FastAPI Tutorial
FastAPI is a modern, fast (high-performance) Python web framework for building APIs, specifically designed for building RESTful APIs.
FastAPI uses Python 3.8+ and is built on standard Python type hints, using Starlette and Pydantic, enabling automatic API documentation generation and data validation.
Who is this tutorial for?
This tutorial is suitable for developers with a foundation in Python. If you already understand Python's basic syntax and type annotations, you will be able to quickly get started with FastAPI.
What you need to know before taking this tutorial
Before learning this tutorial, you need to understand some basic Web knowledge andPython 3.x Basic TutorialIf you are not familiar with HTTP request methods (GET, POST, etc.), it is recommended to read first.HTTP Tutorial。
FastAPI Features
FastAPI stands out among Python web frameworks, mainly due to the following features:
| Features | Description |
|---|---|
| High Performance | Based on Starlette and Pydantic, with performance comparable to NodeJS and Go, it is one of the fastest Python frameworks. |
| Rapid development | Development speed increases by about 200%-300%, with standard type declarations enabling data validation and documentation generation. |
| Reduce errors | Reduces human errors by about 40%, with the type system automatically catching common issues. |
| Automatic documentation | Automatically generates interactive API documentation (Swagger UI and ReDoc), eliminating the need for manual maintenance. |
| Type Safety | Based on standard Python type hints, providing comprehensive auto-completion and error checking in editors. |
| Async support | Native support for async/await, efficiently handling IO-intensive tasks. |
FastAPI applicable scenarios
| Scenarios | Description |
|---|---|
| Building API backends | Used for building RESTful APIs, supporting web applications with separated frontend and backend. |
| Microservices architecture | Lightweight and efficient, suitable as a backend framework for microservices. |
| Data processing APIs | Suitable for data processing services that receive and return JSON data. |
| Real-time communication | Supports WebSocket, suitable for real-time communication scenarios. |
| Machine learning services | Can wrap trained models as APIs, making it convenient for frontends and other services to call. |
FastAPI tech stack
FastAPI is built on top of two core libraries:
| Components | Function | Description |
|---|---|---|
| Starlette | Web framework layer | Provides basic web features such as routing, middleware, and WebSocket; FastAPI directly inherits from Starlette. |
| Pydantic | Data validation layer | Performs data validation, serialization, and documentation generation based on Python type hints. |
| Uvicorn | ASGI server | A high-performance ASGI server based on uvloop and httptools, used to run FastAPI applications |
FastAPI is a subclass of Starlette, so you can use all of Starlette's features. At the same time, FastAPI is fully compatible with Pydantic, including external libraries based on Pydantic ORMs (such as SQLModel).
Why choose FastAPI?
| Comparison Dimension | FastAPI | Flask | Django |
|---|---|---|---|
| Performance | High (async, ASGI) | Medium (sync, WSGI) | Medium (sync, WSGI) |
| Automatic documentation | Built-in (Swagger UI + ReDoc) | Requires third-party extensions | Requires third-party extensions |
| Type validation | Built-in (Pydantic) | Manual implementation required | Manual implementation required |
| Async support | Native support | need to expand | 3.1+ support |
| Learning Curve | Low | Low | Higher |
| Applicable scale | Small and medium-sized / microservices | small and medium-sized | Large / Full-stack |
Related Links
| Resources | Address |
|---|---|
| FastAPI official documentation | https://fastapi.tiangolo.com/zh/ |
| FastAPI Source Code | https://github.com/tiangolo/fastapi |
| Starlette Documentation | https://www.starlette.dev/ |
| Pydantic documentation | https://docs.pydantic.dev/ |