Stove-hust commented on a change in pull request #35363:
URL: https://github.com/apache/spark/pull/35363#discussion_r813650190
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File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/FilterEstimation.scala
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@@ -311,6 +311,16 @@ case class FilterEstimation(plan: Filter) extends Logging {
logDebug("[CBO] No statistics for " + attr)
return None
}
+
+ attr.dataType match {
+ case _: NumericType | DateType | TimestampType | BooleanType =>
+ if (!colStatsMap.hasMinMaxStats(attr)) {
Review comment:
> I'm not confident to merge this PR without the answer of this question.
Although the scenario I encountered is rather specific, but I think this is
still a constructive improvement.
The evaluateEquality() method and evaluateBinary() method should be
consistent.
For numeric types, evaluateBinary() has three judgments: 1.
!colStatsMap.contains(attr)
2. !colStatsMap.hasMinMaxStats(attr)
3. !colStatsMap.hasDistinctCount(attr)
but evaluateEquality() has only one judgment.
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