Github user setjet commented on a diff in the pull request:
https://github.com/apache/spark/pull/18113#discussion_r118939565
--- 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 --
Ah I see my misunderstanding: in reduce I tried to also have an `if` for
`f(a) == null` because of the previously mentioned implicit casting issue. This
would force a `java.lang.Double` to be returned by the function, as `Double ==
null` doesn't make sense in Scala.
I have updated the code, please have a look :) Becuase `OUT` is already a
`java.lang.Double`, we do not need the `toColumnJava`. As a result of `OUT`
being `java.lang.Double` however, we do need a `toColumnScala` to accommodate `
val f = (x: (Double, Double)) => x._2; empty.agg(typed.min(f)).show()`
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