Fork me on GitHub

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 can be integrated in your web stack easily.

RQ requires Redis >= 3.0.0.

Getting started

First, run a Redis server. You can use an existing one. 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):
    resp = requests.get(url)
    return len(resp.text.split())

Then, create a 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
result = q.enqueue(count_words_at_url, 'http://nvie.com')

Scheduling jobs are similarly easy:

# Schedule job to run at 9:15, October 10th
job = queue.enqueue_at(datetime(2019, 10, 8, 9, 15), say_hello)

# Schedule job to be run in 10 seconds
job = queue.enqueue_in(timedelta(seconds=10), say_hello)

You can also ask RQ to retry failed jobs:

from rq import Retry

# Retry up to 3 times, failed job will be requeued immediately
queue.enqueue(say_hello, retry=Retry(max=3))

# Retry up to 3 times, with configurable intervals between retries
queue.enqueue(say_hello, retry=Retry(max=3, interval=[10, 30, 60]))

The worker

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

$ rq worker --with-scheduler
*** 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+git@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 existing queueing frameworks, with a low barrier to entry.