Github user frreiss commented on a diff in the pull request:
https://github.com/apache/spark/pull/13155#discussion_r63768232
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
---
@@ -1648,16 +1648,56 @@ object RewriteCorrelatedScalarSubquery extends
Rule[LogicalPlan] {
}
/**
+ * Statically evaluate an expression containing one or more aggregates
on an empty input.
+ */
+ private def evalOnZeroTups(expr : Expression) : Option[Any] = {
+ // AggregateExpressions are Unevaluable, so we need to replace all
aggregates
+ // in the expression with the value they would return for zero input
tuples.
+ val rewrittenExpr = expr transform {
+ case a @ AggregateExpression(aggFunc, _, _, resultId) =>
+ val resultLit = aggFunc.defaultResult match {
+ case Some(lit) => lit
+ case None => Literal.default(NullType)
+ }
+ Alias(resultLit, "aggVal") (exprId = resultId)
+ }
+ Option(rewrittenExpr.eval())
+ }
+
+ /**
* Construct a new child plan by left joining the given subqueries to a
base plan.
*/
private def constructLeftJoins(
child: LogicalPlan,
subqueries: ArrayBuffer[ScalarSubquery]): LogicalPlan = {
subqueries.foldLeft(child) {
case (currentChild, ScalarSubquery(query, conditions, _)) =>
+ val aggOutputExpr =
query.asInstanceOf[Aggregate].aggregateExpressions.head
--- End diff --
I'm looking into exactly what types of plans can show up in a
ScalarSubquery node that passes all the checks upstream of
RewriteCorrelatedScalarSubquery. Should have an answer in a few hours.
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