xBis7 commented on code in PR #71737:
URL: https://github.com/apache/airflow/pull/71737#discussion_r4312899012
##########
airflow-core/src/airflow/models/dagrun.py:
##########
@@ -1479,8 +1517,27 @@ def recalculate(self) -> _UnfinishedStates:
@provide_session
def task_instance_scheduling_decisions(self, *, session: Session =
NEW_SESSION) -> TISchedulingDecision:
- tis = self.get_task_instances(session=session, state=State.task_states)
- self.log.debug("number of tis tasks for %s: %s task(s)", self,
len(tis))
+ # Fetch finished task instances as lightweight read-only rows and only
the
+ # unfinished ones as full ORM objects. Finished ones are never mutated
during
+ # scheduling, so hydrating them is pure overhead.
+ #
+ # Flush here to avoid task instances ending up in the wrong list or in
both,
+ # in case of a pending in-session state change.
+ # The airflow session maker sets autoflush=False (ref
settings.configure_orm).
+ session.flush()
+ unfinished_tis = self.get_task_instances(session=session,
state=State.unfinished)
+ finished_rows = session.execute(
Review Comment:
@vatsrahul1001 Because of all the new task loops code, I wanted to rerun the
benchmarks. I'm going to check this as well and get back with the results.
When I initially tested the code in multiple scenarios including the one
that you mentioned, I didn't notice any difference at all in the timing. It was
neither good nor bad, just the same. I had to increase the load a lot to see
the side-effects.
The underlying code has changed a lot in the last days.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
