dongjoon-hyun commented on a change in pull request #28876:
URL: https://github.com/apache/spark/pull/28876#discussion_r443291919
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/AggUtils.scala
##########
@@ -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:
If we broaden the scope, `SparkStrategies` already is looking at the
detail of `functionsWithDistinct` like the following.
```scala
val (functionsWithDistinct, functionsWithoutDistinct) =
aggregateExpressions.partition(_.isDistinct)
if
(functionsWithDistinct.map(_.aggregateFunction.children.toSet).distinct.length
> 1) {
// This is a sanity check. We should not reach here when we have
multiple distinct
// column sets. Our `RewriteDistinctAggregates` should take care
this case.
sys.error("You hit a query analyzer bug. Please report your query
to " +
"Spark user mailing list.")
}
```
And the very next line is the same logic block for `groupingExpression`.
```scala
// Ideally this should be done in `NormalizeFloatingNumbers`, but we
do it here because
// `groupingExpressions` is not extracted during logical phase.
val normalizedGroupingExpressions = groupingExpressions.map { e =>
NormalizeFloatingNumbers.normalize(e) match {
case n: NamedExpression => n
case other => Alias(other, e.name)(exprId = e.exprId)
}
}
```
Given the above, I guess what you concerned is only one line source code,
`val distinctExpressions =
functionsWithDistinct.head.aggregateFunction.children`.
And, the following comment is about the definition of
`functionsWithDistinct` which is generated from the above. The comment is not a
detail hidden from `SparkStrategies`. For me, it seems to be an assumption
given by `SparkStrategies`.
```
// functionsWithDistinct is guaranteed to be non-empty. Even though it may
contain more than one
// DISTINCT aggregate function, all of those functions will have the same
column expressions.
// For example, it would be valid for functionsWithDistinct to be
// [COUNT(DISTINCT foo), MAX(DISTINCT foo)], but [COUNT(DISTINCT bar),
COUNT(DISTINCT foo)] is
// disallowed because those two distinct aggregates have different column
expressions.
```
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