


Build high-performance, asynchronous web applications using FastAPI
Use FastAPI to build high-performance, asynchronous Web applications
With the rapid development of the Internet, the performance and efficiency of Web applications have become one of the focuses of users. Traditional web frameworks are often inefficient when handling a large number of requests and cannot meet high concurrency requirements. In order to improve the performance and efficiency of web applications, FastAPI came into being.
FastAPI is a modern Python-based web framework that maintains simplicity and ease of use while delivering outstanding performance. FastAPI adopts an asynchronous programming model and uses Python's coroutines and asynchronous IO mechanisms to enable applications to efficiently handle a large number of concurrent requests. The following will introduce how to use FastAPI to build a high-performance, asynchronous web application.
- Install FastAPI
First, you need to use the pip command to install FastAPI:
pip install fastapi
- Writing code
Next, create a Python file, such as main.py, and write the following code:
from fastapi import FastAPI app = FastAPI() @app.get("/") async def root(): return {"message": "Hello, World!"}
The above code creates a FastAPI application and defines a GET request route, "/" represents the root directory. When accessing the root directory, a JSON response containing "Hello, World!" will be returned.
- Run the application
Use uvicorn to run the FastAPI application:
uvicorn main:app --reload
Now, the FastAPI application is running. Open the browser and visit http://localhost:8000, and you will see the "Hello, World!" response.
- Writing asynchronous processing functions
FastAPI supports the use of asynchronous processing functions to process requests. The following is an example of using an asynchronous processing function:
from fastapi import FastAPI import asyncio app = FastAPI() async def background_task(): while True: print("Running background task...") await asyncio.sleep(1) @app.get("/") async def root(): asyncio.create_task(background_task()) return {"message": "Hello, World!"}
In the above code, we define an asynchronous task background_task(), which prints "Running background task..." every second. In the handler function of the root route "/", we use asyncio.create_task() to create a background task. In this way, when the root directory is accessed, the execution of background_task() will be started at the same time.
- Processing request parameters
FastAPI supports passing parameters through URL path parameters, query parameters, request bodies, etc., which is very flexible. The following is an example of using URL path parameters and query parameters:
from fastapi import FastAPI app = FastAPI() @app.get("/items/{item_id}") async def read_item(item_id: int, q: str = None): return {"item_id": item_id, "q": q}
In the above code, we define a GET request route with the path parameter item_id and the query parameter q. When accessing, for example, /items/42?q=test, the following response will be returned:
{ "item_id": 42, "q": "test" }
- Asynchronous database operation
FastAPI inherently supports asynchronous operations and can be easily used with Asynchronous database interaction. The following is an example of using asynchronous database operations:
from fastapi import FastAPI from databases import Database app = FastAPI() database = Database("sqlite:///test.db") @app.on_event("startup") async def startup(): await database.connect() @app.on_event("shutdown") async def shutdown(): await database.disconnect() @app.get("/") async def root(): query = "SELECT * FROM items" items = await database.fetch_all(query) return {"items": items}
In the above code, we use the databases library to create a SQLite database connection and perform connection and disconnection operations when the application starts and shuts down. In the root route's handler function, we execute a SELECT query and return the results.
- Deploying applications
Use tools such as uvicorn, Gunicorn, etc. to deploy FastAPI applications to the production environment. For example, use Gunicorn to deploy a FastAPI application:
gunicorn -w 4 -k uvicorn.workers.UvicornWorker main:app
The above command will start 4 processes and use UvicornWorker to handle requests.
Summary
FastAPI is a very powerful modern Web framework that can help us build high-performance, asynchronous Web applications. By leveraging Python's asynchronous programming features, we can easily handle large numbers of concurrent requests and achieve efficient database operations. Whether you are developing a personal project or building an enterprise-level application, FastAPI is an option worth trying.
The above is the detailed content of Build high-performance, asynchronous web applications using FastAPI. For more information, please follow other related articles on the PHP Chinese website!

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

Python's real-world applications include data analytics, web development, artificial intelligence and automation. 1) In data analysis, Python uses Pandas and Matplotlib to process and visualize data. 2) In web development, Django and Flask frameworks simplify the creation of web applications. 3) In the field of artificial intelligence, TensorFlow and PyTorch are used to build and train models. 4) In terms of automation, Python scripts can be used for tasks such as copying files.

Python is widely used in data science, web development and automation scripting fields. 1) In data science, Python simplifies data processing and analysis through libraries such as NumPy and Pandas. 2) In web development, the Django and Flask frameworks enable developers to quickly build applications. 3) In automated scripts, Python's simplicity and standard library make it ideal.

Python's flexibility is reflected in multi-paradigm support and dynamic type systems, while ease of use comes from a simple syntax and rich standard library. 1. Flexibility: Supports object-oriented, functional and procedural programming, and dynamic type systems improve development efficiency. 2. Ease of use: The grammar is close to natural language, the standard library covers a wide range of functions, and simplifies the development process.

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.

Yes, learn Python in two hours a day. 1. Develop a reasonable study plan, 2. Select the right learning resources, 3. Consolidate the knowledge learned through practice. These steps can help you master Python in a short time.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Safe Exam Browser
Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.

WebStorm Mac version
Useful JavaScript development tools

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.

MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

Atom editor mac version download
The most popular open source editor