Skip to main content

A multiprocessing distributed task queue for Django

Project description

Q logo

A multiprocessing distributed task queue for Django

image0 image1 Documentation Status image2


  • Multiprocessing worker pool
  • Asynchronous tasks
  • Scheduled and repeated tasks
  • Encrypted and compressed packages
  • Failure and success database or cache
  • Result hooks, groups and chains
  • Django Admin integration
  • PaaS compatible with multiple instances
  • Multi cluster monitor
  • Redis, Disque, IronMQ, SQS, MongoDB or ORM
  • Rollbar support


Tested with: Python 2.7 & 3.6. Django 1.8.18, 1.10.7 and 1.11


  • Install the latest version with pip:

    $ pip install django-q
  • Add django_q to your INSTALLED_APPS in your projects

        # other apps
  • Run Django migrations to create the database tables:

    $ python migrate
  • Choose a message broker , configure and install the appropriate client library.

Read the full documentation at


All configuration settings are optional. e.g:

# example
    'name': 'myproject',
    'workers': 8,
    'recycle': 500,
    'timeout': 60,
    'compress': True,
    'cpu_affinity': 1,
    'save_limit': 250,
    'queue_limit': 500,
    'label': 'Django Q',
    'redis': {
        'host': '',
        'port': 6379,
        'db': 0, }

For full configuration options, see the configuration documentation.

Management Commands

Start a cluster with:

$ python qcluster

Monitor your clusters with:

$ python qmonitor

Check overall statistics with:

$ python qinfo

Creating Tasks

Use async from your code to quickly offload tasks:

from django_q.tasks import async, result

# create the task
async('math.copysign', 2, -2)

# or with a reference
import math.copysign

task_id = async(copysign, 2, -2)

# get the result
task_result = result(task_id)

# result returns None if the task has not been executed yet
# you can wait for it
task_result = result(task_id, 200)

# but in most cases you will want to use a hook:

async('math.modf', 2.5, hook='hooks.print_result')

def print_result(task):

For more info see Tasks


Schedules are regular Django models. You can manage them through the Admin page or directly from your code:

# Use the schedule function
from django_q.tasks import schedule

         2, -2,

# Or create the object directly
from django_q.models import Schedule


# Run a task every 5 minutes, starting at 6 today
# for 2 hours
import arrow

         3, 4,
         next_run=arrow.utcnow().replace(hour=18, minute=0))

For more info check the Schedules documentation.


To run the tests you will need py.test and pytest-django


  • Better tests and coverage
  • Less dependencies?


Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for livingbio-django-q, version 0.8.5
Filename, size File type Python version Upload date Hashes
Filename, size livingbio-django-q-0.8.5.tar.gz (39.3 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page