目录
1.定义采集数据的存储结构
2.定义管道文件
3.定义中间件文件
4.scrapy爬虫设置文件修改
5.商品类目抓取
6.商品信息抓取
7.店铺信息抓取
8.评论信息抓取
9.抓取过程
10.基本数据展示
1.定义采集数据的存储结构
【存储结构说明】class CategoriesItem(Item)：存储京东类目信息class ProductsItem(Item)：存储京东商品信息class ShopItem(Item)：存储京东店铺信息class CommentSummaryItem(Item)：存储京东每个商品的评论概况信息class CommentItem(Item)：存储京东每个商品的评论基本信息class CommentImageItem(Item)：存储京东每个商品中每条评论的图像信息说明：类中所定义字段可依据具体采集要求或response内容进行调整
【items.py程序】
# -*- coding: utf-8 -*-# Define here the models for your scraped items## See documentation in:# /en/latest/topics/items.htmlfrom scrapy import Item, Fieldclass CategoriesItem(Item):"""存储京东类目信息"""name = Field() # 商品三级类目名称url = Field()# 商品三级类目对应url_id = Field()# 商品类目对应id[一级id,二级id,三级id]class ProductsItem(Item):"""存储京东商品信息"""name = Field() # 商品名称url = Field() # 商品url[用于商品主图提取]_id = Field() # 商品skucategory = Field() # 商品三级类目description = Field() # 商品描述shopId = Field()# 商品所在店铺id(名称)commentCount = Field()# 商品评价总数=mentCount# goodComment = Field() # 商品好评数# generalComment = Field() # 商品中评数# poolComment = Field() # 商品差评数# favourableDesc1 = Field() # 商品优惠描述1# favourableDesc2 = Field() # 商品优惠描述2# venderId = Field() # 供应商id# reallyPrice = Field() # 商品现价# originalPrice = Field() # 商品原价class ShopItem(Item):_id = Field() # 店铺urlshopName = Field() # 店铺名称shopItemScore = Field() # 店铺[商品评价]shopLgcScore = Field()# 店铺[物流履约]shopAfterSale = Field() # 店铺[售后服务]class CommentItem(Item):_id = Field() # 评论idproductId = Field() # 商品id=skuguid = Field() # 评论全局唯一标识符firstCategory = Field() # 商品一级类目secondCategory = Field() # 商品二级类目thirdCategory = Field() # 商品三级类目score = Field() # 用户评分nickname = Field() # 用户昵称plusAvailable = Field() # 用户账户等级(201：PLUS, 103:普通用户，0：无价值用户)content = Field()# 评论内容creationTime = Field()# 评论时间replyCount = Field() # 评论的评论数usefulVoteCount = Field() # 用户评论的被点赞数imageCount = Field() # 评论中图片的数量class CommentImageItem(Item):_id = Field() # 晒图对应id(1张图对应1个id)commentGuid = Field() # 晒图所在评论的全局唯一标识符guidimgId = Field() # 晒图对应idimgUrl = Field()# 晒图urlimgTitle = Field() # 晒图标题imgStatus = Field() # 晒图状态class CommentSummaryItem(Item):"""商品评论总结"""_id = Field() # 商品skuproductId = Field() # 商品pidcommentCount = Field()# 商品累计评论数score1Count = Field() # 用户评分为1的数量score2Count = Field() # 用户评分为2的数量score3Count = Field() # 用户评分为3的数量score4Count = Field() # 用户评分为3的数量score5Count = Field() # 用户评分为5的数量
2.定义管道文件
【管道文件说明】数据库：MongoDB数据库名称：JD数据库集合：Categories、Products、Shop、CommentSummary、Comment和CommentImage处理过程：先判断待插入数据库集合类型是否匹配，然后插入，并为重复数据插入抛出异常
【pipelines.py】
# -*- coding: utf-8 -*-# Define your item pipelines here## Don't forget to add your pipeline to the ITEM_PIPELINES setting# See: /en/latest/topics/item-pipeline.htmlimport pymongofrom JDSpider.items import *class MongoDBPipeline(object):def __init__(self):clinet = pymongo.MongoClient("localhost", 27017)db = clinet["JD"]self.Categories = db["Categories"]self.Products = db["Products"]self.Shop = db["Shop"]ment = db["Comment"]mentImage = db["CommentImage"]mentSummary = db["CommentSummary"]def process_item(self, item, spider):""" 判断item的类型，并作相应的处理，再入数据库 """if isinstance(item, CategoriesItem):try:self.Categories.insert(dict(item))except Exception as e:print('get failed:', e)elif isinstance(item, ProductsItem):try:self.Products.insert(dict(item))except Exception as e:print('get failed:', e)elif isinstance(item, ShopItem):try:self.Shop.insert(dict(item))except Exception as e:print('get failed:', e)elif isinstance(item, CommentItem):try:ment.insert(dict(item))except Exception as e:print('get failed:', e)elif isinstance(item, CommentImageItem):try:mentImage.insert(dict(item))except Exception as e:print('get failed:', e)elif isinstance(item, CommentSummaryItem):try:mentSummary.insert(dict(item))except Exception as e:print('get failed:', e)elif isinstance(item, ShopItem):try:self.Shop.insert(dict(item))except Exception as e:print('get failed:', e)return item
3.定义中间件文件
