Simple job queues for Python
Go to file
Selwin Ong c653d2388b Add beta warning to retry result 2024-12-23 17:08:58 +07:00
.github Replace Black with Ruff as formatter tool (#2152) 2024-11-20 15:34:27 +07:00
docs Add beta warning to retry result 2024-12-23 17:08:58 +07:00
examples Update linting configuration (#1915) 2023-05-17 23:19:14 +07:00
rq Jobs can return Retry object to retry execution (#2159) 2024-12-01 15:58:05 +07:00
tests Jobs can return Retry object to retry execution (#2159) 2024-12-01 15:58:05 +07:00
.coveragerc Update linting configuration (#1915) 2023-05-17 23:19:14 +07:00
.deepsource.toml Fix some code quality issues (#1235) 2020-05-03 17:35:01 +07:00
.gitignore Group jobs into batches and retrieve by batch name (#1945) 2024-03-11 15:08:53 +07:00
.mailmap Add .mailmap 2015-08-25 09:08:42 +02:00
.pre-commit-config.yaml Store project metadata in pyproject.toml (PEP 621) (#1952) 2024-02-24 10:07:56 +07:00
CHANGES.md Update docs 2024-11-16 15:06:55 +07:00
Dockerfile Docker (#1471) 2021-06-12 11:51:11 +07:00
LICENSE Fix year. 2012-03-28 10:49:28 +02:00
Makefile Replace Black with Ruff as formatter tool (#2152) 2024-11-20 15:34:27 +07:00
README.md Replace Black with Ruff as formatter tool (#2152) 2024-11-20 15:34:27 +07:00
codecov.yml Update linting configuration (#1915) 2023-05-17 23:19:14 +07:00
pyproject.toml Replace Black with Ruff as formatter tool (#2152) 2024-11-20 15:34:27 +07:00
tox.ini Replace Black with Ruff as formatter tool (#2152) 2024-11-20 15:34:27 +07:00

README.md

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 Code style: Ruff

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())

Then, create an RQ queue:

from redis import Redis
from rq import Queue

queue = Queue(connection=Redis())

And enqueue the function call:

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

Scheduling jobs are also similarly easy:

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

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

Retrying failed jobs is also supported:

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]))

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 --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 git+https://github.com/rq/rq.git@master#egg=rq

Docs

To build and run the docs, install jekyll and run:

cd docs
jekyll serve

If you use RQ, Check out these below repos which might be useful in your rq based project.

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.