#!/usr/bin/python
# -*- coding=utf-8 -*-
# @Create Time:		2024-01-18 13:15:22
# @Last Modified time: 2024-02-21 13:16:17
class BusinessClickRealTimeAnalyzeSpace(object):
    def __init__(self):
        self.aggregatedDStream = pd.DataFrame(columns=['word', 'count'])

    def calculateRealTimeStat(self, sourceDStream: dict) -> DataFrame:
        """计算业务访问流量实时统计
        计算每天业务的访问量
        设计维度：日期时间、用户、业务、访问次数
        可以看到当天所有的实时数据，
        通过DF，直接统计出全局的访问次数，在DF缓存一份，在DM保存一份
        """

        """
        我们要对原始数据进行map，映射成<date_user_domain,1>格式
        然后，对上述格式的数据，执行updateStateByKey算子
        """
        def _filter(data: dict) -> tuple:
            return {"word": data["time"].strftime('%Y-%m-%dT%H:%M:%S') + "_" + data["user"] + "_" + data["domain"], "count": 1}

        mappedDStream = _filter(sourceDStream)

        self.aggregatedDStream = self.aggregatedDStream.append(mappedDStream, ignore_index=True)

        self.aggregatedDStream = self.aggregatedDStream.groupby('word').apply(lambda x: sum(x['count'])).reset_index(name="count")
        # 将计算出来的结果同步一份到DM
        session = DM8Session()
        for row in self.aggregatedDStream.itertuples():
            try:
                result = session.execute("""INSERT INTO TOTALVISIT("Name", "Count") VALUES(?,?)""", (row.word, row.count))
            except Exception as e:
                print(e)
        return self.aggregatedDStream

    def calculateTopKBusiness(self):
        """计算每天用户的TopK访问业务
        计算出每天各业务的点击量
        """
        pass

    def calculateBusinessCountByWindow(self):
        """计算最近1小时滑动窗口内的业务访问趋势
        """
        pass