このドキュメントでは、Django コードをより効率よく、早く、より少ないリソースで実行するためのテクニックとツールについて、その概要を説明します。
一般に、最も関心があるのは、コードが正しく 動作する、つまり、書かれたコードのロジックがが期待通りの出力を生成することです。しかし、それだけでは、期待通りに 効率よく 動作しているとは言えません。
この場合、必要なのは、コードの振る舞いに影響を与えることなく、あるいは影響を最小限に保ちつつ、そのパフォーマンスを向上させる実用的な方法です。
最適化する「パフォーマンス」という言葉で何を意味しているのか、はっきりと考えておくことが大切です。パフォーマンスを測る指標は1つではないからです。
高速化というものがプログラムのために最も明らかな目的かもしれません。しかし、他の観点からパフォーマンスを向上することもできます。たとえば、メモリー消費量を少なくするとか、データベースやネットワークのアクセス量を削減するといったことが考えられます。
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.
なので、何を目的としたパフォーマンスの改善なのかを知っておくべき必要があります。また、その目的とした方針に相応の理由があるかを知っておく必要もあります。
コード中で効率の悪い部分を、単に想像したり当て推量したりするというのは、あまり良い考えではありません。
django-debug-toolbar は、コードの動作や実行時間を確認する手段を提供してくれる、非常に便利なツールです。特に、ページが生成するすべての SQL クエリと、そのそれぞれの実行時間を表示してくれる機能が有用です。
サードパーティ製のパネルをツールバーに追加することも可能です。それにより、たとえば、キャッシュのパフォーマンスや、テンプレートのレンダリング時間を測定することができます。
サイト上のページのパフォーマンスを分析・レポートしてくれる無料のサービスが数多く存在します。これらのサービスでは、実際のユーザーエクスペリエンスをリモートの HTTP クライアントの観点からシミュレートしてくれます。
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. Examples include:
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.
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:
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)
# 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.
注釈
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 documents describes a case where counting in the template would be better.
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.
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.
Django の caching framework は、動的なコンテンツを保存して、リクエストごとに再計算しないで済むようにすることで、パフォーマンスを向上するための非常に大きな可能性を提供します。
利便性のため、Django は様々なレベルのキャッシュの粒度を提供しています。特定のビューの出力をキャッシュしたり、生成するのに時間のかかるパーツだけをキャッシュしたり、あるいは、サイト全体をキャッシュすることでさえ可能です。
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.
cached_property¶クラスのインスタンスのメソッドを複数回呼ぶ必要があるというのはよくあることです。その関数の実行が高価な場合、複数回の呼び出しは資源の浪費になります。
cached_property デコレータを使うことで、プロパティが返す値を保存して、次回同じインスタンスで関数が呼ばれた時、その値を再計算する代わりに、保存しておいた値を返すようにすることができます。ただし、このデコレータが機能するのは、メソッドが引数として self のみを取り、メソッドをプロパティーに変えられる場合のみなので、注意してください。
特定の Django コンポーネントは、独自のキャッシュ機能を実装しています。以下のそれらのコンポーネントに関係するセクションで解説します。
遅延 (laziness) とは、キャッシュ機能を補完するもう1つの戦略です。キャッシュは結果を保存することで再計算を防ぎますが、遅延は実際に値が必要になるまで、計算の実行を遅らせます。
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, lazy translation 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 generator and generator expression constructs. It's worth reading up on laziness in Python to discover opportunities for making use of lazy patterns in your code.
Django is itself quite lazy. A good example of this can be found in the
evaluation of QuerySets. 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, certain operations will force the evaluation of a
QuerySet. Avoiding the premature evaluation of
a QuerySet can save making an expensive and unnecessary trip to the
database.
Django also offers a 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.
Django's database layer provides various ways to help developers get the best performance from their databases. The database optimization documentation gathers together links to the relevant documentation and adds various tips that outline the steps to take when attempting to optimize your database usage.
Django は、サイトのパフォーマンス改善に役立ついくつかの middleware を用意しています。以下のようなものがあります。
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.
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 GZipMiddleware for more information.
Using cached sessions 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.
静的ファイル (定義により、動的に生成されないファイル) は、最適化を施すのに格好のターゲットです。
ManifestStaticFilesStorage¶By taking advantage of web browsers' caching abilities, you can eliminate network hits entirely for a given file after the initial download.
ManifestStaticFilesStorage appends a
content-dependent tag to the filenames of static files 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.
いくつかのサードパーティ製の Django ツールやパッケージは、HTML、CSS、そして JavaScript を最小化する ("minify") 機能を提供します。これらは不要なホワイトスペースや改行、コメントを取り除き、変数名を短縮することで、サイトが公開するドキュメントのサイズを削減してくれます。
Note that:
{% block %} の使用は {% include %} より高速です。cached template loader を有効にすると、パフォーマンスが劇的に改善する場合が多いです。このローダーを使用することで、テンプレートのレンダリングが必要になった場合に、各テンプレートを毎回コンパイルせずに済むようになるためです。
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.
注釈
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.
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.
注釈
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.
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.
注釈
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.
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 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 with existing Python APIs and libraries. Django is compatible, but you will need to check the compatibility of other libraries you rely on.
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).
2022年6月01日