# -*- coding:UTF-8 -*-
# @Time  : 2020/8/31 13:41
# @File  : advertisement_strategy_d.py
# @email : litao@igengmei.com
# @author : litao


import hashlib
import json

import pymysql
import xlwt, datetime
import redis
# from pyhive import hive
from maintenance.func_send_email_with_file import send_file_email
from typing import Dict, List
from elasticsearch_7 import Elasticsearch
from elasticsearch_7.helpers import scan
import sys
import time
from pyspark import SparkConf
from pyspark.sql import SparkSession, DataFrame
from pyspark.sql.functions import lit
import pytispark.pytispark as pti

db = pymysql.connect(host='172.16.40.158', port=4000, user='st_user', passwd='aqpuBLYzEV7tML5RPsN1pntUzFy',
                     db='jerry_prod')
cursor = db.cursor()


def con_sql(sql):
    # 从数据库的表里获取数据

    db = pymysql.connect(host='172.16.40.158', port=4000, user='st_user', passwd='aqpuBLYzEV7tML5RPsN1pntUzFy',
                         db='jerry_prod')
    cursor = db.cursor()
    cursor.execute(sql)
    result = cursor.fetchall()
    db.close()
    return result


startTime = time.time()
sparkConf = SparkConf()
sparkConf.set("spark.sql.crossJoin.enabled", True)
sparkConf.set("spark.debug.maxToStringFields", "100")
sparkConf.set("spark.tispark.plan.allow_index_double_read", False)
sparkConf.set("spark.tispark.plan.allow_index_read", True)
sparkConf.set("spark.hive.mapred.supports.subdirectories", True)
sparkConf.set("spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive", True)
sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
sparkConf.set("mapreduce.output.fileoutputformat.compress", False)
sparkConf.set("mapreduce.map.output.compress", False)
sparkConf.set("prod.gold.jdbcuri",
              "jdbc:mysql://172.16.30.136/doris_prod?user=doris&password=o5gbA27hXHHm&rewriteBatchedStatements=true")
sparkConf.set("prod.mimas.jdbcuri",
              "jdbc:mysql://172.16.30.138/mimas_prod?user=mimas&password=GJL3UJe1Ck9ggL6aKnZCq4cRvM&rewriteBatchedStatements=true")
sparkConf.set("prod.gaia.jdbcuri",
              "jdbc:mysql://172.16.30.143/zhengxing?user=work&password=BJQaT9VzDcuPBqkd&rewriteBatchedStatements=true")
sparkConf.set("prod.tidb.jdbcuri",
              "jdbc:mysql://172.16.40.158:4000/eagle?user=st_user&password=aqpuBLYzEV7tML5RPsN1pntUzFy&rewriteBatchedStatements=true")
sparkConf.set("prod.jerry.jdbcuri",
              "jdbc:mysql://172.16.40.158:4000/jerry_prod?user=st_user&password=aqpuBLYzEV7tML5RPsN1pntUzFy&rewriteBatchedStatements=true")
sparkConf.set("prod.tispark.pd.addresses", "172.16.40.158:2379")
sparkConf.set("prod.tispark.pd.addresses", "172.16.40.170:4000")
sparkConf.set("prod.tidb.database", "jerry_prod")

spark = (SparkSession.builder.config(conf=sparkConf).config("spark.sql.extensions", "org.apache.spark.sql.TiExtensions")
         .config("spark.tispark.pd.addresses", "172.16.40.170:2379").appName(
    "LR PYSPARK TEST").enableHiveSupport().getOrCreate())

spark.sql("ADD JAR hdfs:///user/hive/share/lib/udf/brickhouse-0.7.1-SNAPSHOT.jar")
spark.sql("ADD JAR hdfs:///user/hive/share/lib/udf/hive-udf-1.0-SNAPSHOT.jar")
spark.sql("CREATE TEMPORARY FUNCTION json_map AS 'brickhouse.udf.json.JsonMapUDF'")
spark.sql("CREATE TEMPORARY FUNCTION is_json AS 'com.gmei.hive.common.udf.UDFJsonFormatCheck'")
spark.sql("CREATE TEMPORARY FUNCTION arrayMerge AS 'com.gmei.hive.common.udf.UDFArryMerge'")

task_list = []
task_days = 1
now = datetime.datetime.now()
partition_date_str = (now + datetime.timedelta(days=-1)).strftime("%Y%m%d")

