.. _tut-celery:
.. _first-steps:

=========================
 First Steps with Celery
=========================

Celery is a task queue with batteries included.
It's easy to use so that you can get started without learning
the full complexities of the problem it solves. It's designed
around best practices so that your product can scale
and integrate with other languages, and it comes with the
tools and support you need to run such a system in production.

In this tutorial you'll learn the absolute basics of using Celery.

Learn about:

- Choosing and installing a message transport (broker).
- Installing Celery and creating your first task.
- Starting the worker and calling tasks.
- Keeping track of tasks as they transition through different states,
  and inspecting return values.

Celery may seem daunting at first - but don't worry - this tutorial
will get you started in no time. It's deliberately kept simple, so
as to not confuse you with advanced features.
After you have finished this tutorial,
it's a good idea to browse the rest of the documentation.
For example the :ref:`next-steps` tutorial will
showcase Celery's capabilities.

.. contents::
    :local:

.. _celerytut-broker:

Choosing a Broker
=================

Celery requires a solution to send and receive messages; usually this
comes in the form of a separate service called a *message broker*.

There are several choices available, including:

RabbitMQ
--------

`RabbitMQ`_ is feature-complete, stable, durable and easy to install.
It's an excellent choice for a production environment.
Detailed information about using RabbitMQ with Celery:

    :ref:`broker-rabbitmq`

.. _`RabbitMQ`: http://www.rabbitmq.com/

If you're using Ubuntu or Debian install RabbitMQ by executing this
command:

.. code-block:: console

    $ sudo apt-get install rabbitmq-server

Or, if you want to run it on Docker execute this:

.. code-block:: console

    $ docker run -d -p 5672:5672 rabbitmq

When the command completes, the broker will already be running in the background,
ready to move messages for you: ``Starting rabbitmq-server: SUCCESS``.

Don't worry if you're not running Ubuntu or Debian, you can go to this
website to find similarly simple installation instructions for other
platforms, including Microsoft Windows:

    http://www.rabbitmq.com/download.html

Redis
-----

`Redis`_ is also feature-complete, but is more susceptible to data loss in
the event of abrupt termination or power failures. Detailed information about using Redis:

:ref:`broker-redis`

.. _`Redis`: https://redis.io/

If you want to run it on Docker execute this:

.. code-block:: console

    $ docker run -d -p 6379:6379 redis

Other brokers
-------------

In addition to the above, there are other experimental transport implementations
to choose from, including :ref:`Amazon SQS <broker-sqs>`.

See :ref:`broker-overview` for a full list.

.. _celerytut-installation:

Installing Celery
=================

Celery is on the Python Package Index (PyPI), so it can be installed
with standard Python tools like ``pip``:

.. code-block:: console

    $ pip install celery

Application
===========

The first thing you need is a Celery instance.  We call this the *Celery
application* or just *app* for short. As this instance is used as
the entry-point for everything you want to do in Celery, like creating tasks and
managing workers, it must be possible for other modules to import it.

In this tutorial we keep everything contained in a single module,
but for larger projects you want to create
a :ref:`dedicated module <project-layout>`.

Let's create the file :file:`tasks.py`:

.. code-block:: python

    from celery import Celery

    app = Celery('tasks', broker='pyamqp://guest@localhost//')

    @app.task
    def add(x, y):
        return x + y

The first argument to :class:`~celery.app.Celery` is the name of the current module.
This is only needed so that names can be automatically generated when the tasks are
defined in the `__main__` module.

The second argument is the broker keyword argument, specifying the URL of the
message broker you want to use. Here we are using RabbitMQ (also the default option).

See :ref:`celerytut-broker` above for more choices --
for RabbitMQ you can use ``amqp://localhost``, or for Redis you can
use ``redis://localhost``.

You defined a single task, called ``add``, returning the sum of two numbers.

.. _celerytut-running-the-worker:

Running the Celery worker server
================================

You can now run the worker by executing our program with the ``worker``
argument:

.. code-block:: console

    $ celery -A tasks worker --loglevel=INFO

.. note::

    See the :ref:`celerytut-troubleshooting` section if the worker
    doesn't start.

In production you'll want to run the worker in the
background as a daemon. To do this you need to use the tools provided
by your platform, or something like `supervisord`_ (see :ref:`daemonizing`
for more information).

