========================
Django's Tasks framework
========================

For a web application, there's often more than just turning HTTP requests into
HTTP responses. For some functionality, it may be beneficial to run code
outside the request-response cycle.

That's where background Tasks come in.

Background Tasks can offload work to be run outside the request-response cycle,
to be run elsewhere, potentially at a later date. This keeps requests fast,
reduces latency, and improves the user experience. For example, a user
shouldn't have to wait for an email to send before their page finishes loading.

Django's Tasks framework makes it easy to define and enqueue such work. It
does not provide a worker mechanism to run Tasks. The actual execution must be
handled by infrastructure outside Django, such as a separate process or
service. Given that, a :ref:`task backend <configuring-a-task-backend>` capable
of executing tasks on that service should be evaluated and configured.

Background Task fundamentals
============================

When work needs to be done in the background, Django creates a ``Task``, which
is stored in the Queue Store. This ``Task`` contains all the metadata needed to
execute it, as well as a unique identifier for Django to retrieve the result
later.

A Worker will look at the Queue Store for new Tasks to run. When a new Task is
added, a Worker claims the Task, executes it, and saves the status and result
back to the Queue Store. These workers run outside the request-response
lifecycle.

.. _configuring-a-task-backend:

Configuring a Task backend
==========================

The Task backend determines how and where Tasks are stored for execution and
how they are executed. Different Task backends have different characteristics
and configuration options, which may impact the performance and reliability of
your application. Django comes with :ref:`built-in backends
<task-available-backends>`, but these are for development and testing only.

Django handles task definition, validation, queuing, and result handling, not
execution, so production setups need a backend or worker process that actually
runs queued work. Relevant options are listed in the `Community Ecosystem
<https://www.djangoproject.com/community/ecosystem/>`__ page.

Task backends are configured using the :setting:`TASKS` setting in your
settings file. Whilst most applications will only need a single backend,
multiple are supported.

.. _immediate-task-backend:

Immediate execution
-------------------

This is the default backend if another is not specified in your settings file.
The :class:`.ImmediateBackend` runs enqueued Tasks immediately, rather than in
the background. This allows background Task functionality to be slowly added to
an application, before the required infrastructure is available.

To use it, set :setting:`BACKEND <TASKS-BACKEND>` to
``"django.tasks.backends.immediate.ImmediateBackend"``::

    TASKS = {"default": {"BACKEND": "django.tasks.backends.immediate.ImmediateBackend"}}

The :class:`.ImmediateBackend` may also be useful in tests, to bypass the need
to run a real background worker in your tests.

.. _dummy-task-backend:

Dummy backend
-------------

The :class:`.DummyBackend` doesn't execute enqueued Tasks at all, instead
storing results for later use. Task results will forever remain in the
:attr:`~django.tasks.TaskResultStatus.READY` state.

This backend is not intended for use in production - it is provided as a
convenience that can be used during development and testing.

To use it, set :setting:`BACKEND <TASKS-BACKEND>` to
``"django.tasks.backends.dummy.DummyBackend"``::

    TASKS = {"default": {"BACKEND": "django.tasks.backends.dummy.DummyBackend"}}

The results for enqueued Tasks can be retrieved from the backend's
:attr:`~django.tasks.backends.dummy.DummyBackend.results` attribute:

.. code-block:: pycon

    >>> from django.tasks import default_task_backend
    >>> my_task.enqueue()
    >>> len(default_task_backend.results)
    1

Stored results can be cleared using the
:meth:`~django.tasks.backends.dummy.DummyBackend.clear` method:

.. code-block:: pycon

    >>> default_task_backend.clear()
    >>> len(default_task_backend.results)
    0

Third-party backends
--------------------

As mentioned at the beginning of this section, Django includes backends
suitable for development and testing only. Production systems should rely on
backends that supply a worker process and a durable queue implementation.
Available third-party backends are listed on the `Community Ecosystem page
<https://www.djangoproject.com/community/ecosystem/#tasks>`__ and the `Tasks
framework grid from Django Packages
<https://djangopackages.org/grids/g/task-framework/>`__.

