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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