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.
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.