=================
 Executing Tasks
=================

.. contents::
    :local:

Basics
======

Executing tasks is done with :meth:`~celery.task.Base.Task.apply_async`,
and its shortcut: :meth:`~celery.task.Base.Task.delay`.

``delay`` is simple and convenient, as it looks like calling a regular
function:

.. code-block:: python

    Task.delay(arg1, arg2, kwarg1="x", kwarg2="y")

The same thing using ``apply_async`` is written like this:

.. code-block:: python

    Task.apply_async(args=[arg1, arg2], kwargs={"kwarg1": "x", "kwarg2": "y"})

You can also execute a task by name using :func:`~celery.execute.send_task`, 
if you don't have access to the task's class::

    >>> from celery.execute import send_task
    >>> result = send_task("tasks.add", [2, 2])
    >>> result.get()
    4

While ``delay`` is convenient, it doesn't give you as much control as using ``apply_async``.
With ``apply_async`` you can override the execution options available as attributes on
the ``Task`` class: ``routing_key``, ``exchange``, ``immediate``, ``mandatory``,
``priority``, and ``serializer``.  In addition you can set a countdown/eta, or provide
a custom broker connection.

Let's go over these in more detail. The following examples use this simple
task, which adds together two numbers:

.. code-block:: python

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


ETA and countdown
=================

The ETA (estimated time of arrival) lets you set a specific date and time that
is the earliest time at which your task will execute. ``countdown`` is
a shortcut to set this by seconds in the future.

.. code-block:: python

    >>> result = add.apply_async(args=[10, 10], countdown=3)
    >>> result.get()    # this takes at least 3 seconds to return
    20

Note that your task is guaranteed to be executed at some time *after* the
specified date and time has passed, but not necessarily at that exact time.

While ``countdown`` is an integer, ``eta`` must be a :class:`~datetime.datetime` object,
specifying an exact date and time in the future. This is good if you already
have a :class:`~datetime.datetime`` object and need to modify it with a
:class:`~datetime.timedelta`, or when using time in seconds is not very readable.

.. code-block:: python

    from datetime import datetime, timedelta

    def quickban(username):
        """Ban user for 24 hours."""
        ban(username)
        tomorrow = datetime.now() + timedelta(days=1)
        UnbanTask.apply_async(args=[username], eta=tomorrow)


Serializers
===========

Data passed between celery and workers has to be serialized to be
transferred. The default serializer is :mod:`pickle`, but you can 
change this for each
task. There is built-in support for using :mod:`pickle`, ``JSON`` and ``YAML``,
and you can add your own custom serializers by registering them into the
carrot serializer registry.

The default serializer (pickle) supports Python objects, like ``datetime`` and
any custom datatypes you define yourself. But since pickle has poor support
outside of the Python language, you need to choose another serializer if you
need to communicate with other languages. In that case, ``JSON`` is a very
popular choice.

The serialization method is sent with the message, so the worker knows how to
deserialize any task. Of course, if you use a custom serializer, this must
also be registered in the worker.

When sending a task the serialization method is taken from the following
places in order: The ``serializer`` argument to ``apply_async``, the
Task's ``serializer`` attribute, and finally the global default ``CELERY_SERIALIZER``
configuration directive.

.. code-block:: python

    >>> add.apply_async(args=[10, 10], serializer="json")

Connections and connection timeouts.
====================================

Currently there is no support for broker connection pools in celery,
so this is something you need to be aware of when sending more than
one task at a time, as ``apply_async``/``delay`` establishes and
closes a connection every time.

If you need to send more than one task at the same time, it's a good idea to
establish the connection yourself and pass it to ``apply_async``:

.. code-block:: python

    numbers = [(2, 2), (4, 4), (8, 8), (16, 16)]

    results = []
    publisher = add.get_publisher()
    try:
        for args in numbers:
            res = add.apply_async(args=args, publisher=publisher)
            results.append(res)
    finally:
        publisher.close()
        publisher.connection.close()

    print([res.get() for res in results])


The connection timeout is the number of seconds to wait before we give up
establishing the connection. You can set this with the ``connect_timeout``
argument to ``apply_async``:

.. code-block:: python

    add.apply_async([10, 10], connect_timeout=3)

Or if you handle the connection manually:

.. code-block:: python

    publisher = add.get_publisher(connect_timeout=3)


Routing options
===============

Celery uses the AMQP routing mechanisms to route tasks to different workers.
You can route tasks using the following entities: exchange, queue and routing key.

Messages (tasks) are sent to exchanges, a queue binds to an exchange with a
routing key. Let's look at an example:

Our application has a lot of tasks, some process video, others process images,
and some gather collective intelligence about users. Some of these have
higher priority than others so we want to make sure the high priority tasks
get sent to powerful machines, while low priority tasks are sent to dedicated
machines that can handle these at their own pace.

For the sake of example we have only one exchange called ``tasks``.
There are different types of exchanges that matches the routing key in
different ways, the exchange types are:

* direct

    Matches the routing key exactly.

* topic

    In the topic exchange the routing key is made up of words separated by dots (``.``).
    Words can be matched by the wild cards ``*`` and ``#``, where ``*`` matches one
    exact word, and ``#`` matches one or many.

    For example, ``*.stock.#`` matches the routing keys ``usd.stock`` and
    ``euro.stock.db`` but not ``stock.nasdaq``.

(there are also other exchange types, but these are not used by celery)

So, we create three queues, ``video``, ``image`` and ``lowpri`` that bind to
our ``tasks`` exchange. For the queues we use the following binding keys::

    video: video.#
    image: image.#
    lowpri: misc.#

Now we can send our tasks to different worker machines, by making the workers
listen to different queues:

.. code-block:: python

    >>> CompressVideoTask.apply_async(args=[filename],
    ...                               routing_key="video.compress")

    >>> ImageRotateTask.apply_async(args=[filename, 360],
    ...                             routing_key="image.rotate")

    >>> ImageCropTask.apply_async(args=[filename, selection],
    ...                           routing_key="image.crop")
    >>> UpdateReccomendationsTask.apply_async(routing_key="misc.recommend")


Later, if the crop task is consuming a lot of resources,
we can bind some new workers to handle just the ``"image.crop"`` task,
by creating a new queue that binds to ``"image.crop``".


AMQP options
============

**NOTE** The ``mandatory`` and ``immediate`` flags are not supported by
``amqplib`` at this point.

* mandatory

This sets the delivery to be mandatory. An exception will be raised
if there are no running workers able to take on the task.

* immediate

Request immediate delivery. Will raise an exception
if the task cannot be routed to a worker immediately.

* priority

A number between ``0`` and ``9``, where ``0`` is the highest priority.
Note that RabbitMQ does not implement AMQP priorities, and maybe your broker
does not either, consult your broker's documentation for more
information.
