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closed because of open PR limit
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Hello @fat-catTW - thank you for your contributions to Apache Airflow! The Airflow community has introduced a limit of 5 open pull requests at a time for contributors without write access to the repository. You currently have 17 open pull requests, so - as a one-time step of introducing the limit - we closed the ones where maintainers have not engaged yet:
These pull requests stay open because maintainers are already engaged in them - they count towards your limit:
This is not a judgement of you or of your changes. We never told contributors before that opening many pull requests at once was a problem, so there is nothing to feel bad about - and nothing is lost: your branches, commits and the review history stay where they are. What we ask you to do is to make your first prioritization decision: choose which of the pull requests above matter most to you, and reopen them (up to 5 open at a time, including the ones still open) with the "Reopen pull request" button or While your pull requests are waiting for review, the most valuable thing you can do is help in other ways - reviewing other contributors' pull requests, helping with issues, and taking part in the discussions on the devlist and Slack. Why we introduced the limit, what it means for you and how to reopen or restore a pull request is explained in https://github.com/apache/airflow/blob/main/contributing-docs/32_open_pull_request_limit.rst. Drafted-by: Claude Code (Opus 5); reviewed by @potiuk before posting |
Why
SchedulerJobRunner._create_dag_runs()had a TODO asking whether the scheduler should callsession.flush()orsession.expunge_all()while creating scheduled DagRuns.The concern behind the TODO is that the scheduler may iterate over multiple Dags, create scheduled DagRuns, update
DagModelscheduling fields, and create task instance rows in the same SQLAlchemy session. For larger Dags or larger batches of Dags, leaving ORM state pending until the outer transaction boundary can make it harder to reason about session growth and memory behavior.This PR resolves that TODO by flushing after each successful scheduled DagRun creation and related
DagModelscheduling-state update.Why
flush()Instead ofexpunge_all()The narrower operation is enough for the state observed in this path.
session.flush()writes pending ORM changes to the database transaction while keeping objects attached to the session. That directly addresses the observed pending/dirty scheduler state after creating a scheduled DagRun and updating the correspondingDagModel.session.expunge_all()does something broader: it detaches every ORM object from the session. That can reduce identity-map retention, but it also carries more behavioral risk because later scheduler code may still expect ORM objects to be attached, refreshable, or tracked by the session.To decide whether
expunge_all()was needed, this path was checked for retained ORM objects after DagRun creation. If creating larger Dags left one trackedDagRunorTaskInstanceobject per created row,flush()would not address that memory growth because it does not clear the session identity map.A local pressure check with 20 Dags and 500 tasks per Dag created 10,000 TaskInstance rows. After
_create_dag_runs()returned, the observed session state was:Was generative AI tooling used to co-author this PR?
[X]Yes (please specify the tool below)Generated-by: [Codex] following the guidelines
{pr_number}.significant.rst, in airflow-core/newsfragments. You can add this file in a follow-up commit after the PR is created so you know the PR number.