.. _django-first-steps:

========================= First steps with Django

Using Celery with Django

.. note::

Previous versions of Celery required a separate library to work with Django,
but since 3.1 this is no longer the case. Django is supported out of the
box now so this document only contains a basic way to integrate Celery and
Django. You'll use the same API as non-Django users so you're recommended
to read the :ref:`first-steps` tutorial
first and come back to this tutorial. When you have a working example you can
continue to the :ref:`next-steps` guide.

.. note::

Celery 5.5.x supports Django 2.2 LTS or newer versions.
Please use Celery 5.2.x for versions older than Django 2.2 or Celery 4.4.x if your Django version is older than 1.11.

To use Celery with your Django project you must first define an instance of the Celery library (called an "app")

If you have a modern Django project layout like::

- proj/
  - manage.py
  - proj/
    - __init__.py
    - settings.py
    - urls.py

then the recommended way is to create a new proj/proj/celery.py module that defines the Celery instance:

:file: proj/proj/celery.py

.. literalinclude:: ../../examples/django/proj/celery.py

Then you need to import this app in your :file:proj/proj/__init__.py module. This ensures that the app is loaded when Django starts so that the @shared_task decorator (mentioned later) will use it:

:file:proj/proj/__init__.py:

.. literalinclude:: ../../examples/django/proj/init.py

Note that this example project layout is suitable for larger projects, for simple projects you may use a single contained module that defines both the app and tasks, like in the :ref:tut-celery tutorial.

Let's break down what happens in the first module, first, we set the default :envvar:DJANGO_SETTINGS_MODULE environment variable for the :program:celery command-line program:

.. code-block:: python

os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'proj.settings')

You don't need this line, but it saves you from always passing in the settings module to the celery program. It must always come before creating the app instances, as is what we do next:

.. code-block:: python

app = Celery('proj')

This is our instance of the library, you can have many instances but there's probably no reason for that when using Django.

We also add the Django settings module as a configuration source for Celery. This means that you don't have to use multiple configuration files, and instead configure Celery directly from the Django settings; but you can also separate them if wanted.

.. code-block:: python

app.config_from_object('django.conf:settings', namespace='CELERY')

The uppercase name-space means that all :ref:Celery configuration options <configuration> must be specified in uppercase instead of lowercase, and start with CELERY_, so for example the :setting:task_always_eager setting becomes CELERY_TASK_ALWAYS_EAGER, and the :setting:broker_url setting becomes CELERY_BROKER_URL. This also applies to the workers settings, for instance, the :setting:worker_concurrency setting becomes CELERY_WORKER_CONCURRENCY.

For example, a Django project's configuration file might include:

.. code-block:: python :caption: settings.py

...

# Celery Configuration Options
CELERY_TIMEZONE = "Australia/Tasmania"
CELERY_TASK_TRACK_STARTED = True
CELERY_TASK_TIME_LIMIT = 30 * 60

You can pass the settings object directly instead, but using a string is better since then the worker doesn't have to serialize the object. The CELERY_ namespace is also optional, but recommended (to prevent overlap with other Django settings).

Next, a common practice for reusable apps is to define all tasks in a separate tasks.py module, and Celery does have a way to auto-discover these modules:

.. code-block:: python

app.autodiscover_tasks()

With the line above Celery will automatically discover tasks from all of your installed apps, following the tasks.py convention::

- app1/
    - tasks.py
    - models.py
- app2/
    - tasks.py
    - models.py

This way you don't have to manually add the individual modules to the :setting:CELERY_IMPORTS <imports> setting.

Finally, the debug_task example is a task that dumps its own request information. This is using the new bind=True task option introduced in Celery 3.1 to easily refer to the current task instance.

Using the @shared_task decorator

The tasks you write will probably live in reusable apps, and reusable apps cannot depend on the project itself, so you also cannot import your app instance directly.

The @shared_task decorator lets you create tasks without having any concrete app instance:

:file:demoapp/tasks.py:

.. literalinclude:: ../../examples/django/demoapp/tasks.py

.. seealso::

You can find the full source code for the Django example project at:
https://github.com/celery/celery/tree/main/examples/django/

Trigger tasks at the end of the database transaction

A common pitfall with Django is triggering a task immediately and not wait until the end of the database transaction, which means that the Celery task may run before all changes are persisted to the database. For example:

.. code-block:: python

# views.py
def create_user(request):
    # Note: simplified example, use a form to validate input
    user = User.objects.create(username=request.POST['username'])
    send_email.delay(user.pk)
    return HttpResponse('User created')

# task.py
@shared_task
def send_email(user_pk):
    user = User.objects.get(pk=user_pk)
    # send email ...

In this case, the send_email task could start before the view has committed the transaction to the database, and therefore the task may not be able to find the user.

