Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/7841#discussion_r44500328
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
---
@@ -248,6 +253,38 @@ class Analyzer(
}
}
+ object ResolvePivot extends Rule[LogicalPlan] {
+ def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+ case p: Pivot if !p.childrenResolved => p
+ case Pivot(groupByExprs, pivotColumn, pivotValues, aggregates,
child) =>
+ val singleAgg = aggregates.size == 1
+ val pivotAggregates: Seq[NamedExpression] = pivotValues.flatMap{
value =>
+ aggregates.map{ aggregate =>
+ val filteredAggregate = aggregate.transformDown{
+ case u: UnaryExpression if
u.isInstanceOf[AggregateExpression] =>
+ u.withNewChildren(Seq(
+ If(EqualTo(pivotColumn, Literal(value)), u.child,
Literal(null))
--- End diff --
I guess the underlying assumption of this line is that aggregate function
should ignore `null` input values, right? (Basically, `null` should have no
impact on the result of this aggregate function.) If so, can we add a comment
at here?
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