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'] = sidlogin_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：
<form id="login" method="post" action="/login"><!-- 表单的其他字段 --><input type="text" name="username"><input type="password" name="password"><!-- 其他表单字段 --><input type="submit" value="Login">
</form>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.metameta["replies"] = repliesmeta["views"] = viewsdetail_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_titleyield 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.metameta['pagination'] = pagination+1yield 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，<font 并通过color=#ff0033 size=3>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urlaccount_id = Field() # 账号idaccount_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forumlevel = 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idtitle = 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urllanguage = Field() # 语种, zh_cn|en|esrelease_time = Field() # 发布时间account_id = Field() # 发帖人idaccount_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idlocation_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idaccount_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idlike_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.urlpost_item['area'] = self.namepost_item['msg_type'] = u"贴文"post_item['type'] = u"post"post_item["post_id"] = post_idpost_item["language"] = 'en'post_item["website_name"] = self.allowed_domains[0]post_item["category"] = categorypost_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_idpost_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.nameresponse.meta["post_id"] = post_idresponse.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_itemcomment_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"] = 1next_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.namecomment_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_idcomment_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:continuecomment_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.urlabout_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_descriptionabout_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"] = locationpost_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_numabout_item["reply_num"] = ""about_item["msg_type"] = 'account'about_item["area"] = self.nameyield 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proxyproxy_addr = spider.proxyif 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
