# -*- coding:UTF-8 -*-
# @Time : 2020/9/11 17:37
# @File : ecommerce_income_report.py
# @email : litao@igengmei.com
# @author : litao
# -*- coding:UTF-8 -*-
# @Time : 2020/9/4 17:07
# @File : search_meigou_ctr.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
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")
sparkConf.setAppName("search_diary_ctr")
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(
"search_diary_ctr").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 = 60
for t in range(1, 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")
# quanzhong_dau
quanzhong_dau_sql = """
--quanzhong_dau
SELECT mas.partition_date
,round(count(DISTINCT CASE WHEN device_type = '老活' AND device_os_type = 'android' AND channel_type = 'AI' THEN device_id END)*0.14
+count(DISTINCT CASE WHEN device_type = '老活' AND device_os_type = 'android' AND channel_type = '医美' THEN device_id END)*0.64
+count(DISTINCT CASE WHEN device_type = '新增' AND device_os_type = 'android' AND channel_type = 'AI' THEN device_id END)*0.08
+count(DISTINCT CASE WHEN device_type = '新增' AND device_os_type = 'android' AND channel_type = '医美' THEN device_id END)*0.19
+count(DISTINCT CASE WHEN device_type = '老活' AND device_os_type = 'ios' AND channel_type = 'AI' THEN device_id END)*0.32
+count(DISTINCT CASE WHEN device_type = '老活' AND device_os_type = 'ios' AND channel_type = '积分墙' THEN device_id END)*0.28
+count(DISTINCT CASE WHEN device_type = '老活' AND device_os_type = 'ios' AND channel_type = '医美' THEN device_id END)*1.00
+count(DISTINCT CASE WHEN device_type = '新增' AND device_os_type = 'ios' AND channel_type = 'AI' THEN device_id END)*0.19
+count(DISTINCT CASE WHEN device_type = '新增' AND device_os_type = 'ios' AND channel_type = '积分墙' THEN device_id END)*0.03
+count(DISTINCT CASE WHEN device_type = '新增' AND device_os_type = 'ios' AND channel_type = '医美' THEN device_id END)*0.57,0) as quanzhong_dau
FROM
(
SELECT
partition_date,m.device_id,device_os_type
,case WHEN active_type = '4' THEN '老活'
WHEN active_type in ('1','2') then '新增' END as device_type
,CASE WHEN is_ai_channel = 'true' THEN 'AI'
WHEN first_channel_source_type in ('yqxiu1','yqxiu2','yqxiu3','yqxiu4','yqxiu5','mxyc1','mxyc2','mxyc3'
,'wanpu','jinshan','jx','maimai','zhuoyi','huatian','suopingjingling','mocha','mizhe','meika','lamabang'
,'js-az1','js-az2','js-az3','js-az4','js-az5','jfq-az1','jfq-az2','jfq-az3','jfq-az4','jfq-az5','toufang1'
,'toufang2','toufang3','toufang4','toufang5','toufang6','TF-toufang1','TF-toufang2','TF-toufang3','TF-toufang4'
,'TF-toufang5','tf-toufang1','tf-toufang2','tf-toufang3','tf-toufang4','tf-toufang5','benzhan','promotion_aso100'
,'promotion_qianka','promotion_xiaoyu','promotion_dianru','promotion_malioaso','promotion_malioaso-shequ'
,'promotion_shike','promotion_julang_jl03','promotion_zuimei','','unknown') then '积分墙' ELSE '医美' END as channel_type
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>= '{start_date}' AND partition_day < '{end_date}' ) tmp
ON m.partition_date=tmp.partition_day AND first_channel_source_type=code
where partition_date >= '{start_date}'
AND partition_date < '{end_date}'
AND active_type in ('1','2','4')
) mas
GROUP BY mas.partition_date
""".format(start_date=yesterday_str, end_date=today_str)
print(quanzhong_dau_sql)
quanzhong_dau_df = spark.sql(quanzhong_dau_sql)
quanzhong_dau_df.createOrReplaceTempView("quanzhong_dau_view")
quanzhong_dau_df.show(1)
sql_res = quanzhong_dau_df.collect()
for res in sql_res:
quanzhong_dau = res.quanzhong_dau
partition_date = res.partition_date
# DAU
DAU_sql = """
SELECT mas.partition_date,count(DISTINCT mas.device_id) as dau
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>= '{start_date}' AND partition_day < '{end_date}' ) tmp
ON m.partition_date=tmp.partition_day AND first_channel_source_type=code
where partition_date >= '{start_date}'
AND partition_date < '{end_date}'
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
GROUP BY mas.partition_date
""".format(start_date=yesterday_str, end_date=today_str)
print(DAU_sql)
dau_df = spark.sql(DAU_sql)
dau_df.createOrReplaceTempView("dau_view")
dau_df.show(1)
sql_res = dau_df.collect()
for res in sql_res:
dau = res.dau
# CPT日均点击
cpc_daily_click_sql = r"""
