xBis7 commented on code in PR #71737:
URL: https://github.com/apache/airflow/pull/71737#discussion_r4315428123
##########
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:
I measured it with 30 dags with 11 tasks each, all dependencies linear.
And the difference is negligible and it's actually in favor of the new code.
I run it multiple times and the new code is always 2-3 seconds faster.
It makes sense because the debug spans from this PR
https://github.com/apache/airflow/pull/69809 showed in my initial investigation
that the query took 10% of the time and the ORM hydration 90%. The queries are
very fast and python code is expensive.
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