This document describes Celery 3.1. For development docs, go here.
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, usually 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, node-celery for Node.js and a PHP client, but language interoperability can also be achieved by using webhooks.
Celery requires a message transport to send and receive messages. The RabbitMQ and Redis broker transports are feature complete, but there’s also support for a myriad of other experimental solutions, including using SQLite for local development.
Celery can run on a single machine, on multiple machines, or even across data centers.
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:
Celery is easy to use and maintain, and it doesn’t need configuration files.
Here’s one of the simplest applications you can make:from celery import Celery app = Celery('hello', broker='amqp://guest@localhost//') @app.task def hello(): return 'hello world'
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.
A single Celery process can process millions of tasks a minute, with sub-millisecond round-trip latency (using RabbitMQ, py-librabbitmq, and optimized settings).
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.
Celery is easy to integrate with web frameworks, some of which even have integration packages:
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).
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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
Celery also defines a group of bundles that can be used to install Celery and the dependencies for a given feature.
You can specify these in your requirements or on the pip comand-line by using brackets. Multiple bundles can be specified by separating them by commas.
$ pip install celery[librabbitmq] $ pip install celery[librabbitmq,redis,auth,msgpack]
The following bundles are available:
|celery[auth]:||for using the auth serializer.|
|for using the msgpack serializer.|
|celery[yaml]:||for using the yaml serializer.|
|for using the eventlet pool.|
|celery[gevent]:||for using the gevent pool.|
|for using the thread pool.|
|for using the librabbitmq C library.|
|celery[redis]:||for using Redis as a message transport or as a result backend.|
|for using MongoDB as a message transport (experimental), or as a result backend (supported).|
|celery[sqs]:||for using Amazon SQS as a message transport (experimental).|
|for using memcached as a result backend.|
|for using Apache Cassandra as a result backend.|
|for using CouchDB as a message transport (experimental).|
|for using CouchBase as a result backend.|
|for using Beanstalk as a message transport (experimental).|
|for using Zookeeper as a message transport.|
|celery[zeromq]:||for using ZeroMQ as a message transport (experimental).|
|for using SQLAlchemy as a message transport (experimental), or as a result backend (supported).|
|celery[pyro]:||for using the Pyro4 message transport (experimental).|
|celery[slmq]:||for using the SoftLayer Message Queue transport (experimental).|
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.
The Celery development version also requires the development versions of kombu, amqp and billiard.
You can install the latest snapshot of these using the following pip commands:
$ pip install https://github.com/celery/celery/zipball/master#egg=celery $ pip install https://github.com/celery/billiard/zipball/master#egg=billiard $ pip install https://github.com/celery/py-amqp/zipball/master#egg=amqp $ pip install https://github.com/celery/kombu/zipball/master#egg=kombu
Please the Contributing section.