Celery 1.0.6 (stable) documentation

This Page

First steps with Django

Configuring your Django project to use Celery

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

  1. Add celery to INSTALLED_APPS.

  2. Create the celery database tables:

    $ python manage.py syncdb
  3. Configure celery to use the AMQP user and virtual host we created

    before, by adding the following to your settings.py:

    BROKER_HOST = "localhost"
    BROKER_PORT = 5672
    BROKER_USER = "myuser"
    BROKER_PASSWORD = "mypassword"
    BROKER_VHOST = "myvhost"

That’s it.

There are more options available, like how many processes you want to work in parallel (the CELERY_CONCURRENCY setting). You can also configure the backend used for storing task statuses. For now though, this should do. For all of the options available, please see the configuration directive reference.

Note: If you’re using SQLite as the Django database back-end, celeryd will only be able to process one task at a time, this is because SQLite doesn’t allow concurrent writes.

Running the celery worker server

To test this we’ll be running the worker server in the foreground, so we can see what’s going on without consulting the logfile:

$ python manage.py celeryd

However, in production you probably want to run the worker in the background as a daemon. To do this you need to use to tools provided by your platform. See daemon mode reference.

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

$ python manage.py help celeryd

Defining and executing tasks

Please note: All the tasks have to be stored in a real module, they can’t be defined in the python shell or ipython/bpython. This is because the celery worker server needs access to the task function to be able to run it. Put them in the tasks module of your Django application. The worker server will automatically load any tasks.py file for all of the applications listed in settings.INSTALLED_APPS. Executing tasks using delay and apply_async can be done from the python shell, but keep in mind that since arguments are pickled, you can’t use custom classes defined in the shell session.

This is a task that adds two numbers:

from celery.decorators import task

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

To execute this task, we can use the delay method of the task class. This is a handy shortcut to the apply_async method which gives greater control of the task execution. See Executing Tasks for more information.

>>> from myapp.tasks import MyTask
>>> MyTask.delay(some_arg="foo")

At this point, the task has been sent to the message broker. The message broker will hold on to the task until a celery worker server has successfully picked it up.

Note: If everything is just hanging when you execute delay, please check that RabbitMQ is running, and that the user/password has access to the virtual host you configured earlier.

Right now we have to check the celery worker log files to know what happened with the task. This is because we didn’t keep the AsyncResult object returned by delay.

The AsyncResult lets us find the state of the task, wait for the task to finish and get its return value (or exception if the task failed).

So, let’s execute the task again, but this time we’ll keep track of the task:

>>> result = add.delay(4, 4)
>>> result.ready() # returns True if the task has finished processing.
>>> result.result # task is not ready, so no return value yet.
>>> result.get()   # Waits until the task is done and returns the retval.
>>> result.result # direct access to result, doesn't re-raise errors.
>>> result.successful() # returns True if the task didn't end in failure.

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.