Github user aray commented on a diff in the pull request:
https://github.com/apache/spark/pull/7841#discussion_r44572982
--- 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 --
It should now work fine with aggregate functions that have multiple
children as long as they ignore updates when all values are null. For example
`Corr` should work since it only updates its aggregation buffer if both its
arguments are non null.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
