Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/7841#discussion_r44571373
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
---
@@ -248,6 +253,43 @@ 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 =>
+ def ifExpr(expr: Expression) = {
+ If(EqualTo(pivotColumn, value), expr, Literal(null))
+ }
+ 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))
--- End diff --
Seems we still need to check the number of children and make sure we have a
single child?
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