https://github.com/rq/rq
Simple job queues for Python
https://github.com/rq/rq
async background-jobs delayed-jobs delayed-tasks job-queue python redis rq task task-queue workers
Last synced: 7 days ago
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Simple job queues for Python
- Host: GitHub
- URL: https://github.com/rq/rq
- Owner: rq
- License: other
- Created: 2011-11-14T10:53:48.000Z (over 13 years ago)
- Default Branch: master
- Last Pushed: 2024-05-01T07:54:00.000Z (12 months ago)
- Last Synced: 2024-05-01T22:24:39.957Z (12 months ago)
- Topics: async, background-jobs, delayed-jobs, delayed-tasks, job-queue, python, redis, rq, task, task-queue, workers
- Language: Python
- Homepage: https://python-rq.org
- Size: 3.45 MB
- Stars: 9,523
- Watchers: 207
- Forks: 1,389
- Open Issues: 204
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- awesome-list - RQ - A simple Python library for queueing jobs and processing them in the background with workers. (DevOps / Data Management)
- starred-awesome - rq - Simple job queues for Python (Python)
- awesome-python-machine-learning-resources - GitHub - 19% open · ⏱️ 21.08.2022): (数据管道和流处理)
- stars - rq/rq - Simple job queues for Python (Python)
- stars - rq/rq - Simple job queues for Python (Python)
- xiao-awesome - rq
- best-of-python - GitHub - 17% open · ⏱️ 26.05.2024): (Data Pipelines & Streaming)
README
RQ (_Redis Queue_) is a simple Python library for queueing jobs and processing
them in the background with workers. It is backed by Redis or Valkey and is designed
to have a low barrier to entry while scaling incredibly well for large applications.
It can be integrated into your web stack easily, making it suitable for projects
of any size—from simple applications to high-volume enterprise systems.
RQ requires Redis >= 4 or Valkey >= 7.2.
[](https://github.com/rq/rq/actions?query=workflow%3A%22Test%22)
[](https://pypi.python.org/pypi/rq)
[](https://codecov.io/gh/rq/rq)
[](https://github.com/astral-sh/ruff)
Full documentation can be found [here][d].
## Support RQ
If you find RQ useful, please consider supporting this project via [Tidelift](https://tidelift.com/subscription/pkg/pypi-rq?utm_source=pypi-rq&utm_medium=referral&utm_campaign=readme).
## Getting started
First, run a Redis server, of course:
```console
$ redis-server
```
To put jobs on queues, you don't have to do anything special, just define
your typically lengthy or blocking function:
```python
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:
```python
from redis import Redis
from rq import Queue
queue = Queue(connection=Redis())
```
And enqueue the function call:
```python
from my_module import count_words_at_url
job = queue.enqueue(count_words_at_url, 'http://nvie.com')
```
## Scheduling Jobs
Scheduling jobs is also easy:
```python
# 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)
```
## Repeating Jobs
To execute a `Job` multiple times, use the `Repeat` class:
```python
from rq import Queue, Repeat
# Repeat job 3 times after successful execution, with 30 second intervals
queue.enqueue(my_function, repeat=Repeat(times=3, interval=30))
# Repeat job 3 times with different intervals between runs
queue.enqueue(my_function, repeat=Repeat(times=3, interval=[5, 10, 15]))
```
## Retrying Failed Jobs
Retrying failed jobs is also supported:
```python
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][d]. But this is the essence.
### The Worker
To start executing enqueued function calls in the background, start a worker
from your project's directory:
```console
$ 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
```
To run multiple workers in production, use process managers like `systemd`. RQ also ships with a beta version of `worker-pool` that lets you run multiple worker processes with a single command.
```console
rq worker-pool -n 4
```
More options are documented on [python-rq.org](https://python-rq.org/docs/workers/).
## 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](https://jekyllrb.com/docs/) and run:
```shell
cd docs
jekyll serve
```
## Related Projects
If you use RQ, Check out these below repos which might be useful in your rq based project.
- [django-rq](https://github.com/rq/django-rq)
- [rq-dashboard](https://github.com/Parallels/rq-dashboard)
- [rqmonitor](https://github.com/pranavgupta1234/rqmonitor)
- [Flask-RQ2](https://github.com/rq/Flask-RQ2)
- [rq-scheduler](https://github.com/rq/rq-scheduler)
- [rq-dashboard-fastAPI](https://github.com/Hannes221/rq-dashboard-fast)
## Project history
This project has been inspired by the good parts of [Celery][1], [Resque][2]
and [this snippet][3], and has been created as a lightweight alternative to the
heaviness of Celery or other AMQP-based queueing implementations.
RQ is maintained by [Stamps](https://stamps.id), an Indonesian based company that provides enterprise grade CRM and order management systems.
[d]: http://python-rq.org/
[m]: http://pypi.python.org/pypi/mailer
[p]: http://docs.python.org/library/pickle.html
[1]: http://docs.celeryq.dev/
[2]: https://github.com/resque/resque
[3]: https://github.com/fengsp/flask-snippets/blob/1f65833a4291c5b833b195a09c365aa815baea4e/utilities/rq.py