celery/docs/getting-started/first-steps-with-celery.rst

========================
 First steps with Celery
========================

Creating a simple task
======================

In this example we are creating a simple task that adds two
numbers. Tasks are defined in a normal python module. The module can
be named whatever you like, but the convention is to call it
``tasks.py``.

Our addition task looks like this:

``tasks.py``:

.. code-block:: python

    from celery.decorators import task

    @task
    def add(x, y):
        return x + y


All celery tasks are classes that inherit from the ``Task``
class. In this case we're using a decorator that wraps the add
function in an appropriate class for us automatically. The full
documentation on how to create tasks and task classes are in
:doc:`Executing Tasks<../userguide/tasks>`.



Configuration
=============

Celery is configured by using a configuration module. By convention,
this module is called ``celeryconfig.py``. This module must be in the
Python path so it can be imported.

You can set a custom name for the configuration module with the
``CELERY_CONFIG_MODULE`` variable. In these examples we use the
default name.


Let's create our ``celeryconfig.py``.

1. Configure how we communicate with the broker::

        BROKER_HOST = "localhost"
        BROKER_PORT = 5672
        BROKER_USER = "myuser"
        BROKER_PASSWORD = "mypassword"
        BROKER_VHOST = "myvhost"

2. In this example we don't want to store the results of the tasks, so
   we'll use the simplest backend available; the AMQP backend::

        CELERY_RESULT_BACKEND = "amqp"

3. Finally, we list the modules to import, that is, all the modules
   that contain tasks. This is so celery knows about what tasks it can
   be asked to perform. We only have a single task module,
   ``tasks.py``, which we added earlier::

        CELERY_IMPORTS = ("tasks", )

That's it.

There are more options available, like how many processes you want to
process work in parallel (the ``CELERY_CONCURRENCY`` setting), and we
could use a persistent result store backend, but for now, this should
do. For all of the options available, see the 
:doc:`configuration directive reference<../configuration>`.

Running the celery worker server
================================

To test we will run the worker server in the foreground, so we can
see what's going on in the terminal::

    $ PYTHONPATH="." celeryd --loglevel=INFO

However, in production you probably want to run the worker in the
background as a daemon. To do this you need to use to tools provided
by your platform, or something like `supervisord`_.

For a complete listing of the command line options available, use the
help command::

    $  PYTHONPATH="." celeryd --help

For info on how to run celery as standalone daemon, see 
:doc:`daemon mode reference<../cookbook/daemonizing>`

.. _`supervisord`: http://supervisord.org

Executing the task
==================

Whenever we want to execute our task, we can use the ``delay`` method
of the task class.

This is a handy shortcut to the ``apply_async`` method which gives
greater control of the task execution.
See :doc:`Executing Tasks<../userguide/executing>` for more information.

    >>> from tasks import add
    >>> add.delay(4, 4)
    <AsyncResult: 889143a6-39a2-4e52-837b-d80d33efb22d>

At this point, the task has been sent to the message broker. The message
broker will hold on to the task until a celery worker server has successfully
picked it up.

*Note:* If everything is just hanging when you execute ``delay``, please check
that RabbitMQ is running, and that the user/password has access to the virtual
host you configured earlier.

Right now we have to check the celery worker log files to know what happened
with the task. This is because we didn't keep the ``AsyncResult`` object
returned by ``delay``.

The ``AsyncResult`` lets us find the state of the task, wait for the task to
finish and get its return value (or exception if the task failed).

So, let's execute the task again, but this time we'll keep track of the task:

    >>> result = add.delay(4, 4)
    >>> result.ready() # returns True if the task has finished processing.
    False
    >>> result.result # task is not ready, so no return value yet.
    None
    >>> result.get()   # Waits until the task is done and returns the retval.
    8
    >>> result.result # direct access to result, doesn't re-raise errors.
    8
    >>> result.successful() # returns True if the task didn't end in failure.
    True

If the task raises an exception, the return value of ``result.successful()``
will be ``False``, and ``result.result`` will contain the exception instance
raised by the task.

That's all for now! After this you should probably read the :doc:`User
Guide<../userguide/index>`.