Overview

This is a high level overview of the architecture.

The broker pushes tasks to the worker servers. A worker server is a networked machine running celeryd. This can be one or more machines, depending on the workload.

The result of the task can be stored for later retrieval (called its "tombstone").

Example

You probably want to see some code by now, so I'll give you an example task adding two numbers:

from celery.decorators import task
@task
def add(x, y):
    return x + y

You can execute the task in the background, or wait for it to finish:

>>> result = add.delay(4, 4)
>>> result.wait() # wait for and return the result
8

Simple!

Features

Messaging	Supported brokers include RabbitMQ, Stomp, Redis, and the most common SQL databases.
Robust	Using RabbitMQ, celery survives most error scenarios, and your tasks will never be lost.
Distributed	Runs on one or more machines. Supports clustering when used in combination with RabbitMQ. You can set up new workers without central configuration (e.g. use your dads laptop while the queue is temporarily overloaded).
Concurrency	Tasks are executed in parallel using the multiprocessing module.
Scheduling	Supports recurring tasks like cron, or specifying an exact date or countdown for when after the task should be executed.
Performance	Able to execute tasks while the user waits.
Return Values	Task return values can be saved to the selected result store backend. You can wait for the result, retrieve it later, or ignore it.
Result Stores	Database, MongoDB, Redis, Tokyo Tyrant, AMQP (high performance).
Webhooks	Your tasks can also be HTTP callbacks, enabling cross-language communication.
Rate limiting	Supports rate limiting by using the token bucket algorithm, which accounts for bursts of traffic. Rate limits can be set for each task type, or globally for all.
Routing	Using AMQP you can route tasks arbitrarily to different workers.
Remote-control	You can rate limit and delete (revoke) tasks remotely.
Monitoring	You can capture everything happening with the workers in real-time by subscribing to events. A real-time web monitor is in development.
Serialization	Supports Pickle, JSON, YAML, or easily defined custom schemes. One task invocation can have a different scheme than another.
Tracebacks	Errors and tracebacks are stored and can be investigated after the fact.
UUID	Every task has an UUID (Universally Unique Identifier), which is the task id used to query task status and return value.
Retries	Tasks can be retried if they fail, with configurable maximum number of retries, and delays between each retry.
Task Sets	A Task set is a task consisting of several sub-tasks. You can find out how many, or if all of the sub-tasks has been executed, and even retrieve the results in order. Progress bars, anyone?
Made for Web	You can query status and results via URLs, enabling the ability to poll task status using Ajax.
Error e-mails	Can be configured to send e-mails to the administrators when tasks fails.
Supervised	Pool workers are supervised and automatically replaced if they crash.

Documentation

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

Installation

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

To install using pip,:

$ pip install celery

To install using easy_install,:

$ easy_install celery

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

$ git clone git://github.com/ask/celery.git

Getting Help

Bug tracker

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

Contributing

Development of celery happens at Github: https://github.com/ask/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.

License

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