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Comment by nickjj | Hacker News Reader

A long time ago I wrote a blog post about Celery use cases at https://nickjanetakis.com/blog/4-use-cases-for-when-to-use-c....

It applies to Django, Flask or any Python system using it. All of it still applies today.

It covers a few use cases on the before vs after of using Celery and touches base on why I'd consider using Celery over other solutions such as async / await. The TL;DR is Celery brings a lot to the table around tracking and retrying jobs. It's also nice to separate your web and worker workloads into different processes since they have much different requirements usually.

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I think as a concept a task queue with some workers makes a lot of sense but having used Celery in production, it leaves a lot to be desired

We’ve run into various bugs and weird performance gotchas (like the workers prefetch jobs which is terrible if they aren’t all the same size)

Agreed. I have had more luck writing my own "jobs" engine that does stuff that I need that celery doesn't have anyway (retries, some record of execution, rate limiting).

I'm sure if I really tried, I can get celery to be very reliable... but I never really got there.

Also for whatever reason I have NEVER been able to get celery to be reliable for its scheduling/cron stuff. It just starts to fail. I use this library for that, which I have never had problems with: https://schedule.readthedocs.io/en/stable/

What type of situations did you run into while using it?

I've been using Celery for a long time in production now. Nothing crazy and it's a fairly basic set up of "execute job, thanks!" along with a beat server. Over the last 6-7 years an off the top of my head guess would be that there's been at least 5 million jobs processed. It's not huge volume when measured over years but it's been stable.

One server was running for 6 months without being updated. That's a Celery worker process running for ~180 days uninterrupted. It served hundreds of thousands of jobs without maintenance. A lot of these were pretty beefy tasks too like performing HTTP requests that got 400mb XML responses and then parsed them. I didn't even have things like `worker_max_tasks_per_child` set either.

Celery has a lot of gotchas, but primarily the issue I have had is with resource consumption. Maybe it is a problem with my config, but I haven't had this issue with other solutions.

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