HomePage: http://celeryproject.org

Author: Ask Solem

Download: https://pypi.python.org/packages/source/c/celery/celery-3.1.11.tar.gz

 celery - Distributed Task Queue

.. image:: http://cloud.github.com/downloads/celery/celery/celery_128.png

:Version: 3.1.11 (Cipater)
:Web: http://celeryproject.org/
:Download: http://pypi.python.org/pypi/celery/
:Source: http://github.com/celery/celery/
:Keywords: task queue, job queue, asynchronous, async, rabbitmq, amqp, redis,
  python, webhooks, queue, distributed


What is a Task Queue?

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

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 a library 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.

.. _RCelery: http://leapfrogdevelopment.github.com/rcelery/
.. _`PHP client`: https://github.com/gjedeer/celery-php
.. _`using webhooks`:

What do I need?

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 Celery 3.1, Python 2.6 or later is required.
The last version to support Python 2.4 was Celery series 2.2.

*Celery* is usually used with a message broker to send and receive messages.
The RabbitMQ, Redis transports are feature complete,
but there's also experimental 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 datacenters.

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`_

    Tutorial teaching you the bare minimum needed to get started with Celery.

- `Next steps`_

    A more complete overview, showing more features.

.. _`First steps with Celery`:

.. _`Next steps`:

Celery is...

- **Simple**

    Celery is easy to use and maintain, and does *not need configuration files*.

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

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

        from celery import Celery

        app = Celery('hello', broker='amqp://guest@localhost//')

        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...

    - **Message Transports**

        - RabbitMQ_, Redis_,
        - MongoDB_ (experimental), Amazon SQS (experimental),
        - CouchDB_ (experimental), SQLAlchemy_ (experimental),
        - Django ORM (experim