Github user heary-cao commented on a diff in the pull request:
https://github.com/apache/spark/pull/18969#discussion_r136531688
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
---
@@ -24,6 +24,24 @@ import org.apache.spark.sql.catalyst.plans._
import org.apache.spark.sql.catalyst.plans.logical._
/**
+ * A pattern that matches any number of project if fields is deterministic
+ * or child is LeafNode of project on top of another relational operator.
+ */
+object ProjectOperation extends PredicateHelper {
+ type ReturnType = (Seq[NamedExpression], LogicalPlan)
+
+ def unapply(plan: LogicalPlan): Option[ReturnType] = plan match {
+ case Project(fields, child) if fields.forall(_.deterministic) =>
+ Some((fields, child))
+
+ case Project(fields, child: LeafNode) if
!fields.forall(_.deterministic) =>
--- End diff --
this might be a rare case. but in business scenario, there are still a lot
of scenes to use the rare case. similar business scenarios:
1.Random grouping, add a random factor to each row of data before grouping.
2.Use the random value to fill a field, easy to follow algorithm for
calculation or prevents querying data anomalies.
3.Data skew, data are discretized using random values.
thanks.
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