爬去当当书籍信息

多台机器同时爬取,共用一个redis记录 scrapy_redis

带爬取的request对象储存在redis中,每台机器读取request对象并删除记录,经行爬取。实现分布式爬虫

import scrapy
from scrapy_redis.spiders import RedisSpider
from copy import deepcopy class DangdangSpider(RedisSpider):
name = 'dangdang'
allowed_domains = ['dangdang.com']
# 开始爬虫,会从redis的key中读取start_url.
redis_key = "dangdang" # lpush dangdang 'http://book.dangdang.com/' def parse(self, response):
# 大分类
div_list = response.xpath("//div[@class='con flq_body']/div")[:-4]
print(len(div_list), 'duoshao')
for div in div_list:
item = {}
item['b_cate'] = div.xpath("./dl/dt//text()").extract()
item['b_cate'] = [i.strip() for i in item['b_cate'] if len(i.strip())>0] # 过滤掉空字符
print('b_cate:', item['b_cate'])
# 中间分类
if item['b_cate'] == ['创意文具']:
print(item['b_cate'], "pass......")
item['m_cate'] = None
item['s_cate_url'] = div.xpath("./dl/dt/a/@ddt-src").extract_first()
print('s_cate_url:', item['m_cate'])
# yield scrapy.Request(
# item['s_cate_url'],
# callback=self.parse_special,
# meta={'item': deepcopy(item)}
# )
else:
dl_list = div.xpath(".//dl[@class='inner_dl']")
for dl in dl_list:
item['m_cate'] = dl.xpath("./dt//text()").extract()
item['m_cate'] = [i.strip() for i in item['m_cate'] if len(i.strip())>0]
# 小分类
dd_list = dl.xpath("./dd")
for dd in dd_list:
item['s_cate'] = dd.xpath("./a/@title").extract_first()
item['s_cate_url'] = dd.xpath("./a/@ddt-src").extract_first()
# 小分类的所有书籍
if item['s_cate_url'] is not None:
yield scrapy.Request(
item['s_cate_url'],
callback=self.parse_books,
meta={'item': deepcopy(item)}
) def parse_special(self, response):
''' 文具信息 '''
pass def parse_books(self, response):
item = response.meta['item']
# 当前小分类的书籍
li_list = response.xpath("//ul[@class='list_aa ']/li")
if li_list is not None:
for li in li_list:
try:
item['book_price'] = li.xpath(".//span[@class='num']/text()").extract_first() + \
li.xpath(".//span[@class='tail']/text()").extract_first()
except:
item['book_price'] = 'Unknown'
item['book_url'] = li.xpath("./a/@href").extract_first()
if item['book_url'] is not None:
yield scrapy.Request(
item['book_url'],
callback=self.parse_book_detail,
meta={'item': deepcopy(item)}
) def parse_book_detail(self, response):
item = response.meta['item']
item['book_name'] = response.xpath("//div[@class='name_info']/h1/img/text()").extract_first()
item['book_desc'] = response.xpath("//span[@class='head_title_name']/text()").extract_first()
# 这一本书籍的详细信息
span_list = response.xpath("//div[@class='messbox_info']/span")
item['book_author'] = span_list.xpath("./span[1]/a/text()").extract() # 可能多个作者
item['publisher'] = span_list.xpath("./span[2]/a/text()").extract_first()
item['pub_date'] = span_list.xpath("./span[3]/text()").extract_first()
print(item)
# yield item
05-06 11:29