统计多天重复日记数量

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

@@ -304,13 +304,14 @@ object Repeated_content_recommendation_moreday {
 
 
 //      val stat_date = GmeiConfig.getMinusNDate(1)
-      val stat_date = "2019-01-16"
+//      val stat_date = "2019-01-16"
 //      val partition_date = stat_date.replace("-","")
 
 
       val now= new Date()
       val dateFormat = new SimpleDateFormat("yyyy-MM-dd")
       val date = dateFormat.format(now.getTime - 86400000L * 15)
+      val today=dateFormat.format(now)
 
 
       val exp_diary = sc.sql(
@@ -318,8 +319,7 @@ object Repeated_content_recommendation_moreday {
            |select stat_date,device_id,concat_ws(',',collect_set(distinct cid_id)) as expoure_diary
            |from data_feed_exposure_precise
            |where cid_type = 'diary'
-           |and stat_date >='2018-12-31'
-           |and stat_date <'2019-01-16'
+           |and stat_date >='${date}'
            |group by device_id,stat_date
          """.stripMargin
       ).rdd.map(row=>(row(0).toString,row(1).toString,row(2).toString)).map(row=>(row._2,row._3)).groupByKey()
@@ -338,7 +338,7 @@ object Repeated_content_recommendation_moreday {
       val fenzi =   count_imp.map(x => x._2).reduce((x,y) => x+y)
 
       val repeated_rate= fenmu / fenzi.toDouble
-      val result=List((stat_date,repeated_rate))
+      val result=List((today,repeated_rate))
       val df_result = sc.createDataFrame(result)
 
       GmeiConfig.writeToJDBCTable(df_result, table = "Repeated_content_recommendation_moreday", SaveMode.Append)