Github user ioana-delaney commented on a diff in the pull request:
https://github.com/apache/spark/pull/13418#discussion_r65437967
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
---
@@ -1468,7 +1468,8 @@ object DecimalAggregates extends Rule[LogicalPlan] {
*/
object ConvertToLocalRelation extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan transform {
- case Project(projectList, LocalRelation(output, data)) =>
+ case p @ Project(projectList, LocalRelation(output, data))
+ if !p.expressions.exists(ScalarSubquery.hasScalarSubquery) =>
--- End diff --
@davies Sorry for the delay in replying. I am new to the Spark code. I've
looked at Unevaluable expressions. My findings are that checking for
Unevaluable expressions would be too general since a lot of expressions mix in
this trait. For example, AttributeReference is one of them. If we explicitly
check for Unevaluable expressions, a simple query of the form "select c1 from
t1"
would be regressed. Let me know I misunderstood your requirement. Thanks.
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