将已有的新浪网分类资讯Scrapy爬虫项目,修改为基于RedisSpider类的scrapy-redis分布式爬虫项目
注:items数据直接存储在Redis数据库中,这个功能已经由scrapy-redis自行实现。除非单独做额外处理(比如直接存入本地数据库等),否则不用编写pipelines.py代码。
items.py文件
# items.py
# -*- coding: utf-8 -*-
import scrapy
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
class SinaItem(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()
settings.py文件
# settings.py
SPIDER_MODULES = ['Sina.spiders']
NEWSPIDER_MODULE = 'Sina.spiders'
USER_AGENT = 'scrapy-redis (+https://github.com/rolando/scrapy-redis)'
DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"
SCHEDULER = "scrapy_redis.scheduler.Scheduler"
SCHEDULER_PERSIST = True
SCHEDULER_QUEUE_CLASS = "scrapy_redis.queue.SpiderPriorityQueue"
#SCHEDULER_QUEUE_CLASS = "scrapy_redis.queue.SpiderQueue"
#SCHEDULER_QUEUE_CLASS = "scrapy_redis.queue.SpiderStack"
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
REDIS_HOST = "192.168.13.26"
REDIS_PORT = 6379
spiders/sina.py
# sina.py
# -*- coding: utf-8 -*-
from Sina.items import SinaItem
from scrapy_redis.spiders import RedisSpider
#from scrapy.spiders import Spider
import scrapy
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
#class SinaSpider(Spider):
class SinaSpider(RedisSpider):
name= "sina"
redis_key = "sinaspider:start_urls"
#allowed_domains= ["sina.com.cn"]
#start_urls= [
# "http://news.sina.com.cn/guide/"
#]#起始urls列表
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)):
# 指定大类的路径和目录名
#parentFilename = "./Data/" + parentTitle[i]
#如果目录不存在,则创建目录
#if(not os.path.exists(parentFilename)):
# os.makedirs(parentFilename)
# 爬取所有小类
for j in range(0, len(subUrls)):
item = SinaItem()
# 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):
#subFilename =parentFilename + '/'+ subTitle[j]
# 如果目录不存在,则创建目录
#if(not os.path.exists(subFilename)):
# os.makedirs(subFilename)
# 存储 小类url、title和filename字段数据
item['subUrls'] = subUrls[j]
item['subTitle'] =subTitle[j]
#item['subFilename'] = subFilename
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 = SinaItem()
item['parentTitle'] =meta_1['parentTitle']
item['parentUrls'] =meta_1['parentUrls']
item['subUrls'] =meta_1['subUrls']
item['subTitle'] =meta_1['subTitle']
#item['subFilename'] = meta_1['subFilename']
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()').extract()
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
执行:
slave端:
scrapy runspider sina.py
Master端:
redis-cli> lpush sinaspider:start_urls http://news.sina.com.cn/guide/