[GitHub] [spark] dongjoon-hyun commented on a change in pull request #26738: [SPARK-30082][SQL] Do not replace Zeros when replacing NaNs

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dongjoon-hyun 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_r352996206
 
 

 ##########
 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:
   Thank you for making a PR and I fully understand this issue, @johnhany97 .
   
   One concern is that the current behavior is consistent with Spark's `CAST` operation which converts `NaN` to `0` during DOUBLE-to-INT casting. Theoretically, while Apache Spark casts the given value to the given column type first, `NaN` becomes `0`.
   ```scala
   scala> Seq(Double.NaN, 0.0).toDF.selectExpr("cast(value as int)").show
   +-----+
   |value|
   +-----+
   |    0|
   |    0|
   +-----+
   ```
   So, it's a natural behavior in that sequence. However, I agree that `na` function specially needs this fix.
   
   Hi, @gatorsmile and @cloud-fan . How do you think about this PR?

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