Resque
celery
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Resque | celery | |
---|---|---|
5 | 43 | |
9,388 | 23,439 | |
0.1% | 1.4% | |
4.1 | 9.5 | |
5 months ago | 8 days ago | |
Ruby | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Resque
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Mike Perham of Sidekiq: “If you build something valuable, charge money for it.”
The free version acts exactly like Resque, the previous market leader in Ruby background jobs. If it was good enough reliability for GitHub and Shopify to use for years, it was good enough for Sidekiq OSS too.
Here's Resque literally using `lpop` which is destructive and will lose jobs.
https://github.com/resque/resque/blob/7623b8dfbdd0a07eb04b19...
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Add web scraping data into the database at regular intervals [ruby & ror]
You can use a background job queue like Resque to scrape and process data in the background, and a scheduler like resque-scheduler to schedule jobs to run your scraper periodically.
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How to run a really long task from a Rails web request
So how do we trigger such a long-running process from a Rails request? The first option that comes to mind is a background job run by some of the queuing back-ends such as Sidekiq, Resque or DelayedJob, possibly governed by ActiveJob. While this would surely work, the problem with all these solutions is that they usually have a limited number of workers available on the server and we didn’t want to potentially block other important background tasks for so long.
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Building a dynamic staging platform
Background jobs are another limitation. Since only the Aha! web service runs in a dynamic staging, the host environment's workers would process any Resque jobs that were sent to the shared Redis instance. If your branch hadn't updated any background-able methods, this would be no big deal. But if you were hoping to test changes to these methods, you would be out of luck.
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Autoscaling Redis applications on Kubernetes 🚀🚀
Redis Lists are quite versatile and used as the backbone for implementing scalable architectural patterns such as consumer-producer (based on queues), where producer applications push items into a List, and consumers (also called workers) process those items. Popular projects such as resque, sidekiq, celery etc. use Redis behind the scenes to implement background jobs.
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?
Sidekiq - Simple, efficient background processing for Ruby
dramatiq - A fast and reliable background task processing library for Python 3.
Shoryuken - A super efficient Amazon SQS thread based message processor for Ruby
Apache Kafka - Mirror of Apache Kafka
RabbitMQ - Open source RabbitMQ: core server and tier 1 (built-in) plugins
huey - a little task queue for python
Sneakers - A fast background processing framework for Ruby and RabbitMQ
NATS - High-Performance server for NATS.io, the cloud and edge native messaging system.
Sucker Punch - Sucker Punch is a Ruby asynchronous processing library using concurrent-ruby, heavily influenced by Sidekiq and girl_friday.
rq - Simple job queues for Python
good_job - Multithreaded, Postgres-based, Active Job backend for Ruby on Rails.
kombu - Messaging library for Python.