[GitHub] spark pull request #13418: [SPARK-15677][SQL] Query with scalar sub-query in...

[ioana-delaney <gi...@git.apache.org>]

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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