How to use the FastAPI framework to build high-performance data APIs

How to use the FastAPI framework to build a high-performance data API

Introduction:
In today's Internet era, building a high-performance data API is the key to achieving fast response and availability. The key to scalability. The FastAPI framework is a high-performance web framework in Python that helps developers quickly build high-quality APIs. This article will guide readers to understand the basic concepts of the FastAPI framework and provide sample code to help readers quickly build high-performance data APIs.

1. Introduction to FastAPI framework
FastAPI is a high-performance web framework based on the Starlette framework. It combines the latest technology of Python3.6 and uses advanced features such as type hints and asynchronous support. FastAPI has significant advantages in performance and ease of use, and is widely used to build high-performance data APIs.

2. Install the FastAPI framework
Before we start, we need to install the FastAPI framework. Open a terminal window and execute the following command:

$ pip install fastapi
$ pip install uvicorn

The above command will install the FastAPI framework and its dependent uvicorn server.

3. Build the first FastAPI application
The following example will demonstrate how to build a simple data API through the FastAPI framework. We will build an API for student information, including getting a list of students, getting individual student information, and adding new students. Create a Python file named main.py in the terminal window and write the following code:

from fastapi import FastAPI
from pydantic import BaseModel

class Student(BaseModel):
    id: int
    name: str
    age: int

app = FastAPI()

students = []

@app.get("/students")
async def get_students():
    return students

@app.get("/students/{student_id}")
async def get_student(student_id: int):
    for student in students:
        if student["id"] == student_id:
            return student
    return {"message": "Student not found"}

@app.post("/students")
async def create_student(student: Student):
    students.append(student)
    return student

In the above code, we first introduced the FastAPI and pydantic modules. Then a class named Student is defined, which inherits from BaseModel and is used to define the student's data structure. Next, we create a FastAPI application instance and initialize an empty student list.

In the get_students() function, an HTTP GET request handler is defined using the @app.get decorator, which is used to obtain the student list. Use the @app.get decorator to tell the FastAPI framework the HTTP request method corresponding to the function.

Similarly, we also use the @app.get decorator to define the get_student() function, which is used to obtain the information of a single student. In this function, we search based on the passed in student ID and return the corresponding student information.

Finally, we define the create_student() function through the @app.post decorator, which is used to add new student information. In this function, we add the received student object to the students list.

4. Run the FastAPI application
Execute the following command in the terminal window to start the FastAPI application:

$ uvicorn main:app --reload

The above command will start a uvicorn server and listen to the local 8000 port. After successful startup, you can access http://localhost:8000/students in a browser or HTTP client to test the functionality of the API interface.

Conclusion:
Through the introduction of this article, we have understood the basic concepts and usage of the FastAPI framework, and learned how to build a high-performance data API through a simple example. Using the FastAPI framework can help developers quickly build high-performance data APIs and provides many practical features and functions. I hope this article can help readers understand and use the FastAPI framework.

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