django/docs/topics/performance.txt

============================
Performance and optimization
============================

This document provides an overview of techniques and tools that can help get
your Django code running more efficiently - faster, and using fewer system
resources.

Introduction
============

Generally one's first concern is to write code that *works*, whose logic
functions as required to produce the expected output. Sometimes, however, this
will not be enough to make the code work as *efficiently* as one would like.

In this case, what's needed is something - and in practice, often a collection
of things - to improve the code's performance without, or only minimally,
affecting its behavior.

General approaches
==================

What are you optimizing *for*?
------------------------------

It's important to have a clear idea what you mean by 'performance'. There is
not just one metric of it.

Improved speed might be the most obvious aim for a program, but sometimes other
performance improvements might be sought, such as lower memory consumption or
fewer demands on the database or network.

Improvements in one area will often bring about improved performance in
another, but not always; sometimes one can even be at the expense of another.
For example, an improvement in a program's speed might cause it to use more
memory. Even worse, it can be self-defeating - if the speed improvement is so
memory-hungry that the system starts to run out of memory, you'll have done
more harm than good.

There are other trade-offs to bear in mind. Your own time is a valuable
resource, more precious than CPU time. Some improvements might be too difficult
to be worth implementing, or might affect the portability or maintainability of
the code. Not all performance improvements are worth the effort.

So, you need to know what performance improvements you are aiming for, and you
also need to know that you have a good reason for aiming in that direction -
and for that you need:

Performance benchmarking
------------------------

It's no good just guessing or assuming where the inefficiencies lie in your
code.

Django tools
~~~~~~~~~~~~

:pypi:`django-debug-toolbar` is a very handy tool that provides insights into
what your code is doing and how much time it spends doing it. In particular it
can show you all the SQL queries your page is generating, and how long each one
has taken.

Third-party panels are also available for the toolbar, that can (for example)
report on cache performance and template rendering times.

Third-party services
~~~~~~~~~~~~~~~~~~~~

There are a number of free services that will analyze and report on the
performance of your site's pages from the perspective of a remote HTTP client,
in effect simulating the experience of an actual user.

These can't report on the internals of your code, but can provide a useful
insight into your site's overall performance, including aspects that can't be
adequately measured from within Django environment.

There are also several paid-for services that perform a similar analysis,
including some that are Django-aware and can integrate with your codebase to
profile its performance far more comprehensively.

Get things right from the start
-------------------------------

Some work in optimization involves tackling performance shortcomings, but some
of the work can be built-in to what you'd do anyway, as part of the good
practices you should adopt even before you start thinking about improving
performance.

In this respect Python is an excellent language to work with, because solutions
that look elegant and feel right usually are the best performing ones. As with
most skills, learning what "looks right" takes practice, but one of the most
useful guidelines is:

Work at the appropriate level
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Django offers many different ways of approaching things, but just because it's
possible to do something in a certain way doesn't mean that it's the most
appropriate way to do it. For example, you might find that you could calculate
the same thing - the number of items in a collection, perhaps - in a
``QuerySet``, in Python, or in a template.

However, it will almost always be faster to do this work at lower rather than
higher levels. At higher levels the system has to deal with objects through
multiple levels of abstraction and layers of machinery.

That is, the database can typically do things faster than Python can, which can
do them faster than the template language can::

    # QuerySet operation on the database
    # fast, because that's what databases are good at
    my_bicycles.count()

    # counting Python objects
    # slower, because it requires a database query anyway, and processing
    # of the Python objects
    len(my_bicycles)

.. code-block:: html+django

    <!--
    Django template filter
    slower still, because it will have to count them in Python anyway,
    and because of template language overheads
    -->
    {{ my_bicycles|length }}

Generally speaking, the most appropriate level for the job is the lowest-level
one that it is comfortable to code for.

.. note::

    The example above is merely illustrative.

    Firstly, in a real-life case you need to consider what is happening before
    and after your count to work out what's an optimal way of doing it *in that
    particular context*. The database optimization document describes :ref:`a
    case where counting in the template would be better
    <overuse_of_count_and_exists>`.

    Secondly, there are other options to consider: in a real-life case, ``{{
    my_bicycles.count }}``, which invokes the ``QuerySet`` ``count()`` method
    directly from the template, might be the most appropriate choice.

Caching
=======

Often it is expensive (that is, resource-hungry and slow) to compute a value,
so there can be huge benefit in saving the value to a quickly accessible cache,
ready for the next time it's required.

