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_r349416582
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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.")
Review comment:
The nested fields are not supported since the name is checked first against
`df.schema.fields` which contains the top-level field, and these do not become
candidates for `fill`. `*` is handled the same way and it is ignored. I can
capture this behavior in the unit test.
Also, what's the best way to convert `Column` to `Attribute`? Now that we
have a single point of entry `def fillValue[T](value: T, cols:
Seq[Attribute])`, we need to convert `Column` (result of `df.col`) to
`Attribute`.
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