# -*- coding:UTF-8 -*-
# @Time  : 2020/8/20 9:42
# @File  : recommend_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='172.16.50.175', port=3306, user='doris', passwd='o5gbA27hXHHm',
                     db='doris_olap')
cursor = db.cursor()


def con_sql(sql):
    # 从数据库的表里获取数据

    db = pymysql.connect(host='172.16.50.175', port=3306, user='doris', passwd='o5gbA27hXHHm',
                         db='doris_olap')
    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).appName(
    "recommend_strategy_d").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'")


t = 1
day_num = 0 - t
now = (datetime.datetime.now() + datetime.timedelta(days=day_num))
last_30_day_str = (now + datetime.timedelta(days=-31)).strftime("%Y%m%d")
today_str = now.strftime("%Y%m%d")
today_str_format = now.strftime("%Y-%m-%d")
yesterday_str = (now + datetime.timedelta(days=-1)).strftime("%Y%m%d")
yesterday_str_format = (now + datetime.timedelta(days=-1)).strftime("%Y-%m-%d")
one_week_age_str = (now + datetime.timedelta(days=-7)).strftime("%Y%m%d")
grey_select_sql = """SELECT *,
    NVL(ROUND((navbar_search+highlight_word+self_welfare_card+recommend_welfare_card+recommend_content_card*0.2+transfer_card+video_consultation)/card_exposure,5),0) as recommend_ctr,
    NVL(ROUND(card_click/card_exposure,5),0) as click_ctr,
    NVL(ROUND((navbar_search+highlight_word+self_welfare_card+recommend_welfare_card+recommend_content_card*0.2+transfer_card+video_consultation)/card_click,5),0) as second_jump_ctr ,
    NVL(ROUND(page_pv_20/page_pv,5),0) as page_pv_20_percent 
    FROM pm.tl_pm_recommend_strategy_d"""
device_df = spark.sql(grey_select_sql)

device_df.show(1, False)
sql_res = device_df.collect()
print("-----------------------------------------------------------------------------")
for res in sql_res:
    # print(res)
    day_id = res.day_id
    device_os_type = res.device_os_type
    active_type = res.active_type
    card_content_type = res.card_content_type
    recommend_type = res.recommend_type
    card_click = res.card_click
    card_exposure = res.card_exposure
    avg_page_stay = res.avg_page_stay
    navbar_search = res.navbar_search
    highlight_word = res.highlight_word
    self_welfare_card = res.self_welfare_card
    page_pv_20 = res.page_pv_20
    page_pv_20_percent = res.page_pv_20_percent


    recommend_welfare_card = res.recommend_welfare_card
    recommend_content_card = res.recommend_content_card
    if not recommend_content_card:
        recommend_content_card = 0
    recommend_special_card = res.recommend_special_card
    if not recommend_special_card:
        recommend_special_card = 0
    transfer_card = res.transfer_card
    video_consultation = res.video_consultation
    partition_day = res.partition_day
    recommend_ctr = res.recommend_ctr
    second_jump_ctr = res.second_jump_ctr
    click_ctr = res.click_ctr
    pid = hashlib.md5((day_id + device_os_type + active_type + card_content_type + recommend_type).encode("utf8")).hexdigest()
    instert_sql = """replace into recommend_strategy_d(
    day_id,device_os_type,active_type,card_content_type,recommend_type,card_click,card_exposure,avg_page_stay,navbar_search,
    highlight_word,self_welfare_card,recommend_welfare_card,recommend_content_card,recommend_special_card,transfer_card,video_consultation,
    partition_day,pid,recommend_ctr,second_jump_ctr,click_ctr,page_pv_20_percent
    ) VALUES('{day_id}','{device_os_type}','{active_type}','{card_content_type}','{recommend_type}',{card_click},{card_exposure},
    {avg_page_stay},{navbar_search},{highlight_word},{self_welfare_card},{recommend_welfare_card},{recommend_content_card},{recommend_special_card},
    {transfer_card},{video_consultation},'{partition_day}','{pid}',{recommend_ctr},{second_jump_ctr},{click_ctr},{page_pv_20_percent});""".format(
        day_id=day_id, device_os_type=device_os_type, active_type=active_type, card_content_type=card_content_type,
        card_click=card_click, recommend_type=recommend_type, card_exposure=card_exposure, avg_page_stay=avg_page_stay,
        navbar_search=navbar_search, self_welfare_card=self_welfare_card, recommend_welfare_card=recommend_welfare_card,
        recommend_content_card=recommend_content_card, recommend_special_card=recommend_special_card,page_pv_20_percent=page_pv_20_percent,
        transfer_card=transfer_card,
        video_consultation=video_consultation, partition_day=partition_day, pid=pid, recommend_ctr=recommend_ctr,
        second_jump_ctr=second_jump_ctr, click_ctr=click_ctr,highlight_word=highlight_word
    )
    print(instert_sql)
    # cursor.execute("set names 'UTF8'")
    res = cursor.execute(instert_sql)
    db.commit()
    print(res)
# cursor.executemany()
db.close()
