ] + } + } + + results = list(some_data()) + assert results[0]["data"]["items"] == [ + {"created_at": None}, + {"created_at": 1}, + ] + + p = dlt.pipeline(pipeline_name=uniq_id()) + p.run(some_data(), destination="duckdb") + + assert_query_data(p, "select count(*) from some_data__data__items", [2]) + + +def test_cursor_path_none_nested_can_include_on_none_2() -> None: + # No nested json path support for pandas and arrow. See test_nested_cursor_path_arrow_fails + @dlt.resource + def some_data( + created_at=dlt.sources.incremental( + "data.items[0].created_at", on_cursor_value_missing="include" + ) + ): + yield { + "data": { + "items": [ + {"created_at": None}, + {"created_at": 1}, + ] + } + } + + results = list(some_data()) + assert results[0]["data"]["items"] == [ + {"created_at": None}, + {"created_at": 1}, + ] + + p = dlt.pipeline(pipeline_name=uniq_id()) + p.run(some_data(), destination="duckdb") + + assert_query_data(p, "select count(*) from some_data__data__items", [2]) + + +def test_cursor_path_none_nested_includes_rows_without_cursor_path() -> None: + # No nested json path support for pandas and arrow. See test_nested_cursor_path_arrow_fails + @dlt.resource + def some_data( + created_at=dlt.sources.incremental( + "data.items[*].created_at", on_cursor_value_missing="include" + ) + ): + yield { + "data": { + "items": [ + {"id": 1}, + {"id": 2, "created_at": 2}, + ] + } + } + + results = list(some_data()) + assert results[0]["data"]["items"] == [ + {"id": 1}, + {"id": 2, "created_at": 2}, + ] + + p = dlt.pipeline(pipeline_name=uniq_id()) + p.run(some_data(), destination="duckdb") + + assert_query_data(p, "select count(*) from some_data__data__items", [2]) + + +@pytest.mark.parametrize("item_type", ALL_TEST_DATA_ITEM_FORMATS) +def test_set_default_value_for_incremental_cursor(item_type: TestDataItemFormat) -> None: + @dlt.resource + def some_data(created_at=dlt.sources.incremental("updated_at")): + yield data_to_item_format( + item_type, + [ + {"id": 1, "created_at": 1, "updated_at": 1}, + {"id": 2, "created_at": 4, "updated_at": None}, + {"id": 3, "created_at": 3, "updated_at": 3}, + ], + ) + + def set_default_updated_at(record): + if record.get("updated_at") is None: + record["updated_at"] = record.get("created_at", pendulum.now().int_timestamp) + return record + + def set_default_updated_at_pandas(df): + df["updated_at"] = df["updated_at"].fillna(df["created_at"]) + return df + + def set_default_updated_at_arrow(records): + updated_at_is_null = pa.compute.is_null(records.column("updated_at")) + updated_at_filled = pa.compute.if_else( + updated_at_is_null, records.column("created_at"), records.column("updated_at") + ) + if item_type == "arrow-table": + records = records.set_column( + records.schema.get_field_index("updated_at"), + pa.field("updated_at", records.column("updated_at").type), + updated_at_filled, + ) + elif item_type == "arrow-batch": + columns = [records.column(i) for i in range(records.num_columns)] + columns[2] = updated_at_filled + records = pa.RecordBatch.from_arrays(columns, schema=records.schema) + return records + + if item_type == "object": + func = set_default_updated_at + elif item_type == "pandas": + func = set_default_updated_at_pandas + elif item_type in ["arrow-table", "arrow-batch"]: + func = set_default_updated_at_arrow + + result = list(some_data().add_map(func, insert_at=1)) + values = data_item_to_list(item_type, result) + assert data_item_length(values) == 3 + assert values[1]["updated_at"] == 4 + + # same for pipeline run + p = dlt.pipeline(pipeline_name=uniq_id()) + p.extract(some_data().add_map(func, insert_at=1)) + s = p.state["sources"][p.default_schema_name]["resources"]["some_data"]["incremental"][ + "updated_at" + ] + assert s["last_value"] == 4 + + def test_json_path_cursor() -> None: @dlt.resource def some_data(last_timestamp=dlt.sources.incremental("item.timestamp|modifiedAt")): @@ -819,12 +1325,11 @@ def some_data( ): yield from source_items - info = p.run(some_data()) - info.raise_on_failed_jobs() + p.run(some_data()) norm_info = p.last_trace.last_normalize_info assert norm_info.row_counts["some_data"] == 20 # load incrementally - info = p.run(some_data()) + p.run(some_data()) norm_info = p.last_trace.last_normalize_info assert "some_data" not in norm_info.row_counts @@ -2016,7 +2521,7 @@ def test_type_3(): @pytest.mark.parametrize("yield_pydantic", (True, False)) def test_pydantic_columns_validator(yield_pydantic: bool) -> None: - from pydantic import BaseModel, Field, ConfigDict + from pydantic import BaseModel, ConfigDict, Field # forbid extra fields so "id" in json is not a valid field BUT # add alias for id_ that will serde "id" correctly @@ -2054,11 +2559,8 @@ def test_source(): pip_1_name = "test_pydantic_columns_validator_" + uniq_id() pipeline = dlt.pipeline(pipeline_name=pip_1_name, destination="duckdb") - info = pipeline.run(test_source()) - info.raise_on_failed_jobs() - - info = pipeline.run(test_source_incremental()) - info.raise_on_failed_jobs() + pipeline.run(test_source()) + pipeline.run(test_source_incremental()) # verify that right steps are at right place steps = test_source().table_name._pipe._steps diff --git a/tests/extract/test_sources.py b/tests/extract/test_sources.py index a170c6977d..d111548db0 100644 --- a/tests/extract/test_sources.py +++ b/tests/extract/test_sources.py @@ -1354,15 +1354,15 @@ def empty_gen(): # combine columns with primary key empty_r = empty() empty_r.apply_hints( - columns={"tags": {"data_type": "complex", "primary_key": False}}, + columns={"tags": {"data_type": "json", "primary_key": False}}, primary_key="tags", merge_key="tags", ) # primary key not set here - assert empty_r.columns["tags"] == {"data_type": "complex", "name": "tags", "primary_key": False} + assert empty_r.columns["tags"] == {"data_type": "json", "name": "tags", "primary_key": False} # only in the computed table assert empty_r.compute_table_schema()["columns"]["tags"] == { - "data_type": "complex", + "data_type": "json", "name": "tags", "nullable": False, # NOT NULL because `tags` do not define it "primary_key": True, @@ -1436,6 +1436,36 @@ def empty_gen(): assert table["columns"]["tags"] == {"name": "tags"} +def test_apply_hints_complex_migration() -> None: + def empty_gen(): + yield [1, 2, 3] + + empty = DltResource.from_data(empty_gen) + empty_r = empty() + + def dyn_type(ev): + # must return columns in one of the known formats + return [{"name": "dyn_col", "data_type": ev["dt"]}] + + # start with static columns, update to dynamic + empty_r.apply_hints( + table_name=lambda ev: ev["t"], columns=[{"name": "dyn_col", "data_type": "json"}] + ) + + table = empty_r.compute_table_schema({"t": "table"}) + assert table["columns"]["dyn_col"]["data_type"] == "json" + + empty_r.apply_hints(table_name=lambda ev: ev["t"], columns=dyn_type) + table = empty_r.compute_table_schema({"t": "table", "dt": "complex"}) + assert table["columns"]["dyn_col"]["data_type"] == "json" + + # start with dynamic + empty_r = empty() + empty_r.apply_hints(table_name=lambda ev: ev["t"], columns=dyn_type) + table = empty_r.compute_table_schema({"t": "table", "dt": "complex"}) + assert table["columns"]["dyn_col"]["data_type"] == "json" + + def test_apply_hints_table_variants() -> None: def empty_gen(): yield [1, 2, 3] diff --git a/tests/libs/pyarrow/test_pyarrow_normalizer.py b/tests/libs/pyarrow/test_pyarrow_normalizer.py index d975702ad8..32ee5fdafc 100644 --- a/tests/libs/pyarrow/test_pyarrow_normalizer.py +++ b/tests/libs/pyarrow/test_pyarrow_normalizer.py @@ -4,8 +4,8 @@ import pytest from dlt.common.libs.pyarrow import normalize_py_arrow_item, NameNormalizationCollision -from dlt.common.normalizers.utils import explicit_normalizers, import_normalizers from dlt.common.schema.utils import new_column, TColumnSchema +from dlt.common.schema.normalizers import explicit_normalizers, import_normalizers from dlt.common.destination import DestinationCapabilitiesContext diff --git a/tests/libs/test_deltalake.py b/tests/libs/test_deltalake.py index dc5586eb32..e18fb1abd7 100644 --- a/tests/libs/test_deltalake.py +++ b/tests/libs/test_deltalake.py @@ -143,7 +143,7 @@ def arrow_data( # type: ignore[return] assert dt.to_pyarrow_table().shape == (arrow_table.num_rows, arrow_table.num_columns) # the previous table version should still exist - dt.load_version(1) + dt.load_as_version(1) assert dt.to_pyarrow_table().shape == (arrow_table.num_rows * 2, arrow_table.num_columns) # `merge` should resolve to `append` bevavior diff --git a/tests/libs/test_parquet_writer.py b/tests/libs/test_parquet_writer.py index 158ed047d8..b6a25c5db5 100644 --- a/tests/libs/test_parquet_writer.py +++ b/tests/libs/test_parquet_writer.py @@ -7,7 +7,7 @@ from dlt.common import pendulum, Decimal, json from dlt.common.configuration import inject_section -from dlt.common.data_writers.writers import ParquetDataWriter +from dlt.common.data_writers.writers import ArrowToParquetWriter, ParquetDataWriter from dlt.common.destination import DestinationCapabilitiesContext from dlt.common.schema.utils import new_column from dlt.common.configuration.specs.config_section_context import ConfigSectionContext @@ -76,7 +76,7 @@ def test_parquet_writer_schema_evolution_with_small_buffer() -> None: def test_parquet_writer_json_serialization() -> None: c1 = new_column("col1", "bigint") c2 = new_column("col2", "bigint") - c3 = new_column("col3", "complex") + c3 = new_column("col3", "json") with get_writer(ParquetDataWriter) as writer: writer.write_data_item( @@ -296,7 +296,7 @@ def _assert_arrow_field(field: int, prec: str) -> None: else: assert column_type.tz is None - _assert_arrow_field(0, "us") + _assert_arrow_field(0, "s") _assert_arrow_field(1, "ms") _assert_arrow_field(2, "us") _assert_arrow_field(3, "ns") @@ -306,10 +306,93 @@ def _assert_arrow_field(field: int, prec: str) -> None: def _assert_pq_column(col: int, prec: str) -> None: info = json.loads(reader.metadata.schema.column(col).logical_type.to_json()) + print(info) assert info["isAdjustedToUTC"] is adjusted assert info["timeUnit"] == prec - _assert_pq_column(0, "microseconds") + # apparently storting seconds is not supported + _assert_pq_column(0, "milliseconds") _assert_pq_column(1, "milliseconds") _assert_pq_column(2, "microseconds") _assert_pq_column(3, "nanoseconds") + + +def test_arrow_parquet_row_group_size() -> None: + import pyarrow as pa + + c1 = {"col1": new_column("col1", "bigint")} + + id_ = -1 + + def get_id_() -> int: + nonlocal id_ + id_ += 1 + return id_ + + single_elem_table = lambda: pa.Table.from_pylist([{"col1": get_id_()}]) + single_elem_batch = lambda: pa.RecordBatch.from_pylist([{"col1": get_id_()}]) + + with get_writer(ArrowToParquetWriter, file_max_bytes=2**8, buffer_max_items=2) as writer: + writer.write_data_item(single_elem_table(), columns=c1) + writer._flush_items() + assert writer._writer.items_count == 1 + + with pa.parquet.ParquetFile(writer.closed_files[0].file_path) as reader: + assert reader.num_row_groups == 1 + assert reader.metadata.row_group(0).num_rows == 1 + + # should be packages into single group + with get_writer(ArrowToParquetWriter, file_max_bytes=2**8, buffer_max_items=2) as writer: + writer.write_data_item( + [ + single_elem_table(), + single_elem_batch(), + single_elem_batch(), + single_elem_table(), + single_elem_batch(), + ], + columns=c1, + ) + writer._flush_items() + assert writer._writer.items_count == 5 + + with pa.parquet.ParquetFile(writer.closed_files[0].file_path) as reader: + assert reader.num_row_groups == 1 + assert reader.metadata.row_group(0).num_rows == 5 + + with open(writer.closed_files[0].file_path, "rb") as f: + table = pq.read_table(f) + # all ids are there and in order + assert table["col1"].to_pylist() == list(range(1, 6)) + + # pass also empty and make it to be written with a separate call to parquet writer (by buffer_max_items) + with get_writer(ArrowToParquetWriter, file_max_bytes=2**8, buffer_max_items=1) as writer: + pq_batch = single_elem_batch() + writer.write_data_item(pq_batch, columns=c1) + # writer._flush_items() + # assert writer._writer.items_count == 5 + # this will also create arrow schema + print(pq_batch.schema) + writer.write_data_item(pa.RecordBatch.from_pylist([], schema=pq_batch.schema), columns=c1) + + with pa.parquet.ParquetFile(writer.closed_files[0].file_path) as reader: + assert reader.num_row_groups == 2 + assert reader.metadata.row_group(0).num_rows == 1 + # row group with size 0 for an empty item + assert reader.metadata.row_group(1).num_rows == 0 + + +def test_empty_tables_get_flushed() -> None: + c1 = {"col1": new_column("col1", "bigint")} + single_elem_table = pa.Table.from_pylist([{"col1": 1}]) + empty_batch = pa.RecordBatch.from_pylist([], schema=single_elem_table.schema) + + with get_writer(ArrowToParquetWriter, file_max_bytes=2**8, buffer_max_items=2) as writer: + writer.write_data_item(empty_batch, columns=c1) + writer.write_data_item(empty_batch, columns=c1) + # written + assert len(writer._buffered_items) == 0 + writer.write_data_item(empty_batch, columns=c1) + assert len(writer._buffered_items) == 1 + writer.write_data_item(single_elem_table, columns=c1) + assert len(writer._buffered_items) == 0 diff --git a/tests/libs/test_pyarrow.py b/tests/libs/test_pyarrow.py deleted file mode 100644 index 68541e96e0..0000000000 --- a/tests/libs/test_pyarrow.py +++ /dev/null @@ -1,111 +0,0 @@ -from copy import deepcopy -from datetime import timezone, datetime, timedelta # noqa: I251 -import pyarrow as pa - -from dlt.common import pendulum -from dlt.common.libs.pyarrow import ( - from_arrow_scalar, - get_py_arrow_timestamp, - py_arrow_to_table_schema_columns, - get_py_arrow_datatype, - to_arrow_scalar, -) -from dlt.common.destination import DestinationCapabilitiesContext - -from tests.cases import TABLE_UPDATE_COLUMNS_SCHEMA - - -def test_py_arrow_to_table_schema_columns(): - dlt_schema = deepcopy(TABLE_UPDATE_COLUMNS_SCHEMA) - - caps = DestinationCapabilitiesContext.generic_capabilities() - # The arrow schema will add precision - dlt_schema["col4"]["precision"] = caps.timestamp_precision - dlt_schema["col6"]["precision"], dlt_schema["col6"]["scale"] = caps.decimal_precision - dlt_schema["col11"]["precision"] = caps.timestamp_precision - dlt_schema["col4_null"]["precision"] = caps.timestamp_precision - dlt_schema["col6_null"]["precision"], dlt_schema["col6_null"]["scale"] = caps.decimal_precision - dlt_schema["col11_null"]["precision"] = caps.timestamp_precision - - # Ignoring wei as we can't distinguish from decimal - dlt_schema["col8"]["precision"], dlt_schema["col8"]["scale"] = (76, 0) - dlt_schema["col8"]["data_type"] = "decimal" - dlt_schema["col8_null"]["precision"], dlt_schema["col8_null"]["scale"] = (76, 0) - dlt_schema["col8_null"]["data_type"] = "decimal" - # No json type - dlt_schema["col9"]["data_type"] = "text" - del dlt_schema["col9"]["variant"] - dlt_schema["col9_null"]["data_type"] = "text" - del dlt_schema["col9_null"]["variant"] - - # arrow string fields don't have precision - del dlt_schema["col5_precision"]["precision"] - - # Convert to arrow schema - arrow_schema = pa.schema( - [ - pa.field( - column["name"], - get_py_arrow_datatype(column, caps, "UTC"), - nullable=column["nullable"], - ) - for column in dlt_schema.values() - ] - ) - - result = py_arrow_to_table_schema_columns(arrow_schema) - - # Resulting schema should match the original - assert result == dlt_schema - - -def test_to_arrow_scalar() -> None: - naive_dt = get_py_arrow_timestamp(6, tz=None) - # print(naive_dt) - # naive datetimes are converted as UTC when time aware python objects are used - assert to_arrow_scalar(datetime(2021, 1, 1, 5, 2, 32), naive_dt).as_py() == datetime( - 2021, 1, 1, 5, 2, 32 - ) - assert to_arrow_scalar( - datetime(2021, 1, 1, 5, 2, 32, tzinfo=timezone.utc), naive_dt - ).as_py() == datetime(2021, 1, 1, 5, 2, 32) - assert to_arrow_scalar( - datetime(2021, 1, 1, 5, 2, 32, tzinfo=timezone(timedelta(hours=-8))), naive_dt - ).as_py() == datetime(2021, 1, 1, 5, 2, 32) + timedelta(hours=8) - - # naive datetimes are treated like UTC - utc_dt = get_py_arrow_timestamp(6, tz="UTC") - dt_converted = to_arrow_scalar( - datetime(2021, 1, 1, 5, 2, 32, tzinfo=timezone(timedelta(hours=-8))), utc_dt - ).as_py() - assert dt_converted.utcoffset().seconds == 0 - assert dt_converted == datetime(2021, 1, 1, 13, 2, 32, tzinfo=timezone.utc) - - berlin_dt = get_py_arrow_timestamp(6, tz="Europe/Berlin") - dt_converted = to_arrow_scalar( - datetime(2021, 1, 1, 5, 2, 32, tzinfo=timezone(timedelta(hours=-8))), berlin_dt - ).as_py() - # no dst - assert dt_converted.utcoffset().seconds == 60 * 60 - assert dt_converted == datetime(2021, 1, 1, 13, 2, 32, tzinfo=timezone.utc) - - -def test_from_arrow_scalar() -> None: - naive_dt = get_py_arrow_timestamp(6, tz=None) - sc_dt = to_arrow_scalar(datetime(2021, 1, 1, 5, 2, 32), naive_dt) - - # this value is like UTC - py_dt = from_arrow_scalar(sc_dt) - assert isinstance(py_dt, pendulum.DateTime) - # and we convert to explicit UTC - assert py_dt == datetime(2021, 1, 1, 5, 2, 32, tzinfo=timezone.utc) - - # converts to UTC - berlin_dt = get_py_arrow_timestamp(6, tz="Europe/Berlin") - sc_dt = to_arrow_scalar( - datetime(2021, 1, 1, 5, 2, 32, tzinfo=timezone(timedelta(hours=-8))), berlin_dt - ) - py_dt = from_arrow_scalar(sc_dt) - assert isinstance(py_dt, pendulum.DateTime) - assert py_dt.tzname() == "UTC" - assert py_dt == datetime(2021, 1, 1, 13, 2, 32, tzinfo=timezone.utc) diff --git a/tests/libs/test_pydantic.py b/tests/libs/test_pydantic.py index 2222d13197..70846dcd72 100644 --- a/tests/libs/test_pydantic.py +++ b/tests/libs/test_pydantic.py @@ -168,7 +168,7 @@ class User(BaseModel): final_location: Final[Annotated[Union[str, int], None]] # type: ignore[misc] final_optional: Final[Annotated[Optional[str], None]] # type: ignore[misc] - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": True} + dlt_config: ClassVar[DltConfig] = {"skip_nested_types": True} USER_INSTANCE_DATA = dict( @@ -229,18 +229,18 @@ def test_pydantic_model_to_columns(instance: bool) -> None: assert result["decimal_field"]["data_type"] == "decimal" assert result["double_field"]["data_type"] == "double" assert result["time_field"]["data_type"] == "time" - assert result["nested_field"]["data_type"] == "complex" - assert result["list_field"]["data_type"] == "complex" + assert result["nested_field"]["data_type"] == "json" + assert result["list_field"]["data_type"] == "json" assert result["union_field"]["data_type"] == "bigint" assert result["optional_field"]["data_type"] == "double" assert result["optional_field"]["nullable"] is True - assert result["blank_dict_field"]["data_type"] == "complex" - assert result["parametrized_dict_field"]["data_type"] == "complex" + assert result["blank_dict_field"]["data_type"] == "json" + assert result["parametrized_dict_field"]["data_type"] == "json" assert result["str_enum_field"]["data_type"] == "text" assert result["int_enum_field"]["data_type"] == "bigint" assert result["mixed_enum_int_field"]["data_type"] == "text" assert result["mixed_enum_str_field"]["data_type"] == "text" - assert result["json_field"]["data_type"] == "complex" + assert result["json_field"]["data_type"] == "json" assert result["url_field"]["data_type"] == "text" # Any type fields are excluded from schema @@ -260,9 +260,9 @@ def test_pydantic_model_to_columns_annotated() -> None: assert schema_from_user_class["final_optional"]["nullable"] is True -def test_pydantic_model_skip_complex_types() -> None: +def test_pydantic_model_skip_nested_types() -> None: class SkipNestedModel(Model): - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": True} + dlt_config: ClassVar[DltConfig] = {"skip_nested_types": True} result = pydantic_to_table_schema_columns(SkipNestedModel) @@ -393,7 +393,7 @@ class UserPipe(BaseModel): final_location: Final[Annotated[Union[str, int], None]] # type: ignore[misc, syntax, unused-ignore] final_optional: Final[Annotated[str | None, None]] # type: ignore[misc, syntax, unused-ignore] - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": True} + dlt_config: ClassVar[DltConfig] = {"skip_nested_types": True} # TODO: move to separate test model_freeze = apply_schema_contract_to_model(UserPipe, "evolve", "freeze") @@ -426,7 +426,7 @@ def test_item_list_validation() -> None: class ItemModel(BaseModel): b: bool opt: Optional[int] = None - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": False} + dlt_config: ClassVar[DltConfig] = {"skip_nested_types": False} # non validating items removed from the list (both extra and declared) discard_model = apply_schema_contract_to_model(ItemModel, "discard_row", "discard_row") @@ -563,7 +563,7 @@ class ItemModel(BaseModel): def test_item_validation() -> None: class ItemModel(BaseModel): b: bool - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": False} + dlt_config: ClassVar[DltConfig] = {"skip_nested_types": False} # non validating items removed from the list (both extra and declared) discard_model = apply_schema_contract_to_model(ItemModel, "discard_row", "discard_row") @@ -648,9 +648,10 @@ class Parent(BaseModel): optional_parent_attribute: Optional[str] = None -def test_pydantic_model_flattened_when_skip_complex_types_is_true(): +@pytest.mark.parametrize("config_attr", ("skip_nested_types", "skip_complex_types")) +def test_pydantic_model_flattened_when_skip_nested_types_is_true(config_attr: str): class MyParent(Parent): - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": True} + dlt_config: ClassVar[DltConfig] = {config_attr: True} # type: ignore schema = pydantic_to_table_schema_columns(MyParent) @@ -673,16 +674,17 @@ class MyParent(Parent): } -def test_considers_model_as_complex_when_skip_complex_types_is_false(): +@pytest.mark.parametrize("config_attr", ("skip_nested_types", "skip_complex_types")) +def test_considers_model_as_complex_when_skip_nested_types_is_false(config_attr: str): class MyParent(Parent): data_dictionary: Dict[str, Any] = None - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": False} + dlt_config: ClassVar[DltConfig] = {config_attr: False} # type: ignore schema = pydantic_to_table_schema_columns(MyParent) assert schema == { - "child": {"data_type": "complex", "name": "child", "nullable": False}, - "data_dictionary": {"data_type": "complex", "name": "data_dictionary", "nullable": False}, + "child": {"data_type": "json", "name": "child", "nullable": False}, + "data_dictionary": {"data_type": "json", "name": "data_dictionary", "nullable": False}, "optional_parent_attribute": { "data_type": "text", "name": "optional_parent_attribute", @@ -691,32 +693,32 @@ class MyParent(Parent): } -def test_considers_dictionary_as_complex_when_skip_complex_types_is_false(): +def test_considers_dictionary_as_complex_when_skip_nested_types_is_false(): class MyParent(Parent): data_list: List[str] = [] data_dictionary: Dict[str, Any] = None - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": False} + dlt_config: ClassVar[DltConfig] = {"skip_nested_types": False} schema = pydantic_to_table_schema_columns(MyParent) assert schema["data_dictionary"] == { - "data_type": "complex", + "data_type": "json", "name": "data_dictionary", "nullable": False, } assert schema["data_list"] == { - "data_type": "complex", + "data_type": "json", "name": "data_list", "nullable": False, } -def test_skip_complex_types_when_skip_complex_types_is_true_and_field_is_not_pydantic_model(): +def test_skip_json_types_when_skip_nested_types_is_true_and_field_is_not_pydantic_model(): class MyParent(Parent): data_list: List[str] = [] data_dictionary: Dict[str, Any] = None - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": True} + dlt_config: ClassVar[DltConfig] = {"skip_nested_types": True} schema = pydantic_to_table_schema_columns(MyParent) diff --git a/tests/load/athena_iceberg/test_athena_iceberg.py b/tests/load/athena_iceberg/test_athena_iceberg.py index 0ef935a8bc..6190f8793a 100644 --- a/tests/load/athena_iceberg/test_athena_iceberg.py +++ b/tests/load/athena_iceberg/test_athena_iceberg.py @@ -1,8 +1,8 @@ import pytest -import os from typing import Iterator, Any import dlt +from tests.load.utils import DestinationTestConfiguration, destinations_configs from tests.pipeline.utils import load_table_counts from dlt.destinations.exceptions import DatabaseTerminalException @@ -11,19 +11,22 @@ pytestmark = pytest.mark.essential -def test_iceberg() -> None: +@pytest.mark.parametrize( + "destination_config", + destinations_configs( + default_sql_configs=True, + with_table_format="iceberg", + subset=["athena"], + ), + ids=lambda x: x.name, +) +def test_iceberg(destination_config: DestinationTestConfiguration) -> None: """ We write two tables, one with the iceberg flag, one without. We expect the iceberg table and its subtables to accept update commands and the other table to reject them. """ - os.environ["DESTINATION__FILESYSTEM__BUCKET_URL"] = "s3://dlt-ci-test-bucket" - pipeline = dlt.pipeline( - pipeline_name="athena-iceberg", - destination="athena", - staging="filesystem", - dev_mode=True, - ) + pipeline = destination_config.setup_pipeline("test_iceberg", dev_mode=True) def items() -> Iterator[Any]: yield { @@ -67,3 +70,57 @@ def items_iceberg(): # modifying iceberg table will succeed client.execute_sql("UPDATE items_iceberg SET name='new name'") client.execute_sql("UPDATE items_iceberg__sub_items SET name='super new name'") + + +@pytest.mark.parametrize( + "destination_config", + destinations_configs( + default_sql_configs=True, + with_table_format="iceberg", + subset=["athena"], + ), + ids=lambda x: x.name, +) +def test_force_iceberg_deprecation(destination_config: DestinationTestConfiguration) -> None: + """Fails on deprecated force_iceberg option""" + destination_config.force_iceberg = True + pipeline = destination_config.setup_pipeline("test_force_iceberg_deprecation", dev_mode=True) + + def items() -> Iterator[Any]: + yield { + "id": 1, + "name": "item", + "sub_items": [{"id": 101, "name": "sub item 101"}, {"id": 101, "name": "sub item 102"}], + } + + @dlt.resource(name="items_normal", write_disposition="append") + def items_normal(): + yield from items() + + @dlt.resource(name="items_hive", write_disposition="append", table_format="hive") + def items_hive(): + yield from items() + + print(pipeline.run([items_normal, items_hive])) + + # items_normal should load as iceberg + # _dlt_pipeline_state should load as iceberg (IMPORTANT for backward comp) + + with pipeline.sql_client() as client: + client.execute_sql("SELECT * FROM items_normal") + client.execute_sql("SELECT * FROM items_hive") + + with pytest.raises(DatabaseTerminalException) as dbex: + client.execute_sql("UPDATE items_hive SET name='new name'") + assert "Modifying Hive table rows is only supported for transactional tables" in str(dbex) + + # modifying iceberg table will succeed + client.execute_sql("UPDATE items_normal SET name='new name'") + client.execute_sql("UPDATE items_normal__sub_items SET name='super new name'") + client.execute_sql("UPDATE _dlt_pipeline_state SET pipeline_name='new name'") + + # trigger deprecation warning + from dlt.destinations import athena + + athena_c = athena(force_iceberg=True).configuration(athena().spec()._bind_dataset_name("ds")) + assert athena_c.force_iceberg is True diff --git a/tests/load/bigquery/test_bigquery_client.py b/tests/load/bigquery/test_bigquery_client.py index c92f18e159..10ee55cc6c 100644 --- a/tests/load/bigquery/test_bigquery_client.py +++ b/tests/load/bigquery/test_bigquery_client.py @@ -23,7 +23,10 @@ from dlt.destinations.impl.bigquery.bigquery import BigQueryClient, BigQueryClientConfiguration from dlt.destinations.exceptions import LoadJobNotExistsException, LoadJobTerminalException -from dlt.destinations.impl.bigquery.bigquery_adapter import AUTODETECT_SCHEMA_HINT +from dlt.destinations.impl.bigquery.bigquery_adapter import ( + AUTODETECT_SCHEMA_HINT, + should_autodetect_schema, +) from tests.utils import TEST_STORAGE_ROOT, delete_test_storage from tests.common.utils import json_case_path as common_json_case_path from tests.common.configuration.utils import environment @@ -277,23 +280,35 @@ def test_bigquery_different_project_id(bigquery_project_id) -> None: def test_bigquery_autodetect_configuration(client: BigQueryClient) -> None: # no schema autodetect - assert client._should_autodetect_schema("event_slot") is False - assert client._should_autodetect_schema("_dlt_loads") is False + event_slot = client.prepare_load_table("event_slot") + _dlt_loads = client.prepare_load_table("_dlt_loads") + assert should_autodetect_schema(event_slot) is False + assert should_autodetect_schema(_dlt_loads) is False # add parent table child = new_table("event_slot__values", "event_slot") - client.schema.update_table(child) - assert client._should_autodetect_schema("event_slot__values") is False + client.schema.update_table(child, normalize_identifiers=False) + event_slot__values = client.prepare_load_table("event_slot__values") + assert should_autodetect_schema(event_slot__values) is False + # enable global config client.config.autodetect_schema = True - assert client._should_autodetect_schema("event_slot") is True - assert client._should_autodetect_schema("_dlt_loads") is False - assert client._should_autodetect_schema("event_slot__values") is True + # prepare again + event_slot = client.prepare_load_table("event_slot") + _dlt_loads = client.prepare_load_table("_dlt_loads") + event_slot__values = client.prepare_load_table("event_slot__values") + assert should_autodetect_schema(event_slot) is True + assert should_autodetect_schema(_dlt_loads) is False + assert should_autodetect_schema(event_slot__values) is True + # enable hint per table client.config.autodetect_schema = False client.schema.get_table("event_slot")[AUTODETECT_SCHEMA_HINT] = True # type: ignore[typeddict-unknown-key] - assert client._should_autodetect_schema("event_slot") is True - assert client._should_autodetect_schema("_dlt_loads") is False - assert client._should_autodetect_schema("event_slot__values") is True + event_slot = client.prepare_load_table("event_slot") + _dlt_loads = client.prepare_load_table("_dlt_loads") + event_slot__values = client.prepare_load_table("event_slot__values") + assert should_autodetect_schema(event_slot) is True + assert should_autodetect_schema(_dlt_loads) is False + assert should_autodetect_schema(event_slot__values) is True def test_bigquery_job_resuming(client: BigQueryClient, file_storage: FileStorage) -> None: @@ -311,14 +326,14 @@ def test_bigquery_job_resuming(client: BigQueryClient, file_storage: FileStorage r_job = cast( RunnableLoadJob, client.create_load_job( - client.schema.get_table(user_table_name), + client.prepare_load_table(user_table_name), file_storage.make_full_path(job.file_name()), uniq_id(), ), ) # job will be automatically found and resumed - r_job.set_run_vars(uniq_id(), client.schema, client.schema.tables[user_table_name]) + r_job.set_run_vars(uniq_id(), client.schema, client.prepare_load_table(user_table_name)) r_job.run_managed(client) assert r_job.state() == "completed" assert r_job._resumed_job # type: ignore diff --git a/tests/load/bigquery/test_bigquery_streaming_insert.py b/tests/load/bigquery/test_bigquery_streaming_insert.py index c950a46f91..20d07c7c76 100644 --- a/tests/load/bigquery/test_bigquery_streaming_insert.py +++ b/tests/load/bigquery/test_bigquery_streaming_insert.py @@ -1,7 +1,10 @@ import pytest import dlt +from dlt.common.pipeline import LoadInfo from dlt.destinations.adapters import bigquery_adapter +from dlt.load.exceptions import LoadClientJobFailed +from dlt.pipeline.exceptions import PipelineStepFailed from tests.pipeline.utils import assert_load_info @@ -40,13 +43,16 @@ def test_resource(): test_resource.apply_hints(additional_table_hints={"x-insert-api": "streaming"}) pipe = dlt.pipeline(pipeline_name="insert_test", destination="bigquery") - info = pipe.run(test_resource) + with pytest.raises(PipelineStepFailed) as pip_ex: + pipe.run(test_resource) + assert isinstance(pip_ex.value.step_info, LoadInfo) + assert pip_ex.value.step_info.has_failed_jobs # pick the failed job - failed_job = info.load_packages[0].jobs["failed_jobs"][0] + assert isinstance(pip_ex.value.__cause__, LoadClientJobFailed) assert ( """BigQuery streaming insert can only be used with `append`""" """ write_disposition, while the given resource has `merge`.""" - ) in failed_job.failed_message + ) in pip_ex.value.__cause__.failed_message def test_bigquery_streaming_nested_data(): diff --git a/tests/load/bigquery/test_bigquery_table_builder.py b/tests/load/bigquery/test_bigquery_table_builder.py index 63ac645113..18059767cd 100644 --- a/tests/load/bigquery/test_bigquery_table_builder.py +++ b/tests/load/bigquery/test_bigquery_table_builder.py @@ -1,17 +1,10 @@ import os from copy import deepcopy from typing import Iterator, Dict, Any, List -from dlt.common.destination.exceptions import DestinationSchemaTampered -from dlt.common.schema.exceptions import SchemaIdentifierNormalizationCollision -from dlt.destinations.impl.bigquery.bigquery_adapter import ( - PARTITION_HINT, - CLUSTER_HINT, -) import google import pytest import sqlfluff -from google.cloud.bigquery import Table import dlt from dlt.common.configuration import resolve_configuration @@ -19,26 +12,28 @@ GcpServiceAccountCredentialsWithoutDefaults, GcpServiceAccountCredentials, ) +from dlt.common.destination.exceptions import DestinationSchemaTampered from dlt.common.pendulum import pendulum from dlt.common.schema import Schema, utils +from dlt.common.schema.exceptions import SchemaIdentifierNormalizationCollision from dlt.common.utils import custom_environ from dlt.common.utils import uniq_id - -from dlt.destinations.exceptions import DestinationSchemaWillNotUpdate from dlt.destinations import bigquery -from dlt.destinations.impl.bigquery.bigquery import BigQueryClient from dlt.destinations.adapters import bigquery_adapter +from dlt.destinations.exceptions import DestinationSchemaWillNotUpdate +from dlt.destinations.impl.bigquery.bigquery import BigQueryClient +from dlt.destinations.impl.bigquery.bigquery_adapter import ( + PARTITION_HINT, + CLUSTER_HINT, +) from dlt.destinations.impl.bigquery.configuration import BigQueryClientConfiguration - from dlt.extract import DltResource - from tests.load.utils import ( destinations_configs, DestinationTestConfiguration, drop_active_pipeline_data, TABLE_UPDATE, sequence_generator, - empty_schema, ) # mark all tests as essential, do not remove @@ -197,7 +192,7 @@ def test_create_table_case_insensitive(ci_gcp_client: BigQueryClient) -> None: ) assert "Event_TEST_tablE" in ci_gcp_client.schema.tables with pytest.raises(SchemaIdentifierNormalizationCollision) as coll_ex: - ci_gcp_client.update_stored_schema([]) + ci_gcp_client.verify_schema() assert coll_ex.value.conflict_identifier_name == "Event_test_tablE" assert coll_ex.value.table_name == "Event_TEST_tablE" @@ -205,6 +200,7 @@ def test_create_table_case_insensitive(ci_gcp_client: BigQueryClient) -> None: ci_gcp_client.capabilities.has_case_sensitive_identifiers = True # now the check passes, we are stopped because it is not allowed to change schema in the loader with pytest.raises(DestinationSchemaTampered): + ci_gcp_client.verify_schema() ci_gcp_client.update_stored_schema([]) @@ -1019,8 +1015,7 @@ def sources() -> List[DltResource]: dlt.resource([{"col2": "ABC"}], name="hints"), table_description="Once upon a time a small table got hinted twice.", ) - info = pipeline.run(mod_hints) - info.raise_on_failed_jobs() + pipeline.run(mod_hints) assert pipeline.last_trace.last_normalize_info.row_counts["hints"] == 1 with pipeline.sql_client() as c: @@ -1044,97 +1039,3 @@ def some_data() -> Iterator[Dict[str, str]]: bigquery_adapter(some_data, table_expiration_datetime="2030-01-01") assert some_data._hints["x-bigquery-table-expiration"] == pendulum.datetime(2030, 1, 1) # type: ignore - - -@pytest.mark.parametrize( - "destination_config", - destinations_configs(default_sql_configs=True, subset=["bigquery"]), - ids=lambda x: x.name, -) -def test_adapter_additional_table_hints_table_expiration( - destination_config: DestinationTestConfiguration, -) -> None: - @dlt.resource(columns=[{"name": "col1", "data_type": "text"}]) - def no_hints() -> Iterator[Dict[str, str]]: - yield from [{"col1": str(i)} for i in range(10)] - - hints = bigquery_adapter( - no_hints.with_name(new_name="hints"), table_expiration_datetime="2030-01-01" - ) - - @dlt.source(max_table_nesting=0) - def sources() -> List[DltResource]: - return [no_hints, hints] - - pipeline = destination_config.setup_pipeline( - f"bigquery_{uniq_id()}", - dev_mode=True, - ) - - pipeline.run(sources()) - - with pipeline.sql_client() as c: - nc: google.cloud.bigquery.client.Client = c.native_connection - - fqtn_no_hints = c.make_qualified_table_name("no_hints", escape=False) - fqtn_hints = c.make_qualified_table_name("hints", escape=False) - - no_hints_table = nc.get_table(fqtn_no_hints) - hints_table = nc.get_table(fqtn_hints) - - assert not no_hints_table.expires - assert hints_table.expires == pendulum.datetime(2030, 1, 1, 0) - - -@pytest.mark.parametrize( - "destination_config", - destinations_configs(default_sql_configs=True, subset=["bigquery"]), - ids=lambda x: x.name, -) -def test_adapter_merge_behaviour( - destination_config: DestinationTestConfiguration, -) -> None: - @dlt.resource( - columns=[ - {"name": "col1", "data_type": "text"}, - {"name": "col2", "data_type": "bigint"}, - {"name": "col3", "data_type": "double"}, - ] - ) - def hints() -> Iterator[Dict[str, Any]]: - yield from [{"col1": str(i), "col2": i, "col3": float(i)} for i in range(10)] - - bigquery_adapter(hints, table_expiration_datetime="2030-01-01", cluster=["col1"]) - bigquery_adapter( - hints, - table_description="A small table somewhere in the cosmos...", - partition="col2", - ) - - pipeline = destination_config.setup_pipeline( - f"bigquery_{uniq_id()}", - dev_mode=True, - ) - - pipeline.run(hints) - - with pipeline.sql_client() as c: - nc: google.cloud.bigquery.client.Client = c.native_connection - - table_fqtn = c.make_qualified_table_name("hints", escape=False) - - table: Table = nc.get_table(table_fqtn) - - table_cluster_fields = [] if table.clustering_fields is None else table.clustering_fields - - # Test merging behaviour. - assert table.expires == pendulum.datetime(2030, 1, 1, 0) - assert ["col1"] == table_cluster_fields, "`hints` table IS NOT clustered by `col1`." - assert table.description == "A small table somewhere in the cosmos..." - - if not table.range_partitioning: - raise ValueError("`hints` table IS NOT clustered on a column.") - else: - assert ( - table.range_partitioning.field == "col2" - ), "`hints` table IS NOT clustered on column `col2`." diff --git a/tests/load/clickhouse/test_clickhouse_configuration.py b/tests/load/clickhouse/test_clickhouse_configuration.py index a4e8abc8dd..2b74922c34 100644 --- a/tests/load/clickhouse/test_clickhouse_configuration.py +++ b/tests/load/clickhouse/test_clickhouse_configuration.py @@ -3,7 +3,7 @@ import pytest from dlt.common.configuration.resolve import resolve_configuration -from dlt.common.libs.sql_alchemy import make_url +from dlt.common.libs.sql_alchemy_shims import make_url from dlt.common.utils import digest128 from dlt.destinations.impl.clickhouse.clickhouse import ClickHouseClient from dlt.destinations.impl.clickhouse.configuration import ( diff --git a/tests/load/conftest.py b/tests/load/conftest.py index a110b1198f..76a7248e5b 100644 --- a/tests/load/conftest.py +++ b/tests/load/conftest.py @@ -2,7 +2,13 @@ import pytest from typing import Iterator -from tests.load.utils import ALL_BUCKETS, DEFAULT_BUCKETS, WITH_GDRIVE_BUCKETS, drop_pipeline +from tests.load.utils import ( + ALL_BUCKETS, + DEFAULT_BUCKETS, + WITH_GDRIVE_BUCKETS, + drop_pipeline, + empty_schema, +) from tests.utils import preserve_environ, patch_home_dir diff --git a/tests/load/duckdb/test_duckdb_table_builder.py b/tests/load/duckdb/test_duckdb_table_builder.py index 85f86ce84d..50c18f5587 100644 --- a/tests/load/duckdb/test_duckdb_table_builder.py +++ b/tests/load/duckdb/test_duckdb_table_builder.py @@ -1,7 +1,9 @@ +from typing import List import pytest from copy import deepcopy import sqlfluff +from dlt.common.schema.typing import TColumnSchema from dlt.common.utils import uniq_id from dlt.common.schema import Schema @@ -31,7 +33,9 @@ def client(empty_schema: Schema) -> DuckDbClient: def test_create_table(client: DuckDbClient) -> None: # non existing table - sql = client._get_table_update_sql("event_test_table", TABLE_UPDATE, False)[0] + sql = client._get_table_update_sql( + "event_test_table", add_timezone_false_on_precision(TABLE_UPDATE), False + )[0] sqlfluff.parse(sql, dialect="duckdb") assert "event_test_table" in sql assert '"col1" BIGINT NOT NULL' in sql @@ -57,13 +61,15 @@ def test_create_table_all_precisions(client: DuckDbClient) -> None: # non existing table sql = client._get_table_update_sql( "event_test_table", - TABLE_UPDATE_ALL_TIMESTAMP_PRECISIONS + TABLE_UPDATE_ALL_INT_PRECISIONS, + add_timezone_false_on_precision( + TABLE_UPDATE_ALL_TIMESTAMP_PRECISIONS + TABLE_UPDATE_ALL_INT_PRECISIONS + ), False, )[0] sqlfluff.parse(sql, dialect="duckdb") assert '"col1_ts" TIMESTAMP_S ' in sql assert '"col2_ts" TIMESTAMP_MS ' in sql - assert '"col3_ts" TIMESTAMP WITH TIME ZONE ' in sql + assert '"col3_ts" TIMESTAMP ' in sql assert '"col4_ts" TIMESTAMP_NS ' in sql assert '"col1_int" TINYINT ' in sql assert '"col2_int" SMALLINT ' in sql @@ -74,7 +80,9 @@ def test_create_table_all_precisions(client: DuckDbClient) -> None: def test_alter_table(client: DuckDbClient) -> None: # existing table has no columns - sqls = client._get_table_update_sql("event_test_table", TABLE_UPDATE, True) + sqls = client._get_table_update_sql( + "event_test_table", add_timezone_false_on_precision(TABLE_UPDATE), True + ) for sql in sqls: sqlfluff.parse(sql, dialect="duckdb") canonical_name = client.sql_client.make_qualified_table_name("event_test_table") @@ -107,7 +115,7 @@ def test_create_table_with_hints(client: DuckDbClient) -> None: mod_update[0]["primary_key"] = True mod_update[0]["sort"] = True mod_update[1]["unique"] = True - mod_update[4]["foreign_key"] = True + mod_update[4]["parent_key"] = True sql = ";".join(client._get_table_update_sql("event_test_table", mod_update, False)) assert '"col1" BIGINT NOT NULL' in sql assert '"col2" DOUBLE NOT NULL' in sql @@ -127,3 +135,11 @@ def test_create_table_with_hints(client: DuckDbClient) -> None: sql = client._get_table_update_sql("event_test_table", mod_update, False)[0] sqlfluff.parse(sql) assert '"col2" DOUBLE UNIQUE NOT NULL' in sql + + +def add_timezone_false_on_precision(table_update: List[TColumnSchema]) -> List[TColumnSchema]: + table_update = deepcopy(table_update) + for column in table_update: + if column["data_type"] == "timestamp" and column.get("precision") is not None: + column["timezone"] = False + return table_update diff --git a/tests/load/filesystem/test_filesystem_client.py b/tests/load/filesystem/test_filesystem_client.py index f16e75c7e6..fdd68d4683 100644 --- a/tests/load/filesystem/test_filesystem_client.py +++ b/tests/load/filesystem/test_filesystem_client.py @@ -194,9 +194,10 @@ def test_replace_write_disposition(layout: str, default_buckets_env: str) -> Non # First file from load1 remains, second file is replaced by load2 # assert that only these two files are in the destination folder + is_sftp = urlparse(default_buckets_env).scheme == "sftp" paths = [] for basedir, _dirs, files in client.fs_client.walk( - client.dataset_path, detail=False, refresh=True + client.dataset_path, detail=False, **({"refresh": True} if not is_sftp else {}) ): # remove internal paths if "_dlt" in basedir: @@ -257,9 +258,10 @@ def test_append_write_disposition(layout: str, default_buckets_env: str) -> None ] expected_files = sorted([Path(posixpath.join(root_path, fn)) for fn in expected_files]) # type: ignore[misc] + is_sftp = urlparse(default_buckets_env).scheme == "sftp" paths = [] for basedir, _dirs, files in client.fs_client.walk( - client.dataset_path, detail=False, refresh=True + client.dataset_path, detail=False, **({"refresh": True} if not is_sftp else {}) ): # remove internal paths if "_dlt" in basedir: diff --git a/tests/load/filesystem_sftp/__init__.py b/tests/load/filesystem_sftp/__init__.py new file mode 100644 index 0000000000..0d23f8002b --- /dev/null +++ b/tests/load/filesystem_sftp/__init__.py @@ -0,0 +1,3 @@ +from tests.utils import skip_if_not_active + +skip_if_not_active("filesystem") diff --git a/tests/load/filesystem_sftp/bootstrap/Dockerfile b/tests/load/filesystem_sftp/bootstrap/Dockerfile new file mode 100644 index 0000000000..a6c75d8c88 --- /dev/null +++ b/tests/load/filesystem_sftp/bootstrap/Dockerfile @@ -0,0 +1,117 @@ +FROM ubuntu:noble + +ENV DEBIAN_FRONTEND=noninteractive + +RUN apt-get update && \ + apt-get install -y openssh-server && \ + rm -rf /var/lib/apt/lists/* + +# Certificate Authority (CA): public key +COPY ca_rsa.pub /etc/ssh/ca_rsa.pub +RUN chmod 600 /etc/ssh/ca_rsa.pub + +RUN mkdir -p /etc/ssh/auth_principals && \ + echo "billy" >> /etc/ssh/auth_principals/billy + +RUN mkdir -p /run/sshd && \ + echo "SyslogFacility AUTH" >> /etc/ssh/sshd_config && \ + echo "LogLevel INFO" >> /etc/ssh/sshd_config && \ + echo "TrustedUserCAKeys /etc/ssh/ca_rsa.pub" >> /etc/ssh/sshd_config && \ + echo "AuthorizedPrincipalsFile /etc/ssh/auth_principals/billy" >> /etc/ssh/sshd_config + +# USER foo: set up user for SFTP with no shell login +RUN useradd -m -d /home/foo -s /usr/sbin/nologin foo && \ + mkdir -p /home/foo/.ssh && \ + chown foo:foo /home/foo/.ssh && \ + chmod 700 /home/foo/.ssh + +# USER foo: set password for the user foo +RUN echo 'foo:pass' | chpasswd + +# USER foo: copy the public key and set permissions +COPY foo_rsa.pub /home/foo/.ssh/authorized_keys +RUN chmod 600 /home/foo/.ssh/authorized_keys && \ + chown foo:foo /home/foo/.ssh/authorized_keys + +# USER foo: create a directory for SFTP that the user will have access to +RUN mkdir -p /home/foo/sftp/data && \ + chown root:root /home/foo /home/foo/sftp && \ + chmod 755 /home/foo /home/foo/sftp && \ + chown foo:foo /home/foo/sftp/data && \ + chmod 755 /home/foo/sftp/data + + +# USER foo: configure SSH for SFTP: allow password and pubkey authentication +RUN echo "Match User foo" >> /etc/ssh/sshd_config && \ + echo " ChrootDirectory /home/foo/sftp" >> /etc/ssh/sshd_config && \ + echo " ForceCommand internal-sftp" >> /etc/ssh/sshd_config && \ + echo " PasswordAuthentication yes" >> /etc/ssh/sshd_config && \ + echo " PubkeyAuthentication yes" >> /etc/ssh/sshd_config && \ + echo " PermitTunnel no" >> /etc/ssh/sshd_config && \ + echo " AllowAgentForwarding no" >> /etc/ssh/sshd_config && \ + echo " AllowTcpForwarding no" >> /etc/ssh/sshd_config && \ + echo " X11Forwarding no" >> /etc/ssh/sshd_config + +# USER bobby: set up user for SFTP with no shell login +RUN useradd -m -d /home/bobby -s /usr/sbin/nologin bobby && \ + mkdir -p /home/bobby/.ssh && \ + chown bobby:bobby /home/bobby/.ssh && \ + chmod 700 /home/bobby/.ssh + +# USER bobby: copy the public key and set permissions +COPY bobby_rsa.pub /home/bobby/.ssh/authorized_keys +RUN chmod 600 /home/bobby/.ssh/authorized_keys && \ + chown bobby:bobby /home/bobby/.ssh/authorized_keys + +# USER bobby: create a directory for SFTP that the user will have access to +RUN mkdir -p /home/bobby/sftp/data && \ + chown root:root /home/bobby /home/bobby/sftp && \ + chmod 755 /home/bobby /home/bobby/sftp && \ + chown bobby:bobby /home/bobby/sftp/data && \ + chmod 755 /home/bobby/sftp/data + +# USER bobby: configure SSH for SFTP: allow password and pubkey authentication +RUN echo "Match User bobby" >> /etc/ssh/sshd_config && \ + echo " ChrootDirectory /home/bobby/sftp" >> /etc/ssh/sshd_config && \ + echo " ForceCommand internal-sftp" >> /etc/ssh/sshd_config && \ + echo " PasswordAuthentication no" >> /etc/ssh/sshd_config && \ + echo " PubkeyAuthentication yes" >> /etc/ssh/sshd_config && \ + echo " PermitTunnel no" >> /etc/ssh/sshd_config && \ + echo " AllowAgentForwarding no" >> /etc/ssh/sshd_config && \ + echo " AllowTcpForwarding no" >> /etc/ssh/sshd_config && \ + echo " X11Forwarding no" >> /etc/ssh/sshd_config + +# USER billy: set up user for SFTP with no shell login +RUN useradd -m -d /home/billy -s /usr/sbin/nologin billy && \ + mkdir -p /home/billy/.ssh && \ + chown billy:billy /home/billy/.ssh && \ + chmod 700 /home/billy/.ssh + +# USER billy: create a directory for SFTP that the user will have access to +RUN mkdir -p /home/billy/sftp/data && \ + chown root:root /home/billy /home/billy/sftp && \ + chmod 755 /home/billy /home/billy/sftp && \ + chown billy:billy /home/billy/sftp/data && \ + chmod 755 /home/billy/sftp/data + +# USER billy: certificated signed with CA key +COPY billy_rsa-cert.pub /home/billy/.ssh/billy_rsa-cert.pub + +RUN chown billy:billy /home/billy/.ssh/billy_rsa-cert.pub && \ + chmod 600 /home/billy/.ssh/billy_rsa-cert.pub + +# USER billy: configure SSH for SFTP with certificate authentication +RUN echo "Match User billy" >> /etc/ssh/sshd_config && \ + echo " ChrootDirectory /home/billy/sftp" >> /etc/ssh/sshd_config && \ + echo " ForceCommand internal-sftp" >> /etc/ssh/sshd_config && \ + echo " PasswordAuthentication no" >> /etc/ssh/sshd_config && \ + echo " PubkeyAuthentication yes" >> /etc/ssh/sshd_config && \ + echo " PermitTunnel no" >> /etc/ssh/sshd_config && \ + echo " AllowAgentForwarding no" >> /etc/ssh/sshd_config && \ + echo " AllowTcpForwarding no" >> /etc/ssh/sshd_config && \ + echo " X11Forwarding no" >> /etc/ssh/sshd_config + +EXPOSE 22 + +# run sshd on container start +CMD ["/usr/sbin/sshd", "-D", "-e"] diff --git a/tests/load/filesystem_sftp/bootstrap/SETUP.md b/tests/load/filesystem_sftp/bootstrap/SETUP.md new file mode 100644 index 0000000000..0620bbb075 --- /dev/null +++ b/tests/load/filesystem_sftp/bootstrap/SETUP.md @@ -0,0 +1,49 @@ +## Users: Authentication + +This guide covers the setup of different authentication methods for SSH users, including public/private key pairs, passphrase protection, and certificate-based authentication. + +### User foo: Public/Private Key Pair Without Passphrase + +Generate a key pair for `foo` without a passphrase: +```bash +# Generate the key pair +ssh-keygen -t rsa -b 4096 -C "foo@example.com" -f foo_rsa + +# Secure the private key +chmod 600 foo_rsa +``` + +### User bobby: Public/Private Key Pair With Passphrase + +Generate a key pair for `bobby` with a passphrase (passphrase=passphrase123): +```bash +# Generate the key pair with a passphrase +ssh-keygen -t rsa -b 4096 -C "bobby@example.com" -f bobby_rsa + +# Secure the private key +chmod 600 bobby_rsa +``` + +### Certificate Authority (CA) Setup + +Generate the Certificate Authority (CA) key pair: +```bash +# Generate a self-signed CA key pair +ssh-keygen -t rsa -b 4096 -f ca_rsa -N "" +``` + +### User billy: Public/Private Key Pair with CA-Signed Certificate + +Generate and sign a key pair for `billy` using the CA: +```bash +# Generate the user key pair for billy +ssh-keygen -t rsa -b 4096 -C "billy@example.com" -f billy_rsa + +# Sign billy's public key with the CA +ssh-keygen -s ca_rsa -I billy-cert -n billy billy_rsa.pub +``` + +### Important Files + +- **ca_rsa.pub**: The CA public key. This key is used by the server to verify certificates. +- **billy_rsa-cert.pub**: Billy’s signed certificate. This certificate is used by Billy to authenticate with the server. diff --git a/tests/load/filesystem_sftp/bootstrap/billy_rsa b/tests/load/filesystem_sftp/bootstrap/billy_rsa new file mode 100644 index 0000000000..ceafa496e0 --- /dev/null +++ b/tests/load/filesystem_sftp/bootstrap/billy_rsa @@ -0,0 +1,49 @@ +-----BEGIN OPENSSH PRIVATE KEY----- +b3BlbnNzaC1rZXktdjEAAAAABG5vbmUAAAAEbm9uZQAAAAAAAAABAAACFwAAAAdzc2gtcn +NhAAAAAwEAAQAAAgEAt6vSxFUsoMmftSc2sn7yd/GqN1QuD2DlCzdp+MNF2awKe4JXC+9v +OKDwYWk1DU1Z2r400hUFpsColXrO7kNJKdLNZw7tUcIwusXUWobh3zT0VCpompycJI1ylZ +HTX7cSwuSX43VRNmsj049sawTtwb+5yURsZuc8fLRX4x0EYjbqrP4TxEvumvXf2vjPQg+l +d4QCASTWNijN0aBRV+fnfCXlkkt+9PhyqUYFmLfd//JclEs5JEyOmsX68tUTx90XNKC4ef +0vYDnQsMTDujHOwjla9ZTVMvhXBg10ZUd+rmbWlwD9gCiIqL5YZg/hsJ9QEVYr+03hEq+q +nBAqRTwvFXaJHGUVNg7eCkE3w4RFSqU7FIwag9naHBQvbW0LLMAIpJqRPQ81J0LJtxXPsi +X+xt4Oah+jgWMAXtKkfzzZfu+BRDZ4F/TqRj7xgPrwsCgIFE6n3PpgnBRZb1IczQ0VFuGX +O2kvCpH9bJ5Rb8MEJyZ1z/GqiagBfDhUIXSzGLSxjGfNR6bmrrBL2J3PPviAgd1epjFhMj 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new file mode 100644 index 0000000000..147113c067 --- /dev/null +++ b/tests/load/filesystem_sftp/bootstrap/billy_rsa-cert.pub @@ -0,0 +1 @@ +ssh-rsa-cert-v01@openssh.com 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billy@example.com diff --git a/tests/load/filesystem_sftp/bootstrap/billy_rsa.pub b/tests/load/filesystem_sftp/bootstrap/billy_rsa.pub new file mode 100644 index 0000000000..8b74bdb00b --- /dev/null +++ b/tests/load/filesystem_sftp/bootstrap/billy_rsa.pub @@ -0,0 +1 @@ +ssh-rsa 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 billy@example.com diff --git a/tests/load/filesystem_sftp/bootstrap/bobby_rsa b/tests/load/filesystem_sftp/bootstrap/bobby_rsa new file mode 100644 index 0000000000..1ee49f5776 --- /dev/null +++ b/tests/load/filesystem_sftp/bootstrap/bobby_rsa @@ -0,0 +1,50 @@ +-----BEGIN OPENSSH PRIVATE KEY----- +b3BlbnNzaC1rZXktdjEAAAAACmFlczI1Ni1jdHIAAAAGYmNyeXB0AAAAGAAAABB98Gk5Ak +IB7cgFibVwRnadAAAAGAAAAAEAAAIXAAAAB3NzaC1yc2EAAAADAQABAAACAQD2sf6SyXup +5tWG/cqSTtORJJGM1teEnDpyupHbUP1LHXvq5nHJ0g82YX9v3wUJ3Nkd6ZMbh+bs37BCaQ +zR2oDWs3kIhsx/xesg03N6tdVMpXFw3tgSceSsXe31YS5rUVNGYIUVtcP3xFRQvLG41X0/ +5GEGS+yiicq+LfqyMnE6Np+/FGaCFzuW1nE8hrR4B/YoLKJs8e4kKOs8Jsj8mCCAXvAk+S +NDZLbVhx5pQcybUSNYqC4NExn5EEj4xK71vHa4oA3gtgeubKXVr04fk5C8maku/tr1P63M +UKtuvTtZBmNH+RVs9i9IwsF8VSMBSMyTMuOaa1Tsa3FIwnGZpZ1PcfTBG7CJPMu4drMBSw +5y0DiAY8mdGvdklz1esG0DldAZx5Blfvw6XU16588e3zQKNiApNwQHlvJJEb0Gb7rFw/Tt +HhCT8MtpBqIf+gRMaFhajbgkQI6CDABp6RgmpqcI6xDFwvkQ+JFveFNFy6zXVrd+oYgz16 +owDpEzfxTYp0mrhivpa0dkoKqgjIU8VMuQfegoxQiJUi+mW9IqrmzcGwAlRTzBcT+IJ9Jz +mfZxuZQshWIQe05nPMqCJqpSukg6tN+Fl0kVF8swz6pNjEIcAryIuHlqOxSyQz8pqxUPBS +PqAEcY0i+0VeMfweQ2TNHdtT3IYzOZrT79r/Ba6Jhy6wAAB1DPvTW0+gfDxLsGAokxLfHY +YtTqqu6siXxI1YPVS57m3HV3iL4PZ3yCcofMFjHT3R+QfSlJxmjg4cHLKCe2P4h10fMxSa +1T+H/NHJvh4ZXt2PfCGXKcXl65t/d5KnM1LSCWHxAXH+u8gOL8giRXljzzjjEl2xFchiYn +zf+pGaCBgfbd1232lePkeKHQSyR6gSOe5t15BwUVRoOKXIrFgpyf+a5i4sbOqBr464wcFd +cLrFcXMmtArNzWLrtBfyFgtyv2KqcdQ7PhUh4JZoNXKSI+28P8sj8xoe5PCCUnk9JlHsEe +j09mzPSqrfHm6JcEuuR/685hhTvlB3Wo0q0dc0AHIDNHQjL0WmKyDWGc321A+QquJdhkYb +v/BuDzrSuSA4tTrMfS84LNFvDtB0NtxfCz/doqiqTaMHre1DrA348Cvzrs53SQjiiT8PUC +SI5mO6u+XY5wyi80knGhJdOYZSwB/m8BuGZJlzRrR4hkbCBtrnwVd/R7jlIdobqJm5Y4+i +iocCGCAvEsKUz5RYZdJiCC85PhsUOtIdFeaCltrTNgmpAKO0GQdI4mYRUOrcXoaM6Y/eMi +zkbcDeitvGcT2YYkflmwxg/7G46sRbICgf8s+lcRix0P6grSvAINGdNBE3yz9YfeNKJh43 +BSQQxulk7ZM0juNqbMl/gGBikncsnx/aVusBzqo3jxZ0g2X2nn1rS0So13Fc54I1957YME 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+iJkJZ+UFhMkz+E2m25e/eHmeOmfxHCvPKqQAIzQLC/yR1wkp3hZIhk/6/ImJmGtjoS7/O3 +I3c998HSjQgP2OE6spr7J2YuLfUuz4QOFTs757pzgnfQNIrxioqmLb8dOGCfYhDeW5cEz8 +CE9piaoXXSFpQCsBwer+KFiyOdvNIeNDQBoruElS4KTO/qpYmDkOqzCywpUQCvmx6umqFd +5XaibupiP87chS68oyR3Fs/Ga+qsZ94CFi+6WYTT4GRug5flGtfZP5BeRN4O5AtICr/1pR +7tbw7lgwI1PN1G6jxMSrmstIjOA2vn9KxeRxpPLw+I5SjBZsyfjzjoCsZqTy9gvpnxf5Cu +Il/vgIZXNh3WC8Ypl7zvXsesiISd/7EiHPtncDy9dPk0XMVMfMUxTzWY7tle/xma8atRv7 +vf8w/XfP192OmpYf19+YwNjjUOksuT3WC50NbYAtYasIqJQtxC+2XgaCbf2bheZ01HBEO5 +qOqB+6o2KPz2cxjZIxRsyeX75VMETjPCQvYv9DIMRN4UP14VJEbLbVFDB9HOYpCirJrUC8 +NjUDdlTouyzx2F5Bw4mDnBOKRYuDtIwh7FtkDDCP7Fu9bIzeK+8Tpjn/WkIv600rKmrnZ9 ++a2Fy2LKIKJjyx+oISFiE52kE+rUVW/KKajHdxVZVbdOoHXzst5ZINFJ6uVaPDcyErsIoq +bbsRY8B3WwaU8Qa4AKUiqfTahZ22ducEyiDYs1apLZuE1HWKORXgWPMtdLPAE7+E/zo5ss +d7g+ddymwCBEV6q7p6Nw2f7HlTO++cvkB0EQDmrpWEcoRkSELTBQ6/SND90CX4yo2Rh6qq +Qi1l9MV2/ft4KKZsTqSlVV+po= +-----END OPENSSH PRIVATE KEY----- diff --git a/tests/load/filesystem_sftp/bootstrap/bobby_rsa.pub b/tests/load/filesystem_sftp/bootstrap/bobby_rsa.pub new file mode 100644 index 0000000000..1f9ef6f504 --- /dev/null +++ b/tests/load/filesystem_sftp/bootstrap/bobby_rsa.pub @@ -0,0 +1 @@ +ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAACAQD2sf6SyXup5tWG/cqSTtORJJGM1teEnDpyupHbUP1LHXvq5nHJ0g82YX9v3wUJ3Nkd6ZMbh+bs37BCaQzR2oDWs3kIhsx/xesg03N6tdVMpXFw3tgSceSsXe31YS5rUVNGYIUVtcP3xFRQvLG41X0/5GEGS+yiicq+LfqyMnE6Np+/FGaCFzuW1nE8hrR4B/YoLKJs8e4kKOs8Jsj8mCCAXvAk+SNDZLbVhx5pQcybUSNYqC4NExn5EEj4xK71vHa4oA3gtgeubKXVr04fk5C8maku/tr1P63MUKtuvTtZBmNH+RVs9i9IwsF8VSMBSMyTMuOaa1Tsa3FIwnGZpZ1PcfTBG7CJPMu4drMBSw5y0DiAY8mdGvdklz1esG0DldAZx5Blfvw6XU16588e3zQKNiApNwQHlvJJEb0Gb7rFw/TtHhCT8MtpBqIf+gRMaFhajbgkQI6CDABp6RgmpqcI6xDFwvkQ+JFveFNFy6zXVrd+oYgz16owDpEzfxTYp0mrhivpa0dkoKqgjIU8VMuQfegoxQiJUi+mW9IqrmzcGwAlRTzBcT+IJ9JzmfZxuZQshWIQe05nPMqCJqpSukg6tN+Fl0kVF8swz6pNjEIcAryIuHlqOxSyQz8pqxUPBSPqAEcY0i+0VeMfweQ2TNHdtT3IYzOZrT79r/Ba6Jhy6w== bobby@example.com diff --git a/tests/load/filesystem_sftp/bootstrap/ca_rsa b/tests/load/filesystem_sftp/bootstrap/ca_rsa new file mode 100644 index 0000000000..6616331a46 --- /dev/null +++ b/tests/load/filesystem_sftp/bootstrap/ca_rsa @@ -0,0 +1,49 @@ +-----BEGIN OPENSSH PRIVATE KEY----- +b3BlbnNzaC1rZXktdjEAAAAABG5vbmUAAAAEbm9uZQAAAAAAAAABAAACFwAAAAdzc2gtcn +NhAAAAAwEAAQAAAgEA57aSrYecHkvXEPQYZ3ZXyld2bJ3CeuD+rmEtbGFYuJ4NjobBTle1 +fBDULcemeIs2P7rsybixIMVgAhbVRw9Dkm+8yXJXaUib64m5AkPtDrwPZh6ipxAn6EuuRc +0bUdw8JGrLk6j4Pb2OClHAI9SEvvhYgdE+2bf2xMvpI63g4elBb7Z0SzKWdPnBbyCDmdQT +cC5Fz/8sYzrx/MAhqwZ1kFLr2OahsajpjuJJnL7K7rL+JN2OiBj9eh4Of2IX15FMFV8LHd +LsOxn9E3oOQQxNfkXyT/qcyd2SwqmJHEM6AyQuWkWKowtjrLX410VJMUFw8w9q7oSW+Wr6 +UkTZXg74W+jfRjOBt6CO3I+glEdpeJAV+odaY8Sc3vv+sSRNo+O0Yzskq1voxpw5xQIdFZ +IzQmN5T5M8bNFVoM0dZ61MiC8gYWAj3w/kUF7hjb4PW2MTZSxP11J7sR9vdV86bos9r1ka +d6QEiowkJBRYTGPOZZEj0X/1JjrETahFXjV8696Reub5vPzVcizMKQ93iJNMNf/QX4Xrb3 +FVxmA0HyM6iXM/oLWCQ4A4hdnzONS4e6hURw9BUF7094/dea7Agaw9V97YDXyBBUx80kfw 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b/tests/load/filesystem_sftp/bootstrap/ca_rsa.pub @@ -0,0 +1 @@ +ssh-rsa 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 julian@Julians-Laptop.local diff --git a/tests/load/filesystem_sftp/bootstrap/foo_rsa b/tests/load/filesystem_sftp/bootstrap/foo_rsa new file mode 100644 index 0000000000..9ccc98eccb --- /dev/null +++ b/tests/load/filesystem_sftp/bootstrap/foo_rsa @@ -0,0 +1,49 @@ +-----BEGIN OPENSSH PRIVATE KEY----- +b3BlbnNzaC1rZXktdjEAAAAABG5vbmUAAAAEbm9uZQAAAAAAAAABAAACFwAAAAdzc2gtcn +NhAAAAAwEAAQAAAgEAxz/mzyh9N8KvqW5UVMuKo7reYYyUxU2gGYv/StDs+H3j5UQbrhrA +bwV+R0jgfK4pEAQw2EDDWz5r8pc2LOAq9C+mpha+R6oXZuLwZFCnJOro+peFDEkGVyNReq +Sw2JvKFenRiP2IWqQRtv/zfbcC65TrX4C/DNFuGF/uPjZJH8v4rGN+MtAcrQZDi7V4pvAe +FejIeVumlU5rZ6XN1hOID3BrmybiL8cqcyGTVolYbnKdyaPRmjmXgGBK0vMecJhvbWzVlW +7iLnXnaULXFiaKxXlvizxwMampCNMbD6j0+b6DolYDjEj0BRuEWGr2Ox3opqKhWww1eAtj +spY7PpGAoeCq1Mfvn178Bv0sRZykJT4gM4ELOEhmItl6QSFn5kLg8vDRg0j8CHe5scVSVG +3egjYVcNxL/05okSiWIwNLd7Ma5Qyi06BS6JUqqFEDhANIRgMZuztMs1e6IKtiiREeOhf4 +UQLcMsH8r0AYjJPXFTpXvWLamsiAhsL1tGa8XyyxqdJrqsIktrqS4QnCx7uDAPon0wHoRK +YzCwqNmn1Iwf3uepZ7Z0evh3IRRfSI3LaRxfhM0I63z8uPyzx55C3PAkJpmRiPGzFWd7Nk +1P6OgRsjhlSfEK8OwOFXfZLYoRBEYj8yUXRhJu3CdcJViCIJYuhtY38fST3875aTx1KJOq +sAAAdIRs3hz0bN4c8AAAAHc3NoLXJzYQAAAgEAxz/mzyh9N8KvqW5UVMuKo7reYYyUxU2g 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+IvVXCCjRmXMI/m+4+vIXs6y9SZTxmKNm47Wpfd8fumYzfSBL54o5AnKvUYYGiwwuAlKMz1 +rgvRP4ZrzQJZitCh86/jCgRwCDMniu5GGUDXxhvNPoQqeO1ezLV0v9K1SZvd5U0uXnKWop +SLtx2K4s+yyjpxnWyIZmAwjh5paHjLKegXTYskg4n/3fBW9nEJVrFflLVWdzp+xWsz+u3P +sQ3n0efAZPjOWbAAABAQDQitS0R2KAOci5f8LiGy2KtJipcS2jvzBCZX9DqMInfpU/cY92 +bAn8NgY9G8jChloAu2smHo3Fx6LL4ZdFNTjfIBbFyZsuSPhy9czpxWF733mteyuMW6n2jv +e04YoWRp4uh9YxbtpFx24x0RIu64NM69/N2E94eFEJUhpG7NPPgoL41jEqJVJCPVJsQnFT +RCReMb5D9zWYlKVK4xnuB/NqgD+j5iLMHK5hS6Wt0b0olNoOmTlj7IUK62sQyPelU3I3Sy +hmZquXqCILq/rMbJBo5NjhoodvzSFYw+jDvLq4rK+XGL/DgGV080oemXTAP73Er8tBdJq8 +iv8eCatJLFgxAAAAD2Zvb0BleGFtcGxlLmNvbQECAw== +-----END OPENSSH PRIVATE KEY----- diff --git a/tests/load/filesystem_sftp/bootstrap/foo_rsa.pub b/tests/load/filesystem_sftp/bootstrap/foo_rsa.pub new file mode 100644 index 0000000000..212cbb4d7e --- /dev/null +++ b/tests/load/filesystem_sftp/bootstrap/foo_rsa.pub @@ -0,0 +1 @@ +ssh-rsa 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 foo@example.com diff --git a/tests/load/filesystem_sftp/docker-compose.yml b/tests/load/filesystem_sftp/docker-compose.yml new file mode 100644 index 0000000000..a714219146 --- /dev/null +++ b/tests/load/filesystem_sftp/docker-compose.yml @@ -0,0 +1,21 @@ +version: '3.8' + +services: + + sftpserver: + build: + context: bootstrap + dockerfile: Dockerfile + image: sftpserver:latest + networks: + - sftpserver + ports: + - "2222:22" + volumes: + - ../../common/storages/samples:/home/foo/sftp/data/standard_source/samples + - ../../common/storages/samples:/home/bobby/sftp/data/standard_source/samples + - ../../common/storages/samples:/home/billy/sftp/data/standard_source/samples + +networks: + sftpserver: + name: sftpserver diff --git a/tests/load/filesystem_sftp/test_filesystem_sftp.py b/tests/load/filesystem_sftp/test_filesystem_sftp.py new file mode 100644 index 0000000000..32869ee56e --- /dev/null +++ b/tests/load/filesystem_sftp/test_filesystem_sftp.py @@ -0,0 +1,174 @@ +import os +import pytest +import fsspec +import dlt + +from dlt.common.json import json +from dlt.common.configuration.inject import with_config +from dlt.common.storages import FilesystemConfiguration, fsspec_from_config +from dlt.destinations.impl.filesystem.filesystem import FilesystemClient + +from tests.load.utils import ALL_FILESYSTEM_DRIVERS + +if "sftp" not in ALL_FILESYSTEM_DRIVERS: + pytest.skip("sftp filesystem driver not configured", allow_module_level=True) + + +@with_config(spec=FilesystemConfiguration, sections=("sources", "filesystem")) +def get_config(config: FilesystemConfiguration = None) -> FilesystemConfiguration: + return config + + +def get_key_path(user: str = "foo") -> str: + current_dir = os.path.dirname(os.path.abspath(__file__)) + return os.path.join(current_dir, f"bootstrap/{user}_rsa") + + +def files_are_equal(file1_path, file2_path): + try: + with open(file1_path, "r", encoding="utf-8") as f1, open( + file2_path, "r", encoding="utf-8" + ) as f2: + return f1.read() == f2.read() + except FileNotFoundError: + return False + + +def is_ssh_agent_ready(): + try: + # Never skip tests when running in CI + if os.getenv("CI"): + return True + + # Check if SSH agent is running + ssh_agent_pid = os.getenv("SSH_AGENT_PID") + if not ssh_agent_pid: + return False + + # Check if the key is present and matches + id_rsa_pub_path = os.path.expanduser("~/.ssh/id_rsa") + bobby_rsa_pub_path = os.path.expanduser(get_key_path("bobby")) + if not os.path.isfile(id_rsa_pub_path) or not os.path.isfile(bobby_rsa_pub_path): + return False + + return files_are_equal(id_rsa_pub_path, bobby_rsa_pub_path) + except Exception: + return False + + +@pytest.fixture(scope="module") +def sftp_filesystem(): + fs = fsspec.filesystem( + "sftp", host="localhost", port=2222, username="foo", key_filename=get_key_path() + ) + yield fs + + +def test_filesystem_sftp_server(sftp_filesystem): + test_file = "/data/countries.json" + input_data = { + "countries": [ + {"name": "United States", "code": "US"}, + {"name": "Canada", "code": "CA"}, + {"name": "Mexico", "code": "MX"}, + ] + } + + fs = sftp_filesystem + try: + with fs.open(test_file, "w") as f: + f.write(json.dumps(input_data)) + + files = fs.ls("/data") + assert test_file in files + + with fs.open(test_file, "r") as f: + data = json.load(f) + assert data == input_data + + info = fs.info(test_file) + assert "mtime" in info + + finally: + fs.rm(test_file) + + +def test_filesystem_sftp_pipeline(sftp_filesystem): + import posixpath + import pyarrow.parquet as pq + + os.environ["DESTINATION__FILESYSTEM__BUCKET_URL"] = "sftp://localhost/data" + os.environ["DESTINATION__FILESYSTEM__CREDENTIALS__SFTP_PORT"] = "2222" + os.environ["DESTINATION__FILESYSTEM__CREDENTIALS__SFTP_USERNAME"] = "foo" + os.environ["DESTINATION__FILESYSTEM__CREDENTIALS__SFTP_PASSWORD"] = "pass" + + @dlt.resource() + def states(): + yield [{"id": 1, "name": "DE"}, {"id": 2, "name": "AK"}, {"id": 3, "name": "CA"}] + + pipeline = dlt.pipeline(destination="filesystem", dataset_name="test") + pipeline.run([states], loader_file_format="parquet") + + client: FilesystemClient = pipeline.destination_client() # type: ignore[assignment] + data_glob = posixpath.join(client.dataset_path, "states/*") + data_files = client.fs_client.glob(data_glob) + assert len(data_files) > 0 + + fs = sftp_filesystem + with fs.open(data_files[0], "rb") as f: + rows = pq.read_table(f).to_pylist() + result_states = [r["name"] for r in rows] + + expected_states = ["DE", "AK", "CA"] + assert sorted(result_states) == sorted(expected_states) + + +def run_sftp_auth(user, password=None, key=None, passphrase=None): + env_vars = { + "SOURCES__FILESYSTEM__BUCKET_URL": "sftp://localhost", + "SOURCES__FILESYSTEM__CREDENTIALS__SFTP_PORT": "2222", + "SOURCES__FILESYSTEM__CREDENTIALS__SFTP_USERNAME": user, + } + + if password: + env_vars["SOURCES__FILESYSTEM__CREDENTIALS__SFTP_PASSWORD"] = password + if key: + env_vars["SOURCES__FILESYSTEM__CREDENTIALS__SFTP_KEY_FILENAME"] = get_key_path(user) + if passphrase: + env_vars["SOURCES__FILESYSTEM__CREDENTIALS__SFTP_KEY_PASSPHRASE"] = passphrase + + os.environ.update(env_vars) + + config = get_config() + fs, _ = fsspec_from_config(config) + assert len(fs.ls("/data/standard_source/samples")) > 0 + + +def test_filesystem_sftp_auth_useranme_password(): + run_sftp_auth("foo", "pass") + + +def test_filesystem_sftp_auth_private_key(): + run_sftp_auth("foo", key=get_key_path()) + + +def test_filesystem_sftp_auth_private_key_protected(): + run_sftp_auth("bobby", key=get_key_path("bobby"), passphrase="passphrase123") + + +# Test requires - ssh_agent with user's bobby key loaded. The commands and file names required are: +# eval "$(ssh-agent -s)" +# cp /path/to/tests/load/filesystem_sftp/bobby_rsa* ~/.ssh/id_rsa +# cp /path/to/tests/load/filesystem_sftp/bobby_rsa.pub ~/.ssh/id_rsa.pub +# ssh-add ~/.ssh/id_rsa +@pytest.mark.skipif( + not is_ssh_agent_ready(), + reason="SSH agent is not running or bobby's private key isn't stored in ~/.ssh/id_rsa", +) +def test_filesystem_sftp_auth_private_ssh_agent(): + run_sftp_auth("bobby", passphrase="passphrase123") + + +def test_filesystem_sftp_auth_ca_signed_pub_key(): + # billy_rsa-cert.pub is automatically loaded too + run_sftp_auth("billy", key=get_key_path("billy")) diff --git a/tests/load/lancedb/__init__.py b/tests/load/lancedb/__init__.py index fb4bf0b35d..69eb3fb011 100644 --- a/tests/load/lancedb/__init__.py +++ b/tests/load/lancedb/__init__.py @@ -1,3 +1,5 @@ +import pytest from tests.utils import skip_if_not_active skip_if_not_active("lancedb") +pytest.importorskip("lancedb") diff --git a/tests/load/lancedb/test_pipeline.py b/tests/load/lancedb/test_pipeline.py index e817a2f6c8..3dc2a999d4 100644 --- a/tests/load/lancedb/test_pipeline.py +++ b/tests/load/lancedb/test_pipeline.py @@ -1,6 +1,10 @@ -from typing import Iterator, Generator, Any, List +import multiprocessing +from typing import Iterator, Generator, Any, List, Mapping import pytest +import lancedb # type: ignore +from lancedb import DBConnection +from lancedb.embeddings import EmbeddingFunctionRegistry # type: ignore import dlt from dlt.common import json @@ -15,13 +19,12 @@ from tests.load.utils import sequence_generator, drop_active_pipeline_data from tests.pipeline.utils import assert_load_info - # Mark all tests as essential, do not remove. pytestmark = pytest.mark.essential @pytest.fixture(autouse=True) -def drop_lancedb_data() -> Iterator[None]: +def drop_lancedb_data() -> Iterator[Any]: yield drop_active_pipeline_data() @@ -433,3 +436,126 @@ def test_empty_dataset_allowed() -> None: assert client.dataset_name is None assert client.sentinel_table == "dltSentinelTable" assert_table(pipe, "content", expected_items_count=3) + + +search_data = [ + {"text": "Frodo was a happy puppy"}, + {"text": "There are several kittens playing"}, +] + + +def test_fts_query() -> None: + @dlt.resource + def search_data_resource() -> Generator[Mapping[str, object], Any, None]: + yield from search_data + + pipeline = dlt.pipeline( + pipeline_name="test_fts_query", + destination="lancedb", + dataset_name=f"test_pipeline_append{uniq_id()}", + ) + info = pipeline.run( + search_data_resource(), + ) + assert_load_info(info) + + client: LanceDBClient + with pipeline.destination_client() as client: # type: ignore[assignment] + db_client: DBConnection = client.db_client + + table_name = client.make_qualified_table_name("search_data_resource") + tbl = db_client[table_name] + tbl.checkout_latest() + + tbl.create_fts_index("text") + results = tbl.search("kittens", query_type="fts").select(["text"]).to_list() + assert results[0]["text"] == "There are several kittens playing" + + +def test_semantic_query() -> None: + @dlt.resource + def search_data_resource() -> Generator[Mapping[str, object], Any, None]: + yield from search_data + + lancedb_adapter( + search_data_resource, + embed=["text"], + ) + + pipeline = dlt.pipeline( + pipeline_name="test_fts_query", + destination="lancedb", + dataset_name=f"test_pipeline_append{uniq_id()}", + ) + info = pipeline.run( + search_data_resource(), + ) + assert_load_info(info) + + client: LanceDBClient + with pipeline.destination_client() as client: # type: ignore[assignment] + db_client: DBConnection = client.db_client + + table_name = client.make_qualified_table_name("search_data_resource") + tbl = db_client[table_name] + tbl.checkout_latest() + + results = ( + tbl.search("puppy", query_type="vector", ordering_field_name="_distance") + .select(["text"]) + .to_list() + ) + assert results[0]["text"] == "Frodo was a happy puppy" + + +def test_semantic_query_custom_embedding_functions_registered() -> None: + """Test the LanceDB registry registered custom embedding functions defined in models, if any. + See: https://github.com/dlt-hub/dlt/issues/1765""" + + @dlt.resource + def search_data_resource() -> Generator[Mapping[str, object], Any, None]: + yield from search_data + + lancedb_adapter( + search_data_resource, + embed=["text"], + ) + + pipeline = dlt.pipeline( + pipeline_name="test_fts_query", + destination="lancedb", + dataset_name=f"test_pipeline_append{uniq_id()}", + ) + info = pipeline.run( + search_data_resource(), + ) + assert_load_info(info) + + client: LanceDBClient + with pipeline.destination_client() as client: # type: ignore[assignment] + db_client_uri = client.db_client.uri + table_name = client.make_qualified_table_name("search_data_resource") + + # A new python process doesn't seem to correctly deserialize the custom embedding + # functions into global __REGISTRY__. + # We make sure to reset it as well to make sure no globals are propagated to the spawned process. + EmbeddingFunctionRegistry().reset() + with multiprocessing.get_context("spawn").Pool(1) as pool: + results = pool.apply(run_lance_search_in_separate_process, (db_client_uri, table_name)) + + assert results[0]["text"] == "Frodo was a happy puppy" + + +def run_lance_search_in_separate_process(db_client_uri: str, table_name: str) -> Any: + import lancedb + + # Must read into __REGISTRY__ here. + db = lancedb.connect(db_client_uri) + tbl = db[table_name] + tbl.checkout_latest() + + return ( + tbl.search("puppy", query_type="vector", ordering_field_name="_distance") + .select(["text"]) + .to_list() + ) diff --git a/tests/load/lancedb/utils.py b/tests/load/lancedb/utils.py index dc3ea5304b..7431e895b7 100644 --- a/tests/load/lancedb/utils.py +++ b/tests/load/lancedb/utils.py @@ -52,7 +52,7 @@ def assert_table( "_dlt_id", "_dlt_load_id", dlt.config.get("destination.lancedb.credentials.id_field_name", str) or "id__", - dlt.config.get("destination.lancedb.credentials.vector_field_name", str) or "vector__", + dlt.config.get("destination.lancedb.credentials.vector_field_name", str) or "vector", ] objects_without_dlt_or_special_keys = [ {k: v for k, v in record.items() if k not in drop_keys} for record in records diff --git a/tests/load/pipeline/test_arrow_loading.py b/tests/load/pipeline/test_arrow_loading.py index 6d78968996..f72aaec1d8 100644 --- a/tests/load/pipeline/test_arrow_loading.py +++ b/tests/load/pipeline/test_arrow_loading.py @@ -1,4 +1,4 @@ -from datetime import datetime # noqa: I251 +from datetime import datetime, timedelta, time as dt_time, date # noqa: I251 import os import pytest @@ -9,7 +9,12 @@ import dlt from dlt.common import pendulum -from dlt.common.time import reduce_pendulum_datetime_precision +from dlt.common.time import ( + reduce_pendulum_datetime_precision, + ensure_pendulum_time, + ensure_pendulum_datetime, + ensure_pendulum_date, +) from dlt.common.utils import uniq_id from tests.load.utils import destinations_configs, DestinationTestConfiguration @@ -41,7 +46,7 @@ def test_load_arrow_item( # os.environ["DATA_WRITER__DISABLE_COMPRESSION"] = "True" os.environ["NORMALIZE__PARQUET_NORMALIZER__ADD_DLT_LOAD_ID"] = "True" os.environ["NORMALIZE__PARQUET_NORMALIZER__ADD_DLT_ID"] = "True" - include_time = destination_config.destination not in ( + include_time = destination_config.destination_type not in ( "athena", "redshift", "databricks", @@ -49,15 +54,21 @@ def test_load_arrow_item( "clickhouse", ) # athena/redshift can't load TIME columns include_binary = not ( - destination_config.destination in ("redshift", "databricks") + destination_config.destination_type in ("redshift", "databricks") and destination_config.file_format == "jsonl" ) - include_decimal = not ( - destination_config.destination == "databricks" and destination_config.file_format == "jsonl" - ) + include_decimal = True + + if ( + destination_config.destination_type == "databricks" + and destination_config.file_format == "jsonl" + ) or (destination_config.destination_name == "sqlalchemy_sqlite"): + include_decimal = False + include_date = not ( - destination_config.destination == "databricks" and destination_config.file_format == "jsonl" + destination_config.destination_type == "databricks" + and destination_config.file_format == "jsonl" ) item, records, _ = arrow_table_all_data_types( @@ -76,11 +87,13 @@ def some_data(): yield item # use csv for postgres to get native arrow processing - file_format = ( - destination_config.file_format if destination_config.destination != "postgres" else "csv" + destination_config.file_format = ( + destination_config.file_format + if destination_config.destination_type != "postgres" + else "csv" ) - load_info = pipeline.run(some_data(), loader_file_format=file_format) + load_info = pipeline.run(some_data(), **destination_config.run_kwargs) assert_load_info(load_info) # assert the table types some_table_columns = pipeline.default_schema.get_table("some_data")["columns"] @@ -107,13 +120,13 @@ def some_data(): if isinstance(row[i], memoryview): row[i] = row[i].tobytes() - if destination_config.destination == "redshift": + if destination_config.destination_type == "redshift": # Redshift needs hex string for record in records: if "binary" in record: record["binary"] = record["binary"].hex() - if destination_config.destination == "clickhouse": + if destination_config.destination_type == "clickhouse": for record in records: # Clickhouse needs base64 string for jsonl if "binary" in record and destination_config.file_format == "jsonl": @@ -121,23 +134,29 @@ def some_data(): if "binary" in record and destination_config.file_format == "parquet": record["binary"] = record["binary"].decode("ascii") - for row in rows: - for i in range(len(row)): - if isinstance(row[i], datetime): - row[i] = pendulum.instance(row[i]) + expected = sorted([list(r.values()) for r in records]) + first_record = list(records[0].values()) + for row, expected_row in zip(rows, expected): + for i in range(len(expected_row)): + if isinstance(expected_row[i], datetime): + row[i] = ensure_pendulum_datetime(row[i]) # clickhouse produces rounding errors on double with jsonl, so we round the result coming from there - if ( - destination_config.destination == "clickhouse" + elif ( + destination_config.destination_type == "clickhouse" and destination_config.file_format == "jsonl" and isinstance(row[i], float) ): row[i] = round(row[i], 4) - - expected = sorted([list(r.values()) for r in records]) + elif isinstance(first_record[i], dt_time): + # Some drivers (mysqlclient) return TIME columns as timedelta as seconds since midnight + # sqlite returns iso strings + row[i] = ensure_pendulum_time(row[i]) + elif isinstance(expected_row[i], date): + row[i] = ensure_pendulum_date(row[i]) for row in expected: for i in range(len(row)): - if isinstance(row[i], datetime): + if isinstance(row[i], (datetime, dt_time)): row[i] = reduce_pendulum_datetime_precision( row[i], pipeline.destination.capabilities().timestamp_precision ) @@ -234,6 +253,14 @@ def test_load_arrow_with_not_null_columns( item_type: TestDataItemFormat, destination_config: DestinationTestConfiguration ) -> None: """Resource schema contains non-nullable columns. Arrow schema should be written accordingly""" + if ( + destination_config.destination_type in ("databricks", "redshift") + and destination_config.file_format == "jsonl" + ): + pytest.skip( + "databricks + redshift / json cannot load most of the types so we skip this test" + ) + item, records, _ = arrow_table_all_data_types(item_type, include_json=False, include_time=False) @dlt.resource(primary_key="string", columns=[{"name": "int", "nullable": False}]) @@ -242,7 +269,7 @@ def some_data(): pipeline = destination_config.setup_pipeline("arrow_" + uniq_id()) - pipeline.extract(some_data()) + pipeline.extract(some_data(), table_format=destination_config.table_format) norm_storage = pipeline._get_normalize_storage() extract_files = [ @@ -258,7 +285,7 @@ def some_data(): assert result_tbl.schema.field("int").nullable is False assert result_tbl.schema.field("int").type == pa.int64() - pipeline.normalize() - # Load is succesful + pipeline.normalize(loader_file_format=destination_config.file_format) + # Load is successful info = pipeline.load() assert_load_info(info) diff --git a/tests/load/pipeline/test_athena.py b/tests/load/pipeline/test_athena.py index 3197a19d14..3f63e178ea 100644 --- a/tests/load/pipeline/test_athena.py +++ b/tests/load/pipeline/test_athena.py @@ -4,7 +4,9 @@ import dlt, os from dlt.common import pendulum +from dlt.common.destination.exceptions import UnsupportedDataType from dlt.common.utils import uniq_id +from dlt.pipeline.exceptions import PipelineStepFailed from tests.cases import table_update_and_row, assert_all_data_types_row from tests.pipeline.utils import assert_load_info, load_table_counts from tests.pipeline.utils import load_table_counts @@ -40,7 +42,7 @@ def items(): "sub_items": [{"id": 101, "name": "sub item 101"}, {"id": 101, "name": "sub item 102"}], } - pipeline.run(items, loader_file_format=destination_config.file_format) + pipeline.run(items, **destination_config.run_kwargs) # see if we have athena tables with items table_counts = load_table_counts( @@ -71,7 +73,7 @@ def items2(): ], } - pipeline.run(items2) + pipeline.run(items2, **destination_config.run_kwargs) table_counts = load_table_counts( pipeline, *[t["name"] for t in pipeline.default_schema._schema_tables.values()] ) @@ -93,7 +95,7 @@ def test_athena_all_datatypes_and_timestamps( # TIME is not supported column_schemas, data_types = table_update_and_row(exclude_types=["time"]) - # apply the exact columns definitions so we process complex and wei types correctly! + # apply the exact columns definitions so we process json and wei types correctly! @dlt.resource(table_name="data_types", write_disposition="append", columns=column_schemas) def my_resource() -> Iterator[Any]: nonlocal data_types @@ -103,7 +105,7 @@ def my_resource() -> Iterator[Any]: def my_source() -> Any: return my_resource - info = pipeline.run(my_source()) + info = pipeline.run(my_source(), **destination_config.run_kwargs) assert_load_info(info) with pipeline.sql_client() as sql_client: @@ -113,7 +115,7 @@ def my_source() -> Any: # content must equal assert_all_data_types_row( db_row[:-2], - parse_complex_strings=True, + parse_json_strings=True, timestamp_precision=sql_client.capabilities.timestamp_precision, schema=column_schemas, ) @@ -180,7 +182,7 @@ def test_athena_blocks_time_column(destination_config: DestinationTestConfigurat column_schemas, data_types = table_update_and_row() - # apply the exact columns definitions so we process complex and wei types correctly! + # apply the exact columns definitions so we process json and wei types correctly! @dlt.resource(table_name="data_types", write_disposition="append", columns=column_schemas) def my_resource() -> Iterator[Any]: nonlocal data_types @@ -190,14 +192,10 @@ def my_resource() -> Iterator[Any]: def my_source() -> Any: return my_resource - info = pipeline.run(my_source()) - - assert info.has_failed_jobs - - assert ( - "Athena cannot load TIME columns from parquet tables" - in info.load_packages[0].jobs["failed_jobs"][0].failed_message - ) + with pytest.raises(PipelineStepFailed) as pip_ex: + pipeline.run(my_source(), **destination_config.run_kwargs) + assert isinstance(pip_ex.value.__cause__, UnsupportedDataType) + assert pip_ex.value.__cause__.data_type == "time" @pytest.mark.parametrize( @@ -223,10 +221,10 @@ def test_athena_file_layouts(destination_config: DestinationTestConfiguration, l FILE_LAYOUT_TABLE_NOT_FIRST, # table not the first variable ]: with pytest.raises(CantExtractTablePrefix): - pipeline.run(resources) + pipeline.run(resources, **destination_config.run_kwargs) return - info = pipeline.run(resources) + info = pipeline.run(resources, **destination_config.run_kwargs) assert_load_info(info) table_counts = load_table_counts( @@ -237,11 +235,11 @@ def test_athena_file_layouts(destination_config: DestinationTestConfiguration, l @pytest.mark.parametrize( "destination_config", - destinations_configs(default_sql_configs=True, subset=["athena"], force_iceberg=True), + destinations_configs(default_sql_configs=True, subset=["athena"], with_table_format="iceberg"), ids=lambda x: x.name, ) def test_athena_partitioned_iceberg_table(destination_config: DestinationTestConfiguration): - """Load an iceberg table with partition hints and verifiy partitions are created correctly.""" + """Load an iceberg table with partition hints and verify partitions are created correctly.""" pipeline = destination_config.setup_pipeline("athena_" + uniq_id(), dev_mode=True) data_items = [ @@ -269,7 +267,7 @@ def partitioned_table(): ], ) - info = pipeline.run(partitioned_table) + info = pipeline.run(partitioned_table, **destination_config.run_kwargs) assert_load_info(info) # Get partitions from metadata diff --git a/tests/load/pipeline/test_bigquery.py b/tests/load/pipeline/test_bigquery.py index fd0a55e273..9d2a4abf49 100644 --- a/tests/load/pipeline/test_bigquery.py +++ b/tests/load/pipeline/test_bigquery.py @@ -1,10 +1,15 @@ +from typing import Any, Dict, Iterator +from git import List import pytest import io import dlt -from dlt.common import Decimal, json +from dlt.common import Decimal, json, pendulum from dlt.common.typing import TLoaderFileFormat +from dlt.common.utils import uniq_id +from dlt.destinations.adapters import bigquery_adapter +from dlt.extract.resource import DltResource from tests.pipeline.utils import assert_load_info from tests.load.utils import destinations_configs, DestinationTestConfiguration @@ -33,7 +38,7 @@ def test_bigquery_numeric_types(destination_config: DestinationTestConfiguration }, ] - info = pipeline.run(iter(data), table_name="big_numeric", columns=columns) # type: ignore[arg-type] + info = pipeline.run(iter(data), table_name="big_numeric", columns=columns, **destination_config.run_kwargs) # type: ignore[arg-type] assert_load_info(info) with pipeline.sql_client() as client: @@ -145,3 +150,213 @@ def load_cve(stage: int): field = field.fields[0] # it looks like BigQuery can evolve structs and the field is added nested_field = next(f for f in field.fields if f.name == "refsource") + + +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True, subset=["bigquery"]), + ids=lambda x: x.name, +) +def test_adapter_additional_table_hints_table_expiration( + destination_config: DestinationTestConfiguration, +) -> None: + import google + + @dlt.resource(columns=[{"name": "col1", "data_type": "text"}]) + def no_hints() -> Iterator[Dict[str, str]]: + yield from [{"col1": str(i)} for i in range(10)] + + hints = bigquery_adapter( + no_hints.with_name(new_name="hints"), table_expiration_datetime="2030-01-01" + ) + + @dlt.source(max_table_nesting=0) + def sources() -> List[DltResource]: + return [no_hints, hints] + + pipeline = destination_config.setup_pipeline( + f"bigquery_{uniq_id()}", + dev_mode=True, + ) + + pipeline.run(sources()) + + with pipeline.sql_client() as c: + nc: google.cloud.bigquery.client.Client = c.native_connection + + fqtn_no_hints = c.make_qualified_table_name("no_hints", escape=False) + fqtn_hints = c.make_qualified_table_name("hints", escape=False) + + no_hints_table = nc.get_table(fqtn_no_hints) + hints_table = nc.get_table(fqtn_hints) + + assert not no_hints_table.expires + assert hints_table.expires == pendulum.datetime(2030, 1, 1, 0) + + +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True, subset=["bigquery"]), + ids=lambda x: x.name, +) +def test_adapter_merge_behaviour( + destination_config: DestinationTestConfiguration, +) -> None: + import google + from google.cloud.bigquery import Table + + @dlt.resource( + columns=[ + {"name": "col1", "data_type": "text"}, + {"name": "col2", "data_type": "bigint"}, + {"name": "col3", "data_type": "double"}, + ] + ) + def hints() -> Iterator[Dict[str, Any]]: + yield from [{"col1": str(i), "col2": i, "col3": float(i)} for i in range(10)] + + bigquery_adapter(hints, table_expiration_datetime="2030-01-01", cluster=["col1"]) + bigquery_adapter( + hints, + table_description="A small table somewhere in the cosmos...", + partition="col2", + ) + + pipeline = destination_config.setup_pipeline( + f"bigquery_{uniq_id()}", + dev_mode=True, + ) + + pipeline.run(hints) + + with pipeline.sql_client() as c: + nc: google.cloud.bigquery.client.Client = c.native_connection + + table_fqtn = c.make_qualified_table_name("hints", escape=False) + + table: Table = nc.get_table(table_fqtn) + + table_cluster_fields = [] if table.clustering_fields is None else table.clustering_fields + + # Test merging behaviour. + assert table.expires == pendulum.datetime(2030, 1, 1, 0) + assert ["col1"] == table_cluster_fields, "`hints` table IS NOT clustered by `col1`." + assert table.description == "A small table somewhere in the cosmos..." + + if not table.range_partitioning: + raise ValueError("`hints` table IS NOT clustered on a column.") + else: + assert ( + table.range_partitioning.field == "col2" + ), "`hints` table IS NOT clustered on column `col2`." + + +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True, subset=["bigquery"]), + ids=lambda x: x.name, +) +def test_adapter_autodetect_schema_with_hints( + destination_config: DestinationTestConfiguration, +) -> None: + import google + from google.cloud.bigquery import Table + + @dlt.resource( + columns=[ + {"name": "col1", "data_type": "text"}, + {"name": "col2", "data_type": "bigint"}, + {"name": "col3", "data_type": "double"}, + ] + ) + def general_types() -> Iterator[Dict[str, Any]]: + yield from [{"col1": str(i), "col2": i, "col3": float(i)} for i in range(10)] + + @dlt.resource( + columns=[ + {"name": "my_time_column", "data_type": "timestamp"}, + ] + ) + def partition_time() -> Iterator[Dict[str, Any]]: + for i in range(10): + yield { + "my_time_column": pendulum.from_timestamp(1700784000 + i * 50_000), + } + + @dlt.resource( + columns=[ + {"name": "my_date_column", "data_type": "date"}, + ] + ) + def partition_date() -> Iterator[Dict[str, Any]]: + for i in range(10): + yield { + "my_date_column": pendulum.from_timestamp(1700784000 + i * 50_000).date(), + } + + bigquery_adapter( + general_types, + table_description="A small table somewhere in the cosmos...", + partition="col2", + cluster=["col1"], + autodetect_schema=True, + ) + + pipeline = destination_config.setup_pipeline( + f"bigquery_{uniq_id()}", + dev_mode=True, + ) + + pipeline.run(general_types) + + bigquery_adapter( + partition_time, + partition="my_time_column", + autodetect_schema=True, + ) + + pipeline_time = destination_config.setup_pipeline( + f"bigquery_{uniq_id()}", + dev_mode=True, + ) + + pipeline_time.run(partition_time) + + bigquery_adapter( + partition_date, + partition="my_date_column", + autodetect_schema=True, + ) + + pipeline_date = destination_config.setup_pipeline( + f"bigquery_{uniq_id()}", + dev_mode=True, + ) + + pipeline_date.run(partition_date) + + with pipeline.sql_client() as c: + nc: google.cloud.bigquery.client.Client = c.native_connection + + table_fqtn = c.make_qualified_table_name("general_types", escape=False) + + table: Table = nc.get_table(table_fqtn) + + table_cluster_fields = [] if table.clustering_fields is None else table.clustering_fields + assert ["col1"] == table_cluster_fields, "NOT clustered by `col1`." + + assert table.description == "A small table somewhere in the cosmos..." + assert table.range_partitioning.field == "col2", "NOT partitioned on column `col2`." + + with pipeline_time.sql_client() as c: + nc: google.cloud.bigquery.client.Client = c.native_connection # type: ignore[no-redef] + table_fqtn = c.make_qualified_table_name("partition_time", escape=False) + table: Table = nc.get_table(table_fqtn) # type: ignore[no-redef] + assert table.time_partitioning.field == "my_time_column" + + with pipeline_date.sql_client() as c: + nc: google.cloud.bigquery.client.Client = c.native_connection # type: ignore[no-redef] + table_fqtn = c.make_qualified_table_name("partition_date", escape=False) + table: Table = nc.get_table(table_fqtn) # type: ignore[no-redef] + assert table.time_partitioning.field == "my_date_column" + assert table.time_partitioning.type_ == "DAY" diff --git a/tests/load/pipeline/test_clickhouse.py b/tests/load/pipeline/test_clickhouse.py index 8ad3a7f1a7..9e9c156144 100644 --- a/tests/load/pipeline/test_clickhouse.py +++ b/tests/load/pipeline/test_clickhouse.py @@ -32,7 +32,7 @@ def items() -> Iterator[TDataItem]: pipeline.run( items, - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, staging=destination_config.staging, ) @@ -64,7 +64,7 @@ def items2() -> Iterator[TDataItem]: ], } - pipeline.run(items2) + pipeline.run(items2, **destination_config.run_kwargs) table_counts = load_table_counts( pipeline, *[t["name"] for t in pipeline.default_schema._schema_tables.values()] ) diff --git a/tests/load/pipeline/test_csv_loading.py b/tests/load/pipeline/test_csv_loading.py index 6a2be2eb40..a2cc786915 100644 --- a/tests/load/pipeline/test_csv_loading.py +++ b/tests/load/pipeline/test_csv_loading.py @@ -92,7 +92,6 @@ def test_custom_csv_no_header( table_name="no_header", loader_file_format=file_format, ) - info.raise_on_failed_jobs() print(info) assert_only_table_columns(pipeline, "no_header", [col["name"] for col in columns]) rows = load_tables_to_dicts(pipeline, "no_header") @@ -114,6 +113,8 @@ def test_custom_csv_no_header( ids=lambda x: x.name, ) def test_custom_wrong_header(destination_config: DestinationTestConfiguration) -> None: + # do not raise on failed jobs + os.environ["RAISE_ON_FAILED_JOBS"] = "false" csv_format = CsvFormatConfiguration(delimiter="|", include_header=True) # apply to collected config pipeline = destination_config.setup_pipeline("postgres_" + uniq_id(), dev_mode=True) diff --git a/tests/load/pipeline/test_databricks_pipeline.py b/tests/load/pipeline/test_databricks_pipeline.py index 5f8641f9fa..9d152bb099 100644 --- a/tests/load/pipeline/test_databricks_pipeline.py +++ b/tests/load/pipeline/test_databricks_pipeline.py @@ -20,6 +20,9 @@ def test_databricks_external_location(destination_config: DestinationTestConfiguration) -> None: # do not interfere with state os.environ["RESTORE_FROM_DESTINATION"] = "False" + # let the package complete even with failed jobs + os.environ["RAISE_ON_FAILED_JOBS"] = "false" + dataset_name = "test_databricks_external_location" + uniq_id() from dlt.destinations import databricks, filesystem @@ -52,7 +55,7 @@ def test_databricks_external_location(destination_config: DestinationTestConfigu destination=bricks, staging=stage, ) - info = pipeline.run([1, 2, 3], table_name="digits") + info = pipeline.run([1, 2, 3], table_name="digits", **destination_config.run_kwargs) assert info.has_failed_jobs is True assert ( "Invalid configuration value detected" @@ -67,7 +70,7 @@ def test_databricks_external_location(destination_config: DestinationTestConfigu destination=bricks, staging=stage, ) - info = pipeline.run([1, 2, 3], table_name="digits") + info = pipeline.run([1, 2, 3], table_name="digits", **destination_config.run_kwargs) assert info.has_failed_jobs is True assert ( "credential_x" in pipeline.list_failed_jobs_in_package(info.loads_ids[0])[0].failed_message @@ -78,7 +81,7 @@ def test_databricks_external_location(destination_config: DestinationTestConfigu pipeline = destination_config.setup_pipeline( "test_databricks_external_location", dataset_name=dataset_name, destination=bricks ) - info = pipeline.run([1, 2, 3], table_name="digits") + info = pipeline.run([1, 2, 3], table_name="digits", **destination_config.run_kwargs) assert info.has_failed_jobs is True assert ( "credential_x" in pipeline.list_failed_jobs_in_package(info.loads_ids[0])[0].failed_message diff --git a/tests/load/pipeline/test_dbt_helper.py b/tests/load/pipeline/test_dbt_helper.py index 86ee1a646e..d55c81e998 100644 --- a/tests/load/pipeline/test_dbt_helper.py +++ b/tests/load/pipeline/test_dbt_helper.py @@ -36,7 +36,7 @@ def dbt_venv() -> Iterator[Venv]: def test_run_jaffle_package( destination_config: DestinationTestConfiguration, dbt_venv: Venv ) -> None: - if destination_config.destination == "athena": + if destination_config.destination_type == "athena": pytest.skip( "dbt-athena requires database to be created and we don't do it in case of Jaffle" ) @@ -71,7 +71,7 @@ def test_run_jaffle_package( ids=lambda x: x.name, ) def test_run_chess_dbt(destination_config: DestinationTestConfiguration, dbt_venv: Venv) -> None: - if destination_config.destination == "mssql": + if destination_config.destination_type == "mssql": pytest.skip( "mssql requires non standard SQL syntax and we do not have specialized dbt package" " for it" @@ -93,7 +93,7 @@ def test_run_chess_dbt(destination_config: DestinationTestConfiguration, dbt_ven with pytest.raises(PrerequisitesException): transforms.run_all(source_tests_selector="source:*") # load data - info = pipeline.run(chess(max_players=5, month=9)) + info = pipeline.run(chess(max_players=5, month=9), **destination_config.run_kwargs) print(info) assert pipeline.schema_names == ["chess"] # run all the steps (deps -> seed -> source tests -> run) @@ -130,7 +130,7 @@ def test_run_chess_dbt(destination_config: DestinationTestConfiguration, dbt_ven def test_run_chess_dbt_to_other_dataset( destination_config: DestinationTestConfiguration, dbt_venv: Venv ) -> None: - if destination_config.destination == "mssql": + if destination_config.destination_type == "mssql": pytest.skip( "mssql requires non standard SQL syntax and we do not have specialized dbt package" " for it" @@ -150,7 +150,7 @@ def test_run_chess_dbt_to_other_dataset( transforms = dlt.dbt.package(pipeline, "docs/examples/chess/dbt_transform", venv=dbt_venv) # assert pipeline.default_schema_name is None # load data - info = pipeline.run(chess(max_players=5, month=9)) + info = pipeline.run(chess(max_players=5, month=9), **destination_config.run_kwargs) print(info) assert pipeline.schema_names == ["chess"] # store transformations in alternative dataset diff --git a/tests/load/pipeline/test_dremio.py b/tests/load/pipeline/test_dremio.py index 66d1b0be4f..f19f9f44d9 100644 --- a/tests/load/pipeline/test_dremio.py +++ b/tests/load/pipeline/test_dremio.py @@ -22,7 +22,7 @@ def items() -> Iterator[Any]: "sub_items": [{"id": 101, "name": "sub item 101"}, {"id": 101, "name": "sub item 102"}], } - print(pipeline.run([items])) + print(pipeline.run([items], **destination_config.run_kwargs)) table_counts = load_table_counts( pipeline, *[t["name"] for t in pipeline.default_schema._schema_tables.values()] diff --git a/tests/load/pipeline/test_drop.py b/tests/load/pipeline/test_drop.py index e1c6ec9d79..0e44c754e7 100644 --- a/tests/load/pipeline/test_drop.py +++ b/tests/load/pipeline/test_drop.py @@ -1,11 +1,12 @@ import os -from typing import Any, Iterator, Dict, Any, List +from typing import Iterator, Dict, Any, List from unittest import mock from itertools import chain import pytest import dlt +from dlt.common.destination.reference import JobClientBase from dlt.extract import DltResource from dlt.common.utils import uniq_id from dlt.pipeline import helpers, state_sync, Pipeline @@ -18,6 +19,7 @@ from dlt.destinations.job_client_impl import SqlJobClientBase from tests.load.utils import destinations_configs, DestinationTestConfiguration +from tests.pipeline.utils import assert_load_info, load_table_counts def _attach(pipeline: Pipeline) -> Pipeline: @@ -124,24 +126,45 @@ def assert_destination_state_loaded(pipeline: Pipeline) -> None: assert pipeline_state == destination_state +@pytest.mark.essential @pytest.mark.parametrize( - "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name + "destination_config", + destinations_configs( + default_sql_configs=True, local_filesystem_configs=True, all_buckets_filesystem_configs=True + ), + ids=lambda x: x.name, ) def test_drop_command_resources_and_state(destination_config: DestinationTestConfiguration) -> None: """Test the drop command with resource and state path options and verify correct data is deleted from destination and locally""" source = droppable_source() pipeline = destination_config.setup_pipeline("drop_test_" + uniq_id(), dev_mode=True) - pipeline.run(source) + info = pipeline.run(source, **destination_config.run_kwargs) + assert_load_info(info) + assert load_table_counts(pipeline, *pipeline.default_schema.tables.keys()) == { + "_dlt_version": 1, + "_dlt_loads": 1, + "droppable_a": 2, + "droppable_b": 1, + "droppable_c": 1, + "droppable_d": 2, + "droppable_no_state": 3, + "_dlt_pipeline_state": 1, + "droppable_b__items": 2, + "droppable_c__items": 1, + "droppable_c__items__labels": 2, + } attached = _attach(pipeline) helpers.drop( - attached, resources=["droppable_c", "droppable_d"], state_paths="data_from_d.*.bar" + attached, + resources=["droppable_c", "droppable_d", "droppable_no_state"], + state_paths="data_from_d.*.bar", ) attached = _attach(pipeline) - assert_dropped_resources(attached, ["droppable_c", "droppable_d"]) + assert_dropped_resources(attached, ["droppable_c", "droppable_d", "droppable_no_state"]) # Verify extra json paths are removed from state sources_state = pipeline.state["sources"] @@ -149,15 +172,38 @@ def test_drop_command_resources_and_state(destination_config: DestinationTestCon assert_destination_state_loaded(pipeline) + # now run the same droppable_source to see if tables are recreated and they contain right number of items + info = pipeline.run(source, **destination_config.run_kwargs) + assert_load_info(info) + # 2 versions (one dropped and replaced with schema with dropped tables, then we added missing tables) + # 3 loads (one for drop) + # droppable_no_state correctly replaced + # all other resources stay at the same count (they are incremental so they got loaded again or not loaded at all ie droppable_a) + assert load_table_counts(pipeline, *pipeline.default_schema.tables.keys()) == { + "_dlt_version": 2, + "_dlt_loads": 3, + "droppable_a": 2, + "droppable_b": 1, + "_dlt_pipeline_state": 3, + "droppable_b__items": 2, + "droppable_c": 1, + "droppable_d": 2, + "droppable_no_state": 3, + "droppable_c__items": 1, + "droppable_c__items__labels": 2, + } + @pytest.mark.parametrize( - "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name + "destination_config", + destinations_configs(default_sql_configs=True, local_filesystem_configs=True), + ids=lambda x: x.name, ) def test_drop_command_only_state(destination_config: DestinationTestConfiguration) -> None: """Test drop command that deletes part of the state and syncs with destination""" source = droppable_source() pipeline = destination_config.setup_pipeline("drop_test_" + uniq_id(), dev_mode=True) - pipeline.run(source) + pipeline.run(source, **destination_config.run_kwargs) attached = _attach(pipeline) helpers.drop(attached, state_paths="data_from_d.*.bar") @@ -174,13 +220,15 @@ def test_drop_command_only_state(destination_config: DestinationTestConfiguratio @pytest.mark.parametrize( - "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name + "destination_config", + destinations_configs(default_sql_configs=True, local_filesystem_configs=True), + ids=lambda x: x.name, ) def test_drop_command_only_tables(destination_config: DestinationTestConfiguration) -> None: """Test drop only tables and makes sure that schema and state are synced""" source = droppable_source() pipeline = destination_config.setup_pipeline("drop_test_" + uniq_id(), dev_mode=True) - pipeline.run(source) + pipeline.run(source, **destination_config.run_kwargs) sources_state = pipeline.state["sources"] attached = _attach(pipeline) @@ -196,13 +244,15 @@ def test_drop_command_only_tables(destination_config: DestinationTestConfigurati @pytest.mark.parametrize( - "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name + "destination_config", + destinations_configs(default_sql_configs=True, local_filesystem_configs=True), + ids=lambda x: x.name, ) def test_drop_destination_tables_fails(destination_config: DestinationTestConfiguration) -> None: """Fail on DROP TABLES in destination init. Command runs again.""" source = droppable_source() pipeline = destination_config.setup_pipeline("drop_test_" + uniq_id(), dev_mode=True) - pipeline.run(source) + pipeline.run(source, **destination_config.run_kwargs) attached = _attach(pipeline) @@ -224,13 +274,15 @@ def test_drop_destination_tables_fails(destination_config: DestinationTestConfig @pytest.mark.parametrize( - "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name + "destination_config", + destinations_configs(default_sql_configs=True, local_filesystem_configs=True), + ids=lambda x: x.name, ) def test_fail_after_drop_tables(destination_config: DestinationTestConfiguration) -> None: """Fail directly after drop tables. Command runs again ignoring destination tables missing.""" source = droppable_source() pipeline = destination_config.setup_pipeline("drop_test_" + uniq_id(), dev_mode=True) - pipeline.run(source) + pipeline.run(source, **destination_config.run_kwargs) attached = _attach(pipeline) @@ -255,13 +307,15 @@ def test_fail_after_drop_tables(destination_config: DestinationTestConfiguration @pytest.mark.parametrize( - "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name + "destination_config", + destinations_configs(default_sql_configs=True, local_filesystem_configs=True), + ids=lambda x: x.name, ) def test_load_step_fails(destination_config: DestinationTestConfiguration) -> None: """Test idempotence. pipeline.load() fails. Command can be run again successfully""" source = droppable_source() pipeline = destination_config.setup_pipeline("drop_test_" + uniq_id(), dev_mode=True) - pipeline.run(source) + pipeline.run(source, **destination_config.run_kwargs) attached = _attach(pipeline) @@ -278,12 +332,14 @@ def test_load_step_fails(destination_config: DestinationTestConfiguration) -> No @pytest.mark.parametrize( - "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name + "destination_config", + destinations_configs(default_sql_configs=True, local_filesystem_configs=True), + ids=lambda x: x.name, ) def test_resource_regex(destination_config: DestinationTestConfiguration) -> None: source = droppable_source() pipeline = destination_config.setup_pipeline("drop_test_" + uniq_id(), dev_mode=True) - pipeline.run(source) + pipeline.run(source, **destination_config.run_kwargs) attached = _attach(pipeline) @@ -296,13 +352,15 @@ def test_resource_regex(destination_config: DestinationTestConfiguration) -> Non @pytest.mark.parametrize( - "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name + "destination_config", + destinations_configs(default_sql_configs=True, local_filesystem_configs=True), + ids=lambda x: x.name, ) def test_drop_nothing(destination_config: DestinationTestConfiguration) -> None: """No resources, no state keys. Nothing is changed.""" source = droppable_source() pipeline = destination_config.setup_pipeline("drop_test_" + uniq_id(), dev_mode=True) - pipeline.run(source) + pipeline.run(source, **destination_config.run_kwargs) attached = _attach(pipeline) previous_state = dict(attached.state) @@ -320,7 +378,7 @@ def test_drop_all_flag(destination_config: DestinationTestConfiguration) -> None """Using drop_all flag. Destination dataset and all local state is deleted""" source = droppable_source() pipeline = destination_config.setup_pipeline("drop_test_" + uniq_id(), dev_mode=True) - pipeline.run(source) + pipeline.run(source, **destination_config.run_kwargs) dlt_tables = [ t["name"] for t in pipeline.default_schema.dlt_tables() ] # Original _dlt tables to check for @@ -340,12 +398,14 @@ def test_drop_all_flag(destination_config: DestinationTestConfiguration) -> None @pytest.mark.parametrize( - "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name + "destination_config", + destinations_configs(default_sql_configs=True, local_filesystem_configs=True), + ids=lambda x: x.name, ) def test_run_pipeline_after_partial_drop(destination_config: DestinationTestConfiguration) -> None: """Pipeline can be run again after dropping some resources""" pipeline = destination_config.setup_pipeline("drop_test_" + uniq_id(), dev_mode=True) - pipeline.run(droppable_source()) + pipeline.run(droppable_source(), **destination_config.run_kwargs) attached = _attach(pipeline) @@ -355,16 +415,18 @@ def test_run_pipeline_after_partial_drop(destination_config: DestinationTestConf attached.extract(droppable_source()) # TODO: individual steps cause pipeline.run() never raises attached.normalize() - attached.load(raise_on_failed_jobs=True) + attached.load() @pytest.mark.parametrize( - "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name + "destination_config", + destinations_configs(default_sql_configs=True, local_filesystem_configs=True), + ids=lambda x: x.name, ) def test_drop_state_only(destination_config: DestinationTestConfiguration) -> None: """Pipeline can be run again after dropping some resources""" pipeline = destination_config.setup_pipeline("drop_test_" + uniq_id(), dev_mode=True) - pipeline.run(droppable_source()) + pipeline.run(droppable_source(), **destination_config.run_kwargs) attached = _attach(pipeline) diff --git a/tests/load/pipeline/test_duckdb.py b/tests/load/pipeline/test_duckdb.py index 2fa44d77c5..b028edc1bb 100644 --- a/tests/load/pipeline/test_duckdb.py +++ b/tests/load/pipeline/test_duckdb.py @@ -13,6 +13,7 @@ from dlt.pipeline.exceptions import PipelineStepFailed from tests.cases import TABLE_UPDATE_ALL_INT_PRECISIONS, TABLE_UPDATE_ALL_TIMESTAMP_PRECISIONS +from tests.load.duckdb.test_duckdb_table_builder import add_timezone_false_on_precision from tests.load.utils import destinations_configs, DestinationTestConfiguration from tests.pipeline.utils import airtable_emojis, assert_data_table_counts, load_table_counts @@ -30,17 +31,15 @@ def test_duck_case_names(destination_config: DestinationTestConfiguration) -> No os.environ["SCHEMA__NAMING"] = "duck_case" pipeline = destination_config.setup_pipeline("test_duck_case_names") # create tables and columns with emojis and other special characters - info = pipeline.run( + pipeline.run( airtable_emojis().with_resources("📆 Schedule", "🦚Peacock", "🦚WidePeacock"), - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) - info.raise_on_failed_jobs() - info = pipeline.run( + pipeline.run( [{"🐾Feet": 2, "1+1": "two", "\nhey": "value"}], table_name="🦚Peacocks🦚", - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) - info.raise_on_failed_jobs() table_counts = load_table_counts( pipeline, *[t["name"] for t in pipeline.default_schema.data_tables()] ) @@ -58,7 +57,7 @@ def test_duck_case_names(destination_config: DestinationTestConfiguration) -> No pipeline.run( [{"🐾Feet": 2, "1+1": "two", "🐾feet": "value"}], table_name="🦚peacocks🦚", - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) assert isinstance(pip_ex.value.__context__, SchemaIdentifierNormalizationCollision) assert pip_ex.value.__context__.conflict_identifier_name == "🦚Peacocks🦚" @@ -103,13 +102,14 @@ def test_duck_precision_types(destination_config: DestinationTestConfiguration) "col5_int": 2**64 // 2 - 1, } ] - info = pipeline.run( + pipeline.run( row, table_name="row", - loader_file_format=destination_config.file_format, - columns=TABLE_UPDATE_ALL_TIMESTAMP_PRECISIONS + TABLE_UPDATE_ALL_INT_PRECISIONS, + **destination_config.run_kwargs, + columns=add_timezone_false_on_precision( + TABLE_UPDATE_ALL_TIMESTAMP_PRECISIONS + TABLE_UPDATE_ALL_INT_PRECISIONS + ), ) - info.raise_on_failed_jobs() with pipeline.sql_client() as client: table = client.native_connection.sql("SELECT * FROM row").arrow() @@ -117,7 +117,7 @@ def test_duck_precision_types(destination_config: DestinationTestConfiguration) # only us has TZ aware timestamp in duckdb, also we have UTC here assert table.schema.field(0).type == pa.timestamp("s") assert table.schema.field(1).type == pa.timestamp("ms") - assert table.schema.field(2).type == pa.timestamp("us", tz="UTC") + assert table.schema.field(2).type == pa.timestamp("us") assert table.schema.field(3).type == pa.timestamp("ns") assert table.schema.field(4).type == pa.int8() @@ -129,6 +129,7 @@ def test_duck_precision_types(destination_config: DestinationTestConfiguration) table_row = table.to_pylist()[0] table_row["col1_ts"] = ensure_pendulum_datetime(table_row["col1_ts"]) table_row["col2_ts"] = ensure_pendulum_datetime(table_row["col2_ts"]) + table_row["col3_ts"] = ensure_pendulum_datetime(table_row["col3_ts"]) table_row["col4_ts"] = ensure_pendulum_datetime(table_row["col4_ts"]) table_row.pop("_dlt_id") table_row.pop("_dlt_load_id") @@ -148,7 +149,7 @@ class EventDetail(BaseModel): is_complete: bool class EventV1(BaseModel): - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": True} + dlt_config: ClassVar[DltConfig] = {"skip_nested_types": True} ver: int id: str # noqa @@ -163,14 +164,13 @@ class EventV1(BaseModel): event = {"ver": 1, "id": "id1", "details": {"detail_id": "detail_1", "is_complete": False}} - info = pipeline.run( + pipeline.run( [event], table_name="events", columns=EventV1, loader_file_format="parquet", schema_contract="evolve", ) - info.raise_on_failed_jobs() print(pipeline.default_schema.to_pretty_yaml()) # we will use a different pipeline with a separate schema but writing to the same dataset and to the same table @@ -184,7 +184,7 @@ class EventDetailV2(BaseModel): time: Optional[datetime] class EventV2(BaseModel): - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": True} + dlt_config: ClassVar[DltConfig] = {"skip_nested_types": True} ver: int id: str # noqa @@ -196,14 +196,13 @@ class EventV2(BaseModel): "test_new_nested_prop_parquet_2", dataset_name="test_dataset" ) pipeline.destination = duck_factory # type: ignore - info = pipeline.run( + pipeline.run( [event], table_name="events", columns=EventV2, loader_file_format="parquet", schema_contract="evolve", ) - info.raise_on_failed_jobs() print(pipeline.default_schema.to_pretty_yaml()) @@ -216,8 +215,7 @@ def test_jsonl_reader(destination_config: DestinationTestConfiguration) -> None: pipeline = destination_config.setup_pipeline("test_jsonl_reader") data = [{"a": 1, "b": 2}, {"a": 1}] - info = pipeline.run(data, table_name="data", loader_file_format="jsonl") - info.raise_on_failed_jobs() + pipeline.run(data, table_name="data", loader_file_format="jsonl") @pytest.mark.parametrize( @@ -241,9 +239,8 @@ def _get_shuffled_events(repeat: int = 1): os.environ["DATA_WRITER__FILE_MAX_ITEMS"] = "200" pipeline = destination_config.setup_pipeline("test_provoke_parallel_parquet_same_table") + pipeline.run(_get_shuffled_events(50), **destination_config.run_kwargs) - info = pipeline.run(_get_shuffled_events(50)) - info.raise_on_failed_jobs() assert_data_table_counts( pipeline, expected_counts={ diff --git a/tests/load/pipeline/test_filesystem_pipeline.py b/tests/load/pipeline/test_filesystem_pipeline.py index bc6cbd9848..92e927f438 100644 --- a/tests/load/pipeline/test_filesystem_pipeline.py +++ b/tests/load/pipeline/test_filesystem_pipeline.py @@ -44,7 +44,6 @@ def test_pipeline_merge_write_disposition(default_buckets_env: str) -> None: """Run pipeline twice with merge write disposition Regardless wether primary key is set or not, filesystem appends """ - import pyarrow.parquet as pq # Module is evaluated by other tests os.environ["DATA_WRITER__DISABLE_COMPRESSION"] = "True" @@ -102,7 +101,6 @@ def test_pipeline_csv_filesystem_destination(item_type: TestDataItemFormat) -> N item, rows, _ = arrow_table_all_data_types(item_type, include_json=False, include_time=True) info = pipeline.run(item, table_name="table", loader_file_format="csv") - info.raise_on_failed_jobs() job = info.load_packages[0].jobs["completed_jobs"][0].file_path assert job.endswith("csv") with open(job, "r", encoding="utf-8", newline="") as f: @@ -128,7 +126,6 @@ def test_csv_options(item_type: TestDataItemFormat) -> None: item, rows, _ = arrow_table_all_data_types(item_type, include_json=False, include_time=True) info = pipeline.run(item, table_name="table", loader_file_format="csv") - info.raise_on_failed_jobs() job = info.load_packages[0].jobs["completed_jobs"][0].file_path assert job.endswith("csv") with open(job, "r", encoding="utf-8", newline="") as f: @@ -157,7 +154,6 @@ def test_csv_quoting_style(item_type: TestDataItemFormat) -> None: item, _, _ = arrow_table_all_data_types(item_type, include_json=False, include_time=True) info = pipeline.run(item, table_name="table", loader_file_format="csv") - info.raise_on_failed_jobs() job = info.load_packages[0].jobs["completed_jobs"][0].file_path assert job.endswith("csv") with open(job, "r", encoding="utf-8", newline="") as f: @@ -257,7 +253,7 @@ def foo(): "destination_config", destinations_configs( table_format_filesystem_configs=True, - table_format="delta", + with_table_format="delta", bucket_exclude=(MEMORY_BUCKET), ), ids=lambda x: x.name, @@ -330,7 +326,7 @@ def data_types(): "destination_config", destinations_configs( table_format_filesystem_configs=True, - table_format="delta", + with_table_format="delta", bucket_subset=(FILE_BUCKET), ), ids=lambda x: x.name, @@ -375,7 +371,7 @@ def delta_table(): "destination_config", destinations_configs( table_format_filesystem_configs=True, - table_format="delta", + with_table_format="delta", bucket_subset=(FILE_BUCKET), ), ids=lambda x: x.name, @@ -421,7 +417,7 @@ def delta_table(): "destination_config", destinations_configs( table_format_filesystem_configs=True, - table_format="delta", + with_table_format="delta", bucket_subset=(FILE_BUCKET), ), ids=lambda x: x.name, @@ -432,7 +428,7 @@ def test_delta_table_child_tables( """Tests child table handling for `delta` table format.""" @dlt.resource(table_format="delta") - def complex_table(): + def nested_table(): yield [ { "foo": 1, @@ -448,57 +444,67 @@ def complex_table(): pipeline = destination_config.setup_pipeline("fs_pipe", dev_mode=True) - info = pipeline.run(complex_table()) + info = pipeline.run(nested_table()) assert_load_info(info) rows_dict = load_tables_to_dicts( pipeline, - "complex_table", - "complex_table__child", - "complex_table__child__grandchild", + "nested_table", + "nested_table__child", + "nested_table__child__grandchild", exclude_system_cols=True, ) # assert row counts - assert len(rows_dict["complex_table"]) == 2 - assert len(rows_dict["complex_table__child"]) == 3 - assert len(rows_dict["complex_table__child__grandchild"]) == 5 + assert len(rows_dict["nested_table"]) == 2 + assert len(rows_dict["nested_table__child"]) == 3 + assert len(rows_dict["nested_table__child__grandchild"]) == 5 # assert column names - assert rows_dict["complex_table"][0].keys() == {"foo"} - assert rows_dict["complex_table__child"][0].keys() == {"bar"} - assert rows_dict["complex_table__child__grandchild"][0].keys() == {"value"} + assert rows_dict["nested_table"][0].keys() == {"foo"} + assert rows_dict["nested_table__child"][0].keys() == {"bar"} + assert rows_dict["nested_table__child__grandchild"][0].keys() == {"value"} # test write disposition handling with child tables - info = pipeline.run(complex_table()) + info = pipeline.run(nested_table()) assert_load_info(info) rows_dict = load_tables_to_dicts( pipeline, - "complex_table", - "complex_table__child", - "complex_table__child__grandchild", + "nested_table", + "nested_table__child", + "nested_table__child__grandchild", exclude_system_cols=True, ) - assert len(rows_dict["complex_table"]) == 2 * 2 - assert len(rows_dict["complex_table__child"]) == 3 * 2 - assert len(rows_dict["complex_table__child__grandchild"]) == 5 * 2 + assert len(rows_dict["nested_table"]) == 2 * 2 + assert len(rows_dict["nested_table__child"]) == 3 * 2 + assert len(rows_dict["nested_table__child__grandchild"]) == 5 * 2 - info = pipeline.run(complex_table(), write_disposition="replace") + info = pipeline.run(nested_table(), write_disposition="replace") assert_load_info(info) rows_dict = load_tables_to_dicts( pipeline, - "complex_table", - "complex_table__child", - "complex_table__child__grandchild", + "nested_table", + "nested_table__child", + "nested_table__child__grandchild", exclude_system_cols=True, ) - assert len(rows_dict["complex_table"]) == 2 - assert len(rows_dict["complex_table__child"]) == 3 - assert len(rows_dict["complex_table__child__grandchild"]) == 5 + assert len(rows_dict["nested_table"]) == 2 + assert len(rows_dict["nested_table__child"]) == 3 + assert len(rows_dict["nested_table__child__grandchild"]) == 5 + + # now drop children and grandchildren, use merge write disposition to create and pass full table chain + # also for tables that do not have jobs + info = pipeline.run( + [{"foo": 3}] * 10000, + table_name="nested_table", + primary_key="foo", + write_disposition="merge", + ) + assert_load_info(info) @pytest.mark.parametrize( "destination_config", destinations_configs( table_format_filesystem_configs=True, - table_format="delta", + with_table_format="delta", bucket_subset=(FILE_BUCKET), ), ids=lambda x: x.name, @@ -584,7 +590,7 @@ def two_part(): "destination_config", destinations_configs( table_format_filesystem_configs=True, - table_format="delta", + with_table_format="delta", bucket_subset=(FILE_BUCKET), ), ids=lambda x: x.name, @@ -681,7 +687,7 @@ def delta_table(data): "destination_config", destinations_configs( table_format_filesystem_configs=True, - table_format="delta", + with_table_format="delta", bucket_subset=(FILE_BUCKET, AZ_BUCKET), ), ids=lambda x: x.name, @@ -693,7 +699,6 @@ def test_delta_table_empty_source( Tests both empty Arrow table and `dlt.mark.materialize_table_schema()`. """ - from dlt.common.libs.pyarrow import pyarrow as pa from dlt.common.libs.deltalake import ensure_delta_compatible_arrow_data, get_delta_tables from tests.pipeline.utils import users_materialize_table_schema @@ -740,9 +745,21 @@ def delta_table(data): ensure_delta_compatible_arrow_data(empty_arrow_table).schema ) + # now run the empty frame again + info = pipeline.run(delta_table(empty_arrow_table)) + assert_load_info(info) + + # use materialized list + # NOTE: this will create an empty parquet file with a schema takes from dlt schema. + # the original parquet file had a nested (struct) type in `json` field that is now + # in the delta table schema. the empty parquet file lost this information and had + # string type (converted from dlt `json`) + info = pipeline.run([dlt.mark.materialize_table_schema()], table_name="delta_table") + assert_load_info(info) + # test `dlt.mark.materialize_table_schema()` users_materialize_table_schema.apply_hints(table_format="delta") - info = pipeline.run(users_materialize_table_schema()) + info = pipeline.run(users_materialize_table_schema(), loader_file_format="parquet") assert_load_info(info) dt = get_delta_tables(pipeline, "users")["users"] assert dt.version() == 0 @@ -755,7 +772,7 @@ def delta_table(data): "destination_config", destinations_configs( table_format_filesystem_configs=True, - table_format="delta", + with_table_format="delta", bucket_subset=(FILE_BUCKET), ), ids=lambda x: x.name, @@ -804,7 +821,7 @@ def s(): "destination_config", destinations_configs( table_format_filesystem_configs=True, - table_format="delta", + with_table_format="delta", bucket_subset=(FILE_BUCKET), ), ids=lambda x: x.name, @@ -832,7 +849,7 @@ def github_events(): "destination_config", destinations_configs( table_format_filesystem_configs=True, - table_format="delta", + with_table_format="delta", bucket_subset=(FILE_BUCKET, AZ_BUCKET), ), ids=lambda x: x.name, @@ -912,7 +929,7 @@ def parent_delta(): "destination_config", destinations_configs( table_format_filesystem_configs=True, - table_format="delta", + with_table_format="delta", bucket_subset=(FILE_BUCKET,), ), ids=lambda x: x.name, diff --git a/tests/load/pipeline/test_merge_disposition.py b/tests/load/pipeline/test_merge_disposition.py index b2197dd273..a81c1b13e9 100644 --- a/tests/load/pipeline/test_merge_disposition.py +++ b/tests/load/pipeline/test_merge_disposition.py @@ -9,6 +9,7 @@ from dlt.common import json, pendulum from dlt.common.configuration.container import Container +from dlt.common.destination.utils import resolve_merge_strategy from dlt.common.pipeline import StateInjectableContext from dlt.common.schema.utils import has_table_seen_data from dlt.common.schema.exceptions import ( @@ -41,15 +42,12 @@ AZ_BUCKET, ) -# uncomment add motherduck tests -# NOTE: the tests are passing but we disable them due to frequent ATTACH DATABASE timeouts -# ACTIVE_DESTINATIONS += ["motherduck"] - def skip_if_not_supported( merge_strategy: TLoaderMergeStrategy, destination: TDestination, ) -> None: + # resolve_merge_strategy if merge_strategy not in destination.capabilities().supported_merge_strategies: pytest.skip( f"`{merge_strategy}` merge strategy not supported for `{destination.destination_name}`" @@ -74,36 +72,51 @@ def test_merge_on_keys_in_schema( destination_config: DestinationTestConfiguration, merge_strategy: TLoaderMergeStrategy, ) -> None: + """Tests merge disposition on an annotated schema, no annotations on resource""" p = destination_config.setup_pipeline("eth_2", dev_mode=True) skip_if_not_supported(merge_strategy, p.destination) - with open("tests/common/cases/schemas/eth/ethereum_schema_v5.yml", "r", encoding="utf-8") as f: + with open("tests/common/cases/schemas/eth/ethereum_schema_v9.yml", "r", encoding="utf-8") as f: schema = dlt.Schema.from_dict(yaml.safe_load(f)) - # make block uncles unseen to trigger filtering loader in loader for child tables + # make block uncles unseen to trigger filtering loader in loader for nested tables if has_table_seen_data(schema.tables["blocks__uncles"]): del schema.tables["blocks__uncles"]["x-normalizer"] assert not has_table_seen_data(schema.tables["blocks__uncles"]) - @dlt.resource( - table_name="blocks", - write_disposition={"disposition": "merge", "strategy": merge_strategy}, - table_format=destination_config.table_format, - ) - def data(slice_: slice = None): - with open( - "tests/normalize/cases/ethereum.blocks.9c1d9b504ea240a482b007788d5cd61c_2.json", - "r", - encoding="utf-8", - ) as f: - yield json.load(f) if slice_ is None else json.load(f)[slice_] + @dlt.source(schema=schema) + def ethereum(slice_: slice = None): + @dlt.resource( + table_name="blocks", + write_disposition={"disposition": "merge", "strategy": merge_strategy}, + ) + def data(): + with open( + "tests/normalize/cases/ethereum.blocks.9c1d9b504ea240a482b007788d5cd61c_2.json", + "r", + encoding="utf-8", + ) as f: + yield json.load(f) if slice_ is None else json.load(f)[slice_] + + # also modify the child tables (not nested) + schema_ = dlt.current.source_schema() + blocks__transactions = schema_.tables["blocks__transactions"] + blocks__transactions["write_disposition"] = "merge" + blocks__transactions["x-merge-strategy"] = merge_strategy # type: ignore[typeddict-unknown-key] + blocks__transactions["table_format"] = destination_config.table_format + + blocks__transactions__logs = schema_.tables["blocks__transactions__logs"] + blocks__transactions__logs["write_disposition"] = "merge" + blocks__transactions__logs["x-merge-strategy"] = merge_strategy # type: ignore[typeddict-unknown-key] + blocks__transactions__logs["table_format"] = destination_config.table_format + + return data # take only the first block. the first block does not have uncles so this table should not be created and merged info = p.run( - data(slice(1)), - schema=schema, - loader_file_format=destination_config.file_format, + ethereum(slice(1)), + **destination_config.run_kwargs, ) assert_load_info(info) eth_1_counts = load_table_counts(p, "blocks") @@ -117,18 +130,17 @@ def data(slice_: slice = None): # now we load the whole dataset. blocks should be created which adds columns to blocks # if the table would be created before the whole load would fail because new columns have hints info = p.run( - data, - schema=schema, - loader_file_format=destination_config.file_format, + ethereum(), + **destination_config.run_kwargs, ) + assert_load_info(info) eth_2_counts = load_table_counts(p, *[t["name"] for t in p.default_schema.data_tables()]) # we have 2 blocks in dataset assert eth_2_counts["blocks"] == 2 if destination_config.supports_merge else 3 # make sure we have same record after merging full dataset again info = p.run( - data, - schema=schema, - loader_file_format=destination_config.file_format, + ethereum(), + **destination_config.run_kwargs, ) assert_load_info(info) # for non merge destinations we just check that the run passes @@ -163,7 +175,6 @@ def test_merge_record_updates( table_name="parent", write_disposition={"disposition": "merge", "strategy": merge_strategy}, primary_key="id", - table_format=destination_config.table_format, ) def r(data): yield data @@ -173,7 +184,7 @@ def r(data): {"id": 1, "foo": 1, "child": [{"bar": 1, "grandchild": [{"baz": 1}]}]}, {"id": 2, "foo": 1, "child": [{"bar": 1, "grandchild": [{"baz": 1}]}]}, ] - info = p.run(r(run_1)) + info = p.run(r(run_1), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, "parent", "parent__child", "parent__child__grandchild") == { "parent": 2, @@ -194,7 +205,7 @@ def r(data): {"id": 1, "foo": 2, "child": [{"bar": 1, "grandchild": [{"baz": 1}]}]}, {"id": 2, "foo": 1, "child": [{"bar": 1, "grandchild": [{"baz": 1}]}]}, ] - info = p.run(r(run_2)) + info = p.run(r(run_2), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, "parent", "parent__child", "parent__child__grandchild") == { "parent": 2, @@ -215,7 +226,7 @@ def r(data): {"id": 1, "foo": 2, "child": [{"bar": 2, "grandchild": [{"baz": 1}]}]}, {"id": 2, "foo": 1, "child": [{"bar": 1, "grandchild": [{"baz": 1}]}]}, ] - info = p.run(r(run_3)) + info = p.run(r(run_3), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, "parent", "parent__child", "parent__child__grandchild") == { "parent": 2, @@ -236,7 +247,7 @@ def r(data): {"id": 1, "foo": 2, "child": [{"bar": 2, "grandchild": [{"baz": 2}]}]}, {"id": 2, "foo": 1, "child": [{"bar": 1, "grandchild": [{"baz": 1}]}]}, ] - info = p.run(r(run_3)) + info = p.run(r(run_3), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, "parent", "parent__child", "parent__child__grandchild") == { "parent": 2, @@ -253,6 +264,251 @@ def r(data): ) +@pytest.mark.essential +@pytest.mark.parametrize( + "destination_config", + destinations_configs( + default_sql_configs=True, + local_filesystem_configs=True, + table_format_filesystem_configs=True, + supports_merge=True, + bucket_subset=(FILE_BUCKET), + ), + ids=lambda x: x.name, +) +@pytest.mark.parametrize("merge_strategy", ("delete-insert", "upsert")) +def test_merge_nested_records_inserted_deleted( + destination_config: DestinationTestConfiguration, + merge_strategy: TLoaderMergeStrategy, +) -> None: + p = destination_config.setup_pipeline( + "test_merge_nested_records_inserted_deleted", dev_mode=True + ) + + skip_if_not_supported(merge_strategy, p.destination) + + @dlt.resource( + table_name="parent", + write_disposition={"disposition": "merge", "strategy": merge_strategy}, + primary_key="id", + merge_key="foo", + ) + def r(data): + yield data + + # initial load + run_1 = [ + {"id": 1, "foo": 1, "child": [{"bar": 1, "grandchild": [{"baz": 1}]}]}, + {"id": 2, "foo": 1, "child": [{"bar": 1, "grandchild": [{"baz": 1}]}]}, + {"id": 3, "foo": 1, "child": [{"bar": 3, "grandchild": [{"baz": 1}]}]}, + ] + info = p.run(r(run_1), **destination_config.run_kwargs) + assert_load_info(info) + assert load_table_counts(p, "parent", "parent__child", "parent__child__grandchild") == { + "parent": 3, + "parent__child": 3, + "parent__child__grandchild": 3, + } + tables = load_tables_to_dicts(p, "parent", exclude_system_cols=True) + assert_records_as_set( + tables["parent"], + [ + {"id": 1, "foo": 1}, + {"id": 2, "foo": 1}, + {"id": 3, "foo": 1}, + ], + ) + + # delete records — delete parent (id 3), child (id 2) and grandchild (id 1) + # foo is merge key, should delete id = 3 + run_3 = [ + {"id": 1, "foo": 1, "child": [{"bar": 2}]}, + {"id": 2, "foo": 1}, + ] + info = p.run(r(run_3), **destination_config.run_kwargs) + assert_load_info(info) + + table_counts = load_table_counts(p, "parent", "parent__child", "parent__child__grandchild") + table_data = load_tables_to_dicts(p, "parent", "parent__child", exclude_system_cols=True) + if merge_strategy == "upsert": + # merge keys will not apply and parent will not be deleted + if destination_config.table_format == "delta": + # delta merges cannot delete from nested tables + assert table_counts == { + "parent": 3, # id == 3 not deleted (not present in the data) + "parent__child": 3, # child not deleted + "parent__child__grandchild": 3, # grand child not deleted, + } + else: + assert table_counts == { + "parent": 3, # id == 3 not deleted (not present in the data) + "parent__child": 2, + "parent__child__grandchild": 1, + } + assert_records_as_set( + table_data["parent__child"], + [ + {"bar": 2}, # id 1 updated to bar + {"bar": 3}, # id 3 not deleted + ], + ) + else: + assert table_counts == { + "parent": 2, + "parent__child": 1, + "parent__child__grandchild": 0, + } + assert_records_as_set( + table_data["parent__child"], + [ + {"bar": 2}, + ], + ) + + # insert records id 3 inserted back, id 2 added child, id 1 added grandchild + run_3 = [ + {"id": 1, "foo": 1, "child": [{"bar": 1, "grandchild": [{"baz": 1}, {"baz": 4}]}]}, + {"id": 2, "foo": 1, "child": [{"bar": 2, "grandchild": [{"baz": 2}]}, {"bar": 4}]}, + {"id": 3, "foo": 1, "child": [{"bar": 3, "grandchild": [{"baz": 3}]}]}, + ] + info = p.run(r(run_3), **destination_config.run_kwargs) + assert_load_info(info) + assert load_table_counts(p, "parent", "parent__child", "parent__child__grandchild") == { + "parent": 3, + "parent__child": 4, + "parent__child__grandchild": 4, + } + tables = load_tables_to_dicts( + p, "parent__child", "parent__child__grandchild", exclude_system_cols=True + ) + assert_records_as_set( + tables["parent__child__grandchild"], + [ + {"baz": 2}, + {"baz": 1}, + {"baz": 3}, + {"baz": 4}, + ], + ) + assert_records_as_set( + tables["parent__child"], + [ + {"bar": 2}, + {"bar": 1}, + {"bar": 3}, + {"bar": 4}, + ], + ) + + +@pytest.mark.essential +@pytest.mark.parametrize( + "destination_config", + destinations_configs( + default_sql_configs=True, + local_filesystem_configs=True, + table_format_filesystem_configs=True, + supports_merge=True, + bucket_subset=(FILE_BUCKET), + ), + ids=lambda x: x.name, +) +@pytest.mark.parametrize("merge_strategy", ("delete-insert", "upsert")) +def test_bring_your_own_dlt_id( + destination_config: DestinationTestConfiguration, + merge_strategy: TLoaderMergeStrategy, +) -> None: + p = destination_config.setup_pipeline( + "test_merge_nested_records_inserted_deleted", dev_mode=True + ) + + skip_if_not_supported(merge_strategy, p.destination) + + # sets _dlt_id as both primary key and row key. + @dlt.resource( + table_name="parent", + write_disposition={"disposition": "merge", "strategy": merge_strategy}, + primary_key="_dlt_id", + ) + def r(data): + yield data + + # initial load + run_1 = [ + {"_dlt_id": 1, "foo": 1, "child": [{"bar": 1, "grandchild": [{"baz": 1}]}]}, + ] + info = p.run(r(run_1), **destination_config.run_kwargs) + assert_load_info(info) + run_2 = [ + {"_dlt_id": 1, "foo": 2, "child": [{"bar": 2, "grandchild": [{"baz": 2}]}]}, + ] + info = p.run(r(run_2), **destination_config.run_kwargs) + assert_load_info(info) + # _dlt_id is a bigint and a primary key + parent_dlt_id = p.default_schema.tables["parent"]["columns"]["_dlt_id"] + assert parent_dlt_id["data_type"] == "bigint" + assert parent_dlt_id["primary_key"] is True + assert parent_dlt_id["row_key"] is True + assert parent_dlt_id["unique"] is True + + # parent_key on child refers to the dlt_id above + child_parent_id = p.default_schema.tables["parent__child"]["columns"]["_dlt_parent_id"] + assert child_parent_id["data_type"] == "bigint" + assert child_parent_id["parent_key"] is True + + # same for root key + child_root_id = p.default_schema.tables["parent__child"]["columns"]["_dlt_root_id"] + assert child_root_id["data_type"] == "bigint" + assert child_root_id["root_key"] is True + + # id on child is regular auto dlt id + child_dlt_id = p.default_schema.tables["parent__child"]["columns"]["_dlt_id"] + assert child_dlt_id["data_type"] == "text" + + # check grandchild + grandchild_parent_id = p.default_schema.tables["parent__child__grandchild"]["columns"][ + "_dlt_parent_id" + ] + # refers to child dlt id which is a regular one + assert grandchild_parent_id["data_type"] == "text" + assert grandchild_parent_id["parent_key"] is True + + grandchild_root_id = p.default_schema.tables["parent__child__grandchild"]["columns"][ + "_dlt_root_id" + ] + # root key still to parent + assert grandchild_root_id["data_type"] == "bigint" + assert grandchild_root_id["root_key"] is True + + table_data = load_tables_to_dicts( + p, "parent", "parent__child", "parent__child__grandchild", exclude_system_cols=False + ) + # drop dlt load id + del table_data["parent"][0]["_dlt_load_id"] + # all the ids are deterministic: on parent is set by the user, on child - is derived from parent + assert table_data == { + "parent": [{"_dlt_id": 1, "foo": 2}], + "parent__child": [ + { + "bar": 2, + "_dlt_root_id": 1, + "_dlt_parent_id": 1, + "_dlt_list_idx": 0, + "_dlt_id": "mvMThji/REOKKA", + } + ], + "parent__child__grandchild": [ + { + "baz": 2, + "_dlt_root_id": 1, + "_dlt_parent_id": "mvMThji/REOKKA", + "_dlt_list_idx": 0, + "_dlt_id": "KKZaBWTgbZd74A", + } + ], + } + + @pytest.mark.parametrize( "destination_config", destinations_configs( @@ -285,10 +541,7 @@ def data(slice_: slice = None): yield json.load(f) if slice_ is None else json.load(f)[slice_] # note: NodeId will be normalized to "node_id" which exists in the schema - info = p.run( - data(slice(0, 17)), - loader_file_format=destination_config.file_format, - ) + info = p.run(data(slice(0, 17)), **destination_config.run_kwargs) assert_load_info(info) github_1_counts = load_table_counts(p, *[t["name"] for t in p.default_schema.data_tables()]) # 17 issues @@ -298,10 +551,7 @@ def data(slice_: slice = None): assert p.default_schema.tables["issues"]["columns"]["node_id"]["data_type"] == "text" assert p.default_schema.tables["issues"]["columns"]["node_id"]["nullable"] is False - info = p.run( - data(slice(5, None)), - loader_file_format=destination_config.file_format, - ) + info = p.run(data(slice(5, None)), **destination_config.run_kwargs) assert_load_info(info) # for non merge destinations we just check that the run passes if not destination_config.supports_merge: @@ -339,7 +589,7 @@ def test_merge_source_compound_keys_and_changes( ) -> None: p = destination_config.setup_pipeline("github_3", dev_mode=True) - info = p.run(github(), loader_file_format=destination_config.file_format) + info = p.run(github(), **destination_config.run_kwargs) assert_load_info(info) github_1_counts = load_table_counts(p, *[t["name"] for t in p.default_schema.data_tables()]) # 100 issues total @@ -359,11 +609,7 @@ def test_merge_source_compound_keys_and_changes( ) # append load_issues resource - info = p.run( - github().load_issues, - write_disposition="append", - loader_file_format=destination_config.file_format, - ) + info = p.run(github().load_issues, write_disposition="append", **destination_config.run_kwargs) assert_load_info(info) assert p.default_schema.tables["issues"]["write_disposition"] == "append" # the counts of all tables must be double @@ -371,9 +617,7 @@ def test_merge_source_compound_keys_and_changes( assert {k: v * 2 for k, v in github_1_counts.items()} == github_2_counts # now replace all resources - info = p.run( - github(), write_disposition="replace", loader_file_format=destination_config.file_format - ) + info = p.run(github(), write_disposition="replace", **destination_config.run_kwargs) assert_load_info(info) assert p.default_schema.tables["issues"]["write_disposition"] == "replace" # assert p.default_schema.tables["issues__labels"]["write_disposition"] == "replace" @@ -383,7 +627,9 @@ def test_merge_source_compound_keys_and_changes( @pytest.mark.parametrize( - "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name + "destination_config", + destinations_configs(default_sql_configs=True, supports_merge=True), + ids=lambda x: x.name, ) def test_merge_no_child_tables(destination_config: DestinationTestConfiguration) -> None: p = destination_config.setup_pipeline("github_3", dev_mode=True) @@ -398,7 +644,7 @@ def test_merge_no_child_tables(destination_config: DestinationTestConfiguration) # take only first 15 elements github_data.load_issues.add_filter(take_first(15)) - info = p.run(github_data, loader_file_format=destination_config.file_format) + info = p.run(github_data, **destination_config.run_kwargs) assert len(p.default_schema.data_tables()) == 1 assert "issues" in p.default_schema.tables assert_load_info(info) @@ -408,7 +654,7 @@ def test_merge_no_child_tables(destination_config: DestinationTestConfiguration) # load all github_data = github() github_data.max_table_nesting = 0 - info = p.run(github_data, loader_file_format=destination_config.file_format) + info = p.run(github_data, **destination_config.run_kwargs) assert_load_info(info) github_2_counts = load_table_counts(p, *[t["name"] for t in p.default_schema.data_tables()]) # 100 issues total, or 115 if merge is not supported @@ -432,7 +678,7 @@ def test_merge_no_merge_keys(destination_config: DestinationTestConfiguration) - github_data.load_issues.apply_hints(merge_key=(), primary_key=()) # skip first 45 rows github_data.load_issues.add_filter(skip_first(45)) - info = p.run(github_data, loader_file_format=destination_config.file_format) + info = p.run(github_data, **destination_config.run_kwargs) assert_load_info(info) github_1_counts = load_table_counts(p, *[t["name"] for t in p.default_schema.data_tables()]) assert github_1_counts["issues"] == 100 - 45 @@ -443,7 +689,7 @@ def test_merge_no_merge_keys(destination_config: DestinationTestConfiguration) - github_data.load_issues.apply_hints(merge_key=(), primary_key=()) # skip first 45 rows github_data.load_issues.add_filter(take_first(10)) - info = p.run(github_data, loader_file_format=destination_config.file_format) + info = p.run(github_data, **destination_config.run_kwargs) assert_load_info(info) github_1_counts = load_table_counts(p, *[t["name"] for t in p.default_schema.data_tables()]) # we have 10 rows more, merge falls back to append if no keys present @@ -452,7 +698,9 @@ def test_merge_no_merge_keys(destination_config: DestinationTestConfiguration) - @pytest.mark.parametrize( "destination_config", - destinations_configs(default_sql_configs=True, file_format="parquet"), + destinations_configs( + default_sql_configs=True, with_file_format="parquet", local_filesystem_configs=True + ), ids=lambda x: x.name, ) def test_pipeline_load_parquet(destination_config: DestinationTestConfiguration) -> None: @@ -460,12 +708,14 @@ def test_pipeline_load_parquet(destination_config: DestinationTestConfiguration) # do not save state to destination so jobs counting is easier p.config.restore_from_destination = False github_data = github() - # generate some complex types + # generate some nested types github_data.max_table_nesting = 2 github_data_copy = github() github_data_copy.max_table_nesting = 2 info = p.run( - [github_data, github_data_copy], loader_file_format="parquet", write_disposition="merge" + [github_data, github_data_copy], + write_disposition="merge", + **destination_config.run_kwargs, ) assert_load_info(info) # make sure it