Metadata-Version: 1.0
Name: celery
Version: 0.3.0
Summary: Distributed Task Queue for Django
Home-page: http://github.com/ask/celery/
Author: Ask Solem
Author-email: askh@opera.com
License: UNKNOWN
Description: ============================================
        celery - Distributed Task Queue for Django.
        ============================================
        
        :Version: 0.3.0
        
        Introduction
        ============
        
        ``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.
        
        Features
        ========
        
        * 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
        (http://ask.github.com/celery)
        
        .. _`API Reference`: 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
        
        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
        
        Usage
        =====
        
        Installing RabbitMQ
        -------------------
        
        See `Installing RabbitMQ`_ over at RabbitMQ's website. For Mac OS X
        see `Installing RabbitMQ on OS X`_.
        
        .. _`Installing RabbitMQ`: http://www.rabbitmq.com/install.html
        .. _`Installing RabbitMQ on OS X`:
        http://playtype.net/past/2008/10/9/installing_rabbitmq_on_osx/
        
        
        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 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_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 manage.py celeryd
        
        
        However, in production you'll probably want to run the worker in the
        background as a daemon instead::
        
        $ python manage.py celeryd --daemon
        
        
        For help on command line arguments to the worker daemon, you can execute 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 daemon 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. 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)
        ...         logger.info("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.
        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.success() # 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 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)
        
        **Note:** Periodic tasks does not support arguments, as this doesn't
        really make sense.
        
        License
        =======
        
        This software is licensed under the ``New BSD License``. See the ``LICENSE``
        file in the top distribution directory for the full license text.
        
        .. # vim: syntax=rst expandtab tabstop=4 shiftwidth=4 shiftround
        
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Framework :: Django
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Environment :: No Input/Output (Daemon)
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: POSIX
Classifier: Topic :: Communications
Classifier: Topic :: System :: Distributed Computing
Classifier: Topic :: Software Development :: Libraries :: Python Modules
