文章目录

• 1. 写在前面
• 2. 抓包分析
• 3. Scrapy提交登陆请求
• 4. 列表与详情页面数据解析
• 5. 中间件Middleware配置

  【作者主页】：吴秋霖

  【作者介绍】：Python领域优质创作者、阿里云博客专家、华为云享专家。长期致力于Python与爬虫领域研究与开发工作！

  【作者推荐】：对JS逆向感兴趣的朋友可以关注《爬虫JS逆向实战》，对分布式爬虫平台感兴趣的朋友可以关注《分布式爬虫平台搭建与开发实战》

  还有未来会持续更新的验证码突防、APP逆向、Python领域等一系列文章

  1. 写在前面

    Scrapy是爬虫非常经典的一个框架，深受开发者喜爱！因其简洁高效的设计，被广泛选用于构建强大的爬虫工程。很多人会选择使用它来开发自己的爬虫工程。今天我将用一个论坛网站的示例来全面讲述Scrapy框架的使用

  以前都是底层开始，现在不一样了，一上来都是框架。导致很多人是知其然，但不知其所以然。而忽略了底层原理的理解

  ---

  目标网站（感兴趣的可以练练手）：

  aHR0cHM6Ly9mb3J1bS5heGlzaGlzdG9yeS5jb20v

  ---

  这是一个国外的BBS论坛，随手挑的一个曾经写过的案例。前几年做舆情相关的项目，写的爬虫真的是很多，境内外社交媒体、论坛、新闻资讯

  

  

  2. 抓包分析

    首先，我们打开这个网站，这个网站是要登陆的。我们先解决登陆这块，简单的构造一下登陆请求抓个包分析一下：

  

  上图就是登陆请求提交的参数，接下来我们需要在Scrapy爬虫工程的Spider中构造并实现登陆功能

  3. Scrapy提交登陆请求

    参数都都是明文的比较简单，唯一的一个sid也不是加密生成的，在HTML中就能够拿到

  很多时候一些接口某些参数，你看起来是密文，但是并不一定就是加密算法生成的，很有可能在HTML或者其它接口响应中就能获取的到

  sid获取如下：

  

  现在我们开始编写Scrapy爬虫中登陆的这部分代码，实现代码如下所示：

  def parse(self, response):
  	text = response.headers['Set-Cookie']
  	pa = re.compile("phpbb3_lzhqa_sid=(.*?);")
  	sid = pa.findall(text)[0]
  	response.meta['sid'] = sid
  	login_url = 'https://forum.axishistory.com/ucp.php?mode=login'
  	yield Request(login_url, meta=response.meta, callback=self.parse_login)
          
  def parse_login(self, response):
  	sid=response.meta['sid']
  	username ='用户名'
  	password = '密码'
  	formdata = {
  	    "username": username,
  	    "password": password,
  	    "sid": sid,
  	    "redirect": "index.php",
  	    "login": "Login",
  	}
  	yield FormRequest.from_response(response, formid='login', formdata=formdata, callback=self.parse_after_login)
  

  首先我们它通过parse函数从start_urls请求所响应的response中获取sid的值，然后继续交给parse_login的登陆函数实现模拟登陆

  另外说一下formid这个参数，在HTML文档中，表单通常通过标签定义，并且可以包含id属性，这个id属性就是表单的ID，如下一个HTML的示例：

  在上面的这个例子中，标签有一个id属性，其值为“login”。所以，formid这个参数用于指定表单，去构造登陆提交请求

  4. 列表与详情页面数据解析

    登陆处理完以后，我们就可以使用Scrapy爬虫继续对列表跟详情页构造请求并解析数据，这一部分的无非就是写XPATH规则了，基本对技术的要求并不高，如下使用XPATH测试工具编写列表页链接提取的规则：

  