was parquet or sql transforms @@ -484,13 +734,20 @@ def test_pipeline_load_parquet(destination_config: DestinationTestConfiguration) # now retry with replace github_data = github() - # generate some complex types + # generate some nested types github_data.max_table_nesting = 2 - info = p.run(github_data, loader_file_format="parquet", write_disposition="replace") + info = p.run( + github_data, + write_disposition="replace", + **destination_config.run_kwargs, + ) assert_load_info(info) # make sure it was parquet or sql inserts files = p.get_load_package_info(p.list_completed_load_packages()[1]).jobs["completed_jobs"] - if destination_config.force_iceberg: + if ( + destination_config.destination_type == "athena" + and destination_config.table_format == "iceberg" + ): # iceberg uses sql to copy tables expected_formats.append("sql") assert all(f.job_file_info.file_format in expected_formats for f in files) @@ -535,12 +792,18 @@ def _get_shuffled_events(shuffle: bool = dlt.secrets.value): @pytest.mark.parametrize( - "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name + "destination_config", + destinations_configs(default_sql_configs=True), + ids=lambda x: x.name, ) @pytest.mark.parametrize("github_resource", [github_repo_events, github_repo_events_table_meta]) def test_merge_with_dispatch_and_incremental( destination_config: DestinationTestConfiguration, github_resource: DltResource ) -> None: + if destination_config.destination_name == "sqlalchemy_mysql": + # TODO: Github events have too many columns for MySQL + pytest.skip("MySQL can't handle too many columns") + newest_issues = list( sorted(_get_shuffled_events(True), key=lambda x: x["created_at"], reverse=True) ) @@ -590,7 +853,7 @@ def _updated_event(node_id): p = destination_config.setup_pipeline("github_3", dev_mode=True) info = p.run( _get_shuffled_events(True) | github_resource, - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) assert_load_info(info) # get top tables @@ -602,15 +865,13 @@ def _updated_event(node_id): # this should skip all events due to incremental load info = p.run( _get_shuffled_events(True) | github_resource, - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) # no packages were loaded assert len(info.loads_ids) == 0 # load one more event with a new id - info = p.run( - _new_event("new_node") | github_resource, loader_file_format=destination_config.file_format - ) + info = p.run(_new_event("new_node") | github_resource, **destination_config.run_kwargs) assert_load_info(info) counts = load_table_counts( p, *[t["name"] for t in p.default_schema.data_tables() if t.get("parent") is None] @@ -625,7 +886,7 @@ def _updated_event(node_id): # load updated event info = p.run( _updated_event("new_node_X") | github_resource, - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) assert_load_info(info) # still 101 @@ -656,7 +917,7 @@ def duplicates(): {"id": 1, "name": "row2", "child": [4, 5, 6]}, ] - info = p.run(duplicates(), loader_file_format=destination_config.file_format) + info = p.run(duplicates(), **destination_config.run_kwargs) assert_load_info(info) counts = load_table_counts(p, "duplicates", "duplicates__child") assert counts["duplicates"] == 1 if destination_config.supports_merge else 2 @@ -668,7 +929,7 @@ def duplicates(): def duplicates_no_child(): yield [{"id": 1, "subkey": "AX", "name": "row1"}, {"id": 1, "subkey": "AX", "name": "row2"}] - info = p.run(duplicates_no_child(), loader_file_format=destination_config.file_format) + info = p.run(duplicates_no_child(), **destination_config.run_kwargs) assert_load_info(info) counts = load_table_counts(p, "duplicates_no_child") assert counts["duplicates_no_child"] == 1 if destination_config.supports_merge else 2 @@ -687,7 +948,7 @@ def duplicates(): {"id": 1, "name": "row2", "child": [4, 5, 6]}, ] - info = p.run(duplicates(), loader_file_format=destination_config.file_format) + info = p.run(duplicates(), **destination_config.run_kwargs) assert_load_info(info) counts = load_table_counts(p, "duplicates", "duplicates__child") assert counts["duplicates"] == 2 @@ -697,7 +958,7 @@ def duplicates(): def duplicates_no_child(): yield [{"id": 1, "subkey": "AX", "name": "row1"}, {"id": 1, "subkey": "AX", "name": "row2"}] - info = p.run(duplicates_no_child(), loader_file_format=destination_config.file_format) + info = p.run(duplicates_no_child(), **destination_config.run_kwargs) assert_load_info(info) counts = load_table_counts(p, "duplicates_no_child") assert counts["duplicates_no_child"] == 2 @@ -715,17 +976,17 @@ def duplicates_no_child(): ids=lambda x: x.name, ) @pytest.mark.parametrize("merge_strategy", ("delete-insert", "upsert")) -def test_complex_column_missing( +def test_nested_column_missing( destination_config: DestinationTestConfiguration, merge_strategy: TLoaderMergeStrategy, ) -> None: if destination_config.table_format == "delta": pytest.skip( - "Record updates that involve removing elements from a complex" + "Record updates that involve removing elements from a nested" " column is not supported for `delta` table format." ) - table_name = "test_complex_column_missing" + table_name = "test_nested_column_missing" @dlt.resource( name=table_name, @@ -740,22 +1001,22 @@ def r(data): skip_if_not_supported(merge_strategy, p.destination) data = [ - {"id": 1, "simple": "foo", "complex": [1, 2, 3]}, - {"id": 2, "simple": "foo", "complex": [1, 2]}, + {"id": 1, "simple": "foo", "nested": [1, 2, 3]}, + {"id": 2, "simple": "foo", "nested": [1, 2]}, ] - info = p.run(r(data), loader_file_format=destination_config.file_format) + info = p.run(r(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == 2 - assert load_table_counts(p, table_name + "__complex")[table_name + "__complex"] == 5 + assert load_table_counts(p, table_name + "__nested")[table_name + "__nested"] == 5 - # complex column is missing, previously inserted records should be deleted from child table + # nested column is missing, previously inserted records should be deleted from child table data = [ {"id": 1, "simple": "bar"}, ] - info = p.run(r(data), loader_file_format=destination_config.file_format) + info = p.run(r(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == 2 - assert load_table_counts(p, table_name + "__complex")[table_name + "__complex"] == 2 + assert load_table_counts(p, table_name + "__nested")[table_name + "__nested"] == 2 @pytest.mark.parametrize( @@ -800,7 +1061,7 @@ def data_resource(data): {"id": 1, "val": "foo", "deleted": False}, {"id": 2, "val": "bar", "deleted": False}, ] - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == 2 @@ -808,7 +1069,7 @@ def data_resource(data): data = [ {"id": 1, "deleted": True}, ] - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == (1 if key_type != "no_key" else 2) @@ -816,7 +1077,7 @@ def data_resource(data): data = [ {"id": 2, "val": "baz", "deleted": None}, ] - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == (1 if key_type != "no_key" else 3) @@ -837,7 +1098,7 @@ def data_resource(data): ] if merge_strategy == "upsert": del data[0] # `upsert` requires unique `primary_key` - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) counts = load_table_counts(p, table_name)[table_name] if key_type == "primary_key": @@ -855,12 +1116,12 @@ def data_resource(data): data = [ {"id": 3, "val": "foo", "deleted": True}, ] - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) counts = load_table_counts(p, table_name)[table_name] assert load_table_counts(p, table_name)[table_name] == 1 - table_name = "test_hard_delete_hint_complex" + table_name = "test_hard_delete_hint_nested" data_resource.apply_hints(table_name=table_name) # insert two records with childs and grandchilds @@ -881,7 +1142,7 @@ def data_resource(data): "deleted": False, }, ] - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == 2 assert load_table_counts(p, table_name + "__child_1")[table_name + "__child_1"] == 3 @@ -897,7 +1158,7 @@ def data_resource(data): data = [ {"id": 1, "deleted": True}, ] - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == 1 assert load_table_counts(p, table_name + "__child_1")[table_name + "__child_1"] == 1 @@ -912,7 +1173,7 @@ def data_resource(data): data = [ {"id": 2, "deleted": True}, ] - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == 0 assert load_table_counts(p, table_name + "__child_1")[table_name + "__child_1"] == 0 @@ -955,7 +1216,7 @@ def data_resource(data): {"id": 1, "val": "foo", "deleted_timestamp": None}, {"id": 2, "val": "bar", "deleted_timestamp": None}, ] - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == 2 @@ -963,7 +1224,7 @@ def data_resource(data): data = [ {"id": 1, "deleted_timestamp": "2024-02-15T17:16:53Z"}, ] - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == 1 @@ -986,7 +1247,7 @@ def r(): yield {"id": 1, "val": "foo", "deleted_1": True, "deleted_2": False} with pytest.raises(PipelineStepFailed): - info = p.run(r(), loader_file_format=destination_config.file_format) + info = p.run(r(), **destination_config.run_kwargs) @pytest.mark.essential @@ -1018,7 +1279,7 @@ def data_resource(data): {"id": 1, "val": "baz", "sequence": 3}, {"id": 1, "val": "bar", "sequence": 2}, ] - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == 1 @@ -1035,7 +1296,7 @@ def data_resource(data): # now test "asc" sorting data_resource.apply_hints(columns={"sequence": {"dedup_sort": "asc", "nullable": False}}) - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == 1 @@ -1049,7 +1310,7 @@ def data_resource(data): expected = [{"id": 1, "val": "foo", "sequence": 1}] assert sorted(observed, key=lambda d: d["id"]) == expected - table_name = "test_dedup_sort_hint_complex" + table_name = "test_dedup_sort_hint_nested" data_resource.apply_hints( table_name=table_name, columns={"sequence": {"dedup_sort": "desc", "nullable": False}}, @@ -1062,7 +1323,7 @@ def data_resource(data): {"id": 1, "val": [7, 8, 9], "sequence": 3}, {"id": 1, "val": [4, 5, 6], "sequence": 2}, ] - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == 1 assert load_table_counts(p, table_name + "__val")[table_name + "__val"] == 3 @@ -1089,7 +1350,7 @@ def data_resource(data): {"id": 1, "val": "baz", "sequence": 3, "deleted": True}, {"id": 1, "val": "bar", "sequence": 2, "deleted": False}, ] - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == 0 @@ -1100,7 +1361,7 @@ def data_resource(data): {"id": 1, "val": "bar", "sequence": 2, "deleted": True}, {"id": 1, "val": "baz", "sequence": 3, "deleted": False}, ] - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == 1 @@ -1114,7 +1375,7 @@ def data_resource(data): assert sorted(observed, key=lambda d: d["id"]) == expected # additional tests with two records, run only on duckdb to limit test load - if destination_config.destination == "duckdb": + if destination_config.destination_type == "duckdb": # two records with same primary key # record with highest value in sort column is a delete # existing record is deleted and no record will be inserted @@ -1122,7 +1383,7 @@ def data_resource(data): {"id": 1, "val": "foo", "sequence": 1}, {"id": 1, "val": "bar", "sequence": 2, "deleted": True}, ] - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == 0 @@ -1132,7 +1393,7 @@ def data_resource(data): {"id": 1, "val": "foo", "sequence": 2}, {"id": 1, "val": "bar", "sequence": 1, "deleted": True}, ] - info = p.run(data_resource(data), loader_file_format=destination_config.file_format) + info = p.run(data_resource(data), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, table_name)[table_name] == 1 @@ -1147,14 +1408,14 @@ def r(): # invalid value for "dedup_sort" hint with pytest.raises(PipelineStepFailed): - info = p.run(r(), loader_file_format=destination_config.file_format) + info = p.run(r(), **destination_config.run_kwargs) # more than one "dedup_sort" column hints are provided r.apply_hints( columns={"dedup_sort_1": {"dedup_sort": "desc"}, "dedup_sort_2": {"dedup_sort": "desc"}} ) with pytest.raises(PipelineStepFailed): - info = p.run(r(), loader_file_format=destination_config.file_format) + info = p.run(r(), **destination_config.run_kwargs) def test_merge_strategy_config() -> None: @@ -1177,8 +1438,11 @@ def r(): yield {"foo": "bar"} assert "scd2" not in p.destination.capabilities().supported_merge_strategies - with pytest.raises(DestinationCapabilitiesException): + with pytest.raises(PipelineStepFailed) as pip_ex: p.run(r()) + assert pip_ex.value.step == "normalize" # failed already in normalize when generating row ids + # PipelineStepFailed -> NormalizeJobFailed -> DestinationCapabilitiesException + assert isinstance(pip_ex.value.__cause__.__cause__, DestinationCapabilitiesException) @pytest.mark.parametrize( @@ -1192,7 +1456,7 @@ def r(): ids=lambda x: x.name, ) def test_upsert_merge_strategy_config(destination_config: DestinationTestConfiguration) -> None: - if destination_config.destination == "filesystem": + if destination_config.destination_type == "filesystem": # TODO: implement validation and remove this test exception pytest.skip( "`upsert` merge strategy configuration validation has not yet been" @@ -1207,7 +1471,7 @@ def r(): p = destination_config.setup_pipeline("upsert_pipeline", dev_mode=True) assert "primary_key" not in r._hints with pytest.raises(PipelineStepFailed) as pip_ex: - p.run(r()) + p.run(r(), **destination_config.run_kwargs) assert isinstance(pip_ex.value.__context__, SchemaCorruptedException) @@ -1225,7 +1489,7 @@ def merging_test_table(): p = destination_config.setup_pipeline("abstract", full_refresh=True) with pytest.raises(PipelineStepFailed) as pip_ex: - p.run(merging_test_table()) + p.run(merging_test_table(), **destination_config.run_kwargs) ex = pip_ex.value assert ex.step == "normalize" @@ -1250,7 +1514,7 @@ def r(): p = destination_config.setup_pipeline("abstract", full_refresh=True) with pytest.raises(PipelineStepFailed) as pip_ex: - p.run(r()) + p.run(r(), **destination_config.run_kwargs) ex = pip_ex.value assert ex.step == "normalize" diff --git a/tests/load/pipeline/test_pipelines.py b/tests/load/pipeline/test_pipelines.py index 81c9292570..659bca6cb9 100644 --- a/tests/load/pipeline/test_pipelines.py +++ b/tests/load/pipeline/test_pipelines.py @@ -9,7 +9,6 @@ from dlt.common import json, sleep from dlt.common.pipeline import SupportsPipeline from dlt.common.destination import Destination -from dlt.common.destination.exceptions import DestinationHasFailedJobs from dlt.common.destination.reference import WithStagingDataset from dlt.common.schema.exceptions import CannotCoerceColumnException from dlt.common.schema.schema import Schema @@ -17,12 +16,14 @@ from dlt.common.schema.utils import new_table from dlt.common.typing import TDataItem from dlt.common.utils import uniq_id +from dlt.common.exceptions import TerminalValueError from dlt.destinations.exceptions import DatabaseUndefinedRelation from dlt.destinations import filesystem, redshift from dlt.destinations.job_client_impl import SqlJobClientBase from dlt.extract.exceptions import ResourceNameMissing from dlt.extract.source import DltSource +from dlt.load.exceptions import LoadClientJobFailed from dlt.pipeline.exceptions import ( CannotRestorePipelineException, PipelineConfigMissing, @@ -84,12 +85,12 @@ def data_fun() -> Iterator[Any]: yield data # this will create default schema - p.extract(data_fun) + p.extract(data_fun, table_format=destination_config.table_format) # _pipeline suffix removed when creating default schema name assert p.default_schema_name in ["dlt_pytest", "dlt", "dlt_jb_pytest_runner"] # this will create additional schema - p.extract(data_fun(), schema=dlt.Schema("names")) + p.extract(data_fun(), schema=dlt.Schema("names"), table_format=destination_config.table_format) assert p.default_schema_name in ["dlt_pytest", "dlt", "dlt_jb_pytest_runner"] assert "names" in p.schemas.keys() @@ -99,7 +100,7 @@ def data_fun() -> Iterator[Any]: # mock the correct destinations (never do that in normal code) with p.managed_state(): p._set_destinations( - destination=Destination.from_reference(destination_config.destination), + destination=destination_config.destination_factory(), staging=( Destination.from_reference(destination_config.staging) if destination_config.staging @@ -118,7 +119,7 @@ def data_fun() -> Iterator[Any]: state_package = p.get_load_package_info(last_load_id) assert len(state_package.jobs["new_jobs"]) == 1 assert state_package.schema_name == p.default_schema_name - p.normalize() + p.normalize(loader_file_format=destination_config.file_format) info = p.load(dataset_name="d" + uniq_id()) print(p.dataset_name) assert info.pipeline is p @@ -161,16 +162,20 @@ def test_default_schema_name( for idx, alpha in [(0, "A"), (0, "B"), (0, "C")] ] - p = dlt.pipeline( + p = destination_config.setup_pipeline( "test_default_schema_name", - TEST_STORAGE_ROOT, - destination=destination_config.destination, - staging=destination_config.staging, dataset_name=dataset_name, + pipelines_dir=TEST_STORAGE_ROOT, ) + p.config.use_single_dataset = use_single_dataset - p.extract(data, table_name="test", schema=Schema("default")) - p.normalize() + p.extract( + data, + table_name="test", + schema=Schema("default"), + table_format=destination_config.table_format, + ) + p.normalize(loader_file_format=destination_config.file_format) info = p.load() print(info) @@ -206,10 +211,10 @@ def _data(): destination_config.setup() info = dlt.run( _data(), - destination=destination_config.destination, + destination=destination_config.destination_factory(), staging=destination_config.staging, dataset_name="specific" + uniq_id(), - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) with pytest.raises(CannotRestorePipelineException): @@ -246,7 +251,7 @@ def _data(): yield d p = destination_config.setup_pipeline("test_skip_sync_schema_for_tables", dev_mode=True) - p.extract(_data) + p.extract(_data, table_format=destination_config.table_format) schema = p.default_schema assert "data_table" in schema.tables assert schema.tables["data_table"]["columns"] == {} @@ -282,10 +287,10 @@ def _data(): p = dlt.pipeline(dev_mode=True) info = p.run( _data(), - destination=destination_config.destination, + destination=destination_config.destination_factory(), staging=destination_config.staging, dataset_name="iteration" + uniq_id(), - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) assert info.dataset_name == p.dataset_name assert info.dataset_name.endswith(p._pipeline_instance_id) @@ -356,9 +361,11 @@ def extended_rows(): "my_pipeline", import_schema_path=import_schema_path, export_schema_path=export_schema_path ) - p.extract(source(10).with_resources("simple_rows")) + p.extract( + source(10).with_resources("simple_rows"), table_format=destination_config.table_format + ) # print(p.default_schema.to_pretty_yaml()) - p.normalize() + p.normalize(loader_file_format=destination_config.file_format) info = p.load(dataset_name=dataset_name) # test __str__ print(info) @@ -372,11 +379,13 @@ def extended_rows(): assert "new_column" not in schema.get_table("simple_rows")["columns"] # lets violate unique constraint on postgres, redshift and BQ ignore unique indexes - if destination_config.destination == "postgres": + if destination_config.destination_type == "postgres": + # let it complete even with PK violation (which is a teminal error) + os.environ["RAISE_ON_FAILED_JOBS"] = "false" assert p.dataset_name == dataset_name err_info = p.run( source(1).with_resources("simple_rows"), - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) version_history.append(p.default_schema.stored_version_hash) # print(err_info) @@ -387,8 +396,7 @@ def extended_rows(): # - new column in "simple_rows" table # - new "simple" table info_ext = dlt.run( - source(10).with_resources("extended_rows", "simple"), - loader_file_format=destination_config.file_format, + source(10).with_resources("extended_rows", "simple"), **destination_config.run_kwargs ) print(info_ext) # print(p.default_schema.to_pretty_yaml()) @@ -431,14 +439,14 @@ def test_pipeline_data_writer_compression( "disable_compression": disable_compression } # not sure how else to set this p = destination_config.setup_pipeline("compression_test", dataset_name=dataset_name) - p.extract(dlt.resource(data, name="data")) + p.extract(dlt.resource(data, name="data"), table_format=destination_config.table_format) s = p._get_normalize_storage() # check that files are not compressed if compression is disabled if disable_compression: for f in s.list_files_to_normalize_sorted(): with pytest.raises(gzip.BadGzipFile): gzip.open(s.extracted_packages.storage.make_full_path(f), "rb").read() - p.normalize() + p.normalize(loader_file_format=destination_config.file_format) info = p.load() assert_table(p, "data", data, info=info) @@ -449,383 +457,35 @@ def test_pipeline_data_writer_compression( def test_source_max_nesting(destination_config: DestinationTestConfiguration) -> None: destination_config.setup() - complex_part = {"l": [1, 2, 3], "c": {"a": 1, "b": 12.3}} + nested_part = {"l": [1, 2, 3], "c": {"a": 1, "b": 12.3}} - @dlt.source(name="complex", max_table_nesting=0) - def complex_data(): - return dlt.resource([{"idx": 1, "cn": complex_part}], name="complex_cn") + @dlt.source(name="nested", max_table_nesting=0) + def nested_data(): + return dlt.resource([{"idx": 1, "cn": nested_part}], name="nested_cn") info = dlt.run( - complex_data(), - destination=destination_config.destination, + nested_data(), + destination=destination_config.destination_factory(), staging=destination_config.staging, dataset_name="ds_" + uniq_id(), - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) print(info) with dlt.pipeline().sql_client() as client: - complex_cn_table = client.make_qualified_table_name("complex_cn") - rows = select_data(dlt.pipeline(), f"SELECT cn FROM {complex_cn_table}") + nested_cn_table = client.make_qualified_table_name("nested_cn") + rows = select_data(dlt.pipeline(), f"SELECT cn FROM {nested_cn_table}") assert len(rows) == 1 cn_val = rows[0][0] if isinstance(cn_val, str): cn_val = json.loads(cn_val) - assert cn_val == complex_part - - -@pytest.mark.parametrize( - "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name -) -def test_dataset_name_change(destination_config: DestinationTestConfiguration) -> None: - destination_config.setup() - # standard name - ds_1_name = "iteration" + uniq_id() - # will go to snake case - ds_2_name = "IteRation" + uniq_id() - # illegal name that will be later normalized - ds_3_name = "1it/era 👍 tion__" + uniq_id() - p, s = simple_nested_pipeline(destination_config, dataset_name=ds_1_name, dev_mode=False) - try: - info = p.run(s(), loader_file_format=destination_config.file_format) - assert_load_info(info) - assert info.dataset_name == ds_1_name - ds_1_counts = load_table_counts(p, "lists", "lists__value") - # run to another dataset - info = p.run(s(), dataset_name=ds_2_name, loader_file_format=destination_config.file_format) - assert_load_info(info) - assert info.dataset_name.startswith("ite_ration") - # save normalized dataset name to delete correctly later - ds_2_name = info.dataset_name - ds_2_counts = load_table_counts(p, "lists", "lists__value") - assert ds_1_counts == ds_2_counts - # set name and run to another dataset - p.dataset_name = ds_3_name - info = p.run(s(), loader_file_format=destination_config.file_format) - assert_load_info(info) - assert info.dataset_name.startswith("_1it_era_tion_") - ds_3_counts = load_table_counts(p, "lists", "lists__value") - assert ds_1_counts == ds_3_counts - - finally: - # we have to clean dataset ourselves - with p.sql_client() as client: - delete_dataset(client, ds_1_name) - delete_dataset(client, ds_2_name) - # delete_dataset(client, ds_3_name) # will be deleted by the fixture - - -# do not remove - it allows us to filter tests by destination -@pytest.mark.parametrize( - "destination_config", - destinations_configs(default_sql_configs=True, subset=["postgres"]), - ids=lambda x: x.name, -) -def test_pipeline_explicit_destination_credentials( - destination_config: DestinationTestConfiguration, -) -> None: - from dlt.destinations import postgres - from dlt.destinations.impl.postgres.configuration import PostgresCredentials - - # explicit credentials resolved - p = dlt.pipeline( - destination=Destination.from_reference( - "postgres", - destination_name="mydest", - credentials="postgresql://loader:loader@localhost:7777/dlt_data", - ), - ) - c = p._get_destination_clients(Schema("s"), p._get_destination_client_initial_config())[0] - assert c.config.credentials.port == 7777 # type: ignore[attr-defined] - - # TODO: may want to clear the env completely and ignore/mock config files somehow to avoid side effects - # explicit credentials resolved ignoring the config providers - os.environ["DESTINATION__MYDEST__CREDENTIALS__HOST"] = "HOST" - p = dlt.pipeline( - destination=Destination.from_reference( - "postgres", - destination_name="mydest", - credentials="postgresql://loader:loader@localhost:5432/dlt_data", - ), - ) - c = p._get_destination_clients(Schema("s"), p._get_destination_client_initial_config())[0] - assert c.config.credentials.host == "localhost" # type: ignore[attr-defined] - - # explicit partial credentials will use config providers - os.environ["DESTINATION__MYDEST__CREDENTIALS__USERNAME"] = "UN" - os.environ["DESTINATION__MYDEST__CREDENTIALS__PASSWORD"] = "PW" - p = dlt.pipeline( - destination=Destination.from_reference( - "postgres", - destination_name="mydest", - credentials="postgresql://localhost:5432/dlt_data", - ), - ) - c = p._get_destination_clients(Schema("s"), p._get_destination_client_initial_config())[0] - assert c.config.credentials.username == "UN" # type: ignore[attr-defined] - # host is taken form explicit credentials - assert c.config.credentials.host == "localhost" # type: ignore[attr-defined] - - # instance of credentials will be simply passed - cred = PostgresCredentials("postgresql://user:pass@localhost/dlt_data") - p = dlt.pipeline(destination=postgres(credentials=cred)) - inner_c = p.destination_client() - assert inner_c.config.credentials is cred - - # with staging - p = dlt.pipeline( - pipeline_name="postgres_pipeline", - staging=filesystem("_storage"), - destination=redshift(credentials="redshift://loader:password@localhost:5432/dlt_data"), - ) - config = p.destination_client().config - assert config.credentials.is_resolved() - assert ( - config.credentials.to_native_representation() - == "redshift://loader:password@localhost:5432/dlt_data?connect_timeout=15" - ) + assert cn_val == nested_part -# do not remove - it allows us to filter tests by destination @pytest.mark.parametrize( "destination_config", - destinations_configs(default_sql_configs=True, subset=["postgres"]), - ids=lambda x: x.name, -) -def test_pipeline_with_sources_sharing_schema( - destination_config: DestinationTestConfiguration, -) -> None: - schema = Schema("shared") - - @dlt.source(schema=schema, max_table_nesting=1) - def source_1(): - @dlt.resource(primary_key="user_id") - def gen1(): - dlt.current.source_state()["source_1"] = True - dlt.current.resource_state()["source_1"] = True - yield {"id": "Y", "user_id": "user_y"} - - @dlt.resource(columns={"col": {"data_type": "bigint"}}) - def conflict(): - yield "conflict" - - return gen1, conflict - - @dlt.source(schema=schema, max_table_nesting=2) - def source_2(): - @dlt.resource(primary_key="id") - def gen1(): - dlt.current.source_state()["source_2"] = True - dlt.current.resource_state()["source_2"] = True - yield {"id": "X", "user_id": "user_X"} - - def gen2(): - yield from "CDE" - - @dlt.resource(columns={"col": {"data_type": "bool"}}, selected=False) - def conflict(): - yield "conflict" - - return gen2, gen1, conflict - - # all selected tables with hints should be there - discover_1 = source_1().discover_schema() - assert "gen1" in discover_1.tables - assert discover_1.tables["gen1"]["columns"]["user_id"]["primary_key"] is True - assert "data_type" not in discover_1.tables["gen1"]["columns"]["user_id"] - assert "conflict" in discover_1.tables - assert discover_1.tables["conflict"]["columns"]["col"]["data_type"] == "bigint" - - discover_2 = source_2().discover_schema() - assert "gen1" in discover_2.tables - assert "gen2" in discover_2.tables - # conflict deselected - assert "conflict" not in discover_2.tables - - p = dlt.pipeline(pipeline_name="multi", destination="duckdb", dev_mode=True) - p.extract([source_1(), source_2()]) - default_schema = p.default_schema - gen1_table = default_schema.tables["gen1"] - assert "user_id" in gen1_table["columns"] - assert "id" in gen1_table["columns"] - assert "conflict" in default_schema.tables - assert "gen2" in default_schema.tables - p.normalize() - assert "gen2" in default_schema.tables - p.load() - table_names = [t["name"] for t in default_schema.data_tables()] - counts = load_table_counts(p, *table_names) - assert counts == {"gen1": 2, "gen2": 3, "conflict": 1} - # both sources share the same state - assert p.state["sources"] == { - "shared": { - "source_1": True, - "resources": {"gen1": {"source_1": True, "source_2": True}}, - "source_2": True, - } - } - drop_active_pipeline_data() - - # same pipeline but enable conflict - p = dlt.pipeline(pipeline_name="multi", destination="duckdb", dev_mode=True) - with pytest.raises(PipelineStepFailed) as py_ex: - p.extract([source_1(), source_2().with_resources("conflict")]) - assert isinstance(py_ex.value.__context__, CannotCoerceColumnException) - - -# do not remove - it allows us to filter tests by destination -@pytest.mark.parametrize( - "destination_config", - destinations_configs(default_sql_configs=True, subset=["postgres"]), - ids=lambda x: x.name, -) -def test_many_pipelines_single_dataset(destination_config: DestinationTestConfiguration) -> None: - schema = Schema("shared") - - @dlt.source(schema=schema, max_table_nesting=1) - def source_1(): - @dlt.resource(primary_key="user_id") - def gen1(): - dlt.current.source_state()["source_1"] = True - dlt.current.resource_state()["source_1"] = True - yield {"id": "Y", "user_id": "user_y"} - - return gen1 - - @dlt.source(schema=schema, max_table_nesting=2) - def source_2(): - @dlt.resource(primary_key="id") - def gen1(): - dlt.current.source_state()["source_2"] = True - dlt.current.resource_state()["source_2"] = True - yield {"id": "X", "user_id": "user_X"} - - def gen2(): - yield from "CDE" - - return gen2, gen1 - - # load source_1 to common dataset - p = dlt.pipeline( - pipeline_name="source_1_pipeline", destination="duckdb", dataset_name="shared_dataset" - ) - p.run(source_1(), credentials="duckdb:///_storage/test_quack.duckdb") - counts = load_table_counts(p, *p.default_schema.tables.keys()) - assert counts.items() >= {"gen1": 1, "_dlt_pipeline_state": 1, "_dlt_loads": 1}.items() - p._wipe_working_folder() - p.deactivate() - - p = dlt.pipeline( - pipeline_name="source_2_pipeline", destination="duckdb", dataset_name="shared_dataset" - ) - p.run(source_2(), credentials="duckdb:///_storage/test_quack.duckdb") - # table_names = [t["name"] for t in p.default_schema.data_tables()] - counts = load_table_counts(p, *p.default_schema.tables.keys()) - # gen1: one record comes from source_1, 1 record from source_2 - assert counts.items() >= {"gen1": 2, "_dlt_pipeline_state": 2, "_dlt_loads": 2}.items() - # assert counts == {'gen1': 2, 'gen2': 3} - p._wipe_working_folder() - p.deactivate() - - # restore from destination, check state - p = dlt.pipeline( - pipeline_name="source_1_pipeline", - destination=dlt.destinations.duckdb(credentials="duckdb:///_storage/test_quack.duckdb"), - dataset_name="shared_dataset", - ) - p.sync_destination() - # we have our separate state - assert p.state["sources"]["shared"] == { - "source_1": True, - "resources": {"gen1": {"source_1": True}}, - } - # but the schema was common so we have the earliest one - assert "gen2" in p.default_schema.tables - p._wipe_working_folder() - p.deactivate() - - p = dlt.pipeline( - pipeline_name="source_2_pipeline", - destination=dlt.destinations.duckdb(credentials="duckdb:///_storage/test_quack.duckdb"), - dataset_name="shared_dataset", - ) - p.sync_destination() - # we have our separate state - assert p.state["sources"]["shared"] == { - "source_2": True, - "resources": {"gen1": {"source_2": True}}, - } - - -# do not remove - it allows us to filter tests by destination -@pytest.mark.parametrize( - "destination_config", - destinations_configs(default_sql_configs=True, subset=["snowflake"]), - ids=lambda x: x.name, -) -def test_snowflake_custom_stage(destination_config: DestinationTestConfiguration) -> None: - """Using custom stage name instead of the table stage""" - os.environ["DESTINATION__SNOWFLAKE__STAGE_NAME"] = "my_non_existing_stage" - pipeline, data = simple_nested_pipeline(destination_config, f"custom_stage_{uniq_id()}", False) - info = pipeline.run(data(), loader_file_format=destination_config.file_format) - with pytest.raises(DestinationHasFailedJobs) as f_jobs: - info.raise_on_failed_jobs() - assert "MY_NON_EXISTING_STAGE" in f_jobs.value.failed_jobs[0].failed_message - - drop_active_pipeline_data() - - # NOTE: this stage must be created in DLT_DATA database for this test to pass! - # CREATE STAGE MY_CUSTOM_LOCAL_STAGE; - # GRANT READ, WRITE ON STAGE DLT_DATA.PUBLIC.MY_CUSTOM_LOCAL_STAGE TO ROLE DLT_LOADER_ROLE; - stage_name = "PUBLIC.MY_CUSTOM_LOCAL_STAGE" - os.environ["DESTINATION__SNOWFLAKE__STAGE_NAME"] = stage_name - pipeline, data = simple_nested_pipeline(destination_config, f"custom_stage_{uniq_id()}", False) - info = pipeline.run(data(), loader_file_format=destination_config.file_format) - assert_load_info(info) - - load_id = info.loads_ids[0] - - # Get a list of the staged files and verify correct number of files in the "load_id" dir - with pipeline.sql_client() as client: - staged_files = client.execute_sql(f'LIST @{stage_name}/"{load_id}"') - assert len(staged_files) == 3 - # check data of one table to ensure copy was done successfully - tbl_name = client.make_qualified_table_name("lists") - assert_query_data(pipeline, f"SELECT value FROM {tbl_name}", ["a", None, None]) - - -# do not remove - it allows us to filter tests by destination -@pytest.mark.parametrize( - "destination_config", - destinations_configs(default_sql_configs=True, subset=["snowflake"]), - ids=lambda x: x.name, -) -def test_snowflake_delete_file_after_copy(destination_config: DestinationTestConfiguration) -> None: - """Using keep_staged_files = false option to remove staged files after copy""" - os.environ["DESTINATION__SNOWFLAKE__KEEP_STAGED_FILES"] = "FALSE" - - pipeline, data = simple_nested_pipeline( - destination_config, f"delete_staged_files_{uniq_id()}", False - ) - - info = pipeline.run(data(), loader_file_format=destination_config.file_format) - assert_load_info(info) - - load_id = info.loads_ids[0] - - with pipeline.sql_client() as client: - # no files are left in table stage - stage_name = client.make_qualified_table_name("%lists") - staged_files = client.execute_sql(f'LIST @{stage_name}/"{load_id}"') - assert len(staged_files) == 0 - - # ensure copy was done - tbl_name = client.make_qualified_table_name("lists") - assert_query_data(pipeline, f"SELECT value FROM {tbl_name}", ["a", None, None]) - - -@pytest.mark.parametrize( - "destination_config", - destinations_configs(default_sql_configs=True, all_staging_configs=True, file_format="parquet"), + destinations_configs( + default_sql_configs=True, all_staging_configs=True, with_file_format="parquet" + ), ids=lambda x: x.name, ) def test_parquet_loading(destination_config: DestinationTestConfiguration) -> None: @@ -848,12 +508,12 @@ def other_data(): column_schemas = deepcopy(TABLE_UPDATE_COLUMNS_SCHEMA) # parquet on bigquery and clickhouse does not support JSON but we still want to run the test - if destination_config.destination in ["bigquery"]: + if destination_config.destination_type in ["bigquery"]: column_schemas["col9_null"]["data_type"] = column_schemas["col9"]["data_type"] = "text" # duckdb 0.9.1 does not support TIME other than 6 - if destination_config.destination in ["duckdb", "motherduck"]: - column_schemas["col11_precision"]["precision"] = 0 + if destination_config.destination_type in ["duckdb", "motherduck"]: + column_schemas["col11_precision"]["precision"] = None # also we do not want to test col4_precision (datetime) because # those timestamps are not TZ aware in duckdb and we'd need to # disable TZ when generating parquet @@ -861,7 +521,7 @@ def other_data(): column_schemas["col4_precision"]["precision"] = 6 # drop TIME from databases not supporting it via parquet - if destination_config.destination in [ + if destination_config.destination_type in [ "redshift", "athena", "synapse", @@ -875,11 +535,11 @@ def other_data(): column_schemas.pop("col11_null") column_schemas.pop("col11_precision") - if destination_config.destination in ("redshift", "dremio"): + if destination_config.destination_type in ("redshift", "dremio"): data_types.pop("col7_precision") column_schemas.pop("col7_precision") - # apply the exact columns definitions so we process complex and wei types correctly! + # apply the exact columns definitions so we process nested and wei types correctly! @dlt.resource(table_name="data_types", write_disposition="merge", columns=column_schemas) def my_resource(): nonlocal data_types @@ -889,7 +549,7 @@ def my_resource(): def some_source(): return [some_data(), other_data(), my_resource()] - info = pipeline.run(some_source(), loader_file_format="parquet") + info = pipeline.run(some_source(), **destination_config.run_kwargs) package_info = pipeline.get_load_package_info(info.loads_ids[0]) # print(package_info.asstr(verbosity=2)) assert package_info.state == "loaded" @@ -900,9 +560,9 @@ def some_source(): # add sql merge job if destination_config.supports_merge: expected_completed_jobs += 1 - # add iceberg copy jobs - if destination_config.force_iceberg: - expected_completed_jobs += 3 if destination_config.supports_merge else 4 + # add iceberg copy jobs + if destination_config.destination_type == "athena": + expected_completed_jobs += 2 # if destination_config.supports_merge else 4 assert len(package_info.jobs["completed_jobs"]) == expected_completed_jobs with pipeline.sql_client() as sql_client: @@ -922,13 +582,56 @@ def some_source(): assert_all_data_types_row( db_row, schema=column_schemas, - parse_complex_strings=destination_config.destination + parse_json_strings=destination_config.destination_type in ["snowflake", "bigquery", "redshift"], - allow_string_binary=destination_config.destination == "clickhouse", - timestamp_precision=3 if destination_config.destination in ("athena", "dremio") else 6, + allow_string_binary=destination_config.destination_type == "clickhouse", + timestamp_precision=( + 3 if destination_config.destination_type in ("athena", "dremio") else 6 + ), ) +@pytest.mark.parametrize( + "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name +) +def test_dataset_name_change(destination_config: DestinationTestConfiguration) -> None: + destination_config.setup() + # standard name + ds_1_name = "iteration" + uniq_id() + # will go to snake case + ds_2_name = "IteRation" + uniq_id() + # illegal name that will be later normalized + ds_3_name = "1it/era 👍 tion__" + uniq_id() + p, s = simple_nested_pipeline(destination_config, dataset_name=ds_1_name, dev_mode=False) + try: + info = p.run(s(), **destination_config.run_kwargs) + assert_load_info(info) + assert info.dataset_name == ds_1_name + ds_1_counts = load_table_counts(p, "lists", "lists__value") + # run to another dataset + info = p.run(s(), dataset_name=ds_2_name, **destination_config.run_kwargs) + assert_load_info(info) + assert info.dataset_name.startswith("ite_ration") + # save normalized dataset name to delete correctly later + ds_2_name = info.dataset_name + ds_2_counts = load_table_counts(p, "lists", "lists__value") + assert ds_1_counts == ds_2_counts + # set name and run to another dataset + p.dataset_name = ds_3_name + info = p.run(s(), **destination_config.run_kwargs) + assert_load_info(info) + assert info.dataset_name.startswith("_1it_era_tion_") + ds_3_counts = load_table_counts(p, "lists", "lists__value") + assert ds_1_counts == ds_3_counts + + finally: + # we have to clean dataset ourselves + with p.sql_client() as client: + delete_dataset(client, ds_1_name) + delete_dataset(client, ds_2_name) + # delete_dataset(client, ds_3_name) # will be deleted by the fixture + + @pytest.mark.parametrize( "destination_config", destinations_configs(default_staging_configs=True, default_sql_configs=True), @@ -988,9 +691,7 @@ def table_3(make_data=False): # now we use this schema but load just one resource source = two_tables() # push state, table 3 not created - load_info_1 = pipeline.run( - source.table_3, schema=schema, loader_file_format=destination_config.file_format - ) + load_info_1 = pipeline.run(source.table_3, schema=schema, **destination_config.run_kwargs) assert_load_info(load_info_1) with pytest.raises(DatabaseUndefinedRelation): load_table_counts(pipeline, "table_3") @@ -1000,15 +701,13 @@ def table_3(make_data=False): ) # load with one empty job, table 3 not created - load_info = pipeline.run(source.table_3, loader_file_format=destination_config.file_format) + load_info = pipeline.run(source.table_3, **destination_config.run_kwargs) assert_load_info(load_info, expected_load_packages=0) with pytest.raises(DatabaseUndefinedRelation): load_table_counts(pipeline, "table_3") # print(pipeline.default_schema.to_pretty_yaml()) - load_info_2 = pipeline.run( - [source.table_1, source.table_3], loader_file_format=destination_config.file_format - ) + load_info_2 = pipeline.run([source.table_1, source.table_3], **destination_config.run_kwargs) assert_load_info(load_info_2) # 1 record in table 1 assert pipeline.last_trace.last_normalize_info.row_counts["table_1"] == 1 @@ -1030,7 +729,7 @@ def table_3(make_data=False): # also we make the replace resource to load its 1 record load_info_3 = pipeline.run( [source.table_3(make_data=True), source.table_2], - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) assert_load_info(load_info_3) assert_data_table_counts(pipeline, {"table_1": 1, "table_2": 1, "table_3": 1}) @@ -1049,9 +748,7 @@ def table_3(make_data=False): with pipeline.sql_client() as client: table_name = f"table_{i}" - if job_client.should_load_data_to_staging_dataset( - job_client.schema.tables[table_name] - ): + if job_client.should_load_data_to_staging_dataset(table_name): with client.with_staging_dataset(): tab_name = client.make_qualified_table_name(table_name) with client.execute_query(f"SELECT * FROM {tab_name}") as cur: @@ -1059,14 +756,16 @@ def table_3(make_data=False): @pytest.mark.parametrize( - "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name + "destination_config", + destinations_configs(default_sql_configs=True, exclude=["sqlalchemy"]), + ids=lambda x: x.name, ) def test_query_all_info_tables_fallback(destination_config: DestinationTestConfiguration) -> None: pipeline = destination_config.setup_pipeline( "parquet_test_" + uniq_id(), dataset_name="parquet_test_" + uniq_id() ) with mock.patch.object(SqlJobClientBase, "INFO_TABLES_QUERY_THRESHOLD", 0): - info = pipeline.run([1, 2, 3], table_name="digits_1") + info = pipeline.run([1, 2, 3], table_name="digits_1", **destination_config.run_kwargs) assert_load_info(info) # create empty table client: SqlJobClientBase @@ -1080,7 +779,7 @@ def test_query_all_info_tables_fallback(destination_config: DestinationTestConfi # remove it from schema del pipeline.default_schema._schema_tables["existing_table"] # store another table - info = pipeline.run([1, 2, 3], table_name="digits_2") + info = pipeline.run([1, 2, 3], table_name="digits_2", **destination_config.run_kwargs) assert_data_table_counts(pipeline, {"digits_1": 3, "digits_2": 3}) @@ -1141,8 +840,164 @@ def _data(): p = dlt.pipeline( pipeline_name=f"pipeline_{dataset_name}", dev_mode=dev_mode, - destination=destination_config.destination, + destination=destination_config.destination_factory(), staging=destination_config.staging, dataset_name=dataset_name, ) return p, _data + + +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True, subset=["duckdb", "postgres", "snowflake"]), + ids=lambda x: x.name, +) +def test_dest_column_invalid_timestamp_precision( + destination_config: DestinationTestConfiguration, +) -> None: + invalid_precision = 10 + + @dlt.resource( + columns={ + "event_tstamp": { + "data_type": "timestamp", + "precision": invalid_precision, + "timezone": False, + } + }, + primary_key="event_id", + ) + def events(): + yield [{"event_id": 1, "event_tstamp": "2024-07-30T10:00:00.123+00:00"}] + + pipeline = destination_config.setup_pipeline(uniq_id()) + + with pytest.raises((TerminalValueError, PipelineStepFailed)): + pipeline.run(events(), **destination_config.run_kwargs) + + +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True, subset=["duckdb", "snowflake", "postgres"]), + ids=lambda x: x.name, +) +def test_dest_column_hint_timezone(destination_config: DestinationTestConfiguration) -> None: + destination = destination_config.destination_type + + input_data = [ + {"event_id": 1, "event_tstamp": "2024-07-30T10:00:00.123+00:00"}, + {"event_id": 2, "event_tstamp": "2024-07-30T10:00:00.123456+02:00"}, + {"event_id": 3, "event_tstamp": "2024-07-30T10:00:00.123456"}, + ] + + output_values = [ + "2024-07-30T10:00:00.123000", + "2024-07-30T08:00:00.123456", + "2024-07-30T10:00:00.123456", + ] + + output_map = { + "postgres": { + "tables": { + "events_timezone_off": { + "timestamp_type": "timestamp without time zone", + "timestamp_values": output_values, + }, + "events_timezone_on": { + "timestamp_type": "timestamp with time zone", + "timestamp_values": output_values, + }, + "events_timezone_unset": { + "timestamp_type": "timestamp with time zone", + "timestamp_values": output_values, + }, + }, + "query_data_type": ( + "SELECT data_type FROM information_schema.columns WHERE table_schema ='experiments'" + " AND table_name = '%s' AND column_name = 'event_tstamp'" + ), + }, + "snowflake": { + "tables": { + "EVENTS_TIMEZONE_OFF": { + "timestamp_type": "TIMESTAMP_NTZ", + "timestamp_values": output_values, + }, + "EVENTS_TIMEZONE_ON": { + "timestamp_type": "TIMESTAMP_TZ", + "timestamp_values": output_values, + }, + "EVENTS_TIMEZONE_UNSET": { + "timestamp_type": "TIMESTAMP_TZ", + "timestamp_values": output_values, + }, + }, + "query_data_type": ( + "SELECT data_type FROM information_schema.columns WHERE table_schema ='EXPERIMENTS'" + " AND table_name = '%s' AND column_name = 'EVENT_TSTAMP'" + ), + }, + "duckdb": { + "tables": { + "events_timezone_off": { + "timestamp_type": "TIMESTAMP", + "timestamp_values": output_values, + }, + "events_timezone_on": { + "timestamp_type": "TIMESTAMP WITH TIME ZONE", + "timestamp_values": output_values, + }, + "events_timezone_unset": { + "timestamp_type": "TIMESTAMP WITH TIME ZONE", + "timestamp_values": output_values, + }, + }, + "query_data_type": ( + "SELECT data_type FROM information_schema.columns WHERE table_schema ='experiments'" + " AND table_name = '%s' AND column_name = 'event_tstamp'" + ), + }, + } + + # table: events_timezone_off + @dlt.resource( + columns={"event_tstamp": {"data_type": "timestamp", "timezone": False}}, + primary_key="event_id", + ) + def events_timezone_off(): + yield input_data + + # table: events_timezone_on + @dlt.resource( + columns={"event_tstamp": {"data_type": "timestamp", "timezone": True}}, + primary_key="event_id", + ) + def events_timezone_on(): + yield input_data + + # table: events_timezone_unset + @dlt.resource( + primary_key="event_id", + ) + def events_timezone_unset(): + yield input_data + + pipeline = destination_config.setup_pipeline( + f"{destination}_" + uniq_id(), dataset_name="experiments" + ) + + pipeline.run( + [events_timezone_off(), events_timezone_on(), events_timezone_unset()], + **destination_config.run_kwargs, + ) + + with pipeline.sql_client() as client: + for t in output_map[destination]["tables"].keys(): # type: ignore + # check data type + column_info = client.execute_sql(output_map[destination]["query_data_type"] % t) + assert column_info[0][0] == output_map[destination]["tables"][t]["timestamp_type"] # type: ignore + # check timestamp data + rows = client.execute_sql(f"SELECT event_tstamp FROM {t} ORDER BY event_id") + + values = [r[0].strftime("%Y-%m-%dT%H:%M:%S.%f") for r in rows] + assert values == output_map[destination]["tables"][t]["timestamp_values"] # type: ignore diff --git a/tests/load/pipeline/test_postgres.py b/tests/load/pipeline/test_postgres.py index 5cadf701a2..c8dc0e10cc 100644 --- a/tests/load/pipeline/test_postgres.py +++ b/tests/load/pipeline/test_postgres.py @@ -4,10 +4,22 @@ from string import ascii_lowercase import pytest +import dlt +from dlt.common.destination.reference import Destination +from dlt.common.schema.exceptions import CannotCoerceColumnException +from dlt.common.schema.schema import Schema from dlt.common.utils import uniq_id -from tests.load.utils import destinations_configs, DestinationTestConfiguration -from tests.pipeline.utils import assert_load_info, load_tables_to_dicts +from dlt.destinations import filesystem, redshift + +from dlt.pipeline.exceptions import PipelineStepFailed + +from tests.load.utils import ( + destinations_configs, + DestinationTestConfiguration, + drop_active_pipeline_data, +) +from tests.pipeline.utils import assert_load_info, load_table_counts, load_tables_to_dicts from tests.utils import TestDataItemFormat @@ -44,6 +56,248 @@ def test_postgres_encoded_binary( assert data["table"][0]["hash"].tobytes() == blob +# do not remove - it allows us to filter tests by destination +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True, subset=["postgres"]), + ids=lambda x: x.name, +) +def test_pipeline_explicit_destination_credentials( + destination_config: DestinationTestConfiguration, +) -> None: + from dlt.destinations import postgres + from dlt.destinations.impl.postgres.configuration import PostgresCredentials + + # explicit credentials resolved + p = dlt.pipeline( + destination=Destination.from_reference( + "postgres", + destination_name="mydest", + credentials="postgresql://loader:loader@localhost:7777/dlt_data", + ), + ) + c = p._get_destination_clients(Schema("s"), p._get_destination_client_initial_config())[0] + assert c.config.credentials.port == 7777 # type: ignore[attr-defined] + + # TODO: may want to clear the env completely and ignore/mock config files somehow to avoid side effects + # explicit credentials resolved ignoring the config providers + os.environ["DESTINATION__MYDEST__CREDENTIALS__HOST"] = "HOST" + p = dlt.pipeline( + destination=Destination.from_reference( + "postgres", + destination_name="mydest", + credentials="postgresql://loader:loader@localhost:5432/dlt_data", + ), + ) + c = p._get_destination_clients(Schema("s"), p._get_destination_client_initial_config())[0] + assert c.config.credentials.host == "localhost" # type: ignore[attr-defined] + + # explicit partial credentials will use config providers + os.environ["DESTINATION__MYDEST__CREDENTIALS__USERNAME"] = "UN" + os.environ["DESTINATION__MYDEST__CREDENTIALS__PASSWORD"] = "PW" + p = dlt.pipeline( + destination=Destination.from_reference( + "postgres", + destination_name="mydest", + credentials="postgresql://localhost:5432/dlt_data", + ), + ) + c = p._get_destination_clients(Schema("s"), p._get_destination_client_initial_config())[0] + assert c.config.credentials.username == "UN" # type: ignore[attr-defined] + # host is taken form explicit credentials + assert c.config.credentials.host == "localhost" # type: ignore[attr-defined] + + # instance of credentials will be simply passed + cred = PostgresCredentials("postgresql://user:pass@localhost/dlt_data") + p = dlt.pipeline(destination=postgres(credentials=cred)) + inner_c = p.destination_client() + assert inner_c.config.credentials is cred + + # with staging + p = dlt.pipeline( + pipeline_name="postgres_pipeline", + staging=filesystem("_storage"), + destination=redshift(credentials="redshift://loader:password@localhost:5432/dlt_data"), + ) + config = p.destination_client().config + assert config.credentials.is_resolved() + assert ( + config.credentials.to_native_representation() + == "redshift://loader:password@localhost:5432/dlt_data?connect_timeout=15" + ) + + +# do not remove - it allows us to filter tests by destination +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True, subset=["postgres"]), + ids=lambda x: x.name, +) +def test_pipeline_with_sources_sharing_schema( + destination_config: DestinationTestConfiguration, +) -> None: + schema = Schema("shared") + + @dlt.source(schema=schema, max_table_nesting=1) + def source_1(): + @dlt.resource(primary_key="user_id") + def gen1(): + dlt.current.source_state()["source_1"] = True + dlt.current.resource_state()["source_1"] = True + yield {"id": "Y", "user_id": "user_y"} + + @dlt.resource(columns={"col": {"data_type": "bigint"}}) + def conflict(): + yield "conflict" + + return gen1, conflict + + @dlt.source(schema=schema, max_table_nesting=2) + def source_2(): + @dlt.resource(primary_key="id") + def gen1(): + dlt.current.source_state()["source_2"] = True + dlt.current.resource_state()["source_2"] = True + yield {"id": "X", "user_id": "user_X"} + + def gen2(): + yield from "CDE" + + @dlt.resource(columns={"col": {"data_type": "bool"}}, selected=False) + def conflict(): + yield "conflict" + + return gen2, gen1, conflict + + # all selected tables with hints should be there + discover_1 = source_1().discover_schema() + assert "gen1" in discover_1.tables + assert discover_1.tables["gen1"]["columns"]["user_id"]["primary_key"] is True + assert "data_type" not in discover_1.tables["gen1"]["columns"]["user_id"] + assert "conflict" in discover_1.tables + assert discover_1.tables["conflict"]["columns"]["col"]["data_type"] == "bigint" + + discover_2 = source_2().discover_schema() + assert "gen1" in discover_2.tables + assert "gen2" in discover_2.tables + # conflict deselected + assert "conflict" not in discover_2.tables + + p = dlt.pipeline(pipeline_name="multi", destination="duckdb", dev_mode=True) + p.extract([source_1(), source_2()], table_format=destination_config.table_format) + default_schema = p.default_schema + gen1_table = default_schema.tables["gen1"] + assert "user_id" in gen1_table["columns"] + assert "id" in gen1_table["columns"] + assert "conflict" in default_schema.tables + assert "gen2" in default_schema.tables + p.normalize(loader_file_format=destination_config.file_format) + assert "gen2" in default_schema.tables + p.load() + table_names = [t["name"] for t in default_schema.data_tables()] + counts = load_table_counts(p, *table_names) + assert counts == {"gen1": 2, "gen2": 3, "conflict": 1} + # both sources share the same state + assert p.state["sources"] == { + "shared": { + "source_1": True, + "resources": {"gen1": {"source_1": True, "source_2": True}}, + "source_2": True, + } + } + drop_active_pipeline_data() + + # same pipeline but enable conflict + p = dlt.pipeline(pipeline_name="multi", destination="duckdb", dev_mode=True) + with pytest.raises(PipelineStepFailed) as py_ex: + p.extract([source_1(), source_2().with_resources("conflict")]) + assert isinstance(py_ex.value.__context__, CannotCoerceColumnException) + + +# do not remove - it allows us to filter tests by destination +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True, subset=["postgres"]), + ids=lambda x: x.name, +) +def test_many_pipelines_single_dataset(destination_config: DestinationTestConfiguration) -> None: + schema = Schema("shared") + + @dlt.source(schema=schema, max_table_nesting=1) + def source_1(): + @dlt.resource(primary_key="user_id") + def gen1(): + dlt.current.source_state()["source_1"] = True + dlt.current.resource_state()["source_1"] = True + yield {"id": "Y", "user_id": "user_y"} + + return gen1 + + @dlt.source(schema=schema, max_table_nesting=2) + def source_2(): + @dlt.resource(primary_key="id") + def gen1(): + dlt.current.source_state()["source_2"] = True + dlt.current.resource_state()["source_2"] = True + yield {"id": "X", "user_id": "user_X"} + + def gen2(): + yield from "CDE" + + return gen2, gen1 + + # load source_1 to common dataset + p = dlt.pipeline( + pipeline_name="source_1_pipeline", destination="duckdb", dataset_name="shared_dataset" + ) + p.run(source_1(), credentials="duckdb:///_storage/test_quack.duckdb") + counts = load_table_counts(p, *p.default_schema.tables.keys()) + assert counts.items() >= {"gen1": 1, "_dlt_pipeline_state": 1, "_dlt_loads": 1}.items() + p._wipe_working_folder() + p.deactivate() + + p = dlt.pipeline( + pipeline_name="source_2_pipeline", destination="duckdb", dataset_name="shared_dataset" + ) + p.run(source_2(), credentials="duckdb:///_storage/test_quack.duckdb") + # table_names = [t["name"] for t in p.default_schema.data_tables()] + counts = load_table_counts(p, *p.default_schema.tables.keys()) + # gen1: one record comes from source_1, 1 record from source_2 + assert counts.items() >= {"gen1": 2, "_dlt_pipeline_state": 2, "_dlt_loads": 2}.items() + # assert counts == {'gen1': 2, 'gen2': 3} + p._wipe_working_folder() + p.deactivate() + + # restore from destination, check state + p = dlt.pipeline( + pipeline_name="source_1_pipeline", + destination=dlt.destinations.duckdb(credentials="duckdb:///_storage/test_quack.duckdb"), + dataset_name="shared_dataset", + ) + p.sync_destination() + # we have our separate state + assert p.state["sources"]["shared"] == { + "source_1": True, + "resources": {"gen1": {"source_1": True}}, + } + # but the schema was common so we have the earliest one + assert "gen2" in p.default_schema.tables + p._wipe_working_folder() + p.deactivate() + + p = dlt.pipeline( + pipeline_name="source_2_pipeline", + destination=dlt.destinations.duckdb(credentials="duckdb:///_storage/test_quack.duckdb"), + dataset_name="shared_dataset", + ) + p.sync_destination() + # we have our separate state + assert p.state["sources"]["shared"] == { + "source_2": True, + "resources": {"gen1": {"source_2": True}}, + } + + # TODO: uncomment and finalize when we implement encoding for psycopg2 # @pytest.mark.parametrize( # "destination_config", diff --git a/tests/load/pipeline/test_redshift.py b/tests/load/pipeline/test_redshift.py index bfdc15459c..21d8f60bc2 100644 --- a/tests/load/pipeline/test_redshift.py +++ b/tests/load/pipeline/test_redshift.py @@ -3,7 +3,9 @@ import pytest import dlt +from dlt.common.destination.exceptions import UnsupportedDataType from dlt.common.utils import uniq_id +from dlt.pipeline.exceptions import PipelineStepFailed from tests.load.utils import destinations_configs, DestinationTestConfiguration from tests.cases import table_update_and_row, assert_all_data_types_row from tests.pipeline.utils import assert_load_info @@ -22,7 +24,7 @@ def test_redshift_blocks_time_column(destination_config: DestinationTestConfigur column_schemas, data_types = table_update_and_row() - # apply the exact columns definitions so we process complex and wei types correctly! + # apply the exact columns definitions so we process nested and wei types correctly! @dlt.resource(table_name="data_types", write_disposition="append", columns=column_schemas) def my_resource() -> Iterator[Any]: nonlocal data_types @@ -32,11 +34,10 @@ def my_resource() -> Iterator[Any]: def my_source() -> Any: return my_resource - info = pipeline.run(my_source(), loader_file_format=destination_config.file_format) - - assert info.has_failed_jobs - - assert ( - "Redshift cannot load TIME columns from" - in info.load_packages[0].jobs["failed_jobs"][0].failed_message - ) + with pytest.raises(PipelineStepFailed) as pip_ex: + pipeline.run(my_source(), **destination_config.run_kwargs) + assert isinstance(pip_ex.value.__cause__, UnsupportedDataType) + if destination_config.file_format == "parquet": + assert pip_ex.value.__cause__.data_type == "time" + else: + assert pip_ex.value.__cause__.data_type in ("time", "binary") diff --git a/tests/load/pipeline/test_refresh_modes.py b/tests/load/pipeline/test_refresh_modes.py index f4bf3b0311..dcb2be44dc 100644 --- a/tests/load/pipeline/test_refresh_modes.py +++ b/tests/load/pipeline/test_refresh_modes.py @@ -110,13 +110,16 @@ def test_refresh_drop_sources(destination_config: DestinationTestConfiguration): pipeline = destination_config.setup_pipeline("refresh_full_test", refresh="drop_sources") # First run pipeline so destination so tables are created - info = pipeline.run(refresh_source(first_run=True, drop_sources=True)) + info = pipeline.run( + refresh_source(first_run=True, drop_sources=True), **destination_config.run_kwargs + ) assert_load_info(info) # Second run of pipeline with only selected resources info = pipeline.run( refresh_source(first_run=False, drop_sources=True).with_resources( "some_data_1", "some_data_2" - ) + ), + **destination_config.run_kwargs, ) assert set(t["name"] for t in pipeline.default_schema.data_tables(include_incomplete=True)) == { @@ -154,7 +157,9 @@ def test_existing_schema_hash(destination_config: DestinationTestConfiguration): """ pipeline = destination_config.setup_pipeline("refresh_full_test", refresh="drop_sources") - info = pipeline.run(refresh_source(first_run=True, drop_sources=True)) + info = pipeline.run( + refresh_source(first_run=True, drop_sources=True), **destination_config.run_kwargs + ) assert_load_info(info) first_schema_hash = pipeline.default_schema.version_hash @@ -162,7 +167,8 @@ def test_existing_schema_hash(destination_config: DestinationTestConfiguration): info = pipeline.run( refresh_source(first_run=False, drop_sources=True).with_resources( "some_data_1", "some_data_2" - ) + ), + **destination_config.run_kwargs, ) # Just check the local schema @@ -173,7 +179,9 @@ def test_existing_schema_hash(destination_config: DestinationTestConfiguration): # Run again with all tables to ensure they are re-created # The new schema in this case should match the schema of the first run exactly - info = pipeline.run(refresh_source(first_run=True, drop_sources=True)) + info = pipeline.run( + refresh_source(first_run=True, drop_sources=True), **destination_config.run_kwargs + ) # Check table 3 was re-created data = load_tables_to_dicts(pipeline, "some_data_3")["some_data_3"] result = sorted([(row["id"], row["name"]) for row in data]) @@ -195,12 +203,13 @@ def test_refresh_drop_resources(destination_config: DestinationTestConfiguration # First run pipeline with load to destination so tables are created pipeline = destination_config.setup_pipeline("refresh_full_test", refresh="drop_tables") - info = pipeline.run(refresh_source(first_run=True)) + info = pipeline.run(refresh_source(first_run=True), **destination_config.run_kwargs) assert_load_info(info) # Second run of pipeline with only selected resources info = pipeline.run( - refresh_source(first_run=False).with_resources("some_data_1", "some_data_2") + refresh_source(first_run=False).with_resources("some_data_1", "some_data_2"), + **destination_config.run_kwargs, ) # Confirm resource tables not selected on second run are untouched @@ -244,7 +253,9 @@ def test_refresh_drop_data_only(destination_config: DestinationTestConfiguration # First run pipeline with load to destination so tables are created pipeline = destination_config.setup_pipeline("refresh_full_test", refresh="drop_data") - info = pipeline.run(refresh_source(first_run=True), write_disposition="append") + info = pipeline.run( + refresh_source(first_run=True), write_disposition="append", **destination_config.run_kwargs + ) assert_load_info(info) first_schema_hash = pipeline.default_schema.version_hash @@ -253,6 +264,7 @@ def test_refresh_drop_data_only(destination_config: DestinationTestConfiguration info = pipeline.run( refresh_source(first_run=False).with_resources("some_data_1", "some_data_2"), write_disposition="append", + **destination_config.run_kwargs, ) assert_load_info(info) @@ -263,7 +275,7 @@ def test_refresh_drop_data_only(destination_config: DestinationTestConfiguration data = load_tables_to_dicts(pipeline, "some_data_1", "some_data_2", "some_data_3") # name column still remains when table was truncated instead of dropped # (except on filesystem where truncate and drop are the same) - if destination_config.destination == "filesystem": + if destination_config.destination_type == "filesystem": result = sorted([row["id"] for row in data["some_data_1"]]) assert result == [3, 4] @@ -348,11 +360,15 @@ def source_2_data_2(): # Run both sources info = pipeline.run( - [refresh_source(first_run=True, drop_sources=True), refresh_source_2(first_run=True)] + [refresh_source(first_run=True, drop_sources=True), refresh_source_2(first_run=True)], + **destination_config.run_kwargs, ) assert_load_info(info, 2) # breakpoint() - info = pipeline.run(refresh_source_2(first_run=False).with_resources("source_2_data_1")) + info = pipeline.run( + refresh_source_2(first_run=False).with_resources("source_2_data_1"), + **destination_config.run_kwargs, + ) assert_load_info(info, 2) # Check source 1 schema still has all tables @@ -394,11 +410,12 @@ def source_2_data_2(): def test_refresh_argument_to_run(destination_config: DestinationTestConfiguration): pipeline = destination_config.setup_pipeline("refresh_full_test") - info = pipeline.run(refresh_source(first_run=True)) + info = pipeline.run(refresh_source(first_run=True), **destination_config.run_kwargs) assert_load_info(info) info = pipeline.run( refresh_source(first_run=False).with_resources("some_data_3"), + **destination_config.run_kwargs, refresh="drop_sources", ) assert_load_info(info) @@ -408,7 +425,10 @@ def test_refresh_argument_to_run(destination_config: DestinationTestConfiguratio assert tables == {"some_data_3"} # Run again without refresh to confirm refresh option doesn't persist on pipeline - info = pipeline.run(refresh_source(first_run=False).with_resources("some_data_2")) + info = pipeline.run( + refresh_source(first_run=False).with_resources("some_data_2"), + **destination_config.run_kwargs, + ) assert_load_info(info) # Nothing is dropped @@ -426,11 +446,12 @@ def test_refresh_argument_to_run(destination_config: DestinationTestConfiguratio def test_refresh_argument_to_extract(destination_config: DestinationTestConfiguration): pipeline = destination_config.setup_pipeline("refresh_full_test") - info = pipeline.run(refresh_source(first_run=True)) + info = pipeline.run(refresh_source(first_run=True), **destination_config.run_kwargs) assert_load_info(info) pipeline.extract( refresh_source(first_run=False).with_resources("some_data_3"), + table_format=destination_config.table_format, refresh="drop_sources", ) @@ -439,7 +460,10 @@ def test_refresh_argument_to_extract(destination_config: DestinationTestConfigur assert tables == {"some_data_3"} # Run again without refresh to confirm refresh option doesn't persist on pipeline - pipeline.extract(refresh_source(first_run=False).with_resources("some_data_2")) + pipeline.extract( + refresh_source(first_run=False).with_resources("some_data_2"), + table_format=destination_config.table_format, + ) tables = set(t["name"] for t in pipeline.default_schema.data_tables(include_incomplete=True)) assert tables == {"some_data_2", "some_data_3"} @@ -470,7 +494,7 @@ def test_refresh_staging_dataset(destination_config: DestinationTestConfiguratio ], ) # create two tables so two tables need to be dropped - info = pipeline.run(source) + info = pipeline.run(source, **destination_config.run_kwargs) assert_load_info(info) # make data so inserting on mangled tables is not possible @@ -487,7 +511,7 @@ def test_refresh_staging_dataset(destination_config: DestinationTestConfiguratio dlt.resource(data_i, name="data_2", primary_key="id", write_disposition="append"), ], ) - info = pipeline.run(source_i, refresh="drop_resources") + info = pipeline.run(source_i, refresh="drop_resources", **destination_config.run_kwargs) assert_load_info(info) # now replace the whole source and load different tables @@ -499,7 +523,7 @@ def test_refresh_staging_dataset(destination_config: DestinationTestConfiguratio dlt.resource(data_i, name="data_2_v2", primary_key="id", write_disposition="append"), ], ) - info = pipeline.run(source_i, refresh="drop_sources") + info = pipeline.run(source_i, refresh="drop_sources", **destination_config.run_kwargs) assert_load_info(info) # tables got dropped diff --git a/tests/load/pipeline/test_replace_disposition.py b/tests/load/pipeline/test_replace_disposition.py index d49ce2904f..82cef83019 100644 --- a/tests/load/pipeline/test_replace_disposition.py +++ b/tests/load/pipeline/test_replace_disposition.py @@ -94,9 +94,7 @@ def append_items(): } # first run with offset 0 - info = pipeline.run( - [load_items, append_items], loader_file_format=destination_config.file_format - ) + info = pipeline.run([load_items, append_items], **destination_config.run_kwargs) assert_load_info(info) # count state records that got extracted state_records = increase_state_loads(info) @@ -105,9 +103,7 @@ def append_items(): # second run with higher offset so we can check the results offset = 1000 - info = pipeline.run( - [load_items, append_items], loader_file_format=destination_config.file_format - ) + info = pipeline.run([load_items, append_items], **destination_config.run_kwargs) assert_load_info(info) state_records += increase_state_loads(info) dlt_loads += 1 @@ -153,9 +149,7 @@ def load_items_none(): if False: yield - info = pipeline.run( - [load_items_none, append_items], loader_file_format=destination_config.file_format - ) + info = pipeline.run([load_items_none, append_items], **destination_config.run_kwargs) assert_load_info(info) state_records += increase_state_loads(info) dlt_loads += 1 @@ -186,9 +180,7 @@ def load_items_none(): pipeline_2 = destination_config.setup_pipeline( "test_replace_strategies_2", dataset_name=dataset_name ) - info = pipeline_2.run( - load_items, table_name="items_copy", loader_file_format=destination_config.file_format - ) + info = pipeline_2.run(load_items, table_name="items_copy", **destination_config.run_kwargs) assert_load_info(info) new_state_records = increase_state_loads(info) assert new_state_records == 1 @@ -202,7 +194,7 @@ def load_items_none(): "_dlt_pipeline_state": 1, } - info = pipeline_2.run(append_items, loader_file_format=destination_config.file_format) + info = pipeline_2.run(append_items, **destination_config.run_kwargs) assert_load_info(info) new_state_records = increase_state_loads(info) assert new_state_records == 0 @@ -321,9 +313,7 @@ def yield_empty_list(): yield [] # regular call - pipeline.run( - [items_with_subitems, static_items], loader_file_format=destination_config.file_format - ) + pipeline.run([items_with_subitems, static_items], **destination_config.run_kwargs) table_counts = load_table_counts( pipeline, *[t["name"] for t in pipeline.default_schema.data_tables()] ) @@ -345,7 +335,7 @@ def yield_empty_list(): } # see if child table gets cleared - pipeline.run(items_without_subitems, loader_file_format=destination_config.file_format) + pipeline.run(items_without_subitems, **destination_config.run_kwargs) table_counts = load_table_counts( pipeline, *[t["name"] for t in pipeline.default_schema.data_tables()] ) @@ -360,8 +350,8 @@ def yield_empty_list(): # see if yield none clears everything for empty_resource in [yield_none, no_yield, yield_empty_list]: - pipeline.run(items_with_subitems, loader_file_format=destination_config.file_format) - pipeline.run(empty_resource, loader_file_format=destination_config.file_format) + pipeline.run(items_with_subitems, **destination_config.run_kwargs) + pipeline.run(empty_resource, **destination_config.run_kwargs) table_counts = load_table_counts( pipeline, *[t["name"] for t in pipeline.default_schema.data_tables()] ) @@ -375,7 +365,7 @@ def yield_empty_list(): assert pipeline.last_trace.last_normalize_info.row_counts == {"items": 0, "other_items": 0} # see if yielding something next to other none entries still goes into db - pipeline.run(items_with_subitems_yield_none, loader_file_format=destination_config.file_format) + pipeline.run(items_with_subitems_yield_none, **destination_config.run_kwargs) table_counts = load_table_counts( pipeline, *[t["name"] for t in pipeline.default_schema.data_tables()] ) diff --git a/tests/load/pipeline/test_restore_state.py b/tests/load/pipeline/test_restore_state.py index c3968e2e74..050636c491 100644 --- a/tests/load/pipeline/test_restore_state.py +++ b/tests/load/pipeline/test_restore_state.py @@ -24,7 +24,7 @@ from tests.utils import TEST_STORAGE_ROOT from tests.cases import JSON_TYPED_DICT, JSON_TYPED_DICT_DECODED -from tests.common.utils import IMPORTED_VERSION_HASH_ETH_V9, yml_case_path as common_yml_case_path +from tests.common.utils import IMPORTED_VERSION_HASH_ETH_V10, yml_case_path as common_yml_case_path from tests.common.configuration.utils import environment from tests.pipeline.utils import assert_query_data from tests.load.utils import ( @@ -219,13 +219,13 @@ def test_get_schemas_from_destination( use_single_dataset: bool, naming_convention: str, ) -> None: - set_naming_env(destination_config.destination, naming_convention) + set_naming_env(destination_config.destination_type, naming_convention) pipeline_name = "pipe_" + uniq_id() dataset_name = "state_test_" + uniq_id() p = destination_config.setup_pipeline(pipeline_name=pipeline_name, dataset_name=dataset_name) - assert_naming_to_caps(destination_config.destination, p.destination.capabilities()) + assert_naming_to_caps(destination_config.destination_type, p.destination.capabilities()) p.config.use_single_dataset = use_single_dataset def _make_dn_name(schema_name: str) -> str: @@ -318,13 +318,13 @@ def _make_dn_name(schema_name: str) -> str: def test_restore_state_pipeline( destination_config: DestinationTestConfiguration, naming_convention: str ) -> None: - set_naming_env(destination_config.destination, naming_convention) + set_naming_env(destination_config.destination_type, naming_convention) # enable restoring from destination os.environ["RESTORE_FROM_DESTINATION"] = "True" pipeline_name = "pipe_" + uniq_id() dataset_name = "state_test_" + uniq_id() p = destination_config.setup_pipeline(pipeline_name=pipeline_name, dataset_name=dataset_name) - assert_naming_to_caps(destination_config.destination, p.destination.capabilities()) + assert_naming_to_caps(destination_config.destination_type, p.destination.capabilities()) def some_data_gen(param: str) -> Any: dlt.current.source_state()[param] = param @@ -357,15 +357,15 @@ def some_data(): p.extract([data1, some_data("state2")], schema=Schema("default")) data_two = source_two("state3") - p.extract(data_two) + p.extract(data_two, table_format=destination_config.table_format) data_three = source_three("state4") - p.extract(data_three) + p.extract(data_three, table_format=destination_config.table_format) data_four = source_four() - p.extract(data_four) + p.extract(data_four, table_format=destination_config.table_format) - p.normalize() + p.normalize(loader_file_format=destination_config.file_format) p.load() # keep the orig state orig_state = p.state @@ -374,14 +374,14 @@ def some_data(): p._wipe_working_folder() os.environ["RESTORE_FROM_DESTINATION"] = "False" p = destination_config.setup_pipeline(pipeline_name=pipeline_name, dataset_name=dataset_name) - p.run(loader_file_format=destination_config.file_format) + p.run(**destination_config.run_kwargs) # restore was not requested so schema is empty assert p.default_schema_name is None p._wipe_working_folder() # request restore os.environ["RESTORE_FROM_DESTINATION"] = "True" p = destination_config.setup_pipeline(pipeline_name=pipeline_name, dataset_name=dataset_name) - p.run(loader_file_format=destination_config.file_format) + p.run(**destination_config.run_kwargs) assert p.default_schema_name == "default" assert set(p.schema_names) == set(["default", "two", "three", "four"]) assert p.state["sources"] == { @@ -402,7 +402,7 @@ def some_data(): p = destination_config.setup_pipeline( pipeline_name=pipeline_name, dataset_name=dataset_name, dev_mode=True ) - p.run(loader_file_format=destination_config.file_format) + p.run(**destination_config.run_kwargs) assert p.default_schema_name is None drop_active_pipeline_data() @@ -415,7 +415,7 @@ def some_data(): assert p.dataset_name == dataset_name assert p.default_schema_name is None # restore - p.run(loader_file_format=destination_config.file_format) + p.run(**destination_config.run_kwargs) assert p.default_schema_name is not None restored_state = p.state assert restored_state["_state_version"] == orig_state["_state_version"] @@ -426,7 +426,7 @@ def some_data(): ) # this will modify state, run does not sync if states are identical assert p.state["_state_version"] > orig_state["_state_version"] # print(p.state) - p.run(loader_file_format=destination_config.file_format) + p.run(**destination_config.run_kwargs) assert set(p.schema_names) == set( ["default", "two", "three", "second", "four"] ) # we keep our local copy @@ -435,7 +435,7 @@ def some_data(): state["_state_version"] -= 1 p._save_state(state) p._state_restored = False - p.run(loader_file_format=destination_config.file_format) + p.run(**destination_config.run_kwargs) assert set(p.schema_names) == set(["default", "two", "three", "four"]) @@ -458,9 +458,9 @@ def some_data(param: str) -> Any: job_client: WithStateSync # Load some complete load packages with state to the destination - p.run(some_data("state1"), loader_file_format=destination_config.file_format) - p.run(some_data("state2"), loader_file_format=destination_config.file_format) - p.run(some_data("state3"), loader_file_format=destination_config.file_format) + p.run(some_data("state1"), **destination_config.run_kwargs) + p.run(some_data("state2"), **destination_config.run_kwargs) + p.run(some_data("state3"), **destination_config.run_kwargs) with p._get_destination_clients(p.default_schema)[0] as job_client: # type: ignore[assignment] state = load_pipeline_state_from_destination(pipeline_name, job_client) @@ -472,7 +472,7 @@ def complete_package_mock(self, load_id: str, schema: Schema, aborted: bool = Fa self.load_storage.complete_load_package(load_id, aborted) with patch.object(Load, "complete_package", complete_package_mock): - p.run(some_data("fix_1"), loader_file_format=destination_config.file_format) + p.run(some_data("fix_1"), **destination_config.run_kwargs) # assert complete_package.called with p._get_destination_clients(p.default_schema)[0] as job_client: # type: ignore[assignment] @@ -523,7 +523,7 @@ def test_restore_schemas_while_import_schemas_exist( ["A", "B", "C"], table_name="labels", schema=schema, - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) # schema should be up to date normalized_labels = schema.naming.normalize_table_identifier("labels") @@ -534,9 +534,7 @@ def test_restore_schemas_while_import_schemas_exist( # re-attach the pipeline p = destination_config.attach_pipeline(pipeline_name=pipeline_name) - p.run( - ["C", "D", "E"], table_name="annotations", loader_file_format=destination_config.file_format - ) + p.run(["C", "D", "E"], table_name="annotations", **destination_config.run_kwargs) schema = p.schemas["ethereum"] assert normalized_labels in schema.tables assert normalized_annotations in schema.tables @@ -552,21 +550,19 @@ def test_restore_schemas_while_import_schemas_exist( ) # use run to get changes p.run( - destination=destination_config.destination, + destination=destination_config.destination_factory(), staging=destination_config.staging, dataset_name=dataset_name, - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) schema = p.schemas["ethereum"] assert normalized_labels in schema.tables assert normalized_annotations in schema.tables # check if attached to import schema - assert schema._imported_version_hash == IMPORTED_VERSION_HASH_ETH_V9() + assert schema._imported_version_hash == IMPORTED_VERSION_HASH_ETH_V10() # extract some data with restored pipeline - p.run( - ["C", "D", "E"], table_name="blacklist", loader_file_format=destination_config.file_format - ) + p.run(["C", "D", "E"], table_name="blacklist", **destination_config.run_kwargs) assert normalized_labels in schema.tables assert normalized_annotations in schema.tables assert normalized_blacklist in schema.tables @@ -605,20 +601,20 @@ def some_data(param: str) -> Any: p.run( [data1, some_data("state2")], schema=Schema("default"), - destination=destination_config.destination, + destination=destination_config.destination_factory(), staging=destination_config.staging, dataset_name=dataset_name, - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) orig_state = p.state # create a production pipeline in separate pipelines_dir production_p = dlt.pipeline(pipeline_name=pipeline_name, pipelines_dir=TEST_STORAGE_ROOT) production_p.run( - destination=destination_config.destination, + destination=destination_config.destination_factory(), staging=destination_config.staging, dataset_name=dataset_name, - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) assert production_p.default_schema_name == "default" @@ -630,7 +626,7 @@ def some_data(param: str) -> Any: # rename extract table/ data2.apply_hints(table_name="state1_data2") print("---> run production") - production_p.run(data2, loader_file_format=destination_config.file_format) + production_p.run(data2, **destination_config.run_kwargs) assert production_p.state["_state_version"] == prod_state["_state_version"] normalize = production_p.default_schema.naming.normalize_table_identifier @@ -645,7 +641,7 @@ def some_data(param: str) -> Any: data3 = some_data("state3") data3.apply_hints(table_name="state1_data2") print("---> run production") - production_p.run(data3, loader_file_format=destination_config.file_format) + production_p.run(data3, **destination_config.run_kwargs) assert production_p.state["_state_version"] > prod_state["_state_version"] # and will be detected locally # print(p.default_schema) @@ -658,14 +654,14 @@ def some_data(param: str) -> Any: # change state locally data4 = some_data("state4") data4.apply_hints(table_name="state1_data4") - p.run(data4, loader_file_format=destination_config.file_format) + p.run(data4, **destination_config.run_kwargs) # and on production in parallel data5 = some_data("state5") data5.apply_hints(table_name="state1_data5") - production_p.run(data5, loader_file_format=destination_config.file_format) + production_p.run(data5, **destination_config.run_kwargs) data6 = some_data("state6") data6.apply_hints(table_name="state1_data6") - production_p.run(data6, loader_file_format=destination_config.file_format) + production_p.run(data6, **destination_config.run_kwargs) # production state version ahead of local state version prod_state = production_p.state assert p.state["_state_version"] == prod_state["_state_version"] - 1 @@ -695,7 +691,9 @@ def some_data(param: str) -> Any: [5, 4, 4, 3, 2], ) except SqlClientNotAvailable: - pytest.skip(f"destination {destination_config.destination} does not support sql client") + pytest.skip( + f"destination {destination_config.destination_type} does not support sql client" + ) @pytest.mark.parametrize( @@ -723,14 +721,14 @@ def some_data(param: str) -> Any: p.run( data4, schema=Schema("sch1"), - destination=destination_config.destination, + destination=destination_config.destination_factory(), staging=destination_config.staging, dataset_name=dataset_name, - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) data5 = some_data("state4") data5.apply_hints(table_name="state1_data5") - p.run(data5, schema=Schema("sch2"), loader_file_format=destination_config.file_format) + p.run(data5, schema=Schema("sch2"), **destination_config.run_kwargs) assert p.state["_state_version"] == 3 assert p.first_run is False with p.destination_client() as job_client: @@ -753,10 +751,10 @@ def some_data(param: str) -> Any: p.run( data4, schema=Schema("sch1"), - destination=destination_config.destination, + destination=destination_config.destination_factory(), staging=destination_config.staging, dataset_name=dataset_name, - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) assert p.first_run is False assert p.state["_local"]["first_run"] is False @@ -766,7 +764,7 @@ def some_data(param: str) -> Any: p.config.restore_from_destination = True data5 = some_data("state4") data5.apply_hints(table_name="state1_data5") - p.run(data5, schema=Schema("sch2"), loader_file_format=destination_config.file_format) + p.run(data5, schema=Schema("sch2"), **destination_config.run_kwargs) # the pipeline was not wiped out, the actual presence if the dataset was checked assert set(p.schema_names) == set(["sch2", "sch1"]) diff --git a/tests/load/pipeline/test_scd2.py b/tests/load/pipeline/test_scd2.py index 065da5ce94..c75ff4d3e6 100644 --- a/tests/load/pipeline/test_scd2.py +++ b/tests/load/pipeline/test_scd2.py @@ -134,7 +134,7 @@ def r(data): {"nk": 1, "c1": "foo", "c2": "foo" if simple else {"nc1": "foo"}}, {"nk": 2, "c1": "bar", "c2": "bar" if simple else {"nc1": "bar"}}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) assert_load_info(info) # assert x-hints table = p.default_schema.get_table("dim_test") @@ -171,7 +171,7 @@ def r(data): {"nk": 1, "c1": "foo", "c2": "foo_updated" if simple else {"nc1": "foo_updated"}}, {"nk": 2, "c1": "bar", "c2": "bar" if simple else {"nc1": "bar"}}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) ts_2 = get_load_package_created_at(p, info) assert_load_info(info) assert get_table(p, "dim_test", cname) == [ @@ -196,7 +196,7 @@ def r(data): dim_snap = [ {"nk": 1, "c1": "foo", "c2": "foo_updated" if simple else {"nc1": "foo_updated"}}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) ts_3 = get_load_package_created_at(p, info) assert_load_info(info) assert get_table(p, "dim_test", cname) == [ @@ -216,7 +216,7 @@ def r(data): {"nk": 1, "c1": "foo", "c2": "foo_updated" if simple else {"nc1": "foo_updated"}}, {"nk": 3, "c1": "baz", "c2": "baz" if simple else {"nc1": "baz"}}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) ts_4 = get_load_package_created_at(p, info) assert_load_info(info) assert get_table(p, "dim_test", cname) == [ @@ -263,7 +263,7 @@ def r(data): l1_1 := {"nk": 1, "c1": "foo", "c2": [1] if simple else [{"cc1": 1}]}, l1_2 := {"nk": 2, "c1": "bar", "c2": [2, 3] if simple else [{"cc1": 2}, {"cc1": 3}]}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) ts_1 = get_load_package_created_at(p, info) assert_load_info(info) assert get_table(p, "dim_test", "c1") == [ @@ -277,12 +277,12 @@ def r(data): {"_dlt_root_id": get_row_hash(l1_2), cname: 3}, ] - # load 2 — update a record — change not in complex column + # load 2 — update a record — change not in nested column dim_snap = [ l2_1 := {"nk": 1, "c1": "foo_updated", "c2": [1] if simple else [{"cc1": 1}]}, {"nk": 2, "c1": "bar", "c2": [2, 3] if simple else [{"cc1": 2}, {"cc1": 3}]}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) ts_2 = get_load_package_created_at(p, info) assert_load_info(info) assert get_table(p, "dim_test", "c1") == [ @@ -300,7 +300,7 @@ def r(data): ], ) - # load 3 — update a record — change in complex column + # load 3 — update a record — change in nested column dim_snap = [ l3_1 := { "nk": 1, @@ -309,7 +309,7 @@ def r(data): }, {"nk": 2, "c1": "bar", "c2": [2, 3] if simple else [{"cc1": 2}, {"cc1": 3}]}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) ts_3 = get_load_package_created_at(p, info) assert_load_info(info) assert_records_as_set( @@ -335,7 +335,7 @@ def r(data): dim_snap = [ {"nk": 1, "c1": "foo_updated", "c2": [1, 2] if simple else [{"cc1": 1}, {"cc1": 2}]}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) ts_4 = get_load_package_created_at(p, info) assert_load_info(info) assert_records_as_set( @@ -356,7 +356,7 @@ def r(data): {"nk": 1, "c1": "foo_updated", "c2": [1, 2] if simple else [{"cc1": 1}, {"cc1": 2}]}, l5_3 := {"nk": 3, "c1": "baz", "c2": [1, 2] if simple else [{"cc1": 1}, {"cc1": 2}]}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) ts_5 = get_load_package_created_at(p, info) assert_load_info(info) assert_records_as_set( @@ -403,7 +403,7 @@ def r(data): l1_1 := {"nk": 1, "c1": "foo", "c2": [{"cc1": [1]}]}, l1_2 := {"nk": 2, "c1": "bar", "c2": [{"cc1": [1, 2]}]}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) assert_load_info(info) assert_records_as_set( get_table(p, "dim_test__c2__cc1"), @@ -414,12 +414,12 @@ def r(data): ], ) - # load 2 — update a record — change not in complex column + # load 2 — update a record — change not in nested column dim_snap = [ l2_1 := {"nk": 1, "c1": "foo_updated", "c2": [{"cc1": [1]}]}, l1_2 := {"nk": 2, "c1": "bar", "c2": [{"cc1": [1, 2]}]}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) assert_load_info(info) assert_records_as_set( (get_table(p, "dim_test__c2__cc1")), @@ -431,12 +431,12 @@ def r(data): ], ) - # load 3 — update a record — change in complex column + # load 3 — update a record — change in nested column dim_snap = [ l3_1 := {"nk": 1, "c1": "foo_updated", "c2": [{"cc1": [1, 2]}]}, {"nk": 2, "c1": "bar", "c2": [{"cc1": [1, 2]}]}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) assert_load_info(info) exp_3 = [ {"_dlt_root_id": get_row_hash(l1_1), "value": 1}, @@ -452,7 +452,7 @@ def r(data): dim_snap = [ {"nk": 1, "c1": "foo_updated", "c2": [{"cc1": [1, 2]}]}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) assert_load_info(info) assert_records_as_set(get_table(p, "dim_test__c2__cc1"), exp_3) @@ -461,7 +461,7 @@ def r(data): {"nk": 1, "c1": "foo_updated", "c2": [{"cc1": [1, 2]}]}, l5_3 := {"nk": 3, "c1": "baz", "c2": [{"cc1": [1]}]}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) assert_load_info(info) assert_records_as_set( get_table(p, "dim_test__c2__cc1"), @@ -496,7 +496,7 @@ def r(data): r1 := {"nk": 1, "c1": "foo", "c2": "foo", "child": [1]}, r2 := {"nk": 2, "c1": "bar", "c2": "bar", "child": [2, 3]}, ] - info = p.run(r(dim_snap)) + info = p.run(r(dim_snap), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, "dim_test")["dim_test"] == 2 assert load_table_counts(p, "dim_test__child")["dim_test__child"] == 3 @@ -504,7 +504,7 @@ def r(data): # load 2 — delete natural key 1 dim_snap = [r2] - info = p.run(r(dim_snap)) + info = p.run(r(dim_snap), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, "dim_test")["dim_test"] == 2 assert load_table_counts(p, "dim_test__child")["dim_test__child"] == 3 @@ -512,7 +512,7 @@ def r(data): # load 3 — reinsert natural key 1 dim_snap = [r1, r2] - info = p.run(r(dim_snap)) + info = p.run(r(dim_snap), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, "dim_test")["dim_test"] == 3 assert load_table_counts(p, "dim_test__child")["dim_test__child"] == 3 # no new record @@ -557,15 +557,17 @@ def test_validity_column_name_conflict(destination_config: DestinationTestConfig def r(data): yield data - # configuring a validity column name that appears in the data should cause an exception - dim_snap = {"nk": 1, "foo": 1, "from": 1} # conflict on "from" column - with pytest.raises(PipelineStepFailed) as pip_ex: - p.run(r(dim_snap), loader_file_format=destination_config.file_format) - assert isinstance(pip_ex.value.__context__.__context__, ColumnNameConflictException) + # a schema check against an items got dropped because it was very costly and done on each row + dim_snap = {"nk": 1, "foo": 1, "from": "X"} # conflict on "from" column + p.run(r(dim_snap), **destination_config.run_kwargs) dim_snap = {"nk": 1, "foo": 1, "to": 1} # conflict on "to" column - with pytest.raises(PipelineStepFailed): - p.run(r(dim_snap), loader_file_format=destination_config.file_format) - assert isinstance(pip_ex.value.__context__.__context__, ColumnNameConflictException) + p.run(r(dim_snap), **destination_config.run_kwargs) + + # instead the variant columns got generated + dim_test_table = p.default_schema.tables["dim_test"] + assert "from__v_text" in dim_test_table["columns"] + + # but `to` column was coerced and then overwritten, this is the cost of dropping the check @pytest.mark.parametrize( @@ -610,7 +612,7 @@ def test_active_record_timestamp( def r(): yield {"foo": "bar"} - p.run(r()) + p.run(r(), **destination_config.run_kwargs) actual_active_record_timestamp = ensure_pendulum_datetime( load_tables_to_dicts(p, "dim_test")["dim_test"][0]["_dlt_valid_to"] ) @@ -648,7 +650,7 @@ def r(data): l1_1 := {"nk": 1, "foo": "foo"}, l1_2 := {"nk": 2, "foo": "foo"}, ] - info = p.run(r(dim_snap)) + info = p.run(r(dim_snap), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, "dim_test")["dim_test"] == 2 from_, to = DEFAULT_VALIDITY_COLUMN_NAMES @@ -671,7 +673,7 @@ def r(data): # l1_2, # natural key 2 no longer present l2_3 := {"nk": 3, "foo": "foo"}, # new natural key ] - info = p.run(r(dim_snap)) + info = p.run(r(dim_snap), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, "dim_test")["dim_test"] == 4 expected = [ @@ -693,7 +695,7 @@ def r(data): } ) dim_snap = [l2_1] # natural key 3 no longer present - info = p.run(r(dim_snap)) + info = p.run(r(dim_snap), **destination_config.run_kwargs) assert_load_info(info) assert load_table_counts(p, "dim_test")["dim_test"] == 4 expected = [ @@ -743,7 +745,7 @@ def _make_scd2_r(table_: Any) -> DltResource: ).add_map(add_row_hash_to_table("row_hash")) p = destination_config.setup_pipeline("abstract", dev_mode=True) - info = p.run(_make_scd2_r(table), loader_file_format=destination_config.file_format) + info = p.run(_make_scd2_r(table), **destination_config.run_kwargs) assert_load_info(info) # make sure we have scd2 columns in schema table_schema = p.default_schema.get_table("tabular") @@ -759,14 +761,14 @@ def _make_scd2_r(table_: Any) -> DltResource: if item_type == "pandas": table = orig_table orig_table = table.copy(deep=True) - info = p.run(_make_scd2_r(table), loader_file_format=destination_config.file_format) + info = p.run(_make_scd2_r(table), **destination_config.run_kwargs) assert_load_info(info) # no changes (hopefully hash is deterministic) assert load_table_counts(p, "tabular")["tabular"] == 100 # change single row orig_table.iloc[0, 0] = "Duck 🦆!" - info = p.run(_make_scd2_r(orig_table), loader_file_format=destination_config.file_format) + info = p.run(_make_scd2_r(orig_table), **destination_config.run_kwargs) assert_load_info(info) # on row changed assert load_table_counts(p, "tabular")["tabular"] == 101 @@ -796,7 +798,7 @@ def r(data): {"nk": 1, "c1": "foo", "c2": [1], "row_hash": "mocked_hash_1"}, {"nk": 2, "c1": "bar", "c2": [2, 3], "row_hash": "mocked_hash_2"}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) assert_load_info(info) ts_1 = get_load_package_created_at(p, info) table = p.default_schema.get_table("dim_test") @@ -809,7 +811,7 @@ def r(data): dim_snap = [ {"nk": 1, "c1": "foo_upd", "c2": [1], "row_hash": "mocked_hash_1_upd"}, ] - info = p.run(r(dim_snap), loader_file_format=destination_config.file_format) + info = p.run(r(dim_snap), **destination_config.run_kwargs) assert_load_info(info) ts_2 = get_load_package_created_at(p, info) diff --git a/tests/load/pipeline/test_snowflake_pipeline.py b/tests/load/pipeline/test_snowflake_pipeline.py index 0203a39147..31a62b6409 100644 --- a/tests/load/pipeline/test_snowflake_pipeline.py +++ b/tests/load/pipeline/test_snowflake_pipeline.py @@ -1,15 +1,25 @@ +from copy import deepcopy import os import pytest from pytest_mock import MockerFixture import dlt +from dlt.common.destination.exceptions import DestinationHasFailedJobs from dlt.common.utils import uniq_id from dlt.destinations.exceptions import DatabaseUndefinedRelation +from dlt.load.exceptions import LoadClientJobFailed +from dlt.pipeline.exceptions import PipelineStepFailed +from tests.cases import assert_all_data_types_row +from tests.load.pipeline.test_pipelines import simple_nested_pipeline from tests.load.snowflake.test_snowflake_client import QUERY_TAG -from tests.pipeline.utils import assert_load_info -from tests.load.utils import destinations_configs, DestinationTestConfiguration +from tests.pipeline.utils import assert_load_info, assert_query_data +from tests.load.utils import ( + destinations_configs, + DestinationTestConfiguration, + drop_active_pipeline_data, +) # mark all tests as essential, do not remove pytestmark = pytest.mark.essential @@ -41,7 +51,9 @@ def test_snowflake_case_sensitive_identifiers( assert destination_client.capabilities.casefold_identifier is str # load some case sensitive data - info = pipeline.run([{"Id": 1, "Capital": 0.0}], table_name="Expenses") + info = pipeline.run( + [{"Id": 1, "Capital": 0.0}], table_name="Expenses", **destination_config.run_kwargs + ) assert_load_info(info) tag_query_spy.assert_not_called() with pipeline.sql_client() as client: @@ -76,6 +88,73 @@ def test_snowflake_query_tagging( os.environ["DESTINATION__SNOWFLAKE__QUERY_TAG"] = QUERY_TAG tag_query_spy = mocker.spy(SnowflakeSqlClient, "_tag_session") pipeline = destination_config.setup_pipeline("test_snowflake_case_sensitive_identifiers") - info = pipeline.run([1, 2, 3], table_name="digits") + info = pipeline.run([1, 2, 3], table_name="digits", **destination_config.run_kwargs) assert_load_info(info) assert tag_query_spy.call_count == 2 + + +# do not remove - it allows us to filter tests by destination +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True, subset=["snowflake"]), + ids=lambda x: x.name, +) +def test_snowflake_custom_stage(destination_config: DestinationTestConfiguration) -> None: + """Using custom stage name instead of the table stage""" + os.environ["DESTINATION__SNOWFLAKE__STAGE_NAME"] = "my_non_existing_stage" + pipeline, data = simple_nested_pipeline(destination_config, f"custom_stage_{uniq_id()}", False) + with pytest.raises(PipelineStepFailed) as f_jobs: + pipeline.run(data(), **destination_config.run_kwargs) + assert isinstance(f_jobs.value.__cause__, LoadClientJobFailed) + assert "MY_NON_EXISTING_STAGE" in f_jobs.value.__cause__.failed_message + + drop_active_pipeline_data() + + # NOTE: this stage must be created in DLT_DATA database for this test to pass! + # CREATE STAGE MY_CUSTOM_LOCAL_STAGE; + # GRANT READ, WRITE ON STAGE DLT_DATA.PUBLIC.MY_CUSTOM_LOCAL_STAGE TO ROLE DLT_LOADER_ROLE; + stage_name = "PUBLIC.MY_CUSTOM_LOCAL_STAGE" + os.environ["DESTINATION__SNOWFLAKE__STAGE_NAME"] = stage_name + pipeline, data = simple_nested_pipeline(destination_config, f"custom_stage_{uniq_id()}", False) + info = pipeline.run(data(), **destination_config.run_kwargs) + assert_load_info(info) + + load_id = info.loads_ids[0] + + # Get a list of the staged files and verify correct number of files in the "load_id" dir + with pipeline.sql_client() as client: + staged_files = client.execute_sql(f'LIST @{stage_name}/"{load_id}"') + assert len(staged_files) == 3 + # check data of one table to ensure copy was done successfully + tbl_name = client.make_qualified_table_name("lists") + assert_query_data(pipeline, f"SELECT value FROM {tbl_name}", ["a", None, None]) + + +# do not remove - it allows us to filter tests by destination +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True, subset=["snowflake"]), + ids=lambda x: x.name, +) +def test_snowflake_delete_file_after_copy(destination_config: DestinationTestConfiguration) -> None: + """Using keep_staged_files = false option to remove staged files after copy""" + os.environ["DESTINATION__SNOWFLAKE__KEEP_STAGED_FILES"] = "FALSE" + + pipeline, data = simple_nested_pipeline( + destination_config, f"delete_staged_files_{uniq_id()}", False + ) + + info = pipeline.run(data(), **destination_config.run_kwargs) + assert_load_info(info) + + load_id = info.loads_ids[0] + + with pipeline.sql_client() as client: + # no files are left in table stage + stage_name = client.make_qualified_table_name("%lists") + staged_files = client.execute_sql(f'LIST @{stage_name}/"{load_id}"') + assert len(staged_files) == 0 + + # ensure copy was done + tbl_name = client.make_qualified_table_name("lists") + assert_query_data(pipeline, f"SELECT value FROM {tbl_name}", ["a", None, None]) diff --git a/tests/load/pipeline/test_stage_loading.py b/tests/load/pipeline/test_stage_loading.py index 6c4f6dfec8..cc8175b677 100644 --- a/tests/load/pipeline/test_stage_loading.py +++ b/tests/load/pipeline/test_stage_loading.py @@ -6,7 +6,6 @@ from dlt.common.storages.configuration import FilesystemConfiguration from dlt.common.utils import uniq_id from dlt.common.schema.typing import TDataType -from dlt.destinations.impl.filesystem.filesystem import FilesystemClient from tests.load.pipeline.test_merge_disposition import github from tests.pipeline.utils import load_table_counts, assert_load_info @@ -55,7 +54,7 @@ def test_staging_load(destination_config: DestinationTestConfiguration) -> None: pipeline_name="test_stage_loading_5", dataset_name="test_staging_load" + uniq_id() ) - info = pipeline.run(github(), loader_file_format=destination_config.file_format) + info = pipeline.run(github(), **destination_config.run_kwargs) assert_load_info(info) # checks if remote_url is set correctly on copy jobs metrics = info.metrics[info.loads_ids[0]][0] @@ -78,9 +77,9 @@ def test_staging_load(destination_config: DestinationTestConfiguration) -> None: num_sql_jobs = 0 if destination_config.supports_merge: num_sql_jobs += 1 - # sql job is used to copy parquet to Athena Iceberg table (_dlt_pipeline_state) - if destination_config.destination == "athena" and destination_config.table_format == "iceberg": - num_sql_jobs += 1 + # sql job is used to copy parquet to Athena Iceberg table (_dlt_pipeline_state) + # if destination_config.destination == "athena": + # num_sql_jobs += 1 assert len(package_info.jobs["completed_jobs"]) == num_jobs + num_sql_jobs assert ( len( @@ -127,7 +126,7 @@ def test_staging_load(destination_config: DestinationTestConfiguration) -> None: # check item of first row in db with pipeline.sql_client() as sql_client: qual_name = sql_client.make_qualified_table_name - if destination_config.destination in ["mssql", "synapse"]: + if destination_config.destination_type in ["mssql", "synapse"]: rows = sql_client.execute_sql( f"SELECT TOP 1 url FROM {qual_name('issues')} WHERE id = 388089021" ) @@ -139,7 +138,7 @@ def test_staging_load(destination_config: DestinationTestConfiguration) -> None: if destination_config.supports_merge: # test merging in some changed values - info = pipeline.run(load_modified_issues, loader_file_format=destination_config.file_format) + info = pipeline.run(load_modified_issues, **destination_config.run_kwargs) assert_load_info(info) assert pipeline.default_schema.tables["issues"]["write_disposition"] == "merge" merge_counts = load_table_counts( @@ -149,7 +148,7 @@ def test_staging_load(destination_config: DestinationTestConfiguration) -> None: # check changes where merged in with pipeline.sql_client() as sql_client: - if destination_config.destination in ["mssql", "synapse"]: + if destination_config.destination_type in ["mssql", "synapse"]: qual_name = sql_client.make_qualified_table_name rows_1 = sql_client.execute_sql( f"SELECT TOP 1 number FROM {qual_name('issues')} WHERE id = 1232152492" @@ -171,7 +170,7 @@ def test_staging_load(destination_config: DestinationTestConfiguration) -> None: info = pipeline.run( github().load_issues, write_disposition="append", - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) assert_load_info(info) assert pipeline.default_schema.tables["issues"]["write_disposition"] == "append" @@ -185,7 +184,7 @@ def test_staging_load(destination_config: DestinationTestConfiguration) -> None: info = pipeline.run( github().load_issues, write_disposition="replace", - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) assert_load_info(info) assert pipeline.default_schema.tables["issues"]["write_disposition"] == "replace" @@ -214,19 +213,25 @@ def test_truncate_staging_dataset(destination_config: DestinationTestConfigurati table_name: str = resource.table_name # type: ignore[assignment] # load the data, files stay on the stage after the load - info = pipeline.run(resource) + info = pipeline.run( + resource, + **destination_config.run_kwargs, + ) assert_load_info(info) # load the data without truncating of the staging, should see two files on staging pipeline.destination.config_params["truncate_tables_on_staging_destination_before_load"] = False - info = pipeline.run(resource) + info = pipeline.run( + resource, + **destination_config.run_kwargs, + ) assert_load_info(info) # check there are two staging files _, staging_client = pipeline._get_destination_clients(pipeline.default_schema) with staging_client: # except Athena + Iceberg which does not store tables in staging dataset if ( - destination_config.destination == "athena" + destination_config.destination_type == "athena" and destination_config.table_format == "iceberg" ): table_count = 0 @@ -239,7 +244,10 @@ def test_truncate_staging_dataset(destination_config: DestinationTestConfigurati # load the data with truncating, so only new file is on the staging pipeline.destination.config_params["truncate_tables_on_staging_destination_before_load"] = True - info = pipeline.run(resource) + info = pipeline.run( + resource, + **destination_config.run_kwargs, + ) assert_load_info(info) # check that table exists in the destination with pipeline.sql_client() as sql_client: @@ -249,7 +257,7 @@ def test_truncate_staging_dataset(destination_config: DestinationTestConfigurati _, staging_client = pipeline._get_destination_clients(pipeline.default_schema) with staging_client: # except for Athena which does not delete staging destination tables - if destination_config.destination == "athena": + if destination_config.destination_type == "athena": if destination_config.table_format == "iceberg": table_count = 0 else: @@ -271,7 +279,7 @@ def test_all_data_types(destination_config: DestinationTestConfiguration) -> Non # redshift and athena, parquet and jsonl, exclude time types exclude_types: List[TDataType] = [] exclude_columns: List[str] = [] - if destination_config.destination in ( + if destination_config.destination_type in ( "redshift", "athena", "databricks", @@ -279,10 +287,13 @@ def test_all_data_types(destination_config: DestinationTestConfiguration) -> Non ) and destination_config.file_format in ("parquet", "jsonl"): # Redshift copy doesn't support TIME column exclude_types.append("time") - if destination_config.destination == "synapse" and destination_config.file_format == "parquet": + if ( + destination_config.destination_type == "synapse" + and destination_config.file_format == "parquet" + ): # TIME columns are not supported for staged parquet loads into Synapse exclude_types.append("time") - if destination_config.destination in ( + if destination_config.destination_type in ( "redshift", "dremio", ) and destination_config.file_format in ( @@ -291,8 +302,11 @@ def test_all_data_types(destination_config: DestinationTestConfiguration) -> Non ): # Redshift can't load fixed width binary columns from parquet exclude_columns.append("col7_precision") - if destination_config.destination == "databricks" and destination_config.file_format == "jsonl": - exclude_types.extend(["decimal", "binary", "wei", "complex", "date"]) + if ( + destination_config.destination_type == "databricks" + and destination_config.file_format == "jsonl" + ): + exclude_types.extend(["decimal", "binary", "wei", "json", "date"]) exclude_columns.append("col1_precision") column_schemas, data_types = table_update_and_row( @@ -301,18 +315,18 @@ def test_all_data_types(destination_config: DestinationTestConfiguration) -> Non # bigquery and clickhouse cannot load into JSON fields from parquet if destination_config.file_format == "parquet": - if destination_config.destination in ["bigquery"]: - # change datatype to text and then allow for it in the assert (parse_complex_strings) + if destination_config.destination_type in ["bigquery"]: + # change datatype to text and then allow for it in the assert (parse_json_strings) column_schemas["col9_null"]["data_type"] = column_schemas["col9"]["data_type"] = "text" # redshift cannot load from json into VARBYTE if destination_config.file_format == "jsonl": - if destination_config.destination == "redshift": + if destination_config.destination_type == "redshift": # change the datatype to text which will result in inserting base64 (allow_base64_binary) binary_cols = ["col7", "col7_null"] for col in binary_cols: column_schemas[col]["data_type"] = "text" - # apply the exact columns definitions so we process complex and wei types correctly! + # apply the exact columns definitions so we process nested and wei types correctly! @dlt.resource(table_name="data_types", write_disposition="merge", columns=column_schemas) def my_resource(): nonlocal data_types @@ -322,7 +336,10 @@ def my_resource(): def my_source(): return my_resource - info = pipeline.run(my_source(), loader_file_format=destination_config.file_format) + info = pipeline.run( + my_source(), + **destination_config.run_kwargs, + ) assert_load_info(info) with pipeline.sql_client() as sql_client: @@ -331,22 +348,22 @@ def my_source(): assert len(db_rows) == 10 db_row = list(db_rows[0]) # parquet is not really good at inserting json, best we get are strings in JSON columns - parse_complex_strings = ( + parse_json_strings = ( destination_config.file_format == "parquet" - and destination_config.destination in ["redshift", "bigquery", "snowflake"] + and destination_config.destination_type in ["redshift", "bigquery", "snowflake"] ) allow_base64_binary = ( destination_config.file_format == "jsonl" - and destination_config.destination in ["redshift", "clickhouse"] + and destination_config.destination_type in ["redshift", "clickhouse"] ) allow_string_binary = ( destination_config.file_format == "parquet" - and destination_config.destination in ["clickhouse"] + and destination_config.destination_type in ["clickhouse"] ) # content must equal assert_all_data_types_row( db_row[:-2], - parse_complex_strings=parse_complex_strings, + parse_json_strings=parse_json_strings, allow_base64_binary=allow_base64_binary, allow_string_binary=allow_string_binary, timestamp_precision=sql_client.capabilities.timestamp_precision, diff --git a/tests/load/pipeline/test_write_disposition_changes.py b/tests/load/pipeline/test_write_disposition_changes.py index ba2f6bf172..f7d915903e 100644 --- a/tests/load/pipeline/test_write_disposition_changes.py +++ b/tests/load/pipeline/test_write_disposition_changes.py @@ -34,7 +34,7 @@ def test_switch_from_merge(destination_config: DestinationTestConfiguration): data_with_subtables(10), table_name="items", write_disposition="merge", - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) assert_data_table_counts(pipeline, {"items": 100, "items__sub_items": 100}) assert pipeline.default_schema._normalizers_config["json"]["config"]["propagation"]["tables"][ @@ -45,7 +45,7 @@ def test_switch_from_merge(destination_config: DestinationTestConfiguration): data_with_subtables(10), table_name="items", write_disposition="merge", - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) assert_load_info(info) assert_data_table_counts( @@ -63,7 +63,7 @@ def test_switch_from_merge(destination_config: DestinationTestConfiguration): data_with_subtables(10), table_name="items", write_disposition="append", - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) assert_load_info(info) assert_data_table_counts( @@ -81,7 +81,7 @@ def test_switch_from_merge(destination_config: DestinationTestConfiguration): data_with_subtables(10), table_name="items", write_disposition="replace", - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) assert_load_info(info) assert_data_table_counts(pipeline, {"items": 100, "items__sub_items": 100}) @@ -91,7 +91,9 @@ def test_switch_from_merge(destination_config: DestinationTestConfiguration): @pytest.mark.parametrize( - "destination_config", destinations_configs(default_sql_configs=True), ids=lambda x: x.name + "destination_config", + destinations_configs(default_sql_configs=True, supports_merge=True), + ids=lambda x: x.name, ) @pytest.mark.parametrize("with_root_key", [True, False]) def test_switch_to_merge(destination_config: DestinationTestConfiguration, with_root_key: bool): @@ -110,7 +112,7 @@ def source(): s, table_name="items", write_disposition="append", - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) assert_data_table_counts(pipeline, {"items": 100, "items__sub_items": 100}) @@ -126,7 +128,7 @@ def source(): # schemaless destinations allow adding of root key without the pipeline failing # they do not mind adding NOT NULL columns to tables with existing data (id NOT NULL is supported at all) # doing this will result in somewhat useless behavior - destination_allows_adding_root_key = destination_config.destination in [ + destination_allows_adding_root_key = destination_config.destination_type in [ "dremio", "clickhouse", "athena", @@ -137,7 +139,7 @@ def source(): s, table_name="items", write_disposition="merge", - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) return @@ -148,7 +150,7 @@ def source(): s, table_name="items", write_disposition="merge", - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) return @@ -156,7 +158,7 @@ def source(): s, table_name="items", write_disposition="merge", - loader_file_format=destination_config.file_format, + **destination_config.run_kwargs, ) assert_load_info(info) assert_data_table_counts( diff --git a/tests/load/postgres/postgres/Dockerfile b/tests/load/postgres/postgres/Dockerfile index 1dfd569b51..0a527bba5e 100644 --- a/tests/load/postgres/postgres/Dockerfile +++ b/tests/load/postgres/postgres/Dockerfile @@ -1,2 +1,2 @@ -FROM postgres:14 +FROM postgres:15 COPY 01_init.sql /docker-entrypoint-initdb.d/ \ No newline at end of file diff --git a/tests/load/postgres/test_postgres_table_builder.py b/tests/load/postgres/test_postgres_table_builder.py index 28fd4eec9d..4dac400f2a 100644 --- a/tests/load/postgres/test_postgres_table_builder.py +++ b/tests/load/postgres/test_postgres_table_builder.py @@ -128,7 +128,7 @@ def test_create_table_with_hints(client: PostgresClient, empty_schema: Schema) - mod_update[0]["primary_key"] = True mod_update[0]["sort"] = True mod_update[1]["unique"] = True - mod_update[4]["foreign_key"] = True + mod_update[4]["parent_key"] = True sql = client._get_table_update_sql("event_test_table", mod_update, False)[0] sqlfluff.parse(sql, dialect="postgres") assert '"col1" bigint NOT NULL' in sql diff --git a/tests/load/qdrant/test_restore_state.py b/tests/load/qdrant/test_restore_state.py index 31bc725d24..63f575f6ec 100644 --- a/tests/load/qdrant/test_restore_state.py +++ b/tests/load/qdrant/test_restore_state.py @@ -1,11 +1,9 @@ -from typing import TYPE_CHECKING import pytest from qdrant_client import models import dlt from tests.load.utils import destinations_configs, DestinationTestConfiguration -from dlt.common.destination.reference import JobClientBase, WithStateSync from dlt.destinations.impl.qdrant.qdrant_job_client import QdrantClient @@ -37,7 +35,7 @@ def dummy_table(): pipeline.extract(dummy_table) pipeline.normalize() - info = pipeline.load(raise_on_failed_jobs=True) + info = pipeline.load() client: QdrantClient with pipeline.destination_client() as client: # type: ignore[assignment] diff --git a/tests/load/snowflake/test_snowflake_configuration.py b/tests/load/snowflake/test_snowflake_configuration.py index 10d93d104c..f692b7ae92 100644 --- a/tests/load/snowflake/test_snowflake_configuration.py +++ b/tests/load/snowflake/test_snowflake_configuration.py @@ -8,7 +8,7 @@ pytest.importorskip("snowflake") -from dlt.common.libs.sql_alchemy import make_url +from dlt.common.libs.sql_alchemy_shims import make_url from dlt.common.configuration.resolve import resolve_configuration from dlt.common.configuration.exceptions import ConfigurationValueError from dlt.common.utils import digest128 diff --git a/tests/load/sources/__init__.py b/tests/load/sources/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/load/sources/filesystem/__init__.py b/tests/load/sources/filesystem/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/load/sources/filesystem/cases.py b/tests/load/sources/filesystem/cases.py new file mode 100644 index 0000000000..52f49686a9 --- /dev/null +++ b/tests/load/sources/filesystem/cases.py @@ -0,0 +1,69 @@ +import os + +from tests.load.utils import WITH_GDRIVE_BUCKETS + +TESTS_BUCKET_URLS = [ + os.path.join(bucket_url, "standard_source/samples") + for bucket_url in WITH_GDRIVE_BUCKETS + if not bucket_url.startswith("memory") +] + +GLOB_RESULTS = [ + { + "glob": None, + "relative_paths": ["sample.txt"], + }, + { + "glob": "*/*", + "relative_paths": [ + "csv/freshman_kgs.csv", + "csv/freshman_lbs.csv", + "csv/mlb_players.csv", + "csv/mlb_teams_2012.csv", + "gzip/taxi.csv.gz", + "jsonl/mlb_players.jsonl", + "parquet/mlb_players.parquet", + ], + }, + { + "glob": "**/*.csv", + "relative_paths": [ + "csv/freshman_kgs.csv", + "csv/freshman_lbs.csv", + "csv/mlb_players.csv", + "csv/mlb_teams_2012.csv", + "met_csv/A801/A881_20230920.csv", + "met_csv/A803/A803_20230919.csv", + "met_csv/A803/A803_20230920.csv", + ], + }, + { + "glob": "*/*.csv", + "relative_paths": [ + "csv/freshman_kgs.csv", + "csv/freshman_lbs.csv", + "csv/mlb_players.csv", + "csv/mlb_teams_2012.csv", + ], + }, + { + "glob": "csv/*", + "relative_paths": [ + "csv/freshman_kgs.csv", + "csv/freshman_lbs.csv", + "csv/mlb_players.csv", + "csv/mlb_teams_2012.csv", + ], + }, + { + "glob": "csv/mlb*", + "relative_paths": [ + "csv/mlb_players.csv", + "csv/mlb_teams_2012.csv", + ], + }, + { + "glob": "*", + "relative_paths": ["sample.txt"], + }, +] diff --git a/tests/load/sources/filesystem/test_filesystem_source.py b/tests/load/sources/filesystem/test_filesystem_source.py new file mode 100644 index 0000000000..88796b9c4d --- /dev/null +++ b/tests/load/sources/filesystem/test_filesystem_source.py @@ -0,0 +1,272 @@ +import os +from typing import Any, Dict, List + +import dlt +import pytest +from dlt.common import pendulum + +from dlt.common.storages import fsspec_filesystem +from dlt.sources.filesystem import filesystem, readers, FileItem, FileItemDict, read_csv +from dlt.sources.filesystem.helpers import fsspec_from_resource + +from tests.common.storages.utils import TEST_SAMPLE_FILES +from tests.load.utils import DestinationTestConfiguration, destinations_configs +from tests.pipeline.utils import ( + assert_load_info, + load_table_counts, + assert_query_data, +) +from tests.utils import TEST_STORAGE_ROOT + +from tests.load.sources.filesystem.cases import GLOB_RESULTS, TESTS_BUCKET_URLS + + +@pytest.fixture(autouse=True) +def glob_test_setup() -> None: + file_fs, _ = fsspec_filesystem("file") + file_path = os.path.join(TEST_STORAGE_ROOT, "standard_source") + if not file_fs.isdir(file_path): + file_fs.mkdirs(file_path) + file_fs.upload(TEST_SAMPLE_FILES, file_path, recursive=True) + + +@pytest.mark.parametrize("bucket_url", TESTS_BUCKET_URLS) +@pytest.mark.parametrize("glob_params", GLOB_RESULTS) +def test_file_list(bucket_url: str, glob_params: Dict[str, Any]) -> None: + @dlt.transformer + def bypass(items) -> str: + return items + + # we just pass the glob parameter to the resource if it is not None + if file_glob := glob_params["glob"]: + filesystem_res = filesystem(bucket_url=bucket_url, file_glob=file_glob) | bypass + else: + filesystem_res = filesystem(bucket_url=bucket_url) | bypass + + all_files = list(filesystem_res) + file_count = len(all_files) + relative_paths = [item["relative_path"] for item in all_files] + assert file_count == len(glob_params["relative_paths"]) + assert set(relative_paths) == set(glob_params["relative_paths"]) + + +@pytest.mark.parametrize("extract_content", [True, False]) +@pytest.mark.parametrize("bucket_url", TESTS_BUCKET_URLS) +def test_load_content_resources(bucket_url: str, extract_content: bool) -> None: + @dlt.transformer + def assert_sample_content(items: List[FileItemDict]): + # expect just one file + for item in items: + assert item["file_name"] == "sample.txt" + content = item.read_bytes() + assert content == b"dlthub content" + assert item["size_in_bytes"] == 14 + assert item["file_url"].endswith("/samples/sample.txt") + assert item["mime_type"] == "text/plain" + assert isinstance(item["modification_date"], pendulum.DateTime) + + yield items + + # use transformer to test files + sample_file = ( + filesystem( + bucket_url=bucket_url, + file_glob="sample.txt", + extract_content=extract_content, + ) + | assert_sample_content + ) + # just execute iterator + files = list(sample_file) + assert len(files) == 1 + + # take file from nested dir + # use map function to assert + def assert_csv_file(item: FileItem): + # on windows when checking out, git will convert lf into cr+lf so we have more bytes (+ number of lines: 25) + assert item["size_in_bytes"] in (742, 767) + assert item["relative_path"] == "met_csv/A801/A881_20230920.csv" + assert item["file_url"].endswith("/samples/met_csv/A801/A881_20230920.csv") + assert item["mime_type"] == "text/csv" + # print(item) + return item + + nested_file = filesystem(bucket_url, file_glob="met_csv/A801/A881_20230920.csv") + + assert len(list(nested_file | assert_csv_file)) == 1 + + +@pytest.mark.skip("Needs secrets toml to work..") +def test_fsspec_as_credentials(): + # get gs filesystem + gs_resource = filesystem("gs://ci-test-bucket") + # get authenticated client + fs_client = fsspec_from_resource(gs_resource) + print(fs_client.ls("ci-test-bucket/standard_source/samples")) + # use to create resource instead of credentials + gs_resource = filesystem("gs://ci-test-bucket/standard_source/samples", credentials=fs_client) + print(list(gs_resource)) + + +@pytest.mark.parametrize("bucket_url", TESTS_BUCKET_URLS) +@pytest.mark.parametrize( + "destination_config", + destinations_configs( + default_sql_configs=True, supports_merge=True, all_buckets_filesystem_configs=True + ), + ids=lambda x: x.name, +) +def test_csv_transformers( + bucket_url: str, destination_config: DestinationTestConfiguration +) -> None: + pipeline = destination_config.setup_pipeline("test_csv_transformers", dev_mode=True) + # load all csvs merging data on a date column + met_files = filesystem(bucket_url=bucket_url, file_glob="met_csv/A801/*.csv") | read_csv() + met_files.apply_hints(write_disposition="merge", merge_key="date") + load_info = pipeline.run(met_files.with_name("met_csv")) + assert_load_info(load_info) + + # print(pipeline.last_trace.last_normalize_info) + # must contain 24 rows of A881 + if destination_config.destination_type != "filesystem": + with pipeline.sql_client() as client: + table_name = client.make_qualified_table_name("met_csv") + # TODO: comment out when filesystem destination supports queries (data pond PR) + assert_query_data(pipeline, f"SELECT code FROM {table_name}", ["A881"] * 24) + + # load the other folder that contains data for the same day + one other day + # the previous data will be replaced + met_files = filesystem(bucket_url=bucket_url, file_glob="met_csv/A803/*.csv") | read_csv() + met_files.apply_hints(write_disposition="merge", merge_key="date") + load_info = pipeline.run(met_files.with_name("met_csv")) + assert_load_info(load_info) + # print(pipeline.last_trace.last_normalize_info) + # must contain 48 rows of A803 + if destination_config.destination_type != "filesystem": + with pipeline.sql_client() as client: + table_name = client.make_qualified_table_name("met_csv") + # TODO: comment out when filesystem destination supports queries (data pond PR) + assert_query_data(pipeline, f"SELECT code FROM {table_name}", ["A803"] * 48) + # and 48 rows in total -> A881 got replaced + # print(pipeline.default_schema.to_pretty_yaml()) + assert load_table_counts(pipeline, "met_csv") == {"met_csv": 48} + + +@pytest.mark.parametrize("bucket_url", TESTS_BUCKET_URLS) +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True, all_buckets_filesystem_configs=True), + ids=lambda x: x.name, +) +def test_standard_readers( + bucket_url: str, destination_config: DestinationTestConfiguration +) -> None: + # extract pipes with standard readers + jsonl_reader = readers(bucket_url, file_glob="**/*.jsonl").read_jsonl() + parquet_reader = readers(bucket_url, file_glob="**/*.parquet").read_parquet() + # also read zipped csvs + csv_reader = readers(bucket_url, file_glob="**/*.csv*").read_csv(float_precision="high") + csv_duckdb_reader = readers(bucket_url, file_glob="**/*.csv*").read_csv_duckdb() + + # a step that copies files into test storage + def _copy(item: FileItemDict): + # instantiate fsspec and copy file + dest_file = os.path.join(TEST_STORAGE_ROOT, item["relative_path"]) + # create dest folder + os.makedirs(os.path.dirname(dest_file), exist_ok=True) + # download file + item.fsspec.download(item["file_url"], dest_file) + # return file item unchanged + return item + + downloader = filesystem(bucket_url, file_glob="**").add_map(_copy) + + # load in single pipeline + pipeline = destination_config.setup_pipeline("test_standard_readers", dev_mode=True) + load_info = pipeline.run( + [ + jsonl_reader.with_name("jsonl_example"), + parquet_reader.with_name("parquet_example"), + downloader.with_name("listing"), + csv_reader.with_name("csv_example"), + csv_duckdb_reader.with_name("csv_duckdb_example"), + ] + ) + # pandas incorrectly guesses that taxi dataset has headers so it skips one row + # so we have 1 less row in csv_example than in csv_duckdb_example + assert_load_info(load_info) + assert load_table_counts( + pipeline, + "jsonl_example", + "parquet_example", + "listing", + "csv_example", + "csv_duckdb_example", + ) == { + "jsonl_example": 1034, + "parquet_example": 1034, + "listing": 11, + "csv_example": 1279, + "csv_duckdb_example": 1281, # TODO: i changed this from 1280, what is going on? :) + } + # print(pipeline.last_trace.last_normalize_info) + # print(pipeline.default_schema.to_pretty_yaml()) + + +@pytest.mark.parametrize("bucket_url", TESTS_BUCKET_URLS) +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True, all_buckets_filesystem_configs=True), + ids=lambda x: x.name, +) +def test_incremental_load( + bucket_url: str, destination_config: DestinationTestConfiguration +) -> None: + @dlt.transformer + def bypass(items) -> str: + return items + + pipeline = destination_config.setup_pipeline("test_incremental_load", dev_mode=True) + + # Load all files + all_files = filesystem(bucket_url=bucket_url, file_glob="csv/*") + # add incremental on modification time + all_files.apply_hints(incremental=dlt.sources.incremental("modification_date")) + load_info = pipeline.run((all_files | bypass).with_name("csv_files")) + assert_load_info(load_info) + assert pipeline.last_trace.last_normalize_info.row_counts["csv_files"] == 4 + + table_counts = load_table_counts(pipeline, "csv_files") + assert table_counts["csv_files"] == 4 + + # load again + all_files = filesystem(bucket_url=bucket_url, file_glob="csv/*") + all_files.apply_hints(incremental=dlt.sources.incremental("modification_date")) + load_info = pipeline.run((all_files | bypass).with_name("csv_files")) + # nothing into csv_files + assert "csv_files" not in pipeline.last_trace.last_normalize_info.row_counts + table_counts = load_table_counts(pipeline, "csv_files") + assert table_counts["csv_files"] == 4 + + # load again into different table + all_files = filesystem(bucket_url=bucket_url, file_glob="csv/*") + all_files.apply_hints(incremental=dlt.sources.incremental("modification_date")) + load_info = pipeline.run((all_files | bypass).with_name("csv_files_2")) + assert_load_info(load_info) + assert pipeline.last_trace.last_normalize_info.row_counts["csv_files_2"] == 4 + + +def test_file_chunking() -> None: + resource = filesystem( + bucket_url=TESTS_BUCKET_URLS[0], + file_glob="*/*.csv", + files_per_page=2, + ) + + from dlt.extract.pipe_iterator import PipeIterator + + # use pipe iterator to get items as they go through pipe + for pipe_item in PipeIterator.from_pipe(resource._pipe): + assert len(pipe_item.item) == 2 + # no need to test more chunks + break diff --git a/tests/load/sources/rest_api/__init__.py b/tests/load/sources/rest_api/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/load/sources/rest_api/test_rest_api_source.py b/tests/load/sources/rest_api/test_rest_api_source.py new file mode 100644 index 0000000000..25a9952ba4 --- /dev/null +++ b/tests/load/sources/rest_api/test_rest_api_source.py @@ -0,0 +1,128 @@ +from typing import Any +import dlt +import pytest +from dlt.sources.rest_api.typing import RESTAPIConfig +from dlt.sources.helpers.rest_client.paginators import SinglePagePaginator + +from dlt.sources.rest_api import rest_api_source +from tests.pipeline.utils import assert_load_info, load_table_counts +from tests.load.utils import ( + destinations_configs, + DestinationTestConfiguration, +) + + +def _make_pipeline(destination_name: str): + return dlt.pipeline( + pipeline_name="rest_api", + destination=destination_name, + dataset_name="rest_api_data", + full_refresh=True, + ) + + +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True, local_filesystem_configs=True), + ids=lambda x: x.name, +) +def test_rest_api_source(destination_config: DestinationTestConfiguration, request: Any) -> None: + config: RESTAPIConfig = { + "client": { + "base_url": "https://pokeapi.co/api/v2/", + }, + "resource_defaults": { + "endpoint": { + "params": { + "limit": 1000, + }, + } + }, + "resources": [ + { + "name": "pokemon_list", + "endpoint": "pokemon", + }, + "berry", + "location", + ], + } + data = rest_api_source(config) + pipeline = destination_config.setup_pipeline(request.node.name, dev_mode=True) + load_info = pipeline.run(data) + assert_load_info(load_info) + table_names = [t["name"] for t in pipeline.default_schema.data_tables()] + table_counts = load_table_counts(pipeline, *table_names) + + assert table_counts.keys() == {"pokemon_list", "berry", "location"} + + assert table_counts["pokemon_list"] == 1302 + assert table_counts["berry"] == 64 + assert table_counts["location"] == 1036 + + +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True, local_filesystem_configs=True), + ids=lambda x: x.name, +) +def test_dependent_resource(destination_config: DestinationTestConfiguration, request: Any) -> None: + config: RESTAPIConfig = { + "client": { + "base_url": "https://pokeapi.co/api/v2/", + }, + "resource_defaults": { + "endpoint": { + "params": { + "limit": 1000, + }, + } + }, + "resources": [ + { + "name": "pokemon_list", + "endpoint": { + "path": "pokemon", + "paginator": SinglePagePaginator(), + "data_selector": "results", + "params": { + "limit": 2, + }, + }, + "selected": False, + }, + { + "name": "pokemon", + "endpoint": { + "path": "pokemon/{name}", + "params": { + "name": { + "type": "resolve", + "resource": "pokemon_list", + "field": "name", + }, + }, + }, + }, + ], + } + + data = rest_api_source(config) + pipeline = destination_config.setup_pipeline(request.node.name, dev_mode=True) + load_info = pipeline.run(data) + assert_load_info(load_info) + table_names = [t["name"] for t in pipeline.default_schema.data_tables()] + table_counts = load_table_counts(pipeline, *table_names) + + assert set(table_counts.keys()) == { + "pokemon", + "pokemon__types", + "pokemon__stats", + "pokemon__moves__version_group_details", + "pokemon__moves", + "pokemon__game_indices", + "pokemon__forms", + "pokemon__abilities", + } + + assert table_counts["pokemon"] == 2 diff --git a/tests/load/sources/sql_database/__init__.py b/tests/load/sources/sql_database/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/load/sources/sql_database/conftest.py b/tests/load/sources/sql_database/conftest.py new file mode 100644 index 0000000000..e70467e714 --- /dev/null +++ b/tests/load/sources/sql_database/conftest.py @@ -0,0 +1,40 @@ +from typing import Iterator, Any + +import pytest + +import dlt +from dlt.sources.credentials import ConnectionStringCredentials + +try: + from tests.load.sources.sql_database.sql_source import SQLAlchemySourceDB +except ModuleNotFoundError: + SQLAlchemySourceDB = Any # type: ignore + + +def _create_db(**kwargs) -> Iterator[SQLAlchemySourceDB]: + # TODO: parametrize the fixture so it takes the credentials for all destinations + credentials = dlt.secrets.get( + "destination.postgres.credentials", expected_type=ConnectionStringCredentials + ) + + db = SQLAlchemySourceDB(credentials, **kwargs) + db.create_schema() + try: + db.create_tables() + db.insert_data() + yield db + finally: + db.drop_schema() + + +@pytest.fixture(scope="package") +def sql_source_db(request: pytest.FixtureRequest) -> Iterator[SQLAlchemySourceDB]: + # Without unsupported types so we can test full schema load with connector-x + yield from _create_db(with_unsupported_types=False) + + +@pytest.fixture(scope="package") +def sql_source_db_unsupported_types( + request: pytest.FixtureRequest, +) -> Iterator[SQLAlchemySourceDB]: + yield from _create_db(with_unsupported_types=True) diff --git a/tests/load/sources/sql_database/sql_source.py b/tests/load/sources/sql_database/sql_source.py new file mode 100644 index 0000000000..43ce5406d2 --- /dev/null +++ b/tests/load/sources/sql_database/sql_source.py @@ -0,0 +1,373 @@ +import random +from copy import deepcopy +from typing import Dict, List, TypedDict +from uuid import uuid4 + +import mimesis + + +from sqlalchemy import ( + ARRAY, + BigInteger, + Boolean, + Column, + Date, + DateTime, + Float, + ForeignKey, + Integer, + MetaData, + Numeric, + SmallInteger, + String, + Table, + Text, + Time, + create_engine, + func, + text, +) + +try: + from sqlalchemy import Uuid # type: ignore[attr-defined] +except ImportError: + # sql alchemy 1.4 + Uuid = String + +from sqlalchemy import ( + schema as sqla_schema, +) + +from sqlalchemy.dialects.postgresql import DATERANGE, JSONB + +from dlt.common.pendulum import pendulum, timedelta +from dlt.common.utils import chunks, uniq_id +from dlt.sources.credentials import ConnectionStringCredentials + + +class SQLAlchemySourceDB: + def __init__( + self, + credentials: ConnectionStringCredentials, + schema: str = None, + with_unsupported_types: bool = False, + ) -> None: + self.credentials = credentials + self.database_url = credentials.to_native_representation() + self.schema = schema or "my_dlt_source" + uniq_id() + self.engine = create_engine(self.database_url) + self.metadata = MetaData(schema=self.schema) + self.table_infos: Dict[str, TableInfo] = {} + self.with_unsupported_types = with_unsupported_types + + def create_schema(self) -> None: + with self.engine.begin() as conn: + conn.execute(sqla_schema.CreateSchema(self.schema, if_not_exists=True)) + + def drop_schema(self) -> None: + with self.engine.begin() as conn: + conn.execute(sqla_schema.DropSchema(self.schema, cascade=True, if_exists=True)) + + def get_table(self, name: str) -> Table: + return self.metadata.tables[f"{self.schema}.{name}"] + + def create_tables(self) -> None: + Table( + "app_user", + self.metadata, + Column("id", Integer(), primary_key=True, autoincrement=True), + Column("email", Text(), nullable=False, unique=True), + Column("display_name", Text(), nullable=False), + Column( + "created_at", + DateTime(timezone=True), + nullable=False, + server_default=func.now(), + ), + Column( + "updated_at", + DateTime(timezone=True), + nullable=False, + server_default=func.now(), + ), + ) + Table( + "chat_channel", + self.metadata, + Column("id", Integer(), primary_key=True, autoincrement=True), + Column( + "created_at", + DateTime(timezone=True), + nullable=False, + server_default=func.now(), + ), + Column("name", Text(), nullable=False), + Column("active", Boolean(), nullable=False, server_default=text("true")), + Column( + "updated_at", + DateTime(timezone=True), + nullable=False, + server_default=func.now(), + ), + ) + Table( + "chat_message", + self.metadata, + Column("id", Integer(), primary_key=True, autoincrement=True), + Column( + "created_at", + DateTime(timezone=True), + nullable=False, + server_default=func.now(), + ), + Column("content", Text(), nullable=False), + Column( + "user_id", + Integer(), + ForeignKey("app_user.id"), + nullable=False, + index=True, + ), + Column( + "channel_id", + Integer(), + ForeignKey("chat_channel.id"), + nullable=False, + index=True, + ), + Column( + "updated_at", + DateTime(timezone=True), + nullable=False, + server_default=func.now(), + ), + ) + Table( + "has_composite_key", + self.metadata, + Column("a", Integer(), primary_key=True), + Column("b", Integer(), primary_key=True), + Column("c", Integer(), primary_key=True), + ) + + def _make_precision_table(table_name: str, nullable: bool) -> None: + Table( + table_name, + self.metadata, + Column("int_col", Integer(), nullable=nullable), + Column("bigint_col", BigInteger(), nullable=nullable), + Column("smallint_col", SmallInteger(), nullable=nullable), + Column("numeric_col", Numeric(precision=10, scale=2), nullable=nullable), + Column("numeric_default_col", Numeric(), nullable=nullable), + Column("string_col", String(length=10), nullable=nullable), + Column("string_default_col", String(), nullable=nullable), + Column("datetime_tz_col", DateTime(timezone=True), nullable=nullable), + Column("datetime_ntz_col", DateTime(timezone=False), nullable=nullable), + Column("date_col", Date, nullable=nullable), + Column("time_col", Time, nullable=nullable), + Column("float_col", Float, nullable=nullable), + Column("json_col", JSONB, nullable=nullable), + Column("bool_col", Boolean, nullable=nullable), + ) + + _make_precision_table("has_precision", False) + _make_precision_table("has_precision_nullable", True) + + if self.with_unsupported_types: + Table( + "has_unsupported_types", + self.metadata, + # Column("unsupported_daterange_1", DATERANGE, nullable=False), + Column("supported_text", Text, nullable=False), + Column("supported_int", Integer, nullable=False), + Column("unsupported_array_1", ARRAY(Integer), nullable=False), + # Column("supported_datetime", DateTime(timezone=True), nullable=False), + ) + + self.metadata.create_all(bind=self.engine) + + # Create a view + q = f""" + CREATE VIEW {self.schema}.chat_message_view AS + SELECT + cm.id, + cm.content, + cm.created_at as _created_at, + cm.updated_at as _updated_at, + au.email as user_email, + au.display_name as user_display_name, + cc.name as channel_name, + CAST(NULL as TIMESTAMP) as _null_ts + FROM {self.schema}.chat_message cm + JOIN {self.schema}.app_user au ON cm.user_id = au.id + JOIN {self.schema}.chat_channel cc ON cm.channel_id = cc.id + """ + with self.engine.begin() as conn: + conn.execute(text(q)) + + def _fake_users(self, n: int = 8594) -> List[int]: + person = mimesis.Person() + user_ids: List[int] = [] + table = self.metadata.tables[f"{self.schema}.app_user"] + info = self.table_infos.setdefault( + "app_user", + dict(row_count=0, ids=[], created_at=IncrementingDate(), is_view=False), + ) + dt = info["created_at"] + for chunk in chunks(range(n), 5000): + rows = [ + dict( + email=person.email(unique=True), + display_name=person.name(), + created_at=next(dt), + updated_at=next(dt), + ) + for i in chunk + ] + with self.engine.begin() as conn: + result = conn.execute(table.insert().values(rows).returning(table.c.id)) + user_ids.extend(result.scalars()) + info["row_count"] += n + info["ids"] += user_ids + return user_ids + + def _fake_channels(self, n: int = 500) -> List[int]: + _text = mimesis.Text() + dev = mimesis.Development() + table = self.metadata.tables[f"{self.schema}.chat_channel"] + channel_ids: List[int] = [] + info = self.table_infos.setdefault( + "chat_channel", + dict(row_count=0, ids=[], created_at=IncrementingDate(), is_view=False), + ) + dt = info["created_at"] + for chunk in chunks(range(n), 5000): + rows = [ + dict( + name=" ".join(_text.words()), + active=dev.boolean(), + created_at=next(dt), + updated_at=next(dt), + ) + for i in chunk + ] + with self.engine.begin() as conn: + result = conn.execute(table.insert().values(rows).returning(table.c.id)) + channel_ids.extend(result.scalars()) + info["row_count"] += n + info["ids"] += channel_ids + return channel_ids + + def fake_messages(self, n: int = 9402) -> List[int]: + user_ids = self.table_infos["app_user"]["ids"] + channel_ids = self.table_infos["chat_channel"]["ids"] + _text = mimesis.Text() + choice = mimesis.Choice() + table = self.metadata.tables[f"{self.schema}.chat_message"] + message_ids: List[int] = [] + info = self.table_infos.setdefault( + "chat_message", + dict(row_count=0, ids=[], created_at=IncrementingDate(), is_view=False), + ) + dt = info["created_at"] + for chunk in chunks(range(n), 5000): + rows = [ + dict( + content=_text.random.choice(_text.extract(["questions"])), + user_id=choice(user_ids), + channel_id=choice(channel_ids), + created_at=next(dt), + updated_at=next(dt), + ) + for i in chunk + ] + with self.engine.begin() as conn: + result = conn.execute(table.insert().values(rows).returning(table.c.id)) + message_ids.extend(result.scalars()) + info["row_count"] += len(message_ids) + info["ids"].extend(message_ids) + # View is the same number of rows as the table + view_info = deepcopy(info) + view_info["is_view"] = True + view_info = self.table_infos.setdefault("chat_message_view", view_info) + view_info["row_count"] = info["row_count"] + view_info["ids"] = info["ids"] + return message_ids + + def _fake_precision_data(self, table_name: str, n: int = 100, null_n: int = 0) -> None: + table = self.metadata.tables[f"{self.schema}.