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

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

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

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page