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.
If you find RQ useful, please consider supporting this project via Tidelift.
First, run a Redis server, of course:
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')
By default, jobs are added to the end of a single queue. RQ offers two ways to give certain jobs higher priority:
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)
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:
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 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)
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 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 SchedulingRQ >= 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:
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.
More options are documented on python-rq.org.
Simply use the following command to install the latest released version:
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:
fork() or spawn() latency is a proven bottleneck at your traffic levels.To build and run the docs, install jekyll and run:
If you use RQ, Check out these below repos which might be useful in your rq based project.
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.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4