FastAPIとは

FastAPIは、Pythonの非常に高速な(高性能)、使いやすい、モダンな、高速(高性能)なWebフレームワークです。FastAPIは、Python 3.6以降の型ヒントを使用してAPIパラメータの型を定義します。

FastAPIは、Starlette(Web部分)とPydantic(データ部分)に基づいています。これにより、FastAPIは以下のような機能を提供します:

  • 高速: NodeJSやGoと同等のパフォーマンス
  • 高速なコーディング: 開発スピードを約2倍から3倍に向上
  • 少ないバグ: 開発者エラーを減らす
  • 直感的: 素晴らしいエディタのサポート
  • 簡単: 設計が簡単で使いやすい
  • 短縮: コードの重複を最小限に抑える
  • 堅牢: プロダクションでの使用に適しています
  • 基準に基づく: OpenAPI(以前はSwagger)とJSONスキーマに完全準拠
  • 自動的なインタラクティブなAPIドキュメンテーション
FastAPIは、開発者が高品質なAPIを迅速に開発し、そのAPIを簡単に管理できるように設計されています。これは、FastAPIがPythonの型ヒントを使用してAPIのパラメータとリクエストボディを自動的に検証し、データのシリアライゼーションとデシリアライゼーションを行い、自動的にOpenAPIスキーマを生成するためです。これにより、開発者はAPIのドキュメンテーションとテストを自動化できます。

https://fastapi.tiangolo.com/

https://fastapi.tiangolo.com/features/

https://fastapi.tiangolo.com/features/

https://fastapi.tiangolo.com/features/

https://fastapi.tiangolo.com/features/#fast

https://fastapi.tiangolo.com/features/#fast-to-code

https://fastapi.tiangolo.com/features/#fewer-bugs

https://fastapi.tiangolo.com/features/#intuitive

https://fastapi.tiangolo.com/features/#easy

https://fastapi.tiangolo.com/features/#short

https://fastapi.tiangolo.com/features/#robust

https://fastapi.tiangolo.com/features/#standards-based

https://fastapi.tiangolo.com/features/#automatic-interactive-api-documentation

https://fastapi.tiangolo.com/

https://fastapi.tiangolo.com/features/

https://fastapi.tiangolo.com/features/

UvicornとGunicornの役割

UvicornGunicornは、PythonのWebアプリケーションをデプロイする際に一緒に使用されることが多いツールです。それぞれの役割について詳しく見ていきましょう。

Uvicorn

Uvicornは、非同期Python Webサーバーで、ASGI(Asynchronous Server Gateway Interface)仕様に基づいています。Uvicornは、非同期フレームワーク(例えば、FastAPIやStarlette)をサポートし、非同期I/Oを利用して高速なレスポンスを提供します。

Uvicornの主な特徴は以下の通りです:

  • 高速: Uvicornは、非同期I/OとPythonの非同期機能を利用して、高速なレスポンスを提供します。
  • ASGI互換: UvicornはASGI仕様に準拠しているため、ASGIフレームワークと互換性があります。

Gunicorn

Gunicorn(’Green Unicorn’)は、PythonのWSGI(Web Server Gateway Interface)HTTPサーバーです。Gunicornは、複数のワーカープロセスを管理して、同時に複数のリクエストを処理する能力を提供します。

Gunicornの主な特徴は以下の通りです:

  • プロセスワーカー管理: Gunicornは、複数のワーカープロセスを生成・管理することで、同時に複数のリクエストを処理します。
  • WSGI互換: GunicornはWSGI仕様に準拠しているため、WSGIフレームワークと互換性があります。

