Skip to content

Latest commit

 

History

History
182 lines (132 loc) · 8.04 KB

README.md

File metadata and controls

182 lines (132 loc) · 8.04 KB
Raw

astro

workflows made easy

Python versions License Development Status PyPI downloads Contributors Commit activity pre-commit.ci status CI codecov

Astro Python SDK is a Python SDK for rapid development of extract, transform, and load workflows in Apache Airflow. It allows you to express your workflows as a set of data dependencies without having to worry about ordering and tasks. The Astro Python SDK is maintained by Astronomer.

Prerequisites

  • Apache Airflow >= 2.1.0.

Install

The Astro Python SDK is available at PyPI. Use the standard Python installation tools.

To install a cloud-agnostic version of the SDK, run:

pip install astro-sdk-python

You can also install dependencies for using the SDK with popular cloud providers:

pip install astro-sdk-python[amazon,google,snowflake,postgres]

Quickstart

  1. Ensure that your Airflow environment is set up correctly by running the following commands:

    export AIRFLOW_HOME=`pwd`
airflow db init

    Note:

    • AIRFLOW__CORE__ENABLE_XCOM_PICKLING no longer needs to be enabled from astro-sdk-python release 1.2 and above.
    • For airflow version < 2.5 and astro-sdk-python release < 1.3 Users can either use a custom XCom backend AstroCustomXcomBackend with Xcom pickling disabled (or) enable Xcom pickling.
    • For airflow version >= 2.5 and astro-sdk-python release >= 1.3.3 Users can either use Airflow's Xcom backend with Xcom pickling disabled (or) enable Xcom pickling.

    The data format used by pickle is Python-specific. This has the advantage that there are no restrictions imposed by external standards such as JSON or XDR (which can’t represent pointer sharing); however it means that non-Python programs may not be able to reconstruct pickled Python objects.

    Read more: enable_xcom_pickling and pickle:

  2. Create a SQLite database for the example to run with:

    # The sqlite_default connection has different host for MAC vs. Linux
export SQL_TABLE_NAME=`airflow connections get sqlite_default -o yaml | grep host | awk '{print $2}'`
sqlite3 "$SQL_TABLE_NAME" "VACUUM;"

  3. Copy the following workflow into a file named calculate_popular_movies.py and add it to the dags directory of your Airflow project:

    astro-sdk/example_dags/calculate_popular_movies.py

    Lines 1 to 37 in d5aa768

    from datetime import datetime
    from airflow import DAG
    from astro import sql as aql
    from astro.files import File
    from astro.sql.table import Table
    @aql.transform()
    def top_five_animations(input_table: Table):
    return """
    SELECT Title, Rating
    FROM {{input_table}}
    WHERE Genre1=='Animation'
    ORDER BY Rating desc
    LIMIT 5;
    """
    with DAG(
    "calculate_popular_movies",
    schedule_interval=None,
    start_date=datetime(2000, 1, 1),
    catchup=False,
    ) as dag:
    imdb_movies = aql.load_file(
    File(
    "https://raw.githubusercontent.com/astronomer/astro-sdk/main/tests/data/imdb.csv"
    ),
    output_table=Table(conn_id="sqlite_default"),
    )
    top_five_animations(
    input_table=imdb_movies,
    output_table=Table(name="top_animation"),
    )
    aql.cleanup()

    Alternatively, you can download calculate_popular_movies.py

     curl -O https://raw.githubusercontent.com/astronomer/astro-sdk/main/example_dags/calculate_popular_movies.py

  4. Run the example DAG:

    airflow dags test calculate_popular_movies `date -Iseconds`

  5. Check the result of your DAG by running:

    sqlite3 "$SQL_TABLE_NAME" "select * from top_animation;" ".exit"

    You should see the following output:

    $ sqlite3 "$SQL_TABLE_NAME" "select * from top_animation;" ".exit"
Toy Story 3 (2010)|8.3
Inside Out (2015)|8.2
How to Train Your Dragon (2010)|8.1
Zootopia (2016)|8.1
How to Train Your Dragon 2 (2014)|7.9

Supported technologies

FileLocation
local
http
https
gs
gdrive
s3
wasb
wasbs
azure
sftp
ftp
FileType
csv
json
ndjson
parquet
xls
xlsx
Database
postgres
sqlite
delta
bigquery
snowflake
redshift
mssql
duckdb
mysql

Available operations

The following are some key functions available in the SDK:

  • load_file: Load a given file into a SQL table
  • transform: Applies a SQL select statement to a source table and saves the result to a destination table
  • drop_table: Drops a SQL table
  • run_raw_sql: Run any SQL statement without handling its output
  • append: Insert rows from the source SQL table into the destination SQL table, if there are no conflicts
  • merge: Insert rows from the source SQL table into the destination SQL table, depending on conflicts:
    • ignore: Do not add rows that already exist
    • update: Replace existing rows with new ones
  • export_file: Export SQL table rows into a destination file
  • dataframe: Export given SQL table into in-memory Pandas data-frame

For a full list of available operators, see the SDK reference documentation.

Documentation

The documentation is a work in progress--we aim to follow the Diátaxis system:

  • Getting Started Tutorial: A hands-on introduction to the Astro Python SDK
  • How-to guides: Simple step-by-step user guides to accomplish specific tasks
  • Reference guide: Commands, modules, classes and methods
  • Explanation: Clarification and discussion of key decisions when designing the project

Changelog

The Astro Python SDK follows semantic versioning for releases. Check the changelog for the latest changes.

Release managements

To learn more about our release philosophy and steps, see Managing Releases.

Contribution guidelines

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.

Read the Contribution Guideline for a detailed overview on how to contribute.

Contributors and maintainers should abide by the Contributor Code of Conduct.

License

Apache Licence 2.0