  Scrapy列表页代码实现如下:

  def parse_page_list(self, response):
      pagination = response.meta.get("pagination", 1)
      details = response.xpath("//div[@class='inner']/ul/li")
      for detail in details:
          replies = detail.xpath("dl/dd[@class='posts']/text()").extract_first()
          views = detail.xpath("dl/dd[@class='views']/text()").extract_first()
          meta = response.meta
          meta["replies"] = replies
          meta["views"] = views
          detail_link = detail.xpath("dl//div[@class='list-inner']/a[@class='topictitle']/@href").extract_first()
          detail_title = detail.xpath("dl//div[@class='list-inner']/a[@class='topictitle']/text()").extract_first()
          meta["detail_title"] = detail_title
          yield Request(response.urljoin(detail_link), callback=self.parse_detail, meta=response.meta)
      next_page = response.xpath("//div[@class='pagination']/ul/li/a[@rel='next']/@href").extract_first()
      if next_page and pagination < self.pagination_num:
          meta = response.meta
          meta['pagination'] = pagination+1
          yield Request(response.urljoin(next_page), callback=self.parse_page_list, meta=meta)
  

  self.pagination_num是一个翻页最大采集数的配置，这个自行设定即可

  通过列表页我们拿到了所有贴文的链接，我们并在代码的最后使用了yield对列表页发起了请求，callback=self.parse_detail交给解析函数去提取数据

  首先我们定义在项目的items.py文件中定义Item数据结构，主要帖子跟评论的，如下所示：

  class AccountItem(Item):
      account_url = Field()                # 账号url
      account_id = Field()                 # 账号id
      account_name = Field()               # 账号名称
      nick_name = Field()                  # 昵称
      website_name = Field()               # 论坛名
      account_type = Field()               # 账号类型，固定forum
      level = Field()                      # 账号等级
      account_description = Field()        # 账号描述信息
      account_followed_num = Field()       # 账号关注数
      account_followed_list = Field()      # 账号关注id列表
      account_focus_num = Field()          # 账号粉丝数
      account_focus_list = Field()         # 账号粉丝id列表
      regist_time = Field()                # 账号注册时间
      forum_credits = Field()              # 论坛积分/经验值
      location = Field()                   # 地区
      post_num = Field()                   # 发帖数
      reply_num = Field()                  # 跟帖数
      msg_type = Field()
      area = Field()
      
  class PostItem(Item):
      type = Field()                 # "post"
      post_id = Field()              # 帖子id
      title = Field()                # 帖子标题
      content = Field()              # 帖子内容
      website_name = Field()         # 论坛名
      category = Field()             # 帖子所属版块
      url = Field()                  # 帖子url
      language = Field()             # 语种, zh_cn|en|es
      release_time = Field()         # 发布时间
      account_id = Field()            # 发帖人id
      account_name = Field()          # 发帖人账号名
      page_view_num = Field()        # 帖子浏览数
      comment_num = Field()          # 帖子回复数
      like_num = Field()             # 帖子点赞数
      quote_from =Field()            # 被转载的帖子id
      location_info = Field()        # 发帖地理位置信息
      images_url = Field()           # 帖子图片链接
      image_file = Field()           # 帖子图片存储路径
      msg_type = Field()
      area = Field()
  class CommentItem(Item):
      type = Field()                 # "comment"
      website_name = Field()         # 论坛名
      post_id = Field()
      comment_id = Field()
      content = Field()              # 回帖内容
      release_time = Field()         # 回帖时间
      account_id = Field()           # 帖子回复人id
      account_name = Field()         # 回帖人名称
      comment_level = Field()        # 回帖层级
      parent_id = Field()            # 回复的帖子或评论id
      like_num = Field()             # 回帖点赞数
      comment_floor = Field()        # 回帖楼层
      images_url = Field()           # 评论图片链接
      image_file = Field()           # 评论图片存储路径
      msg_type = Field()
      area = Field()
  