UvicornとGunicornの組み合わせ

FastAPIのようなASGIフレームワークをデプロイする際には、UvicornとGunicornを組み合わせて使用することが一般的です。Gunicornがプロセスワーカーを管理し、各ワーカーがUvicornサーバーを実行します。これにより、非同期I/Oの恩恵を受けつつ、同時に複数のリクエストを効率的に処理することができます。

https://www.uvicorn.org/

https://gunicorn.org/

https://fastapi.tiangolo.com/deployment/

https://www.uvicorn.org/

https://gunicorn.org/

https://fastapi.tiangolo.com/deployment/

Dockerとの組み合わせ

Dockerは、アプリケーションとその依存関係をパッケージ化し、一貫した環境で実行できるようにするコンテナ化技術です。FastAPIとUvicorn(またはGunicorn)をDockerと組み合わせることで、開発環境と本番環境の間で一貫性を保つことができます。

以下に、FastAPIとUvicorn(またはGunicorn)をDockerと組み合わせる主な利点を挙げます:

  • 環境の一貫性: Dockerを使用すると、開発環境と本番環境が一致します。これにより、「私のマシンでは動作する」という問題を防ぐことができます。
  • 依存関係の管理: Dockerを使用すると、アプリケーションの依存関係をDockerイメージにパッケージ化できます。これにより、依存関係のバージョン管理と共有が容易になります。
  • スケーラビリティ: Dockerとオーケストレーションツール(例えば、Kubernetes)を組み合わせることで、アプリケーションのスケーリングと管理が容易になります。

FastAPIとUvicorn(またはGunicorn)をDockerで実行するための基本的なDockerfileは以下のようになります:

FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7

COPY ./app /app

このDockerfileは、tiangoloが提供するFastAPI、Uvicorn、Gunicornを含むDockerイメージをベースにしています。そして、ローカルのappディレクトリをDockerイメージの/appディレクトリにコピーします。

このように、Dockerを使用すると、FastAPIアプリケーションのデプロイが容易になり、一貫性とスケーラビリティが向上します。

https://www.docker.com/what-docker

https://fastapi.tiangolo.com/deployment/docker/

https://www.docker.com/what-docker

https://fastapi.tiangolo.com/deployment/docker/

https://www.docker.com/what-docker

https://fastapi.tiangolo.com/deployment/docker/

https://www.docker.com/what-docker

https://fastapi.tiangolo.com/deployment/docker/

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https://fastapi.t

tiangoloのDockerイメージの利用方法

tiangoloが提供しているFastAPI用のDockerイメージは、FastAPIとUvicorn/Gunicornを組み合わせたもので、これを使用することで簡単にFastAPIアプリケーションをDocker上で実行することができます。

以下に、tiangoloのDockerイメージを利用してFastAPIアプリケーションをDocker上で実行する手順を示します。

  1. Dockerfileの作成: まず、プロジェクトのルートディレクトリにDockerfileを作成します。その内容は以下のようになります。
FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7

COPY ./app /app

このDockerfileは、tiangoloが提供するFastAPI、Uvicorn、Gunicornを含むDockerイメージをベースにしています。そして、ローカルのappディレクトリをDockerイメージの/appディレクトリにコピーします。

  1. Dockerイメージのビルド: 次に、以下のコマンドを実行してDockerイメージをビルドします。
docker build -t myimage .

このコマンドは、現在のディレクトリ(.)のDockerfileを使用してDockerイメージをビルドし、そのイメージにmyimageという名前を付けます。

  1. Dockerコンテナの起動: ビルドが完了したら、以下のコマンドを実行してDockerコンテナを起動します。
docker run -d --name mycontainer -p 80:80 myimage

このコマンドは、myimageというDockerイメージからmycontainerという名前のDockerコンテナを作成し、バックグラウンドで実行します。また、ホストの80番ポートとコンテナの80番ポートをマッピングします。

以上が、tiangoloのDockerイメージを利用してFastAPIアプリケーションをDocker上で実行する基本的な手順です。これにより、開発環境と本番環境の間で一貫性を保つことができ、アプリケーションのデプロイが容易になります。

https://fastapi.tiangolo.com/deployment/docker/

https://fastapi.tiangolo.com/deployment/docker/

https://fastapi.tiangolo.com/deployment/docker/

https://fastapi.tiangolo.com/deployment/docker/

https://fastapi.tiangolo.com/deployment/docker/

https://fastapi.tiangolo.com/deployment/docker/

https://fastapi.tiangolo.com/deployment/docker/

https://fastapi.tiangolo.com/deployment/docker/

https://fastapi.tiangolo.com/deployment/docker/

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https://fastapi.tiangolo.com/deployment/docker/

https://fastapi.tiangolo.com/deployment/docker/

https://fastapi.tiangolo.com/deployment/docker/

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https://fastapi.tiangolo.com/deployment

