拆分新老用户重复曝光指标

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

@@ -307,6 +307,11 @@ object Repeated_content_recommendation {
       println(all_new)
       val repeated_rate_new= more_than2_new / all_new.toDouble
       println(repeated_rate_new)
+      val result1=List((stat_date,more_than2_new,all_new))
+
+      val df1 = sc.createDataFrame(result1).toDF("stat_date","new_rep_count","new_imp_all")
+
+      GmeiConfig.writeToJDBCTable(df1, table = "Repeated_evaluation_indicator_new", SaveMode.Append)
 
       val exp_diary_old = sc.sql(
         s"""
@@ -327,10 +332,10 @@ object Repeated_content_recommendation {
       println(repeated_rate_old)
 
 
-      val result=List((stat_date,more_than2_new,all_new,more_than2_old,all_old))
-      val df = sc.createDataFrame(result).toDF("stat_date","new_rep_count","new_imp_all","old_rep_count","old_imp_all")
+      val result2=List((stat_date,more_than2_old,all_old))
+      val df2 = sc.createDataFrame(result2).toDF("stat_date","old_rep_count","old_imp_all")
 
-      GmeiConfig.writeToJDBCTable(df, table = "Repeated_evaluation_indicator", SaveMode.Append)
+      GmeiConfig.writeToJDBCTable(df2, table = "Repeated_evaluation_indicator_old", SaveMode.Append)
 
 
 //      val temp=get_result.collect()