{table_name}"] + self.table_infos.setdefault(table_name, dict(row_count=n + null_n, is_view=False)) # type: ignore[call-overload] + rows = [ + dict( + int_col=random.randrange(-2147483648, 2147483647), + bigint_col=random.randrange(-9223372036854775808, 9223372036854775807), + smallint_col=random.randrange(-32768, 32767), + numeric_col=random.randrange(-9999999999, 9999999999) / 100, + numeric_default_col=random.randrange(-9999999999, 9999999999) / 100, + string_col=mimesis.Text().word()[:10], + string_default_col=mimesis.Text().word(), + datetime_tz_col=mimesis.Datetime().datetime(timezone="UTC"), + datetime_ntz_col=mimesis.Datetime().datetime(), # no timezone + date_col=mimesis.Datetime().date(), + time_col=mimesis.Datetime().time(), + float_col=random.random(), + json_col='{"data": [1, 2, 3]}', # NOTE: can we do this? + bool_col=random.randint(0, 1) == 1, + ) + for _ in range(n + null_n) + ] + for row in rows[n:]: + # all fields to None + for field in row: + row[field] = None + with self.engine.begin() as conn: + conn.execute(table.insert().values(rows)) + + def _fake_chat_data(self, n: int = 9402) -> None: + self._fake_users() + self._fake_channels() + self.fake_messages() + + def _fake_unsupported_data(self, n: int = 100) -> None: + table = self.metadata.tables[f"{self.schema}.has_unsupported_types"] + self.table_infos.setdefault("has_unsupported_types", dict(row_count=n, is_view=False)) # type: ignore[call-overload] + rows = [ + dict( + # unsupported_daterange_1="[2020-01-01, 2020-09-01]", + supported_text=mimesis.Text().word(), + supported_int=random.randint(0, 100), + unsupported_array_1=[1, 2, 3], + # supported_datetime="2015-08-12T01:25:22.468126+0100", + ) + for _ in range(n) + ] + with self.engine.begin() as conn: + conn.execute(table.insert().values(rows)) + + def insert_data(self) -> None: + self._fake_chat_data() + self._fake_precision_data("has_precision") + self._fake_precision_data("has_precision_nullable", null_n=10) + if self.with_unsupported_types: + self._fake_unsupported_data() + + +class IncrementingDate: + def __init__(self, start_value: pendulum.DateTime = None) -> None: + self.started = False + self.start_value = start_value or pendulum.now() + self.current_value = self.start_value + + def __next__(self) -> pendulum.DateTime: + if not self.started: + self.started = True + return self.current_value + self.current_value += timedelta(seconds=random.randrange(0, 120)) + return self.current_value + + +class TableInfo(TypedDict): + row_count: int + ids: List[int] + created_at: IncrementingDate + is_view: bool diff --git a/tests/load/sources/sql_database/test_helpers.py b/tests/load/sources/sql_database/test_helpers.py new file mode 100644 index 0000000000..cc88fc0080 --- /dev/null +++ b/tests/load/sources/sql_database/test_helpers.py @@ -0,0 +1,173 @@ +import pytest + +import dlt +from dlt.common.typing import TDataItem + + +from dlt.common.exceptions import MissingDependencyException + +try: + from dlt.sources.sql_database.helpers import TableLoader, TableBackend + from dlt.sources.sql_database.schema_types import table_to_columns + from tests.load.sources.sql_database.sql_source import SQLAlchemySourceDB +except MissingDependencyException: + pytest.skip("Tests require sql alchemy", allow_module_level=True) + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +def test_cursor_or_unique_column_not_in_table( + sql_source_db: SQLAlchemySourceDB, backend: TableBackend +) -> None: + table = sql_source_db.get_table("chat_message") + + with pytest.raises(KeyError): + TableLoader( + sql_source_db.engine, + backend, + table, + table_to_columns(table), + incremental=dlt.sources.incremental("not_a_column"), + ) + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +def test_make_query_incremental_max( + sql_source_db: SQLAlchemySourceDB, backend: TableBackend +) -> None: + """Verify query is generated according to incremental settings""" + + class MockIncremental: + last_value = dlt.common.pendulum.now() + last_value_func = max + cursor_path = "created_at" + row_order = "asc" + end_value = None + + table = sql_source_db.get_table("chat_message") + loader = TableLoader( + sql_source_db.engine, + backend, + table, + table_to_columns(table), + incremental=MockIncremental(), # type: ignore[arg-type] + ) + + query = loader.make_query() + expected = ( + table.select() + .order_by(table.c.created_at.asc()) + .where(table.c.created_at >= MockIncremental.last_value) + ) + + assert query.compare(expected) + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +def test_make_query_incremental_min( + sql_source_db: SQLAlchemySourceDB, backend: TableBackend +) -> None: + class MockIncremental: + last_value = dlt.common.pendulum.now() + last_value_func = min + cursor_path = "created_at" + row_order = "desc" + end_value = None + + table = sql_source_db.get_table("chat_message") + loader = TableLoader( + sql_source_db.engine, + backend, + table, + table_to_columns(table), + incremental=MockIncremental(), # type: ignore[arg-type] + ) + + query = loader.make_query() + expected = ( + table.select() + .order_by(table.c.created_at.asc()) # `min` func swaps order + .where(table.c.created_at <= MockIncremental.last_value) + ) + + assert query.compare(expected) + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +def test_make_query_incremental_end_value( + sql_source_db: SQLAlchemySourceDB, backend: TableBackend +) -> None: + now = dlt.common.pendulum.now() + + class MockIncremental: + last_value = now + last_value_func = min + cursor_path = "created_at" + end_value = now.add(hours=1) + row_order = None + + table = sql_source_db.get_table("chat_message") + loader = TableLoader( + sql_source_db.engine, + backend, + table, + table_to_columns(table), + incremental=MockIncremental(), # type: ignore[arg-type] + ) + + query = loader.make_query() + expected = ( + table.select() + .where(table.c.created_at <= MockIncremental.last_value) + .where(table.c.created_at > MockIncremental.end_value) + ) + + assert query.compare(expected) + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +def test_make_query_incremental_any_fun( + sql_source_db: SQLAlchemySourceDB, backend: TableBackend +) -> None: + class MockIncremental: + last_value = dlt.common.pendulum.now() + last_value_func = lambda x: x[-1] + cursor_path = "created_at" + row_order = "asc" + end_value = dlt.common.pendulum.now() + + table = sql_source_db.get_table("chat_message") + loader = TableLoader( + sql_source_db.engine, + backend, + table, + table_to_columns(table), + incremental=MockIncremental(), # type: ignore[arg-type] + ) + + query = loader.make_query() + expected = table.select() + + assert query.compare(expected) + + +def mock_json_column(field: str) -> TDataItem: + """""" + import pyarrow as pa + import pandas as pd + + json_mock_str = '{"data": [1, 2, 3]}' + + def _unwrap(table: TDataItem) -> TDataItem: + if isinstance(table, pd.DataFrame): + table[field] = [None if s is None else json_mock_str for s in table[field]] + return table + else: + col_index = table.column_names.index(field) + json_str_array = pa.array([None if s is None else json_mock_str for s in table[field]]) + return table.set_column( + col_index, + pa.field(field, pa.string(), nullable=table.schema.field(field).nullable), + json_str_array, + ) + + return _unwrap diff --git a/tests/load/sources/sql_database/test_sql_database_source.py b/tests/load/sources/sql_database/test_sql_database_source.py new file mode 100644 index 0000000000..23a6d4eaf4 --- /dev/null +++ b/tests/load/sources/sql_database/test_sql_database_source.py @@ -0,0 +1,1186 @@ +import os +from copy import deepcopy +from typing import Any, Callable, cast, List, Optional, Set + +import pytest + +import dlt +from dlt.common import json +from dlt.common.configuration.exceptions import ConfigFieldMissingException +from dlt.common.exceptions import MissingDependencyException + +from dlt.common.schema.typing import TColumnSchema, TSortOrder, TTableSchemaColumns +from dlt.common.utils import uniq_id +from dlt.extract.exceptions import ResourceExtractionError + +from dlt.sources import DltResource + +from tests.pipeline.utils import ( + assert_load_info, + assert_schema_on_data, + load_tables_to_dicts, +) +from tests.load.sources.sql_database.test_helpers import mock_json_column +from tests.utils import data_item_length, load_table_counts + + +try: + from dlt.sources.sql_database import ( + ReflectionLevel, + TableBackend, + sql_database, + sql_table, + ) + from dlt.sources.sql_database.helpers import unwrap_json_connector_x + from tests.load.sources.sql_database.sql_source import SQLAlchemySourceDB + import sqlalchemy as sa +except MissingDependencyException: + pytest.skip("Tests require sql alchemy", allow_module_level=True) + + +@pytest.fixture(autouse=True) +def dispose_engines(): + yield + import gc + + # will collect and dispose all hanging engines + gc.collect() + + +@pytest.fixture(autouse=True) +def reset_os_environ(): + # Save the current state of os.environ + original_environ = deepcopy(os.environ) + yield + # Restore the original state of os.environ + os.environ.clear() + os.environ.update(original_environ) + + +def make_pipeline(destination_name: str) -> dlt.Pipeline: + return dlt.pipeline( + pipeline_name="sql_database" + uniq_id(), + destination=destination_name, + dataset_name="test_sql_pipeline_" + uniq_id(), + full_refresh=False, + ) + + +def convert_json_to_text(t): + if isinstance(t, sa.JSON): + return sa.Text + return t + + +def default_test_callback( + destination_name: str, backend: TableBackend +) -> Optional[Callable[[sa.types.TypeEngine], sa.types.TypeEngine]]: + if backend == "pyarrow" and destination_name == "bigquery": + return convert_json_to_text + return None + + +def convert_time_to_us(table): + """map transform converting time column to microseconds (ie. from nanoseconds)""" + import pyarrow as pa + from pyarrow import compute as pc + + time_ns_column = table["time_col"] + time_us_column = pc.cast(time_ns_column, pa.time64("us"), safe=False) + new_table = table.set_column( + table.column_names.index("time_col"), + "time_col", + time_us_column, + ) + return new_table + + +def test_pass_engine_credentials(sql_source_db: SQLAlchemySourceDB) -> None: + # verify database + database = sql_database( + sql_source_db.engine, schema=sql_source_db.schema, table_names=["chat_message"] + ) + assert len(list(database)) == sql_source_db.table_infos["chat_message"]["row_count"] + + # verify table + table = sql_table(sql_source_db.engine, table="chat_message", schema=sql_source_db.schema) + assert len(list(table)) == sql_source_db.table_infos["chat_message"]["row_count"] + + +def test_named_sql_table_config(sql_source_db: SQLAlchemySourceDB) -> None: + # set the credentials per table name + os.environ["SOURCES__SQL_DATABASE__CHAT_MESSAGE__CREDENTIALS"] = ( + sql_source_db.engine.url.render_as_string(False) + ) + table = sql_table(table="chat_message", schema=sql_source_db.schema) + assert table.name == "chat_message" + assert len(list(table)) == sql_source_db.table_infos["chat_message"]["row_count"] + + with pytest.raises(ConfigFieldMissingException): + sql_table(table="has_composite_key", schema=sql_source_db.schema) + + # set backend + os.environ["SOURCES__SQL_DATABASE__CHAT_MESSAGE__BACKEND"] = "pandas" + table = sql_table(table="chat_message", schema=sql_source_db.schema) + # just one frame here + assert len(list(table)) == 1 + + os.environ["SOURCES__SQL_DATABASE__CHAT_MESSAGE__CHUNK_SIZE"] = "1000" + table = sql_table(table="chat_message", schema=sql_source_db.schema) + # now 10 frames with chunk size of 1000 + assert len(list(table)) == 10 + + # make it fail on cursor + os.environ["SOURCES__SQL_DATABASE__CHAT_MESSAGE__INCREMENTAL__CURSOR_PATH"] = "updated_at_x" + table = sql_table(table="chat_message", schema=sql_source_db.schema) + with pytest.raises(ResourceExtractionError) as ext_ex: + len(list(table)) + assert "'updated_at_x'" in str(ext_ex.value) + + +def test_general_sql_database_config(sql_source_db: SQLAlchemySourceDB) -> None: + # set the credentials per table name + os.environ["SOURCES__SQL_DATABASE__CREDENTIALS"] = sql_source_db.engine.url.render_as_string( + False + ) + # applies to both sql table and sql database + table = sql_table(table="chat_message", schema=sql_source_db.schema) + assert len(list(table)) == sql_source_db.table_infos["chat_message"]["row_count"] + database = sql_database(schema=sql_source_db.schema).with_resources("chat_message") + assert len(list(database)) == sql_source_db.table_infos["chat_message"]["row_count"] + + # set backend + os.environ["SOURCES__SQL_DATABASE__BACKEND"] = "pandas" + table = sql_table(table="chat_message", schema=sql_source_db.schema) + # just one frame here + assert len(list(table)) == 1 + database = sql_database(schema=sql_source_db.schema).with_resources("chat_message") + assert len(list(database)) == 1 + + os.environ["SOURCES__SQL_DATABASE__CHUNK_SIZE"] = "1000" + table = sql_table(table="chat_message", schema=sql_source_db.schema) + # now 10 frames with chunk size of 1000 + assert len(list(table)) == 10 + database = sql_database(schema=sql_source_db.schema).with_resources("chat_message") + assert len(list(database)) == 10 + + # make it fail on cursor + os.environ["SOURCES__SQL_DATABASE__CHAT_MESSAGE__INCREMENTAL__CURSOR_PATH"] = "updated_at_x" + table = sql_table(table="chat_message", schema=sql_source_db.schema) + with pytest.raises(ResourceExtractionError) as ext_ex: + len(list(table)) + assert "'updated_at_x'" in str(ext_ex.value) + with pytest.raises(ResourceExtractionError) as ext_ex: + list(sql_database(schema=sql_source_db.schema).with_resources("chat_message")) + # other resources will be loaded, incremental is selective + assert len(list(sql_database(schema=sql_source_db.schema).with_resources("app_user"))) > 0 + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pandas", "pyarrow", "connectorx"]) +@pytest.mark.parametrize("row_order", ["asc", "desc", None]) +@pytest.mark.parametrize("last_value_func", [min, max, lambda x: max(x)]) +def test_load_sql_table_resource_incremental_end_value( + sql_source_db: SQLAlchemySourceDB, + backend: TableBackend, + row_order: TSortOrder, + last_value_func: Any, +) -> None: + start_id = sql_source_db.table_infos["chat_message"]["ids"][0] + end_id = sql_source_db.table_infos["chat_message"]["ids"][-1] // 2 + + if last_value_func is min: + start_id, end_id = end_id, start_id + + @dlt.source + def sql_table_source() -> List[DltResource]: + return [ + sql_table( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + table="chat_message", + backend=backend, + incremental=dlt.sources.incremental( + "id", + initial_value=start_id, + end_value=end_id, + row_order=row_order, + last_value_func=last_value_func, + ), + ) + ] + + try: + rows = list(sql_table_source()) + except Exception as exc: + if isinstance(exc.__context__, NotImplementedError): + pytest.skip("Test skipped due to: " + str(exc.__context__)) + raise + # half of the records loaded -1 record. end values is non inclusive + assert data_item_length(rows) == abs(end_id - start_id) + # check first and last id to see if order was applied + if backend == "sqlalchemy": + if row_order == "asc" and last_value_func is max: + assert rows[0]["id"] == start_id + assert rows[-1]["id"] == end_id - 1 # non inclusive + if row_order == "desc" and last_value_func is max: + assert rows[0]["id"] == end_id - 1 # non inclusive + assert rows[-1]["id"] == start_id + if row_order == "asc" and last_value_func is min: + assert rows[0]["id"] == start_id + assert ( + rows[-1]["id"] == end_id + 1 + ) # non inclusive, but + 1 because last value func is min + if row_order == "desc" and last_value_func is min: + assert ( + rows[0]["id"] == end_id + 1 + ) # non inclusive, but + 1 because last value func is min + assert rows[-1]["id"] == start_id + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +@pytest.mark.parametrize("defer_table_reflect", (False, True)) +def test_load_sql_table_resource_select_columns( + sql_source_db: SQLAlchemySourceDB, defer_table_reflect: bool, backend: TableBackend +) -> None: + # get chat messages with content column removed + chat_messages = sql_table( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + table="chat_message", + defer_table_reflect=defer_table_reflect, + table_adapter_callback=lambda table: table._columns.remove(table.columns["content"]), # type: ignore[attr-defined] + backend=backend, + ) + pipeline = make_pipeline("duckdb") + load_info = pipeline.run(chat_messages) + assert_load_info(load_info) + assert_row_counts(pipeline, sql_source_db, ["chat_message"]) + assert "content" not in pipeline.default_schema.tables["chat_message"]["columns"] + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +@pytest.mark.parametrize("defer_table_reflect", (False, True)) +def test_load_sql_table_source_select_columns( + sql_source_db: SQLAlchemySourceDB, defer_table_reflect: bool, backend: TableBackend +) -> None: + mod_tables: Set[str] = set() + + def adapt(table) -> None: + mod_tables.add(table) + if table.name == "chat_message": + table._columns.remove(table.columns["content"]) + + # get chat messages with content column removed + all_tables = sql_database( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + defer_table_reflect=defer_table_reflect, + table_names=(list(sql_source_db.table_infos.keys()) if defer_table_reflect else None), + table_adapter_callback=adapt, + backend=backend, + ) + pipeline = make_pipeline("duckdb") + load_info = pipeline.run(all_tables) + assert_load_info(load_info) + assert_row_counts(pipeline, sql_source_db) + assert "content" not in pipeline.default_schema.tables["chat_message"]["columns"] + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +@pytest.mark.parametrize("reflection_level", ["full", "full_with_precision"]) +@pytest.mark.parametrize("with_defer", [True, False]) +def test_extract_without_pipeline( + sql_source_db: SQLAlchemySourceDB, + backend: TableBackend, + reflection_level: ReflectionLevel, + with_defer: bool, +) -> None: + # make sure that we can evaluate tables without pipeline + source = sql_database( + credentials=sql_source_db.credentials, + table_names=["has_precision", "app_user", "chat_message", "chat_channel"], + schema=sql_source_db.schema, + reflection_level=reflection_level, + defer_table_reflect=with_defer, + backend=backend, + ) + assert len(list(source)) > 0 + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +@pytest.mark.parametrize("reflection_level", ["minimal", "full", "full_with_precision"]) +@pytest.mark.parametrize("with_defer", [False, True]) +@pytest.mark.parametrize("standalone_resource", [True, False]) +def test_reflection_levels( + sql_source_db: SQLAlchemySourceDB, + backend: TableBackend, + reflection_level: ReflectionLevel, + with_defer: bool, + standalone_resource: bool, +) -> None: + """Test all reflection, correct schema is inferred""" + + def prepare_source(): + if standalone_resource: + + @dlt.source + def dummy_source(): + yield sql_table( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + table="has_precision", + backend=backend, + defer_table_reflect=with_defer, + reflection_level=reflection_level, + ) + yield sql_table( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + table="app_user", + backend=backend, + defer_table_reflect=with_defer, + reflection_level=reflection_level, + ) + + return dummy_source() + + return sql_database( + credentials=sql_source_db.credentials, + table_names=["has_precision", "app_user"], + schema=sql_source_db.schema, + reflection_level=reflection_level, + defer_table_reflect=with_defer, + backend=backend, + ) + + source = prepare_source() + + pipeline = make_pipeline("duckdb") + pipeline.extract(source) + + schema = pipeline.default_schema + assert "has_precision" in schema.tables + + col_names = [col["name"] for col in schema.tables["has_precision"]["columns"].values()] + expected_col_names = [col["name"] for col in PRECISION_COLUMNS] + + # on sqlalchemy json col is not written to schema if no types are discovered + if backend == "sqlalchemy" and reflection_level == "minimal" and not with_defer: + expected_col_names = [col for col in expected_col_names if col != "json_col"] + + assert col_names == expected_col_names + + # Pk col is always reflected + pk_col = schema.tables["app_user"]["columns"]["id"] + assert pk_col["primary_key"] is True + + if reflection_level == "minimal": + resource_cols = source.resources["has_precision"].compute_table_schema()["columns"] + schema_cols = pipeline.default_schema.tables["has_precision"]["columns"] + # We should have all column names on resource hints after extract but no data type or precision + for col, schema_col in zip(resource_cols.values(), schema_cols.values()): + assert col.get("data_type") is None + assert col.get("precision") is None + assert col.get("scale") is None + if backend == "sqlalchemy": # Data types are inferred from pandas/arrow during extract + assert schema_col.get("data_type") is None + + pipeline.normalize() + # Check with/out precision after normalize + schema_cols = pipeline.default_schema.tables["has_precision"]["columns"] + if reflection_level == "full": + # Columns have data type set + assert_no_precision_columns(schema_cols, backend, False) + + elif reflection_level == "full_with_precision": + # Columns have data type and precision scale set + assert_precision_columns(schema_cols, backend, False) + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +@pytest.mark.parametrize("standalone_resource", [True, False]) +def test_type_adapter_callback( + sql_source_db: SQLAlchemySourceDB, backend: TableBackend, standalone_resource: bool +) -> None: + def conversion_callback(t): + if isinstance(t, sa.JSON): + return sa.Text + elif hasattr(sa, "Double") and isinstance(t, sa.Double): + return sa.BIGINT + return t + + common_kwargs = dict( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + backend=backend, + type_adapter_callback=conversion_callback, + reflection_level="full", + ) + + if standalone_resource: + source = sql_table( + table="has_precision", + **common_kwargs, # type: ignore[arg-type] + ) + else: + source = sql_database( # type: ignore[assignment] + table_names=["has_precision"], + **common_kwargs, # type: ignore[arg-type] + ) + + pipeline = make_pipeline("duckdb") + pipeline.extract(source) + + schema = pipeline.default_schema + table = schema.tables["has_precision"] + assert table["columns"]["json_col"]["data_type"] == "text" + assert ( + table["columns"]["float_col"]["data_type"] == "bigint" + if hasattr(sa, "Double") + else "double" + ) + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +@pytest.mark.parametrize( + "table_name,nullable", (("has_precision", False), ("has_precision_nullable", True)) +) +def test_all_types_with_precision_hints( + sql_source_db: SQLAlchemySourceDB, + backend: TableBackend, + table_name: str, + nullable: bool, +) -> None: + source = sql_database( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + reflection_level="full_with_precision", + backend=backend, + ) + + pipeline = make_pipeline("duckdb") + + # add JSON unwrap for connectorx + if backend == "connectorx": + source.resources[table_name].add_map(unwrap_json_connector_x("json_col")) + pipeline.extract(source) + pipeline.normalize(loader_file_format="parquet") + info = pipeline.load() + assert_load_info(info) + + schema = pipeline.default_schema + table = schema.tables[table_name] + assert_precision_columns(table["columns"], backend, nullable) + assert_schema_on_data( + table, + load_tables_to_dicts(pipeline, table_name)[table_name], + nullable, + backend in ["sqlalchemy", "pyarrow"], + ) + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +@pytest.mark.parametrize( + "table_name,nullable", (("has_precision", False), ("has_precision_nullable", True)) +) +def test_all_types_no_precision_hints( + sql_source_db: SQLAlchemySourceDB, + backend: TableBackend, + table_name: str, + nullable: bool, +) -> None: + source = sql_database( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + reflection_level="full", + backend=backend, + ) + + pipeline = make_pipeline("duckdb") + + # add JSON unwrap for connectorx + if backend == "connectorx": + source.resources[table_name].add_map(unwrap_json_connector_x("json_col")) + pipeline.extract(source) + pipeline.normalize(loader_file_format="parquet") + pipeline.load() + + schema = pipeline.default_schema + # print(pipeline.default_schema.to_pretty_yaml()) + table = schema.tables[table_name] + assert_no_precision_columns(table["columns"], backend, nullable) + assert_schema_on_data( + table, + load_tables_to_dicts(pipeline, table_name)[table_name], + nullable, + backend in ["sqlalchemy", "pyarrow"], + ) + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +def test_incremental_composite_primary_key_from_table( + sql_source_db: SQLAlchemySourceDB, + backend: TableBackend, +) -> None: + resource = sql_table( + credentials=sql_source_db.credentials, + table="has_composite_key", + schema=sql_source_db.schema, + backend=backend, + ) + + assert resource.incremental.primary_key == ["a", "b", "c"] + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +@pytest.mark.parametrize("upfront_incremental", (True, False)) +def test_set_primary_key_deferred_incremental( + sql_source_db: SQLAlchemySourceDB, + upfront_incremental: bool, + backend: TableBackend, +) -> None: + # this tests dynamically adds primary key to resource and as consequence to incremental + updated_at = dlt.sources.incremental("updated_at") # type: ignore[var-annotated] + resource = sql_table( + credentials=sql_source_db.credentials, + table="chat_message", + schema=sql_source_db.schema, + defer_table_reflect=True, + incremental=updated_at if upfront_incremental else None, + backend=backend, + ) + + resource.apply_hints(incremental=None if upfront_incremental else updated_at) + + # nothing set for deferred reflect + assert resource.incremental.primary_key is None + + def _assert_incremental(item): + # for all the items, all keys must be present + _r = dlt.current.source().resources[dlt.current.resource_name()] + # assert _r.incremental._incremental is updated_at + if len(item) == 0: + # not yet propagated + assert _r.incremental.primary_key is None + else: + assert _r.incremental.primary_key == ["id"] + assert _r.incremental._incremental.primary_key == ["id"] + assert _r.incremental._incremental._transformers["json"].primary_key == ["id"] + assert _r.incremental._incremental._transformers["arrow"].primary_key == ["id"] + return item + + pipeline = make_pipeline("duckdb") + # must evaluate resource for primary key to be set + pipeline.extract(resource.add_step(_assert_incremental)) # type: ignore[arg-type] + + assert resource.incremental.primary_key == ["id"] + assert resource.incremental._incremental.primary_key == ["id"] + assert resource.incremental._incremental._transformers["json"].primary_key == ["id"] + assert resource.incremental._incremental._transformers["arrow"].primary_key == ["id"] + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +def test_deferred_reflect_in_source( + sql_source_db: SQLAlchemySourceDB, backend: TableBackend +) -> None: + source = sql_database( + credentials=sql_source_db.credentials, + table_names=["has_precision", "chat_message"], + schema=sql_source_db.schema, + reflection_level="full_with_precision", + defer_table_reflect=True, + backend=backend, + ) + # mock the right json values for backends not supporting it + if backend in ("connectorx", "pandas"): + source.resources["has_precision"].add_map(mock_json_column("json_col")) + + # no columns in both tables + assert source.has_precision.columns == {} + assert source.chat_message.columns == {} + + pipeline = make_pipeline("duckdb") + pipeline.extract(source) + # use insert values to convert parquet into INSERT + pipeline.normalize(loader_file_format="insert_values") + pipeline.load() + precision_table = pipeline.default_schema.get_table("has_precision") + assert_precision_columns( + precision_table["columns"], + backend, + nullable=False, + ) + assert_schema_on_data( + precision_table, + load_tables_to_dicts(pipeline, "has_precision")["has_precision"], + True, + backend in ["sqlalchemy", "pyarrow"], + ) + assert len(source.chat_message.columns) > 0 # type: ignore[arg-type] + assert source.chat_message.compute_table_schema()["columns"]["id"]["primary_key"] is True + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +def test_deferred_reflect_no_source_connect(backend: TableBackend) -> None: + source = sql_database( + credentials="mysql+pymysql://test@test/test", + table_names=["has_precision", "chat_message"], + schema="schema", + reflection_level="full_with_precision", + defer_table_reflect=True, + backend=backend, + ) + + # no columns in both tables + assert source.has_precision.columns == {} + assert source.chat_message.columns == {} + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +def test_deferred_reflect_in_resource( + sql_source_db: SQLAlchemySourceDB, backend: TableBackend +) -> None: + table = sql_table( + credentials=sql_source_db.credentials, + table="has_precision", + schema=sql_source_db.schema, + reflection_level="full_with_precision", + defer_table_reflect=True, + backend=backend, + ) + # mock the right json values for backends not supporting it + if backend in ("connectorx", "pandas"): + table.add_map(mock_json_column("json_col")) + + # no columns in both tables + assert table.columns == {} + + pipeline = make_pipeline("duckdb") + pipeline.extract(table) + # use insert values to convert parquet into INSERT + pipeline.normalize(loader_file_format="insert_values") + pipeline.load() + precision_table = pipeline.default_schema.get_table("has_precision") + assert_precision_columns( + precision_table["columns"], + backend, + nullable=False, + ) + assert_schema_on_data( + precision_table, + load_tables_to_dicts(pipeline, "has_precision")["has_precision"], + True, + backend in ["sqlalchemy", "pyarrow"], + ) + + +@pytest.mark.parametrize("backend", ["pyarrow", "pandas", "connectorx"]) +def test_destination_caps_context(sql_source_db: SQLAlchemySourceDB, backend: TableBackend) -> None: + # use athena with timestamp precision == 3 + table = sql_table( + credentials=sql_source_db.credentials, + table="has_precision", + schema=sql_source_db.schema, + reflection_level="full_with_precision", + defer_table_reflect=True, + backend=backend, + ) + + # no columns in both tables + assert table.columns == {} + + pipeline = make_pipeline("athena") + pipeline.extract(table) + pipeline.normalize() + # timestamps are milliseconds + columns = pipeline.default_schema.get_table("has_precision")["columns"] + assert columns["datetime_tz_col"]["precision"] == columns["datetime_ntz_col"]["precision"] == 3 + # prevent drop + pipeline.destination = None + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +def test_sql_table_from_view(sql_source_db: SQLAlchemySourceDB, backend: TableBackend) -> None: + """View can be extract by sql_table without any reflect flags""" + table = sql_table( + credentials=sql_source_db.credentials, + table="chat_message_view", + schema=sql_source_db.schema, + backend=backend, + # use minimal level so we infer types from DATA + reflection_level="minimal", + incremental=dlt.sources.incremental("_created_at"), + ) + + pipeline = make_pipeline("duckdb") + info = pipeline.run(table) + assert_load_info(info) + + assert_row_counts(pipeline, sql_source_db, ["chat_message_view"]) + assert "content" in pipeline.default_schema.tables["chat_message_view"]["columns"] + assert "_created_at" in pipeline.default_schema.tables["chat_message_view"]["columns"] + db_data = load_tables_to_dicts(pipeline, "chat_message_view")["chat_message_view"] + assert "content" in db_data[0] + assert "_created_at" in db_data[0] + # make sure that all NULLs is not present + assert "_null_ts" in pipeline.default_schema.tables["chat_message_view"]["columns"] + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +def test_sql_database_include_views( + sql_source_db: SQLAlchemySourceDB, backend: TableBackend +) -> None: + """include_view flag reflects and extracts views as tables""" + source = sql_database( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + include_views=True, + backend=backend, + ) + + pipeline = make_pipeline("duckdb") + pipeline.run(source) + + assert_row_counts(pipeline, sql_source_db, include_views=True) + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +def test_sql_database_include_view_in_table_names( + sql_source_db: SQLAlchemySourceDB, backend: TableBackend +) -> None: + """Passing a view explicitly in table_names should reflect it, regardless of include_views flag""" + source = sql_database( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + table_names=["app_user", "chat_message_view"], + include_views=False, + backend=backend, + ) + + pipeline = make_pipeline("duckdb") + pipeline.run(source) + + assert_row_counts(pipeline, sql_source_db, ["app_user", "chat_message_view"]) + + +@pytest.mark.parametrize("backend", ["pyarrow", "pandas", "sqlalchemy"]) +@pytest.mark.parametrize("standalone_resource", [True, False]) +@pytest.mark.parametrize("reflection_level", ["minimal", "full", "full_with_precision"]) +@pytest.mark.parametrize("type_adapter", [True, False]) +def test_infer_unsupported_types( + sql_source_db_unsupported_types: SQLAlchemySourceDB, + backend: TableBackend, + reflection_level: ReflectionLevel, + standalone_resource: bool, + type_adapter: bool, +) -> None: + def type_adapter_callback(t): + if isinstance(t, sa.ARRAY): + return sa.JSON + return t + + if backend == "pyarrow" and type_adapter: + pytest.skip("Arrow does not support type adapter for arrays") + + common_kwargs = dict( + credentials=sql_source_db_unsupported_types.credentials, + schema=sql_source_db_unsupported_types.schema, + reflection_level=reflection_level, + backend=backend, + type_adapter_callback=type_adapter_callback if type_adapter else None, + ) + if standalone_resource: + + @dlt.source + def dummy_source(): + yield sql_table( + **common_kwargs, # type: ignore[arg-type] + table="has_unsupported_types", + ) + + source = dummy_source() + source.max_table_nesting = 0 + else: + source = sql_database( + **common_kwargs, # type: ignore[arg-type] + table_names=["has_unsupported_types"], + ) + source.max_table_nesting = 0 + + pipeline = make_pipeline("duckdb") + pipeline.extract(source) + + columns = pipeline.default_schema.tables["has_unsupported_types"]["columns"] + + pipeline.normalize() + pipeline.load() + + assert_row_counts(pipeline, sql_source_db_unsupported_types, ["has_unsupported_types"]) + + schema = pipeline.default_schema + assert "has_unsupported_types" in schema.tables + columns = schema.tables["has_unsupported_types"]["columns"] + + rows = load_tables_to_dicts(pipeline, "has_unsupported_types")["has_unsupported_types"] + + if backend == "pyarrow": + # TODO: duckdb writes structs as strings (not json encoded) to json columns + # Just check that it has a value + + assert isinstance(json.loads(rows[0]["unsupported_array_1"]), list) + assert columns["unsupported_array_1"]["data_type"] == "json" + # Other columns are loaded + assert isinstance(rows[0]["supported_text"], str) + assert isinstance(rows[0]["supported_int"], int) + elif backend == "sqlalchemy": + # sqla value is a dataclass and is inferred as json + + assert columns["unsupported_array_1"]["data_type"] == "json" + + elif backend == "pandas": + # pandas parses it as string + if type_adapter and reflection_level != "minimal": + assert columns["unsupported_array_1"]["data_type"] == "json" + + assert isinstance(json.loads(rows[0]["unsupported_array_1"]), list) + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +@pytest.mark.parametrize("defer_table_reflect", (False, True)) +def test_sql_database_included_columns( + sql_source_db: SQLAlchemySourceDB, backend: TableBackend, defer_table_reflect: bool +) -> None: + # include only some columns from the table + os.environ["SOURCES__SQL_DATABASE__CHAT_MESSAGE__INCLUDED_COLUMNS"] = json.dumps( + ["id", "created_at"] + ) + + source = sql_database( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + table_names=["chat_message"], + reflection_level="full", + defer_table_reflect=defer_table_reflect, + backend=backend, + ) + + pipeline = make_pipeline("duckdb") + pipeline.run(source) + + schema = pipeline.default_schema + schema_cols = set( + col + for col in schema.get_table_columns("chat_message", include_incomplete=True) + if not col.startswith("_dlt_") + ) + assert schema_cols == {"id", "created_at"} + + assert_row_counts(pipeline, sql_source_db, ["chat_message"]) + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +@pytest.mark.parametrize("defer_table_reflect", (False, True)) +def test_sql_table_included_columns( + sql_source_db: SQLAlchemySourceDB, backend: TableBackend, defer_table_reflect: bool +) -> None: + source = sql_table( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + table="chat_message", + reflection_level="full", + defer_table_reflect=defer_table_reflect, + backend=backend, + included_columns=["id", "created_at"], + ) + + pipeline = make_pipeline("duckdb") + pipeline.run(source) + + schema = pipeline.default_schema + schema_cols = set( + col + for col in schema.get_table_columns("chat_message", include_incomplete=True) + if not col.startswith("_dlt_") + ) + assert schema_cols == {"id", "created_at"} + + assert_row_counts(pipeline, sql_source_db, ["chat_message"]) + + +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +@pytest.mark.parametrize("standalone_resource", [True, False]) +def test_query_adapter_callback( + sql_source_db: SQLAlchemySourceDB, backend: TableBackend, standalone_resource: bool +) -> None: + def query_adapter_callback(query, table): + if table.name == "chat_channel": + # Only select active channels + return query.where(table.c.active.is_(True)) + # Use the original query for other tables + return query + + common_kwargs = dict( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + reflection_level="full", + backend=backend, + query_adapter_callback=query_adapter_callback, + ) + + if standalone_resource: + + @dlt.source + def dummy_source(): + yield sql_table( + **common_kwargs, # type: ignore[arg-type] + table="chat_channel", + ) + + yield sql_table( + **common_kwargs, # type: ignore[arg-type] + table="chat_message", + ) + + source = dummy_source() + else: + source = sql_database( + **common_kwargs, # type: ignore[arg-type] + table_names=["chat_message", "chat_channel"], + ) + + pipeline = make_pipeline("duckdb") + pipeline.extract(source) + + pipeline.normalize() + pipeline.load() + + channel_rows = load_tables_to_dicts(pipeline, "chat_channel")["chat_channel"] + assert channel_rows and all(row["active"] for row in channel_rows) + + # unfiltered table loads all rows + assert_row_counts(pipeline, sql_source_db, ["chat_message"]) + + +def assert_row_counts( + pipeline: dlt.Pipeline, + sql_source_db: SQLAlchemySourceDB, + tables: Optional[List[str]] = None, + include_views: bool = False, +) -> None: + if not tables: + tables = [ + tbl_name + for tbl_name, info in sql_source_db.table_infos.items() + if include_views or not info["is_view"] + ] + dest_counts = load_table_counts(pipeline, *tables) + for table in tables: + info = sql_source_db.table_infos[table] + assert ( + dest_counts[table] == info["row_count"] + ), f"Table {table} counts do not match with the source" + + +def assert_precision_columns( + columns: TTableSchemaColumns, backend: TableBackend, nullable: bool +) -> None: + actual = list(columns.values()) + expected = NULL_PRECISION_COLUMNS if nullable else NOT_NULL_PRECISION_COLUMNS + # always has nullability set and always has hints + expected = cast(List[TColumnSchema], deepcopy(expected)) + if backend == "sqlalchemy": + expected = remove_timestamp_precision(expected) + actual = remove_dlt_columns(actual) + if backend == "pyarrow": + expected = add_default_decimal_precision(expected) + if backend == "pandas": + expected = remove_timestamp_precision(expected, with_timestamps=False) + if backend == "connectorx": + # connector x emits 32 precision which gets merged with sql alchemy schema + del columns["int_col"]["precision"] + assert actual == expected + + +def assert_no_precision_columns( + columns: TTableSchemaColumns, backend: TableBackend, nullable: bool +) -> None: + actual = list(columns.values()) + # we always infer and emit nullability + expected = cast( + List[TColumnSchema], + deepcopy(NULL_NO_PRECISION_COLUMNS if nullable else NOT_NULL_NO_PRECISION_COLUMNS), + ) + if backend == "pyarrow": + expected = cast( + List[TColumnSchema], + deepcopy(NULL_PRECISION_COLUMNS if nullable else NOT_NULL_PRECISION_COLUMNS), + ) + # always has nullability set and always has hints + # default precision is not set + expected = remove_default_precision(expected) + expected = add_default_decimal_precision(expected) + elif backend == "sqlalchemy": + # no precision, no nullability, all hints inferred + # remove dlt columns + actual = remove_dlt_columns(actual) + elif backend == "pandas": + # no precision, no nullability, all hints inferred + # pandas destroys decimals + expected = convert_non_pandas_types(expected) + # on one of the timestamps somehow there is timezone info..., we only remove values set to false + # to be sure no bad data is coming in + actual = remove_timezone_info(actual, only_falsy=True) + elif backend == "connectorx": + expected = cast( + List[TColumnSchema], + deepcopy(NULL_PRECISION_COLUMNS if nullable else NOT_NULL_PRECISION_COLUMNS), + ) + expected = convert_connectorx_types(expected) + expected = remove_timezone_info(expected, only_falsy=False) + # on one of the timestamps somehow there is timezone info..., we only remove values set to false + # to be sure no bad data is coming in + actual = remove_timezone_info(actual, only_falsy=True) + + assert actual == expected + + +def convert_non_pandas_types(columns: List[TColumnSchema]) -> List[TColumnSchema]: + for column in columns: + if column["data_type"] == "timestamp": + column["precision"] = 6 + return columns + + +def remove_dlt_columns(columns: List[TColumnSchema]) -> List[TColumnSchema]: + return [col for col in columns if not col["name"].startswith("_dlt")] + + +def remove_default_precision(columns: List[TColumnSchema]) -> List[TColumnSchema]: + for column in columns: + if column["data_type"] == "bigint" and column.get("precision") == 32: + del column["precision"] + if column["data_type"] == "text" and column.get("precision"): + del column["precision"] + return remove_timezone_info(columns, only_falsy=False) + + +def remove_timezone_info(columns: List[TColumnSchema], only_falsy: bool) -> List[TColumnSchema]: + for column in columns: + if not only_falsy: + column.pop("timezone", None) + elif column.get("timezone") is False: + column.pop("timezone", None) + return columns + + +def remove_timestamp_precision( + columns: List[TColumnSchema], with_timestamps: bool = True +) -> List[TColumnSchema]: + for column in columns: + if column["data_type"] == "timestamp" and column["precision"] == 6 and with_timestamps: + del column["precision"] + if column["data_type"] == "time" and column["precision"] == 6: + del column["precision"] + return columns + + +def convert_connectorx_types(columns: List[TColumnSchema]) -> List[TColumnSchema]: + """connector x converts decimals to double, otherwise tries to keep data types and precision + nullability is not kept, string precision is not kept + """ + for column in columns: + if column["data_type"] == "bigint": + if column["name"] == "int_col": + column["precision"] = 32 # only int and bigint in connectorx + if column["data_type"] == "text" and column.get("precision"): + del column["precision"] + return columns + + +def add_default_decimal_precision(columns: List[TColumnSchema]) -> List[TColumnSchema]: + for column in columns: + if column["data_type"] == "decimal" and not column.get("precision"): + column["precision"] = 38 + column["scale"] = 9 + return columns + + +PRECISION_COLUMNS: List[TColumnSchema] = [ + { + "data_type": "bigint", + "name": "int_col", + }, + { + "data_type": "bigint", + "name": "bigint_col", + }, + { + "data_type": "bigint", + "precision": 32, + "name": "smallint_col", + }, + { + "data_type": "decimal", + "precision": 10, + "scale": 2, + "name": "numeric_col", + }, + { + "data_type": "decimal", + "name": "numeric_default_col", + }, + { + "data_type": "text", + "precision": 10, + "name": "string_col", + }, + { + "data_type": "text", + "name": "string_default_col", + }, + {"data_type": "timestamp", "precision": 6, "name": "datetime_tz_col", "timezone": True}, + {"data_type": "timestamp", "precision": 6, "name": "datetime_ntz_col", "timezone": False}, + { + "data_type": "date", + "name": "date_col", + }, + { + "data_type": "time", + "name": "time_col", + "precision": 6, + }, + { + "data_type": "double", + "name": "float_col", + }, + { + "data_type": "json", + "name": "json_col", + }, + { + "data_type": "bool", + "name": "bool_col", + }, +] + +NOT_NULL_PRECISION_COLUMNS = [{"nullable": False, **column} for column in PRECISION_COLUMNS] +NULL_PRECISION_COLUMNS: List[TColumnSchema] = [ + {"nullable": True, **column} for column in PRECISION_COLUMNS +] + +# but keep decimal precision +NO_PRECISION_COLUMNS: List[TColumnSchema] = [ + ( + {"name": column["name"], "data_type": column["data_type"]} # type: ignore[misc] + if column["data_type"] != "decimal" + else dict(column) + ) + for column in PRECISION_COLUMNS +] + +NOT_NULL_NO_PRECISION_COLUMNS: List[TColumnSchema] = [ + {"nullable": False, **column} for column in NO_PRECISION_COLUMNS +] +NULL_NO_PRECISION_COLUMNS: List[TColumnSchema] = [ + {"nullable": True, **column} for column in NO_PRECISION_COLUMNS +] diff --git a/tests/load/sources/sql_database/test_sql_database_source_all_destinations.py b/tests/load/sources/sql_database/test_sql_database_source_all_destinations.py new file mode 100644 index 0000000000..4f4e876fb6 --- /dev/null +++ b/tests/load/sources/sql_database/test_sql_database_source_all_destinations.py @@ -0,0 +1,347 @@ +import os +from typing import Any, List + +import humanize +import pytest + +import dlt +from dlt.sources import DltResource +from dlt.sources.credentials import ConnectionStringCredentials +from dlt.common.exceptions import MissingDependencyException + +from tests.load.utils import ( + DestinationTestConfiguration, + destinations_configs, +) +from tests.pipeline.utils import ( + assert_load_info, + load_table_counts, +) + +try: + from dlt.sources.sql_database import TableBackend, sql_database, sql_table + from tests.load.sources.sql_database.test_helpers import mock_json_column + from tests.load.sources.sql_database.test_sql_database_source import ( + assert_row_counts, + convert_time_to_us, + default_test_callback, + ) + from tests.load.sources.sql_database.sql_source import SQLAlchemySourceDB + from dlt.common.libs.sql_alchemy import IS_SQL_ALCHEMY_20 +except MissingDependencyException: + pytest.skip("Tests require sql alchemy", allow_module_level=True) + + +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True), + ids=lambda x: x.name, +) +@pytest.mark.parametrize("backend", ["sqlalchemy", "pandas", "pyarrow", "connectorx"]) +def test_load_sql_schema_loads_all_tables( + sql_source_db: SQLAlchemySourceDB, + destination_config: DestinationTestConfiguration, + backend: TableBackend, + request: Any, +) -> None: + pipeline = destination_config.setup_pipeline(request.node.name, dev_mode=True) + + source = sql_database( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + backend=backend, + reflection_level="minimal", + type_adapter_callback=default_test_callback(destination_config.destination_type, backend), + ) + + if destination_config.destination_type == "bigquery" and backend == "connectorx": + # connectorx generates nanoseconds time which bigquery cannot load + source.has_precision.add_map(convert_time_to_us) + source.has_precision_nullable.add_map(convert_time_to_us) + + if backend != "sqlalchemy": + # always use mock json + source.has_precision.add_map(mock_json_column("json_col")) + source.has_precision_nullable.add_map(mock_json_column("json_col")) + + assert "chat_message_view" not in source.resources # Views are not reflected by default + + load_info = pipeline.run(source) + print(humanize.precisedelta(pipeline.last_trace.finished_at - pipeline.last_trace.started_at)) + assert_load_info(load_info) + + assert_row_counts(pipeline, sql_source_db) + + +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True), + ids=lambda x: x.name, +) +@pytest.mark.parametrize("backend", ["sqlalchemy", "pandas", "pyarrow", "connectorx"]) +def test_load_sql_schema_loads_all_tables_parallel( + sql_source_db: SQLAlchemySourceDB, + destination_config: DestinationTestConfiguration, + backend: TableBackend, + request: Any, +) -> None: + pipeline = destination_config.setup_pipeline(request.node.name, dev_mode=True) + source = sql_database( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + backend=backend, + reflection_level="minimal", + type_adapter_callback=default_test_callback(destination_config.destination_type, backend), + ).parallelize() + + if destination_config.destination_type == "bigquery" and backend == "connectorx": + # connectorx generates nanoseconds time which bigquery cannot load + source.has_precision.add_map(convert_time_to_us) + source.has_precision_nullable.add_map(convert_time_to_us) + + if backend != "sqlalchemy": + # always use mock json + source.has_precision.add_map(mock_json_column("json_col")) + source.has_precision_nullable.add_map(mock_json_column("json_col")) + + load_info = pipeline.run(source) + print(humanize.precisedelta(pipeline.last_trace.finished_at - pipeline.last_trace.started_at)) + assert_load_info(load_info) + + assert_row_counts(pipeline, sql_source_db) + + +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True), + ids=lambda x: x.name, +) +@pytest.mark.parametrize("backend", ["sqlalchemy", "pandas", "pyarrow", "connectorx"]) +def test_load_sql_table_names( + sql_source_db: SQLAlchemySourceDB, + destination_config: DestinationTestConfiguration, + backend: TableBackend, + request: Any, +) -> None: + pipeline = destination_config.setup_pipeline(request.node.name, dev_mode=True) + tables = ["chat_channel", "chat_message"] + load_info = pipeline.run( + sql_database( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + table_names=tables, + reflection_level="minimal", + backend=backend, + ) + ) + assert_load_info(load_info) + + assert_row_counts(pipeline, sql_source_db, tables) + + +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True), + ids=lambda x: x.name, +) +@pytest.mark.parametrize("backend", ["sqlalchemy", "pandas", "pyarrow", "connectorx"]) +def test_load_sql_table_incremental( + sql_source_db: SQLAlchemySourceDB, + destination_config: DestinationTestConfiguration, + backend: TableBackend, + request: Any, +) -> None: + """Run pipeline twice. Insert more rows after first run + and ensure only those rows are stored after the second run. + """ + os.environ["SOURCES__SQL_DATABASE__CHAT_MESSAGE__INCREMENTAL__CURSOR_PATH"] = "updated_at" + + if not IS_SQL_ALCHEMY_20 and backend == "connectorx": + pytest.skip("Test will not run on sqlalchemy 1.4 with connectorx") + + pipeline = destination_config.setup_pipeline(request.node.name, dev_mode=True) + tables = ["chat_message"] + + def make_source(): + return sql_database( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + table_names=tables, + reflection_level="minimal", + backend=backend, + ) + + load_info = pipeline.run(make_source()) + assert_load_info(load_info) + sql_source_db.fake_messages(n=100) + load_info = pipeline.run(make_source()) + assert_load_info(load_info) + + assert_row_counts(pipeline, sql_source_db, tables) + + +@pytest.mark.skip(reason="Skipping this test temporarily") +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True), + ids=lambda x: x.name, +) +@pytest.mark.parametrize("backend", ["sqlalchemy", "pandas", "pyarrow", "connectorx"]) +def test_load_mysql_data_load( + destination_config: DestinationTestConfiguration, backend: TableBackend, request: Any +) -> None: + # reflect a database + credentials = ConnectionStringCredentials( + "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam" + ) + database = sql_database(credentials) + assert "family" in database.resources + + if backend == "connectorx": + # connector-x has different connection string format + backend_kwargs = {"conn": "mysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam"} + else: + backend_kwargs = {} + + # no longer needed: asdecimal used to infer decimal or not + # def _double_as_decimal_adapter(table: sa.Table) -> sa.Table: + # for column in table.columns.values(): + # if isinstance(column.type, sa.Double): + # column.type.asdecimal = False + + # load a single table + family_table = sql_table( + credentials="mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam", + table="family", + backend=backend, + reflection_level="minimal", + backend_kwargs=backend_kwargs, + # table_adapter_callback=_double_as_decimal_adapter, + ) + + pipeline = destination_config.setup_pipeline(request.node.name, dev_mode=True) + load_info = pipeline.run(family_table, write_disposition="merge") + assert_load_info(load_info) + counts_1 = load_table_counts(pipeline, "family") + + # load again also with merge + family_table = sql_table( + credentials="mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam", + table="family", + backend=backend, + reflection_level="minimal", + # we also try to remove dialect automatically + backend_kwargs={}, + # table_adapter_callback=_double_as_decimal_adapter, + ) + load_info = pipeline.run(family_table, write_disposition="merge") + assert_load_info(load_info) + counts_2 = load_table_counts(pipeline, "family") + # no duplicates + assert counts_1 == counts_2 + + +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True), + ids=lambda x: x.name, +) +@pytest.mark.parametrize("backend", ["sqlalchemy", "pandas", "pyarrow", "connectorx"]) +def test_load_sql_table_resource_loads_data( + sql_source_db: SQLAlchemySourceDB, + destination_config: DestinationTestConfiguration, + backend: TableBackend, + request: Any, +) -> None: + @dlt.source + def sql_table_source() -> List[DltResource]: + return [ + sql_table( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + table="chat_message", + reflection_level="minimal", + backend=backend, + ) + ] + + pipeline = destination_config.setup_pipeline(request.node.name, dev_mode=True) + load_info = pipeline.run(sql_table_source()) + assert_load_info(load_info) + + assert_row_counts(pipeline, sql_source_db, ["chat_message"]) + + +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True), + ids=lambda x: x.name, +) +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +def test_load_sql_table_resource_incremental( + sql_source_db: SQLAlchemySourceDB, + destination_config: DestinationTestConfiguration, + backend: TableBackend, + request: Any, +) -> None: + if not IS_SQL_ALCHEMY_20 and backend == "connectorx": + pytest.skip("Test will not run on sqlalchemy 1.4 with connectorx") + + @dlt.source + def sql_table_source() -> List[DltResource]: + return [ + sql_table( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + table="chat_message", + incremental=dlt.sources.incremental("updated_at"), + reflection_level="minimal", + backend=backend, + ) + ] + + pipeline = destination_config.setup_pipeline(request.node.name, dev_mode=True) + load_info = pipeline.run(sql_table_source()) + assert_load_info(load_info) + sql_source_db.fake_messages(n=100) + load_info = pipeline.run(sql_table_source()) + assert_load_info(load_info) + + assert_row_counts(pipeline, sql_source_db, ["chat_message"]) + + +@pytest.mark.parametrize( + "destination_config", + destinations_configs(default_sql_configs=True), + ids=lambda x: x.name, +) +@pytest.mark.parametrize("backend", ["sqlalchemy", "pyarrow", "pandas", "connectorx"]) +def test_load_sql_table_resource_incremental_initial_value( + sql_source_db: SQLAlchemySourceDB, + destination_config: DestinationTestConfiguration, + backend: TableBackend, + request: Any, +) -> None: + if not IS_SQL_ALCHEMY_20 and backend == "connectorx": + pytest.skip("Test will not run on sqlalchemy 1.4 with connectorx") + + @dlt.source + def sql_table_source() -> List[DltResource]: + return [ + sql_table( + credentials=sql_source_db.credentials, + schema=sql_source_db.schema, + table="chat_message", + incremental=dlt.sources.incremental( + "updated_at", + sql_source_db.table_infos["chat_message"]["created_at"].start_value, + ), + reflection_level="minimal", + backend=backend, + ) + ] + + pipeline = destination_config.setup_pipeline(request.node.name, dev_mode=True) + load_info = pipeline.run(sql_table_source()) + assert_load_info(load_info) + assert_row_counts(pipeline, sql_source_db, ["chat_message"]) diff --git a/tests/load/sqlalchemy/__init__.py b/tests/load/sqlalchemy/__init__.py new file mode 100644 index 0000000000..250c1f7626 --- /dev/null +++ b/tests/load/sqlalchemy/__init__.py @@ -0,0 +1,3 @@ +from tests.utils import skip_if_not_active + +skip_if_not_active("sqlalchemy") diff --git a/tests/load/sqlalchemy/test_sqlalchemy_configuration.py b/tests/load/sqlalchemy/test_sqlalchemy_configuration.py new file mode 100644 index 0000000000..281593aaf7 --- /dev/null +++ b/tests/load/sqlalchemy/test_sqlalchemy_configuration.py @@ -0,0 +1,23 @@ +import pytest + +import sqlalchemy as sa + +from dlt.common.configuration import resolve_configuration +from dlt.destinations.impl.sqlalchemy.configuration import ( + SqlalchemyClientConfiguration, + SqlalchemyCredentials, +) + + +def test_sqlalchemy_credentials_from_engine() -> None: + engine = sa.create_engine("sqlite:///:memory:") + + creds = resolve_configuration(SqlalchemyCredentials(engine)) + + # Url is taken from engine + assert creds.to_url() == sa.engine.make_url("sqlite:///:memory:") + # Engine is stored on the instance + assert creds.engine is engine + + assert creds.drivername == "sqlite" + assert creds.database == ":memory:" diff --git a/tests/load/synapse/test_synapse_table_builder.py b/tests/load/synapse/test_synapse_table_builder.py index 1a92a20f1e..5c80f8d7fa 100644 --- a/tests/load/synapse/test_synapse_table_builder.py +++ b/tests/load/synapse/test_synapse_table_builder.py @@ -119,7 +119,7 @@ def test_create_table_with_column_hint( # Case: table with hint, but client does not have indexes enabled. mod_update = deepcopy(TABLE_UPDATE) - mod_update[0][hint] = True # type: ignore[typeddict-unknown-key] + mod_update[0][hint] = True sql = client._get_table_update_sql("event_test_table", mod_update, False)[0] sqlfluff.parse(sql, dialect="tsql") assert f" {attr} " not in sql diff --git a/tests/load/test_dummy_client.py b/tests/load/test_dummy_client.py index 72c5772668..26b90e5a0d 100644 --- a/tests/load/test_dummy_client.py +++ b/tests/load/test_dummy_client.py @@ -9,13 +9,13 @@ from dlt.common.exceptions import TerminalException, TerminalValueError from dlt.common.storages import FileStorage, PackageStorage, ParsedLoadJobFileName from dlt.common.storages.configuration import FilesystemConfiguration -from dlt.common.storages.load_package import LoadJobInfo, TPackageJobState +from dlt.common.storages.load_package import TPackageJobState from dlt.common.storages.load_storage import JobFileFormatUnsupported from dlt.common.destination.reference import RunnableLoadJob, TDestination from dlt.common.schema.utils import ( fill_hints_from_parent_and_clone_table, - get_child_tables, - get_top_level_table, + get_nested_tables, + get_root_table, ) from dlt.destinations.impl.filesystem.configuration import FilesystemDestinationClientConfiguration @@ -24,6 +24,7 @@ from dlt.destinations.impl.dummy.configuration import DummyClientConfiguration from dlt.load import Load +from dlt.load.configuration import LoaderConfiguration from dlt.load.exceptions import ( LoadClientJobFailed, LoadClientJobRetry, @@ -107,14 +108,11 @@ def test_unsupported_write_disposition() -> None: schema.get_table("event_user")["write_disposition"] = "skip" # write back schema load.load_storage.normalized_packages.save_schema(load_id, schema) - with ThreadPoolExecutor() as pool: - load.run(pool) - # job with unsupported write disp. is failed - failed_job = load.load_storage.loaded_packages.list_failed_jobs(load_id)[0] - failed_message = load.load_storage.loaded_packages.get_job_failed_message( - load_id, ParsedLoadJobFileName.parse(failed_job) - ) - assert "LoadClientUnsupportedWriteDisposition" in failed_message + with pytest.raises(LoadClientJobFailed) as e: + with ThreadPoolExecutor() as pool: + load.run(pool) + + assert "LoadClientUnsupportedWriteDisposition" in e.value.failed_message def test_big_loadpackages() -> None: @@ -156,7 +154,7 @@ def test_get_completed_table_chain_single_job_per_table() -> None: for table_name, table in schema.tables.items(): schema.tables[table_name] = fill_hints_from_parent_and_clone_table(schema.tables, table) - top_job_table = get_top_level_table(schema.tables, "event_user") + top_job_table = get_root_table(schema.tables, "event_user") all_jobs = load.load_storage.normalized_packages.list_all_jobs_with_states(load_id) assert get_completed_table_chain(schema, all_jobs, top_job_table) is None # fake being completed @@ -172,7 +170,7 @@ def test_get_completed_table_chain_single_job_per_table() -> None: == 1 ) # actually complete - loop_top_job_table = get_top_level_table(schema.tables, "event_loop_interrupted") + loop_top_job_table = get_root_table(schema.tables, "event_loop_interrupted") load.load_storage.normalized_packages.start_job( load_id, "event_loop_interrupted.839c6e6b514e427687586ccc65bf133f.0.jsonl" ) @@ -228,10 +226,19 @@ def test_spool_job_failed() -> None: started_files = load.load_storage.normalized_packages.list_started_jobs(load_id) assert len(started_files) == 0 - # test the whole flow - load = setup_loader(client_config=DummyClientConfiguration(fail_prob=1.0)) + # test the whole + loader_config = LoaderConfiguration( + raise_on_failed_jobs=False, + workers=1, + pool_type="none", + ) + load = setup_loader( + client_config=DummyClientConfiguration(fail_prob=1.0), + loader_config=loader_config, + ) load_id, schema = prepare_load_package(load.load_storage, NORMALIZED_FILES) run_all(load) + package_info = load.load_storage.get_load_package_info(load_id) assert package_info.state == "loaded" # all jobs failed @@ -246,8 +253,6 @@ def test_spool_job_failed() -> None: def test_spool_job_failed_terminally_exception_init() -> None: - # this config fails job on start - os.environ["LOAD__RAISE_ON_FAILED_JOBS"] = "true" load = setup_loader(client_config=DummyClientConfiguration(fail_terminally_in_init=True)) load_id, _ = prepare_load_package(load.load_storage, NORMALIZED_FILES) with patch.object(dummy_impl.DummyClient, "complete_load") as complete_load: @@ -269,8 +274,6 @@ def test_spool_job_failed_terminally_exception_init() -> None: def test_spool_job_failed_transiently_exception_init() -> None: - # this config fails job on start - os.environ["LOAD__RAISE_ON_FAILED_JOBS"] = "true" load = setup_loader(client_config=DummyClientConfiguration(fail_transiently_in_init=True)) load_id, _ = prepare_load_package(load.load_storage, NORMALIZED_FILES) with patch.object(dummy_impl.DummyClient, "complete_load") as complete_load: @@ -293,8 +296,6 @@ def test_spool_job_failed_transiently_exception_init() -> None: def test_spool_job_failed_exception_complete() -> None: - # this config fails job on start - os.environ["LOAD__RAISE_ON_FAILED_JOBS"] = "true" load = setup_loader(client_config=DummyClientConfiguration(fail_prob=1.0)) load_id, _ = prepare_load_package(load.load_storage, NORMALIZED_FILES) with pytest.raises(LoadClientJobFailed) as py_ex: @@ -520,7 +521,10 @@ def test_failed_loop() -> None: delete_completed_jobs=True, client_config=DummyClientConfiguration(fail_prob=1.0) ) # actually not deleted because one of the jobs failed - assert_complete_job(load, should_delete_completed=False) + with pytest.raises(LoadClientJobFailed) as e: + assert_complete_job(load, should_delete_completed=False) + + assert "a random fail occurred" in e.value.failed_message # two failed jobs assert len(dummy_impl.JOBS) == 2 assert list(dummy_impl.JOBS.values())[0].state() == "failed" @@ -535,7 +539,10 @@ def test_failed_loop_followup_jobs() -> None: client_config=DummyClientConfiguration(fail_prob=1.0, create_followup_jobs=True), ) # actually not deleted because one of the jobs failed - assert_complete_job(load, should_delete_completed=False) + with pytest.raises(LoadClientJobFailed) as e: + assert_complete_job(load, should_delete_completed=False) + + assert "a random fail occurred" in e.value.failed_message # followup jobs were not started assert len(dummy_impl.JOBS) == 2 assert len(dummy_impl.CREATED_FOLLOWUP_JOBS) == 0 @@ -549,7 +556,7 @@ def test_completed_loop_with_delete_completed() -> None: @pytest.mark.parametrize("to_truncate", [True, False]) -def test_truncate_table_before_load_on_stanging(to_truncate) -> None: +def test_truncate_table_before_load_on_staging(to_truncate) -> None: load = setup_loader( client_config=DummyClientConfiguration( truncate_tables_on_staging_destination_before_load=to_truncate @@ -559,7 +566,7 @@ def test_truncate_table_before_load_on_stanging(to_truncate) -> None: destination_client = load.get_destination_client(schema) assert ( destination_client.should_truncate_table_before_load_on_staging_destination( # type: ignore - schema.tables["_dlt_version"] + schema.tables["_dlt_version"]["name"] ) == to_truncate ) @@ -679,7 +686,7 @@ def test_extend_table_chain() -> None: assert tables == user_chain - {"event_user__parse_data__entities"} # exclude the whole chain tables = _extend_tables_with_table_chain( - schema, ["event_user"], ["event_user"], lambda table: table["name"] not in entities_chain + schema, ["event_user"], ["event_user"], lambda table_name: table_name not in entities_chain ) assert tables == user_chain - entities_chain # ask for tables that are not top @@ -753,7 +760,7 @@ def test_get_completed_table_chain_cases() -> None: assert chain == [event_user, event_user_entities] # merge and replace do not require whole chain to be in jobs - user_chain = get_child_tables(schema.tables, "event_user") + user_chain = get_nested_tables(schema.tables, "event_user") for w_d in ["merge", "replace"]: event_user["write_disposition"] = w_d # type:ignore[typeddict-item] @@ -848,11 +855,17 @@ def test_init_client_truncate_tables() -> None: "event_bot", } - replace_ = lambda table: table["write_disposition"] == "replace" - merge_ = lambda table: table["write_disposition"] == "merge" + replace_ = ( + lambda table_name: client.prepare_load_table(table_name)["write_disposition"] + == "replace" + ) + merge_ = ( + lambda table_name: client.prepare_load_table(table_name)["write_disposition"] + == "merge" + ) # set event_bot chain to merge - bot_chain = get_child_tables(schema.tables, "event_bot") + bot_chain = get_nested_tables(schema.tables, "event_bot") for w_d in ["merge", "replace"]: initialize_storage.reset_mock() update_stored_schema.reset_mock() @@ -1039,6 +1052,7 @@ def run_all(load: Load) -> None: def setup_loader( delete_completed_jobs: bool = False, client_config: DummyClientConfiguration = None, + loader_config: LoaderConfiguration = None, filesystem_staging: bool = False, ) -> Load: # reset jobs for a test @@ -1062,7 +1076,7 @@ def setup_loader( staging_system_config = FilesystemDestinationClientConfiguration()._bind_dataset_name( dataset_name="dummy" ) - staging_system_config.as_staging = True + staging_system_config.as_staging_destination = True os.makedirs(REMOTE_FILESYSTEM) staging = filesystem(bucket_url=REMOTE_FILESYSTEM) # patch destination to provide client_config @@ -1072,6 +1086,7 @@ def setup_loader( return Load( destination, initial_client_config=client_config, + config=loader_config, staging_destination=staging, # type: ignore[arg-type] initial_staging_client_config=staging_system_config, ) diff --git a/tests/load/test_insert_job_client.py b/tests/load/test_insert_job_client.py index a957c871bb..4359ac6885 100644 --- a/tests/load/test_insert_job_client.py +++ b/tests/load/test_insert_job_client.py @@ -28,7 +28,7 @@ def file_storage() -> FileStorage: @pytest.fixture(scope="function") def client(request) -> Iterator[InsertValuesJobClient]: - yield from yield_client_with_storage(request.param.destination) # type: ignore[misc] + yield from yield_client_with_storage(request.param.destination_factory()) # type: ignore[misc] @pytest.mark.essential diff --git a/tests/load/test_job_client.py b/tests/load/test_job_client.py index 06b70a49da..84d08a5a89 100644 --- a/tests/load/test_job_client.py +++ b/tests/load/test_job_client.py @@ -27,7 +27,12 @@ ) from dlt.destinations.job_client_impl import SqlJobClientBase -from dlt.common.destination.reference import StateInfo, WithStagingDataset +from dlt.common.destination.reference import ( + StateInfo, + WithStagingDataset, + DestinationClientConfiguration, +) +from dlt.common.time import ensure_pendulum_datetime from tests.cases import table_update_and_row, assert_all_data_types_row from tests.utils import TEST_STORAGE_ROOT, autouse_test_storage @@ -62,7 +67,7 @@ def file_storage() -> FileStorage: @pytest.fixture(scope="function") def client(request, naming) -> Iterator[SqlJobClientBase]: - yield from yield_client_with_storage(request.param.destination) + yield from yield_client_with_storage(request.param.destination_factory()) @pytest.fixture(scope="function") @@ -202,7 +207,7 @@ def test_complete_load(naming: str, client: SqlJobClientBase) -> None: assert load_rows[0][2] == 0 import datetime # noqa: I251 - assert type(load_rows[0][3]) is datetime.datetime + assert isinstance(ensure_pendulum_datetime(load_rows[0][3]), datetime.datetime) assert load_rows[0][4] == client.schema.version_hash # make sure that hash in loads exists in schema versions table versions_table = client.sql_client.make_qualified_table_name(version_table_name) @@ -417,36 +422,46 @@ def test_get_storage_table_with_all_types(client: SqlJobClientBase) -> None: assert len(storage_table) > 0 # column order must match TABLE_UPDATE storage_columns = list(storage_table.values()) - for c, expected_c in zip(TABLE_UPDATE, storage_columns): + for c, expected_c in zip( + TABLE_UPDATE, storage_columns + ): # TODO: c and expected_c need to be swapped # storage columns are returned with column names as in information schema assert client.capabilities.casefold_identifier(c["name"]) == expected_c["name"] # athena does not know wei data type and has no JSON type, time is not supported with parquet tables if client.config.destination_type == "athena" and