Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/11583#discussion_r61764184
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
---
@@ -363,43 +363,68 @@ class Analyzer(
object ResolvePivot extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan transform {
- case p: Pivot if !p.childrenResolved |
!p.aggregates.forall(_.resolved) => p
+ case p: Pivot if !p.childrenResolved |
!p.aggregates.forall(_.resolved)
+ | !p.groupByExprs.forall(_.resolved) | !p.pivotColumn.resolved => p
case Pivot(groupByExprs, pivotColumn, pivotValues, aggregates,
child) =>
val singleAgg = aggregates.size == 1
- val pivotAggregates: Seq[NamedExpression] = pivotValues.flatMap {
value =>
- def ifExpr(expr: Expression) = {
- If(EqualTo(pivotColumn, value), expr, Literal(null))
+ def outputName(value: Literal, aggregate: Expression): String = {
+ if (singleAgg) value.toString else value + "_" + aggregate.sql
+ }
+ if (aggregates.forall(a =>
PivotFirst.supportsDataType(a.dataType))) {
+ // Since evaluating |pivotValues| if statements for each input
row can get slow this is an
+ // alternate plan that instead uses two steps of aggregation.
+ val namedAggExps: Seq[NamedExpression] = aggregates.map(a =>
Alias(a, a.sql)())
+ val namedPivotCol = pivotColumn match {
+ case n: NamedExpression => n
+ case _ => Alias(pivotColumn, "__pivot_col")()
+ }
+ val bigGroup = groupByExprs :+ namedPivotCol
+ val firstAgg = Aggregate(bigGroup, bigGroup ++ namedAggExps,
child)
+ val castPivotValues = pivotValues.map(Cast(_,
pivotColumn.dataType).eval(EmptyRow))
+ val pivotAggs = namedAggExps.map { a =>
+ Alias(PivotFirst(namedPivotCol.toAttribute, a.toAttribute,
castPivotValues)
+ .toAggregateExpression()
+ , "__pivot_" + a.sql)()
+ }
+ val secondAgg = Aggregate(groupByExprs, groupByExprs ++
pivotAggs, firstAgg)
+ val pivotAggAttribute = pivotAggs.map(_.toAttribute)
+ val pivotOutputs = pivotValues.zipWithIndex.flatMap { case
(value, i) =>
+ aggregates.zip(pivotAggAttribute).map { case (aggregate,
pivotAtt) =>
+ Alias(ExtractValue(pivotAtt, Literal(i), resolver),
outputName(value, aggregate))()
+ }
}
- aggregates.map { aggregate =>
- val filteredAggregate = aggregate.transformDown {
- // Assumption is the aggregate function ignores nulls. This
is true for all current
- // AggregateFunction's with the exception of First and Last
in their default mode
- // (which we handle) and possibly some Hive UDAF's.
- case First(expr, _) =>
- First(ifExpr(expr), Literal(true))
- case Last(expr, _) =>
- Last(ifExpr(expr), Literal(true))
- case a: AggregateFunction =>
- a.withNewChildren(a.children.map(ifExpr))
- }.transform {
- // We are duplicating aggregates that are now computing a
different value for each
- // pivot value.
- // TODO: Don't construct the physical container until after
analysis.
- case ae: AggregateExpression => ae.copy(resultId =
NamedExpression.newExprId)
+ Project(groupByExprs ++ pivotOutputs, secondAgg)
+ } else {
--- End diff --
Since we will decide which branch to use based on the datatypes, do we
still have enough test coverage for this else branch?
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