February 19, 2020

306 words 2 mins read

rq/rq

rq/rq

Simple job queues for Python

repo name rq/rq
repo link https://github.com/rq/rq
homepage https://python-rq.org
language Python
size (curr.) 3044 kB
stars (curr.) 6788
created 2011-11-14
license Other

RQ (Redis Queue) is a simple Python library for queueing jobs and processing them in the background with workers. It is backed by Redis and it is designed to have a low barrier to entry. It should be integrated in your web stack easily.

RQ requires Redis >= 3.0.0.

Build status PyPI Coverage

Full documentation can be found here.

Support RQ

If you find RQ useful, please consider supporting this project via Tidelift.

Getting started

First, run a Redis server, of course:

$ redis-server

To put jobs on queues, you don’t have to do anything special, just define your typically lengthy or blocking function:

import requests

def count_words_at_url(url):
    """Just an example function that's called async."""
    resp = requests.get(url)
    return len(resp.text.split())

You do use the excellent requests package, don’t you?

Then, create an RQ queue:

from redis import Redis
from rq import Queue

q = Queue(connection=Redis())

And enqueue the function call:

from my_module import count_words_at_url
job = q.enqueue(count_words_at_url, 'http://nvie.com')

For a more complete example, refer to the docs. But this is the essence.

The worker

To start executing enqueued function calls in the background, start a worker from your project’s directory:

$ rq worker
*** Listening for work on default
Got count_words_at_url('http://nvie.com') from default
Job result = 818
*** Listening for work on default

That’s about it.

Installation

Simply use the following command to install the latest released version:

pip install rq

If you want the cutting edge version (that may well be broken), use this:

pip install -e git+https://github.com/nvie/rq.git@master#egg=rq

Project history

This project has been inspired by the good parts of Celery, Resque and this snippet, and has been created as a lightweight alternative to the heaviness of Celery or other AMQP-based queueing implementations.

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