Github user tejasapatil commented on a diff in the pull request:
https://github.com/apache/spark/pull/17993#discussion_r117619380
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/expressions.scala
---
@@ -54,6 +54,62 @@ object ConstantFolding extends Rule[LogicalPlan] {
}
}
+/**
+ * Substitutes [[Attribute Attributes]] which can be statically evaluated
with their corresponding
+ * value in conjunctive [[Expression Expressions]]
+ * eg.
+ * {{{
+ * SELECT * FROM table WHERE i = 5 AND j = i + 3
+ * ==> SELECT * FROM table WHERE i = 5 AND j = 8
+ * }}}
+ *
+ * Approach used:
+ * - Start from AND operator as the root
+ * - Get all the children conjunctive predicates which are EqualTo /
EqualNullSafe such that they
+ * don't have a `NOT` or `OR` operator in them
+ * - Populate a mapping of attribute => constant value by looking at all
the equals predicates
+ * - Using this mapping, replace occurrence of the attributes with the
corresponding constant values
+ * in the AND node.
+ */
+object ConstantPropagation extends Rule[LogicalPlan] with PredicateHelper {
+ def containsNonConjunctionPredicates(expression: Expression): Boolean =
expression.find {
+ case _: Not | _: Or => true
+ case _ => false
+ }.isDefined
+
+ def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+ case f: Filter => f transformExpressionsUp {
--- End diff --
I was initially doing this for the entire logical plan but now switched to
do only for filter operator.
Reason: Doing this for the entire logical plan will mess up with JOIN
predicates. eg.
```
SELECT * FROM a JOIN b ON a.i = 1 AND b.i = a.i
=>
SELECT * FROM a JOIN b ON a.i = 1 AND b.i = 1
```
.. the result is a cartesian product and Spark fails (asking to set a
config). In case of OUTER JOINs, changing the join predicates might cause
regression.
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