Github user frreiss commented on a diff in the pull request:
https://github.com/apache/spark/pull/13155#discussion_r66539170
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
---
@@ -1695,16 +1695,176 @@ 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[Long,
Option[Any]]) : Option[Any] = {
+ val rewrittenExpr = expr transform {
+ case r @ AttributeReference(_, dataType, _, _) =>
+ bindings(r.exprId.id) 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.
+ val rewrittenExpr = expr transform {
+ case a @ AggregateExpression(aggFunc, _, _, resultId) =>
+ aggFunc.defaultResult.getOrElse(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[Long, Option[Any]] = {
+ lp match {
+ case SubqueryAlias(_, child) => evalPlan(child)
+ case Filter(condition, child) =>
+ val bindings = evalPlan(child)
+ if (bindings.size == 0) bindings
+ else {
+ val exprResult = evalExpr(condition, bindings).getOrElse(false)
+ .asInstanceOf[Boolean]
+ if (exprResult) bindings else Map()
+ }
+
+ case Project(projectList, child) =>
+ val bindings = evalPlan(child)
+ if (bindings.size == 0) {
+ bindings
+ } else {
+ projectList.map(ne => (ne.exprId.id, 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.id, None)
+ case Alias(AttributeReference(_, _, _, _), _) =>
(ne.exprId.id, None)
+ case _ => (ne.exprId.id, 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.id)
+ }
+
+ /**
+ * Split the plan for a scalar subquery into the parts above the
Aggregate node
+ * (first part of returned value) and the parts below the Aggregate
node, including
+ * the Aggregate (second part of returned value)
+ */
+ private def splitSubquery(plan : LogicalPlan) : Tuple2[Seq[LogicalPlan],
Aggregate] = {
+ var topPart = List[LogicalPlan]()
+ var bottomPart : LogicalPlan = plan
+ while (! bottomPart.isInstanceOf[Aggregate]) {
+ topPart = bottomPart :: topPart
+ bottomPart = bottomPart.children.head
+ }
+ (topPart, bottomPart.asInstanceOf[Aggregate])
+ }
+
+ /**
+ * Rewrite the nodes above the Aggregate in a subquery so that they
generate an
+ * auxiliary column "isFiltered"
+ * @param subqueryPlan plan before rewrite
+ * @param filteredId expression ID for the "isFiltered" column
+ */
+ private def addIsFiltered(subqueryPlan : LogicalPlan, filteredId :
ExprId) : LogicalPlan = {
+ val isFilteredRef = AttributeReference("isFiltered",
BooleanType)(exprId = filteredId)
+ val (topPart, aggNode) = splitSubquery(subqueryPlan)
+ var rewrittenQuery: LogicalPlan = null
+ if (topPart.size > 0 && topPart.head.isInstanceOf[Filter]) {
+ // Correlated subquery has a HAVING clause
+ // Rewrite the Filter into a Project that returns the value of the
filtering predicate
+ val origFilter = topPart.head.asInstanceOf[Filter]
+ var topRemainder = topPart.tail
+ val newProjectList =
+ origFilter.output :+ Alias(origFilter.condition,
"isFiltered")(exprId = filteredId)
+ val filterAsProject = Project(newProjectList, origFilter.child)
+
+ rewrittenQuery = filterAsProject
+ while (topRemainder.size > 0) {
+ rewrittenQuery = topRemainder.head match {
+ case Project(origList, _) => Project(origList :+ isFilteredRef,
rewrittenQuery)
+ case SubqueryAlias(alias, _) => SubqueryAlias(alias,
rewrittenQuery)
+ }
+ topRemainder = topRemainder.tail
+ }
+ } else {
+ // Correlated subquery without HAVING clause
+ // Add an additional Project that adds a constant value for
"isFiltered"
+ rewrittenQuery = Project(subqueryPlan.output :+
Alias(Literal(false), "isFiltered")
+ (exprId = filteredId), subqueryPlan)
+ }
+ return rewrittenQuery
+ }
+
+ /**
* Construct a new child plan by left joining the given subqueries to a
base plan.
*/
private def constructLeftJoins(
child: LogicalPlan,
subqueries: ArrayBuffer[ScalarSubquery]): LogicalPlan = {
subqueries.foldLeft(child) {
case (currentChild, ScalarSubquery(query, conditions, _)) =>
- Project(
- currentChild.output :+ query.output.head,
- Join(currentChild, query, LeftOuter,
conditions.reduceOption(And)))
+ val origOutput = query.output.head
+
+ val resultWithZeroTups = evalSubqueryOnZeroTups(query)
+ if (resultWithZeroTups.isEmpty) {
+ Project(
+ currentChild.output :+ origOutput,
+ Join(currentChild, query, LeftOuter,
conditions.reduceOption(And)))
+ } else {
--- End diff --
Sorry, I'm having some trouble understanding this comment. I think you're
suggesting that the rule should first check whether `query.output.head` (the
output column of the scalar subquery) is a nullable column; then the rule
should sometimes skip the call to `evalQueryOnZeroTups()` depending on the
nullability of the column.
I don't see how the check for nullability would allow one to skip the call
to `evalQueryOnZeroTups()`. If the column is nullable, then the correlated
subquery could return either null or a non-null value when zero tuples match;
and the rule needs to call `evalQueryOnZeroTups` to distinguish between those
two cases. If the column is not nullable, then the rule still needs to call
`evalQueryOnZeroTups` to determine what non-null value the subquery returns
when zero tuples match.
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