Python基于多线程实现抓取数据存入数据库的方法

nqmysb

2018-06-22

本文实例讲述了Python基于多线程实现抓取数据存入数据库的方法。分享给大家供大家参考，具体如下：

1. 数据库类

"""
使用须知：
代码中数据表名 aces ，需要更改该数据表名称的注意更改
"""
import pymysql
class Database():
  # 设置本地数据库用户名和密码
  host = "localhost"
  user = "root"
  password = ""
  database = "test"
  port = 3306
  charset = "utf8"
  cursor=''
  connet =''
  def __init__(self):
    #连接到数据库
    self.connet = pymysql.connect(host = self.host , user = self.user,password = self.password , database = self.database, charset = self.charset)
    self.cursor = self.connet.cursor()
  # #删表
  def dropTables(self):
    self.cursor.execute('''''drop table if exists aces''')
    print("删表")
  #建表
  def createTables(self):
    self.cursor.execute('''''create table if not exists aces
            (
              asin  varchar(11) primary key not null,
              checked varchar(200));''')
    print("建表")
  #保存数据
  def save(self,aceslist):
    self.cursor.execute("insert into aces ( asin, checked) values(%s,%s)", (aceslist[0],aceslist[1]))
    self.connet.commit()
  #判断元素是否已经在数据库里，在就返回true ,不在就返回false
  def is_exists_asin(self,asin):
    self.cursor.execute('select * from aces where asin = %s',asin)
    if self.cursor.fetchone() is None:
      return False
    return True
# db =Database()

2. 多线程任务类

import urllib.parse
import urllib.parse
import urllib.request
from queue import Queue
import time
import random
import threading
import logging
import pymysql
from bs4 import BeautifulSoup
from local_data import Database
#一个模块中存储多个类 AmazonSpeder , ThreadCrawl(threading.Thread), AmazonSpiderJob
class AmazonSpider():
  def __init__(self):
    self.db = Database()
  def randHeader(self):
    head_connection = ['Keep-Alive', 'close']
    head_accept = ['text/html, application/xhtml+xml, */*']
    head_accept_language = ['zh-CN,fr-FR;q=0.5', 'en-US,en;q=0.8,zh-Hans-CN;q=0.5,zh-Hans;q=0.3']
    head_user_agent = ['Mozilla/5.0 (Windows NT 6.3; WOW64; Trident/7.0; rv:11.0) like Gecko',
              'Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1500.95 Safari/537.36',
              'Mozilla/5.0 (Windows NT 6.1; WOW64; Trident/7.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; rv:11.0) like Gecko)',
              'Mozilla/5.0 (Windows; U; Windows NT 5.2) Gecko/2008070208 Firefox/3.0.1',
              'Mozilla/5.0 (Windows; U; Windows NT 5.1) Gecko/20070309 Firefox/2.0.0.3',
              'Mozilla/5.0 (Windows; U; Windows NT 5.1) Gecko/20070803 Firefox/1.5.0.12',
              'Opera/9.27 (Windows NT 5.2; U; zh-cn)',
              'Mozilla/5.0 (Macintosh; PPC Mac OS X; U; en) Opera 8.0',
              'Opera/8.0 (Macintosh; PPC Mac OS X; U; en)',
              'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8.1.12) Gecko/20080219 Firefox/2.0.0.12 Navigator/9.0.0.6',
              'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; Win64; x64; Trident/4.0)',
              'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; Trident/4.0)',
              'Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.1; WOW64; Trident/6.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; InfoPath.2; .NET4.0C; .NET4.0E)',
              'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Maxthon/4.0.6.2000 Chrome/26.0.1410.43 Safari/537.1 ',
              'Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.1; WOW64; Trident/6.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; InfoPath.2; .NET4.0C; .NET4.0E; QQBrowser/7.3.9825.400)',
              'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:21.0) Gecko/20100101 Firefox/21.0 ',
              'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/21.0.1180.92 Safari/537.1 LBBROWSER',
              'Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.1; WOW64; Trident/6.0; BIDUBrowser 2.x)',
              'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.11 TaoBrowser/3.0 Safari/536.11']
    header = {
      'Connection': head_connection[0],
      'Accept': head_accept[0],
      'Accept-Language': head_accept_language[1],
      'User-Agent': head_user_agent[random.randrange(0, len(head_user_agent))]
    }
    return header
  def getDataById(self , queryId):
    #如果数据库中有的数据，直接返回不处理
    if self.db.is_exists_asin(queryId):
      return
    req = urllib.request.Request(url="https://www.amazon.com/dp/"+str(queryId) , headers=self.randHeader())
    webpage = urllib.request.urlopen(req)
    html = webpage.read()
    soup = BeautifulSoup(html, 'html.parser')
    content = soup.find_all("span" , id = "asTitle")
    # 加入一种判断，有的asin没有该定位，
    if len(content):
      # 非空
      state = content[0].string
    else:
      # 列表为空，没有定位到
      state = "other"
    print(queryId)
    print(state)
    self.db.save([queryId,state])
class ThreadCrawl(threading.Thread): #ThreadCrawl类继承了Threading.Thread类
  def __init__(self, queue): #子类特有属性， queue
    FORMAT = time.strftime("[%Y-%m-%d %H:%M:%S]", time.localtime()) + "[AmazonSpider]-----%(message)s------"
    logging.basicConfig(level=logging.INFO, format=FORMAT)
    threading.Thread.__init__(self)
    self.queue = queue
    self.spider = AmazonSpider() #子类特有属性spider， 并初始化，将实例用作属性
  def run(self):
    while True:
      success = True
      item = self.queue.get() #调用队列对象的get()方法从队头删除并返回一个项目item
      try:
        self.spider.getDataById(item) #调用实例spider的方法getDataById(item)
      except :
        # print("失败")
        success = False
      if not success :
        self.queue.put(item)
      logging.info("now queue size is: %d" % self.queue.qsize()) #队列对象qsize()方法，返回队列的大小
      self.queue.task_done() #队列对象在完成一项工作后，向任务已经完成的队列发送一个信号
class AmazonSpiderJob():
  def __init__(self , size , qs):
    self.size = size # 将形参size的值存储到属性变量size中
    self.qs = qs
  def work(self):
    toSpiderQueue = Queue() #创建一个Queue队列对象
    for q in self.qs:
      toSpiderQueue.put(q) #调用队列对象的put()方法，在对尾插入一个项目item
    for i in range(self.size):
      t = ThreadCrawl(toSpiderQueue)  #将实例用到一个类的方法中
      t.setDaemon(True)
      t.start()
    toSpiderQueue.join()  #队列对象，等到队列为空，再执行别的操作

3. 主线程类

from amazon_s import AmazonSpiderJob #从一个模块中导入类
import pymysql
import pandas as pd
from local_data import Database
if __name__ == '__main__':
  #初次跑程序的时候，需要删除旧表，然后新建表，之后重启再跑的时候需要注释
  #----------------------
  db = Database()
  db.dropTables()
  db.createTables()
  #---------------------------
  df = pd.read_excel("ASIN检查_viogico_1108.xlsx")
  # print(df.info())
  qs = df["asin1"].values
  print(qs)
  print(len(qs))
  amazonJob = AmazonSpiderJob(8, qs)
  amazonJob.work()

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希望本文所述对大家Python程序设计有所帮助。

大数据数据库多线程 python