scrapy-redis分布式爬虫案例（房天下）
运行效果动图：
简述：本案例中有一台Linux系统运行Redis服务器，两台Windows系统跑分布式爬虫。爬虫从Redis队列中得到要爬取的URL，同时redis负责队列中URL的去重以及爬虫因某些原因暂停或者终止时，下次开启爬虫自动继续上次未完成的URL继续爬取，不会重头开始爬，当爬虫爬空Redis中的URL时，就会处于等待状态，次数可以设置等待一段时间，如果队列中还是没有新增要爬取的URL就自动关闭爬虫，避免爬虫一直处于等待状态而占用资源。
sfw.py
import scrapy
import re
from fang.items import NewHouseItem, OldHouseItem
from scrapy_redis.spiders import RedisSpider
class SfwSpider(RedisSpider):
name = 'sfw'
allowed_domains = ['fang.com']
# start_urls = ['https://www.fang.com/SoufunFamily.htm']
redis_key = 'fang:start_url'
def parse(self, response):
trs = response.xpath("//div[@class='outCont']//tr")
province = None
for tr in trs:
tds = tr.xpath(".//td[not(@class)]")
province_td = tds[0]
province_text = province_td.xpath(".//text()").get()
province_text = re.sub(r'\s', '', province_text)
if province_text:
province = province_text
if province == '其它':
continue
city_td = tds[1]
city_links = city_td.xpath(".//a")
for city_link in city_links:
city = city_link.xpath(".//text()").get()
city_url = city_link.xpath(".//@href").get()
# print("省份", province)
# print("城市", city)
# print("城市链接", city_url)
url_module = city_url.split("//")
scheme = url_module[0]
domain = url_module[1]
if 'bj.' in domain:
newHouse_url = 'https://newhouse.fang.com/house/s/'
oldHouse_url = 'https://esf.fang.com'
else:
# 构建新房URL
newHouse_url = scheme+'//'+'newHouse.'+domain+'/house/s/'
# 构建二手房房URL
oldHouse_url = scheme+'//'+"esf."+domain
# print("城市：%s%s" % (province, city))
# print("新房链接: %s" % newHouse_url )
# print("二手房链接: %s" % esf_url )
yield scrapy.Request(url=newHouse_url, callback=self.parse_newhouse, meta={'info': (province, city)})
yield scrapy.Request(url=oldHouse_url, callback=self.parse_oldhouse, meta={'info': (province, city)})
# break
# break
def parse_newhouse(self, response):
province, city = response.meta.get('info')
lis = response.xpath("//div[contains(@class,'nl_con')]/ul/li")
for li in lis:
name = li.xpath(".//div[@class='nlcd_name']/a/text()").get()
if name is not None:
name = name.strip()
# print(name)
house_type = li.xpath(".//div[contains(@class, 'house_type')]/a/text()").getall()
house_type_list =list(map(lambda x: re.sub(r"\s", "", x), house_type))
rooms =list(filter(lambda x: x.endswith('居'), house_type_list))
# print(house_type_list)
area = " ".join(li.xpath(".//div[contains(@class, 'house_type')]/text()").getall())
area = re.sub(r"\s|－|/", "", area)
# print(area)
address = li.xpath(".//div[@class='address']/a/@title").get()
district = li.xpath(".//div[@class='address']/a//text()").getall()
district_text = "".join(district)
district = re.search(r".*\[(.+)\].*", district_text)
if district is not None:
district = district.group(1)
sale = li.xpath(".//div[contains(@class,'fangyuan')]/span/text()").get()
price = "".join(li.xpath(".//div[@class = 'nhouse_price']//text()").getall())
price = re.sub(r"\s|广告", "", price)
url = li.xpath(".//div[@class = 'nlcd_name']/a/@href").get()
if url is not None:
url = "https:"+url
item = NewHouseItem(province=province, city=city, name=name, rooms=rooms, area=area,
district=district, address=address, price=price, sale=sale, url=url)
yield item
domain_url = "https://newhouse.fang.com"
next_url = response.xpath("//a[@class='next']/@href").get()
# next_url = domain_url+next_url
# print("下一页链接："+next_url)
if next_url:
scrapy.Request(url=response.urljoin(next_url), callback=self.parse_newhouse, meta={'info': (province, city)})
def parse_oldhouse(self, response):
province, city = response.meta.get('info')
print("二手房市场")
dls = response.xpath("//div[contains(@class, 'shop_list')]/dl[contains(@dataflag,'bg')]")
# dls = response.xpath("//div[contains(@class, 'shop_list')]/dl")
for dl in dls:
# print("dl循环")
item = OldHouseItem(province=province, city=city)
# name =dl.xpath(".//p[@class='add_shop']//text()").get()
item['name'] = dl.xpath(".//p[@class='add_shop']/a/text()").get().strip()
infos = dl.xpath(".//p[@class='tel_shop']/text()").getall()
infos = list(map(lambda x:re.sub(r"\s", "", x), infos))
for info in infos:
if '厅' in info:
item['rooms'] = info
elif '㎡' in info:
item['area'] = info
elif '层' in info:
item['floor'] = info
elif '向' in info:
item['toward'] = info
elif '年' in info:
item['year'] = info.replace("年建", "")
# print(item)
item['address'] = dl.xpath(".//p[@class='add_shop']/span/text()").get()
item['price'] = "".join(dl.xpath(".//dd[@class='price_right']/span[1]//text()").getall())
item['unit'] = dl.xpath(".//dd[@class='price_right']/span[2]//text()").get()
detail_url = dl.xpath(".//h4/a/@href").get()
item['url'] = response.urljoin(detail_url)
yield item
next_url = response.xpath("//div[@class='page_al']/p[1]/a/@href").get()
# print(next_url)
if next_url is not None:
scrapy.Request(url=response.urljoin(next_url), callback=self.parse_oldhouse, meta={'info': (province, city)})
pipelines.py
from scrapy.exporters import JsonLinesItemExporter
class FangPipeline(object):
def __init__(self):
self.newHouse_fp = open('newHouse.json', 'wb')
self.oldHouse_fp = open('oldHouse.json', 'wb')
self.newHouse_exporter = JsonLinesItemExporter(self.newHouse_fp, ensure_ascii=False)
self.oldHouse_exporter = JsonLinesItemExporter(self.oldHouse_fp, ensure_ascii=False)
def process_item(self, item, spider):
self.newHouse_exporter.export_item(item)
self.oldHouse_exporter.export_item(item)
return item
def close_spider(self):
self.newHouse_fp.close()
self.oldHouse_fp.close()settings.py
# Scrapy-Redis相关配置
# 确保request存储到redis中
SCHEDULER = "scrapy_redis.scheduler.Scheduler"
# 确保所有爬虫共享相同的去重指纹
DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"
# 设置redis为item pipeline
ITEM_PIPELINES = {
'scrapy_redis.pipelines.RedisPipeline': 300
}
# 在redis中保持scrapy-redis用到的队列，不会清理redis中的队列，从而可以实现暂停和恢复的功能。
SCHEDULER_PERSIST = True
# 设置连接redis信息
# REDIS_HOST = '127.0.0.1'
REDIS_HOST = '192.168.150.134'
REDIS_PORT = 8888
# 分布式爬虫防止爬空，当redis队列中没有要爬的URL时，爬虫仍会处于等待中，
MYEXT_ENABLED = True
# IDLE_NUMBER = 360 # 半个小时 配置空闲持续时间单位为 360个 ，一个时间单位为5s
IDLE_NUMBER = 60
EXTENSIONS = {
'fang.extensions.RedisSpiderSmartIdleClosedExensions': 500,
}