Github user hvanhovell commented on a diff in the pull request:
https://github.com/apache/spark/pull/13155#discussion_r66681029
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
---
@@ -1695,16 +1696,205 @@ object RewriteCorrelatedScalarSubquery extends
Rule[LogicalPlan] {
}
/**
+ * Statically evaluate an expression containing zero or more
placeholders, given a set
+ * of bindings for placeholder values.
+ */
+ private def evalExpr(expr: Expression, bindings: Map[ExprId,
Option[Any]]) : Option[Any] = {
+ val rewrittenExpr = expr transform {
+ case r @ AttributeReference(_, dataType, _, _) =>
+ bindings(r.exprId) match {
+ case Some(v) => Literal.create(v, dataType)
+ case None => Literal.default(NullType)
+ }
+ }
+ Option(rewrittenExpr.eval())
+ }
+
+ /**
+ * Statically evaluate an expression containing one or more aggregates
on an empty input.
+ */
+ private def evalAggOnZeroTups(expr: Expression) : Option[Any] = {
+ // AggregateExpressions are Unevaluable, so we need to replace all
aggregates
+ // in the expression with the value they would return for zero input
tuples.
+ // Also replace attribute refs (for example, for grouping columns)
with NULL.
+ val rewrittenExpr = expr transform {
+ case a @ AggregateExpression(aggFunc, _, _, resultId) =>
+ aggFunc.defaultResult.getOrElse(Literal.default(NullType))
+
+ case AttributeReference(_, _, _, _) => Literal.default(NullType)
+ }
+ Option(rewrittenExpr.eval())
+ }
+
+ /**
+ * Statically evaluate a scalar subquery on an empty input.
+ *
+ * <b>WARNING:</b> This method only covers subqueries that pass the
checks under
+ * [[org.apache.spark.sql.catalyst.analysis.CheckAnalysis]]. If the
checks in
+ * CheckAnalysis become less restrictive, this method will need to
change.
+ */
+ private def evalSubqueryOnZeroTups(plan: LogicalPlan) : Option[Any] = {
+ // Inputs to this method will start with a chain of zero or more
SubqueryAlias
+ // and Project operators, followed by an optional Filter, followed by
an
+ // Aggregate. Traverse the operators recursively.
+ def evalPlan(lp : LogicalPlan) : Map[ExprId, Option[Any]] = {
+ lp match {
+ case SubqueryAlias(_, child) => evalPlan(child)
+ case Filter(condition, child) =>
+ val bindings = evalPlan(child)
+ if (bindings.isEmpty) bindings
+ else {
+ val exprResult = evalExpr(condition, bindings).getOrElse(false)
+ .asInstanceOf[Boolean]
+ if (exprResult) bindings else Map.empty
+ }
+
+ case Project(projectList, child) =>
+ val bindings = evalPlan(child)
+ if (bindings.isEmpty) {
+ bindings
+ } else {
+ projectList.map(ne => (ne.exprId, evalExpr(ne,
bindings))).toMap
+ }
+
+ case Aggregate(_, aggExprs, _) =>
+ // Some of the expressions under the Aggregate node are the join
columns
+ // for joining with the outer query block. Fill those
expressions in with
+ // nulls and statically evaluate the remainder.
+ aggExprs.map(ne => ne match {
+ case AttributeReference(_, _, _, _) => (ne.exprId, None)
+ case Alias(AttributeReference(_, _, _, _), _) => (ne.exprId,
None)
+ case _ => (ne.exprId, evalAggOnZeroTups(ne))
+ }).toMap
+
+ case _ => sys.error(s"Unexpected operator in scalar subquery: $lp")
+ }
+ }
+
+ val resultMap = evalPlan(plan)
+
+ // By convention, the scalar subquery result is the leftmost field.
+ resultMap(plan.output.head.exprId)
+ }
+
+ /**
+ * Split the plan for a scalar subquery into the parts above the
innermost query block
+ * (first part of returned value), the HAVING clause of the innermost
query block
+ * (optional second part) and the parts below the HAVING CLAUSE (third
part).
+ */
+ private def splitSubquery(plan: LogicalPlan) : (Seq[LogicalPlan],
Option[Filter], Aggregate) = {
+ val topPart = ArrayBuffer.empty[LogicalPlan]
+ var bottomPart : LogicalPlan = plan
+ while (true) {
+ bottomPart match {
+ case havingPart@Filter(_, aggPart@Aggregate(_, _, _)) =>
--- End diff --
style spaces between @ elements, i.e.: `havingPart @ Filter`
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