Python爬虫scrapy-redis分布式实例（一）

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

import scrapy
import sys
reload(sys)
sys.setdefaultencoding("utf-8")


class SinanewsItem(scrapy.Item):
    # 大类的标题和url
    parentTitle = scrapy.Field()
    parentUrls = scrapy.Field()

    # 小类的标题和子url
    subTitle = scrapy.Field()
    subUrls = scrapy.Field()

    # 小类目录存储路径
    subFilename = scrapy.Field()

    # 小类下的子链接
    sonUrls = scrapy.Field()

    # 文章标题和内容
    head = scrapy.Field()
    content = scrapy.Field()

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

import scrapy
import os
from sinaNews.items import SinanewsItem
from scrapy_redis.spiders import RedisSpider
import sys
reload(sys)
sys.setdefaultencoding("utf-8")


class SinaSpider(RedisSpider):
    name = "sina"
    # 启动爬虫的命令
    redis_key = "sinaspider:strat_urls"
　　# 动态定义爬虫爬取域范围
    def __init__(self, *args, **kwargs):
        domain = kwargs.pop(‘domain‘, ‘‘)
        self.allowed_domains = filter(None, domain.split(‘,‘))
        super(SinaSpider, self).__init__(*args, **kwargs)
      
    def parse(self, response):
        items= []
        # 所有大类的url 和 标题
        parentUrls = response.xpath(‘//div[@id="tab01"]/div/h3/a/@href‘).extract()
        parentTitle = response.xpath(‘//div[@id="tab01"]/div/h3/a/text()‘).extract()

        # 所有小类的ur 和 标题
        subUrls  = response.xpath(‘//div[@id="tab01"]/div/ul/li/a/@href‘).extract()
        subTitle = response.xpath(‘//div[@id="tab01"]/div/ul/li/a/text()‘).extract()

        #爬取所有大类
        for i in range(0, len(parentTitle)):
            
            # 爬取所有小类
            for j in range(0, len(subUrls)):
                item = SinanewsItem()

                # 保存大类的title和urls
                item[‘parentTitle‘] = parentTitle[i]
                item[‘parentUrls‘] = parentUrls[i]

                # 检查小类的url是否以同类别大类url开头，如果是返回True (sports.sina.com.cn 和 sports.sina.com.cn/nba)
                if_belong = subUrls[j].startswith(item[‘parentUrls‘])

                # 如果属于本大类，将存储目录放在本大类目录下
                if(if_belong):
                    
                    # 存储 小类url、title和filename字段数据
                    item[‘subUrls‘] = subUrls[j]
                    item[‘subTitle‘] =subTitle[j]
                    items.append(item)

        #发送每个小类url的Request请求，得到Response连同包含meta数据 一同交给回调函数 second_parse 方法处理
        for item in items:
            yield scrapy.Request( url = item[‘subUrls‘], meta={‘meta_1‘: item}, callback=self.second_parse)

    #对于返回的小类的url，再进行递归请求
    def second_parse(self, response):
        # 提取每次Response的meta数据
        meta_1= response.meta[‘meta_1‘]

        # 取出小类里所有子链接
        sonUrls = response.xpath(‘//a/@href‘).extract()

        items= []
        for i in range(0, len(sonUrls)):
            # 检查每个链接是否以大类url开头、以.shtml结尾，如果是返回True
            if_belong = sonUrls[i].endswith(‘.shtml‘) and sonUrls[i].startswith(meta_1[‘parentUrls‘])

            # 如果属于本大类，获取字段值放在同一个item下便于传输
            if(if_belong):
                item = SinanewsItem()
                item[‘parentTitle‘] =meta_1[‘parentTitle‘]
                item[‘parentUrls‘] =meta_1[‘parentUrls‘]
                item[‘subUrls‘] = meta_1[‘subUrls‘]
                item[‘subTitle‘] = meta_1[‘subTitle‘]
                item[‘sonUrls‘] = sonUrls[i]
                items.append(item)

        #发送每个小类下子链接url的Request请求，得到Response后连同包含meta数据 一同交给回调函数 detail_parse 方法处理
        for item in items:
                yield scrapy.Request(url=item[‘sonUrls‘], meta={‘meta_2‘:item}, callback = self.detail_parse)

    # 数据解析方法，获取文章标题和内容
    def detail_parse(self, response):
        item = response.meta[‘meta_2‘]
        content = ""
        head = response.xpath(‘//h1[@id="main_title"]/text()‘)
        content_list = response.xpath(‘//div[@id="artibody"]/p/text()‘).extract()

        # 将p标签里的文本内容合并到一起
        for content_one in content_list:
            content += content_one

        item[‘head‘]= head[0] if len(head) > 0 else "NULL"
        item[‘content‘]= content

        yield item

SPIDER_MODULES = [‘sinaNews.spiders‘]
NEWSPIDER_MODULE = ‘sinaNews.spiders‘


# 使用scrapy-redis里的去重组件，不使用scrapy默认的去重方式
DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"
# 使用scrapy-redis里的调度器组件，不使用默认的调度器
SCHEDULER = "scrapy_redis.scheduler.Scheduler"
# 允许暂停，redis请求记录不丢失
SCHEDULER_PERSIST = True
# 默认的scrapy-redis请求队列形式（按优先级）
SCHEDULER_QUEUE_CLASS = "scrapy_redis.queue.SpiderPriorityQueue"
# 队列形式，请求先进先出
#SCHEDULER_QUEUE_CLASS = "scrapy_redis.queue.SpiderQueue"
# 栈形式，请求先进后出
#SCHEDULER_QUEUE_CLASS = "scrapy_redis.queue.SpiderStack"

# 只是将数据放到redis数据库，不需要写pipelines文件
ITEM_PIPELINES = {
#    ‘Sina.pipelines.SinaPipeline‘: 300,
    ‘scrapy_redis.pipelines.RedisPipeline‘: 400,
}

# LOG_LEVEL = ‘DEBUG‘

# Introduce an artifical delay to make use of parallelism. to speed up the
# crawl.
DOWNLOAD_DELAY = 1
# 指定数据库的主机IP
REDIS_HOST = "192.168.13.26"
# 指定数据库的端口号
REDIS_PORT = 6379

scrapy runspider sina.py

redis-cli> lpush sinaspider:start_urls http://news.sina.com.cn/guide/