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A multiprocessing distributed task queue for Django

Project Description

A multiprocessing distributed task queue for Django

Features

  • 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

Requirements

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

Installation

  • Install the latest version with pip:

    $ pip install django-q
    
  • Add django_q to your INSTALLED_APPS in your projects settings.py:

    INSTALLED_APPS = (
        # other apps
        'django_q',
    )
    
  • Run Django migrations to create the database tables:

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

Read the full documentation at https://django-q.readthedocs.org

Configuration

All configuration settings are optional. e.g:

# settings.py example
Q_CLUSTER = {
    'name': 'myproject',
    'workers': 8,
    'recycle': 500,
    'timeout': 60,
    'compress': True,
    'cpu_affinity': 1,
    'save_limit': 250,
    'queue_limit': 500,
    'label': 'Django Q',
    'redis': {
        'host': '127.0.0.1',
        'port': 6379,
        'db': 0, }
}

For full configuration options, see the configuration documentation.

Management Commands

Start a cluster with:

$ python manage.py qcluster

Monitor your clusters with:

$ python manage.py qmonitor

Check overall statistics with:

$ python manage.py 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')

# hooks.py
def print_result(task):
    print(task.result)

For more info see Tasks

Schedule

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

schedule('math.copysign',
         2, -2,
         hook='hooks.print_result',
         schedule_type=Schedule.DAILY)

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

Schedule.objects.create(func='math.copysign',
                        hook='hooks.print_result',
                        args='2,-2',
                        schedule_type=Schedule.DAILY
                        )

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

schedule('math.hypot',
         3, 4,
         schedule_type=Schedule.MINUTES,
         minutes=5,
         repeats=24,
         next_run=arrow.utcnow().replace(hour=18, minute=0))

For more info check the Schedules documentation.

Testing

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

Todo

  • Better tests and coverage
  • Less dependencies?

Acknowledgements

Release History

Release History

This version
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0.8.0

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