celery - Distributed Task Queue for Django.
|Authors:||Ask Solem (email@example.com)|
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
easy and fun, and the performance improvements can make it more than
- 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
multiprocessingmodule (also available as a backport 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 backend.
- If the task raises an exception, the exception instance is stored, instead of the return value.
- All tasks has a Universaly Unique Identifier (UUID), which is the task id, used for querying task status and return values.
- Supports tasksets, which is a task consisting of several subtasks. You can find out how many, or if all of the subtasks has been executed. Excellent for progress-bar like functionality.
- Has a
maplike function that uses tasks, called
- 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 Documentation is hosted at Github.
You can install
celery either via the Python Package Index (PyPI)
or from source.
To install using
$ pip install celery
To install using
$ easy_install celery
If you have downloaded a source tarball you can install it by doing the following,:
$ python setup.py build # python setup.py install # as root
Have to write a cool tutorial, but here is some simple usage info.
Note If you're running
SQLite as the database backend,
only be able to process one message at a time, this is because
doesn't allow concurrent writes.
>>> from celery.task import tasks >>> from celery.log import setup_logger >>> def do_something(some_arg, **kwargs): ... logger = setup_logger(**kwargs) ... logger.info("Did something: %s" % some_arg) ... return 42 >>> task.register(do_something, "do_something")
Tell the celery daemon to run a task
>>> from celery.task import delay_task >>> delay_task("do_something", some_arg="foo bar baz")
Execute a task, and get its return value.
>>> from celery.task import delay_task >>> result = delay_task("do_something", some_arg="foo bar baz") >>> result.ready() False >>> result.get() # Waits until the task is done. 42 >>> result.status() 'DONE'
If the task raises an exception, the tasks status will be
result.result will contain the exception instance raised.
Running the celery daemon
$ cd mydjangoproject $ env DJANGO_SETTINGS_MODULE=settings celeryd [....] [2009-04-23 17:44:05,115: INFO/Process-1] Did something: foo bar baz [2009-04-23 17:44:05,118: INFO/MainProcess] Waiting for queue.
Autodiscovery of tasks
celery has an autodiscovery feature like the Django Admin, that
automatically loads any
tasks.py module in the applications listed
A good place to add this command could be in your
from celery.task import tasks tasks.autodiscover()
Then you can add new tasks in your applications
from celery.task import tasks from celery.log import setup_logger from clickcounter.models import ClickCount def increment_click(for_url, **kwargs): logger = setup_logger(**kwargs) clicks_for_url, cr = ClickCount.objects.get_or_create(url=for_url) clicks_for_url.clicks = clicks_for_url.clicks + 1 clicks_for_url.save() logger.info("Incremented click count for %s (not at %d)" % ( for_url, clicks_for_url.clicks) tasks.register(increment_click, "increment_click")
Periodic tasks are tasks that are run every
n seconds. They don't
support extra arguments. Here's an example of a periodic task:
>>> from celery.task import tasks, PeriodicTask >>> from datetime import timedelta >>> class MyPeriodicTask(PeriodicTask): ... name = "foo.my-periodic-task" ... run_every = timedelta(seconds=30) ... ... def run(self, **kwargs): ... logger = self.get_logger(**kwargs) ... logger.info("Running periodic task!") ... >>> tasks.register(MyPeriodicTask)
For periodic tasks to work you need to add
and issue a
This software is licensed under the
New BSD License. See the
file in the top distribution directory for the full license text.