Github user zsxwing commented on a diff in the pull request:
https://github.com/apache/spark/pull/16970#discussion_r102290771
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -1996,7 +1996,7 @@ 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.flatMap { colName =>
+ val groupCols = colNames.toSet.toSeq.flatMap { (colName: String) =>
--- End diff --
Fixed an issue that `groupCols` may contain duplicated columns. Without
this fix, `org.apache.spark.sql.DatasetSuite.dropDuplicates: columns with same
column name` will fail because the hash keys are different.
Before my change, in ``org.apache.spark.sql.DatasetSuite.dropDuplicates:
columns with same column name` test, it has two columns both called `_2`, so
`groupCols` will contain 4 columns. However, it will be optimized to 2 columns.
After creating a new Deduplication operator, the optimization rule doesn't
apply because it's not an Aggregate. So it will still use 4 columns as the
group keys.
This exposes one potential breaking change: some optimization rules may not
work after this change.
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