Python协程实战：async/await深度解析-优快云博客

Python协程实战：async/await深度解析

引言

在Python开发中，协程是实现异步编程的核心技术。作为一名从Rust转向Python的后端开发者，我深刻体会到协程在并发编程方面的优势。Python 3.5+引入的async/await语法使得协程编程变得更加简洁和直观。

协程核心概念

什么是协程

协程是一种轻量级的并发编程方式，具有以下特点：

轻量级：协程比线程更轻量，创建和切换开销小
协作式：协程主动让出控制权
异步IO：支持异步读写操作
代码简洁：使用async/await语法
高并发：支持大量并发任务

架构设计

┌─────────────────────────────────────────────────────────────┐
│                      协程架构                              │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐ │
│  │   事件循环   │───▶│   协程调度   │───▶│   任务队列   │ │
│  │ (Event Loop)│    │ (Scheduler)  │    │ (Task Queue)│ │
│  └──────────────┘    └──────────────┘    └──────────────┘ │
│          │                  │                              │
│          ▼                  ▼                              │
│  ┌──────────────────────────────────────────────────────┐  │
│  │              async/await 语法与协程执行                  │  │
│  └──────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────┘

环境搭建与基础配置

基本协程

import asyncio

async def hello():
    print("Hello")
    await asyncio.sleep(1)
    print("World")

asyncio.run(hello())

协程函数

import asyncio

async def fetch_data():
    print("Start fetching")
    await asyncio.sleep(2)
    print("Data fetched")
    return {"data": 42}

async def main():
    result = await fetch_data()
    print(result)

asyncio.run(main())

高级特性实战

并发执行

import asyncio

async def task1():
    await asyncio.sleep(2)
    return "Task 1 completed"

async def task2():
    await asyncio.sleep(1)
    return "Task 2 completed"

async def task3():
    await asyncio.sleep(3)
    return "Task 3 completed"

async def main():
    results = await asyncio.gather(task1(), task2(), task3())
    print(results)

asyncio.run(main())

任务取消

import asyncio

async def long_running_task():
    try:
        while True:
            print("Running...")
            await asyncio.sleep(1)
    except asyncio.CancelledError:
        print("Task cancelled")

async def main():
    task = asyncio.create_task(long_running_task())
    await asyncio.sleep(3)
    task.cancel()
    await task

asyncio.run(main())

超时处理

import asyncio

async def slow_operation():
    await asyncio.sleep(5)
    return "Done"

async def main():
    try:
        result = await asyncio.wait_for(slow_operation(), timeout=2)
        print(result)
    except asyncio.TimeoutError:
        print("Timeout")

asyncio.run(main())

实际业务场景

场景一：并发HTTP请求

import asyncio
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://google.com',
        'https://github.com'
    ]
    
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_url(session, url) for url in urls]
        results = await asyncio.gather(*tasks)
        for url, content in zip(urls, results):
            print(f"{url}: {len(content)} bytes")

asyncio.run(main())

场景二：数据库操作

import asyncio
import asyncpg

async def query_database():
    conn = await asyncpg.connect(user='user', password='pass', database='db')
    values = await conn.fetch('SELECT * FROM users')
    await conn.close()
    return values

asyncio.run(query_database())

性能优化

使用任务池

import asyncio

async def process_item(item):
    await asyncio.sleep(0.1)
    return item * 2

async def main():
    items = range(100)
    tasks = [process_item(item) for item in items]
    results = await asyncio.gather(*tasks)
    print(results)

asyncio.run(main())

限制并发数

import asyncio

async def worker(queue):
    while True:
        item = await queue.get()
        await asyncio.sleep(0.1)
        print(f"Processed: {item}")
        queue.task_done()

async def main():
    queue = asyncio.Queue()
    
    for i in range(100):
        queue.put_nowait(i)
    
    workers = [asyncio.create_task(worker(queue)) for _ in range(10)]
    
    await queue.join()
    
    for w in workers:
        w.cancel()

asyncio.run(main())