学习Python爬虫的第二周，完成了爬取赶集网二手市场的10万商品数据。
成果：
url_list.png
item_info.png
代码：
*channel_extract.py *
from bs4 import BeautifulSoup
import requests
# spider1
start_url = 'http://bj.ganji.com/wu/'
url_host= 'http://bj.ganji.com'
def get_channel_urls(url):
wb_data = requests.get(url)
soup = BeautifulSoup(wb_data.text, 'lxml')
links = soup.select('dl.fenlei dt > a ')
for link in links:
page_url = url_host + link.get('href')
print(page_url)
get_channel_urls(start_url)
channel_list = '''
http://bj.ganji.com/jiaju/
http://bj.ganji.com/rirongbaihuo/
http://bj.ganji.com/shouji/
http://bj.ganji.com/shoujihaoma/
http://bj.ganji.com/bangong/
http://bj.ganji.com/nongyongpin/
http://bj.ganji.com/jiadian/
http://bj.ganji.com/ershoubijibendiannao/
http://bj.ganji.com/ruanjiantushu/
http://bj.ganji.com/yingyouyunfu/
http://bj.ganji.com/diannao/
http://bj.ganji.com/xianzhilipin/
http://bj.ganji.com/fushixiaobaxuemao/
http://bj.ganji.com/meironghuazhuang/
http://bj.ganji.com/shuma/
http://bj.ganji.com/laonianyongpin/
http://bj.ganji.com/xuniwupin/
http://bj.ganji.com/qitawupin/
http://bj.ganji.com/ershoufree/
http://bj.ganji.com/wupinjiaohuan/
'''
page_parsing.py
from bs4 import BeautifulSoup
import requests
import time
import pymongo
client = pymongo.MongoClient('localhost', 27017)
ceshi = client['ceshi']
url_list = ceshi['url_list1']
item_info = ceshi['item_info1']
#随机UA
userAgent = random.choice(['Mozilla/5.0 (Linux; Android 5.0; SM-G900P Build/LRX21T) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.23 Mobile Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.86 Safari/537.36',
'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.23 Mobile Safari/537.36',
'Mozilla/5.0 (Linux; Android 5.1.1; Nexus 6 Build/LYZ28E) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.23 Mobile Safari/537.36',
'Mozilla/5.0 (iPhone; CPU iPhone OS 9_1 like Mac OS X) AppleWebKit/601.1.46 (KHTML, like Gecko) Version/9.0 Mobile/13B143 Safari/601.1',
'Mozilla/5.0 (iPad; CPU OS 9_1 like Mac OS X) AppleWebKit/601.1.46 (KHTML, like Gecko) Version/9.0 Mobile/13B143 Safari/601.1'
])
# spider2
def get_links_from(channel, pages, who_sells='o'):
headers = {
'User-Agent': userAgent,
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
'Accept - Encoding': 'gzip, deflate, sdch',
'Accept - Language': 'zh - CN, zh;q = 0.8, en;q = 0.6',
'Cache - Control': 'max - age = 0','Connection': 'keep - alive'
}
client = pymongo.MongoClient('localhost', 27017)
ceshi = client['ceshi']
url_list = ceshi['url_list1']
list_view = '{}{}{}/'.format(channel, str(who_sells), str(pages))
wb_data = requests.get(list_view, headers=headers)
#随机访问延时
i = random.randrange(0, 3)
time.sleep(i)
soup = BeautifulSoup(wb_data.text, 'lxml')
links = soup.select('li.js-item > a')
if soup.find('ul', 'pageLink'):
for link in links:
item_link = link.get('href')
headers = requests.head(item_link, allow_redirects=False).headers
if headers['Server'] == 'nginx':
item_link = headers['Location']
url_list.insert_one({'url': item_link})
else:
url_list.insert_one({'url': item_link})
pass
get_item_info(item_link)
else:
pass
def get_item_info(url):
headers = {
'User-Agent': userAgent,
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
'Accept - Encoding': 'gzip, deflate, sdch',
'Accept - Language': 'zh - CN, zh;q = 0.8, en;q = 0.6',
'Cache - Control': 'max - age = 0','Connection': 'keep - alive'
}
client = pymongo.MongoClient('localhost', 27017)
ceshi = client['ceshi']
item_info = ceshi['item_info1']
wb_data = requests.get(url, headers=headers)
soup = BeautifulSoup(wb_data.text, 'lxml')
no_longer_exist = soup.find('div', 'error')
if no_longer_exist:
pass
else:
title = soup.select('.title-name')[0].text
date = soup.select('.pr-5')[0].text.split('发布')[0].strip() if soup.find('i', 'pr-5') else None
cate = soup.select('.det-infor > li > span > a')[0].text
price = soup.select('.f22.fc-orange.f-type')[0].text
loco_list = list(soup.select('div.leftBox > div:nth-of-type(3) > div > ul > li:nth-of-type(3) > a'))
area = []
for loco in loco_list:
area.append(loco.text)
state = soup.select('ul.second-det-infor.clearfix > li')[0].text.split('：')[-1].strip() if soup.find('ul', 'second-det-infor') and soup.select('ul.second-det-infor.clearfix > li')[0].text.split('：')[0].strip() == '新旧程度' else None
item_info.insert_one({'title': title, 'date': date, 'cate': cate, 'price': price, 'area': area, 'state': state, 'url': url})
main.py
from multiprocessing import Pool
from channel_extract import channel_list
from page_parsing import get_links_from, url_list, item_info, get_item_info
def get_all_links_from(channel):
for num in range(1, 100):
get_links_from(channel, num)
if __name__ == '__main__':
pool = Pool()
pool.map(get_all_links_from, channel_list.split())
# 断点续传
db_urls = [item['url'] for item in url_list.find()]
index_urls = [item['url'] for item in item_info.find()]
x = set(db_urls)
y = set(index_urls)
rest_of_urls = x - y
pool.map(get_item_info, rest_of_urls)
counts.py
import time
from page_parsing import url_list, item_info
while True:
print(url_list.find().count())
print(item_info.find().count())
print('\n')
time.sleep(10)
总结：
- 大规模数据的爬取之前应该做好爬虫工作流程的设计，设计多个爬虫，分别负责URL链接和每个链接的详情页的爬取。同时设计两个数据库，一个用来存放URL，另一个用来存放商品详情。
- 有一些网页进行了重定向，用requests的head方法可以获取响应头，通过响应头中的'location'可以获得真实的URL，通过设置请求参数allow_redirects=True可以启用重定向，默认情况下是禁用的。
- 为了提高爬取效率，可以使用多线程和多进程。使用多进程的前提是拥有足够的CPU内核，因为一个进程会占用一个CPU。对于单核系统，只能使用多线程爬取。
- 在抓取过程中难免会遇到网络问题而导致程序终止，需要设计断点续传功能保证数据库中抓取的数据不会重复。设计思路是存储商品详情的同时增加一个字段，存储每个商品的URL，如果程序中断，则将所有链接与商品详情表中已抓取链接做差集，抓取剩下的链接。
- 通过获取数据库中的数据数目，可以创建一个监控程序统计所抓数据的数目。
