django-rq
An app that provides django integration for RQ (Redis Queue)
Decision gist · record as of 2026-08-14
Yes. Django-RQ is a straightforward, actively maintained solution for adding background job processing to Django projects. It has low install friction, no known vulnerabilities, a permissive MIT license, and a well-established user base. Choose it if you need Redis-backed async tasks with Django integration and prefer a simpler alternative to Celery.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running Redis server; Django 4.2+ and Python 3.10+ are required.
- Low friction installation with a pure-Python wheel.
- Actively maintained with a recent release and 1951 repository stars.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions.
last release 2026-07-04 (41 days) · last repo commit 2026-08-10 · 1,951 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,030,988 downloads/mo, #4,469 on PyPI
Alternatives
Verify before relying
pip install django-rq
# In settings.py, add to INSTALLED_APPS:
INSTALLED_APPS = ['django_rq']
# Configure queues:
RQ_QUEUES = {'default': {'HOST': 'localhost', 'PORT': 6379, 'DB': 0}}
# In your code:
import django_rq
django_rq.enqueue(my_function, arg1, arg2)- Whether the admin dashboard and Prometheus metrics integration are production-ready or experimental.
- Performance characteristics when handling high job volumes or large numbers of workers.
What it is and what it does
Django-RQ is a Django app that wraps RQ (a Redis-based Python job queue) and exposes it through Django's configuration and management system. Instead of writing custom queue setup code, you define your queues in Django's settings.py and use simple utility functions like enqueue() and get_queue() to push tasks into the background. It also provides a @job decorator for marking functions as queueable tasks, a management command (rqworker) to run workers, and an admin interface for monitoring queues, jobs, and workers.
The package depends on Django, Redis, and RQ itself. It supports multiple queue configurations (standard Redis, Redis Sentinel, connection pooling) and allows you to customize worker, queue, and job classes. A new admin integration in version 4.0 adds a dashboard for queue statistics, job registry browsing, and optional Prometheus metrics export.
Use it for
- Offload long-running tasks (file processing, API calls, reports) from web requests to background workers.
- Schedule jobs to run at specific times or with delays using RQ's built-in scheduler.
- Monitor queue health, job status, and worker activity through the Django admin dashboard.
- Run multiple priority queues (high, default, low) and assign workers to each for load balancing.
- Manage Redis connections efficiently across multiple queue definitions in a single Django app.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Django-RQ is a straightforward, actively maintained solution for adding background job processing to Django projects. It has low install friction, no known vulnerabilities, a permissive MIT license, and a well-established user base. Choose it if you need Redis-backed async tasks with Django integration and prefer a simpler alternative to Celery.
Install
django-rq on PyPI
Before you install
Low friction installation with a pure-Python wheel. Actively maintained with a recent release and 1951 repository stars. Requires Django 4.2+ and Python 3.10+.
Requires a running Redis server; Django 4.2+ and Python 3.10+ are required.
License in practice
MIT license permits commercial and private use with minimal restrictions.
Quickstart
pip install django-rq
# In settings.py, add to INSTALLED_APPS:
INSTALLED_APPS = ['django_rq']
# Configure queues:
RQ_QUEUES = {'default': {'HOST': 'localhost', 'PORT': 6379, 'DB': 0}}
# In your code:
import django_rq
django_rq.enqueue(my_function, arg1, arg2)Verify before relying
- Whether the admin dashboard and Prometheus metrics integration are production-ready or experimental.
- Performance characteristics when handling high job volumes or large numbers of workers.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesdjangoredisrq |
| Maintenance | Actively maintained 41 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,030,988 / month, #4,469 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: Web EnvironmentFramework :: DjangoIntended Audience :: DevelopersIntended Audience :: End Users/DesktopIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchIntended Audience :: System AdministratorsOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: InternetTopic :: Internet :: WWW/HTTPTopic :: Scientific/EngineeringTopic :: Software Development :: Libraries :: Python ModulesTopic :: System :: Distributed ComputingTopic :: System :: MonitoringTopic :: System :: Systems Administration |
Evidence: django_rq-4.1.1-py3-none-any.whl
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See also django-q2 · judoscale · rq · rq-scheduler · rq-dashboard · arq · saq · django-dramatiq · django-apscheduler · django-tasks