cloud-fan commented on a change in pull request #35130:
URL: https://github.com/apache/spark/pull/35130#discussion_r783130063
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File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/V2ScanRelationPushDown.scala
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@@ -88,25 +88,40 @@ object V2ScanRelationPushDown extends Rule[LogicalPlan]
with PredicateHelper {
sHolder.builder match {
case r: SupportsPushDownAggregates =>
val aggExprToOutputOrdinal = mutable.HashMap.empty[Expression,
Int]
- var ordinal = 0
- val aggregates = resultExpressions.flatMap { expr =>
- expr.collect {
- // Do not push down duplicated aggregate expressions. For
example,
- // `SELECT max(a) + 1, max(a) + 2 FROM ...`, we should only
push down one
- // `max(a)` to the data source.
- case agg: AggregateExpression
- if !aggExprToOutputOrdinal.contains(agg.canonicalized) =>
- aggExprToOutputOrdinal(agg.canonicalized) = ordinal
- ordinal += 1
- agg
- }
- }
+ val aggregates = collectAggregates(resultExpressions,
aggExprToOutputOrdinal)
val normalizedAggregates = DataSourceStrategy.normalizeExprs(
aggregates,
sHolder.relation.output).asInstanceOf[Seq[AggregateExpression]]
val normalizedGroupingExpressions =
DataSourceStrategy.normalizeExprs(
groupingExpressions, sHolder.relation.output)
- val pushedAggregates = PushDownUtils.pushAggregates(
- r, normalizedAggregates, normalizedGroupingExpressions)
+ val translatedAggregates =
DataSourceStrategy.translateAggregation(
+ normalizedAggregates, normalizedGroupingExpressions)
+ val (finalResultExpressions, finalAggregates,
finalTranslatedAggregates) = {
+ if (translatedAggregates.isEmpty ||
+ r.supportCompletePushDown(translatedAggregates.get)) {
+ (resultExpressions, aggregates, translatedAggregates)
+ } else {
+ val newResultExpressions = resultExpressions.map { expr =>
+ expr.transform {
+ case AggregateExpression(avg: aggregate.Average, _,
isDistinct, _, _) =>
+ val left = addCastIfNeeded(aggregate.Sum(avg.child)
+ .toAggregateExpression(isDistinct), avg.dataType)
Review comment:
I don't think we can retain the original data type of AVG by doing so.
Please check the implementation of Spark AVG.
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