Scrapy-Redis之RedisSpider与RedisCrawlSpider详解

在上一章《scrapy-redis入门实战》中我们利用scrapy-redis实现了京东图书爬虫的分布式部署和数据爬取。但存在以下问题：

每个爬虫实例在启动的时候，都必须从start_urls开始爬取，即每个爬虫实例都会请求start_urls中的地址，属重复请求，浪费系统资源。

为了解决这一问题，scrapy-redis提供了redisspider与rediscrawlspider两个爬虫类，继承自这两个类的spider在启动的时候能够从指定的redis列表中去获取start_urls；任意爬虫实例从redis列表中获取某一 url 时会将其从列表中弹出，因此其他爬虫实例将不能重复读取该 url ；对于那些未从redis列表获取到初始 url 的爬虫实例将一直处于阻塞状态，直到 start_urls列表中被插入新的起始地址或者redis的requests列表中出现待处理的请求。

在这里，我们以爬取当当网图书信息为例对这两个spider的用法进行简单示例。

settings.py 配置如下：

# -*- coding: utf-8 -*-

bot_name = 'dang_dang'

spider_modules = ['dang_dang.spiders']
newspider_module = 'dang_dang.spiders'


# crawl responsibly by identifying yourself (and your website) on the user-agent
user_agent = 'mozilla/5.0 (windows nt 6.1; win64; x64) applewebkit/537.36 (khtml, like gecko) chrome/71.0.3578.98 safari/537.36'

# obey robots.txt rules
robotstxt_obey = false


######################################################
##############下面是scrapy-redis相关配置################
######################################################

# 指定redis的主机名和端口
redis_host = 'localhost'
redis_port = 6379

# 调度器启用redis存储requests队列
scheduler = "scrapy_redis.scheduler.scheduler"

# 确保所有的爬虫实例使用redis进行重复过滤
dupefilter_class = "scrapy_redis.dupefilter.rfpdupefilter"

# 将requests队列持久化到redis，可支持暂停或重启爬虫
scheduler_persist = true

# requests的调度策略，默认优先级队列
scheduler_queue_class = 'scrapy_redis.queue.priorityqueue'

# 将爬取到的items保存到redis 以便进行后续处理
item_pipelines = {
  'scrapy_redis.pipelines.redispipeline': 300
}

redisspider代码示例

# -*- coding: utf-8 -*-
import scrapy
import re
import urllib
from copy import deepcopy
from scrapy_redis.spiders import redisspider


class dangdangspider(redisspider):
  name = 'dangdang'
  allowed_domains = ['dangdang.com']
  redis_key = 'dangdang:book'
  pattern = re.compile(r"(http|https)://category.dangdang.com/cp(.*?).html", re.i)

  # def __init__(self, *args, **kwargs):
  #   # 动态定义可爬取的域范围
  #   domain = kwargs.pop('domain', '')
  #   self.allowed_domains = filter(none, domain.split(','))
  #   super(dangdangspider, self).__init__(*args, **kwargs)

  def parse(self, response): # 从首页提取图书分类信息
    # 提取一级分类元素
    div_list = response.xpath("//div[@class='con flq_body']/div")
    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]
      # 提取二级分类元素
      dl_list = div.xpath("./div//dl[@class='inner_dl']")
      for dl in dl_list:
        item["m_cate"] = dl.xpath(".//dt/a/@title").extract_first()
        # 提取三级分类元素
        a_list = dl.xpath("./dd/a")
        for a in a_list:
          item["s_cate"] = a.xpath("./text()").extract_first()
          item["s_href"] = a.xpath("./@href").extract_first()
          if item["s_href"] is not none and self.pattern.match(item["s_href"]) is not none:
            yield scrapy.request(item["s_href"], callback=self.parse_book_list,
                       meta={"item": deepcopy(item)})

