davidvrba commented on a change in pull request #27231: [SPARK-28478][SQL]
Remove redundant null checks
URL: https://github.com/apache/spark/pull/27231#discussion_r371629505
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File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/expressions.scala
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@@ -434,6 +434,27 @@ object SimplifyConditionals extends Rule[LogicalPlan]
with PredicateHelper {
case _ => false
}
+ /**
+ * Condition for redundant null check based on intolerant expressions.
+ * @param ifNullExpr expression that takes place if checkedExpr is null
+ * @param ifNotNullExpr expression that takes place if checkedExpr is not
null
+ * @param checkedExpr expression that is checked for null value
+ */
+ private def isRedundantNullCheck(
+ ifNullExpr: Expression,
+ ifNotNullExpr: Expression,
+ checkedExpr: Expression): Boolean = {
+ val isNullIntolerant = ifNotNullExpr.find { x =>
+ !x.isInstanceOf[NullIntolerant] && x.find(e =>
e.semanticEquals(checkedExpr)).nonEmpty
Review comment:
Actually i think we need slightly different logic. Consider these two
examples where `x` will be the null-checked column:
1. `substring(x, coalesce(a, b), c)`
2. `substring(coalesce(x, d), a, c)`
For 1. we need to be null-intolerant (even though `coalesce` is
null-tolerant), so if `x` is null, we replace the `substring` with null value
no matter what are the other children. For 2. we need to be null-tolerant and
we will not replace the `substring` by null value. So we need to check the
expression with respect to the position of `x` (the column that is being
null-checked). Does it make sense?
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