Python异步编程实战：5个高效处理IO密集型任务的技巧

1. 基础异步函数与事件循环

理解async/await基本用法：

import asyncio

async def fetch_data():
    print("开始获取数据")
    await asyncio.sleep(2)  # 模拟IO操作
    print("数据获取完成")
    return {"data": 123}

async def main():
    task = asyncio.create_task(fetch_data())
    print("执行其他任务")
    result = await task
    print(f"获取结果: {result}")

asyncio.run(main())

2. 并发执行多个任务

使用gather同时运行多个协程：

async def download_page(url):
    print(f"开始下载: {url}")
    await asyncio.sleep(1)  # 模拟网络请求
    print(f"完成下载: {url}")
    return f"{url}的内容"

async def main():
    urls = ["url1", "url2", "url3"]
    tasks = [download_page(url) for url in urls]
    results = await asyncio.gather(*tasks)
    print(f"所有页面下载完成: {results}")

asyncio.run(main())

3. 异步文件操作

使用aiofiles处理文件IO：

import aiofiles

async def async_write_file():
    async with aiofiles.open('data.txt', mode='w') as f:
        await f.write("异步写入内容")
        print("文件写入完成")

async def async_read_file():
    async with aiofiles.open('data.txt', mode='r') as f:
        content = await f.read()
        print(f"文件内容: {content}")

async def main():
    await async_write_file()
    await async_read_file()

asyncio.run(main())

4. 异步HTTP客户端

使用aiohttp发送并发请求：

import aiohttp

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

async def main():
    urls = [
        "https://example.com",
        "https://example.org",
        "https://example.net"
    ]
    
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_url(session, url) for url in urls]
        results = await asyncio.gather(*tasks)
        print(f"获取到{len(results)}个页面的内容")

asyncio.run(main())

5. 异步数据库访问

import asyncpg

async def query_database():
    conn = await asyncpg.connect(
        user='user', password='pass',
        database='db', host='localhost')
    
    # 执行查询
    result = await conn.fetch('SELECT * FROM users WHERE id = $1', 1)
    print(f"查询结果: {result}")
    
    await conn.close()

asyncio.run(query_database())

通过合理使用异步编程，Python应用处理IO密集型任务的性能可提升5-10倍，是现代Python开发者必须掌握的核心技能。