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。
\n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。
\n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。
\n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。
\n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。
\nImprovements in one area will often bring about improved performance in\nanother, but not always; sometimes one can even be at the expense of another.\nFor example, an improvement in a program's speed might cause it to use more\nmemory. Even worse, it can be self-defeating - if the speed improvement is so\nmemory-hungry that the system starts to run out of memory, you'll have done\nmore harm than good.
\nThere are other trade-offs to bear in mind. Your own time is a valuable\nresource, more precious than CPU time. Some improvements might be too difficult\nto be worth implementing, or might affect the portability or maintainability of\nthe code. Not all performance improvements are worth the effort.
\nSo, you need to know what performance improvements you are aiming for, and you\nalso need to know that you have a good reason for aiming in that direction -\nand for that you need:
\n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。
\ndjango-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。
\n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。
\n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。
\nThese can't report on the internals of your code, but can provide a useful\ninsight into your site's overall performance, including aspects that can't be\nadequately measured from within Django environment. Examples include:
\n\nThere are also several paid-for services that perform a similar analysis,\nincluding some that are Django-aware and can integrate with your codebase to\nprofile its performance far more comprehensively.
\nSome work in optimization involves tackling performance shortcomings, but some\nof the work can simply be built in to what you'd do anyway, as part of the good\npractices you should adopt even before you start thinking about improving\nperformance.
\nIn this respect Python is an excellent language to work with, because solutions\nthat look elegant and feel right usually are the best performing ones. As with\nmost skills, learning what "looks right" takes practice, but one of the most\nuseful guidelines is:
\nDjango offers many different ways of approaching things, but just because it's\npossible to do something in a certain way doesn't mean that it's the most\nappropriate way to do it. For example, you might find that you could calculate\nthe same thing - the number of items in a collection, perhaps - in a\nQuerySet, in Python, or in a template.
However, it will almost always be faster to do this work at lower rather than\nhigher levels. At higher levels the system has to deal with objects through\nmultiple levels of abstraction and layers of machinery.
\nThat is, the database can typically do things faster than Python can, which can\ndo them faster than the template language can:
\n# QuerySet operation on the database\n# fast, because that's what databases are good at\nmy_bicycles.count()\n\n# counting Python objects\n# slower, because it requires a database query anyway, and processing\n# of the Python objects\nlen(my_bicycles)\n\n# Django template filter\n# slower still, because it will have to count them in Python anyway,\n# and because of template language overheads\n{{ my_bicycles|length }}\nGenerally speaking, the most appropriate level for the job is the lowest-level\none that it is comfortable to code for.
\n\nOften it is expensive (that is, resource-hungry and slow) to compute a value,\nso there can be huge benefit in saving the value to a quickly accessible cache,\nready for the next time it's required.
\nIt's a sufficiently significant and powerful technique that Django includes a\ncomprehensive caching framework, as well as other smaller pieces of caching\nfunctionality.
\nDjango 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。
\n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。
\nImplementing caching should not be regarded as an alternative to improving code\nthat's performing poorly because it has been written badly. It's one of the\nfinal steps towards producing well-performing code, not a shortcut.
\ncached_propertyLink to this heading#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。
\ncached_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。
\n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。
\nLaziness allows us to refer to things before they are instantiated, or even\nbefore it's possible to instantiate them. This has numerous uses.
\nFor example, lazy translation can be used before the\ntarget language is even known, because it doesn't take place until the\ntranslated string is actually required, such as in a rendered template.
\nLaziness is also a way to save effort by trying to avoid work in the first\nplace. That is, one aspect of laziness is not doing anything until it has to be\ndone, because it may not turn out to be necessary after all. Laziness can\ntherefore have performance implications, and the more expensive the work\nconcerned, the more there is to gain through laziness.
\nPython provides a number of tools for lazy evaluation, particularly through the\ngenerator and generator expression constructs. It's worth\nreading up on laziness in Python to discover opportunities for making use of\nlazy patterns in your code.
\nDjango is itself quite lazy. A good example of this can be found in the\nevaluation of QuerySets. QuerySets are lazy.\nThus a QuerySet can be created, passed around and combined with other\nQuerySets, without actually incurring any trips to the database to fetch\nthe items it describes. What gets passed around is the QuerySet object, not\nthe collection of items that - eventually - will be required from the database.
