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rq | Flask-RQ2 | |
---|---|---|
27 | 3 | |
9,503 | 223 | |
1.1% | 0.4% | |
8.3 | 0.0 | |
7 days ago | 5 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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rq
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Redis Re-Implemented with SQLite
That's pretty cool. Reckon it would work with existing code that calls Redis over the wire for RQ?
https://python-rq.org
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The Many Problems with Celery
https://github.com/rq/rq is to the rescue.
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Keep the Monolith, but Split the Workloads
We use RQ[0], it has Redis as a dependency. It’s pretty straightforward and we’re very happy with it. If you are using Django you may want to look at Django RQ[1] as well. RQ has built in scheduling capabilities these days, but historically it did not so we used (and still use) RQ Scheduler[2] which I think still has some advantages over the built in stuff.
[0] https://python-rq.org/
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SQL Maxis: Why We Ditched RabbitMQ and Replaced It with a Postgres Queue
Also had a similar experience using RabbitMQ with Django+Celery. Extremely complicated and workers/queues would just stop for no reason.
Moved to Python-RQ [1] + Redis and been rock solid for years now.
[1] https://python-rq.org/
- Ask HN: Redis Queue Hacks and Questions
- What libraries do you use the most alongside django?
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Recommendations other than celery to send an API processing in background, which would only take 5 mins to process and API usage would be once a month or so.
Yep, rq is simple and good: https://python-rq.org/ It also has a Django wrapper: https://github.com/rq/django-rq
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GPU instance crashes when two python processes use the same pt file
We have a GPU (G5) instance that uses Python RQ (https://python-rq.org/).
- Dynamically update periodic tasks in Celery and Django
- Celery + RabbitMQ alternatives
Flask-RQ2
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Wondering if I should use Celery vs threads for what I want to do
From experience i would not use threads for this or any background jobs. I would use Celery or Flask-RQ2 to be your workers, you will also probably end up using them to run other tasks as you encounter the need for other jobs. They both use Redis as a broker and job store and you can use Redis for other things like caching and so many other useful features. I kind of like RQ2 more then Celery because its a little simpler but Celery has a lot more to offer, more features. RQ2 has rq-dashboard for monitoring jobs and Celery has Flower.
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Tutorials on how to build a flask extension?
However, you might need to access the app’s context like how you’d do so in the Flask-RQ2 extension by using ScriptInfo from flask.cli:
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Application structure for CLI and API in same
For the CLI part, i recommend going with Click. Its great and already part of flask and easy to use. Other CLI libraries work too but why have more libraries. For scheduling jobs you might want to look at Flask-APScheduler. i used it in one of my projects at work for a while but ended up needing something that could scale to many more workers so had to rip it out but i didnt have a problem with it otherwise. You might want to fork Flask-APScheduler so you can update some libraries because it hasnt been touched in 2 years now or you can just use APScheduler alone. Flask-RQ2 and Celery are pretty good too but the workers need to run in separate processes and need Redis. You could use Redislite i think and with celery you could use a database as your broker, i have done it but dont recommend it.
What are some alternatives?
celery - Distributed Task Queue (development branch)
rq-scheduler - A lightweight library that adds job scheduling capabilities to RQ (Redis Queue)
huey - a little task queue for python
rq-dashboard - Flask-based web front-end for monitoring RQ queues
RabbitMQ - Open source RabbitMQ: core server and tier 1 (built-in) plugins
flask-apscheduler - Adds APScheduler support to Flask
mrq - Mr. Queue - A distributed worker task queue in Python using Redis & gevent
arq - Fast job queuing and RPC in python with asyncio and redis.
procrastinate - PostgreSQL-based Task Queue for Python
django-todo - A multi-user, multi-group todo/ticketing system for Django projects. Includes CSV import and integrated mail tracking.
Apache Kafka - Mirror of Apache Kafka
flower - Real-time monitor and web admin for Celery distributed task queue