多线程爬虫

应用场景

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

知识点回顾

队列
# 导入模块 from queue import Queue # 使用 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
线程模块
# 导入模块 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
小米应用商店抓取(多线程)

目标

* 网址 ：百度搜 - 小米应用商店，进入官网，应用分类 - 聊天社交
* 目标 ：爬取应用名称和应用链接
实现步骤

1、确认是否为动态加载

1、页面局部刷新

2、右键查看网页源代码，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、F12抓取网络数据包

1、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

2、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

from threading import Lock

lock = Lock()

lock.acquire()

lock.release()

整体思路

* 在 __init__(self) 中创建文件对象，多线程操作此对象进行文件写入
* 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
* 所有数据抓取完成关闭文件 import requests from threading import Thread from queue import
Queueimport time from lxml import etree import csv from threading import Lock
from fake_useragent import UserAgent class XiaomiSpider(object): def __init__
(self): self.url= '
http://app.mi.com/categotyAllListApi?page={}&categoryId={}&pageSize=30' 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 self.ua = UserAgent() def get_cateid(self): # 请求 url = '
http://app.mi.com/' headers = {'User-Agent': self.ua.random} html =
requests.get(url=url, headers=headers).text # 解析 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 # 获取counts的值并计算页数 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={'User-Agent': self.ua.random}).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() headers= {'User-Agent': self.ua.random} html =
requests.get(url=url, headers=headers).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))
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
<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

* 通过查看网页源码，得知所需数据均为 Ajax 动态加载
* 通过F12抓取网络数据包，进行分析
* 一级页面抓取数据: 职位名称
* 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)', ]
我们先来回忆一下原来的腾讯招聘爬虫代码
import time import json import random import requests from useragents import
ua_listclass 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('\r\n', '').replace(
'\n', '') # 去掉换行 require = html['Data']['Requirement'] # 工作要求 require =
require.replace('\r\n', '').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)) View Code
多线程实现

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

代码实现
import requests import json import time import random from useragents import
ua_listfrom 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('\r\n', '').replace('
\n', '') # 处理要求 require = html['Data']['Requirement'] require = require.replace(
'\r\n', '').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_listfrom 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('\r\n', '').replace('
\n', '') # 处理要求 require = html['Data']['Requirement'] require = require.replace(
'\r\n', '').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))
 

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