Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/7194#discussion_r34954778
--- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala ---
@@ -403,20 +403,30 @@ trait Row extends Serializable {
if (!isNullAt(i)) {
val o1 = get(i)
val o2 = other.get(i)
- if (o1.isInstanceOf[Array[Byte]]) {
- // handle equality of Array[Byte]
- val b1 = o1.asInstanceOf[Array[Byte]]
- if (!o2.isInstanceOf[Array[Byte]] ||
- !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
+ o1 match {
+ case b1: Array[Byte] =>
+ if (!o2.isInstanceOf[Array[Byte]] ||
+ !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
+ return false
+ }
+ case f1: Float =>
+ if (!o2.isInstanceOf[Float] ||
+ (java.lang.Float.isNaN(f1) &&
!java.lang.Float.isNaN(o2.asInstanceOf[Float]))) {
--- End diff --
actually, I don't think that we can use nanSafeCompare without breaking
existing test code / user code: the old code would allow integers and floats to
be compared because Java would handle implicit type conversions. Therefore,
for compatibility I think we need to do the same here.
I think it will be clearer to rework this as something like "if f1 is a
float and it's NaN, then the other value had better be a NaN float, otherwise
fall back to the regular `==` branch).
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