Giter VIP home page Giter VIP logo

django-q2's Introduction

A multiprocessing distributed task queue for Django

image0 image1 Documentation Status downloads

Django Q2 is a fork of Django Q. Big thanks to Ilan Steemers for starting this project. Unfortunately, development has stalled since June 2021. Django Q2 is the new updated version of Django Q, with dependencies updates, docs updates and several bug fixes. Original repository: https://github.com/Koed00/django-q

Features

  • Multiprocessing worker pool
  • Asynchronous tasks
  • Scheduled, cron and repeated tasks
  • Signed 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, IronMQ, SQS, MongoDB or ORM
  • Rollbar and Sentry support

Changes compared to the original Django-Q:

  • Dropped support for Disque (hasn't been updated in a long time)
  • Dropped Redis, Arrow and Blessed dependencies
  • Updated all current dependencies
  • Added tests for Django 4.x and 5.x
  • Added Turkish language
  • Improved admin area
  • Fixed a lot of issues

See the changelog for all changes.

Requirements

Tested with: Python 3.8, 3.9, 3.10, 3.11 and 3.12. Works with Django 4.2.X and 5.0.X

Brokers

Installation

  • Install the latest version with pip:

    $ pip install django-q2
    
  • 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-q2.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

For the management commands to work, you will need to install Blessed: <https://github.com/jquast/blessed>

Start a cluster with:

$ python manage.py qcluster

Monitor your clusters with:

$ python manage.py qmonitor

Monitor your clusters' memory usage with:

$ python manage.py qmemory

Check overall statistics with:

$ python manage.py qinfo

Creating Tasks

Use async_task from your code to quickly offload tasks:

from django_q.tasks import async_task, result

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

# or with a reference
import math.copysign

task_id = async_task(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_task('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
from datetime import datetime

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

# Use a cron expression
schedule('math.hypot',
         3, 4,
         schedule_type=Schedule.CRON,
         cron = '0 22 * * 1-5')

For more info check the Schedules documentation.

Testing

Running tests is easy with docker compose, it will also start the necessary databases. Just run:

docker-compose -f test-services-docker-compose.yaml run --rm django-q2 poetry run pytest

Locale

Currently available in English, German, Turkish, and French. Translation pull requests are always welcome.

Todo

  • Better tests and coverage
  • Less dependencies?

Acknowledgements

django-q2's People

Contributors

koed00 avatar gday avatar eagllus avatar jmcvetta avatar danielwelch avatar janneronkko avatar timomeara avatar msabatier avatar yannpom avatar valentinogagliardi avatar telmobarros avatar maerteijn avatar kennyhei avatar benjaoming avatar dependabot[bot] avatar fallenhitokiri avatar p-eb avatar jnoortheen avatar jimjag avatar urth avatar grayknife avatar zws2014 avatar achidlow avatar maximiliankindshofer avatar ac130kz avatar nickpolet avatar bulv1ne avatar nurettin avatar ptrcnull avatar icfly2 avatar

Stargazers

Nat avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.