nailo2c commented on code in PR #58543:
URL: https://github.com/apache/airflow/pull/58543#discussion_r3156561600
##########
airflow-core/src/airflow/jobs/scheduler_job_runner.py:
##########
@@ -2264,6 +2266,63 @@ def _update_state(dag: SerializedDAG, dag_run: DagRun):
dag_run.run_id,
)
continue
+ # For AssetAndTimeSchedule, defer starting until all required
assets are queued.
+ # Only gate scheduled runs; manual and backfill runs should start
immediately.
+ if isinstance(dag.timetable, AssetAndTimeSchedule) and
dag_run.run_type == DagRunType.SCHEDULED:
+ # Reuse dagrun_timeout to fail runs that wait in QUEUED for
assets for too long.
+ if (
+ dag.dagrun_timeout
+ and dag_run.queued_at
+ and dag_run.queued_at < timezone.utcnow() -
dag.dagrun_timeout
+ ):
+ dag_run.set_state(DagRunState.FAILED)
+ session.flush()
+ self.log.info(
+ "Run %s of %s has timed-out while waiting for assets",
+ dag_run.run_id,
+ dag_run.dag_id,
+ )
+ if (
+ dag_run.run_type
+ in (
+ DagRunType.SCHEDULED,
+ DagRunType.MANUAL,
+ DagRunType.ASSET_TRIGGERED,
+ )
+ and dag_run.dag_model is not None
+ ):
+
self._set_exceeds_max_active_runs(dag_model=dag_run.dag_model, session=session)
Review Comment:
I think it's still necessary. Once the queued run fails, we need to update
`dag_model.exceeds_max_non_backfill`. Otherwise Scheduler may still think the
Dag is maxed out and skip creating the next scheduled run.
https://github.com/apache/airflow/blob/b3138e2400eeed96863c9ced3ff2ff3e0ad7a519/airflow-core/src/airflow/jobs/scheduler_job_runner.py#L3314
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