一、简介

为了提高爬虫程序效率,由于python解释器GIL,导致同一进程中即使有多个线程,实际上也只会有一个线程在运行,但通过request.get发送请求获取响应时有阻塞,所以采用了多线程依然可以提高爬虫效率。

多线程爬虫注意点
1.解耦

整个程序分为4部分,url list模块、发送请求,获取响应模块、数据提取模块、保存模块,如果某一模块出现问题,互相之间不会影响。

2. 资源竞争

由于使用了多线程,不同线程在共享数据时,容易产生资源竞争,假设共享数据放入列表中,那么同一时刻有可能2个线程去列表中取同一个数据,重复使用。解决办法是使用队列,使得某一线程get数据时,其他线程无法get同一数据,真正起到保护作用,类似互斥锁。

队列常用方法介绍

from queue import Queue

q = Queue()
q.put(url)
q.get()         # 当队列为空时,阻塞
q.empty()       # 判断队列是否为空,True/False
image

注意:

  • get和get_nowait两者的区别是当队列取完了即队列为空时,get()会阻塞,等待着新数据继续取,而get_nowait()会报错;
  • put和put_nowait 两者的区别是当队列为满时,put_nowait()会报错;

队列其他方法join task_done setDaemon

  • 在python3中,join()会等待子线程、子进程结束之后,主线程、主进程才会结束.
  • 队列中put队列计数会+1,get时计数不会减1,但当get+task_done时,队列计数才会减1,如果没有task_done则程序跑到最后不会终止。task_done()的位置,应该放在方法的最后以保证所有任务全部完成.
  • setDaemon方法把子线程设置为守护线程,即认为该方法不是很重要,记住主线程结束,则该子线程结束
  • join方法和setDaemon方法搭配使用。主线程进行到join()处,join的效果是让主线程阻塞,等待子线程中队列任务完成之后再解阻塞,等子线程结束,join效果失效,之后主线程结束,由于使用了setDaemon(True),所以子线程跟着结束,此时整个程序结束。

线程模块

from threading import Thread
​
# 使用流程  
t = Thread(target=函数名)                   # 创建线程对象
t.start()                                   # 创建并启动线程
t.join()                                    # 阻塞等待回收线程

应用场景

  • 多进程 :CPU密集程序
  • 多线程 :爬虫(网络I/O)、本地磁盘I/O

二、案例

1. 小米应用商店抓取

目标

  1. 网址 :百度搜 – 小米应用商店,进入官网,应用分类 – 聊天社交
  2. 目标 :爬取应用名称和应用链接
image
image

实现步骤

1、确认是否为动态加载:页面局部刷新,查看网页源代码,搜索关键字未搜到,因此此网站为动态加载网站,需要抓取网络数据包分析

2、抓取网络数据包

  • 抓取返回json数据的URL地址(Headers中的Request URL)http://app.mi.com/categotyAllListApi?page={}&categoryId=2&pageSize=30
  • 查看并分析查询参数(headers中的Query String Parameters)只有page在变,0 1 2 3 … … ,这样我们就可以通过控制page的值拼接多个返回json数据的URL地址
page: 1 
categoryId: 2 
pageSize: 30

3、将抓取数据保存到csv文件。注意多线程写入的线程锁问题

lock = Lock()
lock.acquire()
lock.release()

整体实现思路

  1. init(self) 中创建文件对象,多线程操作此对象进行文件写入;
  2. 每个线程抓取数据后将数据进行文件写入,写入文件时需要加锁;
  3. 所有数据抓取完成关闭文件;
import requests
from threading import Thread
from queue import Queue
import time
from lxml import etree
import csv
from threading import Lock


class XiaomiSpider(object):
    def __init__(self):
        self.url = 'http://app.mi.com/categotyAllListApi?page={}&categoryId={}&pageSize=30'
        self.ua = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.1 (KHTML, like Gecko) Chrome/14.0.835.163 Safari/535.1'}
        self.q = Queue()                            # 存放所有URL地址的队列
        self.i = 0
        self.id_list = []                           # 存放所有类型id的空列表
        # 打开文件
        self.f = open('xiaomi.csv', 'a', newline="")
        self.writer = csv.writer(self.f)
        self.lock = Lock()                      # 创建锁

    def get_cateid(self):
        url = 'http://app.mi.com/'
        html = requests.get(url=url, headers=self.ua).text

