b1650cb9b9
Jobs are now stored in separate keys, and only job IDs are put on Redis queues. Much of the code has been hit by this change, but it is for the good. No really. |
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 | |
calcsize.sh | 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 lightweight* Python library for queueing jobs and processing them in the background with workers. It is backed by Redis and it is extremely simple to use.
* It is under 20 kB in size and just over 500 lines of code.
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 urllib2
def count_words_at_url(url):
f = urllib2.urlopen(url)
count = 0
while True:
line = f.readline()
if not line:
break
count += len(line.split())
return count
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.