Python爬虫从入门到放弃（十九）之 Scrapy爬取所有知乎用户信息(下)

在上一篇文章中主要写了关于爬虫过程的分析，下面是代码的实现，完整代码在：

​​https://github.com/pythonsite/spider​​

items中的代码主要是我们要爬取的字段的定义

class UserItem(scrapy.Item):     id = Field()     name = Field()     account_status = Field()     allow_message= Field()     answer_count = Field()     articles_count = Field()     avatar_hue = Field()     avatar_url = Field()     avatar_url_template = Field()     badge = Field()     business = Field()     employments = Field()     columns_count = Field()     commercial_question_count = Field()     cover_url = Field()     description = Field()     educations = Field()     favorite_count = Field()     favorited_count = Field()     follower_count = Field()     following_columns_count = Field()     following_favlists_count = Field()     following_question_count = Field()     following_topic_count = Field()     gender = Field()     headline = Field()     hosted_live_count = Field()     is_active = Field()     is_bind_sina = Field()     is_blocked = Field()     is_advertiser = Field()     is_blocking = Field()     is_followed = Field()     is_following = Field()     is_force_renamed = Field()     is_privacy_protected = Field()     locations = Field()     is_org = Field()     type = Field()     url = Field()     url_token = Field()     user_type = Field()     logs_count = Field()     marked_answers_count = Field()     marked_answers_text = Field()     message_thread_token = Field()     mutual_followees_count = Field()     participated_live_count = Field()     pins_count = Field()     question_count = Field()     show_sina_weibo = Field()     thank_from_count = Field()     thank_to_count = Field()     thanked_count = Field()     type = Field()     vote_from_count = Field()     vote_to_count = Field()     voteup_count = Field()

