uranusjr commented on code in PR #38481:
URL: https://github.com/apache/airflow/pull/38481#discussion_r1542280532
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docs/apache-airflow/authoring-and-scheduling/datasets.rst:
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@@ -224,6 +224,29 @@ If one dataset is updated multiple times before all
consumed datasets have been
}
+Attaching extra information to an emitting Dataset Event
+--------------------------------------------------------
+
+.. versionadded:: 2.10.0
+
+A task with a dataset outlet can optionally attach extra information before it
emits a dataset event. This is different
+from `Extra information on Dataset`_. Extra information on a dataset
statically describes the entity pointed to by the dataset URI; extra
information on the *dataset event* instead should be used to annotate the
triggering data change, such as how many rows in the database are changed by
the update, or the date range covered by it.
+
+The easiest way to attach extra information to the dataset event is by
accessing ``dataset_events`` in a task's execution context:
+
+.. code-block:: python
+
+ example_s3_dataset = Dataset("s3://dataset/example.csv")
+
+
+ @task(outlets=[example_s3_dataset])
+ def write_to_s3(*, dataset_events):
+ df = ... # Get a Pandas DataFrame to write.
+ # Write df to dataset...
+ dataset_events[example_s3_dataset].extras = {"row_count": len(df)}
+
+This can also be done in classic operators by either subclassing the operator
and overriding ``execute``, or by supplying a pre- or post-execution function.
Review Comment:
I believe so, the callback is executed in `_execute_task_with_callbacks`,
while dataset events are emitted in _register_dataset_changes`. Both functions
are called in `_run_raw_task` and the callback is run earlier. I should add a
test for this.
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