It's a sufficiently significant and powerful technique that Django includes a
comprehensive caching framework, as well as other smaller pieces of caching
functionality.

:doc:`The caching framework </topics/cache>`
--------------------------------------------

Django's :doc:`caching framework </topics/cache>` offers very significant
opportunities for performance gains, by saving dynamic content so that it
doesn't need to be calculated for each request.

For convenience, Django offers different levels of cache granularity: you can
cache the output of specific views, or only the pieces that are difficult to
produce, or even an entire site.

Implementing caching should not be regarded as an alternative to improving code
that's performing poorly because it has been written badly. It's one of the
final steps toward producing well-performing code, not a shortcut.

:class:`~django.utils.functional.cached_property`
-------------------------------------------------

It's common to have to call a class instance's method more than once. If
that function is expensive, then doing so can be wasteful.

Using the :class:`~django.utils.functional.cached_property` decorator saves the
value returned by a property; the next time the function is called on that
instance, it will return the saved value rather than re-computing it. Note that
this only works on methods that take ``self`` as their only argument and that
it changes the method to a property.

Certain Django components also have their own caching functionality; these are
discussed below in the sections related to those components.

Understanding laziness
======================

*Laziness* is a strategy complementary to caching. Caching avoids
recomputation by saving results; laziness delays computation until it's
actually required.

Laziness allows us to refer to things before they are instantiated, or even
before it's possible to instantiate them. This has numerous uses.

For example, :ref:`lazy translation <lazy-translations>` can be used before the
target language is even known, because it doesn't take place until the
translated string is actually required, such as in a rendered template.

Laziness is also a way to save effort by trying to avoid work in the first
place. That is, one aspect of laziness is not doing anything until it has to be
done, because it may not turn out to be necessary after all. Laziness can
therefore have performance implications, and the more expensive the work
concerned, the more there is to gain through laziness.

Python provides a number of tools for lazy evaluation, particularly through the
:py:term:`generator` and :py:term:`generator expression` constructs. It's worth
reading up on laziness in Python to discover opportunities for making use of
lazy patterns in your code.

Laziness in Django
------------------

Django is itself quite lazy. A good example of this can be found in the
evaluation of ``QuerySets``. :ref:`QuerySets are lazy <querysets-are-lazy>`.
Thus a ``QuerySet`` can be created, passed around and combined with other
``QuerySets``, without actually incurring any trips to the database to fetch
the items it describes. What gets passed around is the ``QuerySet`` object, not
the collection of items that - eventually - will be required from the database.

On the other hand, :ref:`certain operations will force the evaluation of a
QuerySet <when-querysets-are-evaluated>`. Avoiding the premature evaluation of
a ``QuerySet`` can save making an expensive and unnecessary trip to the
database.

Django also offers a :meth:`~django.utils.functional.keep_lazy` decorator.
This allows a function that has been called with a lazy argument to behave
lazily itself, only being evaluated when it needs to be. Thus the lazy argument
- which could be an expensive one - will not be called upon for evaluation
until it's strictly required.

Databases
=========

Database optimization
---------------------

Django's database layer provides various ways to help developers get the best
performance from their databases. The :doc:`database optimization documentation
</topics/db/optimization>` gathers together links to the relevant
documentation and adds various tips that outline the steps to take when
attempting to optimize your database usage.

Other database-related tips
---------------------------

Enabling :ref:`persistent-database-connections` can speed up connections to the
database accounts for a significant part of the request processing time.

This helps a lot on virtualized hosts with limited network performance, for example.

HTTP performance
================

Middleware
----------

Django comes with a few helpful pieces of :doc:`middleware </ref/middleware>`
that can help optimize your site's performance. They include:

:class:`~django.middleware.http.ConditionalGetMiddleware`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Adds support for modern browsers to conditionally GET responses based on the
``ETag`` and ``Last-Modified`` headers. It also calculates and sets an ETag if
needed.

:class:`~django.middleware.gzip.GZipMiddleware`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Compresses responses for all modern browsers, saving bandwidth and transfer
time. Note that GZipMiddleware is currently considered a security risk, and is
vulnerable to attacks that nullify the protection provided by TLS/SSL. See the
warning in :class:`~django.middleware.gzip.GZipMiddleware` for more information.

