修改统计参数

eda/feededa/src/main/scala/com/gmei/app_list.scala

@@ -151,11 +151,8 @@ object coincidence_xinyang {
       val sc = spark_env._2
 
       val ti = new TiContext(sc)
-      ti.tidbMapTable(dbName = "jerry_prod", tableName = "diary_video")
-      ti.tidbMapTable(dbName = "jerry_prod", tableName = "data_feed_click")
       ti.tidbMapTable(dbName = "jerry_prod", tableName = "blacklist")
       ti.tidbMapTable(dbName = "jerry_test", tableName = "bl_device_list")
-      ti.tidbMapTable(dbName = "jerry_prod", tableName = "data_feed_exposure")
       ti.tidbMapTable(dbName = "jerry_prod", tableName = "device_id_coincidence")
 
 
@@ -204,7 +201,7 @@ object coincidence_xinyang {
            |and cl_id not in (select device_id from bl_device_list)
            |and cl_id not in (select device_id from blacklist)
          """.stripMargin
-      )
+      ).repartition(100)
       all_device_id.createOrReplaceTempView("all_device_id")
 
 
@@ -217,7 +214,7 @@ object coincidence_xinyang {
            |from device_id_coincidence
            |where stat_date = '${data}'
          """.stripMargin
-      )
+      ).repartition(100)
       //2.获得应用列表的每日活跃总用户数
       val yesterday_expoure_num = sc.sql(
         s"""
@@ -228,7 +225,7 @@ object coincidence_xinyang {
            |and cl_id not in (select device_id from bl_device_list)
            |and cl_id not in (select device_id from blacklist)
          """.stripMargin
-      )
+      ).repartition(100)
 
 
       //获取每周活跃用户中与新氧重合用户占比
@@ -240,7 +237,7 @@ object coincidence_xinyang {
            |from device_id_coincidence
            |where stat_date >= '${week_day}'
          """.stripMargin
-      )
+      ).repartition(100)
       //2.获得应用列表的每周活跃总用户
       val week_expoure_num = sc.sql(
         s"""
@@ -251,7 +248,7 @@ object coincidence_xinyang {
            |and cl_id not in (select device_id from bl_device_list)
            |and cl_id not in (select device_id from blacklist)
          """.stripMargin
-      )
+      ).repartition(100)
 
       //获取每月活跃用户中与新氧用户重合用户占比
       //1.0 每月活跃中与新氧用户重合用户数
@@ -262,7 +259,7 @@ object coincidence_xinyang {
            |from device_id_coincidence
            |where stat_date > '${month_day}'
          """.stripMargin
-      )
+      ).repartition(100)
       //2.0 获得应用列表的每月活跃总用户
       val month_expoure_num = sc.sql(
         s"""
@@ -273,7 +270,7 @@ object coincidence_xinyang {
            |and cl_id not in (select device_id from bl_device_list)
            |and cl_id not in (select device_id from blacklist)
          """.stripMargin
-      )
+      ).repartition(100)
 
       val result = yesterday_coincidence.join(yesterday_expoure_num,"stat_date")
           .join(week_coincidence,"stat_date")
@@ -294,7 +291,7 @@ object coincidence_xinyang {
            |and  pay_time  >=  '2017-11-18'
            |and device_id in (select distinct(device_id) from device_id_coincidence)
          """.stripMargin
-      )
+      ).repartition(100)
       meigou_coincidence_num.show()
 
 
@@ -308,7 +305,7 @@ object coincidence_xinyang {
            |and  pay_time  >=  '2017-11-18'
            |and device_id in (select distinct(device_id) from device_id_coincidence)
          """.stripMargin
-      )
+      ).repartition(100)
       meigou_pay_device.show()
       //3.获得应用列表的用户的美购数
       val meigou_pay_all = sc.sql(
@@ -320,7 +317,7 @@ object coincidence_xinyang {
            |and  pay_time  is  not  null
            |and  pay_time  >=  '2017-11-18'
          """.stripMargin
-      )
+      ).repartition(100)
       meigou_pay_all.show()
 
       //4.获得应用列表用户进行美购的用户数
@@ -333,7 +330,7 @@ object coincidence_xinyang {
            |and  pay_time  is  not  null
            |and  pay_time  >=  '2017-11-18'
          """.stripMargin
-      )
+      ).repartition(100)
       meigou_pay_device_all.show()
 
       //截止目前获得的与新氧重合的用户数咨询统计
@@ -345,7 +342,7 @@ object coincidence_xinyang {
            |where partition_date >= '20180501'
            |and action = 'welfare_detail_click_message'
          """.stripMargin
-      )
+      ).repartition(100)
       zixun_num_all.show()
 
       val zixun_device_all = sc.sql(
@@ -356,7 +353,7 @@ object coincidence_xinyang {
            |where partition_date >= '20180501'
            |and action = 'welfare_detail_click_message'
          """.stripMargin
-      )
+      ).repartition(100)
       zixun_device_all.show()
 
       val zixun_num_co = sc.sql(
@@ -367,7 +364,7 @@ object coincidence_xinyang {
            |and action = 'welfare_detail_click_message'
            |and cl_id in (select distinct(device_id) from device_id_coincidence)
          """.stripMargin
-      )
+      ).repartition(100)
       zixun_num_co.show()
 
       val zixun_num_co_dis = sc.sql(
@@ -378,7 +375,7 @@ object coincidence_xinyang {
            |and action = 'welfare_detail_click_message'
            |and cl_id in (select distinct(device_id) from device_id_coincidence)
          """.stripMargin
-      )
+      ).repartition(100)
       zixun_num_co_dis.show()
 
 

eda/feededa/src/main/scala/com/gmei/strategy_other.scala

@@ -222,15 +222,6 @@ object evaluation_indicator_ {
       val spark_env = GmeiConfig.getSparkSession()
       val sc = spark_env._2
 
-      val ti = new TiContext(sc)
-      ti.tidbMapTable(dbName = "jerry_prod", tableName = "diary_video")
-      ti.tidbMapTable(dbName = "jerry_prod", tableName = "data_feed_click")
-      ti.tidbMapTable(dbName = "jerry_prod", tableName = "blacklist")
-      ti.tidbMapTable(dbName = "jerry_test", tableName = "bl_device_list")
-      ti.tidbMapTable(dbName = "jerry_prod", tableName = "data_feed_exposure")
-      ti.tidbMapTable(dbName = "jerry_prod", tableName = "merge_queue_table")
-
-
       import sc.implicits._
       //val stat_date = GmeiConfig.getMinusNDate(1)
       //println(param.date)