  接下来我们需要编写贴文内容的数据解析代码，解析函数代码实现如下所示：

  def parse_detail(self, response):
      dont_parse_post = response.meta.get("dont_parse_post")
      category = " < ".join(response.xpath("//ul[@id='nav-breadcrumbs']/li//span[@itemprop='title']/text()").extract()[1:])
      if dont_parse_post is None:
          msg_ele = response.xpath("//div[@id='page-body']//div[@class='inner']")[0]
          post_id = msg_ele.xpath("div//h3/a/@href").extract_first(default='').strip().replace("#p", "")
          post_item = PostItem()
          post_item["url"] = response.url
          post_item['area'] = self.name
          post_item['msg_type'] = u"贴文"
          post_item['type'] = u"post"
          post_item["post_id"] = post_id
          post_item["language"] = 'en'
          post_item["website_name"] = self.allowed_domains[0]
          post_item["category"] = category
          post_item["title"] = response.meta.get("detail_title")
          post_item["account_name"] = msg_ele.xpath("div//strong/a[@class='username']/text()").extract_first(default='').strip()
          post_item["content"] = "".join(msg_ele.xpath("div//div[@class='content']/text()").extract()).strip()
          post_time = "".join(msg_ele.xpath("div//p[@class='author']/text()").extract()).strip()
          post_item["release_time"] = dateparser.parse(post_time).strftime('%Y-%m-%d %H:%M:%S')
          post_item["collect_time"] = dateparser.parse(str(time.time())).strftime('%Y-%m-%d %H:%M:%S')
          user_link =msg_ele.xpath("div//strong/a[@class='username']/@href").extract_first(default='').strip()
          account_id = "".join(re.compile("&u=(\d+)").findall(user_link))
          post_item["account_id"] = account_id
          post_item["comment_num"] = response.meta.get("replies")
          post_item["page_view_num"] = response.meta.get("views")
          images_urls = msg_ele.xpath("div//div[@class='content']//img/@src").extract() or ""
          post_item["images_url"] = [response.urljoin(url) for url in images_urls]
          post_item["image_file"] = self.image_path(post_item["images_url"])
          post_item["language"] = 'en'
          post_item["website_name"] = self.name
          response.meta["post_id"] = post_id
          response.meta['account_id'] = post_item["account_id"]
          response.meta["account_name"] = post_item["account_name"]
          full_user_link = response.urljoin(user_link)
          yield Request(full_user_link, meta=response.meta, callback=self.parse_account_info)
      for comment_item in self.parse_comments(response):
          yield comment_item
      comment_next_page = response.xpath(u"//div[@class='pagination']/ul/li/a[@rel='next']/@href").extract_first()
      if comment_next_page:
          response.meta["dont_parse_post"] = 1
          next_page_link = response.urljoin(comment_next_page)
          yield Request(next_page_link, callback=self.parse_detail, meta=response.meta)
  

  贴文内容的下方就是评论信息，上面代码中我们拿到评论的链接comment_next_page，直接继续发送请求解析评论内容：

  

  def parse_comments(self, response):
      comments = response.xpath("//div[@id='page-body']//div[@class='inner']")
      if response.meta.get("dont_parse_post") is None:
          comments = comments[1:]
      for comment in comments:
          comment_item = CommentItem()
          comment_item['type'] = "comment"
          comment_item['area'] = self.name
          comment_item['msg_type'] = u"评论"
          comment_item['post_id'] = response.meta.get("post_id")
          comment_item["parent_id"] = response.meta.get("post_id")
          comment_item["website_name"] = self.allowed_domains[0]
          user_link =comment.xpath("div//strong/a[@class='username']/@href").extract_first(default='').strip()
          account_id = "".join(re.compile("&u=(\d+)").findall(user_link))
          comment_item['comment_id'] = comment.xpath("div//h3/a/@href").extract_first(default='').strip().replace("#p","")
          comment_item['account_id'] = account_id
          comment_item['account_name'] = comment.xpath("div//strong/a[@class='username']/text()").extract_first(default='').strip()
          comment_time = "".join(comment.xpath("div//p[@class='author']/text()").extract()).strip()
          if not comment_time:
              continue
          comment_level_text = comment.xpath("div//div[@id='post_content%s']//a[contains(@href,'./viewtopic.php?p')]/text()" % comment_item['comment_id']).extract_first(default='')
          comment_item['comment_level'] = "".join(re.compile("\d+").findall(comment_level_text))
          comment_item['release_time'] = dateparser.parse(comment_time).strftime('%Y-%m-%d %H:%M:%S')
          comment_content_list = "".join(comment.xpath("div//div[@class='content']/text()").extract()).strip()
          comment_item['content'] = "".join(comment_content_list)
          response.meta['account_id'] = comment_item["account_id"]
          response.meta["account_name"] = comment_item["account_name"]
          full_user_link = response.urljoin(user_link)
          yield Request(full_user_link, meta=response.meta, callback=self.parse_account_info)
  