这些字段的是在用户详细信息里找到的，如下图所示，这里一共有58个字段，可以详细研究每个字段代表的意思：

关于spiders中爬虫文件zhihu.py中的主要代码

这段代码是非常重要的，主要的处理逻辑其实都是在这里

class ZhihuSpider(scrapy.Spider):     name = "zhihu"     allowed_domains = ["www.zhihu.com"]     start_urls = ['http://www.zhihu.com/']     #这里定义一个start_user存储我们找的大V账号     start_user = "excited-vczh"      #这里把查询的参数单独存储为user_query,user_url存储的为查询用户信息的url地址     user_url = "https://www.zhihu.com/api/v4/members/{user}?include={include}"     user_query = "locations,employments,gender,educations,business,voteup_count,thanked_Count,follower_count,following_count,cover_url,following_topic_count,following_question_count,following_favlists_count,following_columns_count,avatar_hue,answer_count,articles_count,pins_count,question_count,columns_count,commercial_question_count,favorite_count,favorited_count,logs_count,marked_answers_count,marked_answers_text,message_thread_token,account_status,is_active,is_bind_phone,is_force_renamed,is_bind_sina,is_privacy_protected,sina_weibo_url,sina_weibo_name,show_sina_weibo,is_blocking,is_blocked,is_following,is_followed,mutual_followees_count,vote_to_count,vote_from_count,thank_to_count,thank_from_count,thanked_count,description,hosted_live_count,participated_live_count,allow_message,industry_category,org_name,org_homepage,badge[?(type=best_answerer)].topics"      #follows_url存储的为关注列表的url地址,fllows_query存储的为查询参数。这里涉及到offset和limit是关于翻页的参数，0，20表示第一页     follows_url = "https://www.zhihu.com/api/v4/members/{user}/followees?include={include}&offset={offset}&limit={limit}"     follows_query = "data%5B*%5D.answer_count%2Carticles_count%2Cgender%2Cfollower_count%2Cis_followed%2Cis_following%2Cbadge%5B%3F(type%3Dbest_answerer)%5D.topics"      #followers_url是获取粉丝列表信息的url地址，followers_query存储的为查询参数。     followers_url = "https://www.zhihu.com/api/v4/members/{user}/followers?include={include}&offset={offset}&limit={limit}"     followers_query = "data%5B*%5D.answer_count%2Carticles_count%2Cgender%2Cfollower_count%2Cis_followed%2Cis_following%2Cbadge%5B%3F(type%3Dbest_answerer)%5D.topics"       def start_requests(self):         '''         这里重写了start_requests方法，分别请求了用户查询的url和关注列表的查询以及粉丝列表信息查询         :return:         '''         yield Request(self.user_url.format(user=self.start_user,include=self.user_query),callback=self.parse_user)         yield Request(self.follows_url.format(user=self.start_user,include=self.follows_query,offset=0,limit=20),callback=self.parse_follows)         yield Request(self.followers_url.format(user=self.start_user,include=self.followers_query,offset=0,limit=20),callback=self.parse_followers)      def parse_user(self, response):         '''         因为返回的是json格式的数据，所以这里直接通过json.loads获取结果         :param response:         :return:         '''         result = json.loads(response.text)         item = UserItem()         #这里循环判断获取的字段是否在自己定义的字段中，然后进行赋值         for field in item.fields:             if field in result.keys():                 item[field] = result.get(field)          #这里在返回item的同时返回Request请求，继续递归拿关注用户信息的用户获取他们的关注列表         yield item         yield Request(self.follows_url.format(user = result.get("url_token"),include=self.follows_query,offset=0,limit=20),callback=self.parse_follows)         yield Request(self.followers_url.format(user = result.get("url_token"),include=self.followers_query,offset=0,limit=20),callback=self.parse_followers)         def parse_follows(self, response):         '''         用户关注列表的解析，这里返回的也是json数据 这里有两个字段data和page，其中page是分页信息         :param response:         :return:         '''         results = json.loads(response.text)          if 'data' in results.keys():             for result in results.get('data'):                 yield Request(self.user_url.format(user = result.get("url_token"),include=self.user_query),callback=self.parse_user)          #这里判断page是否存在并且判断page里的参数is_end判断是否为False，如果为False表示不是最后一页，否则则是最后一页         if 'page' in results.keys() and results.get('is_end') == False:             next_page = results.get('paging').get("next")             #获取下一页的地址然后通过yield继续返回Request请求，继续请求自己再次获取下页中的信息             yield Request(next_page,self.parse_follows)      def parse_followers(self, response):         '''         这里其实和关乎列表的处理方法是一样的         用户粉丝列表的解析，这里返回的也是json数据 这里有两个字段data和page，其中page是分页信息         :param response:         :return:         '''         results = json.loads(response.text)          if 'data' in results.keys():             for result in results.get('data'):                 yield Request(self.user_url.format(user = result.get("url_token"),include=self.user_query),callback=self.parse_user)          #这里判断page是否存在并且判断page里的参数is_end判断是否为False，如果为False表示不是最后一页，否则则是最后一页         if 'page' in results.keys() and results.get('is_end') == False:             next_page = results.get('paging').get("next")             #获取下一页的地址然后通过yield继续返回Request请求，继续请求自己再次获取下页中的信息             yield Request(next_page,self.parse_followers)

上述的代码的主要逻辑用下图分析表示：

关于上图的一个简单描述：

1. 当重写start_requests，一会有三个yield，分别的回调函数调用了parse_user,parse_follows,parse_followers，这是第一次会分别获取我们所选取的大V的信息以及关注列表信息和粉丝列表信息

2. 而parse分别会再次回调parse_follows和parse_followers信息，分别递归获取每个用户的关注列表信息和分析列表信息

3. parse_follows获取关注列表里的每个用户的信息回调了parse_user，并进行翻页获取回调了自己parse_follows

4. parse_followers获取粉丝列表里的每个用户的信息回调了parse_user，并进行翻页获取回调了自己parse_followers

通过上面的步骤实现所有用户信息的爬取，最后是关于数据的存储

关于数据存储到mongodb

这里主要是item中的数据存储到mongodb数据库中，这里主要的一个用法是就是插入的时候进行了一个去重检测

class MongoPipeline(object):      def __init__(self, mongo_uri, mongo_db):         self.mongo_uri = mongo_uri         self.mongo_db = mongo_db      @classmethod     def from_crawler(cls, crawler):         return cls(             mongo_uri=crawler.settings.get('MONGO_URI'),             mongo_db=crawler.settings.get('MONGO_DATABASE', 'items')         )      def open_spider(self, spider):         self.client = pymongo.MongoClient(self.mongo_uri)         self.db = self.client[self.mongo_db]      def close_spider(self, spider):         self.client.close()      def process_item(self, item, spider):         #这里通过mongodb进行了一个去重的操作，每次更新插入数据之前都会进行查询，判断要插入的url_token是否已经存在，如果不存在再进行数据插入，否则放弃数据         self.db['user'].update({'url_token':item["url_token"]},{'$set':item},True)         return item