celery/docs/introduction.rst

============================================
celery - Distributed Task Queue for Django.
============================================

:Authors:
    Ask Solem (askh@opera.com)
:Version: 0.1.15

Introduction
------------

``celery`` is a distributed task queue framework for Django.

It is used for executing tasks *asynchronously*, routed to one or more
worker servers, running concurrently using multiprocessing.

It is designed to solve certain problems related to running websites
demanding high-availability and performance.

It is perfect for filling caches and posting updates to twitter
asynchronously, or for mass downloading of data like syndication
feeds or web scraping. Use-cases are plentiful, and while implementing
these features asynchronously is a demanding task, it is a lot easier using
``celery``, and the performance improvements can make it more than worthwhile.
, 

Features
--------

    * Uses AMQP messaging (RabbitMQ, ZeroMQ) to route tasks to the
      worker servers.

    * Tasks can be routed to as many worker servers as you want.
      They will be picked up from the messaging server.

    * Tasks are executed concurrently using the Python 2.6
      ``multiprocessing`` module (also available as a backport
      to older python versions)

    * Supports periodic tasks, which makes it a replacement for cronjobs.

    * When the task has been executed, the return value is stored using either
      a MySQL/Oracle/PostgreSQL/SQLite database, memcached,
      or Tokyo Tyrant.

    * If the task raises an exception, the exception instance is stored,
      instead of the return value.

    * All tasks has a Universaly Unique Identifier (UUID), which is the
      task id, used for querying task status and return values.

    * Supports tasksets, which is a task consisting of several subtasks.
      You can find out if all, or how many of the subtasks have been executed,
      excellent for progress bar like functionality.

    * Has a ``map`` like function that uses tasks, called ``dmap``.

    * However, you rarely want to wait for these results in a web-environment,
      you'd rather want to use Ajax to poll the task status, which is
      available from a URL like ``celery/<task_id>/status/``. This view
      returns a JSON-serialized data structure containing the task status,
      return value if completed, or exception on failure.
      
API Reference Documentation
---------------------------

The `API Reference Documentation`_ is hosted at Github.

.. _`API Reference Docmentation`: http://ask.github.com/celery/

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

If you have downloaded a source tarball you can install it
by doing the following,::

    $ python setup.py build
    # python setup.py install # as root

Usage
=====

Have to write a cool tutorial, but here is some simple usage info.

*Note* You need to have a AMQP message broker running, like `RabbitMQ`_,
and you need to have the amqp server setup in your settings file, as described
in the `carrot distribution README`_.

*Note* If you're running ``SQLite`` as the database backend, ``celeryd`` will
only be able to process one message at a time, this because ``SQLite`` doesn't
allow concurrent writes.

.. _`RabbitMQ`: http://www.rabbitmq.com
.. _`carrot distribution README`: http://pypi.python.org/pypi/carrot/0.3.3


Defining tasks
--------------

    >>> from celery.task import tasks
    >>> from celery.log import setup_logger
    >>> def do_something(some_arg, **kwargs):
    ...     logger = setup_logger(**kwargs)
    ...     logger.info("Did something: %s" % some_arg)
    ...     return 42
    >>> task.register(do_something, "do_something") 

Tell the celery daemon to run a task
-------------------------------------

    >>> from celery.task import delay_task
    >>> delay_task("do_something", some_arg="foo bar baz")


Execute a task, and get its return value.
-----------------------------------------

    >>> from celery.task import delay_task
    >>> result = delay_task("do_something", some_arg="foo bar baz")
    >>> result.ready()
    False
    >>> result.wait()   # Waits until the task is done.
    42
    >>> result.status()
    'DONE'

If the task raises an exception, the tasks status will be ``FAILURE``, and
``result.result`` will contain the exception instance raised.

Running the celery daemon
--------------------------

::

    $ cd mydjangoproject
    $ env DJANGO_SETTINGS_MODULE=settings celeryd
    [....]
    [2009-04-23 17:44:05,115: INFO/Process-1] Did something: foo bar baz
    [2009-04-23 17:44:05,118: INFO/MainProcess] Waiting for queue.


Autodiscovery of tasks
-----------------------

``celery`` has an autodiscovery feature like the Django Admin, that
automatically loads any ``tasks.py`` module in the applications listed
in ``settings.INSTALLED_APPS``.

A good place to add this command could be in your ``urls.py``,
::

    from celery.task import tasks
    tasks.autodiscover()



Then you can add new tasks in your applications ``tasks.py`` module,
::

    from celery.task import tasks
    from celery.log import setup_logger
    from clickcounter.models import ClickCount

    def increment_click(for_url, **kwargs):
        logger = setup_logger(**kwargs)
        clicks_for_url, cr = ClickCount.objects.get_or_create(url=for_url)
        clicks_for_url.clicks = clicks_for_url.clicks + 1
        clicks_for_url.save()
        logger.info("Incremented click count for %s (not at %d)" % (
                        for_url, clicks_for_url.clicks)
    tasks.register(increment_click, "increment_click")


Periodic Tasks
---------------

Periodic tasks are tasks that are run every ``n`` seconds. They don't
support extra arguments. Here's an example of a periodic task:


    >>> from celery.task import tasks, PeriodicTask
    >>> from datetime import timedelta
    >>> class MyPeriodicTask(PeriodicTask):
    ...     name = "foo.my-periodic-task"
    ...     run_every = timedelta(seconds=30)
    ...
    ...     def run(self, **kwargs):
    ...         logger = self.get_logger(**kwargs)
    ...         logger.info("Running periodic task!")
    ...
    >>> tasks.register(MyPeriodicTask)


For periodic tasks to work you need to add ``celery`` to ``INSTALLED_APPS``,
and issue a ``syncdb``.

License
=======

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

.. # vim: syntax=rst expandtab tabstop=4 shiftwidth=4 shiftround