  评论信息采集中还有一个针对评论用户信息采集的功能，通过调用parse_account_info函数进行采集，实现代码如下所示：

  def parse_account_info(self, response):
      about_item = AccountItem()
      about_item["account_id"] = response.meta["account_id"]
      about_item["account_url"] = response.url
      about_item["account_name"] = response.meta["account_name"]
      about_item["nick_name"] = ""
      about_item["website_name"] = self.allowed_domains[0]
      about_item["account_type"] = "forum"
      about_item["level"] = ""
      account_description = "".join(response.xpath("//div[@class='inner']/div[@class='postbody']//text()").extract())
      about_item["account_description"] = account_description
      about_item["account_followed_num"] = ""
      about_item["account_followed_list"] = ""
      about_item["account_focus_num"] = ""
      about_item["account_focus_list"] = ""
      regist_time = "".join(response.xpath("//dl/dt[text()='Joined:']/following-sibling::dd[1]/text()").extract())
      about_item["regist_time"] = dateparser.parse(regist_time).strftime('%Y-%m-%d %H:%M:%S')
      about_item["forum_credits"] = ""
      location = "".join(response.xpath("//dl/dt[text()='Location:']/following-sibling::dd[1]/text()").extract())
      about_item["location"] = location
      post_num_text = response.xpath("//dl/dt[text()='Total posts:']/following-sibling::dd[1]/text()[1]").extract_first(default='')
      post_num = post_num_text.replace(",",'').strip("|").strip()
      about_item["post_num"] = post_num
      about_item["reply_num"] = ""
      about_item["msg_type"] = 'account'
      about_item["area"] = self.name
      yield about_item
  

  最后从帖子到评论再到账号信息，层层采集与调用拿到完整的一个JSON结构化数据，进行yield到数据库

  5. 中间件Middleware配置

    因为是国外的论坛网站案例，所以这里我们需要使用我们的Middleware来解决这个问题：

  class ProxiesMiddleware():
      logfile = logging.getLogger(__name__)
      def process_request(self, request, spider):
          self.logfile.debug("entry ProxyMiddleware")
          try:
              # 依靠meta中的标记，来决定是否需要使用proxy
              proxy_addr = spider.proxy
              if proxy_addr:
                  if request.url.startswith("http://"):
                      request.meta['proxy'] = "http://" + proxy_addr  # http代理
                  elif request.url.startswith("https://"):
                      request.meta['proxy'] = "https://" + proxy_addr  # https代理
          except Exception as e:
              exc_type, exc_obj, exc_tb = sys.exc_info()
              fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
              self.logfile.warning(u"Proxies error: %s, %s, %s, %s" %
                                   (exc_type, e, fname, exc_tb.tb_lineno))
  

  settings文件中配置开启Middleware:

  DOWNLOADER_MIDDLEWARES = {
  	'forum.middlewares.ProxiesMiddleware': 100,
  }
  

    好了，到这里又到了跟大家说再见的时候了。创作不易，帮忙点个赞再走吧。你的支持是我创作的动力，希望能带给大家更多优质的文章

   
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