Github user yhuai commented on a diff in the pull request: https://github.com/apache/spark/pull/9406#discussion_r44207772 --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/Utils.scala --- @@ -213,3 +216,178 @@ object Utils { case other => None } } + +/** + * This rule rewrites an aggregate query with multiple distinct clauses into an expanded double + * aggregation in which the regular aggregation expressions and every distinct clause is aggregated + * in a separate group. The results are then combined in a second aggregate. + * + * TODO Expression cannocalization + * TODO Eliminate foldable expressions from distinct clauses. + * TODO This eliminates all distinct expressions. We could safely pass one to the aggregate + * operator. Perhaps this is a good thing? It is much simpler to plan later on... --- End diff -- Yeah, we can use this path to handle all cases. If I understand correctly, this rewriting approach will first create two logical Aggregate operators and then we shuffle data twice. Our current planning rule for a single distinct agg will shuffle data once, which can be bad if we do not have group by clause (because we will have a single reducer). To make the ideal decision, we need to know the statistics of grouping columns and distinct column. However, for the cases that we have a single distinct column and we do not have a group by clause, I feel your rewriting approach should be strictly better. What do you think?
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