mirror of https://github.com/rq/rq.git
107 lines
2.9 KiB
Markdown
107 lines
2.9 KiB
Markdown
---
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title: "RQ: Simple job queues for Python"
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layout: default
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---
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RQ (_Redis Queue_) is a simple Python library for queueing jobs and processing
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them in the background with workers. It is backed by Redis and it is designed
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to have a low barrier to entry. It can be integrated in your web stack easily.
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RQ requires Redis >= 3.0.0.
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## Getting Started
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First, run a Redis server. You can use an existing one. To put jobs on
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queues, you don't have to do anything special, just define your typically
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lengthy or blocking function:
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```python
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import requests
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def count_words_at_url(url):
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resp = requests.get(url)
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return len(resp.text.split())
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```
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Then, create a RQ queue:
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```python
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from redis import Redis
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from rq import Queue
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q = Queue(connection=Redis())
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```
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And enqueue the function call:
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```python
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from my_module import count_words_at_url
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result = q.enqueue(count_words_at_url, 'http://nvie.com')
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```
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Scheduling jobs are similarly easy:
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```python
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# Schedule job to run at 9:15, October 10th
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job = queue.enqueue_at(datetime(2019, 10, 8, 9, 15), say_hello)
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# Schedule job to be run in 10 seconds
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job = queue.enqueue_in(timedelta(seconds=10), say_hello)
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```
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You can also ask RQ to retry failed jobs:
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```python
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from rq import Retry
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# Retry up to 3 times, failed job will be requeued immediately
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queue.enqueue(say_hello, retry=Retry(max=3))
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# Retry up to 3 times, with configurable intervals between retries
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queue.enqueue(say_hello, retry=Retry(max=3, interval=[10, 30, 60]))
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```
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### The Worker
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To start executing enqueued function calls in the background, start a worker
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from your project's directory:
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```console
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$ rq worker --with-scheduler
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*** Listening for work on default
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Got count_words_at_url('http://nvie.com') from default
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Job result = 818
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*** Listening for work on default
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```
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That's about it.
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## Installation
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Simply use the following command to install the latest released version:
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pip install rq
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## High Level Overview
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There are several important concepts in RQ:
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1. `Queue`: contains a list of `Job` instances to be executed in a FIFO manner.
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2. `Job`: contains the function to be executed by the worker.
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3. `Worker`: responsible for getting `Job` instances from a `Queue` and executing them.
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4. `Execution`: contains runtime data of a `Job`, created by a `Worker` when it executes a `Job`.
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5. `Result`: stores the outcome of an `Execution`, whether it succeeded or failed.
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## Project History
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This project has been inspired by the good parts of [Celery][1], [Resque][2]
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and [this snippet][3], and has been created as a lightweight alternative to
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existing queueing frameworks, with a low barrier to entry.
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[m]: http://pypi.python.org/pypi/mailer
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[p]: http://docs.python.org/library/pickle.html
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[1]: http://www.celeryproject.org/
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[2]: https://github.com/defunkt/resque
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[3]: https://github.com/fengsp/flask-snippets/blob/1f65833a4291c5b833b195a09c365aa815baea4e/utilities/rq.py
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