.. _next-steps:

============
 Next Steps
============

The :ref:`first-steps` guide is intentionally minimal. In this guide
I'll demonstrate what Celery offers in more detail, including
how to add Celery support for your application and library.

This document doesn't document all of Celery's features and
best practices, so it's recommended that you also read the
:ref:`User Guide <guide>`

.. contents::
    :local:
    :depth: 1

Using Celery in your Application
================================

.. _project-layout:

Our Project
-----------

Project layout::

    proj/__init__.py
        /celery.py
        /tasks.py

:file:`proj/celery.py`
~~~~~~~~~~~~~~~~~~~~~~

.. literalinclude:: ../../examples/next-steps/proj/celery.py
    :language: python

In this module you created our :class:`@Celery` instance (sometimes
referred to as the *app*). To use Celery within your project
you simply import this instance.

- The ``broker`` argument specifies the URL of the broker to use.

    See :ref:`celerytut-broker` for more information.

- The ``backend`` argument specifies the result backend to use.

    It's used to keep track of task state and results.
    While results are disabled by default I use the RPC result backend here
    because I demonstrate how retrieving results work later. You may want to use
    a different backend for your application. They all have different
    strengths and weaknesses. If you don't need results, it's better
    to disable them. Results can also be disabled for individual tasks
    by setting the ``@task(ignore_result=True)`` option.

    See :ref:`celerytut-keeping-results` for more information.

- The ``include`` argument is a list of modules to import when
  the worker starts. You need to add our tasks module here so
  that the worker is able to find our tasks.

:file:`proj/tasks.py`
~~~~~~~~~~~~~~~~~~~~~

.. literalinclude:: ../../examples/next-steps/proj/tasks.py
    :language: python


Starting the worker
-------------------

The :program:`celery` program can be used to start the worker (you need to run the worker in the directory above proj):

.. code-block:: console

    $ celery -A proj worker -l info

When the worker starts you should see a banner and some messages::

     --------------- celery@halcyon.local v4.0 (latentcall)
     --- ***** -----
     -- ******* ---- [Configuration]
     - *** --- * --- . broker:      amqp://guest@localhost:5672//
     - ** ---------- . app:         __main__:0x1012d8590
     - ** ---------- . concurrency: 8 (processes)
     - ** ---------- . events:      OFF (enable -E to monitor this worker)
     - ** ----------
     - *** --- * --- [Queues]
     -- ******* ---- . celery:      exchange:celery(direct) binding:celery
     --- ***** -----

     [2012-06-08 16:23:51,078: WARNING/MainProcess] celery@halcyon.local has started.

-- The *broker* is the URL you specified in the broker argument in our ``celery``
module. You can also specify a different broker on the command-line by using
the :option:`-b <celery -b>` option.

-- *Concurrency* is the number of prefork worker process used
to process your tasks concurrently. When all of these are busy doing work,
new tasks will have to wait for one of the tasks to finish before
it can be processed.

The default concurrency number is the number of CPU's on that machine
(including cores). You can specify a custom number using
the :option:`celery worker -c` option.
There's no recommended value, as the optimal number depends on a number of
factors, but if your tasks are mostly I/O-bound then you can try to increase
it. Experimentation has shown that adding more than twice the number
of CPU's is rarely effective, and likely to degrade performance
instead.

Including the default prefork pool, Celery also supports using
Eventlet, Gevent, and running in a single thread (see :ref:`concurrency`).

-- *Events* is an option that causes Celery to send
monitoring messages (events) for actions occurring in the worker.
These can be used by monitor programs like ``celery events``,
and Flower -- the real-time Celery monitor, which you can read about in
the :ref:`Monitoring and Management guide <guide-monitoring>`.

-- *Queues* is the list of queues that the worker will consume
tasks from. The worker can be told to consume from several queues
at once, and this is used to route messages to specific workers
as a means for Quality of Service, separation of concerns,
and prioritization, all described in the :ref:`Routing Guide
<guide-routing>`.

You can get a complete list of command-line arguments
by passing in the :option:`--help <celery --help>` flag:

.. code-block:: console

    $ celery worker --help

These options are described in more detailed in the :ref:`Workers Guide <guide-workers>`.

