cloud-fan commented on a change in pull request #26738: [SPARK-30082][SQL] Do
not replace Zeros when replacing NaNs
URL: https://github.com/apache/spark/pull/26738#discussion_r353008601
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
##########
@@ -456,11 +456,23 @@ final class DataFrameNaFunctions private[sql](df:
DataFrame) {
val keyExpr = df.col(col.name).expr
def buildExpr(v: Any) = Cast(Literal(v), keyExpr.dataType)
val branches = replacementMap.flatMap { case (source, target) =>
- Seq(buildExpr(source), buildExpr(target))
+ if (isNaN(source) || isNaN(target)) {
+ col.dataType match {
+ case IntegerType | LongType | ShortType | ByteType => Seq.empty
Review comment:
checked with scala
```
scala> Float.NaN == 0
res0: Boolean = false
scala> Float.NaN.toInt == 0
res1: Boolean = true
```
This is also true in Spark. When comparing float and int, we cast int to
float to compare, so `NaN != 0`.
I think it's a bug that we cast the value to the column type and compare. We
shouldn't do any cast and let the type coercion rules to do proper cast for
`CaseKeyWhen`
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