[GitHub] [spark] HyukjinKwon commented on a change in pull request #24436: [SPARK-27539][SQL] Fix inaccurate aggregate outputRows estimation with column containing null values

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HyukjinKwon commented on a change in pull request #24436: [SPARK-27539][SQL] Fix inaccurate aggregate outputRows estimation with column containing null values
URL: https://github.com/apache/spark/pull/24436#discussion_r287070146
 
 

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 File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/AggregateEstimation.scala
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 @@ -42,8 +42,8 @@ object AggregateEstimation {
         (res, expr) => {
           val columnStat = childStats.attributeStats(expr.asInstanceOf[Attribute])
           val distinctCount = columnStat.distinctCount.get
-          val distinctValue: BigInt = if (distinctCount == 0 && columnStat.nullCount.get > 0) {
-            1
+          val distinctValue: BigInt = if (columnStat.nullCount.get > 0) {
+            distinctCount + 1
 
 Review comment:
   Hm, actually, do we need to count `null` as distinct value? It's not counted as a distinct value in SQL (`F.countDistinct` or `count(DISTINCT col)`) and Pandas (`unique()` by default) at least.

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