Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/17446#discussion_r108336824
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/FilterEstimation.scala
---
@@ -343,6 +347,26 @@ case class FilterEstimation(plan: Filter,
catalystConf: CatalystConf) extends Lo
}
/**
+ * Returns a percentage of rows meeting a Literal expression.
+ * This method evaluates all the possible literal cases in Filter.
+ *
+ * FalseLiteral and TrueLiteral should be eliminated by optimizer, but
null literal might be added
+ * by optimizer rule NullPropagation. For safety, we handle all the
cases here.
+ *
+ * @param literal a literal value (or constant)
+ * @return an optional double value to show the percentage of rows
meeting a given condition
+ */
+ def evaluateLiteral(literal: Literal): Option[Double] = {
+ literal match {
+ case Literal(null, _) => Some(0.0)
--- End diff --
handling `null` in filter estimation is not trivial, e.g. `null and false`
returns false, `null and true` returns true. If we estimate `cond && null`, we
will report 0 selectivity, which is wrong.
I think we should eliminate null literal in optimizer when it's involved in
filter condition.
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