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celery - Distributed Task Queue

Version: 0.7.0

Introduction

Celery is a distributed task queue.

It was first created for Django, but is now usable from Python. It can also operate with other languages via HTTP+JSON.

This introduction is written for someone who wants to use Celery from within a Django project. For information about using it from pure Python see Can I use Celery without Django?, for calling out to other languages see Executing tasks on a remote web server.

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.

Overview

This is a high level overview of the architecture.

http://cloud.github.com/downloads/ask/celery/Celery-Overview-v4.jpg

The broker is an AMQP server pushing tasks to the worker servers. A worker server is a networked machine running celeryd. This can be one or more machines, depending on the workload. See A look inside the worker to see how the worker server works.

The result of the task can be stored for later retrieval (called its "tombstone").

Features

  • Uses AMQP messaging (RabbitMQ, ZeroMQ, Qpid) to route tasks to the worker servers. Experimental support for STOMP (ActiveMQ) is also available.

  • 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 can be stored using either a MySQL/Oracle/PostgreSQL/SQLite database, Memcached, MongoDB or Tokyo Tyrant back-end. For high-performance you can also use AMQP messages to publish results.

  • 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.

  • Tasks can be retried if they fail, with a configurable maximum number of retries.

  • Tasks can be configured to run at a specific time and date in the future (ETA) or you can set a countdown in seconds for when the task should be executed.

  • 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.

  • The worker can collect statistics, like, how many tasks has been executed by type, and the time it took to process them. Very useful for monitoring and profiling.

  • Pool workers are supervised, so if for some reason a worker crashes

    it is automatically replaced by a new worker.

  • Can be configured to send e-mails to the administrators when a task fails.

API Reference Documentation

The API Reference is hosted at Github (http://ask.github.com/celery)

Installation

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

Downloading and installing from source

Download the latest version of celery from http://pypi.python.org/pypi/celery/

You can install it by doing the following,:

$ tar xvfz celery-0.0.0.tar.gz
$ cd celery-0.0.0
$ python setup.py build
# python setup.py install # as root

Using the development version

You can clone the repository by doing the following:

$ git clone git://github.com/ask/celery.git

Usage

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

From RabbitMQ version 1.6.0 and onward you have to use the new ACL features to allow access:

$ rabbitmqctl set_permissions -p myvhost myuser "" ".*" ".*"

See the RabbitMQ Admin Guide for more information about access control.

If you are still using version 1.5.0 or below, please use map_user_vhost:

$ 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 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:

    AMQP_SERVER = "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 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:

$ python manage.py celeryd --detach

For a complete listing of the command line arguments available, with a short description, you can use the help command:

$ python manage.py 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 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.

While you can use regular functions, the recommended way is to define a task class. 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:

>>> from celery.task import Task
>>> from celery.registry import tasks
>>> class MyTask(Task):
...     def run(self, some_arg, **kwargs):
...         logger = self.get_logger(**kwargs)
...         logger.info("Did something: %s" % some_arg)
...         return 42
>>> tasks.register(MyTask)

As you can see the worker is sending some keyword arguments to this task, this is the default keyword arguments. A task can choose not to take these, or only list the ones it want (the worker will do the right thing). The current default keyword arguments are:

  • logfile

    The currently used log file, can be passed on to self.get_logger to gain access to the workers log file via a logger.Logging instance.

  • loglevel

    The current loglevel used.

  • task_id

    The unique id of the executing task.

  • task_name

    Name of the executing task.

  • task_retries

    How many times the current task has been retried. (an integer starting a 0).

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.

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 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.
False
>>> result.result # task is not ready, so no return value yet.
None
>>> result.get()   # Waits until the task is done and return the retval.
42
>>> result.result
42
>>> result.successful() # returns True if the task didn't end in failure.
True

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

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

A look inside the worker

http://cloud.github.com/downloads/ask/celery/InsideTheWorker-v2.jpg

Getting Help

Mailing list

For discussions about the usage, development, and future of celery, please join the celery-users mailing list.

IRC

Come chat with us on IRC. The #celery channel is located at the Freenode network.

Bug tracker

If you have any suggestions, bug reports or annoyances please report them to our issue tracker at http://github.com/ask/celery/issues/

Contributing

Development of celery happens at Github: http://github.com/ask/celery

You are highly encouraged to participate in the development of celery. If you don't like Github (for some reason) you're welcome to send regular patches.

License

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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