ferruzzi commented on code in PR #55088:
URL: https://github.com/apache/airflow/pull/55088#discussion_r2320316862
##########
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 discussed a few variants of this including rolling averages, average by
dag version, average since a given datetime, average since a given dagrun_id,
etc. I think we decided to do this as the basic one then add a parameterized
version that accepts a dict of conditions that get assembled into `WHERE`
clauses. Would you be cool with that approach?
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