dstandish commented on code in PR #26208:
URL: https://github.com/apache/airflow/pull/26208#discussion_r965183912
##########
docs/apache-airflow/concepts/datasets.rst:
##########
@@ -15,33 +15,185 @@
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 a task 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(...):
+ 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")],
+ ...,
+ ):
+ ...
-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. Datasets may
be updated by upstream "producer" tasks, and dataset updates contribute to
scheduling downstream "consumer" DAGs.
+
+A dataset is defined by a Uniform Resource Identifier (URI):
+
+.. code-block:: python
+
+ from airflow import Dataset
+
+ example_dataset = Dataset('s3://dataset-bucket/example.csv')
+
+Airflow treats the dataset URI as an opaque value intended to be
human-readable, and makes no assumptions about the content or location of the
data represented by the identifier. It is treated as a string, 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 two restrictions on the dataset URI:
+
+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).
+
+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("airflow://example_dataset")
+ not_ascii = Dataset("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("//example/dataset")
+ csv_file = Dataset("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 credentials, in dataset URIs or extra key values!
+
+The URI is also case sensitive throughout, so ``s3://example_dataset`` and
``s3://Example_Dataset`` are considered different, as is
``s3://example_dataset`` and ``S3://example_dataset``.
+
+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='producer', ...):
+ BashOperator(task_id='producer', outlets=[example_dataset], ...)
+
+ with DAG(dag_id='consumer', schedule=[example_dataset], ...):
+ ...
+
+Once the ``producer`` task in the ``producer`` DAG has completed successfully,
Airflow schedules the ``consumer`` DAG. 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
``consumer`` DAG will not be scheduled.
+
+Multiple Datasets
+-----------------
+
+As the ``schedule`` parameter is a list, DAGs can require multiple datasets,
and the DAG will be scheduled once **all** datasets it consumes have been
updated at least once since the last time it was run:
+
+.. code-block:: python
+
+ with DAG(
+ dag_id='multiple_datasets_example',
+ schedule=[
+ example_dataset_1,
+ example_dataset_2,
+ example_dataset_3,
+ ],
+ ...,
+ ):
+ ...
+
+
+If one dataset is updated multiple times before all consumed datasets have
been updated, the downstream DAG will still only be run once, as shown in this
illustration:
+
+.. ::
+ ASCII art representation of this diagram
+
+ example_dataset_1 x----x---x---x----------------------x-
+ example_dataset_2 -------x---x-------x------x----x------
+ example_dataset_3 ---------------x-----x------x---------
+ DAG runs created * *
+
+.. graphviz::
+
+ graph dataset_event_timeline {
+ graph [layout=neato]
+ {
+ node [margin=0 fontcolor=blue width=0.1 shape=point label=""]
+ e1 [pos="1,2.5!"]
+ e2 [pos="2,2.5!"]
+ e3 [pos="2.5,2!"]
+ e4 [pos="4,2.5!"]
+ e5 [pos="5,2!"]
+ e6 [pos="6,2.5!"]
+ e7 [pos="7,1.5!"]
+ r7 [pos="7,1!" shape=star width=0.25 height=0.25 fixedsize=shape]
+ e8 [pos="8,2!"]
+ e9 [pos="9,1.5!"]
+ e10 [pos="10,2!"]
+ e11 [pos="11,1.5!"]
+ e12 [pos="12,2!"]
+ e13 [pos="13,2.5!"]
+ r13 [pos="13,1!" shape=star width=0.25 height=0.25 fixedsize=shape]
+ }
+ {
+ node [shape=none label="" width=0]
+ end_ds1 [pos="14,2.5!"]
+ end_ds2 [pos="14,2!"]
+ end_ds3 [pos="14,1.5!"]
+ }
+
+ {
+ node [shape=none margin=0.25 fontname="roboto,sans-serif"]
+ example_dataset_1 [ pos="-0.5,2.5!"]
+ example_dataset_2 [ pos="-0.5,2!"]
+ example_dataset_3 [ pos="-0.5,1.5!"]
+ dag_runs [label="DagRuns created" pos="-0.5,1!"]
+ }
+
+ edge [color=lightgrey]
+
+ example_dataset_1 -- e1 -- e2 -- e4 -- e6
-- e13 -- end_ds1
+ example_dataset_2 -- e3 -- e5 -- e8
-- e10 -- e12 -- end_ds2
+ example_dataset_3 -- e7 -- e9
-- e11 -- end_ds3
+
+ }
+
+Notes on schedules
+------------------
+
+The ``schedule`` parameter to your DAG can take either a list of datasets to
consume or a timetable-based option. The two cannot currently be mixed.
+
+When using datasets, in this first release (v2.4) waiting for all datasets in
the list to be updated is the only option when multiple datasets are consumed
by a DAG. A later release should introduce more fine-grained options allowing
for greater flexibility.
Review Comment:
```suggestion
When using datasets, in this first release (v2.4) waiting for all datasets
in the list to be updated is the only option when multiple datasets are
consumed by a DAG. A later release may introduce more fine-grained options
allowing for greater flexibility.
```
also this might make more sense as a note in the previous section
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