c["data_type"] in ( "wei", - "complex", + "json", "time", ): continue - # mssql, clickhouse and synapse have no native data type for the complex type. + # mssql, clickhouse and synapse have no native data type for the nested type. if client.config.destination_type in ("mssql", "synapse", "clickhouse") and c[ "data_type" - ] in ("complex"): + ] in ("json"): continue - if client.config.destination_type == "databricks" and c["data_type"] in ("complex", "time"): + if client.config.destination_type == "databricks" and c["data_type"] in ("json", "time"): continue # ClickHouse has no active data type for binary or time type. if client.config.destination_type == "clickhouse": if c["data_type"] in ("binary", "time"): continue - elif c["data_type"] == "complex" and c["nullable"]: + elif c["data_type"] == "json" and c["nullable"]: continue - if client.config.destination_type == "dremio" and c["data_type"] == "complex": + if client.config.destination_type == "dremio" and c["data_type"] == "json": + continue + if not client.capabilities.supports_native_boolean and c["data_type"] == "bool": + # The reflected data type is probably either int or boolean depending on how the client is implemented + assert expected_c["data_type"] in ("bigint", "bool") continue + assert c["data_type"] == expected_c["data_type"] @pytest.mark.parametrize( - "client", destinations_configs(default_sql_configs=True), indirect=True, ids=lambda x: x.name + "client", + destinations_configs(default_sql_configs=True, exclude=("sqlalchemy",)), + indirect=True, + ids=lambda x: x.name, ) def test_preserve_column_order(client: SqlJobClientBase) -> None: schema = client.schema @@ -561,7 +576,7 @@ def test_load_with_all_types( if not client.capabilities.preferred_loader_file_format: pytest.skip("preferred loader file format not set, destination will only work with staging") table_name = "event_test_table" + uniq_id() - column_schemas, data_row = get_columns_and_row_all_types(client.config.destination_type) + column_schemas, data_row = get_columns_and_row_all_types(client.config) # we should have identical content with all disposition types partial = client.schema.update_table( @@ -576,18 +591,21 @@ def test_load_with_all_types( client.schema._bump_version() client.update_stored_schema() - should_load_to_staging = client.should_load_data_to_staging_dataset(client.schema.tables[table_name]) # type: ignore[attr-defined] - if should_load_to_staging: - with client.with_staging_dataset(): # type: ignore[attr-defined] - # create staging for merge dataset - client.initialize_storage() - client.update_stored_schema() + if isinstance(client, WithStagingDataset): + should_load_to_staging = client.should_load_data_to_staging_dataset(table_name) + if should_load_to_staging: + with client.with_staging_dataset(): + # create staging for merge dataset + client.initialize_storage() + client.update_stored_schema() - with client.sql_client.with_alternative_dataset_name( - client.sql_client.staging_dataset_name - if should_load_to_staging - else client.sql_client.dataset_name - ): + with client.sql_client.with_alternative_dataset_name( + client.sql_client.staging_dataset_name + if should_load_to_staging + else client.sql_client.dataset_name + ): + canonical_name = client.sql_client.make_qualified_table_name(table_name) + else: canonical_name = client.sql_client.make_qualified_table_name(table_name) # write row print(data_row) @@ -633,7 +651,7 @@ def test_write_dispositions( os.environ["DESTINATION__REPLACE_STRATEGY"] = replace_strategy table_name = "event_test_table" + uniq_id() - column_schemas, data_row = get_columns_and_row_all_types(client.config.destination_type) + column_schemas, data_row = get_columns_and_row_all_types(client.config) client.schema.update_table( new_table(table_name, write_disposition=write_disposition, columns=column_schemas.values()) ) @@ -646,6 +664,8 @@ def test_write_dispositions( client.update_stored_schema() if write_disposition == "merge": + if not client.capabilities.supported_merge_strategies: + pytest.skip("destination does not support merge") # add root key client.schema.tables[table_name]["columns"]["col1"]["root_key"] = True # create staging for merge dataset @@ -665,9 +685,11 @@ def test_write_dispositions( with io.BytesIO() as f: write_dataset(client, f, [data_row], column_schemas) query = f.getvalue() - if client.should_load_data_to_staging_dataset(client.schema.tables[table_name]): # type: ignore[attr-defined] + if isinstance( + client, WithStagingDataset + ) and client.should_load_data_to_staging_dataset(table_name): # load to staging dataset on merge - with client.with_staging_dataset(): # type: ignore[attr-defined] + with client.with_staging_dataset(): expect_load_file(client, file_storage, query, t) else: # load directly on other @@ -722,7 +744,7 @@ def test_get_resumed_job(client: SqlJobClientBase, file_storage: FileStorage) -> # now try to retrieve the job # TODO: we should re-create client instance as this call is intended to be run after some disruption ie. stopped loader process r_job = client.create_load_job( - client.schema.get_table(user_table_name), + client.prepare_load_table(user_table_name), file_storage.make_full_path(job.file_name()), uniq_id(), restore=True, @@ -737,7 +759,7 @@ def test_get_resumed_job(client: SqlJobClientBase, file_storage: FileStorage) -> ) def test_default_schema_name_init_storage(destination_config: DestinationTestConfiguration) -> None: with cm_yield_client_with_storage( - destination_config.destination, + destination_config.destination_factory(), default_config_values={ "default_schema_name": ( # pass the schema that is a default schema. that should create dataset with the name `dataset_name` "event" @@ -748,7 +770,7 @@ def test_default_schema_name_init_storage(destination_config: DestinationTestCon assert client.sql_client.has_dataset() with cm_yield_client_with_storage( - destination_config.destination, + destination_config.destination_factory(), default_config_values={ "default_schema_name": ( None # no default_schema. that should create dataset with the name `dataset_name` @@ -759,7 +781,7 @@ def test_default_schema_name_init_storage(destination_config: DestinationTestCon assert client.sql_client.has_dataset() with cm_yield_client_with_storage( - destination_config.destination, + destination_config.destination_factory(), default_config_values={ "default_schema_name": ( # the default schema is not event schema . that should create dataset with the name `dataset_name` with schema suffix "event_2" @@ -788,7 +810,8 @@ def test_get_stored_state( os.environ["SCHEMA__NAMING"] = naming_convention with cm_yield_client_with_storage( - destination_config.destination, default_config_values={"default_schema_name": None} + destination_config.destination_factory(), + default_config_values={"default_schema_name": None}, ) as client: # event schema with event table if not client.capabilities.preferred_loader_file_format: @@ -814,6 +837,8 @@ def test_get_stored_state( # get state stored_state = client.get_stored_state("pipeline") + # Ensure timezone aware datetime for comparing + stored_state.created_at = pendulum.instance(stored_state.created_at) assert doc == stored_state.as_doc() @@ -850,7 +875,8 @@ def _load_something(_client: SqlJobClientBase, expected_rows: int) -> None: assert len(db_rows) == expected_rows with cm_yield_client_with_storage( - destination_config.destination, default_config_values={"default_schema_name": None} + destination_config.destination_factory(), + default_config_values={"default_schema_name": None}, ) as client: # event schema with event table if not client.capabilities.preferred_loader_file_format: @@ -909,7 +935,11 @@ def _load_something(_client: SqlJobClientBase, expected_rows: int) -> None: "mandatory_column", "text", nullable=False ) client.schema._bump_version() - if destination_config.destination == "clickhouse": + if destination_config.destination_type == "clickhouse" or ( + # mysql allows adding not-null columns (they have an implicit default) + destination_config.destination_type == "sqlalchemy" + and client.sql_client.dialect_name == "mysql" + ): client.update_stored_schema() else: with pytest.raises(DatabaseException) as py_ex: @@ -942,11 +972,16 @@ def normalize_rows(rows: List[Dict[str, Any]], naming: NamingConvention) -> None row[naming.normalize_identifier(k)] = row.pop(k) -def get_columns_and_row_all_types(destination_type: str): +def get_columns_and_row_all_types(destination_config: DestinationClientConfiguration): + exclude_types = [] + if destination_config.destination_type in ["databricks", "clickhouse", "motherduck"]: + exclude_types.append("time") + if destination_config.destination_name == "sqlalchemy_sqlite": + exclude_types.extend(["decimal", "wei"]) return table_update_and_row( # TIME + parquet is actually a duckdb problem: https://github.com/duckdb/duckdb/pull/13283 - exclude_types=( - ["time"] if destination_type in ["databricks", "clickhouse", "motherduck"] else None + exclude_types=exclude_types, # type: ignore[arg-type] + exclude_columns=( + ["col4_precision"] if destination_config.destination_type in ["motherduck"] else None ), - exclude_columns=["col4_precision"] if destination_type in ["motherduck"] else None, ) diff --git a/tests/load/test_sql_client.py b/tests/load/test_sql_client.py index e167f0ceda..199b4b83b7 100644 --- a/tests/load/test_sql_client.py +++ b/tests/load/test_sql_client.py @@ -1,7 +1,7 @@ import os import pytest import datetime # noqa: I251 -from typing import Iterator, Any +from typing import Iterator, Any, Tuple, Type, Union from threading import Thread, Event from time import sleep @@ -20,7 +20,7 @@ from dlt.destinations.sql_client import DBApiCursor, SqlClientBase from dlt.destinations.job_client_impl import SqlJobClientBase from dlt.destinations.typing import TNativeConn -from dlt.common.time import ensure_pendulum_datetime +from dlt.common.time import ensure_pendulum_datetime, to_py_datetime from tests.utils import TEST_STORAGE_ROOT, autouse_test_storage from tests.load.utils import ( @@ -28,6 +28,7 @@ prepare_table, AWS_BUCKET, destinations_configs, + DestinationTestConfiguration, ) # mark all tests as essential, do not remove @@ -46,7 +47,8 @@ def file_storage() -> FileStorage: @pytest.fixture(scope="function") def client(request, naming) -> Iterator[SqlJobClientBase]: - yield from yield_client_with_storage(request.param.destination) + param: DestinationTestConfiguration = request.param + yield from yield_client_with_storage(param.destination_factory()) @pytest.fixture(scope="function") @@ -62,7 +64,9 @@ def naming(request) -> str: @pytest.mark.parametrize( "client", destinations_configs( - default_sql_configs=True, exclude=["mssql", "synapse", "dremio", "clickhouse"] + # Only databases that support search path or equivalent + default_sql_configs=True, + exclude=["mssql", "synapse", "dremio", "clickhouse", "sqlalchemy"], ), indirect=True, ids=lambda x: x.name, @@ -145,6 +149,7 @@ def test_has_dataset(naming: str, client: SqlJobClientBase) -> None: ) def test_create_drop_dataset(naming: str, client: SqlJobClientBase) -> None: # client.sql_client.create_dataset() + # Dataset is already create in fixture, so next time it fails with pytest.raises(DatabaseException): client.sql_client.create_dataset() client.sql_client.drop_dataset() @@ -208,14 +213,19 @@ def test_execute_sql(client: SqlJobClientBase) -> None: assert len(rows) == 1 # print(rows) assert rows[0][0] == "event" - assert isinstance(rows[0][1], datetime.datetime) + assert isinstance(ensure_pendulum_datetime(rows[0][1]), datetime.datetime) assert rows[0][0] == "event" # print(rows[0][1]) # print(type(rows[0][1])) - # convert to pendulum to make sure it is supported by dbapi + # ensure datetime obj to make sure it is supported by dbapi + inserted_at = to_py_datetime(ensure_pendulum_datetime(rows[0][1])) + if client.config.destination_name == "sqlalchemy_sqlite": + # timezone aware datetime is not supported by sqlite + inserted_at = inserted_at.replace(tzinfo=None) + rows = client.sql_client.execute_sql( f"SELECT schema_name, inserted_at FROM {version_table_name} WHERE inserted_at = %s", - ensure_pendulum_datetime(rows[0][1]), + inserted_at, ) assert len(rows) == 1 # use rows in subsequent test @@ -241,20 +251,20 @@ def test_execute_sql(client: SqlJobClientBase) -> None: def test_execute_ddl(client: SqlJobClientBase) -> None: uniq_suffix = uniq_id() client.update_stored_schema() - table_name = prepare_temp_table(client) + table_name, py_type = prepare_temp_table(client) f_q_table_name = client.sql_client.make_qualified_table_name(table_name) client.sql_client.execute_sql(f"INSERT INTO {f_q_table_name} VALUES (1.0)") rows = client.sql_client.execute_sql(f"SELECT * FROM {f_q_table_name}") - assert rows[0][0] == Decimal("1.0") + assert rows[0][0] == py_type("1.0") if client.config.destination_type == "dremio": username = client.config.credentials["username"] view_name = f'"@{username}"."view_tmp_{uniq_suffix}"' else: # create view, note that bigquery will not let you execute a view that does not have fully qualified table names. view_name = client.sql_client.make_qualified_table_name(f"view_tmp_{uniq_suffix}") - client.sql_client.execute_sql(f"CREATE VIEW {view_name} AS (SELECT * FROM {f_q_table_name});") + client.sql_client.execute_sql(f"CREATE VIEW {view_name} AS SELECT * FROM {f_q_table_name};") rows = client.sql_client.execute_sql(f"SELECT * FROM {view_name}") - assert rows[0][0] == Decimal("1.0") + assert rows[0][0] == py_type("1.0") @pytest.mark.parametrize( @@ -275,7 +285,7 @@ def test_execute_query(client: SqlJobClientBase) -> None: rows = curr.fetchall() assert len(rows) == 1 assert rows[0][0] == "event" - assert isinstance(rows[0][1], datetime.datetime) + assert isinstance(ensure_pendulum_datetime(rows[0][1]), datetime.datetime) with client.sql_client.execute_query( f"SELECT schema_name, inserted_at FROM {version_table_name} WHERE inserted_at = %s", rows[0][1], @@ -285,7 +295,7 @@ def test_execute_query(client: SqlJobClientBase) -> None: assert rows[0][0] == "event" with client.sql_client.execute_query( f"SELECT schema_name, inserted_at FROM {version_table_name} WHERE inserted_at = %s", - pendulum.now().add(seconds=1), + to_py_datetime(pendulum.now().add(seconds=1)), ) as curr: rows = curr.fetchall() assert len(rows) == 0 @@ -293,7 +303,7 @@ def test_execute_query(client: SqlJobClientBase) -> None: with client.sql_client.execute_query( f"SELECT schema_name, inserted_at FROM {version_table_name} WHERE inserted_at =" " %(date)s", - date=pendulum.now().add(seconds=1), + date=to_py_datetime(pendulum.now().add(seconds=1)), ) as curr: rows = curr.fetchall() assert len(rows) == 0 @@ -314,7 +324,7 @@ def test_execute_df(client: SqlJobClientBase) -> None: total_records = 3000 client.update_stored_schema() - table_name = prepare_temp_table(client) + table_name, py_type = prepare_temp_table(client) f_q_table_name = client.sql_client.make_qualified_table_name(table_name) if client.capabilities.insert_values_writer_type == "default": @@ -415,8 +425,7 @@ def test_database_exceptions(client: SqlJobClientBase) -> None: assert client.sql_client.is_dbapi_exception(term_ex.value.dbapi_exception) if client.config.destination_type not in ["dremio", "clickhouse"]: with pytest.raises(DatabaseUndefinedRelation) as term_ex: - with client.sql_client.execute_query("DROP SCHEMA UNKNOWN"): - pass + client.sql_client.drop_dataset() assert client.sql_client.is_dbapi_exception(term_ex.value.dbapi_exception) @@ -427,29 +436,29 @@ def test_database_exceptions(client: SqlJobClientBase) -> None: ids=lambda x: x.name, ) def test_commit_transaction(client: SqlJobClientBase) -> None: - table_name = prepare_temp_table(client) + table_name, py_type = prepare_temp_table(client) f_q_table_name = client.sql_client.make_qualified_table_name(table_name) with client.sql_client.begin_transaction(): - client.sql_client.execute_sql(f"INSERT INTO {f_q_table_name} VALUES (%s)", Decimal("1.0")) + client.sql_client.execute_sql(f"INSERT INTO {f_q_table_name} VALUES (%s)", py_type("1.0")) # check row still in transaction rows = client.sql_client.execute_sql( - f"SELECT col FROM {f_q_table_name} WHERE col = %s", Decimal("1.0") + f"SELECT col FROM {f_q_table_name} WHERE col = %s", py_type("1.0") ) assert len(rows) == 1 # check row after commit rows = client.sql_client.execute_sql( - f"SELECT col FROM {f_q_table_name} WHERE col = %s", Decimal("1.0") + f"SELECT col FROM {f_q_table_name} WHERE col = %s", py_type("1.0") ) assert len(rows) == 1 assert rows[0][0] == 1.0 with client.sql_client.begin_transaction() as tx: client.sql_client.execute_sql( - f"DELETE FROM {f_q_table_name} WHERE col = %s", Decimal("1.0") + f"DELETE FROM {f_q_table_name} WHERE col = %s", py_type("1.0") ) # explicit commit tx.commit_transaction() rows = client.sql_client.execute_sql( - f"SELECT col FROM {f_q_table_name} WHERE col = %s", Decimal("1.0") + f"SELECT col FROM {f_q_table_name} WHERE col = %s", py_type("1.0") ) assert len(rows) == 0 @@ -463,22 +472,22 @@ def test_commit_transaction(client: SqlJobClientBase) -> None: def test_rollback_transaction(client: SqlJobClientBase) -> None: if client.capabilities.supports_transactions is False: pytest.skip("Destination does not support tx") - table_name = prepare_temp_table(client) + table_name, py_type = prepare_temp_table(client) f_q_table_name = client.sql_client.make_qualified_table_name(table_name) # test python exception with pytest.raises(RuntimeError): with client.sql_client.begin_transaction(): client.sql_client.execute_sql( - f"INSERT INTO {f_q_table_name} VALUES (%s)", Decimal("1.0") + f"INSERT INTO {f_q_table_name} VALUES (%s)", py_type("1.0") ) rows = client.sql_client.execute_sql( - f"SELECT col FROM {f_q_table_name} WHERE col = %s", Decimal("1.0") + f"SELECT col FROM {f_q_table_name} WHERE col = %s", py_type("1.0") ) assert len(rows) == 1 # python exception triggers rollback raise RuntimeError("ROLLBACK") rows = client.sql_client.execute_sql( - f"SELECT col FROM {f_q_table_name} WHERE col = %s", Decimal("1.0") + f"SELECT col FROM {f_q_table_name} WHERE col = %s", py_type("1.0") ) assert len(rows) == 0 @@ -487,23 +496,23 @@ def test_rollback_transaction(client: SqlJobClientBase) -> None: with pytest.raises(DatabaseException): with client.sql_client.begin_transaction(): client.sql_client.execute_sql( - f"INSERT INTO {f_q_table_name} VALUES (%s)", Decimal("1.0") + f"INSERT INTO {f_q_table_name} VALUES (%s)", py_type("1.0") ) # table does not exist client.sql_client.execute_sql( - f"SELECT col FROM {f_q_wrong_table_name} WHERE col = %s", Decimal("1.0") + f"SELECT col FROM {f_q_wrong_table_name} WHERE col = %s", py_type("1.0") ) rows = client.sql_client.execute_sql( - f"SELECT col FROM {f_q_table_name} WHERE col = %s", Decimal("1.0") + f"SELECT col FROM {f_q_table_name} WHERE col = %s", py_type("1.0") ) assert len(rows) == 0 # test explicit rollback with client.sql_client.begin_transaction() as tx: - client.sql_client.execute_sql(f"INSERT INTO {f_q_table_name} VALUES (%s)", Decimal("1.0")) + client.sql_client.execute_sql(f"INSERT INTO {f_q_table_name} VALUES (%s)", py_type("1.0")) tx.rollback_transaction() rows = client.sql_client.execute_sql( - f"SELECT col FROM {f_q_table_name} WHERE col = %s", Decimal("1.0") + f"SELECT col FROM {f_q_table_name} WHERE col = %s", py_type("1.0") ) assert len(rows) == 0 @@ -524,12 +533,15 @@ def test_rollback_transaction(client: SqlJobClientBase) -> None: def test_transaction_isolation(client: SqlJobClientBase) -> None: if client.capabilities.supports_transactions is False: pytest.skip("Destination does not support tx") - table_name = prepare_temp_table(client) + if client.config.destination_name == "sqlalchemy_sqlite": + # because other schema names must be attached for each connection + client.sql_client.dataset_name = "main" + table_name, py_type = prepare_temp_table(client) f_q_table_name = client.sql_client.make_qualified_table_name(table_name) event = Event() event.clear() - def test_thread(thread_id: Decimal) -> None: + def test_thread(thread_id: Union[Decimal, float]) -> None: # make a copy of the sql_client thread_client = client.sql_client.__class__( client.sql_client.dataset_name, @@ -543,8 +555,8 @@ def test_thread(thread_id: Decimal) -> None: event.wait() with client.sql_client.begin_transaction() as tx: - client.sql_client.execute_sql(f"INSERT INTO {f_q_table_name} VALUES (%s)", Decimal("1.0")) - t = Thread(target=test_thread, daemon=True, args=(Decimal("2.0"),)) + client.sql_client.execute_sql(f"INSERT INTO {f_q_table_name} VALUES (%s)", py_type("1.0")) + t = Thread(target=test_thread, daemon=True, args=(py_type("2.0"),)) t.start() # thread 2.0 inserts sleep(3.0) @@ -555,17 +567,23 @@ def test_thread(thread_id: Decimal) -> None: t.join() # just in case close the connection - client.sql_client.close_connection() - # re open connection - client.sql_client.open_connection() + if ( + client.config.destination_name != "sqlalchemy_sqlite" + ): # keep sqlite connection to maintain attached datasets + client.sql_client.close_connection() + # re open connection + client.sql_client.open_connection() rows = client.sql_client.execute_sql(f"SELECT col FROM {f_q_table_name} ORDER BY col") assert len(rows) == 1 # only thread 2 is left - assert rows[0][0] == Decimal("2.0") + assert rows[0][0] == py_type("2.0") @pytest.mark.parametrize( - "client", destinations_configs(default_sql_configs=True), indirect=True, ids=lambda x: x.name + "client", + destinations_configs(default_sql_configs=True, exclude=["sqlalchemy"]), + indirect=True, + ids=lambda x: x.name, ) def test_max_table_identifier_length(client: SqlJobClientBase) -> None: if client.capabilities.max_identifier_length >= 65536: @@ -595,7 +613,10 @@ def test_max_table_identifier_length(client: SqlJobClientBase) -> None: @pytest.mark.parametrize( - "client", destinations_configs(default_sql_configs=True), indirect=True, ids=lambda x: x.name + "client", + destinations_configs(default_sql_configs=True, exclude=["sqlalchemy"]), + indirect=True, + ids=lambda x: x.name, ) def test_max_column_identifier_length(client: SqlJobClientBase) -> None: if client.capabilities.max_column_identifier_length >= 65536: @@ -620,7 +641,7 @@ def test_max_column_identifier_length(client: SqlJobClientBase) -> None: @pytest.mark.parametrize( "client", - destinations_configs(default_sql_configs=True, exclude=["databricks"]), + destinations_configs(default_sql_configs=True, exclude=["databricks", "sqlalchemy"]), indirect=True, ids=lambda x: x.name, ) @@ -674,11 +695,13 @@ def assert_load_id(sql_client: SqlClientBase[TNativeConn], load_id: str) -> None assert len(rows) == 1 -def prepare_temp_table(client: SqlJobClientBase) -> str: +def prepare_temp_table(client: SqlJobClientBase) -> Tuple[str, Type[Union[Decimal, float]]]: + """Return the table name and py type of value to insert""" uniq_suffix = uniq_id() table_name = f"tmp_{uniq_suffix}" ddl_suffix = "" coltype = "numeric" + py_type: Union[Type[Decimal], Type[float]] = Decimal if client.config.destination_type == "athena": ddl_suffix = ( f"LOCATION '{AWS_BUCKET}/ci/{table_name}' TBLPROPERTIES ('table_type'='ICEBERG'," @@ -686,6 +709,10 @@ def prepare_temp_table(client: SqlJobClientBase) -> str: ) coltype = "bigint" qualified_table_name = table_name + elif client.config.destination_name == "sqlalchemy_sqlite": + coltype = "float" + py_type = float + qualified_table_name = client.sql_client.make_qualified_table_name(table_name) elif client.config.destination_type == "clickhouse": ddl_suffix = "ENGINE = MergeTree() ORDER BY col" qualified_table_name = client.sql_client.make_qualified_table_name(table_name) @@ -694,4 +721,4 @@ def prepare_temp_table(client: SqlJobClientBase) -> str: client.sql_client.execute_sql( f"CREATE TABLE {qualified_table_name} (col {coltype}) {ddl_suffix};" ) - return table_name + return table_name, py_type diff --git a/tests/load/utils.py b/tests/load/utils.py index 5427904d52..f443748f8e 100644 --- a/tests/load/utils.py +++ b/tests/load/utils.py @@ -53,6 +53,7 @@ from dlt.destinations.sql_client import SqlClientBase from dlt.destinations.job_client_impl import SqlJobClientBase +from dlt.pipeline.exceptions import SqlClientNotAvailable from tests.utils import ( ACTIVE_DESTINATIONS, IMPLEMENTED_DESTINATIONS, @@ -74,6 +75,7 @@ GDRIVE_BUCKET = dlt.config.get("tests.bucket_url_gdrive", str) FILE_BUCKET = dlt.config.get("tests.bucket_url_file", str) R2_BUCKET = dlt.config.get("tests.bucket_url_r2", str) +SFTP_BUCKET = dlt.config.get("tests.bucket_url_sftp", str) MEMORY_BUCKET = dlt.config.get("tests.memory", str) ALL_FILESYSTEM_DRIVERS = dlt.config.get("ALL_FILESYSTEM_DRIVERS", list) or [ @@ -85,6 +87,7 @@ "file", "memory", "r2", + "sftp", ] # Filter out buckets not in all filesystem drivers @@ -96,6 +99,7 @@ ABFS_BUCKET, AZ_BUCKET, GDRIVE_BUCKET, + SFTP_BUCKET, ] WITH_GDRIVE_BUCKETS = [ bucket @@ -143,7 +147,7 @@ class DestinationTestConfiguration: """Class for defining test setup for one destination.""" - destination: str + destination_type: str staging: Optional[TDestinationReferenceArg] = None file_format: Optional[TLoaderFileFormat] = None table_format: Optional[TTableFormat] = None @@ -153,16 +157,23 @@ class DestinationTestConfiguration: staging_use_msi: bool = False extra_info: Optional[str] = None supports_merge: bool = True # TODO: take it from client base class - force_iceberg: bool = False + force_iceberg: bool = None # used only to test deprecation supports_dbt: bool = True disable_compression: bool = False dev_mode: bool = False credentials: Optional[Union[CredentialsConfiguration, Dict[str, Any]]] = None env_vars: Optional[Dict[str, str]] = None + destination_name: Optional[str] = None + + def destination_factory(self, **kwargs) -> Destination[Any, Any]: + dest_type = kwargs.pop("destination", self.destination_type) + dest_name = kwargs.pop("destination_name", self.destination_name) + self.setup() + return Destination.from_reference(dest_type, destination_name=dest_name, **kwargs) @property def name(self) -> str: - name: str = self.destination + name: str = self.destination_name or self.destination_type if self.file_format: name += f"-{self.file_format}" if self.table_format: @@ -195,7 +206,7 @@ def setup(self) -> None: os.environ[f"DESTINATION__{k.upper()}"] = str(v) # For the filesystem destinations we disable compression to make analyzing the result easier - if self.destination == "filesystem" or self.disable_compression: + if self.destination_type == "filesystem" or self.disable_compression: os.environ["DATA_WRITER__DISABLE_COMPRESSION"] = "True" if self.credentials is not None: @@ -210,11 +221,16 @@ def setup_pipeline( self, pipeline_name: str, dataset_name: str = None, dev_mode: bool = False, **kwargs ) -> dlt.Pipeline: """Convenience method to setup pipeline with this configuration""" + self.dev_mode = dev_mode - self.setup() + destination = kwargs.pop("destination", None) + if destination is None: + destination = self.destination_factory(**kwargs) + else: + self.setup() pipeline = dlt.pipeline( pipeline_name=pipeline_name, - destination=kwargs.pop("destination", self.destination), + destination=destination, staging=kwargs.pop("staging", self.staging), dataset_name=dataset_name or pipeline_name, dev_mode=dev_mode, @@ -228,6 +244,19 @@ def attach_pipeline(self, pipeline_name: str, **kwargs) -> dlt.Pipeline: pipeline = dlt.attach(pipeline_name, **kwargs) return pipeline + def supports_sql_client(self, pipeline: dlt.Pipeline) -> bool: + """Checks if destination supports SQL queries""" + try: + pipeline.sql_client() + return True + except SqlClientNotAvailable: + return False + + @property + def run_kwargs(self): + """Returns a dict of kwargs to be passed to pipeline.run method: currently file and table format""" + return dict(loader_file_format=self.file_format, table_format=self.table_format) + def destinations_configs( default_sql_configs: bool = False, @@ -241,11 +270,10 @@ def destinations_configs( bucket_subset: Sequence[str] = (), exclude: Sequence[str] = (), bucket_exclude: Sequence[str] = (), - file_format: Union[TLoaderFileFormat, Sequence[TLoaderFileFormat]] = None, - table_format: Union[TTableFormat, Sequence[TTableFormat]] = None, + with_file_format: Union[TLoaderFileFormat, Sequence[TLoaderFileFormat]] = None, + with_table_format: Union[TTableFormat, Sequence[TTableFormat]] = None, supports_merge: Optional[bool] = None, supports_dbt: Optional[bool] = None, - force_iceberg: Optional[bool] = None, ) -> List[DestinationTestConfiguration]: # sanity check for item in subset: @@ -261,16 +289,15 @@ def destinations_configs( default_sql_configs_with_staging = [ # Athena needs filesystem staging, which will be automatically set; we have to supply a bucket url though. DestinationTestConfiguration( - destination="athena", + destination_type="athena", file_format="parquet", supports_merge=False, bucket_url=AWS_BUCKET, ), DestinationTestConfiguration( - destination="athena", + destination_type="athena", file_format="parquet", bucket_url=AWS_BUCKET, - force_iceberg=True, supports_merge=True, supports_dbt=False, table_format="iceberg", @@ -281,13 +308,16 @@ def destinations_configs( # default non staging sql based configs, one per destination if default_sql_configs: destination_configs += [ - DestinationTestConfiguration(destination=destination) + DestinationTestConfiguration(destination_type=destination) for destination in SQL_DESTINATIONS - if destination not in ("athena", "synapse", "databricks", "dremio", "clickhouse") + if destination + not in ("athena", "synapse", "databricks", "dremio", "clickhouse", "sqlalchemy") ] destination_configs += [ - DestinationTestConfiguration(destination="duckdb", file_format="parquet"), - DestinationTestConfiguration(destination="motherduck", file_format="insert_values"), + DestinationTestConfiguration(destination_type="duckdb", file_format="parquet"), + DestinationTestConfiguration( + destination_type="motherduck", file_format="insert_values" + ), ] # add Athena staging configs @@ -295,12 +325,27 @@ def destinations_configs( destination_configs += [ DestinationTestConfiguration( - destination="clickhouse", file_format="jsonl", supports_dbt=False + destination_type="sqlalchemy", + supports_merge=False, + supports_dbt=False, + destination_name="sqlalchemy_mysql", + ), + DestinationTestConfiguration( + destination_type="sqlalchemy", + supports_merge=False, + supports_dbt=False, + destination_name="sqlalchemy_sqlite", + ), + ] + + destination_configs += [ + DestinationTestConfiguration( + destination_type="clickhouse", file_format="jsonl", supports_dbt=False ) ] destination_configs += [ DestinationTestConfiguration( - destination="databricks", + destination_type="databricks", file_format="parquet", bucket_url=AZ_BUCKET, extra_info="az-authorization", @@ -309,7 +354,7 @@ def destinations_configs( destination_configs += [ DestinationTestConfiguration( - destination="dremio", + destination_type="dremio", staging=filesystem(destination_name="minio"), file_format="parquet", bucket_url=AWS_BUCKET, @@ -317,24 +362,24 @@ def destinations_configs( ) ] destination_configs += [ - # DestinationTestConfiguration(destination="mssql", supports_dbt=False), - DestinationTestConfiguration(destination="synapse", supports_dbt=False), + # DestinationTestConfiguration(destination_type="mssql", supports_dbt=False), + DestinationTestConfiguration(destination_type="synapse", supports_dbt=False), ] # sanity check that when selecting default destinations, one of each sql destination is actually # provided - assert set(SQL_DESTINATIONS) == {d.destination for d in destination_configs} + assert set(SQL_DESTINATIONS) == {d.destination_type for d in destination_configs} if default_vector_configs: destination_configs += [ - DestinationTestConfiguration(destination="weaviate"), - DestinationTestConfiguration(destination="lancedb"), + DestinationTestConfiguration(destination_type="weaviate"), + DestinationTestConfiguration(destination_type="lancedb"), DestinationTestConfiguration( - destination="qdrant", + destination_type="qdrant", credentials=dict(path=str(Path(FILE_BUCKET) / "qdrant_data")), extra_info="local-file", ), - DestinationTestConfiguration(destination="qdrant", extra_info="server"), + DestinationTestConfiguration(destination_type="qdrant", extra_info="server"), ] if (default_sql_configs or all_staging_configs) and not default_sql_configs: @@ -344,7 +389,7 @@ def destinations_configs( if default_staging_configs or all_staging_configs: destination_configs += [ DestinationTestConfiguration( - destination="redshift", + destination_type="redshift", staging="filesystem", file_format="parquet", bucket_url=AWS_BUCKET, @@ -352,14 +397,14 @@ def destinations_configs( extra_info="s3-role", ), DestinationTestConfiguration( - destination="bigquery", + destination_type="bigquery", staging="filesystem", file_format="parquet", bucket_url=GCS_BUCKET, extra_info="gcs-authorization", ), DestinationTestConfiguration( - destination="snowflake", + destination_type="snowflake", staging="filesystem", file_format="jsonl", bucket_url=GCS_BUCKET, @@ -367,14 +412,14 @@ def destinations_configs( extra_info="gcs-integration", ), DestinationTestConfiguration( - destination="snowflake", + destination_type="snowflake", staging="filesystem", file_format="jsonl", bucket_url=AWS_BUCKET, extra_info="s3-integration", ), DestinationTestConfiguration( - destination="snowflake", + destination_type="snowflake", staging="filesystem", file_format="jsonl", bucket_url=AWS_BUCKET, @@ -382,7 +427,7 @@ def destinations_configs( extra_info="s3-integration", ), DestinationTestConfiguration( - destination="snowflake", + destination_type="snowflake", staging="filesystem", file_format="jsonl", bucket_url=AZ_BUCKET, @@ -390,14 +435,14 @@ def destinations_configs( extra_info="az-integration", ), DestinationTestConfiguration( - destination="snowflake", + destination_type="snowflake", staging="filesystem", file_format="jsonl", bucket_url=AZ_BUCKET, extra_info="az-authorization", ), DestinationTestConfiguration( - destination="databricks", + destination_type="databricks", staging="filesystem", file_format="jsonl", bucket_url=AWS_BUCKET, @@ -405,7 +450,7 @@ def destinations_configs( disable_compression=True, ), DestinationTestConfiguration( - destination="databricks", + destination_type="databricks", staging="filesystem", file_format="jsonl", bucket_url=AZ_BUCKET, @@ -413,14 +458,14 @@ def destinations_configs( disable_compression=True, ), DestinationTestConfiguration( - destination="databricks", + destination_type="databricks", staging="filesystem", file_format="parquet", bucket_url=AWS_BUCKET, extra_info="s3-authorization", ), DestinationTestConfiguration( - destination="synapse", + destination_type="synapse", staging="filesystem", file_format="parquet", bucket_url=AZ_BUCKET, @@ -428,35 +473,35 @@ def destinations_configs( disable_compression=True, ), DestinationTestConfiguration( - destination="clickhouse", + destination_type="clickhouse", staging="filesystem", file_format="parquet", bucket_url=AWS_BUCKET, extra_info="s3-authorization", ), DestinationTestConfiguration( - destination="clickhouse", + destination_type="clickhouse", staging="filesystem", file_format="parquet", bucket_url=AZ_BUCKET, extra_info="az-authorization", ), DestinationTestConfiguration( - destination="clickhouse", + destination_type="clickhouse", staging="filesystem", file_format="jsonl", bucket_url=AZ_BUCKET, extra_info="az-authorization", ), DestinationTestConfiguration( - destination="clickhouse", + destination_type="clickhouse", staging="filesystem", file_format="jsonl", bucket_url=AWS_BUCKET, extra_info="s3-authorization", ), DestinationTestConfiguration( - destination="dremio", + destination_type="dremio", staging=filesystem(destination_name="minio"), file_format="parquet", bucket_url=AWS_BUCKET, @@ -467,35 +512,35 @@ def destinations_configs( if all_staging_configs: destination_configs += [ DestinationTestConfiguration( - destination="redshift", + destination_type="redshift", staging="filesystem", file_format="parquet", bucket_url=AWS_BUCKET, extra_info="credential-forwarding", ), DestinationTestConfiguration( - destination="snowflake", + destination_type="snowflake", staging="filesystem", file_format="parquet", bucket_url=AWS_BUCKET, extra_info="credential-forwarding", ), DestinationTestConfiguration( - destination="redshift", + destination_type="redshift", staging="filesystem", file_format="jsonl", bucket_url=AWS_BUCKET, extra_info="credential-forwarding", ), DestinationTestConfiguration( - destination="bigquery", + destination_type="bigquery", staging="filesystem", file_format="jsonl", bucket_url=GCS_BUCKET, extra_info="gcs-authorization", ), DestinationTestConfiguration( - destination="synapse", + destination_type="synapse", staging="filesystem", file_format="parquet", bucket_url=AZ_BUCKET, @@ -508,7 +553,7 @@ def destinations_configs( if local_filesystem_configs: destination_configs += [ DestinationTestConfiguration( - destination="filesystem", + destination_type="filesystem", bucket_url=FILE_BUCKET, file_format="insert_values", supports_merge=False, @@ -516,7 +561,7 @@ def destinations_configs( ] destination_configs += [ DestinationTestConfiguration( - destination="filesystem", + destination_type="filesystem", bucket_url=FILE_BUCKET, file_format="parquet", supports_merge=False, @@ -524,7 +569,7 @@ def destinations_configs( ] destination_configs += [ DestinationTestConfiguration( - destination="filesystem", + destination_type="filesystem", bucket_url=FILE_BUCKET, file_format="jsonl", supports_merge=False, @@ -535,7 +580,7 @@ def destinations_configs( for bucket in DEFAULT_BUCKETS: destination_configs += [ DestinationTestConfiguration( - destination="filesystem", + destination_type="filesystem", bucket_url=bucket, extra_info=bucket, supports_merge=False, @@ -546,7 +591,7 @@ def destinations_configs( for bucket in DEFAULT_BUCKETS: destination_configs += [ DestinationTestConfiguration( - destination="filesystem", + destination_type="filesystem", bucket_url=bucket, extra_info=bucket, table_format="delta", @@ -565,43 +610,45 @@ def destinations_configs( # filter out non active destinations destination_configs = [ - conf for conf in destination_configs if conf.destination in ACTIVE_DESTINATIONS + conf for conf in destination_configs if conf.destination_type in ACTIVE_DESTINATIONS ] # filter out destinations not in subset if subset: - destination_configs = [conf for conf in destination_configs if conf.destination in subset] + destination_configs = [ + conf for conf in destination_configs if conf.destination_type in subset + ] if bucket_subset: destination_configs = [ conf for conf in destination_configs - if conf.destination != "filesystem" or conf.bucket_url in bucket_subset + if conf.destination_type != "filesystem" or conf.bucket_url in bucket_subset ] if exclude: destination_configs = [ - conf for conf in destination_configs if conf.destination not in exclude + conf for conf in destination_configs if conf.destination_type not in exclude ] if bucket_exclude: destination_configs = [ conf for conf in destination_configs - if conf.destination != "filesystem" or conf.bucket_url not in bucket_exclude + if conf.destination_type != "filesystem" or conf.bucket_url not in bucket_exclude ] - if file_format: - if not isinstance(file_format, Sequence): - file_format = [file_format] + if with_file_format: + if not isinstance(with_file_format, Sequence): + with_file_format = [with_file_format] destination_configs = [ conf for conf in destination_configs - if conf.file_format and conf.file_format in file_format + if conf.file_format and conf.file_format in with_file_format ] - if table_format: - if not isinstance(table_format, Sequence): - table_format = [table_format] + if with_table_format: + if not isinstance(with_table_format, Sequence): + with_table_format = [with_table_format] destination_configs = [ conf for conf in destination_configs - if conf.table_format and conf.table_format in table_format + if conf.table_format and conf.table_format in with_table_format ] if supports_merge is not None: destination_configs = [ @@ -617,11 +664,6 @@ def destinations_configs( conf for conf in destination_configs if conf.name not in EXCLUDED_DESTINATION_CONFIGURATIONS ] - if force_iceberg is not None: - destination_configs = [ - conf for conf in destination_configs if conf.force_iceberg is force_iceberg - ] - # add marks destination_configs = [ cast( @@ -759,20 +801,22 @@ def prepare_table( else: user_table_name = table_name client.schema.update_table(new_table(user_table_name, columns=list(user_table.values()))) + print(client.schema.to_pretty_yaml()) + client.verify_schema([user_table_name]) client.schema._bump_version() client.update_stored_schema() return user_table_name def yield_client( - destination_type: str, + destination_ref: TDestinationReferenceArg, dataset_name: str = None, default_config_values: StrAny = None, schema_name: str = "event", ) -> Iterator[SqlJobClientBase]: os.environ.pop("DATASET_NAME", None) # import destination reference by name - destination = Destination.from_reference(destination_type) + destination = Destination.from_reference(destination_ref) # create initial config dest_config: DestinationClientDwhConfiguration = None dest_config = destination.spec() # type: ignore @@ -797,7 +841,7 @@ def yield_client( client: SqlJobClientBase = None # athena requires staging config to be present, so stick this in there here - if destination_type == "athena": + if destination.destination_name == "athena": staging_config = DestinationClientStagingConfiguration( bucket_url=AWS_BUCKET, )._bind_dataset_name(dataset_name=dest_config.dataset_name) @@ -810,7 +854,7 @@ def yield_client( ConfigSectionContext( sections=( "destination", - destination_type, + destination.destination_name, ) ) ): @@ -820,23 +864,23 @@ def yield_client( @contextlib.contextmanager def cm_yield_client( - destination_type: str, + destination: TDestinationReferenceArg, dataset_name: str, default_config_values: StrAny = None, schema_name: str = "event", ) -> Iterator[SqlJobClientBase]: - return yield_client(destination_type, dataset_name, default_config_values, schema_name) + return yield_client(destination, dataset_name, default_config_values, schema_name) def yield_client_with_storage( - destination_type: str, default_config_values: StrAny = None, schema_name: str = "event" + destination: TDestinationReferenceArg, + default_config_values: StrAny = None, + schema_name: str = "event", ) -> Iterator[SqlJobClientBase]: # create dataset with random name dataset_name = "test_" + uniq_id() - with cm_yield_client( - destination_type, dataset_name, default_config_values, schema_name - ) as client: + with cm_yield_client(destination, dataset_name, default_config_values, schema_name) as client: client.initialize_storage() yield client if client.is_storage_initialized(): @@ -857,9 +901,11 @@ def delete_dataset(client: SqlClientBase[Any], normalized_dataset_name: str) -> @contextlib.contextmanager def cm_yield_client_with_storage( - destination_type: str, default_config_values: StrAny = None, schema_name: str = "event" + destination: TDestinationReferenceArg, + default_config_values: StrAny = None, + schema_name: str = "event", ) -> Iterator[SqlJobClientBase]: - return yield_client_with_storage(destination_type, default_config_values, schema_name) + return yield_client_with_storage(destination, default_config_values, schema_name) def write_dataset( diff --git a/tests/load/weaviate/test_weaviate_client.py b/tests/load/weaviate/test_weaviate_client.py index 0a249db0fd..2bf107afd3 100644 --- a/tests/load/weaviate/test_weaviate_client.py +++ b/tests/load/weaviate/test_weaviate_client.py @@ -93,7 +93,7 @@ def test_all_data_types( assert len(table_columns) == len(TABLE_UPDATE_COLUMNS_SCHEMA) for col_name in table_columns: assert col_name in TABLE_UPDATE_COLUMNS_SCHEMA - if TABLE_UPDATE_COLUMNS_SCHEMA[col_name]["data_type"] in ["decimal", "complex", "time"]: + if TABLE_UPDATE_COLUMNS_SCHEMA[col_name]["data_type"] in ["decimal", "json", "time"]: # no native representation assert table_columns[col_name]["data_type"] == "text" elif TABLE_UPDATE_COLUMNS_SCHEMA[col_name]["data_type"] == "wei": @@ -121,6 +121,7 @@ def test_case_sensitive_properties_create(client: WeaviateClient) -> None: ) client.schema._bump_version() with pytest.raises(SchemaIdentifierNormalizationCollision) as clash_ex: + client.verify_schema() client.update_stored_schema() assert clash_ex.value.identifier_type == "column" assert clash_ex.value.identifier_name == "coL1" @@ -170,6 +171,7 @@ def test_case_sensitive_properties_add(client: WeaviateClient) -> None: ) client.schema._bump_version() with pytest.raises(SchemaIdentifierNormalizationCollision): + client.verify_schema() client.update_stored_schema() # _, table_columns = client.get_storage_table("ColClass") diff --git a/tests/normalize/test_max_nesting.py b/tests/normalize/test_max_nesting.py index 5def1617dc..fb2b2d70f6 100644 --- a/tests/normalize/test_max_nesting.py +++ b/tests/normalize/test_max_nesting.py @@ -8,7 +8,7 @@ from tests.common.utils import json_case_path -TOP_LEVEL_TABLES = ["bot_events"] +ROOT_TABLES = ["bot_events"] ALL_TABLES_FOR_RASA_EVENT = [ "bot_events", @@ -37,8 +37,8 @@ def rasa_event_bot_metadata(): @pytest.mark.parametrize( "nesting_level,expected_num_tables,expected_table_names", ( - (0, 1, TOP_LEVEL_TABLES), - (1, 1, TOP_LEVEL_TABLES), + (0, 1, ROOT_TABLES), + (1, 1, ROOT_TABLES), (2, 3, ALL_TABLES_FOR_RASA_EVENT_NESTING_LEVEL_2), (5, 8, ALL_TABLES_FOR_RASA_EVENT), (15, 8, ALL_TABLES_FOR_RASA_EVENT), @@ -61,7 +61,7 @@ def bot_events(): pipeline_name = f"test_max_table_nesting_{nesting_level}_{expected_num_tables}" pipeline = dlt.pipeline( pipeline_name=pipeline_name, - destination=dummy(timeout=0.1), + destination=dummy(timeout=0.1, completed_prob=1), dev_mode=True, ) @@ -168,7 +168,7 @@ def some_data(): pipeline_name = "test_different_table_nesting_levels" pipeline = dlt.pipeline( pipeline_name=pipeline_name, - destination=dummy(timeout=0.1), + destination=dummy(timeout=0.1, completed_prob=1), dev_mode=True, ) diff --git a/tests/pipeline/cases/contracts/trace.schema.yaml b/tests/pipeline/cases/contracts/trace.schema.yaml index c324818338..1d6c31bdd7 100644 --- a/tests/pipeline/cases/contracts/trace.schema.yaml +++ b/tests/pipeline/cases/contracts/trace.schema.yaml @@ -1,6 +1,6 @@ -version: 4 -version_hash: JE62zVwqT2T/qHTi2Qdnn2d1A/JzCzyGtDwc+qUmbTs= -engine_version: 9 +version: 5 +version_hash: zsNXwXS2tlD1Or0sGgLOMI7clOz978WoyDuzVx+KU1s= +engine_version: 10 name: trace tables: _dlt_version: @@ -25,6 +25,7 @@ tables: nullable: false write_disposition: skip description: Created by DLT. Tracks schema updates + resource: _dlt_version _dlt_loads: columns: load_id: @@ -44,6 +45,7 @@ tables: nullable: true write_disposition: skip description: Created by DLT. Tracks completed loads + resource: _dlt_loads trace: columns: transaction_id: @@ -89,7 +91,9 @@ tables: data_type: text nullable: false unique: true + row_key: true write_disposition: append + resource: trace trace__execution_context__exec_info: columns: value: @@ -99,10 +103,11 @@ tables: data_type: text nullable: false unique: true + row_key: true _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -136,7 +141,7 @@ tables: _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -144,6 +149,7 @@ tables: data_type: text nullable: false unique: true + row_key: true load_info__destination_type: data_type: text nullable: true @@ -201,7 +207,7 @@ tables: _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -209,6 +215,7 @@ tables: data_type: text nullable: false unique: true + row_key: true parent: trace__steps trace__steps__extract_info__table_metrics: columns: @@ -239,7 +246,7 @@ tables: _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -247,6 +254,7 @@ tables: data_type: text nullable: false unique: true + row_key: true parent: trace__steps trace__steps__extract_info__resource_metrics: columns: @@ -277,7 +285,7 @@ tables: _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -285,6 +293,7 @@ tables: data_type: text nullable: false unique: true + row_key: true parent: trace__steps trace__steps__extract_info__dag: columns: @@ -303,7 +312,7 @@ tables: _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -311,6 +320,7 @@ tables: data_type: text nullable: false unique: true + row_key: true parent: trace__steps trace__steps__extract_info__hints: columns: @@ -347,7 +357,7 @@ tables: _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -355,6 +365,7 @@ tables: data_type: text nullable: false unique: true + row_key: true parent: trace__steps trace__steps__step_info__loads_ids: columns: @@ -365,10 +376,11 @@ tables: data_type: text nullable: false unique: true + row_key: true _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -393,7 +405,7 @@ tables: _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -401,6 +413,7 @@ tables: data_type: text nullable: false unique: true + row_key: true completed_at: data_type: timestamp nullable: true @@ -440,7 +453,7 @@ tables: _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -448,6 +461,7 @@ tables: data_type: text nullable: false unique: true + row_key: true parent: trace__steps__step_info__load_packages trace__steps__normalize_info__job_metrics: columns: @@ -481,7 +495,7 @@ tables: _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -489,6 +503,7 @@ tables: data_type: text nullable: false unique: true + row_key: true parent: trace__steps trace__steps__normalize_info__table_metrics: columns: @@ -519,7 +534,7 @@ tables: _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -527,6 +542,7 @@ tables: data_type: text nullable: false unique: true + row_key: true parent: trace__steps trace__steps__load_info__job_metrics: columns: @@ -548,7 +564,7 @@ tables: _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -556,6 +572,7 @@ tables: data_type: text nullable: false unique: true + row_key: true started_at: data_type: timestamp nullable: true @@ -595,7 +612,7 @@ tables: _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -603,6 +620,7 @@ tables: data_type: text nullable: false unique: true + row_key: true parent: data_type: text nullable: true @@ -618,9 +636,21 @@ tables: data_type: data_type: text nullable: true + precision: + data_type: bigint + nullable: true + scale: + data_type: bigint + nullable: true + timezone: + data_type: bool + nullable: true nullable: data_type: bool nullable: true + variant: + data_type: bool + nullable: true primary_key: data_type: bool nullable: true @@ -636,7 +666,7 @@ tables: _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -644,10 +674,29 @@ tables: data_type: text nullable: false unique: true + row_key: true unique: data_type: bool nullable: true - foreign_key: + row_key: + data_type: bool + nullable: true + parent_key: + data_type: bool + nullable: true + root_key: + data_type: bool + nullable: true + merge_key: + data_type: bool + nullable: true + partition: + data_type: bool + nullable: true + cluster: + data_type: bool + nullable: true + sort: data_type: bool nullable: true parent: trace__steps__step_info__load_packages__tables @@ -668,7 +717,7 @@ tables: _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -676,6 +725,7 @@ tables: data_type: text nullable: false unique: true + row_key: true parent: trace trace__resolved_config_values__sections: columns: @@ -686,10 +736,11 @@ tables: data_type: text nullable: false unique: true + row_key: true _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -720,7 +771,7 @@ tables: _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -728,6 +779,7 @@ tables: data_type: text nullable: false unique: true + row_key: true parent: trace__steps trace__steps__exception_traces__stack_trace: columns: @@ -738,10 +790,11 @@ tables: data_type: text nullable: false unique: true + row_key: true _dlt_parent_id: data_type: text nullable: false - foreign_key: true + parent_key: true _dlt_list_idx: data_type: bigint nullable: false @@ -756,17 +809,21 @@ settings: - _dlt_parent_id - _dlt_list_idx - _dlt_load_id - foreign_key: - - _dlt_parent_id root_key: - _dlt_root_id unique: - _dlt_id + row_key: + - _dlt_id + parent_key: + - _dlt_parent_id normalizers: names: snake_case json: module: dlt.common.normalizers.json.relational previous_hashes: +- JE62zVwqT2T/qHTi2Qdnn2d1A/JzCzyGtDwc+qUmbTs= - 9Ysjq/W0xpxkI/vBiYm8Qbr2nDP3JMt6KvGKUS/FCyI= - NYeAxJ2r+T+dKFnXFhBEPzBP6SO+ORdhOfgQRo/XqBU= - RV9jvZSD5dM+ZGjEL3HqokLvtf22K4zMNc3zWRahEw4= + diff --git a/tests/pipeline/cases/github_pipeline/github_pipeline.py b/tests/pipeline/cases/github_pipeline/github_pipeline.py index f4cdc2bcf2..c9bf2ecebd 100644 --- a/tests/pipeline/cases/github_pipeline/github_pipeline.py +++ b/tests/pipeline/cases/github_pipeline/github_pipeline.py @@ -13,11 +13,12 @@ def convert_dates(item: TDataItem) -> TDataItem: @dlt.source(root_key=True) def github(): - @dlt.resource( + @dlt.resource( # type: ignore table_name="issues", write_disposition="merge", primary_key="id", merge_key=("node_id", "url"), + columns={"assignee": {"data_type": "complex"}}, ) def load_issues( created_at=dlt.sources.incremental[pendulum.DateTime]("created_at"), # noqa: B008 @@ -34,7 +35,7 @@ def load_issues( if __name__ == "__main__": # pick the destination name - if len(sys.argv) < 1: + if len(sys.argv) < 2: raise RuntimeError(f"Please provide destination name in args ({sys.argv})") dest_ = sys.argv[1] if dest_ == "filesystem": diff --git a/tests/pipeline/cases/github_pipeline/github_scd2.py b/tests/pipeline/cases/github_pipeline/github_scd2.py new file mode 100644 index 0000000000..9d70b2129e --- /dev/null +++ b/tests/pipeline/cases/github_pipeline/github_scd2.py @@ -0,0 +1,36 @@ +import sys + +import dlt +from dlt.common import json + + +@dlt.source +def github(): + @dlt.resource( + table_name="issues", + write_disposition={"disposition": "merge", "strategy": "scd2"}, + primary_key="id", + ) + def load_issues(): + # we should be in TEST_STORAGE folder + with open( + "../tests/normalize/cases/github.issues.load_page_5_duck.json", "r", encoding="utf-8" + ) as f: + yield json.load(f) + + return load_issues + + +if __name__ == "__main__": + # get issue numbers to delete + delete_issues = [] + if len(sys.argv) == 2: + delete_issues = [int(p) for p in sys.argv[1].split(",")] + + def filter_issues(issue): + return issue["number"] not in delete_issues + + p = dlt.pipeline("dlt_github_pipeline", destination="duckdb", dataset_name="github_3") + github_source = github() + info = p.run(github_source.load_issues.add_filter(filter_issues)) + print(info) diff --git a/tests/pipeline/conftest.py b/tests/pipeline/conftest.py index f6c47e35b1..2411999dac 100644 --- a/tests/pipeline/conftest.py +++ b/tests/pipeline/conftest.py @@ -4,5 +4,7 @@ patch_home_dir, wipe_pipeline, duckdb_pipeline_location, + test_storage, ) +from tests.common.configuration.utils import environment, toml_providers from tests.pipeline.utils import drop_dataset_from_env diff --git a/tests/pipeline/test_arrow_sources.py b/tests/pipeline/test_arrow_sources.py index 4cdccb1e34..01e55e03e8 100644 --- a/tests/pipeline/test_arrow_sources.py +++ b/tests/pipeline/test_arrow_sources.py @@ -2,8 +2,6 @@ from typing import Any import pytest import pandas as pd -import os -import io import pyarrow as pa import dlt @@ -111,7 +109,7 @@ def some_data(): assert schema_columns["time"]["data_type"] == "time" assert schema_columns["binary"]["data_type"] == "binary" assert schema_columns["string"]["data_type"] == "text" - assert schema_columns["json"]["data_type"] == "complex" + assert schema_columns["json"]["data_type"] == "json" @pytest.mark.parametrize( @@ -236,7 +234,7 @@ def test_load_arrow_vary_schema(item_type: TPythonTableFormat) -> None: pipeline = dlt.pipeline(pipeline_name=pipeline_name, destination="duckdb") item, _, _ = arrow_table_all_data_types(item_type, include_not_normalized_name=False) - pipeline.run(item, table_name="data").raise_on_failed_jobs() + pipeline.run(item, table_name="data") item, _, _ = arrow_table_all_data_types(item_type, include_not_normalized_name=False) # remove int column @@ -246,7 +244,7 @@ def test_load_arrow_vary_schema(item_type: TPythonTableFormat) -> None: names = item.schema.names names.remove("int") item = item.select(names) - pipeline.run(item, table_name="data").raise_on_failed_jobs() + pipeline.run(item, table_name="data") @pytest.mark.parametrize("item_type", ["pandas", "arrow-table", "arrow-batch"]) @@ -310,10 +308,10 @@ def some_data(): assert schema.tables["some_data"]["columns"]["_dlt_id"]["data_type"] == "text" assert schema.tables["some_data"]["columns"]["_dlt_load_id"]["data_type"] == "text" - pipeline.load().raise_on_failed_jobs() + pipeline.load() # should be able to load again - pipeline.run(some_data()).raise_on_failed_jobs() + pipeline.run(some_data()) # should be able to load arrow without a column try: @@ -322,12 +320,12 @@ def some_data(): names = item.schema.names names.remove("int") item = item.select(names) - pipeline.run(item, table_name="some_data").raise_on_failed_jobs() + pipeline.run(item, table_name="some_data") # should be able to load arrow with a new column item, records, _ = arrow_table_all_data_types(item_type, num_rows=200) item = item.append_column("static_int", [[0] * 200]) - pipeline.run(item, table_name="some_data").raise_on_failed_jobs() + pipeline.run(item, table_name="some_data") schema = pipeline.default_schema assert schema.tables["some_data"]["columns"]["static_int"]["data_type"] == "bigint" @@ -380,8 +378,7 @@ def _to_item(table: Any) -> Any: assert normalize_info.row_counts["table"] == 5432 * 3 # load to duckdb - load_info = pipeline.load() - load_info.raise_on_failed_jobs() + pipeline.load() @pytest.mark.parametrize("item_type", ["arrow-table", "pandas", "arrow-batch"]) @@ -423,7 +420,7 @@ def _to_item(table: Any) -> Any: shuffled_names.append("binary") assert actual_tbl.schema.names == shuffled_names - pipeline.load().raise_on_failed_jobs() + pipeline.load() @pytest.mark.parametrize("item_type", ["arrow-table", "pandas", "arrow-batch"]) @@ -475,7 +472,7 @@ def _to_item(table: Any) -> Any: assert len(actual_tbl) == 5432 * 3 assert actual_tbl.schema.names == shuffled_names - pipeline.load().raise_on_failed_jobs() + pipeline.load() @pytest.mark.parametrize("item_type", ["pandas", "arrow-table", "arrow-batch"]) @@ -543,11 +540,11 @@ def test_import_file_with_arrow_schema() -> None: # columns should be created from empty table import_file = "tests/load/cases/loading/header.jsonl" - info = pipeline.run( + pipeline.run( [dlt.mark.with_file_import(import_file, "jsonl", 2, hints=empty_table)], table_name="no_header", ) - info.raise_on_failed_jobs() + assert_only_table_columns(pipeline, "no_header", schema.names) rows = load_tables_to_dicts(pipeline, "no_header") assert len(rows["no_header"]) == 2 diff --git a/tests/pipeline/test_dlt_versions.py b/tests/pipeline/test_dlt_versions.py index 319055184a..98ac7a3728 100644 --- a/tests/pipeline/test_dlt_versions.py +++ b/tests/pipeline/test_dlt_versions.py @@ -18,6 +18,7 @@ from dlt.common.schema.typing import ( LOADS_TABLE_NAME, PIPELINE_STATE_TABLE_NAME, + SCHEMA_ENGINE_VERSION, VERSION_TABLE_NAME, TStoredSchema, ) @@ -27,17 +28,14 @@ from dlt.destinations.impl.duckdb.sql_client import DuckDbSqlClient from tests.pipeline.utils import airtable_emojis, load_table_counts -from tests.utils import TEST_STORAGE_ROOT, test_storage +from tests.utils import TEST_STORAGE_ROOT def test_simulate_default_naming_convention_change() -> None: # checks that (future) change in the naming convention won't affect existing pipelines pipeline = dlt.pipeline("simulated_snake_case", destination="duckdb") assert pipeline.naming.name() == "snake_case" - info = pipeline.run( - airtable_emojis().with_resources("📆 Schedule", "🦚Peacock", "🦚WidePeacock") - ) - info.raise_on_failed_jobs() + pipeline.run(airtable_emojis().with_resources("📆 Schedule", "🦚Peacock", "🦚WidePeacock")) # normalized names assert pipeline.last_trace.last_normalize_info.row_counts["_schedule"] == 3 assert "_schedule" in pipeline.default_schema.tables @@ -45,25 +43,21 @@ def test_simulate_default_naming_convention_change() -> None: # mock the mod # from dlt.common.normalizers import utils - with patch("dlt.common.normalizers.utils.DEFAULT_NAMING_MODULE", "duck_case"): + with patch("dlt.common.schema.normalizers.DEFAULT_NAMING_MODULE", "duck_case"): duck_pipeline = dlt.pipeline("simulated_duck_case", destination="duckdb") assert duck_pipeline.naming.name() == "duck_case" print(airtable_emojis().schema.naming.name()) # run new and old pipelines - info = duck_pipeline.run( + duck_pipeline.run( airtable_emojis().with_resources("📆 Schedule", "🦚Peacock", "🦚WidePeacock") ) - info.raise_on_failed_jobs() print(duck_pipeline.last_trace.last_normalize_info.row_counts) assert duck_pipeline.last_trace.last_normalize_info.row_counts["📆 Schedule"] == 3 assert "📆 Schedule" in duck_pipeline.default_schema.tables # old pipeline should keep its naming convention - info = pipeline.run( - airtable_emojis().with_resources("📆 Schedule", "🦚Peacock", "🦚WidePeacock") - ) - info.raise_on_failed_jobs() + pipeline.run(airtable_emojis().with_resources("📆 Schedule", "🦚Peacock", "🦚WidePeacock")) # normalized names assert pipeline.last_trace.last_normalize_info.row_counts["_schedule"] == 3 assert pipeline.naming.name() == "snake_case" @@ -116,6 +110,11 @@ def test_pipeline_with_dlt_update(test_storage: FileStorage) -> None: "version_hash" not in github_schema["tables"][PIPELINE_STATE_TABLE_NAME]["columns"] } + # make sure that assignees are complex + assert ( + github_schema["tables"]["issues"]["columns"]["assignee"]["data_type"] + == "complex" + ) # check loads table without attaching to pipeline duckdb_cfg = resolve_configuration( DuckDbClientConfiguration()._bind_dataset_name(dataset_name=GITHUB_DATASET), @@ -168,7 +167,10 @@ def test_pipeline_with_dlt_update(test_storage: FileStorage) -> None: f".dlt/pipelines/{GITHUB_PIPELINE_NAME}/schemas/github.schema.json" ) ) - assert github_schema["engine_version"] == 9 + assert github_schema["engine_version"] == SCHEMA_ENGINE_VERSION + assert ( + github_schema["tables"]["issues"]["columns"]["assignee"]["data_type"] == "json" + ) assert "schema_version_hash" in github_schema["tables"][LOADS_TABLE_NAME]["columns"] # print(github_schema["tables"][PIPELINE_STATE_TABLE_NAME]) # load state @@ -281,7 +283,7 @@ def assert_github_pipeline_end_state( pipeline.sync_destination() # print(pipeline.working_dir) # we have updated schema - assert pipeline.default_schema.ENGINE_VERSION == 9 + assert pipeline.default_schema.ENGINE_VERSION == SCHEMA_ENGINE_VERSION # make sure that schema hash retrieved from the destination is exactly the same as the schema hash that was in storage before the schema was wiped assert pipeline.default_schema.stored_version_hash == orig_schema["version_hash"] @@ -340,7 +342,7 @@ def test_load_package_with_dlt_update(test_storage: FileStorage) -> None: ) pipeline = pipeline.drop() pipeline.sync_destination() - assert pipeline.default_schema.ENGINE_VERSION == 9 + assert pipeline.default_schema.ENGINE_VERSION == SCHEMA_ENGINE_VERSION # schema version does not match `dlt.attach` does not update to the right schema by itself assert pipeline.default_schema.stored_version_hash != github_schema["version_hash"] # state has hash @@ -394,3 +396,90 @@ def test_normalize_package_with_dlt_update(test_storage: FileStorage) -> None: # now we can migrate the storage pipeline.normalize() assert pipeline._get_normalize_storage().version == "1.0.1" + + +def test_scd2_pipeline_update(test_storage: FileStorage) -> None: + shutil.copytree("tests/pipeline/cases/github_pipeline", TEST_STORAGE_ROOT, dirs_exist_ok=True) + + # execute in test storage + with set_working_dir(TEST_STORAGE_ROOT): + # store dlt data in test storage (like patch_home_dir) + with custom_environ({DLT_DATA_DIR: get_dlt_data_dir()}): + # save database outside of pipeline dir + with custom_environ( + {"DESTINATION__DUCKDB__CREDENTIALS": "duckdb:///test_github_3.duckdb"} + ): + # run scd2 pipeline on 0.4.10 + venv_dir = tempfile.mkdtemp() + # venv_dir == "tmp/dlt0410" + with Venv.create(venv_dir, ["dlt[duckdb]==0.4.10"]) as venv: + venv._install_deps(venv.context, ["duckdb" + "==" + pkg_version("duckdb")]) + + print(venv.run_script("../tests/pipeline/cases/github_pipeline/github_scd2.py")) + # get data from original db + duckdb_cfg = resolve_configuration( + DuckDbClientConfiguration()._bind_dataset_name(dataset_name=GITHUB_DATASET), + sections=("destination", "duckdb"), + ) + with DuckDbSqlClient( + GITHUB_DATASET, + "%s_staging", + duckdb_cfg.credentials, + duckdb().capabilities(), + ) as client: + issues = client.execute_sql("SELECT * FROM issues ORDER BY id") + issues__assignees = client.execute_sql( + "SELECT * FROM issues__assignees ORDER BY id" + ) + issues__labels = client.execute_sql( + "SELECT * FROM issues__labels ORDER BY id" + ) + + assert len(issues) == 100 + + venv = Venv.restore_current() + # load same data again + print(venv.run_script("../tests/pipeline/cases/github_pipeline/github_scd2.py")) + pipeline = dlt.attach(GITHUB_PIPELINE_NAME) + # unique on row_key got swapped from True to False + assert ( + pipeline.default_schema.tables["issues"]["columns"]["_dlt_id"]["unique"] + is False + ) + # datasets must be the same + with DuckDbSqlClient( + GITHUB_DATASET, + "%s_staging", + duckdb_cfg.credentials, + duckdb().capabilities(), + ) as client: + issues_n = client.execute_sql("SELECT * FROM issues ORDER BY id") + issues__assignees_n = client.execute_sql( + "SELECT * FROM issues__assignees ORDER BY id" + ) + issues__labels_n = client.execute_sql( + "SELECT * FROM issues__labels ORDER BY id" + ) + assert issues == issues_n + assert issues__assignees == issues__assignees_n + assert issues__labels == issues__labels_n + + # retire some ids + print( + venv.run_script( + "../tests/pipeline/cases/github_pipeline/github_scd2.py", "6272" + ) + ) + with DuckDbSqlClient( + GITHUB_DATASET, + "%s_staging", + duckdb_cfg.credentials, + duckdb().capabilities(), + ) as client: + issues_retired = client.execute_sql( + "SELECT number FROM issues WHERE _dlt_valid_to IS NOT NULL" + ) + + assert len(issues_retired) == 1 + assert issues_retired[0][0] == 6272 + # print(pipeline.default_schema.to_pretty_yaml()) diff --git a/tests/pipeline/test_pipeline.py b/tests/pipeline/test_pipeline.py index 027a2b4e72..73125cbd6c 100644 --- a/tests/pipeline/test_pipeline.py +++ b/tests/pipeline/test_pipeline.py @@ -52,8 +52,7 @@ from dlt.pipeline.pipeline import Pipeline from tests.common.utils import TEST_SENTRY_DSN -from tests.common.configuration.utils import environment -from tests.utils import TEST_STORAGE_ROOT, skipifnotwindows +from tests.utils import TEST_STORAGE_ROOT from tests.extract.utils import expect_extracted_file from tests.pipeline.utils import ( assert_data_table_counts, @@ -65,6 +64,8 @@ many_delayed, ) +DUMMY_COMPLETE = dummy(completed_prob=1) # factory set up to complete jobs + def test_default_pipeline() -> None: p = dlt.pipeline() @@ -398,7 +399,7 @@ def test_destination_staging_config(environment: Any) -> None: staging_config = fs_dest.configuration(initial_config) # type: ignore[arg-type] # Ensure that as_staging flag is set in the final resolved conifg - assert staging_config.as_staging is True + assert staging_config.as_staging_destination is True def test_destination_factory_defaults_resolve_from_config(environment: Any) -> None: @@ -635,10 +636,8 @@ def with_table_hints(): def test_restore_state_on_dummy() -> None: - os.environ["COMPLETED_PROB"] = "1.0" # make it complete immediately - pipeline_name = "pipe_" + uniq_id() - p = dlt.pipeline(pipeline_name=pipeline_name, destination="dummy") + p = dlt.pipeline(pipeline_name=pipeline_name, destination=DUMMY_COMPLETE) p.config.restore_from_destination = True info = p.run([1, 2, 3], table_name="dummy_table") print(info) @@ -649,7 +648,7 @@ def test_restore_state_on_dummy() -> None: # wipe out storage p._wipe_working_folder() - p = dlt.pipeline(pipeline_name=pipeline_name, destination="dummy") + p = dlt.pipeline(pipeline_name=pipeline_name, destination=DUMMY_COMPLETE) assert p.first_run is True p.sync_destination() assert p.first_run is True @@ -657,10 +656,8 @@ def test_restore_state_on_dummy() -> None: def test_first_run_flag() -> None: - os.environ["COMPLETED_PROB"] = "1.0" # make it complete immediately - pipeline_name = "pipe_" + uniq_id() - p = dlt.pipeline(pipeline_name=pipeline_name, destination="dummy") + p = dlt.pipeline(pipeline_name=pipeline_name, destination=DUMMY_COMPLETE) assert p.first_run is True # attach p = dlt.attach(pipeline_name=pipeline_name) @@ -668,7 +665,7 @@ def test_first_run_flag() -> None: p.extract([1, 2, 3], table_name="dummy_table") assert p.first_run is True # attach again - p = dlt.attach(pipeline_name=pipeline_name) + p = dlt.attach(pipeline_name=pipeline_name, destination=DUMMY_COMPLETE) assert p.first_run is True assert len(p.list_extracted_load_packages()) > 0 p.normalize() @@ -689,7 +686,7 @@ def test_first_run_flag() -> None: def test_has_pending_data_flag() -> None: - p = dlt.pipeline(pipeline_name="pipe_" + uniq_id(), destination="dummy") + p = dlt.pipeline(pipeline_name="pipe_" + uniq_id(), destination=DUMMY_COMPLETE) assert p.has_pending_data is False p.extract([1, 2, 3], table_name="dummy_table") assert p.has_pending_data is True @@ -702,11 +699,10 @@ def test_has_pending_data_flag() -> None: def test_sentry_tracing() -> None: import sentry_sdk - os.environ["COMPLETED_PROB"] = "1.0" # make it complete immediately os.environ["RUNTIME__SENTRY_DSN"] = TEST_SENTRY_DSN pipeline_name = "pipe_" + uniq_id() - p = dlt.pipeline(pipeline_name=pipeline_name, destination="dummy") + p = dlt.pipeline(pipeline_name=pipeline_name, destination=DUMMY_COMPLETE) # def inspect_transaction(ctx): # print(ctx) @@ -803,7 +799,7 @@ def data_schema_3(): # new pipeline pipeline_name = "pipe_" + uniq_id() - p = dlt.pipeline(pipeline_name=pipeline_name, destination="dummy") + p = dlt.pipeline(pipeline_name=pipeline_name, destination=DUMMY_COMPLETE) with pytest.raises(PipelineStepFailed): p.run([data_schema_1(), data_schema_2(), data_schema_3()], write_disposition="replace") @@ -815,14 +811,12 @@ def data_schema_3(): assert len(p._schema_storage.list_schemas()) == 0 assert p.default_schema_name is None - os.environ["COMPLETED_PROB"] = "1.0" # make it complete immediately p.run([data_schema_1(), data_schema_2()], write_disposition="replace") assert set(p.schema_names) == set(p._schema_storage.list_schemas()) def test_run_with_table_name_exceeding_path_length() -> None: pipeline_name = "pipe_" + uniq_id() - # os.environ["COMPLETED_PROB"] = "1.0" # make it complete immediately p = dlt.pipeline(pipeline_name=pipeline_name) # we must fix that @@ -833,7 +827,6 @@ def test_run_with_table_name_exceeding_path_length() -> None: def test_raise_on_failed_job() -> None: os.environ["FAIL_PROB"] = "1.0" - os.environ["RAISE_ON_FAILED_JOBS"] = "true" pipeline_name = "pipe_" + uniq_id() p = dlt.pipeline(pipeline_name=pipeline_name, destination="dummy") with pytest.raises(PipelineStepFailed) as py_ex: @@ -850,15 +843,17 @@ def test_raise_on_failed_job() -> None: def test_load_info_raise_on_failed_jobs() -> None: + # By default, raises terminal error on a failed job and aborts load. This pipeline does not fail os.environ["COMPLETED_PROB"] = "1.0" pipeline_name = "pipe_" + uniq_id() p = dlt.pipeline(pipeline_name=pipeline_name, destination="dummy") load_info = p.run([1, 2, 3], table_name="numbers") assert load_info.has_failed_jobs is False - load_info.raise_on_failed_jobs() + + # Test explicit raising on a failed job after the load is completed. Let pipeline fail os.environ["COMPLETED_PROB"] = "0.0" os.environ["FAIL_PROB"] = "1.0" - + os.environ["RAISE_ON_FAILED_JOBS"] = "false" load_info = p.run([1, 2, 3], table_name="numbers") assert load_info.has_failed_jobs is True with pytest.raises(DestinationHasFailedJobs) as py_ex: @@ -866,6 +861,7 @@ def test_load_info_raise_on_failed_jobs() -> None: assert py_ex.value.destination_name == "dummy" assert py_ex.value.load_id == load_info.loads_ids[0] + # Test automatic raising on a failed job which aborts the load. Let pipeline fail os.environ["RAISE_ON_FAILED_JOBS"] = "true" with pytest.raises(PipelineStepFailed) as py_ex_2: p.run([1, 2, 3], table_name="numbers") @@ -879,9 +875,8 @@ def test_load_info_raise_on_failed_jobs() -> None: def test_run_load_pending() -> None: # prepare some data and complete load with run - os.environ["COMPLETED_PROB"] = "1.0" pipeline_name = "pipe_" + uniq_id() - p = dlt.pipeline(pipeline_name=pipeline_name, destination="dummy") + p = dlt.pipeline(pipeline_name=pipeline_name, destination=DUMMY_COMPLETE) def some_data(): yield from [1, 2, 3] @@ -910,9 +905,9 @@ def source(): def test_retry_load() -> None: + os.environ["COMPLETED_PROB"] = "1.0" retry_count = 2 - os.environ["COMPLETED_PROB"] = "1.0" pipeline_name = "pipe_" + uniq_id() p = dlt.pipeline(pipeline_name=pipeline_name, destination="dummy") @@ -949,7 +944,6 @@ def fail_extract(): assert py_ex.value.step == "extract" os.environ["COMPLETED_PROB"] = "0.0" - os.environ["RAISE_ON_FAILED_JOBS"] = "true" os.environ["FAIL_PROB"] = "1.0" with pytest.raises(PipelineStepFailed) as py_ex: for attempt in Retrying( @@ -999,9 +993,8 @@ def _w_local_state(): def test_changed_write_disposition() -> None: - os.environ["COMPLETED_PROB"] = "1.0" pipeline_name = "pipe_" + uniq_id() - p = dlt.pipeline(pipeline_name=pipeline_name, destination="dummy") + p = dlt.pipeline(pipeline_name=pipeline_name, destination=DUMMY_COMPLETE) @dlt.resource def resource_1(): @@ -1048,9 +1041,8 @@ def _get_shuffled_events(repeat: int = 1): @pytest.mark.parametrize("github_resource", (github_repo_events_table_meta, github_repo_events)) def test_dispatch_rows_to_tables(github_resource: DltResource): - os.environ["COMPLETED_PROB"] = "1.0" pipeline_name = "pipe_" + uniq_id() - p = dlt.pipeline(pipeline_name=pipeline_name, destination="dummy") + p = dlt.pipeline(pipeline_name=pipeline_name, destination=DUMMY_COMPLETE) info = p.run(_get_shuffled_events | github_resource) assert_load_info(info) @@ -1096,7 +1088,7 @@ def some_source(): return [static_data(), dynamic_func_data(), dynamic_mark_data(), nested_data()] source = some_source() - p = dlt.pipeline(pipeline_name=uniq_id(), destination="dummy") + p = dlt.pipeline(pipeline_name=uniq_id(), destination=DUMMY_COMPLETE) p.run(source) schema = p.default_schema @@ -1378,8 +1370,6 @@ def test_emojis_resource_names() -> None: def test_apply_hints_infer_hints() -> None: - os.environ["COMPLETED_PROB"] = "1.0" - @dlt.source def infer(): yield dlt.resource( @@ -1391,7 +1381,7 @@ def infer(): new_new_hints = {"not_null": ["timestamp"], "primary_key": ["id"]} s = infer() s.schema.merge_hints(new_new_hints) # type: ignore[arg-type] - pipeline = dlt.pipeline(pipeline_name="inf", destination="dummy") + pipeline = dlt.pipeline(pipeline_name="inf", destination=DUMMY_COMPLETE) pipeline.run(s) # check schema table = pipeline.default_schema.get_table("table1") @@ -1440,7 +1430,7 @@ def test_invalid_data_edge_cases() -> None: def my_source(): return dlt.resource(itertools.count(start=1), name="infinity").add_limit(5) - pipeline = dlt.pipeline(pipeline_name="invalid", destination="dummy") + pipeline = dlt.pipeline(pipeline_name="invalid", destination=DUMMY_COMPLETE) with pytest.raises(PipelineStepFailed) as pip_ex: pipeline.run(my_source) assert isinstance(pip_ex.value.