This document describes Celery 3.0. For development docs, go here.

Introduction to Celery

What is a Task Queue?

Task queues are used as a mechanism to distribute work across threads or machines.

A task queue’s input is a unit of work, called a task, dedicated worker processes then constantly monitor the queue for new work to perform.

Celery communicates via messages using a broker to mediate between clients and workers. To initiate a task a client puts a message on the queue, the broker then delivers the message to a worker.

A Celery system can consist of multiple workers and brokers, giving way to high availability and horizontal scaling.

Celery is written in Python, but the protocol can be implemented in any language. So far there’s RCelery for the Ruby programming language, and a PHP client, but language interoperability can also be achieved by using webhooks.

What do I need?

Version Requirements

Celery version 3.0 runs on

• Python ❨2.5, 2.6, 2.7, 3.2, 3.3❩
• PyPy ❨1.8, 1.9❩
• Jython ❨2.5, 2.7❩.

This is the last version to support Python 2.5, and from the next version Python 2.6 or newer is required. The last version to support Python 2.4 was Celery series 2.2.

Celery requires a message broker to send and receive messages. The RabbitMQ, Redis and MongoDB broker transports are feature complete, but there’s also support for a myriad of other solutions, including using SQLite for local development.

Celery can run on a single machine, on multiple machines, or even across data centers.

Get Started

If this is the first time you’re trying to use Celery, or you are new to Celery 3.0 coming from previous versions then you should read our getting started tutorials:

• First Steps with Celery
• Next Steps

Celery is…

• Simple

  Celery is easy to use and maintain, and it doesn’t need configuration files.

  It has an active, friendly community you can talk to for support, including a mailing-list and an IRC channel.

  Here’s one of the simplest applications you can make:

  from celery import Celery
  
  celery = Celery('hello', broker='amqp://guest@localhost//')
  
  @celery.task
  def hello():
      return 'hello world'
  

• Highly Available

  Workers and clients will automatically retry in the event of connection loss or failure, and some brokers support HA in way of Master/Master or Master/Slave replication.

• Fast

  A single Celery process can process millions of tasks a minute, with sub-millisecond round-trip latency (using RabbitMQ, py-librabbitmq, and optimized settings).

• Flexible

  Almost every part of Celery can be extended or used on its own, Custom pool implementations, serializers, compression schemes, logging, schedulers, consumers, producers, autoscalers, broker transports and much more.

It supports

Brokers RabbitMQ, Redis, MongoDB, Beanstalk CouchDB, SQLAlchemy Django ORM, Amazon SQS, and more… Concurrency multiprocessing, Eventlet, gevent threads/single threaded

Result Stores AMQP, Redis memcached, MongoDB SQLAlchemy, Django ORM Apache Cassandra Serialization pickle, json, yaml, msgpack. zlib, bzip2 compression. Cryptographic message signing.

Features

Monitoring A stream of monitoring events is emitted by workers and is used by built-in and external tools to tell you what your cluster is doing – in real-time. Read more…. Workflows Simple and complex workflows can be composed using a set of powerful primitives we call the “canvas”, including grouping, chaining, chunking and more. Read more…. Time & Rate Limits You can control how many tasks can be executed per second/minute/hour, or how long a task can be allowed to run, and this can be set as a default, for a specific worker or individually for each task type. Read more…. Scheduling You can specify the time to run a task in seconds or a datetime, or or you can use periodic tasks for recurring events based on a simple interval, or crontab expressions supporting minute, hour, day of week, day of month, and month of year. Read more….

Autoreloading In development workers can be configured to automatically reload source code as it changes, including inotify(7) support on Linux. Read more…. Autoscaling Dynamically resizing the worker pool depending on load, or custom metrics specified by the user, used to limit memory usage in shared hosting/cloud environments or to enforce a given quality of service. Read more…. Resource Leak Protection The --maxtasksperchild option is used for user tasks leaking resources, like memory or file descriptors, that are simply out of your control. Read more…. User Components Each worker component can be customized, and additional components can be defined by the user. The worker is built up using “boot steps” — a dependency graph enabling fine grained control of the worker’s internals.

Framework Integration

Celery is easy to integrate with web frameworks, some of which even have integration packages:

Django	django-celery
Pyramid	pyramid_celery
Pylons	celery-pylons
Flask	not needed
web2py	web2py-celery
Tornado	tornado-celery

The integration packages are not strictly necessary, but they can make development easier, and sometimes they add important hooks like closing database connections at fork(2).

Quickjump

I want to ⟶

get the return value of a task use logging from my task learn about best practices create a custom task base class add a callback to a group of tasks split a task into several chunks optimize the worker see a list of built-in task states create custom task states set a custom task name track when a task starts retry a task when it fails get the id of the current task

know what queue a task was delivered to see a list of running workers purge all messages inspect what the workers are doing see what tasks a worker has registerd migrate tasks to a new broker see a list of event message types contribute to Celery learn about available configuration settings receive email when a task fails get a list of people and companies using Celery write my own remote control command change worker queues at runtime

Jump to ⟶

Brokers Applications Tasks Calling

Workers Daemonizing Monitoring Optimizing

Security Routing Configuration Django

Contributing Signals FAQ API Reference

Installation

You can install Celery either via the Python Package Index (PyPI) or from source.

To install using pip,:

$ pip install -U Celery

To install using easy_install,:

$ easy_install -U Celery

Bundles

Celery also defines a group of bundles that can be used to install Celery and the dependencies for a given feature.

The following bundles are available:

celery-with-redis:  for using Redis as a broker. celery-with-mongodb:  for using MongoDB as a broker. django-celery-with-redis:  for Django, and using Redis as a broker. django-celery-with-mongodb:  for Django, and using MongoDB as a broker.

Downloading and installing from source

Download the latest version of Celery from http://pypi.python.org/pypi/celery/

You can install it by doing the following,:

$ tar xvfz celery-0.0.0.tar.gz
$ cd celery-0.0.0
$ python setup.py build
# python setup.py install

The last command must be executed as a privileged user if you are not currently using a virtualenv.

Using the development version

You can clone the repository by doing the following:

$ git clone https://github.com/celery/celery
$ cd celery
$ python setup.py develop

The development version will usually also depend on the development version of kombu, the messaging framework Celery uses to send and receive messages, so you should also install that from git:

$ git clone https://github.com/celery/kombu
$ cd kombu
$ python setup.py develop