celery - Distributed Task Queue
|Keywords:||task queue, job queue, asynchronous, rabbitmq, amqp, redis.|
Celery is a task queue/job queue based on distributed message passing. It is focused on real-time operation, but has support for scheduling as well.
The execution units, called tasks, are executed concurrently on one or more worker servers, asynchronously (in the background) or synchronously (wait until ready).
Celery is already used in production to process millions of tasks a day.
It was first created for Django, but is now usable from Python as well. It can also operate with other languages via HTTP+JSON.
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").
You probably want to see some code by now, so I'll give you an example task adding two numbers:
from celery.decorators import task @task def add(x, y): return x + y
You can execute the task in the background, or wait for it to finish:
>>> result = add.delay(4, 4) >>> result.wait() # wait for and return the result 8
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.
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.
- 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 latest documentation with user guides, tutorials and API reference 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
Downloading and installing from source
Download the latest version of
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
A look inside the components
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/
celery happens at Github: http://github.com/ask/celery
You are highly encouraged to participate in the development
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
file in the top distribution directory for the full license text.