Sessions
--------

Using cached sessions
~~~~~~~~~~~~~~~~~~~~~

:ref:`Using cached sessions <cached-sessions-backend>` may be a way to increase
performance by eliminating the need to load session data from a slower storage
source like the database and instead storing frequently used session data in
memory.

Static files
------------

Static files, which by definition are not dynamic, make an excellent target for
optimization gains.

:class:`~django.contrib.staticfiles.storage.ManifestStaticFilesStorage`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

By taking advantage of web browsers' caching abilities, you can
eliminate network hits entirely for a given file after the initial download.

:class:`~django.contrib.staticfiles.storage.ManifestStaticFilesStorage` appends a
content-dependent tag to the filenames of :doc:`static files
</ref/contrib/staticfiles>` to make it safe for browsers to cache them
long-term without missing future changes - when a file changes, so will the
tag, so browsers will reload the asset automatically.

"Minification"
~~~~~~~~~~~~~~

Several third-party Django tools and packages provide the ability to "minify"
HTML, CSS, and JavaScript. They remove unnecessary whitespace, newlines, and
comments, and shorten variable names, and thus reduce the size of the documents
that your site publishes.

Template performance
====================

Note that:

* using ``{% block %}`` is faster than using ``{% include %}``
* heavily-fragmented templates, assembled from many small pieces, can affect
  performance

The cached template loader
--------------------------

Enabling the :class:`cached template loader
<django.template.loaders.cached.Loader>` often improves performance
drastically, as it avoids compiling each template every time it needs to be
rendered.

Using different versions of available software
==============================================

It can sometimes be worth checking whether different and better-performing
versions of the software that you're using are available.

These techniques are targeted at more advanced users who want to push the
boundaries of performance of an already well-optimized Django site.

However, they are not magic solutions to performance problems, and they're
unlikely to bring better than marginal gains to sites that don't already do the
more basic things the right way.

.. note::

    It's worth repeating: **reaching for alternatives to software you're
    already using is never the first answer to performance problems**. When
    you reach this level of optimization, you need a formal benchmarking
    solution.

Newer is often - but not always - better
----------------------------------------

It's fairly rare for a new release of well-maintained software to be less
efficient, but the maintainers can't anticipate every possible use-case - so
while being aware that newer versions are likely to perform better, don't
assume that they always will.

This is true of Django itself. Successive releases have offered a number of
improvements across the system, but you should still check the real-world
performance of your application, because in some cases you may find that
changes mean it performs worse rather than better.

Newer versions of Python, and also of Python packages, will often perform
better too - but measure, rather than assume.

.. note::

    Unless you've encountered an unusual performance problem in a particular
    version, you'll generally find better features, reliability, and security
    in a new release and that these benefits are far more significant than any
    performance you might win or lose.

Alternatives to Django's template language
------------------------------------------

For nearly all cases, Django's built-in template language is perfectly
adequate. However, if the bottlenecks in your Django project seem to lie in the
template system and you have exhausted other opportunities to remedy this, a
third-party alternative may be the answer.

Jinja2_ can offer performance improvements, particularly when it comes to
speed.

Alternative template systems vary in the extent to which they share Django's
templating language.

.. note::

    *If* you experience performance issues in templates, the first thing to do
    is to understand exactly why. Using an alternative template system may
    prove faster, but the same gains may also be available without going to
    that trouble - for example, expensive processing and logic in your
    templates could be done more efficiently in your views.

Alternative software implementations
------------------------------------

It may be worth checking whether Python software you're using has been
provided in a different implementation that can execute the same code faster.

However: most performance problems in well-written Django sites aren't at the
Python execution level, but rather in inefficient database querying, caching,
and templates. If you're relying on poorly-written Python code, your
performance problems are unlikely to be solved by having it execute faster.

Using an alternative implementation may introduce compatibility, deployment,
portability, or maintenance issues. It goes without saying that before adopting
a non-standard implementation you should ensure it provides sufficient
performance gains for your application to outweigh the potential risks.

With these caveats in mind, you should be aware of:

`PyPy <https://www.pypy.org/>`_
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

`PyPy <https://www.pypy.org/>`_ is an implementation of Python in Python itself
(the 'standard' Python implementation is in C). PyPy can offer substantial
performance gains, typically for heavyweight applications.

A key aim of the PyPy project is `compatibility
<https://www.pypy.org/compat.html>`_ with existing Python APIs and libraries.
Django is compatible, but you will need to check the compatibility of other
libraries you rely on.

C implementations of Python libraries
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Some Python libraries are also implemented in C, and can be much faster. They
aim to offer the same APIs. Note that compatibility issues and behavior
differences are not unknown (and not always immediately evident).

.. _Jinja2: https://jinja.palletsprojects.com/