o-nikolas commented on code in PR #55088:
URL: https://github.com/apache/airflow/pull/55088#discussion_r2320520849
##########
airflow-core/src/airflow/models/deadline.py:
##########
@@ -355,6 +355,31 @@ def _evaluate_with(self, *, session: Session, **kwargs:
Any) -> datetime:
return _fetch_from_db(DagRun.queued_at, session=session, **kwargs)
+ class AverageRuntimeDeadline(BaseDeadlineReference):
+ """A deadline that calculates the average runtime from past DAG
runs."""
+
+ required_kwargs = {"dag_id"}
+
+ @provide_session
+ def _evaluate_with(self, *, session: Session, **kwargs: Any) ->
datetime:
+ from airflow.models import DagRun
+
+ dag_id = kwargs["dag_id"]
+
+ # Query for completed DAG runs with both start and end dates
+ query = select(func.avg(func.extract("epoch", DagRun.end_date -
DagRun.start_date))).filter(
+ DagRun.dag_id == dag_id, DagRun.start_date.isnot(None),
DagRun.end_date.isnot(None)
+ )
+
+ avg_seconds = session.execute(query).scalar()
Review Comment:
We can't error on zero otherwise we'll never get past zero right? I think
the best we can do is log a warning saying that we can't yet compute and
average.
Then it's up to documentation to instruct people ("hey, you really should
only add this deadline to an established dag") but surely people will not
follow that (and can't in all cases of deploying dags via ci/cd).
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