Github user tanejagagan commented on a diff in the pull request:
https://github.com/apache/spark/pull/16497#discussion_r95303054
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/Percentile.scala
---
@@ -126,10 +152,15 @@ case class Percentile(
buffer: OpenHashMap[Number, Long],
input: InternalRow): OpenHashMap[Number, Long] = {
val key = child.eval(input).asInstanceOf[Number]
+ val frqValue = frequency.eval(input)
// Null values are ignored in counts map.
- if (key != null) {
- buffer.changeValue(key, 1L, _ + 1L)
+ if (key != null && frqValue != null) {
+ val frqLong = frqValue.asInstanceOf[Number].longValue()
+ // add only when frequency is positive
+ if (frqLong > 0) {
--- End diff --
I think the option was between either fail or disregard those values. We
can certainly make this a requirement, document and fail when the values are
negatives
I think for the cases where values are either null or 0 we should not be
adding them to Map to unnecessary
bloat the map.
The logic would look like
if ( frqLong < 0 ) {
throw new SomeException
}else if( frqLong > 0 ) {
// process to add them to map
}
Let me know if above look good and i will make the changes accordingly
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