Stopping the worker
~~~~~~~~~~~~~~~~~~~

To stop the worker simply hit :kbd:`Control-c`. A list of signals supported
by the worker is detailed in the :ref:`Workers Guide <guide-workers>`.

In the background
~~~~~~~~~~~~~~~~~

In production you'll want to run the worker in the background,
described in detail in the :ref:`daemonization tutorial <daemonizing>`.

The daemonization scripts uses the :program:`celery multi` command to
start one or more workers in the background:

.. code-block:: console

    $ celery multi start w1 -A proj -l info
    celery multi v4.0.0 (latentcall)
    > Starting nodes...
        > w1.halcyon.local: OK

You can restart it too:

.. code-block:: console

    $ celery  multi restart w1 -A proj -l info
    celery multi v4.0.0 (latentcall)
    > Stopping nodes...
        > w1.halcyon.local: TERM -> 64024
    > Waiting for 1 node.....
        > w1.halcyon.local: OK
    > Restarting node w1.halcyon.local: OK
    celery multi v4.0.0 (latentcall)
    > Stopping nodes...
        > w1.halcyon.local: TERM -> 64052

or stop it:

.. code-block:: console

    $ celery multi stop w1 -A proj -l info

The ``stop`` command is asynchronous so it won't wait for the
worker to shutdown. You'll probably want to use the ``stopwait`` command
instead, which ensures that all currently executing tasks are completed
before exiting:

.. code-block:: console

    $ celery multi stopwait w1 -A proj -l info

.. note::

    :program:`celery multi` doesn't store information about workers
    so you need to use the same command-line arguments when
    restarting. Only the same pidfile and logfile arguments must be
    used when stopping.

By default it'll create pid and log files in the current directory.
To protect against multiple workers launching on top of each other
you're encouraged to put these in a dedicated directory:

.. code-block:: console

    $ mkdir -p /var/run/celery
    $ mkdir -p /var/log/celery
    $ celery multi start w1 -A proj -l info --pidfile=/var/run/celery/%n.pid \
                                            --logfile=/var/log/celery/%n%I.log

With the multi command you can start multiple workers, and there's a powerful
command-line syntax to specify arguments for different workers too,
for example:

.. code-block:: console

    $ celery multi start 10 -A proj -l info -Q:1-3 images,video -Q:4,5 data \
        -Q default -L:4,5 debug

For more examples see the :mod:`~celery.bin.multi` module in the API
reference.

.. _app-argument:

About the :option:`--app <celery --app>` argument
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The :option:`--app <celery --app>` argument specifies the Celery app instance
to use, in the form of ``module.path:attribute``

But it also supports a shortcut form. If only a package name is specified,
it'll try to search for the app instance, in the following order:

With :option:`--app=proj <celery --app>`:

1) an attribute named ``proj.app``, or
2) an attribute named ``proj.celery``, or
3) any attribute in the module ``proj`` where the value is a Celery
   application, or

If none of these are found it'll try a submodule named ``proj.celery``:

4) an attribute named ``proj.celery.app``, or
5) an attribute named ``proj.celery.celery``, or
6) Any attribute in the module ``proj.celery`` where the value is a Celery
   application.

This scheme mimics the practices used in the documentation -- that is,
``proj:app`` for a single contained module, and ``proj.celery:app``
for larger projects.


.. _calling-tasks:

Calling Tasks
=============

You can call a task using the :meth:`delay` method:

.. code-block:: pycon

    >>> from proj.tasks import add

    >>> add.delay(2, 2)

This method is actually a star-argument shortcut to another method called
:meth:`apply_async`:

.. code-block:: pycon

    >>> add.apply_async((2, 2))

The latter enables you to specify execution options like the time to run
(countdown), the queue it should be sent to, and so on:

.. code-block:: pycon

    >>> add.apply_async((2, 2), queue='lopri', countdown=10)

In the above example the task will be sent to a queue named ``lopri`` and the
task will execute, at the earliest, 10 seconds after the message was sent.