  def parse_book_list(self, response): # 从图书列表页提取数据
    item = response.meta['item']
    li_list = response.xpath("//ul[@class='bigimg']/li")
    for li in li_list:
      item["book_img"] = li.xpath("./a[@class='pic']/img/@src").extract_first()
      if item["book_img"] == "images/model/guan/url_none.png":
        item["book_img"] = li.xpath("./a[@class='pic']/img/@data-original").extract_first()
      item["book_name"] = li.xpath("./p[@class='name']/a/@title").extract_first()
      item["book_desc"] = li.xpath("./p[@class='detail']/text()").extract_first()
      item["book_price"] = li.xpath(".//span[@class='search_now_price']/text()").extract_first()
      item["book_author"] = li.xpath("./p[@class='search_book_author']/span[1]/a/text()").extract_first()
      item["book_publish_date"] = li.xpath("./p[@class='search_book_author']/span[2]/text()").extract_first()
      if item["book_publish_date"] is not none:
        item["book_publish_date"] = item["book_publish_date"].replace('/', '')
      item["book_press"] = li.xpath("./p[@class='search_book_author']/span[3]/a/text()").extract_first()
      yield deepcopy(item)

    # 提取下一页地址
    next_url = response.xpath("//li[@class='next']/a/@href").extract_first()
    if next_url is not none:
      next_url = urllib.parse.urljoin(response.url, next_url)
      yield scrapy.request(next_url, callback=self.parse_book_list, meta={"item": item})

当redis 的dangdang:book键所对应的start_urls列表为空时，启动dangdangspider爬虫会进入到阻塞状态等待列表中被插入数据，控制台提示内容类似下面这样：

2019-05-08 14:02:53 [scrapy.core.engine] info: spider opened
2019-05-08 14:02:53 [scrapy.extensions.logstats] info: crawled 0 pages (at 0 pages/min), scraped 0 items (at 0 items/min)
2019-05-08 14:02:53 [scrapy.extensions.telnet] debug: telnet console listening on 127.0.0.1:6023

此时需要向start_urls列表中插入爬虫的初始爬取地址，向redis列表中插入数据可使用如下命令：

lpush dangdang:book http://book.dangdang.com/

命令执行完后稍等片刻dangdangspider便会开始爬取数据，爬取到的数据结构如下图所示：

rediscrawlspider代码示例

# -*- coding: utf-8 -*-
import scrapy
import re
import urllib
from copy import deepcopy
from scrapy.spiders import crawlspider, rule
from scrapy.linkextractors import linkextractor
from scrapy_redis.spiders import rediscrawlspider


class dangdangcrawler(rediscrawlspider):
  name = 'dangdang2'
  allowed_domains = ['dangdang.com']
  redis_key = 'dangdang:book'
  pattern = re.compile(r"(http|https)://category.dangdang.com/cp(.*?).html", re.i)

  rules = (
    rule(linkextractor(allow=r'(http|https)://category.dangdang.com/cp(.*?).html'), callback='parse_book_list',
       follow=false),
  )

  def parse_book_list(self, response): # 从图书列表页提取数据
    item = {}
    item['book_list_page'] = response._url
    li_list = response.xpath("//ul[@class='bigimg']/li")
    for li in li_list:
      item["book_img"] = li.xpath("./a[@class='pic']/img/@src").extract_first()
      if item["book_img"] == "images/model/guan/url_none.png":
        item["book_img"] = li.xpath("./a[@class='pic']/img/@data-original").extract_first()
      item["book_name"] = li.xpath("./p[@class='name']/a/@title").extract_first()
      item["book_desc"] = li.xpath("./p[@class='detail']/text()").extract_first()
      item["book_price"] = li.xpath(".//span[@class='search_now_price']/text()").extract_first()
      item["book_author"] = li.xpath("./p[@class='search_book_author']/span[1]/a/text()").extract_first()
      item["book_publish_date"] = li.xpath("./p[@class='search_book_author']/span[2]/text()").extract_first()
      if item["book_publish_date"] is not none:
        item["book_publish_date"] = item["book_publish_date"].replace('/', '')
      item["book_press"] = li.xpath("./p[@class='search_book_author']/span[3]/a/text()").extract_first()
      yield deepcopy(item)

    # 提取下一页地址
    next_url = response.xpath("//li[@class='next']/a/@href").extract_first()
    if next_url is not none:
      next_url = urllib.parse.urljoin(response.url, next_url)
      yield scrapy.request(next_url, callback=self.parse_book_list)

 与dangdangspider爬虫类似，dangdangcrawler在获取不到初始爬取地址时也会阻塞在等待状态，当start_urls列表中有地址即开始爬取，爬取到的数据结构如下图所示：

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