[GitHub] spark pull request #14527: [SPARK-16938][SQL] `drop/dropDuplicate` should ha...

[cloud-fan <gi...@git.apache.org>]

Github user cloud-fan commented on a diff in the pull request:

    https://github.com/apache/spark/pull/14527#discussion_r78924274
  
    --- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
    @@ -1827,7 +1827,7 @@ class Dataset[T] private[sql](
         val resolver = sparkSession.sessionState.analyzer.resolver
         val allColumns = queryExecution.analyzed.output
         val remainingCols = allColumns.filter { attribute =>
    -      colNames.forall(n => !resolver(attribute.name, n))
    +      colNames.forall(n => !(resolver(attribute.name, n) || resolver(attribute.qualifiedName, n)))
    --- End diff --
    
    This is fragile, what if we wanna drop a qualified column with special characters like  "a.\`b c\`"? Users may wanna do ``df.drop("a.`b c`")`` instead of ``df.drop("a.b c")``, and what if we just wanna drop a column named `a.b c`?
    
    The current semantic of `drop` is, if the parameter is string, we treat it as column name literal, without any parsing, which is different from `select`. If users do wanna use a qualified column name to refer to a specific column, they should use the `drop` with `Column` parameter.
    
    I think the same semantic should apply to `dropDuplicates`, i.e. we should add a new version that takes `Seq[Column]` as parameter.
    
     


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