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Python crawler multi-threading detailed explanation and example code
Python crawler multi-threading detailed explanation and example code
Python supports multi-threading, mainly through the two modules thread and threading. The thread module is a relatively low-level module, and the threading module provides some packaging for thread, making it more convenient to use.
Although python's multi-threading is limited by GIL and is not true multi-threading, it can still significantly improve efficiency for I/O-intensive calculations, such as crawlers.
An example is used below to verify the efficiency of multi-threading. The code only involves page acquisition and does not parse it.
# -*-coding:utf-8 -*- import urllib2, time import threading class MyThread(threading.Thread): def __init__(self, func, args): threading.Thread.__init__(self) self.args = args self.func = func def run(self): apply(self.func, self.args) def open_url(url): request = urllib2.Request(url) html = urllib2.urlopen(request).read() print len(html) return html
if __name__ == '__main__':
# 构造url列表
urlList = []
for p in range(1, 10):
urlList.append('http://s.wanfangdata.com.cn/Paper.aspx?q=%E5%8C%BB%E5%AD%A6&p=' + str(p))
# 一般方式 n_start = time.time() for each in urlList: open_url(each) n_end = time.time() print 'the normal way take %s s' % (n_end-n_start)
# 多线程 t_start = time.time() threadList = [MyThread(open_url, (url,)) for url in urlList] for t in threadList: t.setDaemon(True) t.start() for i in threadList: i.join() t_end = time.time() print 'the thread way take %s s' % (t_end-t_start)
Use two methods to obtain 10 web pages with relatively slow access speed. The general method takes 50 seconds, and multi-threading takes 10 seconds.
Interpretation of multi-threaded code:
# 创建线程类，继承Thread类 class MyThread(threading.Thread): def __init__(self, func, args): threading.Thread.__init__(self) # 调用父类的构造函数 self.args = args self.func = func def run(self): # 线程活动方法 apply(self.func, self.args)
threadList = [MyThread(open_url, (url,)) for url in urlList] # 调用线程类创建新线程，返回线程列表 for t in threadList: t.setDaemon(True) # 设置守护线程，父线程会等待子线程执行完后再退出 t.start() # 线程开启 for i in threadList: i.join() # 等待线程终止，等子线程执行完后再执行父线程
The above is the entire content of this article, I hope it will be helpful to everyone’s study.
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