Github user hvanhovell commented on a diff in the pull request: https://github.com/apache/spark/pull/13155#discussion_r66680828 --- 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 { --- End diff -- style: do the pattern match directly, i.e.: ```scala aggExpr.map { case _: AttributeReference => case ... } ```
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