| Version: | 0.9.0 |
|---|
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.
This is a high level overview of the architecture.
The broker pushes 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.
The result of the task can be stored for later retrieval (called its "tombstone").
- Uses messaging (AMQP: RabbitMQ, ZeroMQ, Qpid) to route tasks to the worker servers. Experimental support for STOMP (ActiveMQ) is also available. For simple setups it's also possible to use Redis or an SQL database as the message queue.
- 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 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, Redis or Tokyo Tyrant back-end. For high-performance you can also use AMQP messages to publish results.
- Supports calling tasks over HTTP to support multiple programming languages and systems.
- Supports several serialization schemes, like pickle, json, yaml and supports registering custom encodings .
- If the task raises an exception, the exception instance is stored, instead of the return value, and it's possible to inspect the traceback after the fact.
- 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
maplike function that uses tasks, calledcelery.task.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.
The API Reference is hosted at Github (http://ask.github.com/celery)
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
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
You can clone the repository by doing the following:
$ git clone git://github.com/ask/celery.git
See Installing RabbitMQ over at RabbitMQ's website. For Mac OS X see Installing RabbitMQ on OS X.
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 set_permissions -p myvhost myuser "" ".*" ".*"
See the RabbitMQ Admin Guide for more information about access control.
You only need three simple steps to use celery with your Django project.
Add
celerytoINSTALLED_APPS.Create the celery database tables:
$ python manage.py syncdb
- Configure celery to use the AMQP user and virtual host we created
before, by adding the following to your
settings.py:BROKER_HOST = "localhost" BROKER_PORT = 5672 BROKER_USER = "myuser" BROKER_PASSWORD = "mypassword" BROKER_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.
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
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.
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.
This is a task that adds two numbers:
from celery.decorators import task
@task()
def add(x, y):
return x + y
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
greater control of the task execution (see userguide/executing for more
information).
>>> 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 = add.delay(4, 4) >>> 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 returns the retval. 8 >>> result.result # direct access to result, doesn't re-raise errors. 8 >>> result.successful() # returns True if the task didn't end in failure. True
If the task raises an exception, the return value of result.successful()
will be False, and result.result will contain the exception instance
raised by the task.
celeryd 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 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)
If you want to use periodic tasks you need to start the celerybeat
service. You have to make sure only one instance of this server is running at
any time, or else you will end up with multiple executions of the same task.
To start the celerybeat service:
$ celerybeat --detach
or if using Django:
$ python manage.py celerybeat
You can also start celerybeat with celeryd by using the -B option,
this is convenient if you only have one server:
$ celeryd --detach -B
or if using Django:
$ python manage.py celeryd --detach -B
For discussions about the usage, development, and future of celery, please join the celery-users mailing list.
Come chat with us on IRC. The #celery channel is located at the Freenode network.
If you have any suggestions, bug reports or annoyances please report them to our issue tracker at http://github.com/ask/celery/issues/
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.
This software is licensed under the New BSD License. See the LICENSE
file in the top distribution directory for the full license text.