[GitHub] dongjoon-hyun edited a comment on issue #23851: [SPARK-26950][SQL][TEST] Make RandomDataGenerator use Float.NaN or Double.NaN for all NaN values

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dongjoon-hyun edited a comment on issue #23851: [SPARK-26950][SQL][TEST] Make RandomDataGenerator use Float.NaN or Double.NaN for all NaN values
URL: https://github.com/apache/spark/pull/23851#issuecomment-466104618
 
 
   Thank you for review, @cloud-fan and @srowen .
   
   To @cloud-fan .
   `checkEvaluationWithUnsafeProjection` should handle more complex expressions like `from_avro(to_avro([NaN]), {"type":"record","name":"topLevelRecord","fields":[{"name":"col_1","type":["float","null"]}]})` described in the PR description. However,
   
   1. `NormalizeNaNAndZero` expects and handles `Float` and `Double` instances only.
   2. `NormalizeFloatingNumbers` expects `Plan`.
   
   We can wrap the first argument `expression`, but the second argument `expected` is `Any` type.
   ```scala
     protected def checkEvaluationWithUnsafeProjection(
         expression: Expression,
         expected: Any,
         inputRow: InternalRow = EmptyRow): Unit = {
   ```

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