• Multiprocessing worker pool
• Encrypted and compressed task packages
• Scheduled tasks
• Result hooks
• Failure and result database
• PaaS compatible with multiple instances
• Django Admin integration
• Multi cluster monitor

• Redis-py
• Django > = 1.7
• Django-picklefield
• Arrow
• Blessed

Tested with: Python 2.7, 3.4. Django 1.7.8, 1.8.2*

*Django Q is currently in Alpha and as such not safe for production, yet.

• Install the latest version with pip: pip install django-q

• Add django_q to INSTALLED_APPS in your settings.py:

  INSTALLED_APPS = (
      # other apps
      'django_q',
  )

• Run python manage.py migrate to create the database tables
• Make sure you have a Redis server running somewhere

All configuration settings are optional. e.g:

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

• name Used to differentiate between projects using the same Redis server* ['default']
• workers The number of workers to use in the cluster [CPU count]
• recycle The number of tasks a worker will process before respawning. Used to release resources. [500]
• compress Compress task packages to Redis. Useful for large payloads. [False]
• save_limit Limits the amount of successful tasks saved to Django. Set to 0 for unlimited. Set to -1 for no success storage at all. Failures are always saved. [250]
• label The label used for the Django Admin page ['Django Q']
• redis Connection settings for Redis. Follows standard Redis-Py syntax. [localhost]

*Django Q uses your SECRET_KEY to encrypt task packages and prevent task crossover

Start a cluster with: python manage.py qcluster

Monitor your clusters with python manage.py qmonitor

Use async from your code to quickly offload tasks:

from django_q import async, result

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

# or with import and storing the id
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
# so 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)

async(func,*args,**kwargs)

• func: Function to execute. Dotted string or reference.
• args: Optional arguments for the function.
• hook: Optional function to call after execution. Dotted string or reference.
• kwargs: Optional keyword arguments for the function.

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

from django_q import Schedule, schedule

# Use the schedule wrapper

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

# Or create the object directly

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

schedule(func,*args,**kwargs)

• func: the function to schedule. Dotted strings only.
• args: arguments for the scheduled function.
• hook: optional result hook function. Dotted strings only.
• schedule_type: (O)nce, (H)ourly, (D)aily, (W)eekly, M(onthly), Q(uarterly), Y(early)
• repeats: Number of times to repeat schedule. -1=Always, 0=Never, n=n.
• next_run: Next or first scheduled execution datetime.
• kwargs: optional keyword arguments for the scheduled function.

• Task and Schedule are Django Models and can therefore be managed by your own code.
• Task objects are only created after an async package has been executed.
• A Schedule creates a new async package for every execution and thus an unique Task
• Success and Failure are convenient proxy models of Task

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

• Write sphinx documentation
• Better tests and coverage
• Get out of Alpha
• Less dependencies?

• Django Q was inspired by working with Django-RQ and RQ
• Human readable hashes by HumanHash