Python3多线程爬虫实例讲解代码

发布日期:2018-01-07 06:33:51来源:初学者
[导读]本篇文章主要介绍了Python3多线程爬虫实例讲解代码,Python3使用threading模块来进行多线程,小编觉得挺不错的,现在分享给大家,也给大家做个参考。一起跟随小编过来看看吧

多线程概述

多线程使得程序内部可以分出多个线程来做多件事情,充分利用CPU空闲时间,提升处理效率。python提供了两个模块来实现多线程thread 和threading ,thread 有一些缺点,在threading 得到了弥补。并且在Python3中废弃了thread模块,保留了更强大的threading模块。

使用场景

在python的原始解释器CPython中存在着GIL(Global Interpreter Lock,全局解释器锁),因此在解释执行python代码时,会产生互斥锁来限制线程对共享资源的访问,直到解释器遇到I/O操作或者操作次数达到一定数目时才会释放GIL。所以,虽然CPython的线程库直接封装了系统的原生线程,但CPython整体作为一个进程,同一时间只会有一个获得GIL的线程在跑,其他线程则处于等待状态。这就造成了即使在多核CPU中,多线程也只是做着分时切换而已。

如果你的程序是CPU密集型,多个线程的代码很有可能是线性执行的。所以这种情况下多线程是鸡肋,效率可能还不如单线程因为有上下文切换开销。但是如果你的代码是IO密集型,涉及到网络、磁盘IO的任务都是IO密集型任务,多线程可以明显提高效率,例如多线程爬虫,多线程文件处理等等

多线程爬虫

多线程爬虫的代码实例

注: 以下代码在python3下运行通过, python2版本差异较大,不能运行成功,如需帮助请下方留意。

# coding=utf-8
import threading, queue, time, urllib
from urllib import request
baseUrl = 'http://www.pythontab.com/html/pythonjichu/'
urlQueue = queue.Queue()
for i in range(2, 10):
 url = baseUrl + str(i) + '.html'
 urlQueue.put(url)
 #print(url)
def fetchUrl(urlQueue):
 while True:
  try:
   #不阻塞的读取队列数据
   url = urlQueue.get_nowait()
   i = urlQueue.qsize()
  except Exception as e:
   break
  print ('Current Thread Name %s, Url: %s ' % (threading.currentThread().name, url))
  try:
   response = urllib.request.urlopen(url)
   responseCode = response.getcode()
  except Exception as e:
   continue
  if responseCode == 200:
   #抓取内容的数据处理可以放到这里
   #为了突出效果, 设置延时
   time.sleep(1)
if __name__ == '__main__':
 startTime = time.time()
 threads = []
 # 可以调节线程数, 进而控制抓取速度
 threadNum = 4
 for i in range(0, threadNum):
  t = threading.Thread(target=fetchUrl, args=(urlQueue,))
  threads.append(t)
 for t in threads:
  t.start()
 for t in threads:
  #多线程多join的情况下,依次执行各线程的join方法, 这样可以确保主线程最后退出, 且各个线程间没有阻塞
  t.join()
 endTime = time.time()
 print ('Done, Time cost: %s ' % (endTime - startTime))

运行结果:

1个线程时:

Current Thread Name Thread-1, Url: http://www.pythontab.com/html/pythonjichu/2.html 
Current Thread Name Thread-1, Url: http://www.pythontab.com/html/pythonjichu/3.html 
Current Thread Name Thread-1, Url: http://www.pythontab.com/html/pythonjichu/4.html 
Current Thread Name Thread-1, Url: http://www.pythontab.com/html/pythonjichu/5.html 
Current Thread Name Thread-1, Url: http://www.pythontab.com/html/pythonjichu/6.html 
Current Thread Name Thread-1, Url: http://www.pythontab.com/html/pythonjichu/7.html 
Current Thread Name Thread-1, Url: http://www.pythontab.com/html/pythonjichu/8.html 
Current Thread Name Thread-1, Url: http://www.pythontab.com/html/pythonjichu/9.html 
Done, Time cost: 8.182249069213867

2个线程时:

Current Thread Name Thread-1, Url: http://www.pythontab.com/html/pythonjichu/2.html 
Current Thread Name Thread-2, Url: http://www.pythontab.com/html/pythonjichu/3.html 
Current Thread Name Thread-2, Url: http://www.pythontab.com/html/pythonjichu/4.html 
Current Thread Name Thread-1, Url: http://www.pythontab.com/html/pythonjichu/5.html 
Current Thread Name Thread-2, Url: http://www.pythontab.com/html/pythonjichu/6.html 
Current Thread Name Thread-1, Url: http://www.pythontab.com/html/pythonjichu/7.html 
Current Thread Name Thread-2, Url: http://www.pythontab.com/html/pythonjichu/8.html 
Current Thread Name Thread-1, Url: http://www.pythontab.com/html/pythonjichu/9.html 
Done, Time cost: 4.0987958908081055

3个线程时:

Current Thread Name Thread-1, Url: http://www.pythontab.com/html/pythonjichu/2.html 
Current Thread Name Thread-2, Url: http://www.pythontab.com/html/pythonjichu/3.html 
Current Thread Name Thread-3, Url: http://www.pythontab.com/html/pythonjichu/4.html 
Current Thread Name Thread-4, Url: http://www.pythontab.com/html/pythonjichu/5.html 
Current Thread Name Thread-2, Url: http://www.pythontab.com/html/pythonjichu/6.html 
Current Thread Name Thread-4, Url: http://www.pythontab.com/html/pythonjichu/7.html 
Current Thread Name Thread-1, Url: http://www.pythontab.com/html/pythonjichu/9.html 
Current Thread Name Thread-3, Url: http://www.pythontab.com/html/pythonjichu/8.html 
Done, Time cost: 2.287320137023926

通过调节线程数可以看到,执行时间会随着线程数的增加而缩短,抓取效率成正比增加。

总结:

Python多线程在IO密集型任务,多线程可以明显提高效率,CPU密集型任务不适合使用多线程处理。

以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持初学者。

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