amoghrajesh commented on code in PR #72100:
URL: https://github.com/apache/airflow/pull/72100#discussion_r3976368077
##########
airflow-core/tests/unit/api_fastapi/core_api/routes/public/test_task_instances.py:
##########
@@ -4339,6 +4339,72 @@ def test_clear_dry_run_does_not_set_note(self,
test_client, session):
ti_id = response_data["task_instances"][0]["id"]
_check_task_instance_note(session, ti_id, {"content":
"placeholder-note", "user_id": None})
+ def _seed_task_state(self, session, dag_id):
+ """Store one task state key for the single TI created by these
tests."""
+ ti = session.scalars(select(TaskInstance).where(TaskInstance.dag_id ==
dag_id)).one()
+ MetastoreBackend().set(
+ TaskScope(dag_id=ti.dag_id, run_id=ti.run_id, task_id=ti.task_id,
map_index=ti.map_index),
+ "job_id",
+ "app_1234",
+ session=session,
+ )
+ session.commit()
+
+ def _task_state_rows(self, session, dag_id):
+ return
session.scalars(select(TaskStateStoreModel).where(TaskStateStoreModel.dag_id ==
dag_id)).all()
+
+ @pytest.mark.db_test
+ @pytest.mark.parametrize(
+ ("payload_extra", "expect_kept"),
+ [
+ pytest.param({}, False, id="default-discards"),
+ pytest.param({"keep_task_state": True}, True, id="keep-preserves"),
+ pytest.param({"keep_task_state": False}, False,
id="explicit-false-discards"),
Review Comment:
Handled in [comments from
kaxil](https://github.com/apache/airflow/pull/72100/commits/3ed605d61172d8c64b611097bc003e21c2ece402)
##########
airflow-core/newsfragments/72100.significant.rst:
##########
@@ -0,0 +1,39 @@
+Clearing a task now discards its task state store entries by default
+
+Clearing a task instance discards its ``task_state_store`` entries, so the
next attempt starts from
+the beginning instead of resuming from a checkpoint or reconnecting to an
external job recorded by
+the attempt that was cleared.
+
+Retries are unaffected. They keep task state exactly as before, which is what
crash recovery relies
+on. Only a deliberate clear discards.
+
+**Why**
+
+Clearing means "run this again". A checkpoint records how far a task got, not
what it got there
+with, so resuming after the code or the upstream data changed left work done
before the fix in place
+and silently mixed it with the corrected work. Clearing a task whose external
job had already
+succeeded was worse: the operator read the stored result back and returned in
seconds having run
+nothing.
+
+**Keeping the old behaviour**
+
+Pass ``keep_task_state=True`` to the clear task instances endpoint, or tick
"keep task state" in the
+clear dialog. Use it when nothing about the inputs or the code changed and the
task should carry on
+where it stopped, or when an external job is still running and you want the
next attempt to
+reconnect rather than submit a duplicate.
Review Comment:
Handled in [comments from
kaxil](https://github.com/apache/airflow/pull/72100/commits/3ed605d61172d8c64b611097bc003e21c2ece402)
##########
airflow-core/docs/core-concepts/resumable-tasks.rst:
##########
@@ -145,26 +145,42 @@ existing job on retry instead of submitting a new one.
For more details and a working example, see
:class:`~airflow.sdk.ResumableJobMixin`.
-**Clearing a task is treated the same as a retry**
-
-Clearing a task instance does not delete its ``task_state_store`` rows -- they
are only removed
-when the ``dag_run`` itself is deleted, or by :ref:`airflow state-store clean
-<task-and-asset-state-store-cleanup>`. For a checkpointed task this is usually
what you want:
-clearing resumes from the last checkpoint rather than starting over.
-
-For an operator with durable execution, it means clearing a task whose
external job already
-succeeded reads that stored result back and returns immediately, without
resubmitting the job. If
-you want clearing to always resubmit regardless of a prior success, set
-``[state_store] clear_on_success = True``, which deletes a task's state store
rows automatically
-when it moves to ``SUCCESS`` (see
:doc:`/administration-and-deployment/task-and-asset-state-store`).
-
-This does not guarantee the external job is still there to reconnect to,
though. Clearing a task
-that is actively running (``deferrable=False``) stops the worker process,
which runs the
-operator's ``on_kill``. Most operators with durable execution cancel the
external job there by
-default, so the next attempt finds it already stopped instead of still running
-- an operator that
-leaves the job running by default on kill is the exception, check its own
docs. Deferred tasks
-(``deferrable=True``) don't have this problem: there is no actively polling
worker process for the
-clear to interrupt.
+**Retries resume, clearing starts over**
+
+A retry keeps the task's ``task_state_store`` entries, which is what makes
crash recovery work: the
+next attempt reads the checkpoint or the external job id written by the
attempt before it.
+
+Clearing a task discards them. Clearing means "run this again", and a
checkpoint records how far a
+task got, not what it got there with. If you fixed the code or the upstream
data and cleared the
+task, resuming would leave the work done before the fix in place and silently
mix it with the
+corrected work. So by default a cleared task starts from the beginning.
+
+To resume from the checkpoint instead, set ``keep_task_state`` when clearing,
or tick the
+corresponding box in the clear dialog. That is the right choice when nothing
about the inputs or the
+code changed and you only want the task to carry on where it stopped.
+
+**Clearing a task that submitted an external job**
+
+For an operator with durable execution the stored value is an external job id,
so discarding it has
+a different consequence: the next attempt submits a new job rather than
reconnecting to the existing
+one.
+
+Whether that matters depends on what happened to the job:
+
+* Most operators cancel the external job in ``on_kill``, so clearing a
*running* task stops the job
+ and there is nothing left to reconnect to. Submitting a fresh one is the
only option anyway.
+* An operator configured to leave the job running on kill (for example
+ ``KubernetesPodOperator`` with ``on_kill_action="keep_pod"``) keeps it
alive, so a fresh submission
+ runs alongside it. Check the operator's own docs.
+* Clearing a *failed* task never runs ``on_kill`` at all, so an external job
that outlived the
+ worker is still running.
Review Comment:
Handled in [comments from
kaxil](https://github.com/apache/airflow/pull/72100/commits/3ed605d61172d8c64b611097bc003e21c2ece402)
##########
airflow-core/src/airflow/api_fastapi/core_api/services/public/task_instances.py:
##########
@@ -74,21 +88,29 @@ def _clear_task_state_store_on_success(tis: Sequence[TI],
session: Session) -> N
try:
backend.clear(scope=scope, session=session)
log.info(
- "Cleared task state on success",
+ event,
Review Comment:
Handled in [comments from
kaxil](https://github.com/apache/airflow/pull/72100/commits/3ed605d61172d8c64b611097bc003e21c2ece402)
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