        parse_html = etree.HTML(html)
        li_list = parse_html.xpath('//ul[@class="category-list"]/li')
        for li in li_list:
            typ_name = li.xpath('./a/text()')[0]
            typ_id = li.xpath('./a/@href')[0].split('/')[-1]
            pages = self.get_pages(typ_id)                      # 计算每个类型的页数
            self.id_list.append((typ_id, pages))

        self.url_in()  # 入队列

    def get_pages(self, typ_id):
        # 每页返回的json数据中,都有count这个key
        url = self.url.format(0, typ_id)
        html = requests.get(url=url, headers=self.ua).json()
        count = html['count']                   # 类别中的数据总数
        pages = int(count) // 30 + 1            # 每页30个,看有多少页

        return pages

    # url入队列
    def url_in(self):
        for id in self.id_list:
            # id为元组,(typ_id, pages)-->('2',pages)
            for page in range(2):
                url = self.url.format(page, id[0])
                print(url)
                # 把URL地址入队列
                self.q.put(url)

    # 线程事件函数: get() - 请求 - 解析 - 处理数据
    def get_data(self):
        while True:
            # 当队列不为空时,获取url地址
            if not self.q.empty():
                url = self.q.get()
                html = requests.get(url=url, headers=self.ua).json()
                self.parse_html(html)
            else:
                break

    # 解析函数
    def parse_html(self, html):
        # 存放1页的数据 - 写入到csv文件
        app_list = []
        for app in html['data']:
            # 应用名称 + 链接 + 分类
            name = app['displayName']
            link = 'http://app.mi.com/details?id=' + app['packageName']
            typ_name = app['level1CategoryName']
            # 把每一条数据放到app_list中,目的为了 writerows()
            app_list.append([name, typ_name, link])
            print(name, typ_name)
            self.i += 1

        # 开始写入1页数据 - app_list
        self.lock.acquire()
        self.writer.writerows(app_list)
        self.lock.release()

    # 主函数
    def main(self):
        self.get_cateid()       # URL入队列
        t_list = []

        # 创建多个线程
        for i in range(1):
            t = Thread(target=self.get_data)
            t_list.append(t)
            t.start()

        # 统一回收线程
        for t in t_list:
            t.join()

        # 关闭文件
        self.f.close()
        print('数量:', self.i)


if __name__ == '__main__':
    start = time.time()
    spider = XiaomiSpider()
    spider.main()
    end = time.time()
    print('执行时间:%.2f' % (end - start))
image
image
image

2.腾讯招聘数据抓取(Ajax)

确定URL地址及目标

  • URL: 百度搜索腾讯招聘 – 查看工作岗位https://careers.tencent.com/search.html
  • 目标: 职位名称、工作职责、岗位要求

要求与分析

  1. 通过查看网页源码,得知所需数据均为 Ajax 动态加载
  2. 通过F12抓取网络数据包,进行分析
  3. 一级页面抓取数据: 职位名称
  4. 二级页面抓取数据: 工作职责、岗位要求

一级页面json地址(pageIndex在变,timestamp未检查)

https://careers.tencent.com/tencentcareer/api/post/Query?timestamp=1563912271089&countryId=&cityId=&bgIds=&productId=&categoryId=&parentCategoryId=&attrId=&keyword=&pageIndex={}&pageSize=10&language=zh-cn&area=cn

二级页面地址(postId在变,在一级页面中可拿到)

https://careers.tencent.com/tencentcareer/api/post/ByPostId?timestamp=1563912374645&postId={}&language=zh-cn

useragents.py文件

ua_list = [
    'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.1 (KHTML, like Gecko) Chrome/14.0.835.163 Safari/535.1',
    'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:6.0) Gecko/20100101 Firefox/6.0',
    'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; WOW64; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; InfoPath.3)',
]

非多线程爬取

import time
import json
import random
import requests
from useragents import ua_list


class TencentSpider(object):
    def __init__(self):
        self.one_url = 'https://careers.tencent.com/tencentcareer/api/post/Query?timestamp=1563912271089&countryId=&cityId=&bgIds=&productId=&categoryId=&parentCategoryId=&attrId=&keyword=&pageIndex={}&pageSize=10&language=zh-cn&area=cn'
        self.two_url = 'https://careers.tencent.com/tencentcareer/api/post/ByPostId?timestamp=1563912374645&postId={}&language=zh-cn'
        self.f = open('tencent.json', 'a')  # 打开文件
        self.item_list = []  # 存放抓取的item字典数据

