imback82 commented on a change in pull request #26593: [SPARK-29890][SQL]
DataFrameNaFunctions.fill should handle duplicate columns
URL: https://github.com/apache/spark/pull/26593#discussion_r348702756
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
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@@ -468,12 +477,26 @@ final class DataFrameNaFunctions private[sql](df:
DataFrame) {
s"Unsupported value type ${v.getClass.getName} ($v).")
}
+ private def toAttributes(cols: Seq[String]): Seq[Attribute] = {
+ def resolve(colName: String) : Attribute = {
+ df.col(colName).named.toAttribute match {
+ case a: Attribute => a
+ case _ => throw new IllegalArgumentException(s"'$colName' is not a top
level column.")
+ }
+ }
+ cols.map(resolve)
+ }
Review comment:
@cloud-fan I noticed that `drop()` is resolving column names by
`cols.map(name => df.resolve(name)` instead of `df.col(name)`. The difference
(other than the return type) is that `df.col()` will try to resolve using regex
and adding metadata. Do you think we need to make this consistent?
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