ashb commented on code in PR #26208:
URL: https://github.com/apache/airflow/pull/26208#discussion_r964998271
##########
docs/apache-airflow/concepts/datasets.rst:
##########
@@ -15,33 +15,137 @@
specific language governing permissions and limitations
under the License.
-Datasets
-========
+Data-aware scheduling
+=====================
.. versionadded:: 2.4
-With datasets, instead of running a DAG on a schedule, a DAG can be configured
to run when a dataset has been updated.
+Quickstart
+----------
-To use this feature, define a dataset:
+In addition to scheduling DAGs based upon time, they can also be scheduled
based upon another DAG updating a dataset.
-.. exampleinclude:: /../../airflow/example_dags/example_datasets.py
- :language: python
- :start-after: [START dataset_def]
- :end-before: [END dataset_def]
+.. code-block:: python
-Then reference the dataset as a task outlet:
+ from airflow import Dataset
-.. exampleinclude:: /../../airflow/example_dags/example_datasets.py
- :language: python
- :dedent: 4
- :start-after: [START task_outlet]
- :end-before: [END task_outlet]
+ with DAG(...):
+ task1 = MyOperator(
+ # this task updates example.csv
+ outlets=[Dataset("s3://dataset-bucket/example.csv")],
+ ...,
+ )
-Finally, define a DAG and reference this dataset in the DAG's ``schedule``
argument:
-.. exampleinclude:: /../../airflow/example_dags/example_datasets.py
- :language: python
- :start-after: [START dag_dep]
- :end-before: [END dag_dep]
+ with DAG(
+ # this DAG should be run when example.csv is updated (by dag1)
+ schedule=[Dataset("s3://dataset-bucket/example.csv")],
+ ...,
+ ) as dag2:
+ Task2 = OtherOperator(...)
+ ...
-You can reference multiple datasets in the DAG's ``schedule`` argument. Once
there has been an update to all of the upstream datasets, the DAG will be
triggered. This means that the DAG will run as frequently as its
least-frequently-updated dataset.
+What is a "dataset"?
+--------------------
+
+An Airflow dataset is a stand-in for a logical grouping of data that flows
through multiple DAGs, possibly being changed or updated by each one. Datasets
are updated by upstream "producer" tasks, and dataset updates contribute to
scheduling downstream "consumer" DAGs.
+
+A dataset is a construct around a Uniform Resource Identifier (URI) that you
create in your code:
+
+.. code-block:: python
+
+ from airflow import Dataset
+
+ example_dataset = Dataset('s3://dataset-bucket/example.csv')
+
+Airflow treats the dataset URI identifiers as opaque values intended to be
human-readable, and makes no assumptions about the content or location of the
data represented by the identifier. They are treated as strings, so any use of
regular expressions (eg ``input_\d+.csv``) or file glob patterns (eg
``input_2022*.csv``) as an attempt to create multiple datasets from one
declaration will not work.
+
+There are three restrictions on the dataset identifier:
+
+1. It must be a valid URI, which means it must only be composed of only ASCII
characters.
+2. The URI scheme cannot be ``airflow`` (this is reserved for future use).
+3. It must be unique (although it is case is sensitive throughout, so
"s3://example_dataset" and "s3://Example_Dataset" are considered different, and
"s3://example_dataset" and "S3://example_dataset" are considered different).
+
+If you try to use either of the examples below, your code will cause a
ValueError to be raised, and Airflow will not import it.
+
+.. code-block:: python
+
+ # invalid datasets:
+ reserved = Dataset(uri="airflow://example_dataset")
+ not_ascii = Dataset(uri="èxample_datašet")
+
+The identifier does not have to be an absolute URI, it can be a scheme-less,
relative URI, or even just a simple path or string:
+
+.. code-block:: python
+
+ # valid datasets:
+ schemeless = Dataset(uri="//example/dataset")
+ csv_file = Dataset(uri="example.csv")
+
+If required, an extra dictionary can be included in a Dataset:
+
+.. code-block:: python
+
+ example_dataset = Dataset(
+ "s3://dataset/example.csv",
+ extra={'team': 'trainees'},
+ )
+
+..note::
+
+ Security Note: Dataset URI and extra fields are not encrypted, they are
stored in cleartext, in Airflow's metadata database. Do NOT store any sensitive
values, especially URL server credentials, in dataset URIs or extra key values!
+
+How to use datasets in your DAGs
+--------------------------------
+
+You can use datasets to specify data dependencies in your DAGs. Take the
following example:
+
+.. code-block:: python
+
+ example_dataset = Dataset("s3://dataset/example.csv")
+
+ with DAG(dag_id='update_example_dataset', ...) as update_example_dataset:
+ BashOperator(task_id='example_producer', outlets=[example_dataset],
...)
+
+ with DAG(dag_id='example_consumer', schedule=[example_dataset], ...):
+ BashOperator(...)
+
+Once the ``example_producer`` task of the first ``update_example_dataset`` DAG
has completed successfully, Airflow schedules ``requires_example_dataset``.
Only a task's success triggers dataset updates — if the task fails or if it
raises an :class:`~airflow.exceptions.AirflowSkipException`, no update occurs,
and the ``requires_example_dataset`` DAG will not be scheduled.
Review Comment:
```suggestion
Once the ``example_producer`` task of the first ``update_example_dataset``
DAG has completed successfully, Airflow schedules ``example_consumer``. Only a
task's success triggers dataset updates — if the task fails or if it raises an
:class:`~airflow.exceptions.AirflowSkipException`, no update occurs, and the
``example_consumer`` DAG will not be scheduled.
```
Which dag id do people think is clearer? `example_consumer` or
`requires_example_dataset`? (i.e. should we fix the prose or the code sample?)
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