This document describes an older version of Celery (2.5). For the latest stable version please go here.

First steps with Django

Configuring your Django project to use Celery

You need four simple steps to use celery with your Django project.

1. Install the django-celery library:

   $ pip install django-celery

2. Add the following lines to settings.py:

   import djcelery
   djcelery.setup_loader()
   

3. Add djcelery to INSTALLED_APPS.

4. Create the celery database tables.

   If you are using south for schema migrations, you’ll want to:

   $ python manage.py migrate djcelery

   For those who are not using south, a normal syncdb will work:

   $ python manage.py syncdb

By default Celery uses RabbitMQ as the broker, but there are several alternatives to choose from, see Choosing your Broker.

All settings mentioned in the Celery documentation should be added to your Django project’s settings.py module. For example we can configure the BROKER_URL setting to specify what broker to use:

BROKER_URL = "amqp://guest:guest@localhost:5672/"

That’s it.

Special note for mod_wsgi users

If you’re using mod_wsgi to deploy your Django application you need to include the following in your .wsgi module:

import djcelery
djcelery.setup_loader()

Defining and executing tasks

Tasks are defined by wrapping functions in the @task decorator. It is a common practice to put these in their own module named tasks.py, and the worker will automatically go through the apps in INSTALLED_APPS to import these modules.

For a simple demonstration we can create a new Django app called celerytest. To create this app you need to be in the directoryw of your Django project where manage.py is located and execute:

$ python manage.py startapp celerytest

After our new app has been created we can define our task by editing a new file called celerytest/tasks.py:

from celery.task import task

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

Our example task is pretty pointless, it just returns the sum of two arguments, but it will do for demonstration, and it is referenced in many parts of the Celery documentation.

Starting the worker process

You can start a worker instance by using the celeryd manage command:

$ python manage.py celeryd --loglevel=info

In production you probably want to run the worker in the background as a daemon, see Running Celery as a daemon. For a complete listing of the command line options available, use the help command:

$ python manage.py help celeryd

Executing our task

Now that the worker is running we can open up a new terminal to actually execute our task:

>>> from celerytest.tasks import add

>>> add.delay(2, 2)

The delay method is a handy shortcut to the apply_async method which enables you to have greater control of the task execution. To read more about executing tasks, including specifying the time at which the task should execute see Executing Tasks.

Note

Tasks need to be stored in a real module, they can’t be defined in the python shell or ipython/bpython. This is because the worker server must be able to import the task function so that it can execute it.

The task should now be executed by the worker you started earlier, and you can verify that by looking at the workers console output.

Applying a task returns an AsyncResult instance, which can be used to check the state of the task, wait for the task to finish or get its return value (or if the task failed, the exception and traceback).

By default django-celery stores this state in the Django database, you may consider choosing an alternate result backend or disabling states alltogether (see Result Backends).

To demonstrate how the results work we can execute the task again, but this time keep the result instance returned:

>>> result = add.delay(4, 4)
>>> result.ready() # returns True if the task has finished processing.
False
>>> result.result # task is not ready, so no return value yet.
None
>>> result.get()   # Waits until the task is done and returns the retval.
8
>>> result.result # direct access to result, doesn't re-raise errors.
8
>>> result.successful() # returns True if the task didn't end in failure.
True

If the task raises an exception, the return value of result.successful() will be False, and result.result will contain the exception instance raised by the task.

Where to go from here

To learn more you should read the Celery User Guide, and the Celery Documentation in general