celery/docs/userguide/tasks.rst

.. _guide-tasks:

=======
 Tasks
=======

Tasks are the building blocks of Celery applications.

A task can be created out of any callable and defines what happens
when the worker receives a particular message.

Every task has unique name which is referenced in the message,
so that the worker can find the right task to execute.

It's not a requirement, but it's a good idea to keep your tasks
*idempotent*.  Idempotence means that a task can be applied multiple
times without changing the result.

This is important because the task message will not disappear
until the message has been *acknowledged*. A worker can reserve
many messages in advance and even if the worker is killed -- caused by a power failure
or otherwise -- the message will be redelivered to another worker.

But the worker cannot know if your tasks are idempotent, so the default
behavior is to acknowledge the message in advance just before it's executed,
this way a task that has been started will not be executed again.

If your task is idempotent you can set the :attr:`acks_late` option
to have the worker acknowledge the message *after* that task has been
executed instead.  This way the task will be redelivered to another
worker, even if the task has already started executing before.
See also the FAQ entry :ref:`faq-acks_late-vs-retry`.

--

In this chapter you will learn all about defining tasks,
and this is the **table of contents**:

.. contents::
    :local:
    :depth: 1


.. _task-basics:

Basics
======

You can easily create a task from any callable by using
the :meth:`~@Celery.task` decorator:

.. code-block:: python

    from .models import User

    @celery.task
    def create_user(username, password):
        User.objects.create(username=username, password=password)


There are also many :ref:`options <task-options>` that can be set for the task,
these can be specified as arguments to the decorator:

.. code-block:: python

    @celery.task(serializer='json')
    def create_user(username, password):
        User.objects.create(username=username, password=password)



.. sidebar:: How do I import the task decorator?

    The task decorator is available on your :class:`@Celery` instance,
    if you don't know what that is then please read :ref:`first-steps`.

    If you're using Django or are still using the "old" module based celery API,
    then you can import the task decorator like this::

        from celery import task

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

.. sidebar:: Multiple decorators

    When using multiple decorators in combination with the task
    decorator you must make sure that the `task`
    decorator is applied last (which in Python oddly means that it must
    be the first in the list):

    .. code-block:: python

        @celery.task
        @decorator2
        @decorator1
        def add(x, y):
            return x + y

.. _task-names:

Names
=====

Every task must have a unique name, and a new name
will be generated out of the function name if a custom name is not provided.

For example:

.. code-block:: python

    >>> @celery.task(name='sum-of-two-numbers')
    >>> def add(x, y):
    ...     return x + y

    >>> add.name
    'sum-of-two-numbers'

A best practice is to use the module name as a namespace,
this way names won't collide if there's already a task with that name
defined in another module.

.. code-block:: python

    >>> @celery.task(name='tasks.add')
    >>> def add(x, y):
    ...     return x + y

You can tell the name of the task by investigating its name attribute::

    >>> add.name
    'tasks.add'

Which is exactly the name that would have been generated anyway,
if the module name is "tasks.py":

:file:`tasks.py`:

.. code-block:: python

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

    >>> from tasks import add
    >>> add.name
    'tasks.add'

.. _task-naming-relative-imports:

Automatic naming and relative imports
-------------------------------------

Relative imports and automatic name generation does not go well together,
so if you're using relative imports you should set the name explicitly.

For example if the client imports the module "myapp.tasks" as ".tasks", and
the worker imports the module as "myapp.tasks", the generated names won't match
and an :exc:`~@NotRegistered` error will be raised by the worker.

This is also the case if using Django and using `project.myapp`::

    INSTALLED_APPS = ('project.myapp', )

The worker will have the tasks registered as "project.myapp.tasks.*",
while this is what happens in the client if the module is imported as
"myapp.tasks":

.. code-block:: python

    >>> from myapp.tasks import add
    >>> add.name
    'myapp.tasks.add'

For this reason you should never use "project.app", but rather
add the project directory to the Python path::

    import os
    import sys
    sys.path.append(os.getcwd())

    INSTALLED_APPS = ('myapp', )

This makes more sense from the reusable app perspective anyway.


.. _task-request-info:

Context
=======

:attr:`~@Task.request` contains information and state related to
the executing task.

The request defines the following attributes:

:id: The unique id of the executing task.

