celery/_sources/userguide/canvas.txt

.. _guide-canvas:

============================
 Canvas: Building Workflows
============================

.. contents::
    :local:

.. _canvas-subtasks:

Subtasks
========

.. versionadded:: 2.0

The :class:`~celery.subtask` type is used to wrap the arguments and
execution options for a single task invocation:

.. code-block:: python

    from celery import subtask

    subtask(task_name_or_cls, args, kwargs, options)

For convenience every task also has a shortcut to create subtasks:

.. code-block:: python

    task.subtask(args, kwargs, options)

:class:`~celery.subtask` is actually a :class:`dict` subclass,
which means it can be serialized with JSON or other encodings that doesn't
support complex Python objects.

Also it can be regarded as a type, as the following usage works::

    >>> s = subtask("tasks.add", args=(2, 2), kwargs={})

    >>> subtask(dict(s))  # coerce dict into subtask

This makes it excellent as a means to pass callbacks around to tasks.

.. _canvas-callbacks:

Callbacks
---------

Callbacks can be added to any task using the ``link`` argument
to ``apply_async``:

    add.apply_async((2, 2), link=other_task.subtask())

The callback will only be applied if the task exited successfully,
and it will be applied with the return value of the parent task as argument.


The best thing is that any arguments you add to `subtask`,
will be prepended to the arguments specified by the subtask itself!

If you have the subtask::

    >>> add.subtask(args=(10, ))

`subtask.delay(result)` becomes::

    >>> add.apply_async(args=(result, 10))

...

Now let's call our ``add`` task with a callback using partial
arguments::

    >>> add.apply_async((2, 2), link=add.subtask((8, )))

As expected this will first launch one task calculating :math:`2 + 2`, then
another task calculating :math:`4 + 8`.

.. _canvas-group:

Groups
======

The :class:`~celery.group` enables easy invocation of several
tasks at once, and is then able to join the results in the same order as the
tasks were invoked.

``group`` takes a list of :class:`~celery.subtask`'s::

    >>> from celery import group
    >>> from tasks import add

    >>> job = group([
    ...             add.subtask((2, 2)),
    ...             add.subtask((4, 4)),
    ...             add.subtask((8, 8)),
    ...             add.subtask((16, 16)),
    ...             add.subtask((32, 32)),
    ... ])

    >>> result = job.apply_async()

    >>> result.ready()  # have all subtasks completed?
    True
    >>> result.successful() # were all subtasks successful?
    True
    >>> result.join()
    [4, 8, 16, 32, 64]

The first argument can alternatively be an iterator, like::

    >>> group(add.subtask((i, i)) for i in range(100))

.. _canvas-group-results:

Group Results
-------------

When a  :class:`~celery.group` is applied it returns a
:class:`~celery.result.GroupResult` object.

:class:`~celery.result.GroupResult` takes a list of
:class:`~celery.result.AsyncResult` instances and operates on them as if it was a
single task.

It supports the following operations:

* :meth:`~celery.result.GroupResult.successful`

    Returns :const:`True` if all of the subtasks finished
    successfully (e.g. did not raise an exception).

* :meth:`~celery.result.GroupResult.failed`

    Returns :const:`True` if any of the subtasks failed.

* :meth:`~celery.result.GroupResult.waiting`

    Returns :const:`True` if any of the subtasks
    is not ready yet.

* :meth:`~celery.result.GroupResult.ready`

    Return :const:`True` if all of the subtasks
    are ready.

* :meth:`~celery.result.GroupResult.completed_count`

    Returns the number of completed subtasks.

* :meth:`~celery.result.GroupResult.revoke`

    Revokes all of the subtasks.

* :meth:`~celery.result.GroupResult.iterate`

    Iterates over the return values of the subtasks
    as they finish, one by one.

* :meth:`~celery.result.GroupResult.join`

    Gather the results for all of the subtasks
    and return a list with them ordered by the order of which they
    were called.

.. _chords:

Chords
======

.. versionadded:: 2.3

A chord is a task that only executes after all of the tasks in a taskset has
finished executing.


Let's calculate the sum of the expression
:math:`1 + 1 + 2 + 2 + 3 + 3 ... n + n` up to a hundred digits.

First we need two tasks, :func:`add` and :func:`tsum` (:func:`sum` is
already a standard function):

.. code-block:: python

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

    @celery.task()
    def tsum(numbers):
        return sum(numbers)


Now we can use a chord to calculate each addition step in parallel, and then
get the sum of the resulting numbers::

    >>> from celery import chord
    >>> from tasks import add, tsum

    >>> chord(add.subtask((i, i))
    ...     for i in xrange(100))(tsum.subtask()).get()
    9900


This is obviously a very contrived example, the overhead of messaging and
synchronization makes this a lot slower than its Python counterpart::

    sum(i + i for i in xrange(100))

The synchronization step is costly, so you should avoid using chords as much
as possible. Still, the chord is a powerful primitive to have in your toolbox
as synchronization is a required step for many parallel algorithms.

Let's break the chord expression down::

    >>> callback = tsum.subtask()
    >>> header = [add.subtask((i, i)) for i in xrange(100)]
    >>> result = chord(header)(callback)
    >>> result.get()
    9900

Remember, the callback can only be executed after all of the tasks in the
header has returned.  Each step in the header is executed as a task, in
parallel, possibly on different nodes.  The callback is then applied with
the return value of each task in the header.  The task id returned by
:meth:`chord` is the id of the callback, so you can wait for it to complete
and get the final return value (but remember to :ref:`never have a task wait
for other tasks <task-synchronous-subtasks>`)

.. _chord-important-notes:

Important Notes
---------------

By default the synchronization step is implemented by having a recurring task
poll the completion of the taskset every second, applying the subtask when
ready.

Example implementation:

.. code-block:: python

    def unlock_chord(taskset, callback, interval=1, max_retries=None):
        if taskset.ready():
            return subtask(callback).delay(taskset.join())
        raise unlock_chord.retry(countdown=interval, max_retries=max_retries)


This is used by all result backends except Redis and Memcached, which increment a
counter after each task in the header, then applying the callback when the
counter exceeds the number of tasks in the set. *Note:* chords do not properly
work with Redis before version 2.2; you will need to upgrade to at least 2.2 to
use them.

The Redis and Memcached approach is a much better solution, but not easily
implemented in other backends (suggestions welcome!).


.. note::

    If you are using chords with the Redis result backend and also overriding
    the :meth:`Task.after_return` method, you need to make sure to call the
    super method or else the chord callback will not be applied.

    .. code-block:: python

        def after_return(self, *args, **kwargs):
            do_something()
            super(MyTask, self).after_return(*args, **kwargs)