DataFrameNaFunctions.fill in Spark 2.4.5 & Spark 2.4.3

2.4.3.scala

 /**
   * Returns a new `DataFrame` that replaces null or NaN values in specified
   * numeric, string columns. If a specified column is not a numeric, string
   * or boolean column it is ignored.
   */
  private def fillValue[T](value: T, cols: Seq[String]): DataFrame = {
    // the fill[T] which T is  Long/Double,
    // should apply on all the NumericType Column, for example:
    // val input = Seq[(java.lang.Integer, java.lang.Double)]((null, 164.3)).toDF("a","b")
    // input.na.fill(3.1)
    // the result is (3,164.3), not (null, 164.3)
    val targetType = value match {
      case _: Double | _: Long => NumericType
      case _: String => StringType
      case _: Boolean => BooleanType
      case _ => throw new IllegalArgumentException(
        s"Unsupported value type ${value.getClass.getName} ($value).")
    }


    val columnEquals = df.sparkSession.sessionState.analyzer.resolver
    val projections = df.schema.fields.map { f =>
      val typeMatches = (targetType, f.dataType) match {
        case (NumericType, dt) => dt.isInstanceOf[NumericType]
        case (StringType, dt) => dt == StringType
        case (BooleanType, dt) => dt == BooleanType
        case _ =>
          throw new IllegalArgumentException(s"$targetType is not matched at fillValue")
      }
      // Only fill if the column is part of the cols list.
      if (typeMatches && cols.exists(col => columnEquals(f.name, col))) {
        fillCol[T](f, value)
      } else {
        df.col(f.name)
      }
    }
    df.select(projections : _*)
  }

2.4.5.scala

 /**
   * Returns a new `DataFrame` that replaces null or NaN values in the specified
   * columns. If a specified column is not a numeric, string or boolean column,
   * it is ignored.
   */
  private def fillValue[T](value: T, cols: Seq[Attribute]): DataFrame = {
    // the fill[T] which T is  Long/Double,
    // should apply on all the NumericType Column, for example:
    // val input = Seq[(java.lang.Integer, java.lang.Double)]((null, 164.3)).toDF("a","b")
    // input.na.fill(3.1)
    // the result is (3,164.3), not (null, 164.3)
    val targetType = value match {
      case _: Double | _: Long => NumericType
      case _: String => StringType
      case _: Boolean => BooleanType
      case _ => throw new IllegalArgumentException(
        s"Unsupported value type ${value.getClass.getName} ($value).")
    }


    val projections = outputAttributes.map { col =>
      val typeMatches = (targetType, col.dataType) match {
        case (NumericType, dt) => dt.isInstanceOf[NumericType]
        case (StringType, dt) => dt == StringType
        case (BooleanType, dt) => dt == BooleanType
        case _ =>
          throw new IllegalArgumentException(s"$targetType is not matched at fillValue")
      }
      
      // Only fill if the column is part of the cols list.
      if (typeMatches && cols.exists(_.semanticEquals(col))) {
        fillCol(col.dataType, col.name, Column(col), value)
      } else {
        Column(col)
      }
    }
    df.select(projections : _*)
  }