For a complete listing of the command-line options available, do:

.. code-block:: console

    $  celery worker --help

There are also several other commands available, and help is also available:

.. code-block:: console

    $ celery --help

.. _`supervisord`: http://supervisord.org

.. _celerytut-calling:

Calling the task
================

To call our task you can use the :meth:`~@Task.delay` method.

This is a handy shortcut to the :meth:`~@Task.apply_async`
method that gives greater control of the task execution (see
:ref:`guide-calling`)::

    >>> from tasks import add
    >>> add.delay(4, 4)

The task has now been processed by the worker you started earlier.
You can verify this by looking at the worker's console output.

Calling a task returns an :class:`~@AsyncResult` instance.
This can be used to check the state of the task, wait for the task to finish,
or get its return value (or if the task failed, to get the exception and traceback).

Results are not enabled by default. In order to do remote procedure calls
or keep track of task results in a database, you will need to configure Celery to use a result
backend.  This is described in the next section.

.. _celerytut-keeping-results:

Keeping Results
===============

If you want to keep track of the tasks' states, Celery needs to store or send
the states somewhere. There are several
built-in result backends to choose from: `SQLAlchemy`_/`Django`_ ORM,
`MongoDB`_, `Memcached`_, `Redis`_, :ref:`RPC <conf-rpc-result-backend>` (`RabbitMQ`_/AMQP),
and -- or you can define your own.

.. _`Memcached`: http://memcached.org
.. _`MongoDB`: http://www.mongodb.org
.. _`SQLAlchemy`: http://www.sqlalchemy.org/
.. _`Django`: http://djangoproject.com

For this example we use the `rpc` result backend, that sends states
back as transient messages. The backend is specified via the ``backend`` argument to
:class:`@Celery`, (or via the :setting:`result_backend` setting if
you choose to use a configuration module). So, you can modify this line in the `tasks.py`
file to enable the `rpc://` backend:

.. code-block:: python

    app = Celery('tasks', backend='rpc://', broker='pyamqp://')

Or if you want to use Redis as the result backend, but still use RabbitMQ as
the message broker (a popular combination):

.. code-block:: python

    app = Celery('tasks', backend='redis://localhost', broker='pyamqp://')

To read more about result backends please see :ref:`task-result-backends`.

Now with the result backend configured, restart the worker, close the current python session and import the
``tasks`` module again to put the changes into effect. This time you'll hold on to the
:class:`~@AsyncResult` instance returned when you call a task:

.. code-block:: pycon

    >>> from tasks import add    # close and reopen to get updated 'app'
    >>> result = add.delay(4, 4)

The :meth:`~@AsyncResult.ready` method returns whether the task
has finished processing or not:

.. code-block:: pycon

    >>> result.ready()
    False

You can wait for the result to complete, but this is rarely used
since it turns the asynchronous call into a synchronous one:

.. code-block:: pycon

    >>> result.get(timeout=1)
    8

In case the task raised an exception, :meth:`~@AsyncResult.get` will
re-raise the exception, but you can override this by specifying
the ``propagate`` argument:

.. code-block:: pycon

    >>> result.get(propagate=False)


If the task raised an exception, you can also gain access to the
original traceback:

.. code-block:: pycon

    >>> result.traceback

.. warning::

    Backends use resources to store and transmit results. To ensure
    that resources are released, you must eventually call
    :meth:`~@AsyncResult.get` or :meth:`~@AsyncResult.forget` on
    EVERY :class:`~@AsyncResult` instance returned after calling
    a task.

See :mod:`celery.result` for the complete result object reference.

.. _celerytut-configuration:

Configuration
=============

Celery, like a consumer appliance, doesn't need much configuration to operate.
It has an input and an output. The input must be connected to a broker, and the output can
be optionally connected to a result backend. However, if you look closely at the back,
there's a lid revealing loads of sliders, dials, and buttons: this is the configuration.

The default configuration should be good enough for most use cases, but there are
many options that can be configured to make Celery work exactly as needed.
Reading about the options available is a good idea to familiarize yourself with what
can be configured. You can read about the options in the
:ref:`configuration` reference.

