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celery - Distributed Task Queue

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

Build status coverage Bitdeli badge

Version:4.0.0rc2 (0today8)
Web:http://celeryproject.org/
Download:http://pypi.python.org/pypi/celery/
Source:https://github.com/celery/celery/
Keywords:task queue, job queue, asynchronous, async, rabbitmq, amqp, redis, python, webhooks, queue, distributed

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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, 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. In addition to Python there's node-celery for Node.js, and a PHP client.

Language interoperability can also be achieved by using webhooks.

What do I need?

Celery version 3.0 runs on,

  • Python (2.7, 3.4, 3.5)
  • PyPy (5.1, 2.4)

This is the last version to support Python 2.7, and from the next version (Celery 5.x) Python 3.6 or newer is required.

If you are running an older version of Python, you need to be running an older version of Celery:

  • Python 2.6: Celery series 3.1 or earlier.
  • Python 2.5: Celery series 3.0 or earlier.
  • Python 2.4 was Celery series 2.2 or earlier.

Celery is a project with minimal funding, so we do not support Microsoft Windows. Please do not open any issues related to that platform.

Celery is usually used with a message broker to send and receive messages. The RabbitMQ transports is feature complete, but there's also Qpid and Amazon SQS broker support.

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:

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//')

@app.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, broker transports and much more.

It supports...

  • Message Transports

  • Concurrency

  • Result Stores

    • AMQP, Redis
    • memcached
    • SQLAlchemy, Django ORM
    • Apache Cassandra, IronCache, Elasticsearch

  • Serialization

    • pickle, json, yaml, msgpack.
    • zlib, bzip2 compression.
    • Cryptographic message signing.

Framework Integration

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

Django not needed
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.

Documentation

The latest documentation with user guides, tutorials and API reference is hosted at Read The Docs.

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.

You can specify these in your requirements or on the pip command-line by using brackets. Multiple bundles can be specified by separating them by commas.

$ pip install "celery[librabbitmq]"

$ pip install "celery[librabbitmq,auth,msgpack]"

The following bundles are available:

Serializers

celery[auth]:for using the auth security serializer.
celery[msgpack]:for using the msgpack serializer.
celery[yaml]:for using the yaml serializer.

Concurrency

celery[eventlet]:for using the eventlet pool.
celery[gevent]:for using the gevent pool.
celery[threads]:for using the thread pool.

Transports and Backends

celery[librabbitmq]:for using the librabbitmq C library.
celery[sqs]:for using Amazon SQS as a message transport (experimental).
:celery[tblib]
for using the task_remote_tracebacks feature.
celery[memcache]:for using Memcached as a result backend (using pylibmc)
celery[pymemcache]:for using Memcached as a result backend (pure-Python implementation).
celery[cassandra]:for using Apache Cassandra as a result backend with DataStax driver.
celery[couchbase]:for using Couchbase as a result backend.
celery[elasticsearch]:for using Elasticsearch as a result backend.
celery[riak]:for using Riak as a result backend.
celery[zookeeper]:for using Zookeeper as a message transport.
celery[zeromq]:for using ZeroMQ as a message transport (experimental).
celery[sqlalchemy]:for using SQLAlchemy as a result backend (supported).
celery[pyro]:for using the Pyro4 message transport (experimental).
celery[slmq]:for using the SoftLayer Message Queue transport (experimental).
celery[consul]:for using the Consul.io Key/Value store as a message transport or result backend (experimental).

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

With pip

The Celery development version also requires the development versions of kombu, amqp, billiard and vine.

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
$ pip install https://github.com/celery/vine/zipball/master#egg=vine

With git

Please the Contributing section.

Getting Help

Mailing list

For discussions about the usage, development, and future of celery, please join the celery-users mailing list.

IRC

Come chat with us on IRC. The #celery channel is located at the Freenode network.

Bug tracker

If you have any suggestions, bug reports or annoyances please report them to our issue tracker at https://github.com/celery/celery/issues/

Wiki

http://wiki.github.com/celery/celery/

Contributing

Development of celery happens at GitHub: https://github.com/celery/celery

You are highly encouraged to participate in the development of celery. If you don't like GitHub (for some reason) you're welcome to send regular patches.

Be sure to also read the Contributing to Celery section in the documentation.

License

This software is licensed under the New BSD License. See the LICENSE file in the top distribution directory for the full license text.