Applying the task directly will execute the task in the current process,
so that no message is sent:

.. code-block:: pycon

    >>> add(2, 2)
    4

These three methods - :meth:`delay`, :meth:`apply_async`, and applying
(``__call__``), make up the Celery calling API, which is also used for
signatures.

A more detailed overview of the Calling API can be found in the
:ref:`Calling User Guide <guide-calling>`.

Every task invocation will be given a unique identifier (an UUID) -- this
is the task id.

The ``delay`` and ``apply_async`` methods return an :class:`~@AsyncResult`
instance, which can be used to keep track of the tasks execution state.
But for this you need to enable a :ref:`result backend <task-result-backends>` so that
the state can be stored somewhere.

Results are disabled by default because there is no result
backend that suits every application; to choose one you need to consider
the drawbacks of each individual backend. For many tasks
keeping the return value isn't even very useful, so it's a sensible default to
have. Also note that result backends aren't used for monitoring tasks and workers:
for that Celery uses dedicated event messages (see :ref:`guide-monitoring`).

If you have a result backend configured you can retrieve the return
value of a task:

.. code-block:: pycon

    >>> res = add.delay(2, 2)
    >>> res.get(timeout=1)
    4

You can find the task's id by looking at the :attr:`id` attribute:

.. code-block:: pycon

    >>> res.id
    d6b3aea2-fb9b-4ebc-8da4-848818db9114

You can also inspect the exception and traceback if the task raised an
exception, in fact ``result.get()`` will propagate any errors by default:

.. code-block:: pycon

    >>> res = add.delay(2, '2')
    >>> res.get(timeout=1)

.. code-block:: pytb

    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      File "celery/result.py", line 221, in get
        return self.backend.wait_for_pending(
      File "celery/backends/asynchronous.py", line 195, in wait_for_pending
        return result.maybe_throw(callback=callback, propagate=propagate)
      File "celery/result.py", line 333, in maybe_throw
        self.throw(value, self._to_remote_traceback(tb))
      File "celery/result.py", line 326, in throw
        self.on_ready.throw(*args, **kwargs)
      File "vine/promises.py", line 244, in throw
        reraise(type(exc), exc, tb)
      File "vine/five.py", line 195, in reraise
        raise value
    TypeError: unsupported operand type(s) for +: 'int' and 'str'

If you don't wish for the errors to propagate, you can disable that by passing ``propagate``:

.. code-block:: pycon

    >>> res.get(propagate=False)
    TypeError("unsupported operand type(s) for +: 'int' and 'str'")

In this case it'll return the exception instance raised instead --
so to check whether the task succeeded or failed, you'll have to
use the corresponding methods on the result instance:

.. code-block:: pycon

    >>> res.failed()
    True

    >>> res.successful()
    False

So how does it know if the task has failed or not?  It can find out by looking
at the tasks *state*:

.. code-block:: pycon

    >>> res.state
    'FAILURE'

A task can only be in a single state, but it can progress through several
states. The stages of a typical task can be::

    PENDING -> STARTED -> SUCCESS

The started state is a special state that's only recorded if the
:setting:`task_track_started` setting is enabled, or if the
``@task(track_started=True)`` option is set for the task.

The pending state is actually not a recorded state, but rather
the default state for any task id that's unknown: this you can see
from this example:

.. code-block:: pycon

    >>> from proj.celery import app

    >>> res = app.AsyncResult('this-id-does-not-exist')
    >>> res.state
    'PENDING'

If the task is retried the stages can become even more complex.
To demonstrate, for a task that's retried two times the stages would be:

.. code-block:: text

    PENDING -> STARTED -> RETRY -> STARTED -> RETRY -> STARTED -> SUCCESS

To read more about task states you should see the :ref:`task-states` section
in the tasks user guide.

Calling tasks is described in detail in the
:ref:`Calling Guide <guide-calling>`.

.. _designing-workflows:

*Canvas*: Designing Work-flows
==============================

You just learned how to call a task using the tasks ``delay`` method,
and this is often all you need. But sometimes you may want to pass the
signature of a task invocation to another process or as an argument to another
function, for which Celery uses something called *signatures*.

A signature wraps the arguments and execution options of a single task
invocation in such a way that it can be passed to functions or even serialized
and sent across the wire.

