6fc9454675
* adds unit test for a deserialization error This tests that deserialization exceptions are properly logged, and fails in the manner described in #1422 . * Catch deserializing errors in Worker.handle_exception() This fixes #1422 , and makes tests/test_worker.py::TestWorker::test_deserializing_failure_is_handled pass. * made unit test less specific This is required to get the test to pass under other serializers / other python versions. * Added generic DeserializationError * switched ValueError to DeserializationError in a test The changed test is creating an invalid job, which now raises DeserializationError when data is accessed, as opposed to ValueError. |
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.github | 4 years ago | |
docker | 4 years ago | |
docs | 4 years ago | |
examples | 11 years ago | |
rq | 4 years ago | |
tests | 4 years ago | |
.coveragerc | 11 years ago | |
.deepsource.toml | 5 years ago | |
.gitignore | 6 years ago | |
.mailmap | 9 years ago | |
CHANGES.md | 4 years ago | |
Dockerfile | 4 years ago | |
LICENSE | 13 years ago | |
MANIFEST.in | 4 years ago | |
Makefile | 10 years ago | |
README.md | 4 years ago | |
dev-requirements.txt | 4 years ago | |
requirements.txt | 5 years ago | |
run_tests_in_docker.sh | 4 years ago | |
setup.cfg | 6 years ago | |
setup.py | 4 years ago | |
tox.ini | 4 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 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
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, 8, 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/nvie/rq.git@master#egg=rq
Related Projects
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.