__context__, PipeGenInvalid) @@ -1463,7 +1453,7 @@ def res_return(): def my_source_yield(): yield dlt.resource(itertools.count(start=1), name="infinity").add_limit(5) - pipeline = dlt.pipeline(pipeline_name="invalid", destination="dummy") + pipeline = dlt.pipeline(pipeline_name="invalid", destination=DUMMY_COMPLETE) with pytest.raises(PipelineStepFailed) as pip_ex: pipeline.run(my_source_yield) assert isinstance(pip_ex.value.__context__, PipeGenInvalid) @@ -1502,7 +1492,7 @@ def generic(start): assert generic(0).with_name("state2").state["start"] == 20 # NOTE: only one resource will be set in table - assert pipeline.default_schema.get_table("single_table")["resource"] == "state2" + assert pipeline.default_schema.get_table("single_table")["resource"] == "state1" # now load only state1 load_info = pipeline.run( @@ -1736,6 +1726,7 @@ def test_pipeline_list_packages() -> None: assert normalized_package.state == "normalized" assert len(normalized_package.jobs["new_jobs"]) == len(extracted_package.jobs["new_jobs"]) # load all 3 packages and fail all jobs in them + os.environ["RAISE_ON_FAILED_JOBS"] = "false" # do not raise, complete package till the end os.environ["FAIL_PROB"] = "1.0" pipeline.load() load_ids_l = pipeline.list_completed_load_packages() @@ -1748,7 +1739,7 @@ def test_pipeline_list_packages() -> None: def test_remove_pending_packages() -> None: - pipeline = dlt.pipeline(pipeline_name="emojis", destination="dummy") + pipeline = dlt.pipeline(pipeline_name="emojis", destination=DUMMY_COMPLETE) pipeline.extract(airtable_emojis()) assert pipeline.has_pending_data pipeline.drop_pending_packages() @@ -2477,26 +2468,28 @@ def test_import_jsonl_file() -> None: loader_file_format="jsonl", columns=columns, ) - info.raise_on_failed_jobs() print(info) assert_imported_file(pipeline, "no_header", columns, 2) # use hints to infer hints = dlt.mark.make_hints(columns=columns) - info = pipeline.run( + pipeline.run( [dlt.mark.with_file_import(import_file, "jsonl", 2, hints=hints)], table_name="no_header_2", ) - info.raise_on_failed_jobs() assert_imported_file(pipeline, "no_header_2", columns, 2, expects_state=False) def test_import_file_without_sniff_schema() -> None: + os.environ["RAISE_ON_FAILED_JOBS"] = "false" + pipeline = dlt.pipeline( pipeline_name="test_jsonl_import", destination="duckdb", dev_mode=True, ) + + # table will not be found which is terminal exception import_file = "tests/load/cases/loading/header.jsonl" info = pipeline.run( [dlt.mark.with_file_import(import_file, "jsonl", 2)], @@ -2561,6 +2554,77 @@ def test_import_unknown_file_format() -> None: assert isinstance(inner_ex.__cause__, ValueError) +def test_resource_transformer_standalone() -> None: + # requires that standalone resources are executes in a single source + page = 1 + + @dlt.resource(name="pages") + def gen_pages(): + nonlocal page + while True: + yield {"page": page} + if page == 10: + return + page += 1 + + @dlt.transformer(name="subpages") + def get_subpages(page_item): + yield from [ + { + "page": page_item["page"], + "subpage": subpage, + } + for subpage in range(1, 11) + ] + + pipeline = dlt.pipeline("test_resource_transformer_standalone", destination="duckdb") + # here we must combine resources and transformers using the same instance + info = pipeline.run([gen_pages, gen_pages | get_subpages]) + assert_load_info(info) + # this works because we extract transformer and resource above in a single source so dlt optimizes + # dag and extracts gen_pages only once. + assert load_data_table_counts(pipeline) == {"subpages": 100, "pages": 10} + + # for two separate sources we have the following + page = 1 + schema = Schema("test") + info = pipeline.run( + [DltSource(schema, "", [gen_pages]), DltSource(schema, "", [gen_pages | get_subpages])], + dataset_name="new_dataset", + ) + assert_load_info(info, 2) + # ten subpages because only 1 page is extracted in the second source (see gen_pages exit condition) + assert load_data_table_counts(pipeline) == {"subpages": 10, "pages": 10} + + +def test_resources_same_name_in_single_source() -> None: + source_ids: List[int] = [] + + @dlt.resource(name="pages") + def gen_pages(): + page = 0 + # also store id of current source instance + source_ids.append(id(dlt.current.source())) + while True: + yield {"page": page} + if page == 10: + return + page += 1 + + pipeline = dlt.pipeline("test_resources_same_name_in_single_source", destination="duckdb") + info = pipeline.run([gen_pages(), gen_pages()]) + assert_load_info(info) + # two separate sources + assert len(set(source_ids)) == 2 + + # check against different names + source_ids.clear() + info = pipeline.run([gen_pages().with_name("page_1"), gen_pages().with_name("page_2")]) + assert_load_info(info) + # one source + assert len(set(source_ids)) == 1 + + def test_static_staging_dataset() -> None: # share database and staging dataset duckdb_ = dlt.destinations.duckdb( @@ -2729,3 +2793,16 @@ def assert_imported_file( extract_info.metrics[extract_info.loads_ids[0]][0]["table_metrics"][table_name].items_count == expected_rows ) + + +def test_duckdb_column_invalid_timestamp() -> None: + # DuckDB does not have timestamps with timezone and precision, will default to timezone + @dlt.resource( + columns={"event_tstamp": {"data_type": "timestamp", "timezone": True, "precision": 3}}, + primary_key="event_id", + ) + def events(): + yield [{"event_id": 1, "event_tstamp": "2024-07-30T10:00:00.123+00:00"}] + + pipeline = dlt.pipeline(destination="duckdb") + pipeline.run(events()) diff --git a/tests/pipeline/test_pipeline_extra.py b/tests/pipeline/test_pipeline_extra.py index d3e44198b4..821bec8e08 100644 --- a/tests/pipeline/test_pipeline_extra.py +++ b/tests/pipeline/test_pipeline_extra.py @@ -22,6 +22,7 @@ class BaseModel: # type: ignore[no-redef] from dlt.common import json, pendulum from dlt.common.destination import DestinationCapabilitiesContext from dlt.common.destination.capabilities import TLoaderFileFormat +from dlt.destinations.impl.filesystem.filesystem import FilesystemClient from dlt.common.runtime.collector import ( AliveCollector, EnlightenCollector, @@ -33,13 +34,17 @@ class BaseModel: # type: ignore[no-redef] from dlt.extract.storage import ExtractStorage from dlt.extract.validation import PydanticValidator +from dlt.destinations import dummy + from dlt.pipeline import TCollectorArg -from tests.utils import TEST_STORAGE_ROOT, test_storage +from tests.utils import TEST_STORAGE_ROOT from tests.extract.utils import expect_extracted_file from tests.load.utils import DestinationTestConfiguration, destinations_configs from tests.pipeline.utils import assert_load_info, load_data_table_counts, many_delayed +DUMMY_COMPLETE = dummy(completed_prob=1) # factory set up to complete jobs + @pytest.mark.parametrize( "destination_config", @@ -51,8 +56,8 @@ class BaseModel: # type: ignore[no-redef] def test_create_pipeline_all_destinations(destination_config: DestinationTestConfiguration) -> None: # create pipelines, extract and normalize. that should be possible without installing any dependencies p = dlt.pipeline( - pipeline_name=destination_config.destination + "_pipeline", - destination=destination_config.destination, + pipeline_name=destination_config.destination_type + "_pipeline", + destination=destination_config.destination_type, staging=destination_config.staging, ) # are capabilities injected @@ -75,6 +80,8 @@ def test_create_pipeline_all_destinations(destination_config: DestinationTestCon @pytest.mark.parametrize("progress", ["tqdm", "enlighten", "log", "alive_progress"]) def test_pipeline_progress(progress: TCollectorArg) -> None: + # do not raise on failed jobs + os.environ["RAISE_ON_FAILED_JOBS"] = "false" os.environ["TIMEOUT"] = "3.0" p = dlt.pipeline(destination="dummy", progress=progress) @@ -140,7 +147,7 @@ class User(BaseModel): user_label: UserLabel user_labels: List[UserLabel] - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": True} + dlt_config: ClassVar[DltConfig] = {"skip_nested_types": True} user = User( user_id=1, @@ -245,7 +252,6 @@ class TestRow(BaseModel): example_string: str # yield model in resource so incremental fails when looking for "id" - # TODO: support pydantic models in incremental @dlt.resource(name="table_name", primary_key="id", write_disposition="replace") def generate_rows_incremental( @@ -283,11 +289,11 @@ class Child(BaseModel): optional_child_attribute: Optional[str] = None -def test_flattens_model_when_skip_complex_types_is_set() -> None: +def test_flattens_model_when_skip_nested_types_is_set() -> None: class Parent(BaseModel): child: Child optional_parent_attribute: Optional[str] = None - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": True} + dlt_config: ClassVar[DltConfig] = {"skip_nested_types": True} example_data = { "optional_parent_attribute": None, @@ -345,12 +351,12 @@ class Parent(BaseModel): } -def test_considers_model_as_complex_when_skip_complex_types_is_not_set(): +def test_considers_model_as_complex_when_skip_nested_types_is_not_set(): class Parent(BaseModel): child: Child optional_parent_attribute: Optional[str] = None data_dictionary: Dict[str, Any] = None - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": False} + dlt_config: ClassVar[DltConfig] = {"skip_nested_types": False} example_data = { "optional_parent_attribute": None, @@ -374,7 +380,7 @@ class Parent(BaseModel): if col[0] not in ("_dlt_id", "_dlt_load_id") } - # Check if complex fields preserved + # Check if nested fields preserved # their contents and were not flattened assert loaded_values == { "child": '{"child_attribute":"any string","optional_child_attribute":null}', @@ -401,16 +407,16 @@ class Parent(BaseModel): assert columns["data_dictionary"] == { "name": "data_dictionary", - "data_type": "complex", + "data_type": "json", "nullable": False, } -def test_skips_complex_fields_when_skip_complex_types_is_true_and_field_is_not_a_pydantic_model(): +def test_skips_complex_fields_when_skip_nested_types_is_true_and_field_is_not_a_pydantic_model(): class Parent(BaseModel): data_list: List[int] = [] data_dictionary: Dict[str, Any] = None - dlt_config: ClassVar[DltConfig] = {"skip_complex_types": True} + dlt_config: ClassVar[DltConfig] = {"skip_nested_types": True} example_data = { "optional_parent_attribute": None, @@ -528,7 +534,6 @@ def jsonl_data(): assert jsonl_pq.compute_table_schema()["file_format"] == "parquet" info = dlt.pipeline("example", destination="duckdb").run([jsonl_preferred, jsonl_r, jsonl_pq]) - info.raise_on_failed_jobs() # check file types on load jobs load_jobs = { job.job_file_info.table_name: job.job_file_info @@ -542,7 +547,6 @@ def jsonl_data(): csv_r = dlt.resource(jsonl_data, file_format="csv", name="csv_r") assert csv_r.compute_table_schema()["file_format"] == "csv" info = dlt.pipeline("example", destination="duckdb").run(csv_r) - info.raise_on_failed_jobs() # fallback to preferred load_jobs = { job.job_file_info.table_name: job.job_file_info @@ -599,3 +603,82 @@ def test_pick_matching_file_format(test_storage: FileStorage) -> None: files = test_storage.list_folder_files("user_data_csv/object") assert len(files) == 1 assert files[0].endswith("csv") + + +def test_filesystem_column_hint_timezone() -> None: + import pyarrow.parquet as pq + import posixpath + + os.environ["DESTINATION__FILESYSTEM__BUCKET_URL"] = "_storage" + + # talbe: events_timezone_off + @dlt.resource( + columns={"event_tstamp": {"data_type": "timestamp", "timezone": False}}, + primary_key="event_id", + ) + def events_timezone_off(): + yield [ + {"event_id": 1, "event_tstamp": "2024-07-30T10:00:00.123+00:00"}, + {"event_id": 2, "event_tstamp": "2024-07-30T10:00:00.123456+02:00"}, + {"event_id": 3, "event_tstamp": "2024-07-30T10:00:00.123456"}, + ] + + # talbe: events_timezone_on + @dlt.resource( + columns={"event_tstamp": {"data_type": "timestamp", "timezone": True}}, + primary_key="event_id", + ) + def events_timezone_on(): + yield [ + {"event_id": 1, "event_tstamp": "2024-07-30T10:00:00.123+00:00"}, + {"event_id": 2, "event_tstamp": "2024-07-30T10:00:00.123456+02:00"}, + {"event_id": 3, "event_tstamp": "2024-07-30T10:00:00.123456"}, + ] + + # talbe: events_timezone_unset + @dlt.resource( + primary_key="event_id", + ) + def events_timezone_unset(): + yield [ + {"event_id": 1, "event_tstamp": "2024-07-30T10:00:00.123+00:00"}, + {"event_id": 2, "event_tstamp": "2024-07-30T10:00:00.123456+02:00"}, + {"event_id": 3, "event_tstamp": "2024-07-30T10:00:00.123456"}, + ] + + pipeline = dlt.pipeline(destination="filesystem") + + pipeline.run( + [events_timezone_off(), events_timezone_on(), events_timezone_unset()], + loader_file_format="parquet", + ) + + client: FilesystemClient = pipeline.destination_client() # type: ignore[assignment] + + expected_results = { + "events_timezone_off": None, + "events_timezone_on": "UTC", + "events_timezone_unset": "UTC", + } + + for t in expected_results.keys(): + events_glob = posixpath.join(client.dataset_path, f"{t}/*") + events_files = client.fs_client.glob(events_glob) + + with open(events_files[0], "rb") as f: + table = pq.read_table(f) + + # convert the timestamps to strings + timestamps = [ + ts.as_py().strftime("%Y-%m-%dT%H:%M:%S.%f") for ts in table.column("event_tstamp") + ] + assert timestamps == [ + "2024-07-30T10:00:00.123000", + "2024-07-30T08:00:00.123456", + "2024-07-30T10:00:00.123456", + ] + + # check if the Parquet file contains timezone information + schema = table.schema + field = schema.field("event_tstamp") + assert field.type.tz == expected_results[t] diff --git a/tests/pipeline/test_pipeline_trace.py b/tests/pipeline/test_pipeline_trace.py index d2bb035a17..433913851f 100644 --- a/tests/pipeline/test_pipeline_trace.py +++ b/tests/pipeline/test_pipeline_trace.py @@ -17,7 +17,7 @@ from dlt.common.pipeline import ExtractInfo, NormalizeInfo, LoadInfo from dlt.common.schema import Schema from dlt.common.runtime.telemetry import stop_telemetry -from dlt.common.typing import DictStrAny, StrStr, DictStrStr, TSecretValue +from dlt.common.typing import DictStrAny, DictStrStr, TSecretValue from dlt.common.utils import digest128 from dlt.destinations import dummy, filesystem @@ -36,7 +36,6 @@ from tests.pipeline.utils import PIPELINE_TEST_CASES_PATH from tests.utils import TEST_STORAGE_ROOT, start_test_telemetry -from tests.common.configuration.utils import toml_providers, environment def test_create_trace(toml_providers: ConfigProvidersContext, environment: Any) -> None: @@ -116,17 +115,18 @@ def data(): assert resolved.is_secret_hint is False assert resolved.default_value is None assert resolved.provider_name == "config.toml" - # dictionaries are not returned anymore + # dictionaries are not returned anymore, secrets are masked resolved = _find_resolved_value(trace.resolved_config_values, "credentials", []) assert resolved is None or isinstance(resolved.value, str) resolved = _find_resolved_value(trace.resolved_config_values, "secret_value", []) assert resolved.is_secret_hint is True - assert resolved.value == "2137" - assert resolved.default_value == "123" + assert resolved.value is None, "Credential is not masked" + assert resolved.default_value is None, "Credential is not masked" resolved = _find_resolved_value(trace.resolved_config_values, "credentials", ["databricks"]) assert resolved.is_secret_hint is True - assert resolved.value == databricks_creds + assert resolved.value is None, "Credential is not masked" assert_trace_serializable(trace) + # activate pipeline because other was running in assert trace p.activate() @@ -328,8 +328,7 @@ def data(): os.environ["API_TYPE"] = "REST" os.environ["SOURCES__MANY_HINTS__CREDENTIALS"] = "CREDS" - info = pipeline.run([many_hints(), github()]) - info.raise_on_failed_jobs() + pipeline.run([many_hints(), github()]) trace = pipeline.last_trace pipeline._schema_storage.storage.save("trace.json", json.dumps(trace, pretty=True)) @@ -338,8 +337,7 @@ def data(): trace_pipeline = dlt.pipeline( pipeline_name="test_trace_schema_traces", destination=dummy(completed_prob=1.0) ) - info = trace_pipeline.run([trace], table_name="trace", schema=schema) - info.raise_on_failed_jobs() + trace_pipeline.run([trace], table_name="trace", schema=schema) # add exception trace with pytest.raises(PipelineStepFailed): @@ -350,8 +348,7 @@ def data(): "trace_exception.json", json.dumps(trace_exception, pretty=True) ) - info = trace_pipeline.run([trace_exception], table_name="trace") - info.raise_on_failed_jobs() + trace_pipeline.run([trace_exception], table_name="trace") inferred_trace_contract = trace_pipeline.schemas["trace"] inferred_contract_str = inferred_trace_contract.to_pretty_yaml(remove_processing_hints=True) @@ -373,7 +370,7 @@ def data(): contract_trace_pipeline = dlt.pipeline( pipeline_name="test_trace_schema_traces_contract", destination=dummy(completed_prob=1.0) ) - info = contract_trace_pipeline.run( + contract_trace_pipeline.run( [trace_exception, trace], table_name="trace", schema=trace_contract, @@ -517,6 +514,8 @@ def test_trace_telemetry() -> None: SENTRY_SENT_ITEMS.clear() # make dummy fail all files os.environ["FAIL_PROB"] = "1.0" + # but do not raise exceptions + os.environ["RAISE_ON_FAILED_JOBS"] = "false" load_info = dlt.pipeline().run( [1, 2, 3], table_name="data", destination="dummy", dataset_name="data_data" ) @@ -694,7 +693,6 @@ def assert_trace_serializable(trace: PipelineTrace) -> None: from dlt.destinations import duckdb trace_pipeline = dlt.pipeline("trace", destination=duckdb(":pipeline:")).drop() - load_info = trace_pipeline.run([trace], table_name="trace_data") - load_info.raise_on_failed_jobs() + trace_pipeline.run([trace], table_name="trace_data") # print(trace_pipeline.default_schema.to_pretty_yaml()) diff --git a/tests/pipeline/test_schema_contracts.py b/tests/pipeline/test_schema_contracts.py index 4b46bb7c3e..bf48e347c2 100644 --- a/tests/pipeline/test_schema_contracts.py +++ b/tests/pipeline/test_schema_contracts.py @@ -733,7 +733,8 @@ def test_pydantic_contract_implementation(contract_setting: str, as_list: bool) from pydantic import BaseModel class Items(BaseModel): - id: int # noqa: A003 + # for variant test below we must allow allow id to be nullable + id: Optional[int] # noqa: A003 name: str def get_items(as_list: bool = False): diff --git a/tests/pipeline/utils.py b/tests/pipeline/utils.py index dfb5f3f82d..0ae734f72e 100644 --- a/tests/pipeline/utils.py +++ b/tests/pipeline/utils.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Callable, Sequence +from typing import Any, Dict, List, Set, Callable, Sequence import pytest import random from os import environ @@ -6,16 +6,17 @@ import dlt from dlt.common import json, sleep -from dlt.common.destination.exceptions import DestinationUndefinedEntity +from dlt.common.configuration.utils import auto_cast +from dlt.common.data_types import py_type_to_sc_type from dlt.common.pipeline import LoadInfo from dlt.common.schema.utils import get_table_format from dlt.common.typing import DictStrAny from dlt.destinations.impl.filesystem.filesystem import FilesystemClient from dlt.destinations.fs_client import FSClientBase -from dlt.pipeline.exceptions import SqlClientNotAvailable -from dlt.common.storages import FileStorage from dlt.destinations.exceptions import DatabaseUndefinedRelation +from dlt.common.schema.typing import TTableSchema + PIPELINE_TEST_CASES_PATH = "./tests/pipeline/cases/" @@ -147,7 +148,7 @@ def _load_file(client: FSClientBase, filepath) -> List[Dict[str, Any]]: cols = lines[0][15:-2].split(",") for line in lines[2:]: if line: - values = line[1:-3].split(",") + values = map(auto_cast, line[1:-3].split(",")) result.append(dict(zip(cols, values))) # load parquet @@ -420,3 +421,68 @@ def assert_query_data( # the second is load id if info: assert row[1] in info.loads_ids + + +def assert_schema_on_data( + table_schema: TTableSchema, + rows: List[Dict[str, Any]], + requires_nulls: bool, + check_nested: bool, +) -> None: + """Asserts that `rows` conform to `table_schema`. Fields and their order must conform to columns. Null values and + python data types are checked. + """ + table_columns = table_schema["columns"] + columns_with_nulls: Set[str] = set() + for row in rows: + # check columns + assert set(table_schema["columns"].keys()) == set(row.keys()) + # check column order + assert list(table_schema["columns"].keys()) == list(row.keys()) + # check data types + for key, value in row.items(): + print(key) + print(value) + if value is None: + assert table_columns[key][ + "nullable" + ], f"column {key} must be nullable: value is None" + # next value. we cannot validate data type + columns_with_nulls.add(key) + continue + expected_dt = table_columns[key]["data_type"] + # allow json strings + if expected_dt == "json": + if check_nested: + # NOTE: we expect a dict or a list here. simple types of null will fail the test + value = json.loads(value) + else: + # skip checking nested types + continue + actual_dt = py_type_to_sc_type(type(value)) + assert actual_dt == expected_dt + + if requires_nulls: + # make sure that all nullable columns in table received nulls + assert ( + set(col["name"] for col in table_columns.values() if col["nullable"]) + == columns_with_nulls + ), "Some columns didn't receive NULLs which is required" + + +def load_table_distinct_counts( + p: dlt.Pipeline, distinct_column: str, *table_names: str +) -> DictStrAny: + """Returns counts of distinct values for column `distinct_column` for `table_names` as dict""" + with p.sql_client() as c: + query = "\nUNION ALL\n".join( + [ + f"SELECT '{name}' as name, COUNT(DISTINCT {distinct_column}) as c FROM" + f" {c.make_qualified_table_name(name)}" + for name in table_names + ] + ) + + with c.execute_query(query) as cur: + rows = list(cur.fetchall()) + return {r[0]: r[1] for r in rows} diff --git a/tests/sources/conftest.py b/tests/sources/conftest.py new file mode 100644 index 0000000000..89f7cdffed --- /dev/null +++ b/tests/sources/conftest.py @@ -0,0 +1,7 @@ +from tests.utils import ( + preserve_environ, + autouse_test_storage, + patch_home_dir, + wipe_pipeline, + duckdb_pipeline_location, +) diff --git a/tests/sources/filesystem/__init__.py b/tests/sources/filesystem/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/sources/filesystem/test_filesystem_pipeline_template.py b/tests/sources/filesystem/test_filesystem_pipeline_template.py new file mode 100644 index 0000000000..38c51c110c --- /dev/null +++ b/tests/sources/filesystem/test_filesystem_pipeline_template.py @@ -0,0 +1,22 @@ +import pytest + +from tests.common.storages.utils import TEST_SAMPLE_FILES + + +@pytest.mark.parametrize( + "example_name", + ( + "read_custom_file_type_excel", + "stream_and_merge_csv", + "read_csv_with_duckdb", + "read_csv_duckdb_compressed", + "read_parquet_and_jsonl_chunked", + "read_files_incrementally_mtime", + ), +) +def test_all_examples(example_name: str) -> None: + from dlt.sources import filesystem_pipeline + + filesystem_pipeline.TESTS_BUCKET_URL = TEST_SAMPLE_FILES + + getattr(filesystem_pipeline, example_name)() diff --git a/tests/sources/helpers/rest_client/conftest.py b/tests/sources/helpers/rest_client/conftest.py index 10dd23877d..d59df3a4bb 100644 --- a/tests/sources/helpers/rest_client/conftest.py +++ b/tests/sources/helpers/rest_client/conftest.py @@ -1,258 +1 @@ -import base64 -from urllib.parse import parse_qs, urlsplit, urlunsplit, urlencode - -import pytest -import requests_mock - -from dlt.sources.helpers.rest_client import RESTClient - -from .api_router import APIRouter -from .paginators import PageNumberPaginator, OffsetPaginator, CursorPaginator - - -MOCK_BASE_URL = "https://api.example.com" -DEFAULT_PAGE_SIZE = 5 -DEFAULT_TOTAL_PAGES = 5 -DEFAULT_LIMIT = 10 - - -router = APIRouter(MOCK_BASE_URL) - - -def generate_posts(count=DEFAULT_PAGE_SIZE * DEFAULT_TOTAL_PAGES): - return [{"id": i, "title": f"Post {i}"} for i in range(count)] - - -def generate_comments(post_id, count=50): - return [{"id": i, "body": f"Comment {i} for post {post_id}"} for i in range(count)] - - -def get_page_number(qs, key="page", default=1): - return int(qs.get(key, [default])[0]) - - -def create_next_page_url(request, paginator, use_absolute_url=True): - scheme, netloc, path, _, _ = urlsplit(request.url) - query = urlencode(paginator.next_page_url_params) - if use_absolute_url: - return urlunsplit([scheme, netloc, path, query, ""]) - else: - return f"{path}?{query}" - - -def paginate_by_page_number( - request, records, records_key="data", use_absolute_url=True, index_base=1 -): - page_number = get_page_number(request.qs, default=index_base) - paginator = PageNumberPaginator(records, page_number, index_base=index_base) - - response = { - records_key: paginator.page_records, - **paginator.metadata, - } - - if paginator.next_page_url_params: - response["next_page"] = create_next_page_url(request, paginator, use_absolute_url) - - return response - - -@pytest.fixture(scope="module") -def mock_api_server(): - with requests_mock.Mocker() as m: - - @router.get(r"/posts(\?page=\d+)?$") - def posts(request, context): - return paginate_by_page_number(request, generate_posts()) - - @router.get(r"/posts_zero_based(\?page=\d+)?$") - def posts_zero_based(request, context): - return paginate_by_page_number(request, generate_posts(), index_base=0) - - @router.get(r"/posts_header_link(\?page=\d+)?$") - def posts_header_link(request, context): - records = generate_posts() - page_number = get_page_number(request.qs) - paginator = PageNumberPaginator(records, page_number) - - response = paginator.page_records - - if paginator.next_page_url_params: - next_page_url = create_next_page_url(request, paginator) - context.headers["Link"] = f'<{next_page_url}>; rel="next"' - - return response - - @router.get(r"/posts_relative_next_url(\?page=\d+)?$") - def posts_relative_next_url(request, context): - return paginate_by_page_number(request, generate_posts(), use_absolute_url=False) - - @router.get(r"/posts_offset_limit(\?offset=\d+&limit=\d+)?$") - def posts_offset_limit(request, context): - records = generate_posts() - offset = int(request.qs.get("offset", [0])[0]) - limit = int(request.qs.get("limit", [DEFAULT_LIMIT])[0]) - paginator = OffsetPaginator(records, offset, limit) - - return { - "data": paginator.page_records, - **paginator.metadata, - } - - @router.get(r"/posts_cursor(\?cursor=\d+)?$") - def posts_cursor(request, context): - records = generate_posts() - cursor = int(request.qs.get("cursor", [0])[0]) - paginator = CursorPaginator(records, cursor) - - return { - "data": paginator.page_records, - **paginator.metadata, - } - - @router.get(r"/posts/(\d+)/comments") - def post_comments(request, context): - post_id = int(request.url.split("/")[-2]) - return paginate_by_page_number(request, generate_comments(post_id)) - - @router.get(r"/posts/\d+$") - def post_detail(request, context): - post_id = request.url.split("/")[-1] - return {"id": post_id, "body": f"Post body {post_id}"} - - @router.get(r"/posts/\d+/some_details_404") - def post_detail_404(request, context): - """Return 404 for post with id > 0. Used to test ignoring 404 errors.""" - post_id = int(request.url.split("/")[-2]) - if post_id < 1: - return {"id": post_id, "body": f"Post body {post_id}"} - else: - context.status_code = 404 - return {"error": "Post not found"} - - @router.get(r"/posts_under_a_different_key$") - def posts_with_results_key(request, context): - return paginate_by_page_number(request, generate_posts(), records_key="many-results") - - @router.post(r"/posts/search$") - def search_posts(request, context): - body = request.json() - page_size = body.get("page_size", DEFAULT_PAGE_SIZE) - page_number = body.get("page", 1) - - # Simulate a search with filtering - records = generate_posts() - ids_greater_than = body.get("ids_greater_than", 0) - records = [r for r in records if r["id"] > ids_greater_than] - - total_records = len(records) - total_pages = (total_records + page_size - 1) // page_size - start_index = (page_number - 1) * page_size - end_index = start_index + page_size - records_slice = records[start_index:end_index] - - return { - "data": records_slice, - "next_page": page_number + 1 if page_number < total_pages else None, - } - - @router.get("/protected/posts/basic-auth") - def protected_basic_auth(request, context): - auth = request.headers.get("Authorization") - creds = "user:password" - creds_base64 = base64.b64encode(creds.encode()).decode() - if auth == f"Basic {creds_base64}": - return paginate_by_page_number(request, generate_posts()) - context.status_code = 401 - return {"error": "Unauthorized"} - - @router.get("/protected/posts/bearer-token") - def protected_bearer_token(request, context): - auth = request.headers.get("Authorization") - if auth == "Bearer test-token": - return paginate_by_page_number(request, generate_posts()) - context.status_code = 401 - return {"error": "Unauthorized"} - - @router.get("/protected/posts/bearer-token-plain-text-error") - def protected_bearer_token_plain_text_erorr(request, context): - auth = request.headers.get("Authorization") - if auth == "Bearer test-token": - return paginate_by_page_number(request, generate_posts()) - context.status_code = 401 - return "Unauthorized" - - @router.get("/protected/posts/api-key") - def protected_api_key(request, context): - api_key = request.headers.get("x-api-key") - if api_key == "test-api-key": - return paginate_by_page_number(request, generate_posts()) - context.status_code = 401 - return {"error": "Unauthorized"} - - @router.post("/oauth/token") - def oauth_token(request, context): - if oauth_authorize(request): - return {"access_token": "test-token", "expires_in": 3600} - context.status_code = 401 - return {"error": "Unauthorized"} - - @router.post("/oauth/token-expires-now") - def oauth_token_expires_now(request, context): - if oauth_authorize(request): - return {"access_token": "test-token", "expires_in": 0} - context.status_code = 401 - return {"error": "Unauthorized"} - - @router.post("/auth/refresh") - def refresh_token(request, context): - body = request.json() - if body.get("refresh_token") == "valid-refresh-token": - return {"access_token": "new-valid-token"} - context.status_code = 401 - return {"error": "Invalid refresh token"} - - @router.post("/custom-oauth/token") - def custom_oauth_token(request, context): - qs = parse_qs(request.text) - if ( - qs.get("grant_type")[0] == "account_credentials" - and qs.get("account_id")[0] == "test-account-id" - and request.headers["Authorization"] - == "Basic dGVzdC1hY2NvdW50LWlkOnRlc3QtY2xpZW50LXNlY3JldA==" - ): - return {"access_token": "test-token", "expires_in": 3600} - context.status_code = 401 - return {"error": "Unauthorized"} - - router.register_routes(m) - - yield m - - -@pytest.fixture -def rest_client() -> RESTClient: - return RESTClient( - base_url="https://api.example.com", - headers={"Accept": "application/json"}, - ) - - -def oauth_authorize(request): - qs = parse_qs(request.text) - grant_type = qs.get("grant_type")[0] - if "jwt-bearer" in grant_type: - return True - if "client_credentials" in grant_type: - return ( - qs["client_secret"][0] == "test-client-secret" - and qs["client_id"][0] == "test-client-id" - ) - - -def assert_pagination(pages, page_size=DEFAULT_PAGE_SIZE, total_pages=DEFAULT_TOTAL_PAGES): - assert len(pages) == total_pages - for i, page in enumerate(pages): - assert page == [ - {"id": i, "title": f"Post {i}"} for i in range(i * page_size, (i + 1) * page_size) - ] +from tests.sources.rest_api.conftest import * # noqa: F403 diff --git a/tests/sources/helpers/rest_client/test_client.py b/tests/sources/helpers/rest_client/test_client.py index af914bf89d..5ec48e2972 100644 --- a/tests/sources/helpers/rest_client/test_client.py +++ b/tests/sources/helpers/rest_client/test_client.py @@ -77,7 +77,7 @@ class TestRESTClient: def test_get_single_resource(self, rest_client): response = rest_client.get("/posts/1") assert response.status_code == 200 - assert response.json() == {"id": "1", "body": "Post body 1"} + assert response.json() == {"id": 1, "body": "Post body 1"} def test_pagination(self, rest_client: RESTClient): pages_iter = rest_client.paginate( @@ -412,7 +412,7 @@ def update_request(self, request): page_generator = rest_client.paginate( path="/posts/search", method="POST", - json={"ids_greater_than": posts_skip - 1}, + json={"ids_greater_than": posts_skip - 1, "page_size": 5, "page_count": 5}, paginator=JSONBodyPageCursorPaginator(), ) result = [post for page in list(page_generator) for post in page] diff --git a/tests/sources/rest_api/__init__.py b/tests/sources/rest_api/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/sources/rest_api/configurations/__init__.py b/tests/sources/rest_api/configurations/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/sources/rest_api/configurations/source_configs.py b/tests/sources/rest_api/configurations/source_configs.py new file mode 100644 index 0000000000..334bfdd230 --- /dev/null +++ b/tests/sources/rest_api/configurations/source_configs.py @@ -0,0 +1,335 @@ +from collections import namedtuple +from typing import cast, List + +import dlt +from dlt.common.typing import TSecretStrValue +from dlt.common.exceptions import DictValidationException +from dlt.common.configuration.specs import configspec +from dlt.sources.helpers.rest_client.paginators import HeaderLinkPaginator +from dlt.sources.helpers.rest_client.auth import OAuth2AuthBase + +from dlt.sources.helpers.rest_client.paginators import SinglePagePaginator +from dlt.sources.helpers.rest_client.auth import HttpBasicAuth + +from dlt.sources.rest_api.typing import RESTAPIConfig + + +ConfigTest = namedtuple("ConfigTest", ["expected_message", "exception", "config"]) + +INVALID_CONFIGS = [ + ConfigTest( + expected_message="following required fields are missing {'resources'}", + exception=DictValidationException, + config={"client": {"base_url": ""}}, + ), + ConfigTest( + expected_message="following required fields are missing {'client'}", + exception=DictValidationException, + config={"resources": []}, + ), + ConfigTest( + expected_message="In path ./client: following fields are unexpected {'invalid_key'}", + exception=DictValidationException, + config={ + "client": { + "base_url": "https://api.example.com", + "invalid_key": "value", + }, + "resources": ["posts"], + }, + ), + ConfigTest( + expected_message="field 'paginator' with value invalid_paginator is not one of:", + exception=DictValidationException, + config={ + "client": { + "base_url": "https://api.example.com", + "paginator": "invalid_paginator", + }, + "resources": ["posts"], + }, + ), + ConfigTest( + expected_message="issuess", + exception=ValueError, + config={ + "client": {"base_url": "https://github.com/api/v2"}, + "resources": [ + "issues", + { + "name": "comments", + "endpoint": { + "path": "issues/{id}/comments", + "params": { + "id": { + "type": "resolve", + "resource": "issuess", + "field": "id", + }, + }, + }, + }, + ], + }, + ), + ConfigTest( + expected_message="{org}/{repo}/issues/", + exception=ValueError, + config={ + "client": {"base_url": "https://github.com/api/v2"}, + "resources": [ + {"name": "issues", "endpoint": {"path": "{org}/{repo}/issues/"}}, + { + "name": "comments", + "endpoint": { + "path": "{org}/{repo}/issues/{id}/comments", + "params": { + "id": { + "type": "resolve", + "resource": "issues", + "field": "id", + }, + }, + }, + }, + ], + }, + ), +] + + +class CustomPaginator(HeaderLinkPaginator): + def __init__(self) -> None: + super().__init__(links_next_key="prev") + + +@configspec +class CustomOAuthAuth(OAuth2AuthBase): + pass + + +VALID_CONFIGS: List[RESTAPIConfig] = [ + { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + "posts", + { + "name": "post_comments", + "endpoint": { + "path": "posts/{post_id}/comments", + "params": { + "post_id": { + "type": "resolve", + "resource": "posts", + "field": "id", + }, + }, + }, + }, + ], + }, + { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "posts", + "endpoint": { + "path": "posts", + "params": { + "limit": 100, + }, + "paginator": "json_link", + }, + }, + ], + }, + { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "posts", + "endpoint": { + "path": "posts", + "params": { + "limit": 1, + }, + "paginator": SinglePagePaginator(), + }, + }, + ], + }, + { + "client": { + "base_url": "https://example.com", + "auth": {"type": "bearer", "token": "X"}, + }, + "resources": ["users"], + }, + { + "client": { + "base_url": "https://example.com", + "auth": {"token": "X"}, + }, + "resources": ["users"], + }, + { + "client": { + "base_url": "https://example.com", + "paginator": CustomPaginator(), + "auth": CustomOAuthAuth(access_token=cast(TSecretStrValue, "X")), + }, + "resource_defaults": { + "table_name": lambda event: event["type"], + "endpoint": { + "paginator": CustomPaginator(), + "params": {"since": dlt.sources.incremental[str]("user_id")}, + }, + }, + "resources": [ + { + "name": "users", + "endpoint": { + "paginator": CustomPaginator(), + "params": {"since": dlt.sources.incremental[str]("user_id")}, + }, + } + ], + }, + { + "client": { + "base_url": "https://example.com", + "paginator": "header_link", + "auth": HttpBasicAuth("my-secret", cast(TSecretStrValue, "")), + }, + "resources": ["users"], + }, + { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "posts", + "endpoint": { + "path": "posts", + "params": { + "limit": 100, + "since": { + "type": "incremental", + "cursor_path": "updated_at", + "initial_value": "2024-01-25T11:21:28Z", + }, + }, + "paginator": "json_link", + }, + }, + ], + }, + { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "posts", + "endpoint": { + "path": "posts", + "params": { + "limit": 100, + }, + "paginator": "json_link", + "incremental": { + "start_param": "since", + "end_param": "until", + "cursor_path": "updated_at", + "initial_value": "2024-01-25T11:21:28Z", + }, + }, + }, + ], + }, + { + "client": { + "base_url": "https://api.example.com", + "headers": { + "X-Test-Header": "test42", + }, + }, + "resources": [ + "users", + {"name": "users_2"}, + {"name": "users_list", "endpoint": "users_list"}, + ], + }, + { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + "posts", + { + "name": "post_comments", + "table_name": lambda item: item["type"], + "endpoint": { + "path": "posts/{post_id}/comments", + "params": { + "post_id": { + "type": "resolve", + "resource": "posts", + "field": "id", + }, + }, + }, + }, + ], + }, + { + "client": {"base_url": "https://github.com/api/v2"}, + "resources": [ + { + "name": "issues", + "endpoint": { + "path": "{org}/{repo}/issues/", + "params": {"org": "dlt-hub", "repo": "dlt"}, + }, + }, + { + "name": "comments", + "endpoint": { + "path": "{org}/{repo}/issues/{id}/comments", + "params": { + "org": "dlt-hub", + "repo": "dlt", + "id": { + "type": "resolve", + "resource": "issues", + "field": "id", + }, + }, + }, + }, + ], + }, +] + + +# NOTE: leaves some parameters as defaults to test if they are set correctly +PAGINATOR_TYPE_CONFIGS = [ + {"type": "auto"}, + {"type": "single_page"}, + {"type": "page_number", "page": 10, "base_page": 1, "total_path": "response.pages"}, + {"type": "offset", "limit": 100, "maximum_offset": 1000}, + {"type": "header_link", "links_next_key": "next_page"}, + {"type": "json_link", "next_url_path": "response.nex_page_link"}, + {"type": "cursor", "cursor_param": "cursor"}, +] + + +# NOTE: leaves some required parameters to inject them from config +AUTH_TYPE_CONFIGS = [ + {"type": "bearer", "token": "token"}, + {"type": "api_key", "location": "cookie"}, + {"type": "http_basic", "username": "username"}, + { + "type": "oauth2_client_credentials", + "access_token_url": "https://example.com/oauth/token", + "client_id": "a_client_id", + "client_secret": "a_client_secret", + "access_token_request_data": {"foo": "bar"}, + "default_token_expiration": 60, + }, +] diff --git a/tests/sources/rest_api/configurations/test_auth_config.py b/tests/sources/rest_api/configurations/test_auth_config.py new file mode 100644 index 0000000000..4c925c05b1 --- /dev/null +++ b/tests/sources/rest_api/configurations/test_auth_config.py @@ -0,0 +1,311 @@ +import re +from typing import Any, Dict, List, Literal, NamedTuple, Optional, Union, cast, get_args + +import pytest +from requests.auth import AuthBase + +import dlt +import dlt.common +import dlt.common.exceptions +import dlt.extract +from dlt.common.configuration import inject_section +from dlt.common.configuration.specs import ConfigSectionContext +from dlt.common.typing import TSecretStrValue +from dlt.common.utils import custom_environ +from dlt.sources.rest_api import ( + _mask_secrets, + rest_api_source, +) +from dlt.sources.rest_api.config_setup import ( + AUTH_MAP, + create_auth, +) +from dlt.sources.rest_api.typing import ( + AuthConfigBase, + AuthType, + AuthTypeConfig, + RESTAPIConfig, +) + +try: + from dlt.sources.helpers.rest_client.paginators import JSONLinkPaginator +except ImportError: + pass + + +from dlt.sources.helpers.rest_client.auth import ( + APIKeyAuth, + BearerTokenAuth, + HttpBasicAuth, + OAuth2ClientCredentials, +) + +from .source_configs import ( + AUTH_TYPE_CONFIGS, +) + + +@pytest.mark.parametrize("auth_type", get_args(AuthType)) +@pytest.mark.parametrize( + "section", ("SOURCES__REST_API__CREDENTIALS", "SOURCES__CREDENTIALS", "CREDENTIALS") +) +def test_auth_shorthands(auth_type: AuthType, section: str) -> None: + # TODO: remove when changes in rest_client/auth.py are released + if auth_type == "oauth2_client_credentials": + pytest.skip("Waiting for release of changes in rest_client/auth.py") + + # mock all required envs + with custom_environ( + { + f"{section}__TOKEN": "token", + f"{section}__API_KEY": "api_key", + f"{section}__USERNAME": "username", + f"{section}__PASSWORD": "password", + # TODO: uncomment when changes in rest_client/auth.py are released + # f"{section}__ACCESS_TOKEN_URL": "https://example.com/oauth/token", + # f"{section}__CLIENT_ID": "a_client_id", + # f"{section}__CLIENT_SECRET": "a_client_secret", + } + ): + # shorthands need to instantiate from config + with inject_section( + ConfigSectionContext(sections=("sources", "rest_api")), merge_existing=False + ): + import os + + print(os.environ) + auth = create_auth(auth_type) + assert isinstance(auth, AUTH_MAP[auth_type]) + if isinstance(auth, BearerTokenAuth): + assert auth.token == "token" + if isinstance(auth, APIKeyAuth): + assert auth.api_key == "api_key" + assert auth.location == "header" + assert auth.name == "Authorization" + if isinstance(auth, HttpBasicAuth): + assert auth.username == "username" + assert auth.password == "password" + # TODO: uncomment when changes in rest_client/auth.py are released + # if isinstance(auth, OAuth2ClientCredentials): + # assert auth.access_token_url == "https://example.com/oauth/token" + # assert auth.client_id == "a_client_id" + # assert auth.client_secret == "a_client_secret" + # assert auth.default_token_expiration == 3600 + + +@pytest.mark.parametrize("auth_type_config", AUTH_TYPE_CONFIGS) +@pytest.mark.parametrize( + "section", ("SOURCES__REST_API__CREDENTIALS", "SOURCES__CREDENTIALS", "CREDENTIALS") +) +def test_auth_type_configs(auth_type_config: AuthTypeConfig, section: str) -> None: + # mock all required envs + with custom_environ( + { + f"{section}__API_KEY": "api_key", + f"{section}__NAME": "session-cookie", + f"{section}__PASSWORD": "password", + } + ): + # shorthands need to instantiate from config + with inject_section( + ConfigSectionContext(sections=("sources", "rest_api")), merge_existing=False + ): + auth = create_auth(auth_type_config) # type: ignore + assert isinstance(auth, AUTH_MAP[auth_type_config["type"]]) + if isinstance(auth, BearerTokenAuth): + # from typed dict + assert auth.token == "token" + if isinstance(auth, APIKeyAuth): + assert auth.location == "cookie" + # injected + assert auth.api_key == "api_key" + assert auth.name == "session-cookie" + if isinstance(auth, HttpBasicAuth): + # typed dict + assert auth.username == "username" + # injected + assert auth.password == "password" + if isinstance(auth, OAuth2ClientCredentials): + assert auth.access_token_url == "https://example.com/oauth/token" + assert auth.client_id == "a_client_id" + assert auth.client_secret == "a_client_secret" + assert auth.default_token_expiration == 60 + + +@pytest.mark.parametrize( + "section", ("SOURCES__REST_API__CREDENTIALS", "SOURCES__CREDENTIALS", "CREDENTIALS") +) +def test_auth_instance_config(section: str) -> None: + auth = APIKeyAuth(location="param", name="token") + with custom_environ( + { + f"{section}__API_KEY": "api_key", + f"{section}__NAME": "session-cookie", + } + ): + # shorthands need to instantiate from config + with inject_section( + ConfigSectionContext(sections=("sources", "rest_api")), merge_existing=False + ): + # this also resolved configuration + resolved_auth = create_auth(auth) + assert resolved_auth is auth + # explicit + assert auth.location == "param" + # injected + assert auth.api_key == "api_key" + # config overrides explicit (TODO: reverse) + assert auth.name == "session-cookie" + + +def test_bearer_token_fallback() -> None: + auth = create_auth({"token": "secret"}) + assert isinstance(auth, BearerTokenAuth) + assert auth.token == "secret" + + +def test_error_message_invalid_auth_type() -> None: + with pytest.raises(ValueError) as e: + create_auth("non_existing_method") # type: ignore + assert ( + str(e.value) + == "Invalid authentication: non_existing_method." + " Available options: bearer, api_key, http_basic, oauth2_client_credentials." + ) + + +class AuthConfigTest(NamedTuple): + secret_keys: List[Literal["token", "api_key", "password", "username"]] + config: Union[Dict[str, Any], AuthConfigBase] + masked_secrets: Optional[List[str]] = ["s*****t"] + + +AUTH_CONFIGS = [ + AuthConfigTest( + secret_keys=["token"], + config={ + "type": "bearer", + "token": "sensitive-secret", + }, + ), + AuthConfigTest( + secret_keys=["api_key"], + config={ + "type": "api_key", + "api_key": "sensitive-secret", + }, + ), + AuthConfigTest( + secret_keys=["username", "password"], + config={ + "type": "http_basic", + "username": "sensitive-secret", + "password": "sensitive-secret", + }, + masked_secrets=["s*****t", "s*****t"], + ), + AuthConfigTest( + secret_keys=["username", "password"], + config={ + "type": "http_basic", + "username": "", + "password": "sensitive-secret", + }, + masked_secrets=["*****", "s*****t"], + ), + AuthConfigTest( + secret_keys=["username", "password"], + config={ + "type": "http_basic", + "username": "sensitive-secret", + "password": "", + }, + masked_secrets=["s*****t", "*****"], + ), + AuthConfigTest( + secret_keys=["token"], + config=BearerTokenAuth(token=cast(TSecretStrValue, "sensitive-secret")), + ), + AuthConfigTest( + secret_keys=["api_key"], + config=APIKeyAuth(api_key=cast(TSecretStrValue, "sensitive-secret")), + ), + AuthConfigTest( + secret_keys=["username", "password"], + config=HttpBasicAuth("sensitive-secret", cast(TSecretStrValue, "sensitive-secret")), + masked_secrets=["s*****t", "s*****t"], + ), + AuthConfigTest( + secret_keys=["username", "password"], + config=HttpBasicAuth("sensitive-secret", cast(TSecretStrValue, "")), + masked_secrets=["s*****t", "*****"], + ), + AuthConfigTest( + secret_keys=["username", "password"], + config=HttpBasicAuth("", cast(TSecretStrValue, "sensitive-secret")), + masked_secrets=["*****", "s*****t"], + ), +] + + +@pytest.mark.parametrize("secret_keys, config, masked_secrets", AUTH_CONFIGS) +def test_secret_masking_auth_config(secret_keys, config, masked_secrets): + masked = _mask_secrets(config) + for key, mask in zip(secret_keys, masked_secrets): + assert masked[key] == mask # type: ignore[literal-required] + + +def test_secret_masking_oauth() -> None: + config = OAuth2ClientCredentials( + access_token_url=cast(TSecretStrValue, ""), + client_id=cast(TSecretStrValue, "sensitive-secret"), + client_secret=cast(TSecretStrValue, "sensitive-secret"), + ) + + obj = _mask_secrets(config) + assert "sensitive-secret" not in str(obj) + + # TODO + # assert masked.access_token == "None" + # assert masked.client_id == "s*****t" + # assert masked.client_secret == "s*****t" + + +def test_secret_masking_custom_auth() -> None: + class CustomAuthConfigBase(AuthConfigBase): + def __init__(self, token: str = "sensitive-secret"): + self.token = token + + class CustomAuthBase(AuthBase): + def __init__(self, token: str = "sensitive-secret"): + self.token = token + + auth = _mask_secrets(CustomAuthConfigBase()) + assert "s*****t" not in str(auth) + # TODO + # assert auth.token == "s*****t" + + auth_2 = _mask_secrets(CustomAuthBase()) # type: ignore[arg-type] + assert "s*****t" not in str(auth_2) + # TODO + # assert auth_2.token == "s*****t" + + +def test_validation_masks_auth_secrets() -> None: + incorrect_config: RESTAPIConfig = { + "client": { + "base_url": "https://api.example.com", + "auth": { # type: ignore[typeddict-item] + "type": "bearer", + "location": "header", + "token": "sensitive-secret", + }, + }, + "resources": ["posts"], + } + with pytest.raises(dlt.common.exceptions.DictValidationException) as e: + rest_api_source(incorrect_config) + assert ( + re.search("sensitive-secret", str(e.value)) is None + ), "unexpectedly printed 'sensitive-secret'" + assert e.match(re.escape("'{'type': 'bearer', 'location': 'header', 'token': 's*****t'}'")) diff --git a/tests/sources/rest_api/configurations/test_configuration.py b/tests/sources/rest_api/configurations/test_configuration.py new file mode 100644 index 0000000000..0167ea1eb8 --- /dev/null +++ b/tests/sources/rest_api/configurations/test_configuration.py @@ -0,0 +1,403 @@ +from copy import copy +from typing import cast +from unittest.mock import patch + +import pytest + +import dlt +import dlt.common +import dlt.common.exceptions +import dlt.extract +from dlt.common.utils import update_dict_nested +from dlt.sources.helpers.rest_client.paginators import ( + HeaderLinkPaginator, + SinglePagePaginator, +) +from dlt.sources.rest_api import ( + rest_api_resources, + rest_api_source, +) +from dlt.sources.rest_api.config_setup import ( + _make_endpoint_resource, + _merge_resource_endpoints, + _setup_single_entity_endpoint, +) +from dlt.sources.rest_api.typing import ( + Endpoint, + EndpointResource, + EndpointResourceBase, + RESTAPIConfig, +) + +try: + from dlt.sources.helpers.rest_client.paginators import JSONLinkPaginator +except ImportError: + pass + + +from .source_configs import ( + INVALID_CONFIGS, + VALID_CONFIGS, +) + + +@pytest.mark.parametrize("expected_message, exception, invalid_config", INVALID_CONFIGS) +def test_invalid_configurations(expected_message, exception, invalid_config): + with pytest.raises(exception, match=expected_message): + rest_api_source(invalid_config) + + +@pytest.mark.parametrize("valid_config", VALID_CONFIGS) +def test_valid_configurations(valid_config): + rest_api_source(valid_config) + + +@pytest.mark.parametrize("config", VALID_CONFIGS) +def test_configurations_dict_is_not_modified_in_place(config): + # deep clone dicts but do not touch instances of classes so ids still compare + config_copy = update_dict_nested({}, config) + rest_api_source(config) + assert config_copy == config + + +def test_resource_expand() -> None: + # convert str into name / path + assert _make_endpoint_resource("path", {}) == { + "name": "path", + "endpoint": {"path": "path"}, + } + # expand endpoint str into path + assert _make_endpoint_resource({"name": "resource", "endpoint": "path"}, {}) == { + "name": "resource", + "endpoint": {"path": "path"}, + } + # expand name into path with optional endpoint + assert _make_endpoint_resource({"name": "resource"}, {}) == { + "name": "resource", + "endpoint": {"path": "resource"}, + } + # endpoint path is optional + assert _make_endpoint_resource({"name": "resource", "endpoint": {}}, {}) == { + "name": "resource", + "endpoint": {"path": "resource"}, + } + + +def test_resource_endpoint_deep_merge() -> None: + # columns deep merged + resource = _make_endpoint_resource( + { + "name": "resources", + "columns": [ + {"name": "col_a", "data_type": "bigint"}, + {"name": "col_b"}, + ], + }, + { + "columns": [ + {"name": "col_a", "data_type": "text", "primary_key": True}, + {"name": "col_c", "data_type": "timestamp", "partition": True}, + ] + }, + ) + assert resource["columns"] == { + # data_type and primary_key merged + "col_a": {"name": "col_a", "data_type": "bigint", "primary_key": True}, + # from defaults + "col_c": {"name": "col_c", "data_type": "timestamp", "partition": True}, + # from resource (partial column moved to the end) + "col_b": {"name": "col_b"}, + } + # json and params deep merged + resource = _make_endpoint_resource( + { + "name": "resources", + "endpoint": { + "json": {"param1": "A", "param2": "B"}, + "params": {"param1": "A", "param2": "B"}, + }, + }, + { + "endpoint": { + "json": {"param1": "X", "param3": "Y"}, + "params": {"param1": "X", "param3": "Y"}, + } + }, + ) + assert resource["endpoint"] == { + "json": {"param1": "A", "param3": "Y", "param2": "B"}, + "params": {"param1": "A", "param3": "Y", "param2": "B"}, + "path": "resources", + } + + +def test_resource_endpoint_shallow_merge() -> None: + # merge paginators and other typed dicts as whole + resource_config: EndpointResource = { + "name": "resources", + "max_table_nesting": 5, + "write_disposition": {"disposition": "merge", "strategy": "scd2"}, + "schema_contract": {"tables": "freeze"}, + "endpoint": { + "paginator": {"type": "cursor", "cursor_param": "cursor"}, + "incremental": {"cursor_path": "$", "start_param": "since"}, + }, + } + + resource = _make_endpoint_resource( + resource_config, + { + "max_table_nesting": 1, + "parallelized": True, + "write_disposition": { + "disposition": "replace", + }, + "schema_contract": {"columns": "freeze"}, + "endpoint": { + "paginator": { + "type": "header_link", + }, + "incremental": { + "cursor_path": "response.id", + "start_param": "since", + "end_param": "before", + }, + }, + }, + ) + # resource should keep all values, just parallel is added + expected_resource = copy(resource_config) + expected_resource["parallelized"] = True + assert resource == expected_resource + + +def test_resource_merge_with_objects() -> None: + paginator = SinglePagePaginator() + incremental = dlt.sources.incremental[int]("id", row_order="asc") + resource = _make_endpoint_resource( + { + "name": "resource", + "endpoint": { + "path": "path/to", + "paginator": paginator, + "params": {"since": incremental}, + }, + }, + { + "table_name": lambda item: item["type"], + "endpoint": { + "paginator": HeaderLinkPaginator(), + "params": {"since": dlt.sources.incremental[int]("id", row_order="desc")}, + }, + }, + ) + # objects are as is, not cloned + assert resource["endpoint"]["paginator"] is paginator # type: ignore[index] + assert resource["endpoint"]["params"]["since"] is incremental # type: ignore[index] + # callable coming from default + assert callable(resource["table_name"]) + + +def test_resource_merge_with_none() -> None: + endpoint_config: EndpointResource = { + "name": "resource", + "endpoint": {"path": "user/{id}", "paginator": None, "data_selector": None}, + } + # None should be able to reset the default + resource = _make_endpoint_resource( + endpoint_config, + {"endpoint": {"paginator": SinglePagePaginator(), "data_selector": "data"}}, + ) + # nones will overwrite defaults + assert resource == endpoint_config + + +def test_setup_for_single_item_endpoint() -> None: + # single item should revert to single page validator + endpoint = _setup_single_entity_endpoint({"path": "user/{id}"}) + assert endpoint["data_selector"] == "$" + assert isinstance(endpoint["paginator"], SinglePagePaginator) + + # this is not single page + endpoint = _setup_single_entity_endpoint({"path": "user/{id}/messages"}) + assert "data_selector" not in endpoint + + # simulate using None to remove defaults + endpoint_config: EndpointResource = { + "name": "resource", + "endpoint": {"path": "user/{id}", "paginator": None, "data_selector": None}, + } + # None should be able to reset the default + resource = _make_endpoint_resource( + endpoint_config, + {"endpoint": {"paginator": HeaderLinkPaginator(), "data_selector": "data"}}, + ) + + endpoint = _setup_single_entity_endpoint(cast(Endpoint, resource["endpoint"])) + assert endpoint["data_selector"] == "$" + assert isinstance(endpoint["paginator"], SinglePagePaginator) + + +def test_resource_schema() -> None: + config: RESTAPIConfig = { + "client": { + "base_url": "https://api.example.com", + }, + "resources": [ + "users", + { + "name": "user", + "endpoint": { + "path": "user/{id}", + "paginator": None, + "data_selector": None, + "params": { + "id": { + "type": "resolve", + "field": "id", + "resource": "users", + }, + }, + }, + }, + ], + } + resources = rest_api_resources(config) + assert len(resources) == 2 + resource = resources[0] + assert resource.name == "users" + assert resources[1].name == "user" + + +def test_resource_hints_are_passed_to_resource_constructor() -> None: + config: RESTAPIConfig = { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "posts", + "endpoint": { + "params": { + "limit": 100, + }, + }, + "table_name": "a_table", + "max_table_nesting": 2, + "write_disposition": "merge", + "columns": {"a_text": {"name": "a_text", "data_type": "text"}}, + "primary_key": "a_pk", + "merge_key": "a_merge_key", + "schema_contract": {"tables": "evolve"}, + "table_format": "iceberg", + "selected": False, + }, + ], + } + + with patch.object(dlt, "resource", wraps=dlt.resource) as mock_resource_constructor: + rest_api_resources(config) + mock_resource_constructor.assert_called_once() + expected_kwargs = { + "table_name": "a_table", + "max_table_nesting": 2, + "write_disposition": "merge", + "columns": {"a_text": {"name": "a_text", "data_type": "text"}}, + "primary_key": "a_pk", + "merge_key": "a_merge_key", + "schema_contract": {"tables": "evolve"}, + "table_format": "iceberg", + "selected": False, + } + for arg in expected_kwargs.items(): + _, kwargs = mock_resource_constructor.call_args_list[0] + assert arg in kwargs.items() + + +def test_resource_defaults_params_get_merged() -> None: + resource_defaults: EndpointResourceBase = { + "primary_key": "id", + "write_disposition": "merge", + "endpoint": { + "params": { + "per_page": 30, + }, + }, + } + + resource: EndpointResource = { + "endpoint": { + "path": "issues", + "params": { + "sort": "updated", + "direction": "desc", + "state": "open", + }, + }, + } + merged_resource = _merge_resource_endpoints(resource_defaults, resource) + assert merged_resource["endpoint"]["params"]["per_page"] == 30 # type: ignore[index] + + +def test_resource_defaults_params_get_overwritten() -> None: + resource_defaults: EndpointResourceBase = { + "primary_key": "id", + "write_disposition": "merge", + "endpoint": { + "params": { + "per_page": 30, + }, + }, + } + + resource: EndpointResource = { + "endpoint": { + "path": "issues", + "params": { + "per_page": 50, + "sort": "updated", + }, + }, + } + merged_resource = _merge_resource_endpoints(resource_defaults, resource) + assert merged_resource["endpoint"]["params"]["per_page"] == 50 # type: ignore[index] + + +def test_resource_defaults_params_no_resource_params() -> None: + resource_defaults: EndpointResourceBase = { + "primary_key": "id", + "write_disposition": "merge", + "endpoint": { + "params": { + "per_page": 30, + }, + }, + } + + resource: EndpointResource = { + "endpoint": { + "path": "issues", + }, + } + merged_resource = _merge_resource_endpoints(resource_defaults, resource) + assert merged_resource["endpoint"]["params"]["per_page"] == 30 # type: ignore[index] + + +def test_resource_defaults_no_params() -> None: + resource_defaults: EndpointResourceBase = { + "primary_key": "id", + "write_disposition": "merge", + } + + resource: EndpointResource = { + "endpoint": { + "path": "issues", + "params": { + "per_page": 50, + "sort": "updated", + }, + }, + } + merged_resource = _merge_resource_endpoints(resource_defaults, resource) + assert merged_resource["endpoint"]["params"] == { # type: ignore[index] + "per_page": 50, + "sort": "updated", + } diff --git a/tests/sources/rest_api/configurations/test_custom_auth_config.py b/tests/sources/rest_api/configurations/test_custom_auth_config.py new file mode 100644 index 0000000000..1a5a2e58a3 --- /dev/null +++ b/tests/sources/rest_api/configurations/test_custom_auth_config.py @@ -0,0 +1,79 @@ +from base64 import b64encode +from typing import Any, Dict, cast + +import pytest + +from dlt.sources import rest_api +from dlt.sources.helpers.rest_client.auth import APIKeyAuth, OAuth2ClientCredentials +from dlt.sources.rest_api.typing import ApiKeyAuthConfig, AuthConfig + + +class CustomOAuth2(OAuth2ClientCredentials): + def build_access_token_request(self) -> Dict[str, Any]: + """Used e.g. by Zoom Zoom Video Communications, Inc.""" + authentication: str = b64encode(f"{self.client_id}:{self.client_secret}".encode()).decode() + return { + "headers": { + "Authorization": f"Basic {authentication}", + "Content-Type": "application/x-www-form-urlencoded", + }, + "data": self.access_token_request_data, + } + + +class TestCustomAuth: + @pytest.fixture + def custom_auth_config(self) -> AuthConfig: + config: AuthConfig = { + "type": "custom_oauth_2", # type: ignore + "access_token_url": "https://example.com/oauth/token", + "client_id": "test_client_id", + "client_secret": "test_client_secret", + "access_token_request_data": { + "grant_type": "account_credentials", + "account_id": "test_account_id", + }, + } + return config + + def test_creates_builtin_auth_without_registering(self) -> None: + config: ApiKeyAuthConfig = { + "type": "api_key", + "api_key": "test-secret", + "location": "header", + } + auth = cast(APIKeyAuth, rest_api.config_setup.create_auth(config)) + assert auth.api_key == "test-secret" + + def test_not_registering_throws_error(self, custom_auth_config: AuthConfig) -> None: + with pytest.raises(ValueError) as e: + rest_api.config_setup.create_auth(custom_auth_config) + + assert e.match("Invalid authentication: custom_oauth_2.") + + def test_registering_adds_to_AUTH_MAP(self, custom_auth_config: AuthConfig) -> None: + rest_api.config_setup.register_auth("custom_oauth_2", CustomOAuth2) + cls = rest_api.config_setup.get_auth_class("custom_oauth_2") + assert cls is CustomOAuth2 + + # teardown test + del rest_api.config_setup.AUTH_MAP["custom_oauth_2"] + + def test_registering_allows_usage(self, custom_auth_config: AuthConfig) -> None: + rest_api.config_setup.register_auth("custom_oauth_2", CustomOAuth2) + auth = cast(CustomOAuth2, rest_api.config_setup.create_auth(custom_auth_config)) + request = auth.build_access_token_request() + assert request["data"]["account_id"] == "test_account_id" + + # teardown test + del rest_api.config_setup.AUTH_MAP["custom_oauth_2"] + + def test_registering_not_auth_config_base_throws_error(self) -> None: + class NotAuthConfigBase: + pass + + with pytest.raises(ValueError) as e: + rest_api.config_setup.register_auth( + "not_an_auth_config_base", NotAuthConfigBase # type: ignore + ) + assert e.match("Invalid auth: NotAuthConfigBase.") diff --git a/tests/sources/rest_api/configurations/test_custom_paginator_config.py b/tests/sources/rest_api/configurations/test_custom_paginator_config.py new file mode 100644 index 0000000000..f8ac060218 --- /dev/null +++ b/tests/sources/rest_api/configurations/test_custom_paginator_config.py @@ -0,0 +1,69 @@ +from typing import cast + +import pytest + +from dlt.sources import rest_api +from dlt.sources.helpers.rest_client.paginators import JSONLinkPaginator +from dlt.sources.rest_api.typing import PaginatorConfig + + +class CustomPaginator(JSONLinkPaginator): + """A paginator that uses a specific key in the JSON response to find + the next page URL. + """ + + def __init__( + self, + next_url_path="$['@odata.nextLink']", + ): + super().