自分でDockerイメージを作成する方法

Dockerイメージを自分で作成するためには、Dockerfileという特殊なファイルを作成し、その中にイメージの作成手順を記述します。以下に、基本的なDockerfileの作成とDockerイメージのビルド手順を示します。

  1. Dockerfileの作成: まず、プロジェクトのルートディレクトリにDockerfileという名前のファイルを作成します。その内容は以下のようになります。
# ベースとなるDockerイメージを指定
FROM python:3.7

# 作業ディレクトリを設定
WORKDIR /app

# 必要なパッケージをインストール
RUN pip install fastapi uvicorn

# アプリケーションのソースコードをコピー
COPY ./app /app

# アプリケーションを起動するコマンドを指定
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80"]

このDockerfileは、Python 3.7のDockerイメージをベースにしています。そして、FastAPIとUvicornをインストールし、ローカルのappディレクトリをDockerイメージの/appディレクトリにコピーします。最後に、Uvicornを使用してFastAPIアプリケーションを起動するコマンドを指定します。

  1. Dockerイメージのビルド: 次に、以下のコマンドを実行してDockerイメージをビルドします。
docker build -t myimage .

このコマンドは、現在のディレクトリ(.)のDockerfileを使用してDockerイメージをビルドし、そのイメージにmyimageという名前を付けます。

以上が、自分でDockerイメージを作成する基本的な手順です。これにより、自分のニーズに合わせたカスタムDockerイメージを作成することができます。

https://docs.docker.com/engine/reference/builder/

https://docs.docker.com/engine/reference/builder/

https://docs.docker.com/engine/reference/builder/

https://docs.docker.com/engine/reference/builder/

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パフォーマンスチューニングと最適化

FastAPIとUvicorn/Gunicornを使用したWebアプリケーションのパフォーマンスを最適化するためには、いくつかの手法があります。以下に、その一部を紹介します。

ワーカー数の調整

Gunicornを使用している場合、ワーカー数(同時に処理できるリクエスト数)を調整することでパフォーマンスを最適化できます。ワーカー数は、使用しているCPUのコア数に依存します。一般的には、ワーカー数はCPUコア数の2倍+1が推奨されます。

プリロード

Gunicornにはプリロードという機能があります。これは、アプリケーションをワーカーがフォークする前にロードする機能で、大規模なアプリケーションでは起動時間を大幅に短縮できます。

Uvicornの設定

Uvicornには、パフォーマンスを最適化するためのいくつかの設定オプションがあります。例えば、--workersオプションでワーカー数を指定したり、--limit-concurrencyオプションで同時接続数を制限したりできます。

FastAPIの設定

FastAPI自体も、パフォーマンスを最適化するための設定を提供しています。例えば、response_modelパラメータを使用してレスポンスモデルを指定すると、FastAPIは出力データを自動的にシリアライズします。これにより、大量のデータを扱う場合でもレスポンス時間を短縮できます。

以上のような手法を用いて、FastAPIとUvicorn/Gunicornを使用したWebアプリケーションのパフォーマンスを最適化することができます。ただし、最適な設定はアプリケーションの要件や環境により異なるため、具体的な設定値は適切に調整する必要があります。

https://docs.gunicorn.org/en/stable/design.html

https://docs.gunicorn.org/en/stable/settings.html#preload-app

https://www.uvicorn.org/settings/

https://fastapi.tiangolo.com/tutorial/response-model/

https://docs.gunicorn.org/en/stable/design.html

https://docs.gunicorn.org/en/stable/settings.html#preload-app

https://www.uvicorn.org/settings/

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https://docs

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