This document describes Celery 3.0. 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 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.
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.
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 celery = Celery('hello', broker='amqp://guest@localhost//') @celery.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.
The following bundles are available:
|for using Redis as a broker.|
|for using MongoDB as a broker.|
|for Django, and using Redis as a broker.|
|for Django, and using MongoDB as a broker.|
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.
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