Github user viirya commented on a diff in the pull request:
    --- 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 = { colName =>
    -      allColumns.find(col => resolver(, 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(, 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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