Asynchronous IO performance optimization skills in Python web development

Python, as a high-level programming language, has become one of the mainstream choices for web development. However, with the rapid development of the Internet, the data volume and concurrency of web applications are getting higher and higher, which makes the performance of web applications become an important issue. In order to meet business needs and user experience, asynchronous IO performance optimization techniques in Python web development have increasingly become a research hotspot.

Introduction to asynchronous IO

Asynchronous IO refers to the use of non-blocking IO operations in a single thread, and leaves the IO operations to the operating system to complete, in order to process multiple IO requests in the same thread. Purpose. In other words, asynchronous IO processing does not require multi-process, multi-thread and other multi-tasking methods.

Asynchronous IO principle

In Python, asynchronous IO is usually implemented through coroutines. Coroutine is called a lightweight thread that implements alternate execution of multiple tasks through suspension and recovery. In coroutines, we can use asynchronous optimization of IO operations to improve the performance of web applications.

As for coroutines, it is a difficult concept to understand. Here is a simple coroutine example:

import asyncio

async def my_coroutine():
    print('开始执行协程...')
    await asyncio.sleep(1)
    print('协程执行完毕 !')
    
async def main():
    # 第一个协程
    coro1 = my_coroutine()
    # 第二个协程
    coro2 = my_coroutine()
    print("开始执行两个协程...")
    await coro1
    await coro2

# 实例化一个事件循环
loop = asyncio.get_event_loop()
# 执行主协程
loop.run_until_complete(main())

As you can see, in the above example, we created two coroutine objects, Hand them over to the event loop for execution. The event loop can be regarded as a central scheduler that can control the execution sequence and switching of coroutines.

The biggest feature of coroutines and asynchronous IO is that it can "pause" and wait at the IO operation, wait until the IO operation returns the result, and then "wake up" to continue executing subsequent code, so as to avoid the program from being interrupted during the IO operation. Blocking occurs, thereby improving program performance.

Asynchronous IO skills in Python web development

1. Use an asynchronous non-blocking framework: aiohttp

aiohttp It is a Python web framework that supports asynchronous IO. It uses coroutines to implement efficient IO operations and can fully support HTTP/1.1 and WebSocket. This framework is easy to learn and can help developers implement fast asynchronous IO applications.

Some usage methods:

from aiohttp import web

async def handle(request):
    # 获取get参数，即 ?name=test
    name = request.query.get('name', "Anonymous")
    text = "Hello, " + name
    # 返回Response对象
    return web.Response(text=text)

# 初始化web应用，创建一个app对象
app = web.Application()
# 把URL路径'/'和函数句柄handle绑定到一起
app.add_routes([web.get('/', handle)])

# 启动web应用服务
if __name__ == '__main__':
    web.run_app(app, port=8080)

2. Asynchronous optimization of IO-intensive tasks

I/O operations for website data tend to be more CPU-intensive than It is more time-consuming, so asynchronous optimization for IO-intensive tasks can significantly improve the performance of web applications. For example, the following sample code:

import aiohttp
import asyncio

async def fetch(session, url):
    async with session.get(url) as response:
        return await response.text()

async def main():
    async with aiohttp.ClientSession() as session:
        tasks = []
        for i in range(50):
            task = asyncio.create_task(fetch(session, f'https://jsonplaceholder.typicode.com/todos/{i}'))
            tasks.append(task)

        responses = await asyncio.gather(*tasks)
        # do something with responses...

# 实例化一个事件循环
loop = asyncio.get_event_loop()
# 执行异步任务
loop.run_until_complete(main())

In the above example, we used Python’s asynchronous library aiohttp, and used coroutines to implement requests for 50 JSON data in the same thread, thus Efficient multi-task concurrent execution is achieved in a short period of time.

3. Using an asynchronous database driver

Database operations are often one of the performance bottlenecks in web applications. We can use the asynchronous database driver provided in Python, such as aiomysql , asyncpg, etc., realize asynchronous operations on the database, thereby improving the performance of web applications.

import asyncio
import aiomysql

async def test_example():
    async with aiomysql.create_pool(host='localhost', user='root',
                                    password='', db='test', charset='utf8mb4',
                                    autocommit=True) as pool:
           async with pool.acquire() as conn:
               async with conn.cursor() as cur:
                    await cur.execute("select id, name from tb_user where id=1")
                    result = await cur.fetchone()
                    print(result)

if __name__ == '__main__':
    # 实例化一个事件循环
    loop = asyncio.get_event_loop()
    # 执行异步任务
    loop.run_until_complete(test_example())

The above code shows how to use the aiomysql library to perform asynchronous MySQL database operations. Since database operations are usually very slow, we usually use the async with syntax structure to complete it. Coroutine control; if the code needs to perform multiple database operations, then we can use connection pooling and other methods to improve the performance of MySQL operations.

Summary

Asynchronous IO in Python can be said to be a major advantage in web development and can help us quickly implement efficient web applications. During the development process, we can use asynchronous non-blocking frameworks, concurrency optimization for IO-intensive tasks, and asynchronous database drivers to improve the performance of web applications.

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