[GitHub] spark pull request: [SPARK-14051][SQL] Implement `Double.NaN==Floa...

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

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

    https://github.com/apache/spark/pull/11868#discussion_r59145545
  
    --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala ---
    @@ -402,11 +402,13 @@ trait Row extends Serializable {
                   return false
                 }
               case f1: Float if java.lang.Float.isNaN(f1) =>
    -            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
    +            if (!(o2.isInstanceOf[Float] && java.lang.Float.isNaN(o2.asInstanceOf[Float]) ||
    --- End diff --
    
    Hm, in general NaN never equals NaN. There might be some reason to treat it differently here. On the one hand I tend to agree with this change anyway, on the grounds that it implements something like automatic promotion in Scala/Java. But clearly we're already not implementing the language's semantics, and trying to achieve something more like bitwise-equal semantics. In that case this wouldn't quite be right. Ask the author of the original change why it was made?


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