Tips for Writing Efficient and Maintainable Code with FastAPI

In this article, we will be discussing various strategies and techniques to help you write efficient and maintainable code with FastAPI, a modern and high-performance web framework for building APIs with Python. FastAPI is built on top of the popular library pydantic and provides numerous features to assist you in writing clean, efficient, and well-documented code. Whether you are new to FastAPI or have experience using the framework, we hope you will find these tips useful. We will cover a range of topics including the use of type hints, managing dependencies, testing, and debugging. So, without further ado, let's get started!

It's important to understand that APIs (Application Programming Interfaces) are a crucial component for creating modern and connected applications. They allow different systems and services to communicate with each other and exchange data in a standardized way. Building APIs can be a challenging and time-consuming task, but FastAPI aims to make it as easy and efficient as possible. FastAPI is a modern and high-performance web framework for building APIs with Python.

Before we dive into the tips, it's essential to understand what makes code efficient and maintainable. Efficient code is fast and uses resources effectively, while maintainable code is easy to read, understand, and modify. Both of these characteristics are essential for building successful APIs with FastAPI.

1. Using Type Hints for Efficient and Documented Code in FastAPI: 

Use standard Python type hints to define the inputs and outputs of your functions and endpoints. This helps FastAPI automatically generate documentation and validation for your API, and also allows other tools to provide type-checking and auto-completion. For example:

def add(x: int, y: int) -> int:
    return x + y

In this example, the type hints int and int indicate that the x and y parameters are integers, and the type hint int indicates that the return value is an integer. FastAPI will use these type hints to generate documentation and validation for the add function.

2. Using Dependency Injection to Manage Dependencies in FastAPI: 

Use dependency injection to manage the dependencies of your functions and endpoints. This makes it easier to test your code and to swap out different implementations of your dependencies. FastAPI provides a dependency injection system built on top of pydantic models. For example:

from fastapi import FastAPI, Depends
from pydantic import BaseModel

app = FastAPI()

class User(BaseModel):
    username: str
    password: str

def get_current_user(user: User = Depends()):
    return user

@app.get("/users/me")
def read_current_user(current_user: User = Depends(get_current_user)):
    return current_user

In this example, the get_current_user function is used to retrieve the current user from the request. It is decorated with the Depends decorator, which indicates that it is a dependency. The read_current_user function depends on the current_user parameter, which is injected by calling the get_current_user function. This allows the read_current_user function to focus on its core logic, rather than on retrieving the current user.

3. Accessing and Customizing Request and Response Data with FastAPI: 

Use the FastAPI request and response objects to access request and response data. These objects provide a convenient way to access request and response data and also provide a way to customize the behavior of your endpoints. For example:

from fastapi import FastAPI, Request

app = FastAPI()


@app.get("/")
def read_request(request: Request):
    return {"method": request.method, "url": request.url}

In this example, the read_request function uses the Request object to access the request data. It returns a dictionary containing the request method and URL.

4. Automate Input Validation and Documentation with FastAPI: 

Use the FastAPI built-in validation and documentation features to automatically validate inputs and generate documentation for your API. This helps ensure that your API is easy to use and maintain. For example:

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
def read_items(q: str = Query(..., min_length = 3, max_length = 50)):
    results = search_items(q)
    return results

In this example, the q parameter is decorated with the Query object, which indicates that it is a query parameter and provides validation rules for its value. FastAPI will automatically generate documentation for this parameter, indicating that it is required, has a minimum and maximum length, and is a string.

5. Async Database Operations with asyncpg or aiomysql: 

Use a database library that integrates with asyncio, such as asyncpg or aiomysql, to handle database operations in an async manner. This will allow your application to scale better and handle more requests concurrently. For example:

import asyncpg


async def create_user(conn, user):
    await conn.execute(
        "INSERT INTO users (name, email) VALUES ($1, $2)", user.name, user.email)

This example shows a function called create_user that takes two arguments: conn, which is a connection to the database, and user, which is an object containing the user's name and email. The function uses the await conn.execute line to asynchronously execute a SQL INSERT statement, which inserts a new row into the users table with the name and email values taken from the user.name and user.email fields, respectively.

This code assumes that the user object has a name and email attributes and that the conn object has an execute method that can be used to execute SQL queries. It also assumes that the create_user function is being called within an async context (such as an async function or an asyncio event loop).

6. Asynchronous I/O for Improved Performance and Scalability: 

Using asyncio-based libraries for blocking operations can significantly improve the performance and scalability of your application. Blocking operations are tasks that take a long time to complete and prevent your application from performing other tasks while they are running. Examples of blocking operations include making HTTP requests, reading or writing to a file, or accessing a database. By using asyncio-based libraries to perform these operations, you can allow your application to perform other tasks while waiting for the blocking operation to complete, rather than blocking and waiting for the operation to finish. This can allow your application to handle more requests concurrently and scale better. Here is an example of using the aiohttp library to asynchronously make an HTTP request in a FastAPI application:

import aiohttp
from fastapi import FastAPI

app = FastAPI()


@app.get("/")
async def read_root():
    async with aiohttp.ClientSession() as session:
        async with session.get("https://www.geeksforgeeks.org/") as response:
            return await response.text()

This example shows a FastAPI endpoint that makes an HTTP GET request to the URL https://www.geeksforgeeks.org/ using the aiohttp library. The await session.get line is an asynchronous call to the get method of the session object, which sends an HTTP request and returns a response object. The await response.text line is another asynchronous call to the text method of the response object, which returns the response body as a string.

