Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/15427#discussion_r83140093
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -1878,17 +1878,25 @@ class Dataset[T] private[sql](
def dropDuplicates(colNames: Seq[String]): Dataset[T] = withTypedPlan {
val resolver = sparkSession.sessionState.analyzer.resolver
val allColumns = queryExecution.analyzed.output
- val groupCols = colNames.map { colName =>
- allColumns.find(col => resolver(col.name, colName)).getOrElse(
+ val groupCols = colNames.flatMap { colName =>
+ // It is possibly there are more than one columns with the same name,
+ // so we call filter instead of find.
+ val cols = allColumns.filter(col => resolver(col.name, colName))
+ if (cols.isEmpty) {
throw new AnalysisException(
--- End diff --
My thought is:
When an user mistakenly gives wrong column to `Dataset.drop`, it can be
easily found out.
But for `Dataset.dropDuplicates`, it might be harder to figure out
duplicate rows are still there. So to throw an explicit exception looks more
proper to me.
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