:taskset: The unique id of the taskset this task is a member of (if any).

:args: Positional arguments.

:kwargs: Keyword arguments.

:retries: How many times the current task has been retried.
          An integer starting at `0`.

:is_eager: Set to :const:`True` if the task is executed locally in
           the client, and not by a worker.

:logfile: The file the worker logs to.  See `Logging`_.

:loglevel: The current log level used.

:hostname: Hostname of the worker instance executing the task.

:delivery_info: Additional message delivery information. This is a mapping
                containing the exchange and routing key used to deliver this
                task.  Used by e.g. :meth:`~@Task.retry`
                to resend the task to the same destination queue.
                Availability of keys in this dict depends on the
                message broker used.


An example task accessing information in the context is:

.. code-block:: python

    @celery.task
    def add(x, y):
        print('Executing task id %r, args: %r kwargs: %r' % (
            add.request.id, add.request.args, add.request.kwargs))
        return x + y


:data:`~celery.current_task` can also be used:

.. code-block:: python

    from celery import current_task

    @celery.task
    def add(x, y):
        request = current_task.request
        print('Executing task id %r, args: %r kwargs: %r' % (
            request.id, request.args, request.kwargs))
        return x + y

.. _task-logging:

Logging
=======

The worker will automatically set up logging for you, or you can
configure logging manually.

A special logger is available named "celery.task", you can inherit
from this logger to automatically get the task name and unique id as part
of the logs.

The best practice is to create a common logger
for all of your tasks at the top of your module:

.. code-block:: python

    from celery.utils.log import get_task_logger

    logger = get_task_logger(__name__)

    @celery.task
    def add(x, y):
        logger.info('Adding %s + %s' % (x, y))
        return x + y

Celery uses the standard Python logger library,
for which documentation can be found in the :mod:`logging`
module.

You can also simply use :func:`print`, as anything written to standard
out/-err will be redirected to the workers logs by default (see
:setting:`CELERY_REDIRECT_STDOUTS`).

.. _task-retry:

Retrying
========

:meth:`~@Task.retry` can be used to re-execute the task,
for example in the event of recoverable errors.

When you call ``retry`` it will send a new message, using the same
task-id, and it will take care to make sure the message is delivered
to the same queue as the originating task.

When a task is retried this is also recorded as a task state,
so that you can track the progress of the task using the result
instance (see :ref:`task-states`).

Here's an example using ``retry``:

.. code-block:: python

    @celery.task
    def send_twitter_status(oauth, tweet):
        try:
            twitter = Twitter(oauth)
            twitter.update_status(tweet)
        except (Twitter.FailWhaleError, Twitter.LoginError), exc:
            raise send_twitter_status.retry(exc=exc)

Here the `exc` argument was used to pass the current exception to
:meth:`~@Task.retry`.  Both the exception and the traceback will
be available in the task state (if a result backend is enabled).

.. note::

    The :meth:`~@Task.retry` call will raise an exception so any code after the retry
    will not be reached.  This is the :exc:`~@RetryTaskError`
    exception, it is not handled as an error but rather as a semi-predicate
    to signify to the worker that the task is to be retried,
    so that it can store the correct state when a result backend is enabled.

    This is normal operation and always happens unless the
    ``throw`` argument to retry is set to :const:`False`.

.. _task-retry-custom-delay:

Using a custom retry delay
--------------------------

When a task is to be retried, it can wait for a given amount of time
before doing so, and the default delay is defined by the
:attr:`~@Task.default_retry_delay`
attribute. By default this is set to 3 minutes. Note that the
unit for setting the delay is in seconds (int or float).

You can also provide the `countdown` argument to :meth:`~@Task.retry` to
override this default.

.. code-block:: python

    @celery.task(default_retry_delay=30 * 60)  # retry in 30 minutes.
    def add(x, y):
        try:
            ...
        except Exception, exc:
            raise add.retry(exc=exc, countdown=60)  # override the default and
                                                    # retry in 1 minute

.. _task-options:

List of Options
===============

The task decorator can take a number of options that change the way
the task behaves, for example you can set the rate limit for a task
using the :attr:`rate_limit` option.

Any keyword argument passed to the task decorator will actually be set
as an attribute of the resulting task class, and this is a list
of the built-in attributes.

