Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/11868#discussion_r59162191
--- 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 --
Thank you for attention, @srowen . I agree with you and @rxin in the
current Spark status view point.
Today, when I'm reading [Spark SQL, DataFrames and Datasets Guide: NaN
Semantics](http://spark.apache.org/docs/latest/sql-programming-guide.html), I
just suddenly want to update this PR.
> **NaN Semantics**
> There is specially handling for not-a-number (NaN) when dealing with
float or double types that does not exactly match standard floating point
semantics. Specifically:
> - NaN = NaN returns true.
I'm still digging to find some useful cases for this PR outside SQL layers.
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