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Made docs/introduction.rst a symlink to README.rst instead of the other way around, so the README is shown at github.

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+============================================
+celery - Distributed Task Queue for Django.
+============================================
+
+:Authors:
+    Ask Solem (askh@opera.com)
+:Version: 0.2.0-pre1
+
+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 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 ``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 Documentation`_ is hosted at Github.
+
+.. _`API Reference Docmentation`: 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
+=====
+
+Have to write a cool tutorial, but here is some simple usage info.
+
+*Note* You need to have a AMQP message broker running, like `RabbitMQ`_,
+and you need to have the amqp server setup in your settings file, as described
+in the `carrot distribution README`_.
+
+*Note* If you're running ``SQLite`` as the database backend, ``celeryd`` will
+only be able to process one message at a time, this is because ``SQLite``
+doesn't allow concurrent writes.
+
+.. _`RabbitMQ`: http://www.rabbitmq.com
+.. _`carrot distribution README`: http://pypi.python.org/pypi/carrot/0.3.3
+
+
+Defining tasks
+--------------
+
+    >>> 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 ``FAILURE``, and
+``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
+in ``settings.INSTALLED_APPS``.
+
+A good place to add this command could be in your ``urls.py``,
+::
+
+    from celery.task import tasks
+    tasks.autodiscover()
+
+
+Then you can add new tasks in your applications ``tasks.py`` module,
+::
+
+    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
+---------------
+
+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 ``celery`` to ``INSTALLED_APPS``,
+and issue a ``syncdb``.
+
+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

+ 0 - 213
docs/introduction.rst

@@ -1,213 +0,0 @@
-============================================
-celery - Distributed Task Queue for Django.
-============================================
-
-:Authors:
-    Ask Solem (askh@opera.com)
-:Version: 0.2.0-pre1
-
-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 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 ``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 Documentation`_ is hosted at Github.
-
-.. _`API Reference Docmentation`: 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
-=====
-
-Have to write a cool tutorial, but here is some simple usage info.
-
-*Note* You need to have a AMQP message broker running, like `RabbitMQ`_,
-and you need to have the amqp server setup in your settings file, as described
-in the `carrot distribution README`_.
-
-*Note* If you're running ``SQLite`` as the database backend, ``celeryd`` will
-only be able to process one message at a time, this is because ``SQLite``
-doesn't allow concurrent writes.
-
-.. _`RabbitMQ`: http://www.rabbitmq.com
-.. _`carrot distribution README`: http://pypi.python.org/pypi/carrot/0.3.3
-
-
-Defining tasks
---------------
-
-    >>> 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 ``FAILURE``, and
-``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
-in ``settings.INSTALLED_APPS``.
-
-A good place to add this command could be in your ``urls.py``,
-::
-
-    from celery.task import tasks
-    tasks.autodiscover()
-
-
-Then you can add new tasks in your applications ``tasks.py`` module,
-::
-
-    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
----------------
-
-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 ``celery`` to ``INSTALLED_APPS``,
-and issue a ``syncdb``.
-
-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

+ 1 - 0
docs/introduction.rst

@@ -0,0 +1 @@
+../README.rst