一、背景介绍

传统同步爬虫效率有限，遇到大量网页时容易阻塞。
借助 Python 的 Asyncio 结合 aiohttp，实现异步非阻塞爬虫，大幅提升爬取效率。

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二、环境准备

bash

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pip install aiohttp asyncio

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三、基础示例

python

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import asyncio import aiohttp async def fetch(session, url): async with session.get(url) as response: return await response.text() async def main(): urls = ["https://httpbin.org/get" for _ in range(10)] async with aiohttp.ClientSession() as session: tasks = [fetch(session, url) for url in urls] results = await asyncio.gather(*tasks) for i, content in enumerate(results): print(f"第{i+1}个请求内容长度: {len(content)}") asyncio.run(main())

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四、控制并发数（限速）

使用 asyncio.Semaphore 控制同时运行任务数量：

python

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sem = asyncio.Semaphore(5) # 同时最多5个请求 async def fetch_with_sem(session, url): async with sem: async with session.get(url) as response: return await response.text()

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五、异常处理与重试

python

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import async_timeout async def fetch(session, url): try: async with async_timeout.timeout(10): async with session.get(url) as response: return await response.text() except Exception as e: print(f"请求 {url} 失败: {e}") return None

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六、结合代理、User-Agent 伪装

python

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headers = {"User-Agent": "Mozilla/5.0 ..."} proxy = "http://127.0.0.1:8080" async with aiohttp.ClientSession(headers=headers) as session: async with session.get(url, proxy=proxy) as resp: ...

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七、实战小贴士

• 使用异步数据库或缓存存储结果，提高效率

• 利用队列管理 URL，实现广度优先爬取

• 合理设置超时与重试，避免爬虫崩溃

• 遵守网站 robots.txt 和法律法规

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八、总结

技术点	作用
Asyncio	异步事件循环，提升并发
aiohttp	异步 HTTP 客户端
Semaphore	控制并发量，避免压力大
async_timeout	请求超时控制

利用 Asyncio + aiohttp，Python 爬虫效率可提升数倍，适合大规模数据采集。

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