Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/18113#discussion_r154289888
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/typedaggregators.scala
---
@@ -99,3 +94,91 @@ 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
+ override def reduce(b: Double, a: IN): Double = math.min(b, f(a))
+ override def merge(b1: Double, b2: Double): Double = math.min(b1, b2)
+ override def finish(reduction: Double): Double = {
+ if (Double.PositiveInfinity == reduction) {
--- End diff --
After some more thoughts, options 3 is not reasonable as throwing exception
is not a good idea in big data, especially in the last stage of a long-running
job.
option 2 is weird as it doesn't follow either java/scala or SQL.
Let's go with option 1.
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