FastAPI Best Practices: A Condensed Guide with Examples

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

It was designed to be fast, easy to use, and highly compatible with other web frameworks and tools.

FastAPI leverages the power of async/await and Python 3.7+ type hints to provide an efficient and developer-friendly experience for building web applications and APIs.

Key Features and Importance of Best Pratices

Some key features of FastAPI include:

• High performance: FastAPI is built on top of Starlette and Pydantic, which makes it one of the fastest web frameworks available for Python.
• Easy to learn and use: FastAPI is designed to be simple and intuitive, making it easy for developers to get started and be productive quickly.
• Automatic documentation: FastAPI automatically generates interactive API documentation using OpenAPI (formerly Swagger) and JSON Schema.
• Type safety: FastAPI enforces type safety using Python type hints, which helps catch bugs and errors at development time rather than runtime.
• Async/await support: FastAPI supports asynchronous programming, allowing developers to build highly concurrent and scalable applications.

Following best practices when working with FastAPI is crucial for several reasons:

• Maintainability: Adhering to best practices ensures that your code is well-organized, easy to understand, and maintainable, making it simpler for other developers to contribute to your project.
• Scalability: Implementing best practices helps you create a solid foundation for your application, allowing it to scale efficiently as it grows in complexity and size.
• Security: Following best practices can help you avoid common security pitfalls and vulnerabilities, ensuring that your application is more secure and robust.
• Performance: By adhering to best practices, you can optimize your application's performance, taking full advantage of FastAPI's speed and efficiency.
• Community standards: Following best practices aligns your code with community standards and expectations, making it easier for others to collaborate, review, and provide feedback on your work.

Setting Up and Structuring Your FastAPI Project

Before starting your FastAPI project, it's essential to set up a virtual environment to isolate your project's dependencies from other Python projects on your system.

You can use tools like venv or pipenv to create a virtual environment. In this example, we'll use venv:

• Create a new directory for your project and navigate to it in your terminal.
• Create a virtual environment using the following command:

python3 -m venv venv

This command creates a new virtual environment named venv in your project directory.

• Activate the virtual environment. The activation command depends on your operating system:

For Linux and macOS:

source venv/bin/activate

For Windows:

venv\Scripts\activate

• With the virtual environment activated, install FastAPI and an ASGI server like Uvicorn or Hypercorn. In this example, we'll use Uvicorn:

pip install fastapi uvicorn

Structuring your project for scalability

Structuring your FastAPI project properly is crucial for maintaining a scalable and maintainable codebase. Here's a suggested project structure that you can adapt to your needs:

my_fastapi_project/
│
├── app/
│   ├── __init__.py
│   ├── main.py
│   ├── api/
│   │   ├── __init__.py
│   │   ├── v1/
│   │   │   ├── __init__.py
│   │   │   ├── users.py
│   │   │   ├── posts.py
│   │   │   └── ...
│   │   └── v2/
│   │       └── ...
│   ├── models/
│   │   ├── __init__.py
│   │   ├── user.py
│   │   ├── post.py
│   │   └── ...
│   ├── config.py
│   ├── database.py
│   └── utils.py
│
├── tests/
│   ├── __init__.py
│   ├── test_users.py
│   ├── test_posts.py
│   └── ...
│
├── .env
├── requirements.txt
├── Dockerfile
└── README.md

Key components of the project structure:

• app/: Contains the main application logic, including the API routes, models, configuration, database, and utility functions.
• app/main.py: The entry point for your FastAPI application, where you initialize the app and mount the API routes.
• app/api/: Contains the API routes organized by version (e.g., v1, v2).
• app/models/: Contains the Pydantic data models used for data validation and serialization.
• app/config.py: Contains the application configuration settings.
• app/database.py: Contains the database connection and related functions.
• app/utils.py: Contains utility functions used throughout the application.
• tests/: Contains the unit tests for your application.
• .env: Contains environment variables for your application.
• requirements.txt: Lists the Python dependencies for your application.
• Dockerfile: Used for building a Docker image of your application.
• README.md: Provides an overview and documentation for your project.

Data Validation and Handling HTTP Requests

FastAPI uses Pydantic for data validation and serialization. Pydantic enforces type safety and provides helpful error messages when invalid data is submitted.

To define a data model with Pydantic, create a class with the required attributes and their corresponding types:

from pydantic import BaseModel

class UserIn(BaseModel):
    username: str
    email: str
    password: str

class UserOut(BaseModel):
    id: int
    username: str
    email: str
    class Config:
        orm_mode = True

In this example, we define two Pydantic models, UserIn and UserOut, for handling input and output user data, respectively.

Implementing path and query parameters

FastAPI allows you to define path and query parameters for your API endpoints.

Path parameters are part of the URL path, while query parameters are added to the URL as key-value pairs.

Example:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    name: str
    description: str

@app.get("/items/{item_id}")
async def read_item(item_id: int, q: str | None = None):
    return {"item_id": item_id, "q": q}

@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):
    return {"item_id": item_id, "item": item}

In this example, we define two API endpoints:

• /items/{item_id}: A GET endpoint that accepts an item_id path parameter and an optional q query parameter.
• /items/{item_id}: A PUT endpoint that accepts an item_id path parameter and a Pydantic Item model as the request body.

Handling HTTP requests and responses

FastAPI simplifies handling HTTP requests and responses.

You can define the required HTTP method (e.g., GET, POST, PUT, DELETE) for each endpoint, set status codes, and return responses with custom headers.

Example:

from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse

app = FastAPI()

@app.get("/users", response_model=List[UserOut])
async def get_users():
    users = await get_users_from_db()
    return users

@app.post("/users", response_model=UserOut, status_code=201)
async def create_user(user: UserIn):
    user_id = await create_user_in_db(user)
    return await get_user_by_id_from_db(user_id)

@app.delete("/users/{user_id}", response_class=JSONResponse)
async def delete_user(user_id: int):
    result = await delete_user_from_db(user_id)
    if not result:
        raise HTTPException(status_code=404, detail="User not found")
    return {"detail": "User deleted"}

In this example, we define three API endpoints:

• /users: A GET endpoint that returns a list of users with a 200 OK status code.
• /users: A POST endpoint that creates a new user and returns the created user with a 201 Created status code.
• /users/{user_id}: A DELETE endpoint that deletes a user and returns a custom response with a 200 OK status code. If the user is not found, it raises an HTTPException with a 404 Not Found status code.

Exception Handling, Middleware, and CORS

FastAPI provides built-in support for exception and error handling.