【中间件文件说明】包括“爬虫代理中间件”和“缓存中间件”爬虫代理中间件：防止连续请求被京东后台发现并拉黑缓存中间件：判断京东后台服务器响应情况，并作出针对性处理
【middlewares.py】
# -*- coding: utf-8 -*-# Define here the models for your spider middleware## See documentation in:# /en/latest/topics/spider-middleware.htmlimport osimport loggingfrom scrapy.exceptions import IgnoreRequestfrom scrapy.utils.response import response_status_messagefrom scrapy.downloadermiddlewares.retry import RetryMiddlewareimport randomlogger = logging.getLogger(__name__)class UserAgentMiddleware(object):""" 换User-Agent """def process_request(self, request, spider):"""设置爬虫代理"""with open("E://proxy.txt", "r") as f:PROXIES = f.readlines()agent = random.choice(PROXIES)agent = agent.strip()request.headers["User-Agent"] = agentclass CookiesMiddleware(RetryMiddleware):""" 维护Cookie """def process_request(self, request, spider):passdef process_response(self, request, response, spider):if response.status in [300, 301, 302, 303]:try:reason = response_status_message(response.status)return self._retry(request, reason, spider) or response # 重试except Exception as e:raise IgnoreRequestelif response.status in [403, 414]:logger.error("%s! Stopping..." % response.status)os.system("pause")else:return response
4.scrapy爬虫设置文件修改
【修改说明】robot协议：置位False，防止京东网站不允许爬虫抓取数据爬虫最大并发请求：可依据电脑实际性能进行设置下载中间件优先级：值越小，优先级越高管道文件优先级：值越小，优先级越高说明：代码文件过长，故不再展示
5.商品类目抓取
【商品类目抓取说明】有些类别里面包含有很多子类别，所以对于这样的url，需要再次yield并进行抓取
texts = selector.xpath('//div[@class="category-item m"]/div[@class="mc"]/div[@class="items"]/dl/dd/a').extract()for text in texts:# 获取全部三级类目链接+三级类目名称items = re.findall(r'<a href="(.*?)" target="_blank">(.*?)</a>', text)for item in items:# 判断“商品链接”是否需要继续请求if item[0].split('.')[0][2:] in key_word:if item[0].split('.')[0][2:] != 'list':yield Request(url='https:' + item[0], callback=self.parse_category)else:# 记录一级类目：名称/可提数URL/id编码categoriesItem = CategoriesItem()categoriesItem['name'] = item[1]categoriesItem['url'] = 'https:' + item[0]categoriesItem['_id'] = item[0].split('=')[1].split('&')[0]yield categoriesItemmeta = dict()meta["category"] = item[0].split("=")[1]yield Request(url='https:' + item[0], callback=self.parse_list, meta=meta)
6.商品信息抓取
【店铺信息抓取说明】流程：访问每个类别的url，在产品列表中获取每个商品对应的url,进入详情页面抓取产品的详情注意：此处要通过分析得出翻页请求对应的response地址，并解析规律进行翻页
【获取商品链接】
selector = Selector(response)texts = selector.xpath('//*[@id="J_goodsList"]/ul/li/div/div[@class="p-img"]/a').extract()for text in texts:items = text.split("=")[3].split('"')[1]yield Request(url='https:' + items, callback=self.parse_product, meta=meta)# 翻页[仅翻前50页]maxPage = int(response.xpath('//div[@id="J_filter"]/div/div/span/i/text()').extract()[0])if maxPage > 1:if maxPage > 50:maxPage = 50for i in range(2, maxPage):num = 2*i - 1caterory = meta["category"].split(",")[0]+'%2C' + meta["category"].split(",")[1] + '%2C' + meta["category"].split(",")[2]url = list_url % (caterory, num, 30*num)print('products next page:', url)yield Request(url=url, callback=self.parse_list2, meta=meta)
7.店铺信息抓取
【店铺信息抓取说明】店铺信息在抓取商品信息的页面便可以获取但是，要区分自营和非自营，因为自营缺少一些内容
