[GitHub] spark pull request #13876: [SPARK-16174][SQL] Improve `OptimizeIn` optimizer...

[rxin <gi...@git.apache.org>]

Github user rxin commented on a diff in the pull request:

    https://github.com/apache/spark/pull/13876#discussion_r69775645
  
    --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala ---
    @@ -820,16 +820,26 @@ object ConstantFolding extends Rule[LogicalPlan] {
     }
     
     /**
    - * Replaces [[In (value, seq[Literal])]] with optimized version[[InSet (value, HashSet[Literal])]]
    - * which is much faster
    + * Optimize IN predicates:
    + * 1. Removes deterministic repetitions.
    + * 2. Replaces [[In (value, seq[Literal])]] with optimized version
    + *    [[InSet (value, HashSet[Literal])]] which is much faster.
      */
     case class OptimizeIn(conf: CatalystConf) extends Rule[LogicalPlan] {
       def apply(plan: LogicalPlan): LogicalPlan = plan transform {
         case q: LogicalPlan => q transformExpressionsDown {
    -      case In(v, list) if !list.exists(!_.isInstanceOf[Literal]) &&
    -          list.size > conf.optimizerInSetConversionThreshold =>
    -        val hSet = list.map(e => e.eval(EmptyRow))
    -        InSet(v, HashSet() ++ hSet)
    +      case i @ In(v, list) =>
    +        val (deterministics, others) = list.partition(_.deterministic)
    --- End diff --
    
    one question i have is how often do we see an in expression with some expressions being deterministic and some nondeterministic? if not, i'd just simplify this so we only do it if everything is deterministic.



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