    # 获取响应内容函数
    def get_page(self, url):
        headers = {'User-Agent': random.choice(ua_list)}
        html = requests.get(url=url, headers=headers).text
        html = json.loads(html)  # json格式字符串转为Python数据类型

        return html

    # 主线函数: 获取所有数据
    def parse_page(self, one_url):
        html = self.get_page(one_url)
        item = {}
        for job in html['Data']['Posts']:
            item['name'] = job['RecruitPostName']  # 名称
            post_id = job['PostId']  # postId,拿postid为了拼接二级页面地址
            # 拼接二级地址,获取职责和要求
            two_url = self.two_url.format(post_id)
            item['duty'], item['require'] = self.parse_two_page(two_url)
            print(item)
            self.item_list.append(item)  # 添加到大列表中

    # 解析二级页面函数
    def parse_two_page(self, two_url):
        html = self.get_page(two_url)
        duty = html['Data']['Responsibility']  # 工作责任
        duty = duty.replace('rn', '').replace('n', '')  # 去掉换行
        require = html['Data']['Requirement']  # 工作要求
        require = require.replace('rn', '').replace('n', '')  # 去掉换行

        return duty, require

    # 获取总页数
    def get_numbers(self):
        url = self.one_url.format(1)
        html = self.get_page(url)
        numbers = int(html['Data']['Count']) // 10 + 1  # 每页有10个推荐

        return numbers

    def main(self):
        number = self.get_numbers()
        for page in range(1, 3):
            one_url = self.one_url.format(page)
            self.parse_page(one_url)

        # 保存到本地json文件:json.dump
        json.dump(self.item_list, self.f, ensure_ascii=False)
        self.f.close()


if __name__ == '__main__':
    start = time.time()
    spider = TencentSpider()
    spider.main()
    end = time.time()
    print('执行时间:%.2f' % (end - start))

多线程爬取

多线程即把所有一级页面链接提交到队列,进行多线程数据抓取

import requests
import json
import time
import random
from useragents import ua_list
from threading import Thread
from queue import Queue


class TencentSpider(object):
    def __init__(self):
        self.one_url = 'https://careers.tencent.com/tencentcareer/api/post/Query?timestamp=1563912271089&countryId=&cityId=&bgIds=&productId=&categoryId=&parentCategoryId=&attrId=&keyword=&pageIndex={}&pageSize=10&language=zh-cn&area=cn'
        self.two_url = 'https://careers.tencent.com/tencentcareer/api/post/ByPostId?timestamp=1563912374645&postId={}&language=zh-cn'
        self.q = Queue()
        self.i = 0  # 计数

    # 获取响应内容函数
    def get_page(self, url):
        headers = {'User-Agent': random.choice(ua_list)}
        html = requests.get(url=url, headers=headers).text
        # json.loads()把json格式的字符串转为python数据类型
        html = json.loads(html)

        return html

    # 主线函数: 获取所有数据
    def parse_page(self):
        while True:
            if not self.q.empty():
                one_url = self.q.get()
                html = self.get_page(one_url)
                item = {}
                for job in html['Data']['Posts']:
                    item['name'] = job['RecruitPostName']  # 名称
                    post_id = job['PostId']  # 拿postid为了拼接二级页面地址
                    # 拼接二级地址,获取职责和要求
                    two_url = self.two_url.format(post_id)
                    item['duty'], item['require'] = self.parse_two_page(two_url)
                    print(item)
                # 每爬取按完成1页随机休眠
                time.sleep(random.uniform(0, 1))
            else:
                break

    # 解析二级页面函数
    def parse_two_page(self, two_url):
        html = self.get_page(two_url)
        # 用replace处理一下特殊字符
        duty = html['Data']['Responsibility']
        duty = duty.replace('rn', '').replace('n', '')
        # 处理要求
        require = html['Data']['Requirement']
        require = require.replace('rn', '').replace('n', '')

        return duty, require

    # 获取总页数
    def get_numbers(self):
        url = self.one_url.format(1)
        html = self.get_page(url)
        numbers = int(html['Data']['Count']) // 10 + 1

        return numbers

    def main(self):
        # one_url入队列
        number = self.get_numbers()
        for page in range(1, number + 1):
            one_url = self.one_url.format(page)
            self.q.put(one_url)

        t_list = []
        for i in range(5):
            t = Thread(target=self.parse_page)
            t_list.append(t)
            t.start()

        for t in t_list:
            t.join()

        print('数量:', self.i)


if __name__ == '__main__':
    start = time.time()
    spider = TencentSpider()
    spider.main()
    end = time.time()
    print('执行时间:%.2f' % (end - start))