A common solution is to use Django's on_commit_ hook to trigger the task after the transaction has been committed:

.. _on_commit: https://docs.djangoproject.com/en/stable/topics/db/transactions/#django.db.transaction.on_commit

.. code-block:: diff

- send_email.delay(user.pk)
+ transaction.on_commit(lambda: send_email.delay(user.pk))

.. versionadded:: 5.4

Since this is such a common pattern, Celery 5.4 introduced a handy shortcut for this, using a :class:~celery.contrib.django.task.DjangoTask. Instead of calling :meth:~celery.app.task.Task.delay, you should call :meth:~celery.contrib.django.task.DjangoTask.delay_on_commit:

.. code-block:: diff

- send_email.delay(user.pk)
+ send_email.delay_on_commit(user.pk)

This API takes care of wrapping the call into the on_commit_ hook for you. In rare cases where you want to trigger a task without waiting, the existing :meth:~celery.app.task.Task.delay API is still available.

One key difference compared to the delay method, is that delay_on_commit will NOT return the task ID back to the caller. The task is not sent to the broker when you call the method, only when the Django transaction finishes. If you need the task ID, best to stick to :meth:~celery.app.task.Task.delay.

This task class should be used automatically if you've follow the setup steps above. However, if your app :ref:uses a custom task base class <task-custom-classes>, you'll need inherit from :class:~celery.contrib.django.task.DjangoTask instead of :class:~celery.app.task.Task to get this behaviour.

Django Connection pool

From Django 5.1+ there is built-in support for database connection pooling. If you enable it in Django DATABASES settings Celery will automatically handle connection pool closing in worker processes via close_pool database backend method as sharing connections across processes is not possible. <https://github.com/psycopg/psycopg/issues/544#issuecomment-1500886864>_

You can find more about Connection pool at Django docs. <https://docs.djangoproject.com/en/dev/ref/databases/#connection-pool>_

Extensions

.. _django-celery-results:

django-celery-results - Using the Django ORM/Cache as a result backend

The :pypi:django-celery-results extension provides result backends using either the Django ORM, or the Django Cache framework.

To use this with your project you need to follow these steps:

#. Install the :pypi:django-celery-results library:

.. code-block:: console

    $ pip install django-celery-results

#. Add django_celery_results to INSTALLED_APPS in your Django project's :file:settings.py::

    INSTALLED_APPS = (
        ...,
        'django_celery_results',
    )

Note that there is no dash in the module name, only underscores.

#. Create the Celery database tables by performing a database migrations:

.. code-block:: console

    $ python manage.py migrate django_celery_results

#. Configure Celery to use the :pypi:django-celery-results backend.

Assuming you are using Django's :file:`settings.py` to also configure
Celery, add the following settings:

.. code-block:: python

    CELERY_RESULT_BACKEND = 'django-db'

When using the cache backend, you can specify a cache defined within
Django's CACHES setting.

.. code-block:: python

    CELERY_RESULT_BACKEND = 'django-cache'

    # pick which cache from the CACHES setting.
    CELERY_CACHE_BACKEND = 'default'

    # django setting.
    CACHES = {
        'default': {
            'BACKEND': 'django.core.cache.backends.db.DatabaseCache',
            'LOCATION': 'my_cache_table',
        }
    }

For additional configuration options, view the
:ref:`conf-result-backend` reference.

django-celery-beat - Database-backed Periodic Tasks with Admin interface.

See :ref:beat-custom-schedulers for more information.

Starting the worker process

In a production environment you'll want to run the worker in the background as a daemon - see :ref:daemonizing - but for testing and development it is useful to be able to start a worker instance by using the :program:celery worker manage command, much as you'd use Django's :command:manage.py runserver:

.. code-block:: console

$ celery -A proj worker -l INFO

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

.. code-block:: console

$ celery --help

Where to go from here

If you want to learn more you should continue to the :ref:Next Steps <next-steps> tutorial, and after that you can study the :ref:User Guide <guide>.