General
-------

.. _task-general-options:

.. attribute:: Task.name

    The name the task is registered as.

    You can set this name manually, or a name will be
    automatically generated using the module and class name.  See
    :ref:`task-names`.

.. attribute:: Task.request

    If the task is being executed this will contain information
    about the current request.  Thread local storage is used.

    See :ref:`task-request-info`.

.. attribute:: Task.abstract

    Abstract classes are not registered, but are used as the
    base class for new task types.

.. attribute:: Task.max_retries

    The maximum number of attempted retries before giving up.
    If the number of retries exceeds this value a :exc:`~@MaxRetriesExceeded`
    exception will be raised.  *NOTE:* You have to call :meth:`~@Task.retry`
    manually, as it will not automatically retry on exception..

.. attribute:: Task.default_retry_delay

    Default time in seconds before a retry of the task
    should be executed.  Can be either :class:`int` or :class:`float`.
    Default is a 3 minute delay.

.. attribute:: Task.rate_limit

    Set the rate limit for this task type, i.e. how many times in
    a given period of time is the task allowed to run.

    If this is :const:`None` no rate limit is in effect.
    If it is an integer, it is interpreted as "tasks per second".

    The rate limits can be specified in seconds, minutes or hours
    by appending `"/s"`, `"/m"` or `"/h"` to the value.
    Example: `"100/m"` (hundred tasks a minute).  Default is the
    :setting:`CELERY_DEFAULT_RATE_LIMIT` setting, which if not specified means
    rate limiting for tasks is disabled by default.

.. attribute:: Task.time_limit

    The hard time limit for this task.  If not set then the workers default
    will be used.

.. attribute:: Task.soft_time_limit

    The soft time limit for this task.  If not set then the workers default
    will be used.

.. attribute:: Task.ignore_result

    Don't store task state.    Note that this means you can't use
    :class:`~celery.result.AsyncResult` to check if the task is ready,
    or get its return value.

.. attribute:: Task.store_errors_even_if_ignored

    If :const:`True`, errors will be stored even if the task is configured
    to ignore results.

.. attribute:: Task.send_error_emails

    Send an email whenever a task of this type fails.
    Defaults to the :setting:`CELERY_SEND_TASK_ERROR_EMAILS` setting.
    See :ref:`conf-error-mails` for more information.

.. attribute:: Task.ErrorMail

    If the sending of error emails is enabled for this task, then
    this is the class defining the logic to send error mails.

.. attribute:: Task.serializer

    A string identifying the default serialization
    method to use. Defaults to the :setting:`CELERY_TASK_SERIALIZER`
    setting.  Can be `pickle` `json`, `yaml`, or any custom
    serialization methods that have been registered with
    :mod:`kombu.serialization.registry`.

    Please see :ref:`calling-serializers` for more information.

.. attribute:: Task.compression

    A string identifying the default compression scheme to use.

    Defaults to the :setting:`CELERY_MESSAGE_COMPRESSION` setting.
    Can be `gzip`, or `bzip2`, or any custom compression schemes
    that have been registered with the :mod:`kombu.compression` registry.

    Please see :ref:`calling-compression` for more information.

.. attribute:: Task.backend

    The result store backend to use for this task.  Defaults to the
    :setting:`CELERY_RESULT_BACKEND` setting.

.. attribute:: Task.acks_late

    If set to :const:`True` messages for this task will be acknowledged
    **after** the task has been executed, not *just before*, which is
    the default behavior.

    Note that this means the task may be executed twice if the worker
    crashes in the middle of execution, which may be acceptable for some
    applications.

    The global default can be overridden by the :setting:`CELERY_ACKS_LATE`
    setting.

.. _task-track-started:

.. attribute:: Task.track_started

    If :const:`True` the task will report its status as "started"
    when the task is executed by a worker.
    The default value is :const:`False` as the normal behaviour is to not
    report that level of granularity. Tasks are either pending, finished,
    or waiting to be retried.  Having a "started" status can be useful for
    when there are long running tasks and there is a need to report which
    task is currently running.

    The host name and process id of the worker executing the task
    will be available in the state metadata (e.g. `result.info['pid']`)

    The global default can be overridden by the
    :setting:`CELERY_TRACK_STARTED` setting.


.. seealso::

    The API reference for :class:`~@Task`.

