pankajastro commented on code in PR #32000:
URL: https://github.com/apache/airflow/pull/32000#discussion_r1244825723
##########
airflow/models/dag.py:
##########
@@ -2690,22 +2690,50 @@ def add_logger_if_needed(ti: TaskInstance):
)
tasks = self.task_dict
+ # Special case when retry is set and DAG is paused
+ task_try_number = {}
+
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 == State.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,
- )
+ if ti.task_id not in task_try_number:
+ task_try_number[ti.task_id] = 0
+ ti = _run_task(ti, session=session)
+ except Exception:
+ if ti.state == State.UP_FOR_RETRY:
+ try_number = task_try_number.get(ti.task_id, 0)
+ if try_number > ti.max_tries:
+ ti.set_state(State.FAILED)
+ else:
+ task_try_number[ti.task_id] = 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():
+ # Special case TI resume from deferred state
+ if ti.state == State.SCHEDULED:
Review Comment:
first we are calling `_run_task` L2709 for a task which set the task in the
running state. Here, since a task has run previously and now is in a scheduled
state so I'm assuming it is a deferrable task, wdyt?
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