Github user hvanhovell commented on a diff in the pull request:

    https://github.com/apache/spark/pull/13155#discussion_r66540105
  
    --- 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 --
    
    @frreiss this bug only occurs when the result of the scalar subquery cannot 
be null. `COUNT(...)` being the prime example of this. We can identify this 
problem early and easily by checking the nullable flag. Am I missing something?
    
    BTW: Nice talk on SystemML!


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