XD-DENG opened a new pull request, #62893:
URL: https://github.com/apache/airflow/pull/62893
# What's the issue
Previously, `_schedule_all_dag_runs()` used a list comprehension to process
all DagRuns. If any `_schedule_dag_run()` raised an exception for any single
dag run, the entire comprehension would abort, no other DagRun would be
processed, and the exception would propagate up to crash the scheduler process
— **stopping scheduling for ALL DAGs**.
# How to reproduce
I tried to run a DagRun then go to database to mark one TaskInstace's state
to `up_for_retry` and the `end_date` to `None`. In this case, the scheduler
simply crashed with the error below:
```
scheduler | File
"/Users/xiaodong/Downloads/airflow-test-venv/lib/python3.11/site-packages/airflow/models/taskinstance.py",
line 1005, in next_retry_datetime
scheduler | return self.end_date + delay
scheduler | ~~~~~~~~~~~~~~^~~~~~~
scheduler | TypeError: unsupported operand type(s) for +: 'NoneType' and
'datetime.timedelta'
scheduler | 2026-03-04T20:48:14.558116Z [info ] Shutting down
LocalExecutor; waiting for running tasks to finish. Signal again if you don't
want to wait. [airflow.executors.local_executor.LocalExecutor]
loc=local_executor.py:252
```
While the specific scenario used to reproduce this (a TaskInstance with
`state=UP_FOR_RETRY` and `end_date=NULL`) is nearly impossible under normal
operation, the lack of **per-dag-run fault isolation** means ANY unexpected
exception from ANY DagRun would have the same fatal effect.
# What's the fix
Replace the list comprehension with an explicit loop that catches exceptions
per DagRun, logs the error with full traceback, and continues processing the
remaining DagRuns as well as the future DagRuns.
<!-- SPDX-License-Identifier: Apache-2.0
https://www.apache.org/licenses/LICENSE-2.0 -->
<!--
Thank you for contributing!
Please provide above a brief description of the changes made in this pull
request.
Write a good git commit message following this guide:
http://chris.beams.io/posts/git-commit/
Please make sure that your code changes are covered with tests.
And in case of new features or big changes remember to adjust the
documentation.
Feel free to ping (in general) for the review if you do not see reaction for
a few days
(72 Hours is the minimum reaction time you can expect from volunteers) - we
sometimes miss notifications.
In case of an existing issue, reference it using one of the following:
* closes: #ISSUE
* related: #ISSUE
-->
---
##### Was generative AI tooling used to co-author this PR?
<!--
If generative AI tooling has been used in the process of authoring this PR,
please
change below checkbox to `[X]` followed by the name of the tool, uncomment
the "Generated-by".
-->
- [x] Yes (please specify the tool below)
Generated-by: [Claude Opus 4.6] following [the
guidelines](https://github.com/apache/airflow/blob/main/contributing-docs/05_pull_requests.rst#gen-ai-assisted-contributions)
---
* Read the **[Pull Request
Guidelines](https://github.com/apache/airflow/blob/main/contributing-docs/05_pull_requests.rst#pull-request-guidelines)**
for more information. Note: commit author/co-author name and email in commits
become permanently public when merged.
* For fundamental code changes, an Airflow Improvement Proposal
([AIP](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Improvement+Proposals))
is needed.
* When adding dependency, check compliance with the [ASF 3rd Party License
Policy](https://www.apache.org/legal/resolved.html#category-x).
* For significant user-facing changes create newsfragment:
`{pr_number}.significant.rst` or `{issue_number}.significant.rst`, in
[airflow-core/newsfragments](https://github.com/apache/airflow/tree/main/airflow-core/newsfragments).
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