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(partition_day=yesterday_str, end_date=today_str, start_date=yesterday_str)
print(cpc_daily_click_sql)
cpc_daily_click_df = spark.sql(cpc_daily_click_sql)
cpc_daily_click_df.createOrReplaceTempView("cpc_daily_click")
cpc_daily_click_df.show(1)
sql_res = cpc_daily_click_df.collect()
for res in sql_res:
pv = res.pv
# 商详页PV
bus_detail_sql = r"""
--页面浏览pvuv
SELECT
page.partition_date as partition_date
,count(case when page_name in ('search_home','search_home_more','search_home_welfare','search_home_diary','search_home_wiki','search_home_post','search_home_hospital','search_home_doctor') then page.cl_id else NULL end) as search_home_pv
,count(distinct case when page_name in ('search_home','search_home_more','search_home_welfare','search_home_diary','search_home_wiki','search_home_post','search_home_hospital','search_home_doctor') then page.cl_id else NULL end) as search_home_uv
,count(CASE when referrer in ('search_result_diary','search_result_doctor','search_result_hospital','search_result_more'
,'search_result_more_infomation','search_result_more_user','search_result_post','search_result_welfare'
,'search_result_wiki','search_result_question_answer') and page_name in ('welfare_detail','organization_detail','expert_detail') THEN page.cl_id else NULL END) as referrer_search_hexin_pv
,count(CASE when referrer in ('search_result_diary','search_result_doctor','search_result_hospital','search_result_more'
,'search_result_more_infomation','search_result_more_user','search_result_post','search_result_welfare'
,'search_result_wiki','search_result_question_answer') and page_name in ('welfare_detail') THEN page.cl_id else NULL END) as referrer_search_welfare_pv
,count(CASE when referrer in ('search_result_diary','search_result_doctor','search_result_hospital','search_result_more'
,'search_result_more_infomation','search_result_more_user','search_result_post','search_result_welfare'
,'search_result_wiki','search_result_question_answer') and page_name in ('diary_detail','topic_detail','post_detail','user_post_detail','doctor_post_detail','question_detail','answer_detail'
,'question_answer_detail','article_detail') THEN page.cl_id else NULL END) as referrer_search_neirong_pv
,count(DISTINCT CASE WHEN referrer in ('search_result_diary','search_result_doctor','search_result_hospital','search_result_more'
,'search_result_more_infomation','search_result_more_user','search_result_post','search_result_welfare'
,'search_result_wiki','search_result_question_answer') and page_name in ('diary_detail','topic_detail','post_detail','user_post_detail','doctor_post_detail','question_detail','answer_detail'
,'question_answer_detail','article_detail') and page_stay >= '0' and page_stay < '1000' THEN page.cl_id else NULL END) as referrer_search_neirong_uv_1000
,sum(CASE WHEN referrer in ('search_result_diary','search_result_doctor','search_result_hospital','search_result_more'
,'search_result_more_infomation','search_result_more_user','search_result_post','search_result_welfare'
,'search_result_wiki','search_result_question_answer') and page_name in ('diary_detail','topic_detail','post_detail','user_post_detail','doctor_post_detail','question_detail','answer_detail'
,'question_answer_detail','article_detail') and page_stay >= '0' and page_stay < '1000' THEN page.page_stay else NULL END) as referrer_search_neirong_pagestay
FROM
(
SELECT cl_id,partition_date,page_name,params['referrer'] as referrer,page_stay
FROM online.bl_hdfs_maidian_updates
WHERE partition_date >= '{start_date}'
AND partition_date < '{end_date}'
AND action='page_view'
AND page_name in ('search_home','search_home_more','search_home_welfare','search_home_diary','search_home_wiki','search_home_post','search_home_hospital','search_home_doctor'
,'diary_detail','topic_detail','post_detail','user_post_detail','doctor_post_detail','question_detail','answer_detail'
,'question_answer_detail','article_detail','welfare_detail','organization_detail','expert_detail','level_one_plan_detail')
)page
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>= '{start_date}' AND partition_day < '{end_date}' ) tmp
ON m.partition_date=tmp.partition_day AND first_channel_source_type=code
where partition_date >= '{start_date}'
AND partition_date < '{end_date}'