.. _task-states:

States
======

Celery can keep track of the tasks current state.  The state also contains the
result of a successful task, or the exception and traceback information of a
failed task.

There are several *result backends* to choose from, and they all have
different strengths and weaknesses (see :ref:`task-result-backends`).

During its lifetime a task will transition through several possible states,
and each state may have arbitrary metadata attached to it.  When a task
moves into a new state the previous state is
forgotten about, but some transitions can be deducted, (e.g. a task now
in the :state:`FAILED` state, is implied to have been in the
:state:`STARTED` state at some point).

There are also sets of states, like the set of
:state:`FAILURE_STATES`, and the set of :state:`READY_STATES`.

The client uses the membership of these sets to decide whether
the exception should be re-raised (:state:`PROPAGATE_STATES`), or whether
the state can be cached (it can if the task is ready).

You can also define :ref:`custom-states`.

.. _task-result-backends:

Result Backends
---------------

Celery needs to store or send the states somewhere.  There are several
built-in backends to choose from: SQLAlchemy/Django ORM, Memcached,
RabbitMQ (amqp), MongoDB, and Redis -- or you can define your own.

No backend works well for every use case.
You should read about the strengths and weaknesses of each backend, and choose
the most appropriate for your needs.


.. seealso::

    :ref:`conf-result-backend`

RabbitMQ Result Backend
~~~~~~~~~~~~~~~~~~~~~~~

The RabbitMQ result backend (amqp) is special as it does not actually *store*
the states, but rather sends them as messages.  This is an important difference as it
means that a result *can only be retrieved once*; If you have two processes
waiting for the same result, one of the processes will never receive the
result!

Even with that limitation, it is an excellent choice if you need to receive
state changes in real-time.  Using messaging means the client does not have to
poll for new states.

There are several other pitfalls you should be aware of when using the
RabbitMQ result backend:

* Every new task creates a new queue on the server, with thousands of tasks
  the broker may be overloaded with queues and this will affect performance in
  negative ways. If you're using RabbitMQ then each queue will be a separate
  Erlang process, so if you're planning to keep many results simultaneously you
  may have to increase the Erlang process limit, and the maximum number of file
  descriptors your OS allows.

* Old results will be cleaned automatically, based on the
  :setting:`CELERY_TASK_RESULT_EXPIRES` setting.  By default this is set to
  expire after 1 day: if you have a very busy cluster you should lower
  this value.

For a list of options supported by the RabbitMQ result backend, please see
:ref:`conf-amqp-result-backend`.


Database Result Backend
~~~~~~~~~~~~~~~~~~~~~~~

Keeping state in the database can be convenient for many, especially for
web applications with a database already in place, but it also comes with
limitations.

* Polling the database for new states is expensive, and so you should
  increase the polling intervals of operations such as `result.get()`.

* Some databases use a default transaction isolation level that
  is not suitable for polling tables for changes.

  In MySQL the default transaction isolation level is `REPEATABLE-READ`, which
  means the transaction will not see changes by other transactions until the
  transaction is committed.  It is recommended that you change to the
  `READ-COMMITTED` isolation level.


.. _task-builtin-states:

Built-in States
---------------

.. state:: PENDING

PENDING
~~~~~~~

Task is waiting for execution or unknown.
Any task id that is not known is implied to be in the pending state.

.. state:: STARTED

STARTED
~~~~~~~

Task has been started.
Not reported by default, to enable please see :attr:`@Task.track_started`.

:metadata: `pid` and `hostname` of the worker process executing
           the task.

.. state:: SUCCESS

SUCCESS
~~~~~~~

Task has been successfully executed.

:metadata: `result` contains the return value of the task.
:propagates: Yes
:ready: Yes

.. state:: FAILURE

FAILURE
~~~~~~~

Task execution resulted in failure.

:metadata: `result` contains the exception occurred, and `traceback`
           contains the backtrace of the stack at the point when the
           exception was raised.
:propagates: Yes

.. state:: RETRY

RETRY
~~~~~

Task is being retried.

:metadata: `result` contains the exception that caused the retry,
           and `traceback` contains the backtrace of the stack at the point
           when the exceptions was raised.
:propagates: No

.. state:: REVOKED

REVOKED
~~~~~~~

Task has been revoked.