for t in range(0, task_days):
    day_num = 0 - t
    now = (datetime.datetime.now() + datetime.timedelta(days=day_num))
    last_30_day_str = (now + datetime.timedelta(days=-30)).strftime("%Y%m%d")
    today_str = now.strftime("%Y%m%d")
    yesterday_str = (now + datetime.timedelta(days=-1)).strftime("%Y%m%d")
    one_week_age_str = (now + datetime.timedelta(days=-7)).strftime("%Y%m%d")
    # CPT日均点击
    # CPT_daily_click_sql = """SELECT partition_date,count(1) as pv
    # FROM online.bl_hdfs_maidian_updates
    # WHERE partition_date >=  '{start_date}'
    # and partition_date < '{end_date}'
    # AND ((ACTION = 'search_result_welfare_click_item' AND PAGE_NAME = 'search_result_welfare' AND PARAMS['transaction_type'] = 'advertise')
    # OR (ACTION = 'goto_welfare_detail' AND PARAMS['from'] = 'category' AND PARAMS['transaction_type'] = 'operating' AND PARAMS['tab_name'] = 'service')
    # OR (ACTION = 'goto_welfare_detail' AND PARAMS['from'] = 'welfare_home_list_item' and PARAMS['transaction_type'] = 'advertise')
    # OR (ACTION = 'goto_welfare_detail' AND PARAMS['from'] = 'welfare_list' AND PARAMS['transaction_type'] = 'advertise')
    # OR (ACTION = 'on_click_card' AND PARAMS['card_content_type'] = 'service' AND PARAMS['page_name'] IN ('new_sign','search_result_welfare','category','welfare_home_list_item','welfare_list') AND PARAMS['transaction_type'] = 'advertise'))
    # group BY partition_date""".format(start_date=yesterday_str, end_date=today_str)
    # CPT_daily_click_df = spark.sql(CPT_daily_click_sql)
    # # CPT_daily_click_df.createOrReplaceTempView("cpt_daily_click_df")
    # sql_res = CPT_daily_click_df.collect()
    # for res in sql_res:
    #     print(res)
    #
    # print("0-----------------------------------------------------------------------------")
    # # 商详页PV
    # bus_detail_pv_sql = """SELECT
    # a2.partition_date,count(1) welfare_pv
    # FROM
    # (
    #     SELECT cl_id,partition_date
    #     FROM online.bl_hdfs_maidian_updates
    #     WHERE partition_date >='{start_date}'and partition_date < '{end_date}'
    #     AND action='page_view'
    #     AND params['page_name'] = 'welfare_detail'
    # )a1
    # JOIN
    # (
    #     SELECT device_id,partition_date
    #     from online.ml_device_day_active_status
    #     WHERE partition_date >='{start_date}'and partition_date < '{end_date}'
    #     AND active_type in ('1','2','4')
    # )a2
    # on a2.device_id = a1.cl_id
    # AND a2.partition_date=a1.partition_date
    # group by a2.partition_date""".format(start_date=yesterday_str, end_date=today_str, )
    # bus_detail_pv_df = spark.sql(bus_detail_pv_sql)
    # # bus_detail_pv_df.createOrReplaceTempView("bus_detail_pv_df")
    # sql_res = bus_detail_pv_df.collect()
    # for res in sql_res:
    #     print(res)
    # print("1-----------------------------------------------------------------------------")
    # cpc当日预算(有效口径)

    cpc_budget_sql = """SELECT day_id,sum(budget) as budget
        FROM
        (
            SELECT T1.day_id,T1.merchant_doctor_id,case when merchant_budget>=tot_service_budget then tot_service_budget else merchant_budget end as budget
            FROM
            (
                SELECT 
                  substr(clicklog.create_time,1,10) AS day_id
                  ,clicklog.merchant_doctor_id 
                  ,max(merchant_budget) as merchant_budget 
                FROM 
                (
                    SELECT id,promote_id,price,service_budget,merchant_budget,merchant_doctor_id,create_time,recharge
                    FROM online.tl_hdfs_cpc_clicklog_view
                    WHERE partition_date='{partition_date}'
                    AND regexp_replace(substr(create_time,1,10),'-','')>= '{start_date}'
                    AND regexp_replace(substr(create_time,1,10),'-','')<'{end_date}'
                )clicklog
                group by substr(clicklog.create_time,1,10),clicklog.merchant_doctor_id 
            )T1 
            LEFT JOIN
            (
                SELECT 
                     day_id
                     ,merchant_doctor_id
                    ,sum(service_budget) as tot_service_budget
                FROM
                (
                    SELECT 
                      substr(clicklog.create_time,1,10) AS day_id
                      ,clicklog.merchant_doctor_id,clicklog.service_id
                      ,max(service_budget) as service_budget
                    
                    FROM 
                    (
                        SELECT id,promote_id,price,service_budget,merchant_budget,merchant_doctor_id,service_id,create_time
                        FROM  online.tl_hdfs_cpc_clicklog_view
                        WHERE partition_date='{partition_date}'
                        AND regexp_replace(substr(create_time,1,10),'-','')>= '{start_date}'
                        AND regexp_replace(substr(create_time,1,10),'-','')<'{end_date}'
                    )clicklog
                    GROUP BY substr(clicklog.create_time,1,10),clicklog.merchant_doctor_id,clicklog.service_id
                )service_budget
                GROUP BY day_id,merchant_doctor_id
            )T2 
            ON T1.day_id=T2.day_id
            AND T1.merchant_doctor_id=T2.merchant_doctor_id
        )T
        GROUP BY day_id
        """.format(start_date=yesterday_str, end_date=today_str, partition_date=partition_date_str)
    cpc_budget_df = spark.sql(cpc_budget_sql)

