Github user maryannxue commented on a diff in the pull request: https://github.com/apache/spark/pull/19488#discussion_r144881001 --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala --- @@ -205,14 +205,17 @@ object PhysicalAggregation { case logical.Aggregate(groupingExpressions, resultExpressions, child) => // A single aggregate expression might appear multiple times in resultExpressions. // In order to avoid evaluating an individual aggregate function multiple times, we'll - // build a set of the distinct aggregate expressions and build a function which can + // build a map of the distinct aggregate expressions and build a function which can // be used to re-write expressions so that they reference the single copy of the - // aggregate function which actually gets computed. - val aggregateExpressions = resultExpressions.flatMap { expr => + // aggregate function which actually gets computed. Note that aggregate expressions + // should always be deterministic, so we can use its canonicalized expression as its --- End diff -- Then I think the solution we have right now (in my latest ci) is what we need. But we need to refine the code comment there to further illustrate cases like "first_value". And in general we may need better documentation regarding the behavior of FIRST_VALUE and similar functions. Agree?
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