The configuration can be set on the app directly or by using a dedicated
configuration module.
As an example you can configure the default serializer used for serializing
task payloads by changing the :setting:`task_serializer` setting:

.. code-block:: python

    app.conf.task_serializer = 'json'

If you're configuring many settings at once you can use ``update``:

.. code-block:: python

    app.conf.update(
        task_serializer='json',
        accept_content=['json'],  # Ignore other content
        result_serializer='json',
        timezone='Europe/Oslo',
        enable_utc=True,
    )

For larger projects, a dedicated configuration module is recommended.
Hard coding periodic task intervals and task routing options is discouraged.
It is much better to keep these in a centralized location. This is especially
true for libraries, as it enables users to control how their tasks behave.
A centralized configuration will also allow your SysAdmin to make simple changes
in the event of system trouble.

You can tell your Celery instance to use a configuration module
by calling the :meth:`@config_from_object` method:

.. code-block:: python

    app.config_from_object('celeryconfig')

This module is often called "``celeryconfig``", but you can use any
module name.

In the above case, a module named ``celeryconfig.py`` must be available to load from the
current directory or on the Python path. It could look something like this:

:file:`celeryconfig.py`:

.. code-block:: python

    broker_url = 'pyamqp://'
    result_backend = 'rpc://'

    task_serializer = 'json'
    result_serializer = 'json'
    accept_content = ['json']
    timezone = 'Europe/Oslo'
    enable_utc = True

To verify that your configuration file works properly and doesn't
contain any syntax errors, you can try to import it:

.. code-block:: console

    $ python -m celeryconfig

For a complete reference of configuration options, see :ref:`configuration`.

To demonstrate the power of configuration files, this is how you'd
route a misbehaving task to a dedicated queue:

:file:`celeryconfig.py`:

.. code-block:: python

    task_routes = {
        'tasks.add': 'low-priority',
    }

Or instead of routing it you could rate limit the task
instead, so that only 10 tasks of this type can be processed in a minute
(10/m):

:file:`celeryconfig.py`:

.. code-block:: python

    task_annotations = {
        'tasks.add': {'rate_limit': '10/m'}
    }

If you're using RabbitMQ or Redis as the
broker then you can also direct the workers to set a new rate limit
for the task at runtime:

.. code-block:: console

    $ celery -A tasks control rate_limit tasks.add 10/m
    worker@example.com: OK
        new rate limit set successfully

See :ref:`guide-routing` to read more about task routing,
and the :setting:`task_annotations` setting for more about annotations,
or :ref:`guide-monitoring` for more about remote control commands
and how to monitor what your workers are doing.

Where to go from here
=====================

If you want to learn more you should continue to the
:ref:`Next Steps <next-steps>` tutorial, and after that you
can read the :ref:`User Guide <guide>`.

.. _celerytut-troubleshooting:

Troubleshooting
===============

There's also a troubleshooting section in the :ref:`faq`.

Worker doesn't start: Permission Error
--------------------------------------

- If you're using Debian, Ubuntu or other Debian-based distributions:

    Debian recently renamed the :file:`/dev/shm` special file
    to :file:`/run/shm`.

    A simple workaround is to create a symbolic link:

    .. code-block:: console

        # ln -s /run/shm /dev/shm

- Others:

    If you provide any of the :option:`--pidfile <celery worker --pidfile>`,
    :option:`--logfile <celery worker --logfile>` or
    :option:`--statedb <celery worker --statedb>` arguments, then you must
    make sure that they point to a file or directory that's writable and
    readable by the user starting the worker.

Result backend doesn't work or tasks are always in ``PENDING`` state
--------------------------------------------------------------------

All tasks are :state:`PENDING` by default, so the state would've been
better named "unknown". Celery doesn't update the state when a task
is sent, and any task with no history is assumed to be pending (you know
the task id, after all).

1) Make sure that the task doesn't have ``ignore_result`` enabled.

    Enabling this option will force the worker to skip updating
    states.

2) Make sure the :setting:`task_ignore_result` setting isn't enabled.

3) Make sure that you don't have any old workers still running.

    It's easy to start multiple workers by accident, so make sure
    that the previous worker is properly shut down before you start a new one.

    An old worker that isn't configured with the expected result backend
    may be running and is hijacking the tasks.

    The :option:`--pidfile <celery worker --pidfile>` argument can be set to
    an absolute path to make sure this doesn't happen.

4) Make sure the client is configured with the right backend.

    If, for some reason, the client is configured to use a different backend
    than the worker, you won't be able to receive the result.
    Make sure the backend is configured correctly:

    .. code-block:: pycon

        >>> result = task.delay()
        >>> print(result.backend)