You can create a signature for the ``add`` task using the arguments ``(2, 2)``,
and a countdown of 10 seconds like this:

.. code-block:: pycon

    >>> add.signature((2, 2), countdown=10)
    tasks.add(2, 2)

There's also a shortcut using star arguments:

.. code-block:: pycon

    >>> add.s(2, 2)
    tasks.add(2, 2)

And there's that calling API again…
-----------------------------------

Signature instances also support the calling API, meaning they
have ``delay`` and ``apply_async`` methods.

But there's a difference in that the signature may already have
an argument signature specified. The ``add`` task takes two arguments,
so a signature specifying two arguments would make a complete signature:

.. code-block:: pycon

    >>> s1 = add.s(2, 2)
    >>> res = s1.delay()
    >>> res.get()
    4

But, you can also make incomplete signatures to create what we call
*partials*:

.. code-block:: pycon

    # incomplete partial: add(?, 2)
    >>> s2 = add.s(2)

``s2`` is now a partial signature that needs another argument to be complete,
and this can be resolved when calling the signature:

.. code-block:: pycon

    # resolves the partial: add(8, 2)
    >>> res = s2.delay(8)
    >>> res.get()
    10

Here you added the argument 8 that was prepended to the existing argument 2
forming a complete signature of ``add(8, 2)``.

Keyword arguments can also be added later; these are then merged with any
existing keyword arguments, but with new arguments taking precedence:

.. code-block:: pycon

    >>> s3 = add.s(2, 2, debug=True)
    >>> s3.delay(debug=False)   # debug is now False.

As stated, signatures support the calling API: meaning that

- ``sig.apply_async(args=(), kwargs={}, **options)``

    Calls the signature with optional partial arguments and partial
    keyword arguments. Also supports partial execution options.

- ``sig.delay(*args, **kwargs)``

  Star argument version of ``apply_async``. Any arguments will be prepended
  to the arguments in the signature, and keyword arguments is merged with any
  existing keys.

So this all seems very useful, but what can you actually do with these?
To get to that I must introduce the canvas primitives…

The Primitives
--------------

.. topic:: \

    .. hlist::
        :columns: 2

        - :ref:`group <canvas-group>`
        - :ref:`chain <canvas-chain>`
        - :ref:`chord <canvas-chord>`
        - :ref:`map <canvas-map>`
        - :ref:`starmap <canvas-map>`
        - :ref:`chunks <canvas-chunks>`

These primitives are signature objects themselves, so they can be combined
in any number of ways to compose complex work-flows.

.. note::

    These examples retrieve results, so to try them out you need
    to configure a result backend. The example project
    above already does that (see the backend argument to :class:`~celery.Celery`).

Let's look at some examples:

Groups
~~~~~~

A :class:`~celery.group` calls a list of tasks in parallel,
and it returns a special result instance that lets you inspect the results
as a group, and retrieve the return values in order.

.. code-block:: pycon

    >>> from celery import group
    >>> from proj.tasks import add

    >>> group(add.s(i, i) for i in range(10))().get()
    [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

- Partial group

.. code-block:: pycon

    >>> g = group(add.s(i) for i in range(10))
    >>> g(10).get()
    [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]

Chains
~~~~~~

Tasks can be linked together so that after one task returns the other
is called:

.. code-block:: pycon

    >>> from celery import chain
    >>> from proj.tasks import add, mul

    # (4 + 4) * 8
    >>> chain(add.s(4, 4) | mul.s(8))().get()
    64


or a partial chain:

.. code-block:: pycon

    >>> # (? + 4) * 8
    >>> g = chain(add.s(4) | mul.s(8))
    >>> g(4).get()
    64


Chains can also be written like this:

