多线程爬虫案例

Queue（队列对象）

Queue是python中的标准库，可以直接import Queue引用;队列是线程间最常用的交换数据的形式

python下多线程的思考

对于资源，加锁是个重要的环节。因为python原生的list,dict等，都是not thread safe的。而Queue，是线程安全的，因此在满足使用条件下，建议使用队列

1. 初始化： class Queue.Queue(maxsize) FIFO 先进先出

2. 包中的常用方法:

   • Queue.qsize() 返回队列的大小

   • Queue.empty() 如果队列为空，返回True,反之False

   • Queue.full() 如果队列满了，返回True,反之False

   • Queue.full 与 maxsize 大小对应

   • Queue.get([block[, timeout]])获取队列，timeout等待时间

3. 创建一个“队列”对象

   • import Queue
   • myqueue = Queue.Queue(maxsize = 10)

4. 将一个值放入队列中

   • myqueue.put(10)

5. 将一个值从队列中取出

   • myqueue.get()
   

多线程示意图

# -*- coding:utf-8 -*-

# 使用了线程库
import threading
# 队列
from Queue import Queue
# 解析库
from lxml import etree
# 请求处理
import requests
# json处理
import json
import time


class ThreadCrawl(threading.Thread):
    def __init__(self, threadName, pageQueue, dataQueue):
        # threading.Thread.__init__(self)
        # 调用父类初始化方法
        super(ThreadCrawl, self).__init__()
        # 线程名
        self.threadName = threadName
        # 页码队列
        self.pageQueue = pageQueue
        # 数据队列
        self.dataQueue = dataQueue
        # 请求报头
        self.headers = {"User-Agent": "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0;"}

    def run(self):
        print "启动 " + self.threadName
        while not CRAWL_EXIT:
            try:
                # 取出一个数字，先进先出
                # 可选参数block，默认值为True
                # 1. 如果对列为空，block为True的话，不会结束，会进入阻塞状态，直到队列有新的数据
                # 2. 如果队列为空，block为False的话，就弹出一个Queue.empty()异常，
                page = self.pageQueue.get(False)
                url = "https://www.cnblogs.com/loaderman/default.html?page=" + str(page)
                # print url
                content = requests.get(url, headers=self.headers).text
                time.sleep(1)
                self.dataQueue.put(content)
                # print len(content)
            except:
                pass
        print "结束 " + self.threadName


class ThreadParse(threading.Thread):
    def __init__(self, threadName, dataQueue, filename, lock):
        super(ThreadParse, self).__init__()
        # 线程名
        self.threadName = threadName
        # 数据队列
        self.dataQueue = dataQueue
        # 保存解析后数据的文件名
        self.filename = filename
        # 锁
        self.lock = lock

    def run(self):
        print "启动" + self.threadName
        while not PARSE_EXIT:
            try:
                html = self.dataQueue.get(False)
                self.parse(html)
            except:
                pass
        print "退出" + self.threadName

    def parse(self, html):
        # 解析为HTML DOM
        html = etree.HTML(html)

        node_list = html.xpath('//div[contains(@class, "post")]')
        print (node_list)
        items = {}
        for each in node_list:
            print (each)
            title = each.xpath(".//h2/a[@class='postTitle2']/text()")[0]
            detailUrl = each.xpath(".//a[@class='postTitle2']/@href")[0]
            content = each.xpath(".//div[@class='c_b_p_desc']/text()")[0]
            date = each.xpath(".//p[@class='postfoot']/text()")[0]

            items = {
                "title": title,
                "image": detailUrl,
                "content": content,
                "date": date,

            }

            with open("loadermanThread.json", "a") as f:
                f.write(json.dumps(items, ensure_ascii=False).encode("utf-8") + "
")


CRAWL_EXIT = False
PARSE_EXIT = False


def main():
    # 页码的队列，表示20个页面
    pageQueue = Queue(20)
    # 放入1~10的数字，先进先出
    for i in range(1, 21):
        pageQueue.put(i)

    # 采集结果(每页的HTML源码)的数据队列，参数为空表示不限制
    dataQueue = Queue()

    filename = open("duanzi.json", "a")
    # 创建锁
    lock = threading.Lock()

    # 三个采集线程的名字
    crawlList = ["采集线程1号", "采集线程2号", "采集线程3号"]
    # 存储三个采集线程的列表集合
    threadcrawl = []
    for threadName in crawlList:
        thread = ThreadCrawl(threadName, pageQueue, dataQueue)
        thread.start()
        threadcrawl.append(thread)

    # 三个解析线程的名字
    parseList = ["解析线程1号", "解析线程2号", "解析线程3号"]
    # 存储三个解析线程
    threadparse = []
    for threadName in parseList:
        thread = ThreadParse(threadName, dataQueue, filename, lock)
        thread.start()
        threadparse.append(thread)

    # 等待pageQueue队列为空，也就是等待之前的操作执行完毕
    while not pageQueue.empty():
        pass

    # 如果pageQueue为空，采集线程退出循环
    global CRAWL_EXIT
    CRAWL_EXIT = True

    print "pageQueue为空"

    for thread in threadcrawl:
        thread.join()
        print "1"

    while not dataQueue.empty():
        pass

    global PARSE_EXIT
    PARSE_EXIT = True

    for thread in threadparse:
        thread.join()
        print "2"

    with lock:
        # 关闭文件
        filename.close()
    print "谢谢使用！"


if __name__ == "__main__":
    main()

 效果：