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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