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

High Level Overview

There are several important concepts in RQ:

1. Queue: contains a list of Job instances to be executed in a FIFO manner.
2. Job: contains the function to be executed by the worker.
3. Worker: responsible for getting Job instances from a Queue and executing them.
4. Execution: contains runtime data of a Job, created by a Worker when it executes a Job.
5. Result: stores the outcome of an Execution, whether it succeeded or failed.

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.