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new 1e11443ed4 Refactor DatasetOrTimeSchedule timetable docs (#37771)
1e11443ed4 is described below
commit 1e11443ed479276fd765868c818bd59de96eeb86
Author: vatsrahul1001 <[email protected]>
AuthorDate: Thu Feb 29 14:15:43 2024 +0530
Refactor DatasetOrTimeSchedule timetable docs (#37771)
---------
Co-authored-by: Ankit Chaurasia <[email protected]>
---
.../authoring-and-scheduling/timetable.rst | 30 +++++-----------------
1 file changed, 6 insertions(+), 24 deletions(-)
diff --git a/docs/apache-airflow/authoring-and-scheduling/timetable.rst
b/docs/apache-airflow/authoring-and-scheduling/timetable.rst
index 5ea18b36c4..78234910a6 100644
--- a/docs/apache-airflow/authoring-and-scheduling/timetable.rst
+++ b/docs/apache-airflow/authoring-and-scheduling/timetable.rst
@@ -179,34 +179,15 @@ first) event for the data interval, otherwise manual runs
will run with a ``data
.. _dataset-timetable-section:
-DatasetTimetable
-^^^^^^^^^^^^^^^^
+Dataset event based scheduling with time based scheduling
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Combining conditional dataset expressions with time-based schedules enhances
scheduling flexibility:
-The ``DatasetTimetable`` is a specialized timetable allowing for the
scheduling of DAGs based on both time-based schedules and dataset events. It
facilitates the creation of scheduled runs (as per traditional timetables) and
dataset-triggered runs, which operate independently.
+The ``DatasetOrTimeSchedule`` is a specialized timetable allowing for the
scheduling of DAGs based on both time-based schedules and dataset events. It
facilitates the creation of scheduled runs (as per traditional timetables) and
dataset-triggered runs, which operate independently.
This feature is particularly useful in scenarios where a DAG needs to run on
dataset updates and also at periodic intervals. It ensures that the workflow
remains responsive to data changes and consistently runs regular checks or
updates.
-Here's an example of a DAG using ``DatasetTimetable``:
-
-.. code-block:: python
-
- from airflow.timetables.dataset import DatasetTimetable
- from airflow.timetables.trigger import CronTriggerTimetable
-
-
- @dag(
- schedule=DatasetTimetable(time=CronTriggerTimetable("0 1 * * 3",
timezone="UTC"), event=[dag1_dataset])
- # Additional arguments here, replace this comment with actual arguments
- )
- def example_dag():
- # DAG tasks go here
- pass
-
-In this example, the DAG is scheduled to run every Wednesday at 01:00 UTC
based on the ``CronTriggerTimetable``, and it is also triggered by updates to
``dag1_dataset``.
-
-Integrate conditional dataset with Time-Based Scheduling
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Combining conditional dataset expressions with time-based schedules enhances
scheduling flexibility:
+Here's an example of a DAG using ``DatasetOrTimeSchedule``:
.. code-block:: python
@@ -225,6 +206,7 @@ Combining conditional dataset expressions with time-based
schedules enhances sch
pass
+
Timetables comparisons
----------------------
