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Distributed Task Queue for Django

Project description

Version: 0.3.0


celery is a distributed task queue framework for Django.

It is used for executing tasks asynchronously, routed to one or more worker servers, running concurrently using multiprocessing.

It is designed to solve certain problems related to running websites demanding high-availability and performance.

It is perfect for filling caches, posting updates to twitter, mass downloading data like syndication feeds or web scraping. Use-cases are plentiful. Implementing these features asynchronously using celery is easy and fun, and the performance improvements can make it more than worthwhile.


  • Uses AMQP messaging (RabbitMQ, ZeroMQ) to route tasks to the worker servers.
  • You can run as many worker servers as you want, and still be guaranteed that the task is only executed once.
  • Tasks are executed concurrently using the Python 2.6 multiprocessing module (also available as a back-port to older python versions)
  • Supports periodic tasks, which makes it a (better) replacement for cronjobs.
  • When a task has been executed, the return value is stored using either a MySQL/Oracle/PostgreSQL/SQLite database, memcached, or Tokyo Tyrant back-end.
  • If the task raises an exception, the exception instance is stored, instead of the return value.
  • All tasks has a Universally Unique Identifier (UUID), which is the task id, used for querying task status and return values.
  • Supports task-sets, which is a task consisting of several sub-tasks. You can find out how many, or if all of the sub-tasks has been executed. Excellent for progress-bar like functionality.
  • Has a map like function that uses tasks, called dmap.
  • However, you rarely want to wait for these results in a web-environment. You’d rather want to use Ajax to poll the task status, which is available from a URL like celery/<task_id>/status/. This view returns a JSON-serialized data structure containing the task status, and the return value if completed, or exception on failure.

API Reference Documentation

The API Reference is hosted at Github (


You can install celery either via the Python Package Index (PyPI) or from source.

To install using pip,:

$ pip install celery

To install using easy_install,:

$ easy_install celery

If you have downloaded a source tarball you can install it by doing the following,:

$ python build
# python install # as root


Installing RabbitMQ

See Installing RabbitMQ over at RabbitMQ’s website. For Mac OS X see Installing RabbitMQ on OS X.

Setting up RabbitMQ

To use celery we need to create a RabbitMQ user, a virtual host and allow that user access to that virtual host:

$ rabbitmqctl add_user myuser mypassword

$ rabbitmqctl add_vhost myvhost

$ rabbitmqctl map_user_vhost myuser myvhost

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 syncdb
  3. Configure celery to use the AMQP user and virtual host we created

    before, by adding the following to your

    AMQP_HOST = "localhost"
    AMQP_PORT = 5672
    AMQP_USER = "myuser"
    AMQP_PASSWORD = "mypassword"
    AMQP_VHOST = "myvhost"

That’s it.

There are more options available, like how many processes you want to process work in parallel (the CELERY_CONCURRENCY setting), and the backend used for storing task statuses. But for now, this should do. For all of the options available, please consult the API 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 daemon

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

$ python celeryd

However, in production you’ll probably want to run the worker in the background as a daemon instead:

$ python celeryd --daemon

For help on command line arguments to the worker daemon, you can execute the help command:

$ python help celeryd

Defining and executing tasks

Please note All of these tasks has 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. So while it looks like we use the python shell to define the tasks in these examples, you can’t do it this way. Put them in the tasks module of your Django application. The worker daemon will automatically load any 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.

While you can use regular functions, the recommended way is to define a task class. With this way you can cleanly upgrade the task to use the more advanced features of celery later.

This is a task that basically does nothing but take some arguments, and return a value:

>>> class MyTask(Task):
...     name = "myapp.mytask"
...     def run(self, some_arg, **kwargs):
...         logger = self.get_logger(**kwargs)
..."Did something: %s" % some_arg)
...         return 42
>>> tasks.register(MyTask)

Now if we want 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 you greater control of the task execution).

>>> 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.

Right now we have to check the celery worker logfiles 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 = MyTask.delay("do_something", some_arg="foo bar baz")
>>> 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 return the retval.
>>> result.result
>>> result.success() # returns True if the task didn't end in failure.

If the task raises an exception, the result.success() will be False, and result.result will contain the exception instance raised.

Auto-discovery of tasks

celery has an auto-discovery feature like the Django Admin, that automatically loads any module in the applications listed in settings.INSTALLED_APPS. This autodiscovery is used by the celery worker to find registered tasks for your Django project.

Periodic Tasks

Periodic tasks are tasks that are run every n seconds. Here’s an example of a periodic task:

>>> from celery.task import tasks, PeriodicTask
>>> from datetime import timedelta
>>> class MyPeriodicTask(PeriodicTask):
...     name = ""
...     run_every = timedelta(seconds=30)
...     def run(self, **kwargs):
...         logger = self.get_logger(**kwargs)
..."Running periodic task!")
>>> tasks.register(MyPeriodicTask)

Note: Periodic tasks does not support arguments, as this doesn’t really make sense.


This software is licensed under the New BSD License. See the LICENSE file in the top distribution directory for the full license text.

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