kombu
celery
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kombu | celery | |
---|---|---|
2 | 43 | |
2,748 | 23,439 | |
1.3% | 1.4% | |
8.9 | 9.5 | |
3 days ago | 8 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
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kombu
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Variables pool system
It is feasible and there are a number of ways to do it. There are a few issues, though, mainly how to make sure that you are accessing the latest data and not getting part of the data mid write. A database handles these issues pretty well, since generally they are meant to be an intermediary between different processes. Another solution, that is often more flexible, is a message queue, like this one: https://github.com/celery/kombu
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Messaging and Madness: Sending Messages with AMQP and Amazon MQ
Above I use the library Kombu to create some connections and send some stuff. I started by setting up our environment variables. Then created exchange and queue objects. Finally, I made our connection object and the producer object, and then we sent a simple “Hello” message.
celery
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Streaming responses to websockets with multiple LLMs, am I going about this wrong?
So this might be my understanding, but stuff like celery is more like an orchestrator that chunks up workloads (think Hadoop with multiple nodes).
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Examples of using task scheduler with Go?
In the Django world, you'd probably rely on Celery to do this for you. You're probably looking for something similar that works with Go. https://github.com/celery/celery
- SynchronousOnlyOperation from celery task using gevent execution pool on django orm
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FastAPI + Celery problem: Celery task is still getting exectued even though I'm raising an exception on task_prerun
I've been doing some research and there doesn't seem to be much information on this issue, aditionally there's this but without a fix yet or any workaround: https://github.com/celery/celery/issues/7792
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Taskiq: async celery alternative
RabbitMQ Classic mirror queues are very fragile to network partitioning. They are deprecated in favor of Quorum queues, but Celery doesn't support them yet : https://github.com/celery/celery/issues/6067
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Use Celery with any Django Storage as a Result Backend
The Celery package provides some number of (undocumented!) result backends to store task results in different local, network, and cloud storages. The django-celery-result package adds options to use Django-specific ORM-based result storage, as well as Django-specific cache subsystem.
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Django Styleguide
I spent 3 years building a high scale crawler on top of Celery.
I can't recommend it. We found many bugs in the more advanced features of Celery (like Canvas) we also ran into some really weird issues like tasks getting duplicated for no reason [1].
The most concerning problem is that the project was abandoned. The original creator is not working on it anymore and all issues that we raised were ignored. We had to fork the project and apply our own fixes to it. This was 4 years ago so maybe things improved since them.
Celery is also extremely complex.
I would recommend https://dramatiq.io/ instead.
[1]: https://github.com/celery/celery/issues/4426
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Processing input and letting user download the result
You can use celery to process the file for extraction, saving and creating rar/zip.
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RQ-Scheduler for tasks in far future?
Celery not usefull for long term future tasks (far future) · Issue #4522 · celery/celery (github.com)
What are some alternatives?
pika - Pure Python RabbitMQ/AMQP 0-9-1 client library
dramatiq - A fast and reliable background task processing library for Python 3.
rq - Simple job queues for Python
Apache Kafka - Mirror of Apache Kafka
huey - a little task queue for python
gevent - Coroutine-based concurrency library for Python
NATS - High-Performance server for NATS.io, the cloud and edge native messaging system.
rele - Easy to use Google Pub/Sub
KQ - Kafka-based Job Queue for Python
arq - Fast job queuing and RPC in python with asyncio and redis.