总体思路

使用多线程爬虫可以提高爬取和储存的速度，虽然python中的线程是假的，但对于io操作来说，多线程是起作用的。
总体思路用生产者与消费者的模型来设计。

1. 将要爬取的url放入urlQUeue的队列中
2. 负责爬取网页信息的工人（线程），从url队列获取url，进行请求，把爬取的网页信息放入一个dataQueue的队列中。
3. 负责解析的工人，从dataQueue中获取网页信息，进行解析后，存储。

# coding=utf8
import urllib
import urllib2
from lxml import etree
import json
from threading import Thread
from Queue import Queue

CRAWL_EXIT = False
PARSE_EXIT = False

class ThreadCrawl(Thread):
    def __init__(self, threadName, pageQueue, dataQueue):
        super(ThreadCrawl, self).__init__()
        self.pageQueue = pageQueue
        self.dataQueue = dataQueue
        self.threadName = threadName
        self.headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36'}

    def run(self):
        print '%s启动' %self.threadName
        while not CRAWL_EXIT:
            try:
                # 默认block为true，当队列空时堵塞，直到有新的元素加入队列
                page = self.pageQueue.get()
                url = 'https://www.qiushibaike.com/text/page/%d/' % page
                request = urllib2.Request(url, headers=self.headers)
                response = urllib2.urlopen(request).read()
                self.dataQueue.put(response)
            except:
                pass


class ThreadParse(Thread):
    def __init__(self, parseName, dataQueue, fileName):
        super(ThreadParse, self).__init__()
        self.dataQueue = dataQueue
        self.parseName = parseName
        self.fileName = fileName

    def run(self):
        print '%s启动' %self.parseName
        while not PARSE_EXIT:
            try:
                html = self.dataQueue.get(False)
                self.parse(html)
            except:
                pass

    def parse(self, html):
        text = etree.HTML(html)

        # 创建 模糊查询的根节点，包含每条段子的全部信息
        node_list = text.xpath('//div[contains(@id,"qiushi_tag")]')

        items = {}
        for node in node_list:
            # 内容,取出标签下的内容 第一个标签 text
            content = node.xpath('.//div[@class="content"]/span')[0].text

            # 用户名
            try:
                username = node.xpath('./div[1]/a[2]/h2')[0].text
            except:
                print '没有用户'
            items = {'username': username,
                     'content': content}
            self.fileName.write(json.dumps(items, ensure_ascii=False).encode('utf8') + '\n')


def main():
    # 页码队列
    pageQueue = Queue(10)
    for i in range(1, 11):
        pageQueue.put(i)

    # 表示采集好的html源码队列
    dataQueue = Queue()

    crawlList = ['采集线程一号', '采集线程二号', '采集线程三号']

    # 启动三个采集线程
    thread_carwl = []
    for tname in crawlList:
        thread = ThreadCrawl(tname, pageQueue, dataQueue)
        thread.start()
        thread_carwl.append(thread)

    praseList = ['解析线程一号', '解析线程二号', '解析线程三号']
    prase_thread = []
    fileName = open('duanzi.json', 'a')
    for tname in praseList:
        thread = ThreadParse(tname, dataQueue, fileName)
        thread.start()
        prase_thread.append(thread)

    # 页码对列不为空时
    while not pageQueue.empty():
        pass

    global CRAWL_EXIT
    CRAWL_EXIT = True

    while not dataQueue.empty():
        pass
    global PARSE_EXIT
    PARSE_EXIT = True

    # 主线程堵塞，等待采集线程完成
    for thread in thread_carwl:
        thread.join()
        print('采集完成')

    for thread in prase_thread:
        thread.join()
        print('写入完成')

    fileName.close()


if __name__ == '__main__':
    main()