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Python异步编程高并发执行爬虫采集，用回调函数解析响应

news 2025/7/19 22:36:49

一、问题：当发送API请求，读写数据库任务较重时，程序运行效率急剧下降。

异步技术是Python编程中对提升性能非常重要的一项技术。在实际应用，经常面临对外发送网络请求，调用外部接口，或者不断更新数据库或文件等操作。这这些操作，通常90%以上时间是在等待，如通过REST, gRPC向服务器发送请求，通常可能等待几十毫秒至几秒，甚至更长。如果业务较重，按顺序执行编程，会导致大量时间用在等待上，程序运行效率急剧下降。
常见的场景，就是爬虫软件通常会发起很多请求，如果采用同步编程方式工，往往运行时间很长。

二、异步编程的优势

通常的编程，如果有4个任务，采用同步编程模式，4个任务是按顺序执行的，分别用时:10s，7s，5s，6s，共耗时28s; 而异步方式，就是让4个任务同时执行，总耗时降为10s，改善效果是很明显的。
在这里插入图片描述

那时异步编程是如何做到的？

异步编程，将每个任务改成协程执行，在遇到需要等待的语句时，即暂时将执行权交还给主程序的控制循环event loop，其它协程可以继续使用CPU等资源。而当该协程收到响应后，会用事件通知event loop，申请继续执行。这样就避免了由于等待期间还占用CPU资源的情形。因此程序执行效率大为提高。

但如果任务是计算密集型的，那么异步技术对性能提升帮助不大，需要采用其它方式，如多进程编程。或者Cython 等。

三、用同步编程方式，抓取多个网站数据

先看一下，采用同步编程顺序执行，抓取多个网站数据的耗时。这些网站中，
其中http://www.google.com 是无响应的，会超时。因此在 requests.get()方法，设置 timeout=3, 即超过3秒，会抛出TimeOutException 异常。

代码如下：


import requests
import time# 测试时将测试网址替换
urls = ["http://www.bxxxx.com","http://www.aaaa.com","http://www.bbbb.com","http://www.cccc.com","http://www.sdddd.com","http://www.jdddd.com","http://www.zeeee.com","http://www.tffff.com","http://www.cgggg.com","http://www.zhhhhh.com.cn","http://www.google.com","https://www.yiiiii.com/",
]def check_one_ip(url):headers = {"user-ageng": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 \(KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36 Edg/116.0.1938.69"}TIMEOUT = 3result = ()try:response = requests.get(url, headers=headers, timeout=TIMEOUT)print(f"response from {url} is : {response.status_code}")if 200 <= response.status_code < 300:print(f"length of response body is {len(response.text)}")result = (url, response.status_code)except Exception as e:print(f"{url} met timeout error")return (url, 999)return resultdef main():results = []for url in urls:result = check_one_ip(url)results.append(result)if __name__ == "__main__":t1 = time.time()main()t2 = time.time()print(f"total time: {t2-t1:.3f}s")

运行代码，向12个网站发送request, 打印response的状态码，总耗时为：6.035s,

response from url is : 200
length of response body is 2381
response from url is : 200
length of response body is 24000
response from url is : 200
length of response body is 106117
response from url is : 403
response from url is : 404
response from url is : 200
length of response body is 177104
response from url is : 200
length of response body is 37989
response from url is : 200
length of response body is 89513
response from url is : 200
length of response body is 32642
response from url is : 403
url met timeout error
response from url is : 200
length of response body is 834
total time: 6.035s

四、用异步方式，同时抓取多个网站数据

现在，采用Asyncio异步编程，以并发的运行方式，向多个网站同时发送request, 总耗时，应该是用时最长那个协程的用时。这里我们使用了timeout, 就是3秒左右。

AsyncIO异步编程步骤：

定义异步任务函数
使用 asyc / await 关键字。在耗时操作前加await
创建asyncio.create_task() 方法创建协程任务
在main()方法中用gather() 汇集协程任务，以便并发执行。
gather()方法返回结果是一个由所有返回值聚合而成的迭代器
在主线程的event loop中运行main()
asyncio模块提供了1个.run()来启动 event loop 异步控制循环，并执行main()方法，
可选，给协程添加回调函数来解析网站响应结果
对于每个Task, 可用 add_done_callback(task_callback) 方法添加回调函数，此例中，对显示response的状态码。

其它说明

由于requests库的 response对象不支持 await语句，因此这里使用htppx 库来代替requests，除了异步接口外，其它使用方式完全一致。

完整代码

import asyncio
import httpx
from concurrent.futures import ThreadPoolExecutor, Future
import time
import contextvars# 测试时将测试网址替换
urls = ["http://www.bxxxx.com","http://www.aaaa.com","http://www.bbbb.com","http://www.cccc.com","http://www.sdddd.com","http://www.jdddd.com","http://www.zeeee.com","http://www.tffff.com","http://www.cgggg.com","http://www.zhhhhh.com.cn","http://www.google.com","https://www.yiiiii.com/",
]async def check_one_ip(url):headers = {"user-ageng": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 \(KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36 Edg/116.0.1938.69"}TIMEOUT = 3result = ()try:async with httpx.AsyncClient() as client:response = await client.get(url, headers=headers,timeout=TIMEOUT)print(f"response from {url} is : {response.status_code}")if 200 <= response.status_code < 300:print(f"length of response body is {len(response.text)}")result = (url, response.status_code)except  Exception as e:print(f"{url} met timeout error")return (url, 999)return result def task_callback(context):# print response.status_code url, code = context.result()print(f"It is callback,  got status_code: {code} of {url}")async def main():tasks=[]for url in urls:task = asyncio.create_task(check_one_ip(url))task.add_done_callback(task_callback)tasks.append(task)await asyncio.gather(*tasks) if __name__=="__main__":t1 = time.time()asyncio.run(main())t2 = time.time()print(f"total time: {t2-t1:.3f}s")

运行结果如下，可以看到，总耗时： 3.161s，相比同步编程方式，耗时减少了1半。随着发送请求量的增加，可以看到更加明显的效果。

response from url is : 302
It is callback,  got status_code: 302 of url
response from url is : 302
It is callback,  got status_code: 302 of url
response from url is : 200
length of response body is 23508
It is callback,  got status_code: 200 of url
response from url is : 302
response from url is : 301
It is callback,  got status_code: 302 of url
It is callback,  got status_code: 301 of url
response from url is : 301
response from url is : 301
response from url is : 301
response from url is : 200
length of response body is 396837
It is callback,  got status_code: 301 of url
It is callback,  got status_code: 301 of url
It is callback,  got status_code: 301 of url
It is callback,  got status_code: 200 of url
response from url is : 404
It is callback,  got status_code: 404 of url
response from url is : 200
length of response body is 1151330
It is callback,  got status_code: 200 of url
url met timeout error
It is callback,  got status_code: 999 of url
total time: 3.161s