对于IO密集型任务来说，多线程仍然有一定的效果。因为在I/O请求的时候，当一个线程因I/O请求阻塞，可以调用另一个线程来继续发起对另一个Url的请求。当第一个线程的I/O请求得到回应时，它再次得到运行。

multi_thread.py

#! /usr/bin/env python
# -*- coding: utf-8 -*-
# @Last Modified time: 2016-06-28 10:50:58

import urllib2
import time
from threading import Thread

# 在大量访问url的时候，多线程很有优势，实际上也是空间换时间
URLs = ["http://www.baidu.com",
        "https://www.taobao.com/",
        "http://weibo.com/hi/",
        "http://www.jd.com/"
        ]


def normal_way():
    start = time.time()
    for url in URLs:
        urllib2.urlopen(url)
    print time.time() - start


class URL(Thread):
    """
    定义多线程访问url的类
    """

    def __init__(self, url):
        self.url = url
        super(URL, self).__init__()

    def run(self):
        urllib2.urlopen(self.url)


def multi_process_way():
    num = len(URLs)
    threads = []
    start = time.time()
    for i in xrange(num):
        t = URL(URLs[i])
        threads.append(t)
        t.start()
    for i in xrange(num):
        threads[i].join()  # 等线程执行完毕再执行主线程，保证时间上的统计是没问题的
    print time.time() - start


if __name__ == '__main__':
    normal_way()
    multi_process_way()