kachayev commented on a change in pull request #28133: [SPARK-31156][SQL]
DataFrameStatFunctions API to be consistent with respect to Column type
URL: https://github.com/apache/spark/pull/28133#discussion_r407295549
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File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
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@@ -66,6 +68,19 @@ object FrequentItems extends Logging {
}
}
+ /** Helper function to resolve column to expr (if not yet) */
+ // TODO: it might be helpful to have this helper in Dataset.scala,
+ // e.g. `drop` function uses exactly the same flow to deal with
+ // `Column` arguments
+ private def resolveColumn(df: DataFrame, col: Column): Column = {
+ col match {
+ case Column(u: UnresolvedAttribute) =>
+ Column(df.queryExecution.analyzed.resolveQuoted(
+ u.name, df.sparkSession.sessionState.analyzer.resolver).getOrElse(u))
+ case Column(_expr: Expression) => col
+ }
+ }
Review comment:
Exactly the same approach is used for `drop` implementation here:
https://github.com/apache/spark/blob/22bb6b0fddb3ecd3ac0ad2b41a5024c86b8a6fc7/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala#L2500-L2505
-- is there a better approach?
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