# 商品在售店铺id+店铺信息获取shopItem["shopName"] = response.xpath('//div[@class="m m-aside popbox"]/div/div/h3/a/text()').extract()[0]shopItem["_id"] = "https:" + response.xpath('//div[@class="m m-aside popbox"]/div/div/h3/a/@href').extract()[0]productsItem['shopId'] = shopItem["_id"]# 区分是否自营res = response.xpath('//div[@class="score-parts"]/div/span/em/@title').extract()if len(res) == 0:shopItem["shopItemScore"] = "京东自营"shopItem["shopLgcScore"] = "京东自营"shopItem["shopAfterSale"] = "京东自营"else:shopItem["shopItemScore"] = res[0]shopItem["shopLgcScore"] = res[1]shopItem["shopAfterSale"] = res[2]# shopItem["_id"] = response.xpath('//div[@class="m m-aside popbox"]/div/div/h3/a/@href').extract()[0].split("-")[1].split(".")[0]yield shopItem
8.评论信息抓取
【评论信息抓取说明】评论的信息也是动态加载，返回的格式也是json，且会不定期进行更新，访问格式如下：
comment_url = '/comment/productPageComments.action?productId=%s&score=0&sortType=5&page=%s&pageSize=10'
def parse_comments(self, response):"""获取商品评论:param response: 评论相应的json脚本:return:"""try:data = json.loads(response.text)except Exception as e:print('get comment failed:', e)return Noneproduct_id = response.meta['product_id']# 商品评论概况获取[仅导入一次]commentSummaryItem = CommentSummaryItem()commentSummary = data.get('productCommentSummary')commentSummaryItem['_id'] = commentSummary.get('skuId')commentSummaryItem['productId'] = commentSummary.get('productId')commentSummaryItem['commentCount'] = commentSummary.get('commentCount')commentSummaryItem['score1Count'] = commentSummary.get('score1Count')commentSummaryItem['score2Count'] = commentSummary.get('score2Count')commentSummaryItem['score3Count'] = commentSummary.get('score3Count')commentSummaryItem['score4Count'] = commentSummary.get('score4Count')commentSummaryItem['score5Count'] = commentSummary.get('score5Count')# 判断commentSummaryItem类型yield commentSummaryItem# 商品评论[第一页，剩余页面评论由，parse_comments2]for comment_item in data['comments']:comment = CommentItem()comment['_id'] = str(product_id)+","+str(comment_item.get("id"))comment['productId'] = product_idcomment["guid"] = comment_item.get('guid')comment['firstCategory'] = comment_item.get('firstCategory')comment['secondCategory'] = comment_item.get('secondCategory')comment['thirdCategory'] = comment_item.get('thirdCategory')comment['score'] = comment_item.get('score')comment['nickname'] = comment_item.get('nickname')comment['plusAvailable'] = comment_item.get('plusAvailable')comment['content'] = comment_item.get('content')comment['creationTime'] = comment_item.get('creationTime')comment['replyCount'] = comment_item.get('replyCount')comment['usefulVoteCount'] = comment_item.get('usefulVoteCount')comment['imageCount'] = comment_item.get('imageCount')yield comment# 存储当前用户评论中的图片if 'images' in comment_item:for image in comment_item['images']:commentImageItem = CommentImageItem()commentImageItem['commentGuid'] = comment_item.get('guid')commentImageItem['imgId'] = image.get('id')commentImageItem['_id'] = str(product_id)+","+str(comment_item.get('id'))+","+str(image.get('id'))commentImageItem['imgUrl'] = 'http:' + image.get('imgUrl')commentImageItem['imgTitle'] = image.get('imgTitle')commentImageItem['imgStatus'] = image.get('status')yield commentImageItem# 评论翻页[尽量保证评分充足]max_page = int(data.get('maxPage', '1'))# if max_page > 60:## 设置评论的最大翻页数#max_page = 60for i in range(1, max_page):url = comment_url % (product_id, str(i))meta = dict()meta['product_id'] = product_idyield Request(url=url, callback=self.parse_comments2, meta=meta)
9.抓取过程
10.基本数据展示
有数据需要的可以联系，数据非常大
