Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/8931#discussion_r41425821
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
---
@@ -69,6 +72,9 @@ case class TungstenAggregate(
protected override def doExecute(): RDD[InternalRow] = attachTree(this,
"execute") {
val numInputRows = longMetric("numInputRows")
val numOutputRows = longMetric("numOutputRows")
+ val totalPeakMemory = longMetric("totalPeakMemory")
--- End diff --
I see, given limited space, I question the utility of this metric (these
tasks might not have even been running at the same time). The percentiles seem
way more useful (i.e. is skew causing some partitions to spill or are they all
spilling). We can leave it in if @pwendell and @rxin feel strongly though.
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