rq 2.6.0

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 >= 5 or Valkey >= 7.2.

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())

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, 'https://stamps.id')

Job Prioritization

By default, jobs are added to the end of a single queue. RQ offers two ways to give certain jobs higher priority:

1. Enqueue at the front

You can enqueue a job at the front of its queue so it’s picked up before other jobs:

job = queue.enqueue(count_words_at_url, 'https://stamps.id', at_front=True)

2. Use multiple queues

You can create multiple queues and enqueue jobs into different queues based on their priority:

from rq import Queue
high_priority_queue = Queue('high', connection=Redis())
low_priority_queue = Queue('low', connection=Redis())

# This job will be picked up before jobs in the low priority queue
# even if it was enqueued later
high_priority_queue.enqueue(urgent_task)
low_priority_queue.enqueue(non_urgent_task)

Then start workers with a prioritized queue list:

$ rq worker high low

This command starts a worker that listens to both high and low queues. The worker will process jobs from the high queue first, followed by the low queue. You can also run different workers for different queues, allowing you to scale your workers based on the number of jobs in each queue.

Scheduling Jobs

Scheduling jobs is also easy:

# 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:

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:

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.

Interval and Cron Job Scheduling

RQ >= 2.5 provides built-in job scheduling functionality that supports both simple interval-based scheduling and flexible cron syntax.

First, create a configuration file (e.g., cron_config.py) that defines the jobs you want to run periodically.

from rq import cron
from myapp import cleanup_database, send_daily_report

# Run database cleanup every 5 minutes
from rq import cron
from myapp import cleanup_temp_files, generate_analytics_report

# Clean up temporary files every 30 minutes
cron.register(
    cleanup_temp_files,
    queue_name='maintenance',
    interval=1800  # 30 minutes in seconds
)

# Generate analytics report every 6 hours
cron.register(
    generate_analytics_report,
    queue_name='reports',
    args=('daily_metrics',),
    kwargs={'format': 'json', 'recipients': ['bob@example.com']},
    interval=21600  # 6 hours in seconds
)

And then start the rq cron command to enqueue these jobs at specified intervals:

$ rq cron cron_config.py

You can also use standard cron syntax for more flexible scheduling:

from rq import cron
from myapp import send_newsletter, backup_database

# Database backup every day at 3:00 AM
cron.register(
    backup_database,
    queue_name='maintenance',
    cron_string='0 3 * * *'
)

# Monthly report on the first day of each month at 8:00 AM
cron.register(
    generate_monthly_report,
    queue_name='reports',
    cron_string='0 8 1 * *'
)
```python

More details on functionality can be found in the [docs](https://python-rq.org/docs/cron/).

### 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.

$ rq worker-pool -n 4

More options are documented on python-rq.org.

Installation

Simply use the following command to install the latest released version:

$ pip install rq

Notes on Performance

TL;DR — run Worker or SpawnWorker in production.

In a simple hello world microbenchmark, SimpleWorker processed 1,000 jobs in just 1.02 seconds vs. 6.64 seconds with the default Worker), more than 6x faster.

SimpleWorker is faster because it skips fork() or spawn() and runs jobs in process. Worker and SpawnWorker run each job in a separate process, acting as a sandbox that isolates crashes, memory leaks and enforce hard time-outs.

Although SimpleWorker is faster in benchmarks, this overhead is negligible in most real world applications like sending emails, generating reports, processing images, etc. In production systems, the time spent performing jobs usually dwarfs any queueing/worker overhead.

Use SimpleWorker in production only if:

• Your jobs are extremely short-lived (single digit milliseconds).
• The fork() or spawn() latency is a proven bottleneck at your traffic levels.
• Your job code is 100% trusted and known to be free of resource leaks and the possibility of crashing/segfaults.

Docs

To build and run the docs, install jekyll and run:

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
• rq-dashboard
• rqmonitor
• Flask-RQ2
• rq-scheduler
• rq-dashboard-fastAPI

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.

RQ is maintained by Stamps, an Indonesian based company that provides enterprise grade CRM and order management systems.