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
- 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:
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[*].answer_count,articles_count,gender,follower_count,is_followed,is_following,badge[?(type=best_answerer)].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[*].answer_count,articles_count,gender,follower_count,is_followed,is_following,badge[?(type=best_answerer)].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
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
来源: http://www.cnblogs.com/zhaof/p/7228131.html