# -*- 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(distinct user_id) FROM online.tl_hdfs_doctor_view_backup WHERE partition_date = '20200827'"""
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()