To use an external Task backend with Django, use the Python import path as the
:setting:`BACKEND <TASKS-BACKEND>` of the :setting:`TASKS` setting, like so::

    TASKS = {
        "default": {
            "BACKEND": "path.to.backend",
        }
    }

A Task backend is a class that inherits
:class:`~django.tasks.backends.base.BaseTaskBackend`. At a minimum, it must
implement :meth:`.BaseTaskBackend.enqueue`. If you're building your own
backend, you can use the built-in Task backends as reference implementations.
You'll find the code in the :source:`django/tasks/backends/` directory of the
Django source.

Asynchronous support
--------------------

Django has developing support for asynchronous Task backends.

:class:`~django.tasks.backends.base.BaseTaskBackend` has async variants of all
base methods. By convention, the asynchronous versions of all methods are
prefixed with ``a``. The arguments for both variants are the same.

Retrieving backends
-------------------

Backends can be retrieved using the ``task_backends`` connection handler::

    from django.tasks import task_backends

    task_backends["default"]  # The default backend
    task_backends["reserve"]  # Another backend

The "default" backend is available as ``default_task_backend``::

    from django.tasks import default_task_backend

.. _defining-tasks:

Defining Tasks
==============

Tasks are defined using the :meth:`django.tasks.task` decorator on a
module-level function::

    from django.core.mail import send_mail
    from django.tasks import task


    @task
    def email_users(emails, subject, message):
        return send_mail(
            subject=subject, message=message, from_email=None, recipient_list=emails
        )


The return value of the decorator is a :class:`~django.tasks.Task` instance.

:class:`~django.tasks.Task` attributes can be customized via the ``@task``
decorator arguments::

    from django.core.mail import send_mail
    from django.tasks import task


    @task(priority=2, queue_name="emails")
    def email_users(emails, subject, message):
        return send_mail(
            subject=subject, message=message, from_email=None, recipient_list=emails
        )

By convention, Tasks are defined in a ``tasks.py`` file, however this is not
enforced.

.. _task-context:

Task context
------------

Sometimes, the running ``Task`` may need to know context about how it was
enqueued, and how it is being executed. This can be accessed by taking a
``context`` argument, which is an instance of
:class:`~django.tasks.TaskContext`.

To receive the Task context as an argument to your Task function, pass
``takes_context`` when defining it::

    import logging
    from django.core.mail import send_mail
    from django.tasks import task

    logger = logging.getLogger(__name__)


    @task(takes_context=True)
    def email_users(context, emails, subject, message):
        logger.debug(
            f"Attempt {context.attempt} to send user email. Task result id: {context.task_result.id}."
        )
        return send_mail(
            subject=subject, message=message, from_email=None, recipient_list=emails
        )

.. _modifying-tasks:

Modifying Tasks
---------------

Before enqueueing Tasks, it may be necessary to modify certain parameters of
the Task. For example, to give it a higher priority than it would normally.

A ``Task`` instance cannot be modified directly. Instead, a modified instance
can be created with the :meth:`~django.tasks.Task.using` method, leaving the
original as-is. For example:

.. code-block:: pycon

    >>> email_users.priority
    0
    >>> email_users.using(priority=10).priority
    10

.. _enqueueing-tasks:

Enqueueing Tasks
================

To add the Task to the queue store, so it will be executed, call the
:meth:`~django.tasks.Task.enqueue` method on it. If the Task takes arguments,
these can be passed as-is. For example::

    result = email_users.enqueue(
        emails=["user@example.com"],
        subject="You have a message",
        message="Hello there!",
    )

This returns a :class:`~django.tasks.TaskResult`, which can be used to retrieve
the result of the Task once it has finished executing.

To enqueue Tasks in an ``async`` context, :meth:`~django.tasks.Task.aenqueue`
is available as an ``async`` variant of :meth:`~django.tasks.Task.enqueue`.

Because both Task arguments and return values are serialized to JSON, they must
be JSON-serializable:

.. code-block:: pycon

    >>> process_data.enqueue(datetime.now())
    Traceback (most recent call last):
    ...
    TypeError: Object of type datetime is not JSON serializable

Arguments must also be able to round-trip through a :func:`json.dumps`/
:func:`json.loads` cycle without changing type. For example, consider this
Task::

    @task()
    def double_dictionary(key):
        return {key: key * 2}

With the ``ImmediateBackend`` configured as the default backend:

.. code-block:: pycon

    >>> result = double_dictionary.enqueue((1, 2, 3))
    >>> result.status
    FAILED
    >>> result.errors[0].traceback
    Traceback (most recent call last):
    ...
    TypeError: unhashable type: 'list'

The ``double_dictionary`` Task fails because after the JSON round-trip the
tuple ``(1, 2, 3)`` becomes the list ``[1, 2, 3]``, which cannot be used as a
dictionary key.

