docs: add typechecking to embedded snippets

docs/tools/check_embedded_snippets.py

@@ -18,7 +18,7 @@
 LINT_TEMPLATE = "./lint_setup/template.py"
 LINT_FILE = "./lint_setup/lint_me.py"
 
-ENABLE_MYPY = False
+ENABLE_MYPY = True
 
 
 @dataclass
@@ -225,6 +225,7 @@ def typecheck_snippets(snippets: List[Snippet], verbose: bool) -> None:
             failed_count += 1
             fmt.warning(f"Failed to type check {str(snippet)}")
             fmt.echo(result.stdout.strip())
+            fmt.echo(result.stderr.strip())
 
     if failed_count:
         fmt.error(f"Failed to type check {failed_count} snippets")

docs/tools/lint_setup/template.py

@@ -8,8 +8,8 @@
 import os
 
 import pendulum
-from pendulum import DateTime
 from datetime import datetime  # noqa: I251
+from pendulum import DateTime
 
 import dlt
 from dlt.common import json
@@ -26,10 +26,12 @@
     BaseConfiguration,
 )
 from dlt.common.storages.configuration import FileSystemCredentials
+from dlt.pipeline.exceptions import PipelineStepFailed
 
 # some universal variables
 pipeline: dlt.Pipeline = None  # type: ignore[assignment]
 p: dlt.Pipeline = None  # type: ignore[assignment]
 ex: Exception = None  # type: ignore[assignment]
 load_info: LoadInfo = None  # type: ignore[assignment]
 url: str = None  # type: ignore[assignment]
+my_resource: DltResource = None  # type: ignore[assignment]

docs/tools/utils.py

@@ -1,5 +1,6 @@
 from typing import List
 import os
+import glob
 
 import dlt.cli.echo as fmt
 
@@ -15,24 +16,20 @@ def collect_markdown_files(verbose: bool) -> List[str]:
 
     # collect docs pages
     markdown_files: List[str] = []
-    for path, _, files in os.walk(DOCS_DIR):
-        if "api_reference" in path:
+    for filepath in glob.glob(f"{DOCS_DIR}/**/*.md", recursive=True):
+        if "api_reference" in filepath:
             continue
-        if "jaffle_shop" in path:
+        if "jaffle_shop" in filepath:
             continue
-        for file in files:
-            if file.endswith(".md"):
-                markdown_files.append(os.path.join(path, file))
-                if verbose:
-                    fmt.echo(f"Discovered {os.path.join(path, file)}")
+        markdown_files.append(filepath)
+        if verbose:
+            fmt.echo(f"Discovered {filepath}")
 
     # collect blog pages
-    for path, _, files in os.walk(BLOG_DIR):
-        for file in files:
-            if file.endswith(".md"):
-                markdown_files.append(os.path.join(path, file))
-                if verbose:
-                    fmt.echo(f"Discovered {os.path.join(path, file)}")
+    for filepath in glob.glob(f"{BLOG_DIR}/**/*.md", recursive=True):
+        markdown_files.append(filepath)
+        if verbose:
+            fmt.echo(f"Discovered {filepath}")
 
     if len(markdown_files) < 50:  # sanity check
         fmt.error("Found too few files. Something went wrong.")

docs/website/blog/2023-08-24-dlt-etlt.md

@@ -150,16 +150,16 @@ def pseudonymize_name(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_source = data_source.dummy_data().add_map(pseudonymize_name)
+data_resource = data_source.dummy_data().add_map(pseudonymize_name)
 # 3. Inspect your result
-for row in data_source:
+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_source)
+load_info = pipeline.run(data_resource)
 
 ```
 

docs/website/blog/2023-10-26-dlt-prefect.md

@@ -132,7 +132,6 @@ from typing import List
 
 import dlt
 import pendulum
-from pendulum import datetime
 
 from slack import slack_source
 
@@ -144,7 +143,7 @@ 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: datetime) -> None:
+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."""
     # ...
 
@@ -201,7 +200,6 @@ from typing import List
 
 import dlt
 import pendulum
-from pendulum import datetime
 from prefect import flow, task
 from slack import slack_source
 
@@ -214,7 +212,7 @@ def get_resources() -> List[str]:
    ...
 
 @task
-def load_channel_history(channel: str, start_date: datetime) -> None:
+def load_channel_history(channel: str, start_date: pendulum.Date) -> None:
    ...
 
 @task

docs/website/blog/2023-11-01-dlt-dagster.md

@@ -141,13 +141,13 @@ This will generate the default files for Dagster that we will use as a starting
 
 ### Step 4: Add configurable resources and define the asset
 
-- Define a `DltResource` class in `resources/__init__.py` as a Dagster configurable resource. This class allows you to reuse pipeline code inside an 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 DltResource(ConfigurableResource):
+class DDltResource(ConfigurableResource):
     pipeline_name: str
     dataset_name: str
     destination: str
@@ -169,18 +169,18 @@ class DltResource(ConfigurableResource):
 
