maropu commented on a change in pull request #30018:
URL: https://github.com/apache/spark/pull/30018#discussion_r510098048



##########
File path: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
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@@ -475,6 +476,58 @@ object RemoveRedundantAliases extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = removeRedundantAliases(plan, 
AttributeSet.empty)
 }
 
+/**
+ * Remove redundant aggregates from a query plan. A redundant aggregate is an 
aggregate whose
+ * only goal is to keep distinct values, while its parent aggregate would 
ignore duplicate values.
+ */
+object RemoveRedundantAggregates extends Rule[LogicalPlan] with AliasHelper {
+  def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
+    case upper @ Aggregate(_, _, lower: Aggregate) if lowerIsRedundant(upper, 
lower) =>
+      val aliasMap = getAliasMap(lower)
+      upper.copy(
+        child = lower.child,
+        groupingExpressions = upper.groupingExpressions.map(replaceAlias(_, 
aliasMap)),
+        aggregateExpressions = upper.aggregateExpressions.map(
+          replaceAliasButKeepOuter(_, aliasMap))
+      )
+  }
+
+  private def lowerIsRedundant(upper: Aggregate, lower: Aggregate): Boolean = {
+    val isDeterministic = upper.aggregateExpressions.forall(_.deterministic) &&
+      lower.aggregateExpressions.forall(_.deterministic)

Review comment:
       Ah, yes, you're right. I've checked the related code again and I think 
we don't need to check if exprs are deterministic or not because of 
`PullOutNondeterministic`. Seems like it is okay just to check if grouping 
exprs in a upper node is a subset of grouping exprs in a lower node.




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