Pandelo0398 opened a new issue, #51235:
URL: https://github.com/apache/airflow/issues/51235
### Apache Airflow version
3.0.1
### If "Other Airflow 2 version" selected, which one?
_No response_
### What happened?
Apache Airflow version:
3.0.1
Executor:
CeleryExecutor (distributed workers, multi-host setup)
Deployment method:
Docker Compose (official apache/airflow:3.0.1 images)
Summary:
When running a distributed Airflow 3.0.1 setup with CeleryExecutor and
remote workers, my worker(s) consistently fail with a 405 Method Not Allowed
error. The worker tries to PATCH to /api/v2/task-instances/{id}/run, but this
endpoint is not implemented/exposed in the REST API, causing all DAG tasks to
immediately fail.
Repro steps:
Deploy Airflow 3.0.1 central node (webserver, scheduler, API server) and
remote worker nodes via Docker Compose. All use the official images, no custom
pip install.
Trigger a DAG run.
Worker logs show:
´´´
ServerResponseError: Method Not Allowed
airflow.sdk.api.client.Client.patch("task-instances/{id}/run", ...)
´´´
Detailed logs and stacktrace:
´´´
/home/airflow/.local/lib/python3.12/site-packages/airflow/sdk/api/client.py:146
in start
resp = self.client.patch(f"task-instances/{id}/run",
content=body.model_dump_json())
...
ServerResponseError: Method Not Allowed
´´´
### What you think should happen instead?
Remote worker should be able to communicate and update the state via the API
without 405 errors.
DAGs should run to completion.
What happened instead?
Every task execution fails with the above 405 error.
The worker is unable to PATCH to a non-existent endpoint.
Images used:
apache/airflow:3.0.1 (both central node and worker)
Steps already tried:
Checked for custom pip installs (none).
All images use the official version, verified with docker image ls.
Restarted from scratch, wiped all volumes.
Searched the docs and API spec: PATCH /api/v2/task-instances/{id}/run does
not exist.
Possible cause (hypothesis):
Internal SDK/API client in the worker references endpoints not exposed in
the public API, or the distributed DAG processing implementation is incomplete
for this executor setup.
There might be a version mismatch or a bug in the distributed execution
support.
Is this a regression?
Unknown. Did not test with Airflow <3.0.1 distributed DAG processing.
### How to reproduce
How to Reproduce the Issue
Environment Setup
Prepare a distributed Airflow 3.0.1 environment using the official Docker
images.
Use CeleryExecutor.
Set up Redis as the Celery broker.
Set up PostgreSQL as the metadata database.
The environment must have:
A central node (running webserver, scheduler, and API server).
At least one remote worker node (only running Celery worker).
Use Docker Compose (attach your docker-compose.yml if possible).
Start the services in this order:
docker-compose up -d postgres redis
Wait for DB and Redis to be healthy.
docker-compose up airflow-init
docker-compose up -d airflow-api-server airflow-scheduler
Start the Celery worker on a separate node/container, using the same DB and
Redis.
### Operating System
Ubuntu 22
### Versions of Apache Airflow Providers
Airflow 3.0.1
### Deployment
Docker-Compose
### Deployment details
_No response_
### Anything else?
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [x] I agree to follow this project's [Code of
Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
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