Github user setjet commented on a diff in the pull request:
https://github.com/apache/spark/pull/18113#discussion_r118843548
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/typedaggregators.scala
---
@@ -99,3 +97,67 @@ class TypedAverage[IN](val f: IN => Double) extends
Aggregator[IN, (Double, Long
toColumn.asInstanceOf[TypedColumn[IN, java.lang.Double]]
}
}
+
+class TypedMinDouble[IN](val f: IN => Double) extends Aggregator[IN,
Double, Double] {
+ override def zero: Double = Double.PositiveInfinity
--- End diff --
@viirya I just had a go at your suggestion, but it seems to be more
complicated than anticipated. Spark performs some implicit casts (I think as
part of Catalyst) between `java.lang.Double` and `scala.Double`, causing a
nullpointer:
`java.lang.NullPointerException at
scala.Predef$.Double2double(Predef.scala:365!`
I am not sure if this method is feasible.
Sample of the `merge` function:
`override def merge(b1: java.lang.Double, b2: java.lang.Double):
java.lang.Double = java.lang.Math.min(b1, b2)`
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