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
)dev_channel
on dev_channel.device_id = page.cl_id
AND dev_channel.partition_date = page.partition_date
GROUP BY page.partition_date
""".format(partition_day=yesterday_str, end_date=today_str, start_date=yesterday_str)
print(bus_detail_sql)
bus_detail_df = spark.sql(bus_detail_sql)
bus_detail_df.createOrReplaceTempView("bus_detail")
bus_detail_df.show(1)
sql_res = bus_detail_df.collect()
for res in sql_res:
search_home_pv = res.search_home_pv
search_home_uv = res.search_home_uv
referrer_search_hexin_pv = res.referrer_search_hexin_pv
referrer_search_welfare_pv = res.referrer_search_welfare_pv
referrer_search_neirong_pv = res.referrer_search_neirong_pv
referrer_search_neirong_uv_1000 = res.referrer_search_neirong_uv_1000
referrer_search_neirong_pagestay = res.referrer_search_neirong_pagestay
# print(res)
# --cpc当日预算(有效口径)
cpc_budget_sql = r"""
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 --商户预算
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(partition_date=yesterday_str, end_date=today_str, start_date=yesterday_str)
print(cpc_budget_sql)
cpc_budget_df = spark.sql(cpc_budget_sql)
cpc_budget_df.createOrReplaceTempView("cpc_budget")
cpc_budget_df.show(1)
sql_res = cpc_budget_df.collect()
for res in sql_res:
budget = res.budget
print(res)
# cpc收入、广告总消耗
cpc_income_sql = r"""
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,--- 当天cpc商品点击量
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,--- 当天cpc总收入（含返点）
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,--- 当天cpc收入（不含返点）
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 --等比例返点消耗总金额
from ml.ml_c_ct_mc_merchantadclassify_indic_d
where partition_day>='{start_date}' AND partition_day <'{end_date}'
group by partition_day
""".format(partition_day=yesterday_str, end_date=today_str, start_date=yesterday_str)
print(cpc_income_sql)
cpc_income_df = spark.sql(cpc_income_sql)
cpc_income_df.createOrReplaceTempView("cpc_income")
cpc_income_df.show(1)
sql_res = cpc_income_df.collect()
for res in sql_res:
cpc_click_num = res.cpc_click_num
cpc_proportion_expend_amount = res.cpc_proportion_expend_amount
cpc_proportion_expend_recharge_amount = res.cpc_proportion_expend_recharge_amount
tol_proportion_expend_amount = res.tol_proportion_expend_amount
print(res)
#
# out_put_sql = """
# select bus_detail.referrer_search_welfare_pv / dau_view.dau as pv_div_dau,
# bus_detail.referrer_search_welfare_pv / quanzhong_dau_view.quanzhong_dau as pv_div_quanzhong_dau,
# (cpc_income.cpt_click_num + cpc_income.cpc_click_num) / bus_detail.referrer_search_welfare_pv as ad_flow_rat,
# cpc_income.cpc_proportion_expend_amount/cpc_budget.budget as budget_consumption_rate,
# cpc_income.cpc_proportion_expend_recharge_amount/cpc_income.cpc_click_num as cpc_item_pricing,
# cpc_income.tol_proportion_expend_amount as tol_proportion_expend_amount
# """
pv_div_dau = referrer_search_welfare_pv/dau
pv_div_quanzhong_dau = referrer_search_welfare_pv/quanzhong_dau
ad_flow_rat = (pv + cpc_click_num)/referrer_search_welfare_pv
budget_consumption_rate = cpc_proportion_expend_amount/budget
cpc_item_pricing = cpc_proportion_expend_recharge_amount/cpc_click_num
# tol_proportion_expend_amount
db = pymysql.connect(host='172.16.40.158', port=4000, user='st_user', passwd='aqpuBLYzEV7tML5RPsN1pntUzFy',
db='jerry_prod')
cursor = db.cursor()
partition_date = yesterday_str
pid = hashlib.md5(partition_date.encode("utf8")).hexdigest()
cpc_daily_click_sql = """replace into ecommerce_income_report(
pv_div_dau,pv_div_quanzhong_dau,ad_flow_rat,budget_consumption_rate,cpc_item_pricing,tol_proportion_expend_amount,partition_day,day_id,pid) VALUES(
{pv_div_dau},{pv_div_quanzhong_dau},{ad_flow_rat},{budget_consumption_rate},{cpc_item_pricing},{tol_proportion_expend_amount},'{partition_day}','{day_id}','{pid}');""".format(
pv_div_dau=pv_div_dau,pv_div_quanzhong_dau=pv_div_quanzhong_dau,ad_flow_rat=ad_flow_rat,budget_consumption_rate=budget_consumption_rate,
cpc_item_pricing=cpc_item_pricing,tol_proportion_expend_amount=tol_proportion_expend_amount,partition_day=today_str,
day_id=today_str,pid=pid
)
print(cpc_daily_click_sql)
# cursor.execute("set names 'UTF8'")
res = cursor.execute(cpc_daily_click_sql)
db.commit()
print(res)
# cursor.executemany()
db.close()