:propagates: Yes

.. _custom-states:

Custom states
-------------

You can easily define your own states, all you need is a unique name.
The name of the state is usually an uppercase string.  As an example
you could have a look at :mod:`abortable tasks <~celery.contrib.abortable>`
which defines its own custom :state:`ABORTED` state.

Use :meth:`~@Task.update_state` to update a task's state::

    from celery import current_task

    @celery.task
    def upload_files(filenames):
        for i, file in enumerate(filenames):
            current_task.update_state(state='PROGRESS',
                meta={'current': i, 'total': len(filenames)})


Here I created the state `"PROGRESS"`, which tells any application
aware of this state that the task is currently in progress, and also where
it is in the process by having `current` and `total` counts as part of the
state metadata.  This can then be used to create e.g. progress bars.

.. _pickling_exceptions:

Creating pickleable exceptions
------------------------------

A little known Python fact is that exceptions must behave a certain
way to support being pickled.

Tasks that raise exceptions that are not pickleable will not work
properly when Pickle is used as the serializer.

To make sure that your exceptions are pickleable the exception
*MUST* provide the original arguments it was instantiated
with in its ``.args`` attribute.  The simplest way
to ensure this is to have the exception call ``Exception.__init__``.

Let's look at some examples that work, and one that doesn't:

.. code-block:: python


    # OK:
    class HttpError(Exception):
        pass

    # BAD:
    class HttpError(Exception):

        def __init__(self, status_code):
            self.status_code = status_code

    # OK:
    class HttpError(Exception):

        def __init__(self, status_code):
            self.status_code = status_code
            Exception.__init__(self, status_code)  # <-- REQUIRED


So the rule is:
For any exception that supports custom arguments ``*args``,
``Exception.__init__(self, *args)`` must be used.

There is no special support for *keyword arguments*, so if you
want to preserve keyword arguments when the exception is unpickled
you have to pass them as regular args:

.. code-block:: python

    class HttpError(Exception):

        def __init__(self, status_code, headers=None, body=None):
            self.status_code = status_code
            self.headers = headers
            self.body = body

            super(HttpError, self).__init__(status_code, headers, body)

.. _task-custom-classes:

Custom task classes
===================

All tasks inherit from the :class:`@Task` class.
The :meth:`~@Task.run` method becomes the task body.

As an example, the following code,

.. code-block:: python

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


will do roughly this behind the scenes:

.. code-block:: python

    @celery.task
    class AddTask(Task):

        def run(self, x, y):
            return x + y
    add = registry.tasks[AddTask.name]


Instantiation
-------------

A task is **not** instantiated for every request, but is registered
in the task registry as a global instance.

This means that the ``__init__`` constructor will only be called
once per process, and that the task class is semantically closer to an
Actor.

If you have a task,

.. code-block:: python

    from celery import Task

    class NaiveAuthenticateServer(Task):

        def __init__(self):
            self.users = {'george': 'password'}

        def run(self, username, password):
            try:
                return self.users[username] == password
            except KeyError:
                return False

And you route every request to the same process, then it
will keep state between requests.

This can also be useful to cache resources,
e.g. a base Task class that caches a database connection:

.. code-block:: python

    from celery import Task

    class DatabaseTask(Task):
        abstract = True
        _db = None

        @property
        def db(self):
            if self._db is None:
                self._db = Database.connect()
            return self._db


that can be added to tasks like this:

.. code-block:: python


    @celery.task(base=DatabaseTask)
    def process_rows():
        for row in self.db.table.all():
            ...

The ``db`` attribute of the ``process_rows`` task will then
always stay the same in each process.

Abstract classes
----------------

Abstract classes are not registered, but are used as the
base class for new task types.

.. code-block:: python

    from celery import Task

    class DebugTask(Task):
        abstract = True

        def after_return(self, *args, **kwargs):
            print('Task returned: %r' % (self.request, ))


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


Handlers
--------

.. method:: after_return(self, status, retval, task_id, args, kwargs, einfo)

    Handler called after the task returns.

    :param status: Current task state.
    :param retval: Task return value/exception.
    :param task_id: Unique id of the task.
    :param args: Original arguments for the task that failed.
    :param kwargs: Original keyword arguments for the task
                   that failed.

    :keyword einfo: :class:`~celery.datastructures.ExceptionInfo`
                    instance, containing the traceback (if any).

