Github user windpiger commented on a diff in the pull request:
https://github.com/apache/spark/pull/15994#discussion_r89727312
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala ---
@@ -437,4 +444,38 @@ final class DataFrameNaFunctions private[sql](df:
DataFrame) {
case v => throw new IllegalArgumentException(
s"Unsupported value type ${v.getClass.getName} ($v).")
}
+
+ /**
+ * (Scala-specific) Returns a new [[DataFrame]] that replaces null or
NaN values in specified
+ * numeric, bool, string columns. If a specified column is not a
numeric, boolean, string column,
+ * it is ignored.
+ *
+ * @since 2.1.0
+ */
+ private def fill1[T](value: T, cols: Seq[String]): DataFrame = {
+ value match {
+ case _: jl.Double | _: jl.Integer | _: jl.Float | _: jl.Boolean | _:
jl.Long | _: String =>
+ case _ =>
+ throw new IllegalArgumentException(
+ s"Unsupported value type ${value.getClass.getName} ($value).")
+ }
+
+ val targetColumnType = value match {
+ case _: jl.Double | _: jl.Integer | _: jl.Float | _: jl.Long =>
NumericType
+ case _: jl.Boolean => BooleanType
+ case _: String => StringType
+ }
+
+ val columnEquals = df.sparkSession.sessionState.analyzer.resolver
+ val projections = df.schema.fields.map { f =>
+ // Only fill if the column is part of the cols list.
+ if (((f.dataType.isInstanceOf[NumericType] && targetColumnType ==
NumericType)
--- End diff --
Thanks!
I have modified except one:
If T is a double type , this should be apply to all Numeric columns(include
LongType/IntegerType), or just apply to FractionType?
The fill(value Double) apply to all Numeric columns, and I think fill(value
Long) also keep the logic.
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