.. _guide-optimizing:

============
 Optimizing
============

Introduction
============
The default configuration makes a lot of compromises.  It's not optimal for
any single case, but works well enough for most situations.

There are optimizations that can be applied based on specific use cases.

Optimizations can apply to different properties of the running environment,
be it the time tasks take to execute, the amount of memory used, or
responsiveness at times of high load.

Ensuring Operations
===================

In the book `Programming Pearls`_, Jon Bentley presents the concept of
back-of-the-envelope calculations by asking the question;

    ❝ How much water flows out of the Mississippi River in a day? ❞

The point of this exercise [*]_ is to show that there is a limit
to how much data a system can process in a timely manner.
Back of the envelope calculations can be used as a means to plan for this
ahead of time.

In Celery; If a task takes 10 minutes to complete,
and there are 10 new tasks coming in every minute, the queue will never
be empty.  This is why it's very important
that you monitor queue lengths!

A way to do this is by :ref:`using Munin <monitoring-munin>`.
You should set up alerts, that will notify you as soon as any queue has
reached an unacceptable size.  This way you can take appropriate action
like adding new worker nodes, or revoking unnecessary tasks.

.. [*] The chapter is available to read for free here:
       `The back of the envelope`_.  The book is a classic text. Highly
       recommended.

.. _`Programming Pearls`: http://www.cs.bell-labs.com/cm/cs/pearls/

.. _`The back of the envelope`:
    http://books.google.com/books?id=kse_7qbWbjsC&pg=PA67

.. _optimizing-general-settings:

General Settings
================

.. _optimizing-librabbitmq:

librabbitmq
-----------

If you're using RabbitMQ (AMQP) as the broker then you can install the
:mod:`librabbitmq` module to use an optimized client written in C:

.. code-block:: bash

    $ pip install librabbitmq

The 'amqp' transport will automatically use the librabbitmq module if it's
installed, or you can also specify the transport you want directly by using
the ``pyamqp://`` or ``librabbitmq://`` prefixes.

.. _optimizing-connection-pools:

Broker Connection Pools
-----------------------

The broker connection pool is enabled by default since version 2.5.

You can tweak the :setting:`BROKER_POOL_LIMIT` setting to minimize
contention, and the value should be based on the number of
active threads/greenthreads using broker connections.

.. _optimizing-worker-settings:

Worker Settings
===============

.. _optimizing-prefetch-limit:

Prefetch Limits
---------------

*Prefetch* is a term inherited from AMQP that is often misunderstood
by users.

The prefetch limit is a **limit** for the number of tasks (messages) a worker
can reserve for itself.  If it is zero, the worker will keep
consuming messages, not respecting that there may be other
available worker nodes that may be able to process them sooner [*]_,
or that the messages may not even fit in memory.

The workers' default prefetch count is the
:setting:`CELERYD_PREFETCH_MULTIPLIER` setting multiplied by the number
of child worker processes [*]_.

If you have many tasks with a long duration you want
the multiplier value to be 1, which means it will only reserve one
task per worker process at a time.

However -- If you have many short-running tasks, and throughput/round trip
latency is important to you, this number should be large. The worker is
able to process more tasks per second if the messages have already been
prefetched, and is available in memory.  You may have to experiment to find
the best value that works for you.  Values like 50 or 150 might make sense in
these circumstances. Say 64, or 128.

If you have a combination of long- and short-running tasks, the best option
is to use two worker nodes that are configured separately, and route
the tasks according to the run-time. (see :ref:`guide-routing`).

.. [*] RabbitMQ and other brokers deliver messages round-robin,
       so this doesn't apply to an active system.  If there is no prefetch
       limit and you restart the cluster, there will be timing delays between
       nodes starting. If there are 3 offline nodes and one active node,
       all messages will be delivered to the active node.

.. [*] This is the concurrency setting; :setting:`CELERYD_CONCURRENCY` or the
       :option:`-c` option to the :program:`celery worker` program.


Reserve one task at a time
--------------------------

When using early acknowledgement (default), a prefetch multiplier of 1
means the worker will reserve at most one extra task for every active
worker process.

When users ask if it's possible to disable "prefetching of tasks", often
what they really want is to have a worker only reserve as many tasks as there
are child processes.

But this is not possible without enabling late acknowledgements
acknowledgements; A task that has been started, will be
retried if the worker crashes mid execution so the task must be `idempotent`_
(see also notes at :ref:`faq-acks_late-vs-retry`).

.. _`idempotent`: http://en.wikipedia.org/wiki/Idempotent

You can enable this behavior by using the following configuration options:

.. code-block:: python

    CELERY_ACKS_LATE = True
    CELERYD_PREFETCH_MULTIPLIER = 1

.. optimizing-rate-limits:

Rate Limits
-----------

The system responsible for enforcing rate limits introduces some overhead,
so if you're not using rate limits it may be a good idea to
disable them completely.  This will disable one thread, and it won't
spend as many CPU cycles when the queue is inactive.

Set the :setting:`CELERY_DISABLE_RATE_LIMITS` setting to disable
the rate limit subsystem:

.. code-block:: python

    CELERY_DISABLE_RATE_LIMITS = True
