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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Scrapy-Redis之RedisSpider与RedisCrawlSpider详解
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