    The return value of this handler is ignored.

.. method:: on_failure(self, exc, task_id, args, kwargs, einfo)

    This is run by the worker when the task fails.

    :param exc: The exception raised by the task.
    :param task_id: Unique id of the failed task.
    :param args: Original arguments for the task that failed.
    :param kwargs: Original keyword arguments for the task
                       that failed.

    :keyword einfo: :class:`~celery.datastructures.ExceptionInfo`
                           instance, containing the traceback.

    The return value of this handler is ignored.

.. method:: on_retry(self, exc, task_id, args, kwargs, einfo)

    This is run by the worker when the task is to be retried.

    :param exc: The exception sent to :meth:`~@Task.retry`.
    :param task_id: Unique id of the retried task.
    :param args: Original arguments for the retried task.
    :param kwargs: Original keyword arguments for the retried task.

    :keyword einfo: :class:`~celery.datastructures.ExceptionInfo`
                    instance, containing the traceback.

    The return value of this handler is ignored.

.. method:: on_success(self, retval, task_id, args, kwargs)

    Run by the worker if the task executes successfully.

    :param retval: The return value of the task.
    :param task_id: Unique id of the executed task.
    :param args: Original arguments for the executed task.
    :param kwargs: Original keyword arguments for the executed task.

    The return value of this handler is ignored.

