dongjoon-hyun commented on a change in pull request #28876:
URL: https://github.com/apache/spark/pull/28876#discussion_r443266734
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File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/AggUtils.scala
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@@ -144,11 +145,16 @@ object AggUtils {
// [COUNT(DISTINCT foo), MAX(DISTINCT foo)], but [COUNT(DISTINCT bar),
COUNT(DISTINCT foo)] is
// disallowed because those two distinct aggregates have different column
expressions.
val distinctExpressions =
functionsWithDistinct.head.aggregateFunction.children
- val namedDistinctExpressions = distinctExpressions.map {
- case ne: NamedExpression => ne
- case other => Alias(other, other.toString)()
+ val normalizedNamedDistinctExpressions = distinctExpressions.map { e =>
+ // Ideally this should be done in `NormalizeFloatingNumbers`, but we do
it here because
+ // `groupingExpressions` is not extracted during logical phase.
+ NormalizeFloatingNumbers.normalize(e) match {
+ case ne: NamedExpression => ne
+ case other => Alias(other, other.toString)()
Review comment:
This works correctly, @viirya . However, as an architectural
perspective, I'm wondering if we can collect these
`NormalizeFloatingNumbers.normalize` application in `SparkStrategies` instead
of putting `AggUtils`. Actually, in `SparkStragegies`, we have the same logic
for `normalizedGroupingExpressions` and missed this conversion at
`functionsWithDistinct`. IMO, it would be easier if we keep this converting in
one place, `SparkStragegies`, in order to avoid missing corner cases like this.
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