In general, complex objects such as model instances, or built-in types like
``datetime`` and ``tuple`` cannot be used in Tasks without additional
conversion.

.. _task-transactions:

Transactions
------------

For most backends, Tasks are run in a separate process, using a different
database connection. When using a transaction, without waiting for it to
commit, workers could start to process a Task which uses objects it can't
access yet.

For example, consider this simplified example::

    @task
    def my_task(thing_num):
        Thing.objects.get(num=thing_num)


    with transaction.atomic():
        Thing.objects.create(num=1)
        my_task.enqueue(thing_num=1)

To prevent the scenario where ``my_task`` runs before the ``Thing`` is
committed to the database, use :func:`transaction.on_commit()
<django.db.transaction.on_commit>`, binding all arguments to
:meth:`~django.tasks.Task.enqueue` via :func:`functools.partial`::

    from functools import partial

    from django.db import transaction

    with transaction.atomic():
        Thing.objects.create(num=1)
        transaction.on_commit(partial(my_task.enqueue, thing_num=1))

.. _task-results:

Task results
============

When enqueueing a ``Task``, you receive a :class:`~django.tasks.TaskResult`,
however it's likely useful to retrieve the result from somewhere else (for
example another request or another Task).

Each ``TaskResult`` has a unique :attr:`~django.tasks.TaskResult.id`, which can
be used to identify and retrieve the result once the code which enqueued the
Task has finished.

The :meth:`~django.tasks.Task.get_result` method can retrieve a result based on
its ``id``::

    # Later, somewhere else...
    result = email_users.get_result(result_id)

To retrieve a ``TaskResult``, regardless of which kind of ``Task`` it was from,
use the :meth:`~django.tasks.Task.get_result` method on the backend::

    from django.tasks import default_task_backend

    result = default_task_backend.get_result(result_id)

To retrieve results in an ``async`` context,
:meth:`~django.tasks.Task.aget_result` is available as an ``async`` variant of
:meth:`~django.tasks.Task.get_result` on both the backend and ``Task``.

Some backends, such as the built-in ``ImmediateBackend`` do not support
``get_result()``. Calling ``get_result()`` on these backends will
raise :exc:`NotImplementedError`.

Updating results
----------------

A ``TaskResult`` contains the status of a Task's execution at the point it was
retrieved. If the Task finishes after :meth:`~django.tasks.Task.get_result` is
called, it will not update.

To refresh the values, call the :meth:`django.tasks.TaskResult.refresh`
method:

.. code-block:: pycon

    >>> result.status
    RUNNING
    >>> result.refresh()  # or await result.arefresh()
    >>> result.status
    SUCCESSFUL

.. _task-return-values:

Return values
-------------

If your Task function returns something, it can be retrieved from the
:attr:`django.tasks.TaskResult.return_value` attribute:

.. code-block:: pycon

    >>> result.status
    SUCCESSFUL
    >>> result.return_value
    42

If the Task has not finished executing, or has failed, :exc:`ValueError` is
raised.

.. code-block:: pycon

    >>> result.status
    RUNNING
    >>> result.return_value
    Traceback (most recent call last):
    ...
    ValueError: Task has not finished yet

Errors
------

If the Task doesn't succeed, and instead raises an exception, either as part of
the Task or as part of running it, the exception and traceback are saved to the
:attr:`django.tasks.TaskResult.errors` list.

Each entry in ``errors`` is a :class:`~django.tasks.TaskError` containing
information about error raised during the execution:

.. code-block:: pycon

    >>> result.errors[0].exception_class
    <class 'ValueError'>

Note that this is just the type of exception, and contains no other values. The
traceback information is reduced to a string which you can use to help
debugging:

.. code-block:: pycon

    >>> result.errors[0].traceback
    Traceback (most recent call last):
    ...
    TypeError: Object of type datetime is not JSON serializable
