增加app使用时间

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

@@ -558,80 +558,104 @@ object alpha_ctr {
       //println(param.date)
       val partition_date = stat_date.replace("-","")
 
-      val click_count_recommend = sc.sql(
-        s"""
-           |select '${stat_date}' as stat_date,count(*) as click_count_recommend
-           |from bl.bl_alpha_et_mg_maidianlog_inc_d
-           |where params['tab_name']='recommend'
-           |and params['page_name']='home'
-           |and type='on_click_feed_topic_card'
-           |and partition_day='${partition_date}'
-       """.stripMargin
-      )
-      click_count_recommend.show()
-
-      val click_count_focus = sc.sql(
-        s"""
-           |select '${stat_date}' as stat_date,count(*) as click_count_focus
-           |from bl.bl_alpha_et_mg_maidianlog_inc_d
-           |where params['tab_name']='focus'
-           |and params['page_name']='home'
-           |and type='on_click_feed_topic_card'
-           |and partition_day='${partition_date}'
-       """.stripMargin
-      )
-      click_count_focus.show()
-
-
-      def parse_json(str:String): Int ={
-        var t = List[Map[String, Any]]()
-        val result = JSON.parseFull(str)
-        result match {
-          case Some(b: List[Map[String, Any]]) => t = t ++ b
-          case None => println("Parsing failed")
-          case other => println("Unknown data structure: " + other)
-        }
-        t.size
-
-      }
-
-      val expoure_cards=sc.sql(
+//      val click_count_recommend = sc.sql(
+//        s"""
+//           |select '${stat_date}' as stat_date,count(*) as click_count_recommend
+//           |from bl.bl_alpha_et_mg_maidianlog_inc_d
+//           |where params['tab_name']='recommend'
+//           |and params['page_name']='home'
+//           |and type='on_click_feed_topic_card'
+//           |and partition_day='${partition_date}'
+//       """.stripMargin
+//      )
+//      click_count_recommend.show()
+//
+//      val click_count_focus = sc.sql(
+//        s"""
+//           |select '${stat_date}' as stat_date,count(*) as click_count_focus
+//           |from bl.bl_alpha_et_mg_maidianlog_inc_d
+//           |where params['tab_name']='focus'
+//           |and params['page_name']='home'
+//           |and type='on_click_feed_topic_card'
+//           |and partition_day='${partition_date}'
+//       """.stripMargin
+//      )
+//      click_count_focus.show()
+//
+//
+//      def parse_json(str:String): Int ={
+//        var t = List[Map[String, Any]]()
+//        val result = JSON.parseFull(str)
+//        result match {
+//          case Some(b: List[Map[String, Any]]) => t = t ++ b
+//          case None => println("Parsing failed")
+//          case other => println("Unknown data structure: " + other)
+//        }
+//        t.size
+//
+//      }
+//
+//      val expoure_cards=sc.sql(
+//        s"""
+//           |select params['exposure_cards'] as exposure_cards
+//           |from bl.bl_alpha_et_mg_maidianlog_inc_d
+//           |where params['tab_name'] = 'recommend'
+//           |and params['page_name'] = 'home'
+//           |and type = 'page_precise_exposure'
+//           |and partition_day='${partition_date}'
+//       """.stripMargin
+//      )
+//      val a =expoure_cards.rdd.map(row => row(0).toString).map(row=>parse_json(row)).collect().sum
+//      val result1=List((stat_date,a))
+//      val df1 = sc.createDataFrame(result1).toDF("stat_date","expoure_count_recommend")
+//
+//      val expoure_cards2=sc.sql(
+//        s"""
+//           |select params['exposure_cards'] as exposure_cards
+//           |from bl.bl_alpha_et_mg_maidianlog_inc_d
+//           |where params['tab_name'] = 'focus'
+//           |and params['page_name'] = 'home'
+//           |and type = 'page_precise_exposure'
+//           |and partition_day='${partition_date}'
+//       """.stripMargin
+//      )
+//      val b =expoure_cards2.rdd.map(row => row(0).toString).map(row=>parse_json(row)).collect().sum
+//      val result2=List((stat_date,b))
+//      val df2 = sc.createDataFrame(result2).toDF("stat_date","expoure_count_focus")
+//
+//
+//
+//      val result=click_count_recommend.join(click_count_focus,"stat_date")
+//        .join(df1,"stat_date")
+//        .join(df2,"stat_date")
+//
+//
+//
+//      GmeiConfig.writeToJDBCTable(result, "alpha_ctr", SaveMode.Append)
+
+
+      val device_duration_avge = sc.sql(
         s"""
-           |select params['exposure_cards'] as exposure_cards
+           |SELECT '${stat_date}' as stat_date,sum(a.time_all)/count(a.device_id) as averge_time from (select device_id,sum(params['duration']) as time_all
            |from bl.bl_alpha_et_mg_maidianlog_inc_d
-           |where params['tab_name'] = 'recommend'
-           |and params['page_name'] = 'home'
-           |and type = 'page_precise_exposure'
+           |where type='on_app_session_over'
            |and partition_day='${partition_date}'
+           |GROUP BY device_id) a
        """.stripMargin
       )
-      val a =expoure_cards.rdd.map(row => row(0).toString).map(row=>parse_json(row)).collect().sum
-      val result1=List((stat_date,a))
-      val df1 = sc.createDataFrame(result1).toDF("stat_date","expoure_count_recommend")
+      device_duration_avge.show()
 
-      val expoure_cards2=sc.sql(
+      val duration_everytime_avge=sc.sql(
         s"""
-           |select params['exposure_cards'] as exposure_cards
+           |SELECT '${stat_date}' as stat_date,sum(a.time_duration)/count(a.device_id) as averge_time from (select device_id,params['duration'] as time_duration
            |from bl.bl_alpha_et_mg_maidianlog_inc_d
-           |where params['tab_name'] = 'focus'
-           |and params['page_name'] = 'home'
-           |and type = 'page_precise_exposure'
-           |and partition_day='${partition_date}'
+           |where type='on_app_session_over'
+           |and partition_day='20190404') a
        """.stripMargin
       )
-      val b =expoure_cards2.rdd.map(row => row(0).toString).map(row=>parse_json(row)).collect().sum
-      val result2=List((stat_date,b))
-      val df2 = sc.createDataFrame(result2).toDF("stat_date","expoure_count_focus")
-
-
-
-      val result=click_count_recommend.join(click_count_focus,"stat_date")
-        .join(df1,"stat_date")
-        .join(df2,"stat_date")
-
-
 
-      GmeiConfig.writeToJDBCTable(result, "alpha_ctr", SaveMode.Append)
+      val result3=device_duration_avge.join(duration_everytime_avge,"stat_date")
+      GmeiConfig.writeToJDBCTable(result3, "alpha_duration", SaveMode.Append)
 
 
     }