typedCount.scala

  def summarize2(dsSEMUnionClean: Dataset[SEMUnionClean]): Dataset[SEMUnionClean] = {
    import dsSEMUnionClean.sparkSession.implicits._
    import org.apache.spark.sql.expressions.scalalang.typed.{
      count => typedCount,
      sum => typedSum
    }

    val xxx =
      dsSEMUnionClean.groupByKey(r => (r.account_descriptive_name, r.advertiser_id, r.advertiser_name, r.campaign_id, r.campaign_name, r.source))
        .agg(
          // typedSum[SEMUnionClean](_.assists).name("sum(assists)"),
          typedSum[SEMUnionClean](_.clicks).name("sum(clicks)"),
          typedSum[SEMUnionClean](_.conversions).name("sum(conversions)"),
          typedSum[SEMUnionClean](_.conversions_many_per_click).name("sum(conversions_many_per_click)"),
          typedSum[SEMUnionClean](_.impressions).name("sum(depth)")
        )

    val zzz = xxx.joinWith(xxx, xxx.col("_1") === xxx.col("_1"))

    val yyy = xxx.map {
      case ((accountName, advId, advName, campaignId, campaignName, src), howManyAssists, sumOfImpressions) =>
    }
    // .withColumnRenamed("value", "group")
    // .alias("Summary by color level")
    // .show()

  }