HsiuChuanHsu opened a new pull request, #52840:
URL: https://github.com/apache/airflow/pull/52840
<!--
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
-->
<!--
Thank you for contributing! 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 committers for the review!
In case of an existing issue, reference it using one of the following:
closes: #ISSUE
related: #ISSUE
How to write a good git commit message:
http://chris.beams.io/posts/git-commit/
-->
<!-- Please keep an empty line above the dashes. -->
## Description:
When multiple executors (e.g., `CeleryExecutor,KubernetesExecutor`) are
configured, the worker default `KEDA` query uses the `queue` column in the
Airflow metadata database to determine Celery worker scaling.
However, task routing in Airflow is based on the `executor` attribute, not
`queue`. This causes Celery workers to scale up unnecessarily for tasks
explicitly configured with `executor='KubernetesExecutor'` but using the
`default` queue
## Changes:
This PR updates the KEDA query logic in `values.yaml` to correctly filter
tasks based on the executor field when multiple `executors` are configured:
- For `CeleryKubernetesExecutor`, the existing logic is retained, using
`queue != '{{ .Values.config.celery_kubernetes_executor.kubernetes_queue }}'`.
- For configurations including `KubernetesExecutor` (alone or with
`CeleryExecutor`), the query is updated to use `executor !=
'KubernetesExecutor'` ensuring tasks assigned to `KubernetesExecutor` not
trigger Celery worker scaling.
## Testing Steps
1. Deploy Airflow using Helm Chart with `KEDA` enabled and multiple
executors:
```
executor: CeleryExecutor,KubernetesExecutor
workers:
keda:
enabled: true
```
2. Create a DAG with tasks explicitly using `KubernetesExecutor` and
`queue='default'`
3. Verify that Celery workers do not scale up for tasks with
`executor='KubernetesExecutor'`
## Additional Notes:
## Related Issues:
#49001
---
**^ Add meaningful description above**
Read the **[Pull Request
Guidelines](https://github.com/apache/airflow/blob/main/contributing-docs/05_pull_requests.rst#pull-request-guidelines)**
for more information.
In case of fundamental code changes, an Airflow Improvement Proposal
([AIP](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Improvement+Proposals))
is needed.
In case of a new dependency, check compliance with the [ASF 3rd Party
License Policy](https://www.apache.org/legal/resolved.html#category-x).
In case of backwards incompatible changes please leave a note in a
newsfragment file, named `{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]
