Integrate Django-RQ as an alternative task queue#6210
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- Added configuration options for Django-RQ in cookiecutter.json and project generation options documentation. - Updated README.md to include Django-RQ features and setup instructions. - Created a comprehensive guide for using Django-RQ, detailing task creation, enqueuing, and monitoring. - Modified Docker and Docker Compose files to include services for Valkey and RQ components. - Updated environment files to include VALKEY_URL for local and production setups. - Adjusted Procfile to support RQ worker and scheduler commands. - Enhanced base.py settings to configure RQ queues and added necessary imports in tasks and tests. - Implemented cleanup hooks in post_gen_project.py to remove RQ files if not used. - Added new start scripts for RQ worker, scheduler, and dashboard in both local and production setups.
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…es based on Celery and Django-RQ usage
…k dependencies for compatibility
…ration with django-rq
browniebroke
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browniebroke
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I didn't look into too much details but noted a few things
| "use_celery": "n", | ||
| "use_django_rq": "n", |
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Should use_celery and use_django_rq be mutually exclusive, or allow both for advanced setups?
AFAIU, Celery and RQ are solving the same problem, so we should design the questions to avoid having both installed. I would change this question to something like "background_queue": ["None", "Celery", "Django-RQ"]. Basically turn 2 Y/N questions into one drop-down question.
| valkey: | ||
| image: docker.io/valkey/valkey:8.0 | ||
| container_name: {{ cookiecutter.project_slug }}_local_valkey | ||
| volumes: | ||
| - {{ cookiecutter.project_slug }}_local_valkey_data:/data |
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I know we're discussing migrating our Redis setup to Valkey, but we should perhaps stick to Redis for now. I would like to avoid us being in a situation where have different datastores, depending on which background queue framework was used.
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I'm happy for this file to describe what's specific in our setup, but plkease avoid general explanantions about how to use django-rq. The package has docs for that (I hope), and when things will change on their end, I would like to avoid this content to go stale.
You can give a high level intro about django-rq, and explain how it's configured in the generated project, but then link to their docs please.
| RQ_QUEUES = { | ||
| "default": { | ||
| "URL": VALKEY_URL, | ||
| "DEFAULT_TIMEOUT": 360, | ||
| }, | ||
| "high": { | ||
| "URL": VALKEY_URL, | ||
| "DEFAULT_TIMEOUT": 500, | ||
| }, | ||
| "low": { | ||
| "URL": VALKEY_URL, | ||
| }, | ||
| } |
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Not sure we need 3 queues by default, I think the Celery setup has a single queue... Maybe keep it simple for now?
(I haven't worker with djkango-rq so I can't assess the implications/caveats/footguns that comes with multiple queues followed by a single worker)
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Description
This PR adds django-rq as an optional background task backend. Projects can now opt into django-rq as a simpler alternative to Celery when generating a new project. Celery remains fully supported and unchanged.
What's included:
use_django_rqcookiecutter optionrqworkerservice and Procfile worker definitionKey features:
Checklist:
Rationale
Many Django projects need a simple Redis-backed job queue without the complexity of Celery. django-rq is production-ready and provides exactly that. Since cookiecutter-django already integrates Redis for Celery, adding django-rq as an option has minimal overhead while giving users a lighter-weight choice for background tasks.
Open questions for maintainers:
use_celeryanduse_django_rqbe mutually exclusive, or allow both for advanced setups?Resolves #6209