# -*- coding:UTF-8 -*-
# @Time  : 2020/8/21 16:43
# @File  : search_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()

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.sql.adaptive.enabled", True)
sparkConf.set("spark.sql.adaptive.skewedJoin.enabled", True)
sparkConf.set("spark.shuffle.statistics.verbose", True)
sparkConf.set("spark.sql.adaptive.shuffle.targetPostShuffleInputSize", "67108864")
sparkConf.set("spark.sql.adaptive.shuffle.targetPostShuffleRowCount", "20000000")
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

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")
    sql_distinct_device_id = """
            SELECT count(*) FROM online.tl_hdfs_doctor_view"""
    print(sql_distinct_device_id)
    distinct_device_id_df = spark.sql(sql_distinct_device_id,)
    distinct_device_id_df.show(1)
    sql_res = distinct_device_id_df.collect()
    for res in sql_res:
        print(res)

    print("-------------------------------")

   #  sql = r"""SELECT
   #          ,t3.device_os_type as device_type
   #          ,t3.active_type as active_type
   #          ,t3.channel as channel_type
   #          ,NVL(t3.search_pv,0) as pv
   #          ,NVL(t3.search_uv,0) as uv
   #  FROM
   #  (
   #      SELECT active_type,device_os_type,channel,search_pv,search_uv
   #      FROM
   #      (
   #          SELECT active_type,device_os_type,channel
   #                  ,count(t1.cl_id) as search_pv
   #                  ,count(distinct t1.cl_id) as search_uv
   #          FROM
   #          (
   #              SELECT partition_date
   #                      ,cl_id
   #              FROM online.bl_hdfs_maidian_updates
   #              WHERE partition_date >= {yesterday_str}
   #              AND partition_date < {today_str}
   #              AND action in ('do_search','search_result_click_search')
   #
   #              UNION ALL
   #              SELECT cl_id
   #              FROM online.bl_hdfs_maidian_updates
   #              WHERE partition_date >= {yesterday_str}
   #              AND partition_date < {today_str}
   #              AND action = 'on_click_card'
   #              AND params['page_name']='search_home'
   #
   #              UNION ALL
   #              SELECT partition_date
   #                      ,cl_id
   #              FROM online.bl_hdfs_maidian_updates
   #              WHERE partition_date >= {yesterday_str}
   #              AND partition_date < {today_str}
   #              AND action = 'on_click_card'
   #              AND params['in_page_pos']='猜你喜欢'
   #              AND params['tab_name']='精选'
   #              AND params['card_type']='search_word'
   #
   #
   #              UNION ALL
   #              SELECT partition_date
   #                      ,cl_id
   #              FROM online.bl_hdfs_maidian_updates
   #              WHERE partition_date >= {yesterday_str}
   #              AND partition_date < {today_str}
   #              AND action = 'on_click_card'
   #              AND page_name='welfare_home'
   #              AND params['card_type'] ='search_word'
   #              AND params['in_page_pos']='大家都在搜'
   #
   #              UNION ALL
   #              SELECT partition_date
   #                      ,cl_id
   #              FROM online.bl_hdfs_maidian_updates
   #              WHERE partition_date >= {yesterday_str}
   #              AND partition_date < {today_str}
   #              AND int(split(app_version,'\\.')[1]) >= 27
   #              AND action='on_click_card'
   #              AND params['card_type']='highlight_word'
   #          )t1
   #          JOIN
   #          (
   #              SELECT partition_date,device_id,t2.active_type,t2.channel,t2.device_os_type
   #              FROM
   #              (
   #                  SELECT
   #                  partition_date,m.device_id
   #                  ,array(device_os_type ,'合计') as device_os_type
   #                  ,array(case WHEN active_type = '4'  THEN '老活'
   #                        WHEN active_type  in ('1','2')  then '新增' END ,'合计') as active_type
   #                  ,array(CASE WHEN is_ai_channel = 'true' THEN 'AI'  ELSE '其他' END , '合计') as channel
   #                  FROM online.ml_device_day_active_status m
   #                      LEFT JOIN
   #                      (SELECT code,is_ai_channel,partition_day
   #                       FROM DIM.DIM_AI_CHANNEL_ZP_NEW
   #                       WHERE partition_day>= {yesterday_str}
   #                       AND partition_day < {today_str}) tmp
   #                      ON  m.partition_date=tmp.partition_day AND first_channel_source_type=code
   #                  WHERE partition_date >= {yesterday_str}
   #                  AND partition_date < {today_str}
   #                  AND active_type in ('1','2','4')
   #              ) mas
   #              LATERAL VIEW explode(mas.channel) t2 AS channel
   #              LATERAL VIEW explode(mas.device_os_type) t2 AS device_os_type
   #              LATERAL VIEW explode(mas.active_type) t2 AS active_type
   #          )t2
   #          on t1.cl_id=t2.device_id AND t1.partition_date = t2.partition_date
   #  GROUP BY active_type,device_os_type,channel
   #      )t
   #  )t3
   # """.format(today_str=today_str, yesterday_str=yesterday_str, )
   #  device_df = spark.sql(sql)
   #  device_df.show(1, False)
   #  sql_res = device_df.collect()
   #  for res in sql_res:
   #      print(res)
    # device_df.createOrReplaceTempView("data_table")

#     collects_sql = """
#     SELECT device_type,active_type,channel_type,ROUND(if(NVL(sum(uv),0) <> 0 ,NVL(sum(search_core_pv),0)/NVL(sum(uv),0) ,0),5) as core_pv_division_uv,
#     ROUND(if(NVL(sum(uv),0) <> 0 ,NVL(sum(search_pv),0)/NVL(sum(uv),0) , 0),5) as pv_division_uv
#     FROM data_table GROUP BY device_type,active_type,channel_type
#     """
#     finnal_df = spark.sql(collects_sql)
#
#     finnal_df.show(1, False)
#     sql_res = finnal_df.collect()
#     for res in sql_res:
#         # print(res)
#         device_type = res.device_type
#         active_type = res.active_type
#         channel_type = res.channel_type
#         core_pv_division_uv = res.core_pv_division_uv
#         pv_division_uv = res.pv_division_uv
#         pid = hashlib.md5(
#             (today_str + device_type + active_type + channel_type).encode("utf8")).hexdigest()
#         instert_sql = """replace into search_strategy_d(
#             day_id,device_type,active_type,channel_type,core_pv_division_uv,pv_division_uv,pid
#             ) VALUES('{day_id}','{device_type}','{active_type}','{channel_type}',{core_pv_division_uv},{pv_division_uv},'{pid}');""".format(
#             day_id=today_str, device_type=device_type,
#             active_type=active_type, channel_type=channel_type, core_pv_division_uv=core_pv_division_uv,pv_division_uv=pv_division_uv,pid=pid
#
#         )
#         print(instert_sql)
#         # cursor.execute("set names 'UTF8'")
#         res = cursor.execute(instert_sql)
#         db.commit()
#         print(res)
# db.close()