7. Use a Code Formatter for Consistent and Readable Code: 

Using a code formatter to automatically format your code can significantly improve the readability and maintainability of your code. A code formatter is a tool that automatically formats your code according to a set of rules, such as indentation style, line length, and naming conventions. By using a code formatter, you can ensure that your code follows a consistent style and is easy to read and understand. This can make it easier for you and other developers to work with your code and make changes to it over time.

One popular code formatter for Python is Black. Here is an example of using Black to format your code in a FastAPI project

$ pip install black
$ black my_fastapi_project/

The first line installs the Black package, and the second line runs Black on the my_fastapi_project directory, formatting all Python files in that directory according to Black's rules.

Using a code formatter like Black can help you write clean, readable, and maintainable code with FastAPI. It is a good idea to integrate code formatting into your development workflow and run it regularly to ensure that your code is consistently formatted.

8. Use a Linter for Error Checking and Improved Code Quality: 

Using a linter to automatically check your code for errors and style issues can significantly improve the quality of your code. A linter is a tool that analyzes your code and checks for problems such as syntax errors, formatting issues, and potential bugs. By using a linter, you can catch problems early in the development process and fix them before they become more significant issues. This can help you write more reliable and maintainable code.

One popular linter for Python is Pylint. Here is an example of using Pylint to lint your code in a FastAPI project:

$ pip install pylint
$ pylint my_fastapi_project/

The first line installs the Pylint package, and the second line runs Pylint on the my_fastapi_project directory, checking all Python files in that directory for errors and style issues.

Using a linter like Pylint can help you write high-quality code with FastAPI. It is a good idea to integrate linting into your development workflow and run it regularly to ensure that your code is free of errors and follows best practices.

9. Include Automated Testing in Your Code Development Process: 

Writing tests for your code is an essential step in ensuring that it is correct and maintainable. Tests are automated checks that verify that your code is working as expected and catching any problems early on. By writing tests, you can ensure that your code is reliable and maintainable over time. This is especially important for APIs, as they are often the foundation of other applications and services, and any problems with the API can have cascading effects.

There are many tools and libraries available for writing tests in Python, and one popular choice for testing FastAPI applications is the pytest library. Here is an example of writing a test for a FastAPI endpoint using pytest:

import pytest
from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():
    return {"Hello": "GeeksforGeeks"}


def test_read_root():
    response = app.get("/")
    assert response.status_code == 200
    assert response.json() == {"Hello": "GeeksforGeeks"}

This example shows a test function called test_read_root that sends an HTTP GET request to the / endpoint of the FastAPI app and verifies that the response has a status code of 200 (OK) and a JSON body of {"Hello": "GeeksforGeeks"}. The assert statements are used to check that the actual response matches the expected values.

Run the live server using Uvicorn:

Writing tests is an essential part of the development process, and it can help you write reliable and maintainable code with FastAPI. It is a good idea to write tests for all of your code, including your FastAPI endpoints, to ensure that your application is working as expected and to catch any problems early on.

10. Using a CVCS for Collaboration and Version Control: 

Using a code version control system (CVCS) is an important aspect of building successful APIs, as it allows developers to track changes to the code over time and easily collaborate with other developers. A CVCS saves different versions of the code, called "revisions" so that developers can switch between them as needed. This is particularly useful when working on a large project with multiple developers, as it allows developers to see what changes have been made and by whom.

• One example of using a CVCS with FastAPI is by using Git. Git is a popular open-source CVCS that allows developers to track changes to the code, revert to previous versions if necessary, and collaborate with other developers. To use Git with FastAPI, you would first need to install Git. Then, you would create a new repository for your FastAPI project and commit your initial code to the repository. You can then push your code to a remote repository, such as GitHub, to make it accessible to other developers.
• To track changes to your code with Git, you would make changes to the code, stage the changes, and commit them to the repository. You can then push your committed changes to the remote repository to share them with other developers. Other developers can then pull the changes from the remote repository and merge them into their local copy of the code. This allows multiple developers to work on the same project and collaborate effectively.
• In addition to the tips discussed in this article, it is important to follow best practices for using a CVCS, such as regularly committing changes, creating branches for different features, and merging those branches back into the main codebase. By using a CVCS and following best practices, you can ensure that your code is well-organized, easy to understand, and easy to maintain.

In summary, this article discussed various strategies and techniques for writing efficient and maintainable code with FastAPI, a modern and high-performance web framework for building APIs with Python. By using standard Python type hints for input and output validation, managing dependencies with dependency injection, customizing request and response data with FastAPI's request and response objects, and automating input validation and documentation, developers can ensure their APIs are efficient, maintainable, and well-documented. The article provided examples and explanations of how to use these techniques in a FastAPI application. By following these tips and best practices, developers can easily and efficiently build successful APIs with Python using FastAPI.