多进程实现

import requests
import json
import time
import random
from useragents import ua_list
from multiprocessing import Process
from queue import Queue


class TencentSpider(object):
    def __init__(self):
        self.one_url = 'https://careers.tencent.com/tencentcareer/api/post/Query?timestamp=1563912271089&countryId=&cityId=&bgIds=&productId=&categoryId=&parentCategoryId=&attrId=&keyword=&pageIndex={}&pageSize=10&language=zh-cn&area=cn'
        self.two_url = 'https://careers.tencent.com/tencentcareer/api/post/ByPostId?timestamp=1563912374645&postId={}&language=zh-cn'
        self.q = Queue()

    # 获取响应内容函数
    def get_page(self, url):
        headers = {'User-Agent': random.choice(ua_list)}
        html = requests.get(url=url, headers=headers).text
        # json格式字符串 -> Python
        html = json.loads(html)

        return html

    # 主线函数: 获取所有数据
    def parse_page(self):
        while True:
            if not self.q.empty():
                one_url = self.q.get()
                html = self.get_page(one_url)
                item = {}
                for job in html['Data']['Posts']:
                    # 名称
                    item['name'] = job['RecruitPostName']
                    # postId
                    post_id = job['PostId']
                    # 拼接二级地址,获取职责和要求
                    two_url = self.two_url.format(post_id)
                    item['duty'], item['require'] = self.parse_two_page(two_url)

                    print(item)
            else:
                break

    # 解析二级页面函数
    def parse_two_page(self, two_url):
        html = self.get_page(two_url)
        # 用replace处理一下特殊字符
        duty = html['Data']['Responsibility']
        duty = duty.replace('rn', '').replace('n', '')
        # 处理要求
        require = html['Data']['Requirement']
        require = require.replace('rn', '').replace('n', '')

        return duty, require

    # 获取总页数
    def get_numbers(self):
        url = self.one_url.format(1)
        html = self.get_page(url)
        numbers = int(html['Data']['Count']) // 10 + 1

        return numbers

    def main(self):
        # url入队列
        number = self.get_numbers()
        for page in range(1, number + 1):
            one_url = self.one_url.format(page)
            self.q.put(one_url)

        t_list = []
        for i in range(4):
            t = Process(target=self.parse_page)
            t_list.append(t)
            t.start()

        for t in t_list:
            t.join()


if __name__ == '__main__':
    start = time.time()
    spider = TencentSpider()
    spider.main()
    end = time.time()
    print('执行时间:%.2f' % (end - start))

基于multiprocessing.dummy线程池的数据爬取

案例:爬取梨视频数据。在爬取和持久化存储方面比较耗时,所以两个都需要多线程

import requests
import re
from lxml import etree
from multiprocessing.dummy import Pool
import random

pool = Pool(5)                        # 实例化一个线程池对象

url = 'https://www.pearvideo.com/category_1'
headers = {
    'User-Agent':'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.119 Safari/537.36'
}
page_text = requests.get(url=url,headers=headers).text
tree = etree.HTML(page_text)
li_list = tree.xpath('//div[@id="listvideoList"]/ul/li')

video_url_list = []
for li in li_list:
    detail_url = 'https://www.pearvideo.com/'+li.xpath('./div/a/@href')[0]
    detail_page = requests.get(url=detail_url,headers=headers).text
    video_url = re.findall('srcUrl="(.*?)",vdoUrl',detail_page,re.S)[0]
    video_url_list.append(video_url)
   
#  pool.map(回调函数,可迭代对象)函数依次执行对象
video_data_list = pool.map(getVideoData,video_url_list)        # 获取视频

pool.map(saveVideo,video_data_list)                    # 持久化存储

def getVideoData(url):                            
    return requests.get(url=url,headers=headers).content

def saveVideo(data):
    fileName = str(random.randint(0,5000))+'.mp4'            # 因回调函数只能传一个参数,所以没办法再传名字了,只能自己取名
    with open(fileName,'wb') as fp:
        fp.write(data)


pool.close()
pool.join()

文章来源于互联网:Python爬虫进阶 | 多线程

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