on_retry
~~~~~~~~

.. _task-how-they-work:

How it works
============

Here comes the technical details, this part isn't something you need to know,
but you may be interested.

All defined tasks are listed in a registry.  The registry contains
a list of task names and their task classes.  You can investigate this registry
yourself:

.. code-block:: python

    >>> from celery import current_app
    >>> current_app.tasks
    {'celery.chord_unlock':
        <@task: celery.chord_unlock>,
     'celery.backend_cleanup':
        <@task: celery.backend_cleanup>,
     'celery.chord':
        <@task: celery.chord>}

This is the list of tasks built-in to celery.  Note that tasks
will only be registered when the module they are defined in is imported.

The default loader imports any modules listed in the
:setting:`CELERY_IMPORTS` setting.

The entity responsible for registering your task in the registry is the
metaclass: :class:`~celery.task.base.TaskType`.

If you want to register your task manually you can mark the
task as :attr:`~@Task.abstract`:

.. code-block:: python

    class MyTask(Task):
        abstract = True

This way the task won't be registered, but any task inheriting from
it will be.

When tasks are sent, no actual function code is sent with it, just the name
of the task to execute.  When the worker then receives the message it can look
up the name in its task registry to find the execution code.

This means that your workers should always be updated with the same software
as the client.  This is a drawback, but the alternative is a technical
challenge that has yet to be solved.

.. _task-best-practices:

Tips and Best Practices
=======================

.. _task-ignore_results:

Ignore results you don't want
-----------------------------

If you don't care about the results of a task, be sure to set the
:attr:`~@Task.ignore_result` option, as storing results
wastes time and resources.

.. code-block:: python

    @celery.task(ignore_result=True)
    def mytask(...)
        something()

Results can even be disabled globally using the :setting:`CELERY_IGNORE_RESULT`
setting.

.. _task-disable-rate-limits:

Disable rate limits if they're not used
---------------------------------------

Disabling rate limits altogether is recommended if you don't have
any tasks using them.  This is because the rate limit subsystem introduces
quite a lot of complexity.

Set the :setting:`CELERY_DISABLE_RATE_LIMITS` setting to globally disable
rate limits:

.. code-block:: python

    CELERY_DISABLE_RATE_LIMITS = True

You find additional optimization tips in the
:ref:`Optimizing Guide <guide-optimizing>`.

.. _task-synchronous-subtasks:

Avoid launching synchronous subtasks
------------------------------------

Having a task wait for the result of another task is really inefficient,
and may even cause a deadlock if the worker pool is exhausted.

Make your design asynchronous instead, for example by using *callbacks*.

**Bad**:

.. code-block:: python

    @celery.task
    def update_page_info(url):
        page = fetch_page.delay(url).get()
        info = parse_page.delay(url, page).get()
        store_page_info.delay(url, info)

    @celery.task
    def fetch_page(url):
        return myhttplib.get(url)

    @celery.task
    def parse_page(url, page):
        return myparser.parse_document(page)

    @celery.task
    def store_page_info(url, info):
        return PageInfo.objects.create(url, info)


**Good**:

.. code-block:: python

    def update_page_info(url):
        # fetch_page -> parse_page -> store_page
        chain = fetch_page.s() | parse_page.s(url) | store_page_info.s(url)
        chain.apply_async()

    @celery.task(ignore_result=True)
    def fetch_page(url):
        return myhttplib.get(url)

    @celery.task(ignore_result=True)
    def parse_page(url, page):
        return myparser.parse_document(page)

    @celery.task(ignore_result=True)
    def store_page_info(url, info):
        PageInfo.objects.create(url, info)


Here I instead created a chain of tasks by linking together
different :func:`~celery.subtask`'s.
You can read about chains and other powerful constructs
at :ref:`designing-workflows`.

.. _task-performance-and-strategies:

Performance and Strategies
==========================

.. _task-granularity:

Granularity
-----------

The task granularity is the amount of computation needed by each subtask.
In general it is better to split the problem up into many small tasks, than
have a few long running tasks.

With smaller tasks you can process more tasks in parallel and the tasks
won't run long enough to block the worker from processing other waiting tasks.

However, executing a task does have overhead. A message needs to be sent, data
may not be local, etc. So if the tasks are too fine-grained the additional
overhead may not be worth it in the end.

.. seealso::

    The book `Art of Concurrency`_ has a whole section dedicated to the topic
    of task granularity.

.. _`Art of Concurrency`: http://oreilly.com/catalog/9780596521547

.. _task-data-locality:

Data locality
-------------

The worker processing the task should be as close to the data as
possible.  The best would be to have a copy in memory, the worst would be a
full transfer from another continent.

If the data is far away, you could try to run another worker at location, or
if that's not possible - cache often used data, or preload data you know
is going to be used.

The easiest way to share data between workers is to use a distributed cache
system, like `memcached`_.

.. seealso::

    The paper `Distributed Computing Economics`_ by Jim Gray is an excellent
    introduction to the topic of data locality.

.. _`Distributed Computing Economics`:
    http://research.microsoft.com/pubs/70001/tr-2003-24.pdf

.. _`memcached`: http://memcached.org/

.. _task-state:

State
-----

Since celery is a distributed system, you can't know in which process, or
on what machine the task will be executed.  You can't even know if the task will
run in a timely manner.

The ancient async sayings tells us that “asserting the world is the
responsibility of the task”.  What this means is that the world view may
have changed since the task was requested, so the task is responsible for
making sure the world is how it should be;  If you have a task
that re-indexes a search engine, and the search engine should only be
re-indexed at maximum every 5 minutes, then it must be the tasks
responsibility to assert that, not the callers.

Another gotcha is Django model objects.  They shouldn't be passed on as
arguments to tasks.  It's almost always better to re-fetch the object from
the database when the task is running instead,  as using old data may lead
to race conditions.

