uranusjr commented on code in PR #32000:
URL: https://github.com/apache/airflow/pull/32000#discussion_r1282763851
##########
airflow/models/dag.py:
##########
@@ -2738,22 +2738,48 @@ def add_logger_if_needed(ti: TaskInstance):
)
tasks = self.task_dict
+
+ task_try_numbers: dict[tuple[str, int], int] =
collections.defaultdict(int)
+
self.log.debug("starting dagrun")
# Instead of starting a scheduler, we run the minimal loop possible to
check
# for task readiness and dependency management. This is notably faster
# than creating a BackfillJob and allows us to surface logs to the user
while dr.state == DagRunState.RUNNING:
schedulable_tis, _ = dr.update_state(session=session)
- try:
- for ti in schedulable_tis:
+
+ for ti in schedulable_tis:
+ try:
add_logger_if_needed(ti)
ti.task = tasks[ti.task_id]
_run_task(ti, session=session)
- except Exception:
- self.log.info(
- "Task failed. DAG will continue to run until finished and
be marked as failed.",
- exc_info=True,
- )
+ except Exception:
+ if ti.state == TaskInstanceState.UP_FOR_RETRY:
+ try_number = task_try_numbers[ti.task_id, ti.map_index]
+ if try_number > ti.max_tries:
+ ti.set_state(TaskInstanceState.FAILED)
+ else:
+ task_try_numbers[ti.task_id, ti.map_index] =
try_number + 1
+ self.log.info(
+ "Task failed. DAG will continue to run until finished
and be marked as failed.",
+ exc_info=True,
+ )
+ for ti in dr.get_task_instances(session=session,
state=TaskInstanceState.SCHEDULED):
Review Comment:
Depends on how you view things I guess. The context of all this is pretty
undefined to begin with. I would not object if someone wants to expand the
scope to include the triggering part. It would be quite more involved than this
PR though.
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