On the other hand, certain operations will force the evaluation of a\nQuerySet. Avoiding the premature evaluation of\na QuerySet can save making an expensive and unnecessary trip to the\ndatabase.
Django also offers a keep_lazy() decorator.\nThis allows a function that has been called with a lazy argument to behave\nlazily itself, only being evaluated when it needs to be. Thus the lazy argument\n- which could be an expensive one - will not be called upon for evaluation\nuntil it's strictly required.
Django's database layer provides various ways to help developers get the best\nperformance from their databases. The database optimization documentation gathers together links to the relevant\ndocumentation and adds various tips that outline the steps to take when\nattempting to optimize your database usage.
\nDjango 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。
\nConditionalGetMiddlewareLink to this heading#Adds support for modern browsers to conditionally GET responses based on the\nETag and Last-Modified headers. It also calculates and sets an ETag if\nneeded.
GZipMiddlewareLink to this heading#Compresses responses for all modern browsers, saving bandwidth and transfer\ntime. Note that GZipMiddleware is currently considered a security risk, and is\nvulnerable to attacks that nullify the protection provided by TLS/SSL. See the\nwarning in GZipMiddleware for more information.
Using cached sessions may be a way to increase\nperformance by eliminating the need to load session data from a slower storage\nsource like the database and instead storing frequently used session data in\nmemory.
\n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。
\nManifestStaticFilesStorageLink to this heading#By taking advantage of web browsers' caching abilities, you can\neliminate network hits entirely for a given file after the initial download.
\nManifestStaticFilesStorage appends a\ncontent-dependent tag to the filenames of static files to make it safe for browsers to cache them\nlong-term without missing future changes - when a file changes, so will the\ntag, 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。
\nNote that:
\n{% block %} の使用は {% include %} より高速です。
多数の分割されたテンプレートパーツからページを生成すると、パフォーマンスに影響することがあります。
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\nversions of the software that you're using are available.
\nThese techniques are targeted at more advanced users who want to push the\nboundaries of performance of an already well-optimized Django site.
\nHowever, they are not magic solutions to performance problems, and they're\nunlikely to bring better than marginal gains to sites that don't already do the\nmore basic things the right way.
\n\nIt's fairly rare for a new release of well-maintained software to be less\nefficient, but the maintainers can't anticipate every possible use-case - so\nwhile being aware that newer versions are likely to perform better, don't\nsimply assume that they always will.
\nThis is true of Django itself. Successive releases have offered a number of\nimprovements across the system, but you should still check the real-world\nperformance of your application, because in some cases you may find that\nchanges mean it performs worse rather than better.
\nNewer versions of Python, and also of Python packages, will often perform\nbetter too - but measure, rather than assume.
\n\nFor nearly all cases, Django's built-in template language is perfectly\nadequate. However, if the bottlenecks in your Django project seem to lie in the\ntemplate system and you have exhausted other opportunities to remedy this, a\nthird-party alternative may be the answer.
\nJinja2 を使うと、パフォーマンス、特に速度の向上が得られる可能性があります。
\nAlternative template systems vary in the extent to which they share Django's\ntemplating language.
\n\nIt may be worth checking whether Python software you're using has been\nprovided in a different implementation that can execute the same code faster.
\nHowever: most performance problems in well-written Django sites aren't at the\nPython execution level, but rather in inefficient database querying, caching,\nand templates. If you're relying on poorly-written Python code, your\nperformance problems are unlikely to be solved by having it execute faster.
\nUsing an alternative implementation may introduce compatibility, deployment,\nportability, or maintenance issues. It goes without saying that before adopting\na non-standard implementation you should ensure it provides sufficient\nperformance gains for your application to outweigh the potential risks.
\nWith these caveats in mind, you should be aware of:
\nPyPy is an implementation of Python in Python itself\n(the 'standard' Python implementation is in C). PyPy can offer substantial\nperformance gains, typically for heavyweight applications.
\nA key aim of the PyPy project is compatibility with existing Python APIs and libraries.\nDjango is compatible, but you will need to check the compatibility of other\nlibraries you rely on.
\nSome Python libraries are also implemented in C, and can be much faster. They\naim to offer the same APIs. Note that compatibility issues and behavior\ndifferences are not unknown (and not always immediately evident).
\n