FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints.

FastAPI는 표준 Python 타입 힌트를 기반으로 한 Python 3.7+용 모던하고 빠른(고성능) 웹 프레임워크입니다.

Features

• Based on open standards
  • OpenAPI for API creation, including declarations of path operations, parameters, body requests, security, etc.
• Automatic docs
  • Swagger UI, Redoc
• Just Modern Python
• Dependency Injection https://fastapi.tiangolo.com/tutorial/dependencies/

  • Even dependencies can have dependencies, creating a hierarchy or "graph" of dependencies.

    → 의존성은 의존성을 가질 수 있으며, 의존성의 계층 또는 "그래프"를 만듭니다.

  • All automatically handled by the framework.

  • All the dependencies can require data from requests and augment the path operation constraints and automatic documentation.

    → 모든 종속성은 요청에서 데이터를 요구하고 경로 작업 제약과 자동 문서화를 보완할 수 있습니다.

  • Automatic validation even for path operation parameters defined in dependencies.

    → 의존성에 정의된 경로 작업 매개변수에 대해서도 자동 유효성 검사를 수행합니다.

  • Support for complex user authentication systems, database connections, etc.

  • No compromise with databases, frontends, etc. But easy integration with all of them.

• Starlette features https://www.starlette.io/
  • Seriously impressive performance.
  • Web socket
  • In-process background tasks.
  • Startup and shutdown events.
  • Test client built on HTTPX.
  • CORS, GZip, Static Files, Streaming responses.
  • Session and Cookie support.
  • 100% test coverage.
  • 100% type annotated codebase.
• Pydantic features https://pydantic-docs.helpmanual.io/

  • No brainfuck: https://en.wikipedia.org/wiki/Brainfuck

    • No new schema definition micro-language to learn.
    • If you know Python types you know how to use Pydantic.

  • Plays nicely with your IDE/linter/brain

  • Fast: in benchmarks Pydantic is faster than all other tested libraries.

  • Validate complex structures:

    • Use of hierarchical Pydantic models, Python typing’s List and Dict, etc.
    • And validators allow complex data schemas to be clearly and easily defined, checked and documented as JSON Schema.
    • You can have deeply nested JSON objects and have them all validated and annotated.

  • Extensible:

    • Pydantic allows custom data types to be defined or you can extend validation with methods on a model decorated with the validator decorator.

      → Pydantic는 사용자 정의 데이터 유형을 정의할 수 있도록 하거나, validator 데코레이터가 있는 모델의 메서드로 유효성 검사를 확장할 수 있습니다.

  • 100% test coverage.

Dependency Injection

from typing import Union
from fastapi import Depends, FastAPI

app = FastAPI()

async def common_parameters(
    q: Union[str, None] = None, skip: int = 0, limit: int = 100
):
    return {"q": q, "skip": skip, "limit": limit}

@app.get("/items/")
async def read_items(commons: dict = Depends(common_parameters)):
    return commons

@app.get("/users/")
async def read_users(commons: dict = Depends(common_parameters)):
    return commons

• @app.get(~~): path operation function
• 리퀘스트 도달시
  1. dependency function call
  2. result of dependency function call → parameter of path operation function