Imagine the following scenario where you have an article and a task
that automatically expands some abbreviations in it:

.. code-block:: python

    class Article(models.Model):
        title = models.CharField()
        body = models.TextField()

    @celery.task
    def expand_abbreviations(article):
        article.body.replace('MyCorp', 'My Corporation')
        article.save()

First, an author creates an article and saves it, then the author
clicks on a button that initiates the abbreviation task::

    >>> article = Article.objects.get(id=102)
    >>> expand_abbreviations.delay(model_object)

Now, the queue is very busy, so the task won't be run for another 2 minutes.
In the meantime another author makes changes to the article, so
when the task is finally run, the body of the article is reverted to the old
version because the task had the old body in its argument.

Fixing the race condition is easy, just use the article id instead, and
re-fetch the article in the task body:

.. code-block:: python

    @celery.task
    def expand_abbreviations(article_id):
        article = Article.objects.get(id=article_id)
        article.body.replace('MyCorp', 'My Corporation')
        article.save()

    >>> expand_abbreviations(article_id)

There might even be performance benefits to this approach, as sending large
messages may be expensive.

.. _task-database-transactions:

Database transactions
---------------------

Let's have a look at another example:

.. code-block:: python

    from django.db import transaction

    @transaction.commit_on_success
    def create_article(request):
        article = Article.objects.create(....)
        expand_abbreviations.delay(article.pk)

This is a Django view creating an article object in the database,
then passing the primary key to a task.  It uses the `commit_on_success`
decorator, which will commit the transaction when the view returns, or
roll back if the view raises an exception.

There is a race condition if the task starts executing
before the transaction has been committed; The database object does not exist
yet!

The solution is to *always commit transactions before sending tasks
depending on state from the current transaction*:

.. code-block:: python

    @transaction.commit_manually
    def create_article(request):
        try:
            article = Article.objects.create(...)
        except:
            transaction.rollback()
            raise
        else:
            transaction.commit()
            expand_abbreviations.delay(article.pk)

.. _task-example:

Example
=======

Let's take a real wold example; A blog where comments posted needs to be
filtered for spam.  When the comment is created, the spam filter runs in the
background, so the user doesn't have to wait for it to finish.

I have a Django blog application allowing comments
on blog posts.  I'll describe parts of the models/views and tasks for this
application.

blog/models.py
--------------

The comment model looks like this:

.. code-block:: python

    from django.db import models
    from django.utils.translation import ugettext_lazy as _


    class Comment(models.Model):
        name = models.CharField(_('name'), max_length=64)
        email_address = models.EmailField(_('email address'))
        homepage = models.URLField(_('home page'),
                                   blank=True, verify_exists=False)
        comment = models.TextField(_('comment'))
        pub_date = models.DateTimeField(_('Published date'),
                                        editable=False, auto_add_now=True)
        is_spam = models.BooleanField(_('spam?'),
                                      default=False, editable=False)

        class Meta:
            verbose_name = _('comment')
            verbose_name_plural = _('comments')


In the view where the comment is posted, I first write the comment
to the database, then I launch the spam filter task in the background.

.. _task-example-blog-views:

blog/views.py
-------------

.. code-block:: python

    from django import forms
    from django.http import HttpResponseRedirect
    from django.template.context import RequestContext
    from django.shortcuts import get_object_or_404, render_to_response

    from blog import tasks
    from blog.models import Comment


    class CommentForm(forms.ModelForm):

        class Meta:
            model = Comment


    def add_comment(request, slug, template_name='comments/create.html'):
        post = get_object_or_404(Entry, slug=slug)
        remote_addr = request.META.get('REMOTE_ADDR')

        if request.method == 'post':
            form = CommentForm(request.POST, request.FILES)
            if form.is_valid():
                comment = form.save()
                # Check spam asynchronously.
                tasks.spam_filter.delay(comment_id=comment.id,
                                        remote_addr=remote_addr)
                return HttpResponseRedirect(post.get_absolute_url())
        else:
            form = CommentForm()

        context = RequestContext(request, {'form': form})
        return render_to_response(template_name, context_instance=context)


To filter spam in comments I use `Akismet`_, the service
used to filter spam in comments posted to the free weblog platform
`Wordpress`.  `Akismet`_ is free for personal use, but for commercial use you
need to pay.  You have to sign up to their service to get an API key.

To make API calls to `Akismet`_ I use the `akismet.py`_ library written by
`Michael Foord`_.

.. _task-example-blog-tasks:

blog/tasks.py
-------------

.. code-block:: python

    import celery

    from akismet import Akismet

    from django.core.exceptions import ImproperlyConfigured
    from django.contrib.sites.models import Site

    from blog.models import Comment


    @celery.task
    def spam_filter(comment_id, remote_addr=None):
        logger = spam_filter.get_logger()
        logger.info('Running spam filter for comment %s' % comment_id)

        comment = Comment.objects.get(pk=comment_id)
        current_domain = Site.objects.get_current().domain
        akismet = Akismet(settings.AKISMET_KEY, 'http://%s' % domain)
        if not akismet.verify_key():
            raise ImproperlyConfigured('Invalid AKISMET_KEY')


        is_spam = akismet.comment_check(user_ip=remote_addr,
                            comment_content=comment.comment,
                            comment_author=comment.name,
                            comment_author_email=comment.email_address)
        if is_spam:
            comment.is_spam = True
            comment.save()

        return is_spam

.. _`Akismet`: http://akismet.com/faq/
.. _`akismet.py`: http://www.voidspace.org.uk/downloads/akismet.py
.. _`Michael Foord`: http://www.voidspace.org.uk/