__init__(next_url_path=next_url_path) + + +class TestCustomPaginator: + @pytest.fixture + def custom_paginator_config(self) -> PaginatorConfig: + config: PaginatorConfig = { + "type": "custom_paginator", # type: ignore + "next_url_path": "response.next_page_link", + } + return config + + def teardown_method(self, method): + try: + del rest_api.config_setup.PAGINATOR_MAP["custom_paginator"] + except KeyError: + pass + + def test_creates_builtin_paginator_without_registering(self) -> None: + config: PaginatorConfig = { + "type": "json_response", + "next_url_path": "response.next_page_link", + } + paginator = rest_api.config_setup.create_paginator(config) + assert paginator.has_next_page is True + + def test_not_registering_throws_error(self, custom_paginator_config) -> None: + with pytest.raises(ValueError) as e: + rest_api.config_setup.create_paginator(custom_paginator_config) + + assert e.match("Invalid paginator: custom_paginator.") + + def test_registering_adds_to_PAGINATOR_MAP(self, custom_paginator_config) -> None: + rest_api.config_setup.register_paginator("custom_paginator", CustomPaginator) + cls = rest_api.config_setup.get_paginator_class("custom_paginator") + assert cls is CustomPaginator + + def test_registering_allows_usage(self, custom_paginator_config) -> None: + rest_api.config_setup.register_paginator("custom_paginator", CustomPaginator) + paginator = rest_api.config_setup.create_paginator(custom_paginator_config) + paginator = cast(CustomPaginator, paginator) + assert paginator.has_next_page is True + assert str(paginator.next_url_path) == "response.next_page_link" + + def test_registering_not_base_paginator_throws_error(self) -> None: + class NotAPaginator: + pass + + with pytest.raises(ValueError) as e: + rest_api.config_setup.register_paginator("not_a_paginator", NotAPaginator) # type: ignore[arg-type] + assert e.match("Invalid paginator: NotAPaginator.") diff --git a/tests/sources/rest_api/configurations/test_incremental_config.py b/tests/sources/rest_api/configurations/test_incremental_config.py new file mode 100644 index 0000000000..a374b644df --- /dev/null +++ b/tests/sources/rest_api/configurations/test_incremental_config.py @@ -0,0 +1,352 @@ +import re +import dlt.common +import dlt.common.exceptions +from dlt.common import pendulum + +import dlt.extract +import pytest +from typing import cast + + +import dlt + +from dlt.extract.incremental import Incremental + +from dlt.sources.rest_api import ( + _validate_param_type, + _set_incremental_params, +) + +from dlt.sources.rest_api.config_setup import ( + IncrementalParam, + setup_incremental_object, +) +from dlt.sources.rest_api.typing import ( + IncrementalConfig, +) + +try: + from dlt.sources.helpers.rest_client.paginators import JSONLinkPaginator +except ImportError: + pass + + +@pytest.fixture() +def incremental_with_init_and_end() -> Incremental[str]: + return dlt.sources.incremental( + cursor_path="updated_at", + initial_value="2024-01-01T00:00:00Z", + end_value="2024-06-30T00:00:00Z", + ) + + +@pytest.fixture() +def incremental_with_init() -> Incremental[str]: + return dlt.sources.incremental( + cursor_path="updated_at", + initial_value="2024-01-01T00:00:00Z", + ) + + +def test_invalid_incremental_type_is_not_accepted() -> None: + request_params = { + "foo": "bar", + "since": { + "type": "no_incremental", + "cursor_path": "updated_at", + "initial_value": "2024-01-01T00:00:00Z", + }, + } + with pytest.raises(ValueError) as e: + _validate_param_type(request_params) + + assert e.match("Invalid param type: no_incremental.") + + +def test_one_resource_cannot_have_many_incrementals() -> None: + request_params = { + "foo": "bar", + "first_incremental": { + "type": "incremental", + "cursor_path": "updated_at", + "initial_value": "2024-01-01T00:00:00Z", + }, + "second_incremental": { + "type": "incremental", + "cursor_path": "created_at", + "initial_value": "2024-01-01T00:00:00Z", + }, + } + with pytest.raises(ValueError) as e: + setup_incremental_object(request_params) + error_message = re.escape( + "Only a single incremental parameter is allower per endpoint. Found: ['first_incremental'," + " 'second_incremental']" + ) + assert e.match(error_message) + + +def test_one_resource_cannot_have_many_incrementals_2(incremental_with_init) -> None: + request_params = { + "foo": "bar", + "first_incremental": { + "type": "incremental", + "cursor_path": "created_at", + "initial_value": "2024-02-02T00:00:00Z", + }, + "second_incremental": incremental_with_init, + } + with pytest.raises(ValueError) as e: + setup_incremental_object(request_params) + error_message = re.escape( + "Only a single incremental parameter is allower per endpoint. Found: ['first_incremental'," + " 'second_incremental']" + ) + assert e.match(error_message) + + +def test_constructs_incremental_from_request_param() -> None: + request_params = { + "foo": "bar", + "since": { + "type": "incremental", + "cursor_path": "updated_at", + "initial_value": "2024-01-01T00:00:00Z", + }, + } + (incremental_config, incremental_param, _) = setup_incremental_object(request_params) + assert incremental_config == dlt.sources.incremental( + cursor_path="updated_at", initial_value="2024-01-01T00:00:00Z" + ) + assert incremental_param == IncrementalParam(start="since", end=None) + + +def test_constructs_incremental_from_request_param_with_incremental_object( + incremental_with_init, +) -> None: + request_params = { + "foo": "bar", + "since": dlt.sources.incremental( + cursor_path="updated_at", initial_value="2024-01-01T00:00:00Z" + ), + } + (incremental_obj, incremental_param, _) = setup_incremental_object(request_params) + assert incremental_param == IncrementalParam(start="since", end=None) + + assert incremental_with_init == incremental_obj + + +def test_constructs_incremental_from_request_param_with_convert( + incremental_with_init, +) -> None: + def epoch_to_datetime(epoch: str): + return pendulum.from_timestamp(int(epoch)) + + param_config = { + "since": { + "type": "incremental", + "cursor_path": "updated_at", + "initial_value": "2024-01-01T00:00:00Z", + "convert": epoch_to_datetime, + } + } + + (incremental_obj, incremental_param, convert) = setup_incremental_object(param_config, None) + assert incremental_param == IncrementalParam(start="since", end=None) + assert convert == epoch_to_datetime + + assert incremental_with_init == incremental_obj + + +def test_does_not_construct_incremental_from_request_param_with_unsupported_incremental( + incremental_with_init_and_end, +) -> None: + param_config = { + "since": { + "type": "incremental", + "cursor_path": "updated_at", + "initial_value": "2024-01-01T00:00:00Z", + "end_value": "2024-06-30T00:00:00Z", # This is ignored + } + } + + with pytest.raises(ValueError) as e: + setup_incremental_object(param_config) + + assert e.match( + "Only start_param and initial_value are allowed in the configuration of param: since." + ) + + param_config_2 = { + "since_2": { + "type": "incremental", + "cursor_path": "updated_at", + "initial_value": "2024-01-01T00:00:00Z", + "end_param": "2024-06-30T00:00:00Z", # This is ignored + } + } + + with pytest.raises(ValueError) as e: + setup_incremental_object(param_config_2) + + assert e.match( + "Only start_param and initial_value are allowed in the configuration of param: since_2." + ) + + param_config_3 = {"since_3": incremental_with_init_and_end} + + with pytest.raises(ValueError) as e: + setup_incremental_object(param_config_3) + + assert e.match("Only initial_value is allowed in the configuration of param: since_3.") + + +def test_constructs_incremental_from_endpoint_config_incremental( + incremental_with_init, +) -> None: + config = { + "incremental": { + "start_param": "since", + "end_param": "until", + "cursor_path": "updated_at", + "initial_value": "2024-01-01T00:00:00Z", + } + } + incremental_config = cast(IncrementalConfig, config.get("incremental")) + (incremental_obj, incremental_param, _) = setup_incremental_object( + {}, + incremental_config, + ) + assert incremental_param == IncrementalParam(start="since", end="until") + + assert incremental_with_init == incremental_obj + + +def test_constructs_incremental_from_endpoint_config_incremental_with_convert( + incremental_with_init_and_end, +) -> None: + def epoch_to_datetime(epoch): + return pendulum.from_timestamp(int(epoch)) + + resource_config_incremental: IncrementalConfig = { + "start_param": "since", + "end_param": "until", + "cursor_path": "updated_at", + "initial_value": "2024-01-01T00:00:00Z", + "end_value": "2024-06-30T00:00:00Z", + "convert": epoch_to_datetime, + } + + (incremental_obj, incremental_param, convert) = setup_incremental_object( + {}, resource_config_incremental + ) + assert incremental_param == IncrementalParam(start="since", end="until") + assert convert == epoch_to_datetime + assert incremental_with_init_and_end == incremental_obj + + +def test_calls_convert_from_endpoint_config_incremental(mocker) -> None: + def epoch_to_date(epoch: str): + return pendulum.from_timestamp(int(epoch)).to_date_string() + + callback = mocker.Mock(side_effect=epoch_to_date) + incremental_obj = mocker.Mock() + incremental_obj.last_value = "1" + + incremental_param = IncrementalParam(start="since", end=None) + created_param = _set_incremental_params({}, incremental_obj, incremental_param, callback) + assert created_param == {"since": "1970-01-01"} + assert callback.call_args_list[0].args == ("1",) + + +def test_calls_convert_from_request_param(mocker) -> None: + def epoch_to_datetime(epoch: str): + return pendulum.from_timestamp(int(epoch)).to_date_string() + + callback = mocker.Mock(side_effect=epoch_to_datetime) + start = 1 + one_day_later = 60 * 60 * 24 + incremental_config: IncrementalConfig = { + "start_param": "since", + "end_param": "until", + "cursor_path": "updated_at", + "initial_value": str(start), + "end_value": str(one_day_later), + "convert": callback, + } + + (incremental_obj, incremental_param, _) = setup_incremental_object({}, incremental_config) + assert incremental_param is not None + assert incremental_obj is not None + created_param = _set_incremental_params({}, incremental_obj, incremental_param, callback) + assert created_param == {"since": "1970-01-01", "until": "1970-01-02"} + assert callback.call_args_list[0].args == (str(start),) + assert callback.call_args_list[1].args == (str(one_day_later),) + + +def test_default_convert_is_identity() -> None: + start = 1 + one_day_later = 60 * 60 * 24 + incremental_config: IncrementalConfig = { + "start_param": "since", + "end_param": "until", + "cursor_path": "updated_at", + "initial_value": str(start), + "end_value": str(one_day_later), + } + + (incremental_obj, incremental_param, _) = setup_incremental_object({}, incremental_config) + assert incremental_param is not None + assert incremental_obj is not None + created_param = _set_incremental_params({}, incremental_obj, incremental_param, None) + assert created_param == {"since": str(start), "until": str(one_day_later)} + + +def test_incremental_param_transform_is_deprecated(incremental_with_init) -> None: + """Tests that deprecated interface works but issues deprecation warning""" + + def epoch_to_datetime(epoch: str): + return pendulum.from_timestamp(int(epoch)) + + param_config = { + "since": { + "type": "incremental", + "cursor_path": "updated_at", + "initial_value": "2024-01-01T00:00:00Z", + "transform": epoch_to_datetime, + } + } + + with pytest.deprecated_call(): + (incremental_obj, incremental_param, convert) = setup_incremental_object(param_config, None) + + assert incremental_param == IncrementalParam(start="since", end=None) + assert convert == epoch_to_datetime + + assert incremental_with_init == incremental_obj + + +def test_incremental_endpoint_config_transform_is_deprecated( + incremental_with_init_and_end, +) -> None: + """Tests that deprecated interface works but issues deprecation warning""" + + def epoch_to_datetime(epoch): + return pendulum.from_timestamp(int(epoch)) + + resource_config_incremental: IncrementalConfig = { + "start_param": "since", + "end_param": "until", + "cursor_path": "updated_at", + "initial_value": "2024-01-01T00:00:00Z", + "end_value": "2024-06-30T00:00:00Z", + "transform": epoch_to_datetime, # type: ignore[typeddict-unknown-key] + } + + with pytest.deprecated_call(): + (incremental_obj, incremental_param, convert) = setup_incremental_object( + {}, resource_config_incremental + ) + assert incremental_param == IncrementalParam(start="since", end="until") + assert convert == epoch_to_datetime + assert incremental_with_init_and_end == incremental_obj diff --git a/tests/sources/rest_api/configurations/test_paginator_config.py b/tests/sources/rest_api/configurations/test_paginator_config.py new file mode 100644 index 0000000000..6513daf15c --- /dev/null +++ b/tests/sources/rest_api/configurations/test_paginator_config.py @@ -0,0 +1,161 @@ +from typing import get_args + +import pytest + +import dlt +import dlt.common +import dlt.common.exceptions +import dlt.extract +from dlt.common.jsonpath import compile_path +from dlt.sources.helpers.rest_client.paginators import ( + HeaderLinkPaginator, + JSONResponseCursorPaginator, + JSONResponsePaginator, + OffsetPaginator, + PageNumberPaginator, +) +from dlt.sources.rest_api import ( + rest_api_source, +) +from dlt.sources.rest_api.config_setup import ( + PAGINATOR_MAP, + create_paginator, +) +from dlt.sources.rest_api.typing import ( + PaginatorConfig, + PaginatorType, + RESTAPIConfig, +) + +try: + from dlt.sources.helpers.rest_client.paginators import JSONLinkPaginator +except ImportError: + from dlt.sources.helpers.rest_client.paginators import ( + JSONResponsePaginator as JSONLinkPaginator, + ) + + +from .source_configs import ( + PAGINATOR_TYPE_CONFIGS, +) + + +@pytest.mark.parametrize("paginator_type", get_args(PaginatorType)) +def test_paginator_shorthands(paginator_type: PaginatorConfig) -> None: + try: + create_paginator(paginator_type) + except ValueError as v_ex: + # offset paginator cannot be instantiated + assert paginator_type == "offset" + assert "offset" in str(v_ex) + + +@pytest.mark.parametrize("paginator_type_config", PAGINATOR_TYPE_CONFIGS) +def test_paginator_type_configs(paginator_type_config: PaginatorConfig) -> None: + paginator = create_paginator(paginator_type_config) + if paginator_type_config["type"] == "auto": # type: ignore[index] + assert paginator is None + else: + # assert types and default params + assert isinstance(paginator, PAGINATOR_MAP[paginator_type_config["type"]]) # type: ignore[index] + # check if params are bound + if isinstance(paginator, HeaderLinkPaginator): + assert paginator.links_next_key == "next_page" + if isinstance(paginator, PageNumberPaginator): + assert paginator.current_value == 10 + assert paginator.base_index == 1 + assert paginator.param_name == "page" + assert paginator.total_path == compile_path("response.pages") + assert paginator.maximum_value is None + if isinstance(paginator, OffsetPaginator): + assert paginator.current_value == 0 + assert paginator.param_name == "offset" + assert paginator.limit == 100 + assert paginator.limit_param == "limit" + assert paginator.total_path == compile_path("total") + assert paginator.maximum_value == 1000 + if isinstance(paginator, JSONLinkPaginator): + assert paginator.next_url_path == compile_path("response.nex_page_link") + if isinstance(paginator, JSONResponseCursorPaginator): + assert paginator.cursor_path == compile_path("cursors.next") + assert paginator.cursor_param == "cursor" + + +def test_paginator_instance_config() -> None: + paginator = OffsetPaginator(limit=100) + assert create_paginator(paginator) is paginator + + +def test_page_number_paginator_creation() -> None: + config: RESTAPIConfig = { + "client": { + "base_url": "https://api.example.com", + "paginator": { + "type": "page_number", + "page_param": "foobar", + "total_path": "response.pages", + "base_page": 1, + "maximum_page": 5, + }, + }, + "resources": ["posts"], + } + try: + rest_api_source(config) + except dlt.common.exceptions.DictValidationException: + pytest.fail("DictValidationException was unexpectedly raised") + + +def test_allow_deprecated_json_response_paginator(mock_api_server) -> None: + """ + Delete this test as soon as we stop supporting the deprecated key json_response + for the JSONLinkPaginator + """ + config: RESTAPIConfig = { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "posts", + "endpoint": { + "path": "posts", + "paginator": { + "type": "json_response", + "next_url_path": "links.next", + }, + }, + }, + ], + } + + rest_api_source(config) + + +def test_allow_deprecated_json_response_paginator_2(mock_api_server) -> None: + """ + Delete this test as soon as we stop supporting the deprecated key json_response + for the JSONLinkPaginator + """ + config: RESTAPIConfig = { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "posts", + "endpoint": { + "path": "posts", + "paginator": JSONResponsePaginator(next_url_path="links.next"), + }, + }, + ], + } + + rest_api_source(config) + + +def test_error_message_invalid_paginator() -> None: + with pytest.raises(ValueError) as e: + create_paginator("non_existing_method") # type: ignore + assert ( + str(e.value) + == "Invalid paginator: non_existing_method. Available options: json_link, json_response," + " header_link, auto, single_page, cursor, offset, page_number." + ) diff --git a/tests/sources/rest_api/configurations/test_resolve_config.py b/tests/sources/rest_api/configurations/test_resolve_config.py new file mode 100644 index 0000000000..a0ca7ce890 --- /dev/null +++ b/tests/sources/rest_api/configurations/test_resolve_config.py @@ -0,0 +1,332 @@ +import re +from copy import deepcopy + +import pytest +from graphlib import CycleError # type: ignore + +from dlt.sources.rest_api import ( + rest_api_resources, + rest_api_source, +) +from dlt.sources.rest_api.config_setup import ( + _bind_path_params, + process_parent_data_item, +) +from dlt.sources.rest_api.typing import ( + EndpointResource, + ResolvedParam, + RESTAPIConfig, +) + +try: + from dlt.sources.helpers.rest_client.paginators import JSONLinkPaginator +except ImportError: + from dlt.sources.helpers.rest_client.paginators import ( + JSONResponsePaginator as JSONLinkPaginator, + ) + + +try: + from dlt.sources.helpers.rest_client.paginators import JSONLinkPaginator +except ImportError: + pass + + +def test_bind_path_param() -> None: + three_params: EndpointResource = { + "name": "comments", + "endpoint": { + "path": "{org}/{repo}/issues/{id}/comments", + "params": { + "org": "dlt-hub", + "repo": "dlt", + "id": { + "type": "resolve", + "field": "id", + "resource": "issues", + }, + }, + }, + } + tp_1 = deepcopy(three_params) + _bind_path_params(tp_1) + + # do not replace resolved params + assert tp_1["endpoint"]["path"] == "dlt-hub/dlt/issues/{id}/comments" # type: ignore[index] + # bound params popped + assert len(tp_1["endpoint"]["params"]) == 1 # type: ignore[index] + assert "id" in tp_1["endpoint"]["params"] # type: ignore[index] + + tp_2 = deepcopy(three_params) + tp_2["endpoint"]["params"]["id"] = 12345 # type: ignore[index] + _bind_path_params(tp_2) + assert tp_2["endpoint"]["path"] == "dlt-hub/dlt/issues/12345/comments" # type: ignore[index] + assert len(tp_2["endpoint"]["params"]) == 0 # type: ignore[index] + + # param missing + tp_3 = deepcopy(three_params) + with pytest.raises(ValueError) as val_ex: + del tp_3["endpoint"]["params"]["id"] # type: ignore[index, union-attr] + _bind_path_params(tp_3) + # path is a part of an exception + assert tp_3["endpoint"]["path"] in str(val_ex.value) # type: ignore[index] + + # path without params + tp_4 = deepcopy(three_params) + tp_4["endpoint"]["path"] = "comments" # type: ignore[index] + # no unbound params + del tp_4["endpoint"]["params"]["id"] # type: ignore[index, union-attr] + tp_5 = deepcopy(tp_4) + _bind_path_params(tp_4) + assert tp_4 == tp_5 + + # resolved param will remain unbounded and + tp_6 = deepcopy(three_params) + tp_6["endpoint"]["path"] = "{org}/{repo}/issues/1234/comments" # type: ignore[index] + with pytest.raises(NotImplementedError): + _bind_path_params(tp_6) + + +def test_process_parent_data_item() -> None: + resolve_param = ResolvedParam( + "id", {"field": "obj_id", "resource": "issues", "type": "resolve"} + ) + bound_path, parent_record = process_parent_data_item( + "dlt-hub/dlt/issues/{id}/comments", {"obj_id": 12345}, resolve_param, None + ) + assert bound_path == "dlt-hub/dlt/issues/12345/comments" + assert parent_record == {} + + bound_path, parent_record = process_parent_data_item( + "dlt-hub/dlt/issues/{id}/comments", {"obj_id": 12345}, resolve_param, ["obj_id"] + ) + assert parent_record == {"_issues_obj_id": 12345} + + bound_path, parent_record = process_parent_data_item( + "dlt-hub/dlt/issues/{id}/comments", + {"obj_id": 12345, "obj_node": "node_1"}, + resolve_param, + ["obj_id", "obj_node"], + ) + assert parent_record == {"_issues_obj_id": 12345, "_issues_obj_node": "node_1"} + + # test nested data + resolve_param_nested = ResolvedParam( + "id", {"field": "some_results.obj_id", "resource": "issues", "type": "resolve"} + ) + item = {"some_results": {"obj_id": 12345}} + bound_path, parent_record = process_parent_data_item( + "dlt-hub/dlt/issues/{id}/comments", item, resolve_param_nested, None + ) + assert bound_path == "dlt-hub/dlt/issues/12345/comments" + + # param path not found + with pytest.raises(ValueError) as val_ex: + bound_path, parent_record = process_parent_data_item( + "dlt-hub/dlt/issues/{id}/comments", {"_id": 12345}, resolve_param, None + ) + assert "Transformer expects a field 'obj_id'" in str(val_ex.value) + + # included path not found + with pytest.raises(ValueError) as val_ex: + bound_path, parent_record = process_parent_data_item( + "dlt-hub/dlt/issues/{id}/comments", + {"obj_id": 12345, "obj_node": "node_1"}, + resolve_param, + ["obj_id", "node"], + ) + assert "in order to include it in child records under _issues_node" in str(val_ex.value) + + +def test_two_resources_can_depend_on_one_parent_resource() -> None: + user_id = { + "user_id": { + "type": "resolve", + "field": "id", + "resource": "users", + } + } + config: RESTAPIConfig = { + "client": { + "base_url": "https://api.example.com", + }, + "resources": [ + "users", + { + "name": "user_details", + "endpoint": { + "path": "user/{user_id}/", + "params": user_id, # type: ignore[typeddict-item] + }, + }, + { + "name": "meetings", + "endpoint": { + "path": "meetings/{user_id}/", + "params": user_id, # type: ignore[typeddict-item] + }, + }, + ], + } + resources = rest_api_source(config).resources + assert resources["meetings"]._pipe.parent.name == "users" + assert resources["user_details"]._pipe.parent.name == "users" + + +def test_dependent_resource_cannot_bind_multiple_parameters() -> None: + config: RESTAPIConfig = { + "client": { + "base_url": "https://api.example.com", + }, + "resources": [ + "users", + { + "name": "user_details", + "endpoint": { + "path": "user/{user_id}/{group_id}", + "params": { + "user_id": { + "type": "resolve", + "field": "id", + "resource": "users", + }, + "group_id": { + "type": "resolve", + "field": "group", + "resource": "users", + }, + }, + }, + }, + ], + } + with pytest.raises(ValueError) as e: + rest_api_resources(config) + + error_part_1 = re.escape( + "Multiple resolved params for resource user_details: [ResolvedParam(param_name='user_id'" + ) + error_part_2 = re.escape("ResolvedParam(param_name='group_id'") + assert e.match(error_part_1) + assert e.match(error_part_2) + + +def test_one_resource_cannot_bind_two_parents() -> None: + config: RESTAPIConfig = { + "client": { + "base_url": "https://api.example.com", + }, + "resources": [ + "users", + "groups", + { + "name": "user_details", + "endpoint": { + "path": "user/{user_id}/{group_id}", + "params": { + "user_id": { + "type": "resolve", + "field": "id", + "resource": "users", + }, + "group_id": { + "type": "resolve", + "field": "id", + "resource": "groups", + }, + }, + }, + }, + ], + } + + with pytest.raises(ValueError) as e: + rest_api_resources(config) + + error_part_1 = re.escape( + "Multiple resolved params for resource user_details: [ResolvedParam(param_name='user_id'" + ) + error_part_2 = re.escape("ResolvedParam(param_name='group_id'") + assert e.match(error_part_1) + assert e.match(error_part_2) + + +def test_resource_dependent_dependent() -> None: + config: RESTAPIConfig = { + "client": { + "base_url": "https://api.example.com", + }, + "resources": [ + "locations", + { + "name": "location_details", + "endpoint": { + "path": "location/{location_id}", + "params": { + "location_id": { + "type": "resolve", + "field": "id", + "resource": "locations", + }, + }, + }, + }, + { + "name": "meetings", + "endpoint": { + "path": "/meetings/{room_id}", + "params": { + "room_id": { + "type": "resolve", + "field": "room_id", + "resource": "location_details", + }, + }, + }, + }, + ], + } + + resources = rest_api_source(config).resources + assert resources["meetings"]._pipe.parent.name == "location_details" + assert resources["location_details"]._pipe.parent.name == "locations" + + +def test_circular_resource_bindingis_invalid() -> None: + config: RESTAPIConfig = { + "client": { + "base_url": "https://api.example.com", + }, + "resources": [ + { + "name": "chicken", + "endpoint": { + "path": "chicken/{egg_id}/", + "params": { + "egg_id": { + "type": "resolve", + "field": "id", + "resource": "egg", + }, + }, + }, + }, + { + "name": "egg", + "endpoint": { + "path": "egg/{chicken_id}/", + "params": { + "chicken_id": { + "type": "resolve", + "field": "id", + "resource": "chicken", + }, + }, + }, + }, + ], + } + + with pytest.raises(CycleError) as e: + rest_api_resources(config) + assert e.match(re.escape("'nodes are in a cycle', ['chicken', 'egg', 'chicken']")) diff --git a/tests/sources/rest_api/configurations/test_response_actions_config.py b/tests/sources/rest_api/configurations/test_response_actions_config.py new file mode 100644 index 0000000000..c9889b1e09 --- /dev/null +++ b/tests/sources/rest_api/configurations/test_response_actions_config.py @@ -0,0 +1,138 @@ +import pytest +from typing import List + +from dlt.sources.rest_api import ( + rest_api_source, +) + +from dlt.sources.rest_api.config_setup import ( + create_response_hooks, + _handle_response_action, +) +from dlt.sources.rest_api.typing import ( + RESTAPIConfig, + ResponseAction, +) + +try: + from dlt.sources.helpers.rest_client.paginators import JSONLinkPaginator +except ImportError: + pass + + +def test_create_multiple_response_actions(): + def custom_hook(response, *args, **kwargs): + return response + + response_actions: List[ResponseAction] = [ + custom_hook, + {"status_code": 404, "action": "ignore"}, + {"content": "Not found", "action": "ignore"}, + {"status_code": 200, "content": "some text", "action": "ignore"}, + ] + hooks = create_response_hooks(response_actions) + assert len(hooks["response"]) == 4 + + response_actions_2: List[ResponseAction] = [ + custom_hook, + {"status_code": 200, "action": custom_hook}, + ] + hooks_2 = create_response_hooks(response_actions_2) + assert len(hooks_2["response"]) == 2 + + +def test_response_action_raises_type_error(mocker): + class C: + pass + + response = mocker.Mock() + response.status_code = 200 + + with pytest.raises(ValueError) as e_1: + _handle_response_action(response, {"status_code": 200, "action": C()}) # type: ignore[typeddict-item] + assert e_1.match("does not conform to expected type") + + with pytest.raises(ValueError) as e_2: + _handle_response_action(response, {"status_code": 200, "action": 123}) # type: ignore[typeddict-item] + assert e_2.match("does not conform to expected type") + + assert ("ignore", None) == _handle_response_action( + response, {"status_code": 200, "action": "ignore"} + ) + assert ("foobar", None) == _handle_response_action( + response, {"status_code": 200, "action": "foobar"} + ) + + +def test_parses_hooks_from_response_actions(mocker): + response = mocker.Mock() + response.status_code = 200 + + hook_1 = mocker.Mock() + hook_2 = mocker.Mock() + + assert (None, [hook_1]) == _handle_response_action( + response, {"status_code": 200, "action": hook_1} + ) + assert (None, [hook_1, hook_2]) == _handle_response_action( + response, {"status_code": 200, "action": [hook_1, hook_2]} + ) + + +def test_config_validation_for_response_actions(mocker): + mock_response_hook_1 = mocker.Mock() + mock_response_hook_2 = mocker.Mock() + config_1: RESTAPIConfig = { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "posts", + "endpoint": { + "response_actions": [ + { + "status_code": 200, + "action": mock_response_hook_1, + }, + ], + }, + }, + ], + } + + rest_api_source(config_1) + + config_2: RESTAPIConfig = { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "posts", + "endpoint": { + "response_actions": [ + mock_response_hook_1, + mock_response_hook_2, + ], + }, + }, + ], + } + + rest_api_source(config_2) + + config_3: RESTAPIConfig = { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "posts", + "endpoint": { + "response_actions": [ + { + "status_code": 200, + "action": [mock_response_hook_1, mock_response_hook_2], + }, + ], + }, + }, + ], + } + + rest_api_source(config_3) diff --git a/tests/sources/rest_api/conftest.py b/tests/sources/rest_api/conftest.py new file mode 100644 index 0000000000..8ef4e41255 --- /dev/null +++ b/tests/sources/rest_api/conftest.py @@ -0,0 +1,270 @@ +import base64 +from urllib.parse import parse_qs, urlsplit, urlunsplit, urlencode + +import pytest +import requests_mock + +from dlt.sources.helpers.rest_client import RESTClient + +from tests.sources.helpers.rest_client.api_router import APIRouter +from tests.sources.helpers.rest_client.paginators import ( + PageNumberPaginator, + OffsetPaginator, + CursorPaginator, +) + + +MOCK_BASE_URL = "https://api.example.com" +DEFAULT_PAGE_SIZE = 5 +DEFAULT_TOTAL_PAGES = 5 +DEFAULT_LIMIT = 10 + + +router = APIRouter(MOCK_BASE_URL) + + +def generate_posts(count=DEFAULT_PAGE_SIZE * DEFAULT_TOTAL_PAGES): + return [{"id": i, "title": f"Post {i}"} for i in range(count)] + + +def generate_comments(post_id, count=50): + return [ + {"id": i, "post_id": post_id, "body": f"Comment {i} for post {post_id}"} + for i in range(count) + ] + + +def get_page_number(qs, key="page", default=1): + return int(qs.get(key, [default])[0]) + + +def create_next_page_url(request, paginator, use_absolute_url=True): + scheme, netloc, path, _, _ = urlsplit(request.url) + query = urlencode(paginator.next_page_url_params) + if use_absolute_url: + return urlunsplit([scheme, netloc, path, query, ""]) + else: + return f"{path}?{query}" + + +def paginate_by_page_number( + request, records, records_key="data", use_absolute_url=True, index_base=1 +): + page_number = get_page_number(request.qs, default=index_base) + paginator = PageNumberPaginator(records, page_number, index_base=index_base) + + response = { + records_key: paginator.page_records, + **paginator.metadata, + } + + if paginator.next_page_url_params: + response["next_page"] = create_next_page_url(request, paginator, use_absolute_url) + + return response + + +@pytest.fixture(scope="module") +def mock_api_server(): + with requests_mock.Mocker() as m: + + @router.get(r"/posts_no_key(\?page=\d+)?$") + def posts_no_key(request, context): + return paginate_by_page_number(request, generate_posts(), records_key=None) + + @router.get(r"/posts(\?page=\d+)?$") + def posts(request, context): + return paginate_by_page_number(request, generate_posts()) + + @router.get(r"/posts_zero_based(\?page=\d+)?$") + def posts_zero_based(request, context): + return paginate_by_page_number(request, generate_posts(), index_base=0) + + @router.get(r"/posts_header_link(\?page=\d+)?$") + def posts_header_link(request, context): + records = generate_posts() + page_number = get_page_number(request.qs) + paginator = PageNumberPaginator(records, page_number) + + response = paginator.page_records + + if paginator.next_page_url_params: + next_page_url = create_next_page_url(request, paginator) + context.headers["Link"] = f'<{next_page_url}>; rel="next"' + + return response + + @router.get(r"/posts_relative_next_url(\?page=\d+)?$") + def posts_relative_next_url(request, context): + return paginate_by_page_number(request, generate_posts(), use_absolute_url=False) + + @router.get(r"/posts_offset_limit(\?offset=\d+&limit=\d+)?$") + def posts_offset_limit(request, context): + records = generate_posts() + offset = int(request.qs.get("offset", [0])[0]) + limit = int(request.qs.get("limit", [DEFAULT_LIMIT])[0]) + paginator = OffsetPaginator(records, offset, limit) + + return { + "data": paginator.page_records, + **paginator.metadata, + } + + @router.get(r"/posts_cursor(\?cursor=\d+)?$") + def posts_cursor(request, context): + records = generate_posts() + cursor = int(request.qs.get("cursor", [0])[0]) + paginator = CursorPaginator(records, cursor) + + return { + "data": paginator.page_records, + **paginator.metadata, + } + + @router.get(r"/posts/(\d+)/comments") + def post_comments(request, context): + post_id = int(request.url.split("/")[-2]) + return paginate_by_page_number(request, generate_comments(post_id)) + + @router.get(r"/posts/\d+$") + def post_detail(request, context): + post_id = request.url.split("/")[-1] + return {"id": int(post_id), "body": f"Post body {post_id}"} + + @router.get(r"/posts/\d+/some_details_404") + def post_detail_404(request, context): + """Return 404 for post with id > 0. Used to test ignoring 404 errors.""" + post_id = int(request.url.split("/")[-2]) + if post_id < 1: + return {"id": post_id, "body": f"Post body {post_id}"} + else: + context.status_code = 404 + return {"error": "Post not found"} + + @router.get(r"/posts_under_a_different_key$") + def posts_with_results_key(request, context): + return paginate_by_page_number(request, generate_posts(), records_key="many-results") + + @router.post(r"/posts/search$") + def search_posts(request, context): + body = request.json() + page_size = body.get("page_size", DEFAULT_PAGE_SIZE) + page_count = body.get("page_count", DEFAULT_TOTAL_PAGES) + page_number = body.get("page", 1) + + # Simulate a search with filtering + records = generate_posts(page_size * page_count) + ids_greater_than = body.get("ids_greater_than", 0) + records = [r for r in records if r["id"] > ids_greater_than] + + total_records = len(records) + total_pages = (total_records + page_size - 1) // page_size + start_index = (page_number - 1) * page_size + end_index = start_index + page_size + records_slice = records[start_index:end_index] + + return { + "data": records_slice, + "next_page": page_number + 1 if page_number < total_pages else None, + } + + @router.get("/protected/posts/basic-auth") + def protected_basic_auth(request, context): + auth = request.headers.get("Authorization") + creds = "user:password" + creds_base64 = base64.b64encode(creds.encode()).decode() + if auth == f"Basic {creds_base64}": + return paginate_by_page_number(request, generate_posts()) + context.status_code = 401 + return {"error": "Unauthorized"} + + @router.get("/protected/posts/bearer-token") + def protected_bearer_token(request, context): + auth = request.headers.get("Authorization") + if auth == "Bearer test-token": + return paginate_by_page_number(request, generate_posts()) + context.status_code = 401 + return {"error": "Unauthorized"} + + @router.get("/protected/posts/bearer-token-plain-text-error") + def protected_bearer_token_plain_text_erorr(request, context): + auth = request.headers.get("Authorization") + if auth == "Bearer test-token": + return paginate_by_page_number(request, generate_posts()) + context.status_code = 401 + return "Unauthorized" + + @router.get("/protected/posts/api-key") + def protected_api_key(request, context): + api_key = request.headers.get("x-api-key") + if api_key == "test-api-key": + return paginate_by_page_number(request, generate_posts()) + context.status_code = 401 + return {"error": "Unauthorized"} + + @router.post("/oauth/token") + def oauth_token(request, context): + if oauth_authorize(request): + return {"access_token": "test-token", "expires_in": 3600} + context.status_code = 401 + return {"error": "Unauthorized"} + + @router.post("/oauth/token-expires-now") + def oauth_token_expires_now(request, context): + if oauth_authorize(request): + return {"access_token": "test-token", "expires_in": 0} + context.status_code = 401 + return {"error": "Unauthorized"} + + @router.post("/auth/refresh") + def refresh_token(request, context): + body = request.json() + if body.get("refresh_token") == "valid-refresh-token": + return {"access_token": "new-valid-token"} + context.status_code = 401 + return {"error": "Invalid refresh token"} + + @router.post("/custom-oauth/token") + def custom_oauth_token(request, context): + qs = parse_qs(request.text) + if ( + qs.get("grant_type")[0] == "account_credentials" + and qs.get("account_id")[0] == "test-account-id" + and request.headers["Authorization"] + == "Basic dGVzdC1hY2NvdW50LWlkOnRlc3QtY2xpZW50LXNlY3JldA==" + ): + return {"access_token": "test-token", "expires_in": 3600} + context.status_code = 401 + return {"error": "Unauthorized"} + + router.register_routes(m) + + yield m + + +@pytest.fixture +def rest_client() -> RESTClient: + return RESTClient( + base_url="https://api.example.com", + headers={"Accept": "application/json"}, + ) + + +def oauth_authorize(request): + qs = parse_qs(request.text) + grant_type = qs.get("grant_type")[0] + if "jwt-bearer" in grant_type: + return True + if "client_credentials" in grant_type: + return ( + qs["client_secret"][0] == "test-client-secret" + and qs["client_id"][0] == "test-client-id" + ) + + +def assert_pagination(pages, page_size=DEFAULT_PAGE_SIZE, total_pages=DEFAULT_TOTAL_PAGES): + assert len(pages) == total_pages + for i, page in enumerate(pages): + assert page == [ + {"id": i, "title": f"Post {i}"} for i in range(i * page_size, (i + 1) * page_size) + ] diff --git a/tests/sources/rest_api/integration/__init__.py b/tests/sources/rest_api/integration/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/sources/rest_api/integration/test_offline.py b/tests/sources/rest_api/integration/test_offline.py new file mode 100644 index 0000000000..2c1f48537b --- /dev/null +++ b/tests/sources/rest_api/integration/test_offline.py @@ -0,0 +1,329 @@ +from typing import Any, List, Optional +from unittest import mock + +import pytest +from requests import Request, Response + +import dlt +from dlt.common import pendulum +from dlt.pipeline.exceptions import PipelineStepFailed +from dlt.sources.helpers.rest_client.paginators import BaseReferencePaginator +from dlt.sources.rest_api import ( + ClientConfig, + Endpoint, + EndpointResource, + RESTAPIConfig, + rest_api_source, +) +from tests.sources.rest_api.conftest import DEFAULT_PAGE_SIZE, DEFAULT_TOTAL_PAGES +from tests.utils import assert_load_info, assert_query_data, load_table_counts + + +def test_load_mock_api(mock_api_server): + pipeline = dlt.pipeline( + pipeline_name="rest_api_mock", + destination="duckdb", + dataset_name="rest_api_mock", + full_refresh=True, + ) + + mock_source = rest_api_source( + { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + "posts", + { + "name": "post_comments", + "endpoint": { + "path": "posts/{post_id}/comments", + "params": { + "post_id": { + "type": "resolve", + "resource": "posts", + "field": "id", + } + }, + }, + }, + { + "name": "post_details", + "endpoint": { + "path": "posts/{post_id}", + "params": { + "post_id": { + "type": "resolve", + "resource": "posts", + "field": "id", + } + }, + }, + }, + ], + } + ) + + load_info = pipeline.run(mock_source) + print(load_info) + assert_load_info(load_info) + table_names = [t["name"] for t in pipeline.default_schema.data_tables()] + table_counts = load_table_counts(pipeline, *table_names) + + assert table_counts.keys() == {"posts", "post_comments", "post_details"} + + assert table_counts["posts"] == DEFAULT_PAGE_SIZE * DEFAULT_TOTAL_PAGES + assert table_counts["post_details"] == DEFAULT_PAGE_SIZE * DEFAULT_TOTAL_PAGES + assert table_counts["post_comments"] == 50 * DEFAULT_PAGE_SIZE * DEFAULT_TOTAL_PAGES + + with pipeline.sql_client() as client: + posts_table = client.make_qualified_table_name("posts") + posts_details_table = client.make_qualified_table_name("post_details") + post_comments_table = client.make_qualified_table_name("post_comments") + + print(pipeline.default_schema.to_pretty_yaml()) + + assert_query_data( + pipeline, + f"SELECT title FROM {posts_table} ORDER BY id limit 25", + [f"Post {i}" for i in range(25)], + ) + + assert_query_data( + pipeline, + f"SELECT body FROM {posts_details_table} ORDER BY id limit 25", + [f"Post body {i}" for i in range(25)], + ) + + assert_query_data( + pipeline, + f"SELECT body FROM {post_comments_table} ORDER BY post_id, id limit 5", + [f"Comment {i} for post 0" for i in range(5)], + ) + + +def test_ignoring_endpoint_returning_404(mock_api_server): + mock_source = rest_api_source( + { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + "posts", + { + "name": "post_details", + "endpoint": { + "path": "posts/{post_id}/some_details_404", + "params": { + "post_id": { + "type": "resolve", + "resource": "posts", + "field": "id", + } + }, + "response_actions": [ + { + "status_code": 404, + "action": "ignore", + }, + ], + }, + }, + ], + } + ) + + res = list(mock_source.with_resources("posts", "post_details").add_limit(1)) + + assert res[:5] == [ + {"id": 0, "body": "Post body 0"}, + {"id": 0, "title": "Post 0"}, + {"id": 1, "title": "Post 1"}, + {"id": 2, "title": "Post 2"}, + {"id": 3, "title": "Post 3"}, + ] + + +def test_source_with_post_request(mock_api_server): + class JSONBodyPageCursorPaginator(BaseReferencePaginator): + def update_state(self, response: Response, data: Optional[List[Any]] = None) -> None: + self._next_reference = response.json().get("next_page") + + def update_request(self, request: Request) -> None: + if request.json is None: + request.json = {} + + request.json["page"] = self._next_reference + + mock_source = rest_api_source( + { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "search_posts", + "endpoint": { + "path": "/posts/search", + "method": "POST", + "json": {"ids_greater_than": 50, "page_size": 25, "page_count": 4}, + "paginator": JSONBodyPageCursorPaginator(), + }, + } + ], + } + ) + + res = list(mock_source.with_resources("search_posts")) + + for i in range(49): + assert res[i] == {"id": 51 + i, "title": f"Post {51 + i}"} + + +def test_unauthorized_access_to_protected_endpoint(mock_api_server): + pipeline = dlt.pipeline( + pipeline_name="rest_api_mock", + destination="duckdb", + dataset_name="rest_api_mock", + full_refresh=True, + ) + + mock_source = rest_api_source( + { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + "/protected/posts/bearer-token-plain-text-error", + ], + } + ) + + with pytest.raises(PipelineStepFailed) as e: + pipeline.run(mock_source) + assert e.match("401 Client Error") + + +def test_posts_under_results_key(mock_api_server): + mock_source = rest_api_source( + { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "posts", + "endpoint": { + "path": "posts_under_a_different_key", + "data_selector": "many-results", + "paginator": "json_link", + }, + }, + ], + } + ) + + res = list(mock_source.with_resources("posts").add_limit(1)) + + assert res[:5] == [ + {"id": 0, "title": "Post 0"}, + {"id": 1, "title": "Post 1"}, + {"id": 2, "title": "Post 2"}, + {"id": 3, "title": "Post 3"}, + {"id": 4, "title": "Post 4"}, + ] + + +def test_posts_without_key(mock_api_server): + mock_source = rest_api_source( + { + "client": { + "base_url": "https://api.example.com", + "paginator": "header_link", + }, + "resources": [ + { + "name": "posts_no_key", + "endpoint": { + "path": "posts_no_key", + }, + }, + ], + } + ) + + res = list(mock_source.with_resources("posts_no_key").add_limit(1)) + + assert res[:5] == [ + {"id": 0, "title": "Post 0"}, + {"id": 1, "title": "Post 1"}, + {"id": 2, "title": "Post 2"}, + {"id": 3, "title": "Post 3"}, + {"id": 4, "title": "Post 4"}, + ] + + +def test_load_mock_api_typeddict_config(mock_api_server): + pipeline = dlt.pipeline( + pipeline_name="rest_api_mock", + destination="duckdb", + dataset_name="rest_api_mock", + full_refresh=True, + ) + + mock_source = rest_api_source( + RESTAPIConfig( + client=ClientConfig(base_url="https://api.example.com"), + resources=[ + "posts", + EndpointResource( + name="post_comments", + endpoint=Endpoint( + path="posts/{post_id}/comments", + params={ + "post_id": { + "type": "resolve", + "resource": "posts", + "field": "id", + } + }, + ), + ), + ], + ) + ) + + load_info = pipeline.run(mock_source) + print(load_info) + assert_load_info(load_info) + table_names = [t["name"] for t in pipeline.default_schema.data_tables()] + table_counts = load_table_counts(pipeline, *table_names) + + assert table_counts.keys() == {"posts", "post_comments"} + + assert table_counts["posts"] == DEFAULT_PAGE_SIZE * DEFAULT_TOTAL_PAGES + assert table_counts["post_comments"] == DEFAULT_PAGE_SIZE * DEFAULT_TOTAL_PAGES * 50 + + +def test_posts_with_inremental_date_conversion(mock_api_server) -> None: + start_time = pendulum.from_timestamp(1) + one_day_later = start_time.add(days=1) + config: RESTAPIConfig = { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "posts", + "endpoint": { + "path": "posts", + "incremental": { + "start_param": "since", + "end_param": "until", + "cursor_path": "updated_at", + "initial_value": str(start_time.int_timestamp), + "end_value": str(one_day_later.int_timestamp), + "convert": lambda epoch: pendulum.from_timestamp( + int(epoch) + ).to_date_string(), + }, + }, + }, + ], + } + RESTClient = dlt.sources.helpers.rest_client.RESTClient + with mock.patch.object(RESTClient, "paginate") as mock_paginate: + source = rest_api_source(config).add_limit(1) + _ = list(source.with_resources("posts")) + assert mock_paginate.call_count == 1 + _, called_kwargs = mock_paginate.call_args_list[0] + assert called_kwargs["params"] == {"since": "1970-01-01", "until": "1970-01-02"} + assert called_kwargs["path"] == "posts" diff --git a/tests/sources/rest_api/integration/test_processing_steps.py b/tests/sources/rest_api/integration/test_processing_steps.py new file mode 100644 index 0000000000..bbe90dda06 --- /dev/null +++ b/tests/sources/rest_api/integration/test_processing_steps.py @@ -0,0 +1,245 @@ +from typing import Any, Callable, Dict, List + +import dlt +from dlt.sources.rest_api import RESTAPIConfig, rest_api_source + + +def _make_pipeline(destination_name: str): + return dlt.pipeline( + pipeline_name="rest_api", + destination=destination_name, + dataset_name="rest_api_data", + full_refresh=True, + ) + + +def test_rest_api_source_filtered(mock_api_server) -> None: + config: RESTAPIConfig = { + "client": { + "base_url": "https://api.example.com", + }, + "resources": [ + { + "name": "posts", + "endpoint": "posts", + "processing_steps": [ + {"filter": lambda x: x["id"] == 1}, # type: ignore[typeddict-item] + ], + }, + ], + } + mock_source = rest_api_source(config) + + data = list(mock_source.with_resources("posts")) + assert len(data) == 1 + assert data[0]["title"] == "Post 1" + + +def test_rest_api_source_exclude_columns(mock_api_server) -> None: + def exclude_columns(columns: List[str]) -> Callable[..., Any]: + def pop_columns(record: Dict[str, Any]) -> Dict[str, Any]: + for col in columns: + record.pop(col) + return record + + return pop_columns + + config: RESTAPIConfig = { + "client": { + "base_url": "https://api.example.com", + }, + "resources": [ + { + "name": "posts", + "endpoint": "posts", + "processing_steps": [ + { + "map": exclude_columns(["title"]), # type: ignore[typeddict-item] + }, + ], + }, + ], + } + mock_source = rest_api_source(config) + + data = list(mock_source.with_resources("posts")) + + assert all("title" not in record for record in data) + + +def test_rest_api_source_anonymize_columns(mock_api_server) -> None: + def anonymize_columns(columns: List[str]) -> Callable[..., Any]: + def empty_columns(record: Dict[str, Any]) -> Dict[str, Any]: + for col in columns: + record[col] = "dummy" + return record + + return empty_columns + + config: RESTAPIConfig = { + "client": { + "base_url": "https://api.example.com", + }, + "resources": [ + { + "name": "posts", + "endpoint": "posts", + "processing_steps": [ + { + "map": anonymize_columns(["title"]), # type: ignore[typeddict-item] + }, + ], + }, + ], + } + mock_source = rest_api_source(config) + + data = list(mock_source.with_resources("posts")) + + assert all(record["title"] == "dummy" for record in data) + + +def test_rest_api_source_map(mock_api_server) -> None: + def lower_title(row): + row["title"] = row["title"].lower() + return row + + config: RESTAPIConfig = { + "client": { + "base_url": "https://api.example.com", + }, + "resources": [ + { + "name": "posts", + "endpoint": "posts", + "processing_steps": [ + {"map": lower_title}, # type: ignore[typeddict-item] + ], + }, + ], + } + mock_source = rest_api_source(config) + + data = list(mock_source.with_resources("posts")) + + assert all(record["title"].startswith("post ") for record in data) + + +def test_rest_api_source_filter_and_map(mock_api_server) -> None: + def id_by_10(row): + row["id"] = row["id"] * 10 + return row + + config: RESTAPIConfig = { + "client": { + "base_url": "https://api.example.com", + }, + "resources": [ + { + "name": "posts", + "endpoint": "posts", + "processing_steps": [ + {"map": id_by_10}, # type: ignore[typeddict-item] + {"filter": lambda x: x["id"] == 10}, # type: ignore[typeddict-item] + ], + }, + { + "name": "posts_2", + "endpoint": "posts", + "processing_steps": [ + {"filter": lambda x: x["id"] == 10}, # type: ignore[typeddict-item] + {"map": id_by_10}, # type: ignore[typeddict-item] + ], + }, + ], + } + mock_source = rest_api_source(config) + + data = list(mock_source.with_resources("posts")) + assert len(data) == 1 + assert data[0]["title"] == "Post 1" + + data = list(mock_source.with_resources("posts_2")) + assert len(data) == 1 + assert data[0]["id"] == 100 + assert data[0]["title"] == "Post 10" + + +def test_rest_api_source_filtered_child(mock_api_server) -> None: + config: RESTAPIConfig = { + "client": { + "base_url": "https://api.example.com", + }, + "resources": [ + { + "name": "posts", + "endpoint": "posts", + "processing_steps": [ + {"filter": lambda x: x["id"] in (1, 2)}, # type: ignore[typeddict-item] + ], + }, + { + "name": "comments", + "endpoint": { + "path": "/posts/{post_id}/comments", + "params": { + "post_id": { + "type": "resolve", + "resource": "posts", + "field": "id", + } + }, + }, + "processing_steps": [ + {"filter": lambda x: x["id"] == 1}, # type: ignore[typeddict-item] + ], + }, + ], + } + mock_source = rest_api_source(config) + + data = list(mock_source.with_resources("comments")) + assert len(data) == 2 + + +def test_rest_api_source_filtered_and_map_child(mock_api_server) -> None: + def extend_body(row): + row["body"] = f"{row['_posts_title']} - {row['body']}" + return row + + config: RESTAPIConfig = { + "client": { + "base_url": "https://api.example.com", + }, + "resources": [ + { + "name": "posts", + "endpoint": "posts", + "processing_steps": [ + {"filter": lambda x: x["id"] in (1, 2)}, # type: ignore[typeddict-item] + ], + }, + { + "name": "comments", + "endpoint": { + "path": "/posts/{post_id}/comments", + "params": { + "post_id": { + "type": "resolve", + "resource": "posts", + "field": "id", + } + }, + }, + "include_from_parent": ["title"], + "processing_steps": [ + {"map": extend_body}, # type: ignore[typeddict-item] + {"filter": lambda x: x["body"].startswith("Post 2")}, # type: ignore[typeddict-item] + ], + }, + ], + } + mock_source = rest_api_source(config) + + data = list(mock_source.with_resources("comments")) + assert data[0]["body"] == "Post 2 - Comment 0 for post 2" diff --git a/tests/sources/rest_api/integration/test_response_actions.py b/tests/sources/rest_api/integration/test_response_actions.py new file mode 100644 index 0000000000..36a7990db3 --- /dev/null +++ b/tests/sources/rest_api/integration/test_response_actions.py @@ -0,0 +1,135 @@ +from dlt.common import json +from dlt.sources.helpers.requests import Response +from dlt.sources.rest_api import create_response_hooks, rest_api_source + + +def test_response_action_on_status_code(mock_api_server, mocker): + mock_response_hook = mocker.Mock() + mock_source = rest_api_source( + { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "post_details", + "endpoint": { + "path": "posts/1/some_details_404", + "response_actions": [ + { + "status_code": 404, + "action": mock_response_hook, + }, + ], + }, + }, + ], + } + ) + + list(mock_source.with_resources("post_details").add_limit(1)) + + mock_response_hook.assert_called_once() + + +def test_response_action_on_every_response(mock_api_server, mocker): + def custom_hook(request, *args, **kwargs): + return request + + mock_response_hook = mocker.Mock(side_effect=custom_hook) + mock_source = rest_api_source( + { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "posts", + "endpoint": { + "response_actions": [ + mock_response_hook, + ], + }, + }, + ], + } + ) + + list(mock_source.with_resources("posts").add_limit(1)) + + mock_response_hook.assert_called_once() + + +def test_multiple_response_actions_on_every_response(mock_api_server, mocker): + def custom_hook(response, *args, **kwargs): + return response + + mock_response_hook_1 = mocker.Mock(side_effect=custom_hook) + mock_response_hook_2 = mocker.Mock(side_effect=custom_hook) + mock_source = rest_api_source( + { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "posts", + "endpoint": { + "response_actions": [ + mock_response_hook_1, + mock_response_hook_2, + ], + }, + }, + ], + } + ) + + list(mock_source.with_resources("posts").add_limit(1)) + + mock_response_hook_1.assert_called_once() + mock_response_hook_2.assert_called_once() + + +def test_response_actions_called_in_order(mock_api_server, mocker): + def set_encoding(response: Response, *args, **kwargs) -> Response: + assert response.encoding != "windows-1252" + response.encoding = "windows-1252" + return response + + def add_field(response: Response, *args, **kwargs) -> Response: + assert response.encoding == "windows-1252" + payload = response.json() + for record in payload["data"]: + record["custom_field"] = "foobar" + modified_content: bytes = json.dumps(payload).encode("utf-8") + response._content = modified_content + return response + + mock_response_hook_1 = mocker.Mock(side_effect=set_encoding) + mock_response_hook_2 = mocker.Mock(side_effect=add_field) + + response_actions = [ + mock_response_hook_1, + {"status_code": 200, "action": mock_response_hook_2}, + ] + hooks = create_response_hooks(response_actions) + assert len(hooks.get("response")) == 2 + + mock_source = rest_api_source( + { + "client": {"base_url": "https://api.example.com"}, + "resources": [ + { + "name": "posts", + "endpoint": { + "response_actions": [ + mock_response_hook_1, + {"status_code": 200, "action": mock_response_hook_2}, + ], + }, + }, + ], + } + ) + + data = list(mock_source.with_resources("posts").add_limit(1)) + + mock_response_hook_1.assert_called_once() + mock_response_hook_2.assert_called_once() + + assert all(record["custom_field"] == "foobar" for record in data) diff --git a/tests/sources/rest_api/test_rest_api_pipeline_template.py b/tests/sources/rest_api/test_rest_api_pipeline_template.py new file mode 100644 index 0000000000..cd5cca0b10 --- /dev/null +++ b/tests/sources/rest_api/test_rest_api_pipeline_template.py @@ -0,0 +1,23 @@ +import os +import dlt +import pytest +from dlt.common.typing import TSecretStrValue + + +@pytest.mark.parametrize( + "example_name", + ( + "load_github", + "load_pokemon", + ), +) +def test_all_examples(example_name: str) -> None: + from dlt.sources import rest_api_pipeline + + # reroute token location from secrets + github_token: TSecretStrValue = dlt.secrets.get("sources.github.access_token") + if not github_token: + # try to get GITHUB TOKEN which is available on github actions, fallback to None if not available + github_token = os.environ.get("GITHUB_TOKEN", None) # type: ignore + dlt.secrets["sources.rest_api_pipeline.github.access_token"] = github_token + getattr(rest_api_pipeline, example_name)() diff --git a/tests/sources/rest_api/test_rest_api_source.py b/tests/sources/rest_api/test_rest_api_source.py new file mode 100644 index 0000000000..f6b97a7f47 --- /dev/null +++ b/tests/sources/rest_api/test_rest_api_source.py @@ -0,0 +1,116 @@ +import dlt +import pytest +from dlt.sources.rest_api.typing import RESTAPIConfig +from dlt.sources.helpers.rest_client.paginators import SinglePagePaginator + +from dlt.sources.rest_api import rest_api_source +from tests.utils import ALL_DESTINATIONS, assert_load_info, load_table_counts + + +def _make_pipeline(destination_name: str): + return dlt.pipeline( + pipeline_name="rest_api", + destination=destination_name, + dataset_name="rest_api_data", + full_refresh=True, + ) + + +@pytest.mark.parametrize("destination_name", ALL_DESTINATIONS) +def test_rest_api_source(destination_name: str) -> None: + config: RESTAPIConfig = { + "client": { + "base_url": "https://pokeapi.co/api/v2/", + }, + "resource_defaults": { + "endpoint": { + "params": { + "limit": 1000, + }, + } + }, + "resources": [ + { + "name": "pokemon_list", + "endpoint": "pokemon", + }, + "berry", + "location", + ], + } + data = rest_api_source(config) + pipeline = _make_pipeline(destination_name) + load_info = pipeline.run(data) + print(load_info) + assert_load_info(load_info) + table_names = [t["name"] for t in pipeline.default_schema.data_tables()] + table_counts = load_table_counts(pipeline, *table_names) + + assert table_counts.keys() == {"pokemon_list", "berry", "location"} + + assert table_counts["pokemon_list"] == 1302 + assert table_counts["berry"] == 64 + assert table_counts["location"] == 1036 + + +@pytest.mark.parametrize("destination_name", ALL_DESTINATIONS) +def test_dependent_resource(destination_name: str) -> None: + config: RESTAPIConfig = { + "client": { + "base_url": "https://pokeapi.co/api/v2/", + }, + "resource_defaults": { + "endpoint": { + "params": { + "limit": 1000, + }, + } + }, + "resources": [ + { + "name": "pokemon_list", + "endpoint": { + "path": "pokemon", + "paginator": SinglePagePaginator(), + "data_selector": "results", + "params": { + "limit": 2, + }, + }, + "selected": False, + }, + { + "name": "pokemon", + "endpoint": { + "path": "pokemon/{name}", + "params": { + "name": { + "type": "resolve", + "resource": "pokemon_list", + "field": "name", + }, + }, + }, + }, + ], + } + + data = rest_api_source(config) + pipeline = _make_pipeline(destination_name) + load_info = pipeline.run(data) + assert_load_info(load_info) + table_names = [t["name"] for t in pipeline.default_schema.data_tables()] + table_counts = load_table_counts(pipeline, *table_names) + + assert set(table_counts.keys()) == { + "pokemon", + "pokemon__types", + "pokemon__stats", + "pokemon__moves__version_group_details", + "pokemon__moves", + "pokemon__game_indices", + "pokemon__forms", + "pokemon__abilities", + } + + assert table_counts["pokemon"] == 2 diff --git a/tests/sources/sql_database/__init__.py b/tests/sources/sql_database/__init__.py new file mode 100644 index 0000000000..f10ab98368 --- /dev/null +++ b/tests/sources/sql_database/__init__.py @@ -0,0 +1 @@ +# almost all tests are in tests/load since a postgres instance is required for this to work diff --git a/tests/sources/sql_database/test_arrow_helpers.py b/tests/sources/sql_database/test_arrow_helpers.py new file mode 100644 index 0000000000..8328bed89b --- /dev/null +++ b/tests/sources/sql_database/test_arrow_helpers.py @@ -0,0 +1,114 @@ +from datetime import date, datetime, timezone # noqa: I251 +from uuid import uuid4 + +import pyarrow as pa +import pytest + +from dlt.sources.sql_database.arrow_helpers import row_tuples_to_arrow + + +@pytest.mark.parametrize("all_unknown", [True, False]) +def test_row_tuples_to_arrow_unknown_types(all_unknown: bool) -> None: + """Test inferring data types with pyarrow""" + + rows = [ + ( + 1, + "a", + 1.1, + True, + date.today(), + uuid4(), + datetime.now(timezone.utc), + [1, 2, 3], + ), + ( + 2, + "b", + 2.2, + False, + date.today(), + uuid4(), + datetime.now(timezone.utc), + [4, 5, 6], + ), + ( + 3, + "c", + 3.3, + True, + date.today(), + uuid4(), + datetime.now(timezone.utc), + [7, 8, 9], + ), + ] + + # Some columns don't specify data type and should be inferred + columns = { + "int_col": {"name": "int_col", "data_type": "bigint", "nullable": False}, + "str_col": {"name": "str_col", "data_type": "text", "nullable": False}, + "float_col": {"name": "float_col", "nullable": False}, + "bool_col": {"name": "bool_col", "data_type": "bool", "nullable": False}, + "date_col": {"name": "date_col", "nullable": False}, + "uuid_col": {"name": "uuid_col", "nullable": False}, + "datetime_col": { + "name": "datetime_col", + "data_type": "timestamp", + "nullable": False, + }, + "array_col": {"name": "array_col", "nullable": False}, + } + + if all_unknown: + for col in columns.values(): + col.pop("data_type", None) + + # Call the function + result = row_tuples_to_arrow(rows, columns, tz="UTC") # type: ignore[arg-type] + + # Result is arrow table containing all columns in original order with correct types + assert result.num_columns == len(columns) + result_col_names = [f.name for f in result.schema] + expected_names = list(columns) + assert result_col_names == expected_names + + assert pa.types.is_int64(result[0].type) + assert pa.types.is_string(result[1].type) + assert pa.types.is_float64(result[2].type) + assert pa.types.is_boolean(result[3].type) + assert pa.types.is_date(result[4].type) + assert pa.types.is_string(result[5].type) + assert pa.types.is_timestamp(result[6].type) + assert pa.types.is_list(result[7].type) + + +pytest.importorskip("sqlalchemy", minversion="2.0") + + +def test_row_tuples_to_arrow_detects_range_type() -> None: + from sqlalchemy.dialects.postgresql import Range # type: ignore[attr-defined] + + # Applies to NUMRANGE, DATERANGE, etc sql types. Sqlalchemy returns a Range dataclass + IntRange = Range + + rows = [ + (IntRange(1, 10),), + (IntRange(2, 20),), + (IntRange(3, 30),), + ] + result = row_tuples_to_arrow( + rows=rows, # type: ignore[arg-type] + columns={"range_col": {"name": "range_col", "nullable": False}}, + tz="UTC", + ) + assert result.num_columns == 1 + assert pa.types.is_struct(result[0].type) + + # Check range has all fields + range_type = result[0].type + range_fields = {f.name: f for f in range_type} + assert pa.types.is_int64(range_fields["lower"].type) + assert pa.types.is_int64(range_fields["upper"].type) + assert pa.types.is_boolean(range_fields["empty"].type) + assert pa.types.is_string(range_fields["bounds"].type) diff --git a/tests/sources/sql_database/test_sql_database_pipeline_template.py b/tests/sources/sql_database/test_sql_database_pipeline_template.py new file mode 100644 index 0000000000..88c05ea333 --- /dev/null +++ b/tests/sources/sql_database/test_sql_database_pipeline_template.py @@ -0,0 +1,22 @@ +import pytest + + +# TODO: not all template functions are tested here +# we may be able to test more in tests/load/sources +@pytest.mark.parametrize( + "example_name", + ( + "load_select_tables_from_database", + # "load_entire_database", + "load_standalone_table_resource", + "select_columns", + "specify_columns_to_load", + "test_pandas_backend_verbatim_decimals", + "select_with_end_value_and_row_order", + "my_sql_via_pyarrow", + ), +) +def test_all_examples(example_name: str) -> None: + from dlt.sources import sql_database_pipeline + + getattr(sql_database_pipeline, example_name)() diff --git a/tests/sources/test_pipeline_templates.py b/tests/sources/test_pipeline_templates.py new file mode 100644 index 0000000000..0743a21fef --- /dev/null +++ b/tests/sources/test_pipeline_templates.py @@ -0,0 +1,61 @@ +import pytest + + +@pytest.mark.parametrize( + "example_name", + ("load_all_datatypes",), +) +def test_debug_pipeline(example_name: str) -> None: + from dlt.sources.pipeline_templates import debug_pipeline + + getattr(debug_pipeline, example_name)() + + +@pytest.mark.parametrize( + "example_name", + ("load_arrow_tables",), +) +def test_arrow_pipeline(example_name: str) -> None: + from dlt.sources.pipeline_templates import arrow_pipeline + + getattr(arrow_pipeline, example_name)() + + +@pytest.mark.parametrize( + "example_name", + ("load_dataframe",), +) +def test_dataframe_pipeline(example_name: str) -> None: + from dlt.sources.pipeline_templates import dataframe_pipeline + + getattr(dataframe_pipeline, example_name)() + + +@pytest.mark.parametrize( + "example_name", + ("load_stuff",), +) +def test_default_pipeline(example_name: str) -> None: + from dlt.sources.pipeline_templates import default_pipeline + + getattr(default_pipeline, example_name)() + + +@pytest.mark.parametrize( + "example_name", + ("load_chess_data",), +) +def test_requests_pipeline(example_name: str) -> None: + from dlt.sources.pipeline_templates import requests_pipeline + + getattr(requests_pipeline, example_name)() + + +@pytest.mark.parametrize( + "example_name", + ("load_api_data", "load_sql_data", "load_pandas_data"), +) +def test_intro_pipeline(example_name: str) -> None: + from dlt.sources.pipeline_templates import intro_pipeline + + getattr(intro_pipeline, example_name)() diff --git a/tests/tools/clean_athena.py b/tests/tools/clean_athena.py new file mode 100644 index 0000000000..163cf4a4e7 --- /dev/null +++ b/tests/tools/clean_athena.py @@ -0,0 +1,20 @@ +"""WARNING: Running this script will drop add schemas in the athena destination set up in your secrets.toml""" + +import dlt +from dlt.destinations.exceptions import DatabaseUndefinedRelation + +if __name__ == "__main__": + pipeline = dlt.pipeline(pipeline_name="drop_athena", destination="athena") + + with pipeline.sql_client() as client: + with client.execute_query("SHOW DATABASES") as cur: + dbs = cur.fetchall() + for db in dbs: + db = db[0] + sql = f"DROP SCHEMA `{db}` CASCADE;" + try: + print(sql) + with client.execute_query(sql): + pass # + except DatabaseUndefinedRelation: + print("Could not delete schema") diff --git a/tests/tools/clean_redshift.py b/tests/tools/clean_redshift.py index 96364d68fb..2783820cc5 100644 --- a/tests/tools/clean_redshift.py +++ b/tests/tools/clean_redshift.py @@ -1,32 +1,34 @@ -from dlt.destinations.impl.postgres.postgres import PostgresClient -from dlt.destinations.impl.postgres.sql_client import psycopg2 -from psycopg2.errors import InsufficientPrivilege, InternalError_, SyntaxError +"""WARNING: Running this script will drop add schemas in the redshift destination set up in your secrets.toml""" -CONNECTION_STRING = "" +import dlt +from dlt.destinations.exceptions import ( + DatabaseUndefinedRelation, + DatabaseTerminalException, + DatabaseTransientException, +) if __name__ == "__main__": - # connect - connection = psycopg2.connect(CONNECTION_STRING) - connection.set_isolation_level(0) + pipeline = dlt.pipeline(pipeline_name="drop_redshift", destination="redshift") - # list all schemas - with connection.cursor() as curr: - curr.execute("""select s.nspname as table_schema, + with pipeline.sql_client() as client: + with client.execute_query("""select s.nspname as table_schema, s.oid as schema_id, u.usename as owner from pg_catalog.pg_namespace s join pg_catalog.pg_user u on u.usesysid = s.nspowner - order by table_schema;""") - schemas = [row[0] for row in curr.fetchall()] - - # delete all schemas, skipp expected errors - with connection.cursor() as curr: - print(f"Deleting {len(schemas)} schemas") - for schema in schemas: - print(f"Deleting {schema}...") + order by table_schema;""") as cur: + dbs = [row[0] for row in cur.fetchall()] + for db in dbs: + if db.startswith("<"): + continue + sql = f"DROP SCHEMA {db} CASCADE;" try: - curr.execute(f"DROP SCHEMA IF EXISTS {schema} CASCADE;") - except (InsufficientPrivilege, InternalError_, SyntaxError) as ex: - print(ex) - pass - print(f"Deleted {schema}...") + print(sql) + with client.execute_query(sql): + pass # + except ( + DatabaseUndefinedRelation, + DatabaseTerminalException, + DatabaseTransientException, + ): + print("Could not delete schema") diff --git a/tests/utils.py b/tests/utils.py index 1b81881470..813deea69f 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -1,9 +1,10 @@ +import contextlib import multiprocessing import os import platform import sys from os import environ -from typing import Any, Iterable, Iterator, List, Literal, Union, get_args +from typing import Any, Iterable, Iterator, Literal, Union, get_args, List from unittest.mock import patch import pytest @@ -12,24 +13,32 @@ import dlt from dlt.common.configuration.container import Container -from dlt.common.configuration.providers import DictionaryProvider +from dlt.common.configuration.providers import ( + DictionaryProvider, + EnvironProvider, + SecretsTomlProvider, + ConfigTomlProvider, +) from dlt.common.configuration.resolve import resolve_configuration from dlt.common.configuration.specs import RunConfiguration from dlt.common.configuration.specs.config_providers_context import ( ConfigProvidersContext, ) -from dlt.common.pipeline import PipelineContext +from dlt.common.pipeline import LoadInfo, PipelineContext, SupportsPipeline from dlt.common.runtime.init import init_logging from dlt.common.runtime.telemetry import start_telemetry, stop_telemetry from dlt.common.schema import Schema from dlt.common.storages import FileStorage from dlt.common.storages.versioned_storage import VersionedStorage -from dlt.common.typing import StrAny, TDataItem +from dlt.common.typing import DictStrAny, StrAny, TDataItem from dlt.common.utils import custom_environ, uniq_id -from dlt.common.pipeline import PipelineContext, SupportsPipeline TEST_STORAGE_ROOT = "_storage" +ALL_DESTINATIONS = dlt.config.get("ALL_DESTINATIONS", list) or [ + "duckdb", +] + # destination constants IMPLEMENTED_DESTINATIONS = { @@ -51,6 +60,7 @@ "databricks", "clickhouse", "dremio", + "sqlalchemy", } NON_SQL_DESTINATIONS = { "filesystem", @@ -334,3 +344,63 @@ def is_running_in_github_fork() -> bool: skipifgithubfork = pytest.mark.skipif( is_running_in_github_fork(), reason="Skipping test because it runs on a PR coming from fork" ) + + +def assert_load_info(info: LoadInfo, expected_load_packages: int = 1) -> None: + """Asserts that expected number of packages was loaded and there are no failed jobs""" + assert len(info.loads_ids) == expected_load_packages + # all packages loaded + assert all(package.state == "loaded" for package in info.load_packages) is True + # Explicitly check for no failed job in any load package. In case a terminal exception was disabled by raise_on_failed_jobs=False + info.raise_on_failed_jobs() + + +def load_table_counts(p: dlt.Pipeline, *table_names: str) -> DictStrAny: + """Returns row counts for `table_names` as dict""" + with p.sql_client() as c: + query = "\nUNION ALL\n".join( + [ + f"SELECT '{name}' as name, COUNT(1) as c FROM {c.make_qualified_table_name(name)}" + for name in table_names + ] + ) + with c.execute_query(query) as cur: + rows = list(cur.fetchall()) + return {r[0]: r[1] for r in rows} + + +def assert_query_data( + p: dlt.Pipeline, + sql: str, + table_data: List[Any], + schema_name: str = None, + info: LoadInfo = None, +) -> None: + """Asserts that query selecting single column of values matches `table_data`. If `info` is provided, second column must contain one of load_ids in `info`""" + with p.sql_client(schema_name=schema_name) as c: + with c.execute_query(sql) as cur: + rows = list(cur.fetchall()) + assert len(rows) == len(table_data) + for r, d in zip(rows, table_data): + row = list(r) + # first element comes from the data + assert row[0] == d + # the second is load id + if info: + assert row[1] in info.loads_ids + + +@contextlib.contextmanager +def reset_providers(project_dir: str) -> Iterator[ConfigProvidersContext]: + """Context manager injecting standard set of providers where toml providers are initialized from `project_dir`""" + return _reset_providers(project_dir) + + +def _reset_providers(project_dir: str) -> Iterator[ConfigProvidersContext]: + ctx = ConfigProvidersContext() + ctx.providers.clear() + ctx.add_provider(EnvironProvider()) + ctx.add_provider(SecretsTomlProvider(project_dir=project_dir)) + ctx.add_provider(ConfigTomlProvider(project_dir=project_dir)) + with Container().injectable_context(ctx): + yield ctx