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Subject [GitHub] [spark] cloud-fan commented on a change in pull request #26738: [SPARK-30082][SQL] Do not replace Zeros when replacing NaNs
Date Tue, 03 Dec 2019 06:53:01 GMT
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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