# -*- 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()