2e5f537abd
The options to show only partial info (only workers, only queues) are kept, but not default anymore. This makes the default invocation (without args) the most useful one. The default refresh interval is 2.5 seconds. This fixes #42. |
13 years ago | |
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bin | 13 years ago | |
examples | 13 years ago | |
rq | 13 years ago | |
tests | 13 years ago | |
.gitignore | 13 years ago | |
LICENSE | 13 years ago | |
README.md | 13 years ago | |
run_tests | 13 years ago | |
setup.cfg | 13 years ago | |
setup.py | 13 years ago |
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 easily be integrated in your web stack.
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):
resp = requests.get(url)
return len(resp.text.split())
Then, create a RQ queue:
import rq import *
use_redis()
q = Queue()
And enqueue the function call:
from my_module import count_words_at_url
result = 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:
$ rqworker
*** 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 the heaviness of Celery or other AMQP-based queueing implementations.