Best practices

API Best Practices with FastAPI in Python⚑

Building APIs is a common task for backend developers, and they serve as the backbone of many modern web and mobile applications. However, designing and building an API can be a complex process, and it's important to follow best practices to ensure the resulting API is robust, reliable, and easy to use. This article presents API best practices with specific examples using FastAPI, a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.

1. Design First, Code Later⚑

It's important to have a clear plan before starting to code your API. This means having a detailed specification of your API, including the routes, methods, parameters, and expected responses. You can create such a specification using the OpenAPI standard, which FastAPI supports out of the box.

from fastapi import FastAPI

app = FastAPI()

@app.get('/items/{item_id}')
async def read_item(item_id: int):
    return {'item_id': item_id}

In the above example, the OpenAPI schema is automatically generated and can be accessed at the /docs endpoint.

2. Consistent and RESTful Routing⚑

Make sure your API routes are consistent and follow RESTful principles. This means using the correct HTTP methods (GET, POST, PUT, DELETE, etc.) and having meaningful, predictable URLs. FastAPI makes it easy to define these with Python decorators.

@app.get('/users/{user_id}')
async def read_user(user_id: int):
    # code to get user

@app.post('/users/')
async def create_user(user: User):
    # code to create user

• Idempotent means that multiple identical requests should have the same effect as a single request.
• Safe means that the method only retrieves data and does not modify it.

3. Use Request Validation⚑

FastAPI supports request validation using Pydantic models, which allow you to define the expected shape of the data using Python type hints. This can significantly reduce the amount of boilerplate validation code you need to write.

from pydantic import BaseModel

class Item(BaseModel):
    name: str
    description: str
    price: float
    tax: float = None

@app.post('/items/')
async def create_item(item: Item):
    # code to create item

In this example, FastAPI will automatically validate that the incoming request data matches the Item model, and if it doesn't, it will return a helpful error message.

4. Error Handling⚑

You should anticipate and handle errors gracefully in your API. FastAPI provides the HTTPException class which you can raise to return a specific HTTP status code and message.

from fastapi import HTTPException

@app.get('/items/{item_id}')
async def read_item(item_id: int):
    item = get_item(item_id)
    if not item:
        raise HTTPException(status_code=404, detail='Item not found')
    return item

5. Rate Limiting⚑

Protect your API from abuse and overuse by implementing rate limiting. While FastAPI doesn't have built-in support for rate limiting, you can use third-party libraries such as SlowApi.

6. Use Asynchronous Code⚑

FastAPI supports asynchronous request handling using Python's async and await keywords. This can improve the performance of your API by allowing it to handle other requests while waiting for IO-bound tasks (like database queries) to complete.

@app.get('/items/')
async def read_items():
    items = await get_all_items()  # (1)
    return items

1. An async function that gets all items from the database

7. Logging and Monitoring⚑

An essential aspect of maintaining and troubleshooting APIs is having robust logging and monitoring in place. Here are some best practices for logging and monitoring in FastAPI:

1. Use the standard library logging module: The logging module is a built-in Python library that provides a flexible and powerful way to log messages from your application. FastAPI has built-in support for the logging module, so you can easily integrate it into your application​1​.

   import logging
   
   logger = logging.getLogger(__name__)
   
   @app.post('/items/')
   async def create_item(item: Item):
       logger.info(f'Creating item: {item.name}')
       # code to create item
   

2. Avoid using print() to log messages: print() does not provide the same level of control and flexibility as other logging methods. Instead, use FastAPI’s built-in logger for more control over how messages are logged, including setting log levels, adding contextual information, and formatting logs for easier readability​1​.

3. Log as much information as possible: By logging detailed information, such as the request URL, query parameters, headers, body, response status code, and more, developers can easily pinpoint where an issue occurred and what caused it. Use structured loggers to log data in a consistent format that can be easily parsed by other tools​1​.

4. Log exceptions: Logging exceptions allows developers to quickly identify and debug errors in their applications. By logging exceptions, developers can easily pinpoint where an issue occurred and what caused it. To log exceptions with FastAPI, use a library like Python’s built-in logging module​1​.

5. Add context to your logs: Adding context to your logs helps you quickly identify the source of an issue. By adding contextual information such as request and response data, user IDs, or other relevant details, you can easily pinpoint where the issue originated from​1​.

6. Use a structured logger: Structured logging is a way of formatting log messages so that they are easier to read and parse. FastAPI provides built-in support for structured logging via its Logging middleware​1​.

7. Configure your logger for production: Configuring your logger for production ensures that the right information is being logged at the right time. This includes setting up log levels so that only important messages are recorded, and configuring the format of the logs so they are easy to read and interpret. Additionally, ensure that sensitive data is not included in the logs, and that access to the logs is restricted to authorized personnel​1​.

8. Use an external service to store and analyze logs: Using an external service to store logs is beneficial because it allows for more efficient log management. Additionally, these services typically offer a range of features such as real-time monitoring, alerting, and reporting capabilities​1​.