    cpc_budget_df.show(1, False)
    sql_res = cpc_budget_df.collect()
    for res in sql_res:
        print(res)
    print("2-----------------------------------------------------------------------------")
    cpc_income_total_consume_sql = """
    select partition_day,
sum(case when advertise_type = 'cpc' AND advertise_business_type in('service') and advertise_calculate_type='cpc_log' then cpc_click_num end)   cpc_click_num,
sum(case when advertise_type = 'cpc' AND advertise_business_type in('service') and advertise_calculate_type='cpc_flownext' then proportion_expend_amount end)  cpc_proportion_expend_amount,
sum(case when advertise_type = 'cpc' AND advertise_business_type in('service') and advertise_calculate_type='cpc_flownext' then proportion_expend_recharge_amount end)   cpc_proportion_expend_recharge_amount,
SUM(CASE
      WHEN advertise_type = 'cpc' AND advertise_calculate_type = 'cpc_flownext' THEN
       proportion_expend_amount
      WHEN advertise_type = 'cpt' AND advertise_calculate_type = 'cpt_schedule' THEN
       proportion_expend_amount
      WHEN advertise_type IN ('browse', 'message', 'valueadded','rechargededuction') THEN
       proportion_expend_amount
      WHEN advertise_type = 'adjustment' AND advertise_calculate_type ='adjustment_flow' THEN
       proportion_expend_amount
      ELSE
       0
    END) tol_proportion_expend_amount

from ml.ml_c_ct_mc_merchantadclassify_indic_d
where partition_day>='{start_date}' AND partition_day <'{end_date}'
group by partition_day
    """.format(start_date=yesterday_str, end_date=today_str)
    cpc_income_total_consume_df = spark.sql(cpc_income_total_consume_sql)

    cpc_income_total_consume_df.show(1, False)
    cpc_income_total_consume_df_res = cpc_income_total_consume_df.collect()
    for res in cpc_income_total_consume_df_res:
        print(res)
    print("3-----------------------------------------------------------------------------")
#     for res in sql_res:
#         # print(res)
#         day_id = res.day_id
#         device_os_type = res.device_os_type
#         active_type = res.active_type
#         grey_type = res.grey_type
#         page_name = res.page_name
#         content_pv = res.content_pv
#         content_uv = res.content_uv
#         wel_exp_pv = res.wel_exp_pv
#         content_exp_pv = res.content_exp_pv
#         meigou_ctr = res.meigou_ctr
#         if not meigou_ctr: meigou_ctr = 0
#         grey_meigou_ctr = res.grey_meigou_ctr
#         neirong_ctr = res.neirong_ctr
#         if not neirong_ctr: neirong_ctr = 0
#         grey_neirong_ctr = res.grey_neirong_ctr
#
#         wel_click_pv = res.wel_click_pv
#         content_click_pv = res.content_click_pv
#         slide_wel_click_pv = res.slide_wel_click_pv
#         self_wel_click_pv = res.self_wel_click_pv
#         partition_day = res.PARTITION_DAY
#         pid = hashlib.md5((day_id + device_os_type + active_type + grey_type + page_name).encode("utf8")).hexdigest()
#         instert_sql = """replace into conent_detail_page_grayscale_ctr(
#         day_id,device_os_type,active_type,grey_type,page_name,content_pv,content_uv,wel_exp_pv,
#         content_exp_pv,wel_click_pv,content_click_pv,slide_wel_click_pv,self_wel_click_pv,partition_day,pid,meigou_ctr,neirong_ctr,
#         grey_meigou_ctr,grey_neirong_ctr) VALUES('{day_id}','{device_os_type}','{active_type}','{grey_type}','{page_name}',{content_pv},{content_uv},
#         {wel_exp_pv},{content_exp_pv},{wel_click_pv},{content_click_pv},{slide_wel_click_pv},{self_wel_click_pv},'{partition_day}','{pid}',{meigou_ctr},{neirong_ctr},{grey_meigou_ctr},{grey_neirong_ctr});""".format(
#             day_id=day_id, device_os_type=device_os_type, active_type=active_type, grey_type=grey_type,
#             page_name=page_name,
#             content_pv=content_pv, content_uv=content_uv, wel_exp_pv=wel_exp_pv, content_exp_pv=content_exp_pv,
#             wel_click_pv=wel_click_pv,
#             content_click_pv=content_click_pv, slide_wel_click_pv=slide_wel_click_pv,
#             self_wel_click_pv=self_wel_click_pv, meigou_ctr=meigou_ctr, neirong_ctr=neirong_ctr,
#             partition_day=partition_day, pid=pid, grey_neirong_ctr=grey_neirong_ctr, grey_meigou_ctr=grey_meigou_ctr
#         )
#         print(instert_sql)
#         # cursor.execute("set names 'UTF8'")
#         res = cursor.execute(instert_sql)
#         db.commit()
#         print(res)
# # cursor.executemany()
# db.close()