 ```py
 from dagster import asset, get_dagster_logger
-from ..resources import DltResource
+from ..resources import DDltResource
 from ..dlt import github_issues_resource
 
 @asset
-def issues_pipeline(pipeline: DltResource):
+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 (**DltResource**). In the asset, we use the configurable resource to create a dlt pipeline by using an instance of the configurable resource (**DltResource**) 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.
+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
 
@@ -191,7 +191,7 @@ The defined asset (**issues_pipeline**) takes as input the configurable resource
 ```py
 from dagster import AssetExecutionContext
 @asset
-def issues_pipeline(context: AssetExecutionContext, pipeline: DltResource):
+def issues_pipeline(context: AssetExecutionContext, pipeline: DDltResource):
     ...
     md_content=""
     for package in result.load_packages:
@@ -215,7 +215,7 @@ defs = Definitions(
     assets=all_assets,
     jobs=[simple_pipeline],
     resources={
-        "pipeline": DltResource(
+        "pipeline": DDltResource(
             pipeline_name = "github_issues",
             dataset_name = "dagster_github_issues",
             destination = "bigquery",
@@ -299,7 +299,7 @@ For this example, we are using MongoDB Atlas. Set up the account for MongoDB Atl
 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 DltResource
+### Step 3: Adding the DDltResource
 
  Create a `DltResouce` under the **resources** directory. Add the following code to the `__init__.py`:
 
@@ -308,7 +308,7 @@ from dagster import ConfigurableResource
 
 import dlt
 
-class DltResource(ConfigurableResource):
+class DDltResource(ConfigurableResource):
     pipeline_name: str
     dataset_name: str
     destination: str
@@ -337,7 +337,7 @@ In the `mongodb_pipeline.py` file, locate the `load_select_collection_hint_db` f
 
 ```py
 from ..mongodb import mongodb
-from ..resources import DltResource
+from ..resources import DDltResource
 
 import dlt
 import os
@@ -363,7 +363,7 @@ def dlt_asset_factory(collection_list):
                 for stream in collection_name}
 
         )
-        def collections_asset(context: OpExecutionContext, pipeline: DltResource):
+        def collections_asset(context: OpExecutionContext, pipeline: DDltResource):
 
             # Getting Data From MongoDB    
             data = mongodb(URL, db).with_resources(*collection_name)
@@ -390,12 +390,12 @@ Add the definitions in the `__init__.py` in the root directory:
 from dagster import Definitions
 
 from .assets import dlt_assets
-from .resources import DltResource
+from .resources import DDltResource
 
 defs = Definitions(
     assets=dlt_assets,
     resources={
-        "pipeline": DltResource(
+        "pipeline": DDltResource(
             pipeline_name = "mongo",
             dataset_name = "dagster_mongo",
             destination = "bigquery"

docs/website/blog/2023-11-22-dlt-webhooks-event-based-ingestion.md

@@ -81,9 +81,9 @@ in-depth guide, please refer to the detailed documentation.
 1. Set the environment to Python 3.10 and prepare to insert code into main.py:
    ```py
    import dlt
-   import json
    import time
    from google.cloud import bigquery
+   from dlt.common import json
 
    def github_webhook(request):
        # Extract relevant data from the request payload

docs/website/blog/2024-01-15-dlt-dbt-runner-on-cloud-functions.md

@@ -194,9 +194,9 @@ To integrate dlt and dbt in cloud functions, use the dlt-dbt runner; here’s ho
    ```py
    import dlt
    import logging
-   import json
    from flask import jsonify
    from dlt.common.runtime.slack import send_slack_message
+   from dlt.common import json
 
    def run_pipeline(request):
        """

docs/website/docs/dlt-ecosystem/destinations/destination.md

@@ -54,7 +54,7 @@ The full signature of the destination decorator plus its function is the followi
     loader_file_format="jsonl",
     name="my_custom_destination",
     naming_convention="direct",
-    max_nesting_level=0,
+    max_table_nesting=0,
     skip_dlt_columns_and_tables=True
 )
 def my_destination(items: TDataItems, table: TTableSchema) -> None:

docs/website/docs/dlt-ecosystem/verified-sources/airtable.md

@@ -215,7 +215,7 @@ verified source.
       base_id = base_id,
       table_names = table_names
    )
-   load_info = pipeline.run(airtables, write_deposition = "replace")
+   load_info = pipeline.run(airtables, write_disposition = "replace")
    ```
 
