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[SPARK-31990][SS] Use toSet.toSeq in Dataset.dropDuplicates #28830

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[SPARK-31990][SS] Use toSet.toSeq in Dataset.dropDuplicates #28830

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Fix
maropu Jun 15, 2020
2bcb5c5
Revert "Fix"
maropu Jun 15, 2020
7546ba4
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maropu Jun 15, 2020
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4 changes: 3 additions & 1 deletion sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -2541,7 +2541,9 @@ 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.distinct.flatMap { (colName: String) =>
// SPARK-31990: We must keep `toSet.toSeq` here because of the backward compatibility issue
// (the Streaming's state store depends on the `groupCols` order).
val groupCols = colNames.toSet.toSeq.flatMap { (colName: String) =>
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@cloud-fan cloud-fan Jun 15, 2020 •
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I think this is good for now.

In the future, this may still be broken by Scala version upgrade, and hopefully @xuanyuanking 's unsafe row validation can detect it. Then we can change it and use a deterministic order, as it will be broken anyway.

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@xuanyuanking xuanyuanking Jun 15, 2020

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Yep, I also mentioned this at #28830 (comment), we can relay on the validation checking and integrated tests.

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@HeartSaVioR HeartSaVioR Jun 15, 2020 •
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Worth noting that we need to have "concrete" solution eventually - if columns are all having same type neither #28830 nor #24173 catch the change and the result becomes silently incorrect. I roughly remember the similar issue on pyspark, which was trying to fix the issue on order vs name, don't remember how it ended up. cc. @HyukjinKwon

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@HyukjinKwon HyukjinKwon Jun 15, 2020

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Ah, that was fixed in a way by adding an env variable. That case also was specific to Python 2 which is deprecated now so it's rather a corner case.

// 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))
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