spark dataframe dataset reducebykey用法

case class Record(ts: Long, id: Int, value: Int)
如果是rdd,我们经常会用reducebykey获取到最新时间戳的一条记录，用下面的方法
def findLatest(records: RDD[Record])(implicit spark: SparkSession) = {
records.keyBy(_.id).reduceByKey{
(x, y) => if(x.ts > y.ts) x else y
}.values
}
在dataset中可以用一下方法：
import org.apache.spark.sql.functions._
val newDF = df.groupBy('id).agg.max(struct('ts, 'val)) as 'tmp).select($"id", $"tmp.*")
为什么可以这样操作呢？因为对于struct,或者tuple类型而言，max方法默认按照第一个元素进行排序处理
举个详细点的例子：
import org.apache.spark.sql.functions._

val data = Seq(
("michael", 1, "event 1"),
("michael", 2, "event 2"),
("reynold", 1, "event 3"),
("reynold", 3, "event 4")).toDF("user", "time", "event")

val newestEventPerUser =
data
.groupBy('user)
.agg(max(struct('time, 'event)) as 'event)
.select($"user", $"event.*") // Unnest the struct into top-level columns.
scala> newestEventPerUser.show()
+-------+----+-------+
|   user|time|  event|
+-------+----+-------+
|reynold|   3|event 4|
|michael|   2|event 2|
+-------+----+-------+
复杂一点可参考如下：
case class AggregateResultModel(id: String,
mtype: String,
healthScore: Int,
mortality: Float,
reimbursement: Float)
// assume that the rawScores are loaded behorehand from json,csv files

val groupedResultSet = rawScores.as[AggregateResultModel].groupByKey( item => (item.id,item.mtype ))
.reduceGroups( (x,y) => getMinHealthScore(x,y)).map(_._2)

// the binary function used in the reduceGroups
def getMinHealthScore(x : AggregateResultModel, y : AggregateResultModel): AggregateResultModel = {
// complex logic for deciding between which row to keep
if (x.healthScore > y.healthScore) { return y }
else if (x.healthScore < y.healthScore) { return x }
else {
if (x.mortality < y.mortality) { return y }
else if (x.mortality > y.mortality) { return x }
else  {
if(x.reimbursement < y.reimbursement)
return x
else
return y
}
}
}