    > You have the option to use table names or table IDs in the code above, in place of "Table1" and

docs/website/docs/dlt-ecosystem/verified-sources/chess.md

@@ -127,7 +127,9 @@ def players_profiles(players: List[str]) -> Iterator[TDataItem]:
     @dlt.defer
     def _get_profile(username: str) -> TDataItem:
         return get_path_with_retry(f"player/{username}")
-    ...
+
+    for username in players:
+        yield _get_profile(username)
 ```
 
 `players`: Is a list of player usernames for which you want to fetch profile data.
@@ -158,10 +160,10 @@ specified otherwise.
 @dlt.resource(write_disposition="append")
 def players_games(
     players: List[str], start_month: str = None, end_month: str = None
-) -> Iterator[Callable[[], List[TDataItem]]]:
+) -> Iterator[TDataItems]:
     # gets a list of already checked(loaded) archives.
     checked_archives = dlt.current.resource_state().setdefault("archives", [])
-    ...
+    yield {}  # return your retrieved data here
 ```
 
 `players`: Is a list of player usernames for which you want to fetch games.

docs/website/docs/dlt-ecosystem/verified-sources/filesystem.md

@@ -453,7 +453,8 @@ verified source.
     )
     # pretty print the information on data that was loaded
     print(load_info)
-    print(listing)(pipeline.last_trace.last_normalize_info)
+    print(listing)
+    print(pipeline.last_trace.last_normalize_info)
    ```
 
 1. Cleanup after loading:

docs/website/docs/dlt-ecosystem/verified-sources/jira.md

@@ -173,7 +173,8 @@ The resource function searches issues using JQL queries and then loads them to t
 ```py
 @dlt.resource(write_disposition="replace")
 def issues(jql_queries: List[str]) -> Iterable[TDataItem]:
-    api_path = "rest/api/3/search"
+   api_path = "rest/api/3/search"
+   return {}  # return the retrieved values here
 ```
 
 `jql_queries`: Accepts a list of JQL queries.

docs/website/docs/dlt-ecosystem/verified-sources/pipedrive.md

@@ -213,10 +213,11 @@ create and store a mapping of custom fields for different entities in the source
 ```py
 @dlt.resource(selected=False)
 def create_state(pipedrive_api_key: str) -> Iterator[Dict[str, Any]]:
-    def _get_pages_for_rename(
-        entity: str, fields_entity: str, pipedrive_api_key: str
-    ) -> Dict[str, Any]:
+   def _get_pages_for_rename(
+      entity: str, fields_entity: str, pipedrive_api_key: str
+   ) -> Dict[str, Any]:
       ...
+   yield _get_pages_for_rename("", "", "")
 ```
 
 It processes each entity in ENTITY_MAPPINGS, updating the custom fields mapping if a related fields

docs/website/docs/general-usage/credentials/config_specs.md

@@ -94,7 +94,7 @@ credentials = ConnectionStringCredentials()
 credentials.drivername = "postgresql"
 credentials.database = "my_database"
 credentials.username = "my_user"
-credentials.password = "my_password"
+credentials.password = "my_password"  # type: ignore
 credentials.host = "localhost"
 credentials.port = 5432
 
@@ -120,8 +120,8 @@ Usage:
 ```py
 credentials = OAuth2Credentials(
     client_id="CLIENT_ID",
-    client_secret="CLIENT_SECRET",
-    refresh_token="REFRESH_TOKEN",
+    client_secret="CLIENT_SECRET",  # type: ignore
+    refresh_token="REFRESH_TOKEN",  # type: ignore
     scopes=["scope1", "scope2"]
 )
 

docs/website/docs/general-usage/customising-pipelines/pseudonymizing_columns.md

@@ -51,11 +51,11 @@ for row in dummy_source().dummy_data.add_map(pseudonymize_name):
 # 1. Create an instance of the source so you can edit it.
 data_source = dummy_source()
 # 2. Modify this source instance's resource
-data_source = data_source.dummy_data.add_map(pseudonymize_name)
+data_resource = data_source.dummy_data.add_map(pseudonymize_name)
 # 3. Inspect your result
-for row in data_source:
+for row in data_resource:
     print(row)
 
 pipeline = dlt.pipeline(pipeline_name='example', destination='bigquery', dataset_name='normalized_data')
-load_info = pipeline.run(data_source)
+load_info = pipeline.run(data_resource)
 ```

docs/website/docs/general-usage/customising-pipelines/renaming_columns.md

@@ -44,10 +44,10 @@ def replace_umlauts_in_dict_keys(d):
 data_source = dummy_source()
 
 # 2. Modify this source instance's resource
-data_source = data_source.dummy_data().add_map(replace_umlauts_in_dict_keys)
+data_resource = data_source.dummy_data().add_map(replace_umlauts_in_dict_keys)
 
 # 3. Inspect your result
-for row in data_source:
+for row in data_resource:
     print(row)
 
 # {'Objekt_0': {'Groesse': 0, 'Aequivalenzpruefung': True}}