.. code-block:: pycon

    >>> (add.s(4, 4) | mul.s(8))().get()
    64

Chords
~~~~~~

A chord is a group with a callback:

.. code-block:: pycon

    >>> from celery import chord
    >>> from proj.tasks import add, xsum

    >>> chord((add.s(i, i) for i in range(10)), xsum.s())().get()
    90


A group chained to another task will be automatically converted
to a chord:

.. code-block:: pycon

    >>> (group(add.s(i, i) for i in range(10)) | xsum.s())().get()
    90


Since these primitives are all of the signature type they
can be combined almost however you want, for example:

.. code-block:: pycon

    >>> upload_document.s(file) | group(apply_filter.s() for filter in filters)

Be sure to read more about work-flows in the :ref:`Canvas <guide-canvas>` user
guide.

Routing
=======

Celery supports all of the routing facilities provided by AMQP,
but it also supports simple routing where messages are sent to named queues.

The :setting:`task_routes` setting enables you to route tasks by name
and keep everything centralized in one location:

.. code-block:: python

    app.conf.update(
        task_routes = {
            'proj.tasks.add': {'queue': 'hipri'},
        },
    )

You can also specify the queue at runtime
with the ``queue`` argument to ``apply_async``:

.. code-block:: pycon

    >>> from proj.tasks import add
    >>> add.apply_async((2, 2), queue='hipri')

You can then make a worker consume from this queue by
specifying the :option:`celery worker -Q` option:

.. code-block:: console

    $ celery -A proj worker -Q hipri

You may specify multiple queues by using a comma-separated list.
For example, you can make the worker consume from both the default
queue and the ``hipri`` queue, where
the default queue is named ``celery`` for historical reasons:

.. code-block:: console

    $ celery -A proj worker -Q hipri,celery

The order of the queues doesn't matter as the worker will
give equal weight to the queues.

To learn more about routing, including taking use of the full
power of AMQP routing, see the :ref:`Routing Guide <guide-routing>`.

Remote Control
==============

If you're using RabbitMQ (AMQP), Redis, or Qpid as the broker then
you can control and inspect the worker at runtime.

For example you can see what tasks the worker is currently working on:

.. code-block:: console

    $ celery -A proj inspect active

This is implemented by using broadcast messaging, so all remote
control commands are received by every worker in the cluster.

You can also specify one or more workers to act on the request
using the :option:`--destination <celery inspect --destination>` option.
This is a comma-separated list of worker host names:

.. code-block:: console

    $ celery -A proj inspect active --destination=celery@example.com

If a destination isn't provided then every worker will act and reply
to the request.

The :program:`celery inspect` command contains commands that
don't change anything in the worker; it only returns information
and statistics about what's going on inside the worker.
For a list of inspect commands you can execute:

.. code-block:: console

    $ celery -A proj inspect --help

Then there's the :program:`celery control` command, which contains
commands that actually change things in the worker at runtime:

.. code-block:: console

    $ celery -A proj control --help

For example you can force workers to enable event messages (used
for monitoring tasks and workers):

.. code-block:: console

    $ celery -A proj control enable_events

When events are enabled you can then start the event dumper
to see what the workers are doing:

.. code-block:: console

    $ celery -A proj events --dump

or you can start the curses interface:

.. code-block:: console

    $ celery -A proj events

when you're finished monitoring you can disable events again:

.. code-block:: console

    $ celery -A proj control disable_events

The :program:`celery status` command also uses remote control commands
and shows a list of online workers in the cluster:

.. code-block:: console

    $ celery -A proj status

You can read more about the :program:`celery` command and monitoring
in the :ref:`Monitoring Guide <guide-monitoring>`.

Timezone
========

All times and dates, internally and in messages use the UTC timezone.

When the worker receives a message, for example with a countdown set it
converts that UTC time to local time. If you wish to use
a different timezone than the system timezone then you must
configure that using the :setting:`timezone` setting:

.. code-block:: python

    app.conf.timezone = 'Europe/London'

Optimization
============

The default configuration isn't optimized for throughput. By default,
it tries to walk the middle way between many short tasks and fewer long
tasks, a compromise between throughput and fair scheduling.

If you have strict fair scheduling requirements, or want to optimize
for throughput then you should read the :ref:`Optimizing Guide
<guide-optimizing>`.

If you're using RabbitMQ then you can install the :pypi:`librabbitmq`
module, an AMQP client implemented in C:

.. code-block:: console

    $ pip install librabbitmq

What to do now?
===============

Now that you have read this document you should continue
to the :ref:`User Guide <guide>`.

There's also an :ref:`API reference <apiref>` if you're so inclined.
