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[SPARK-13306] [SQL] uncorrelated scalar subquery #11190

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Original file line number Diff line number Diff line change
Expand Up @@ -205,6 +205,8 @@ atomExpression
| whenExpression
| (functionName LPAREN) => function
| tableOrColumn
| (LPAREN KW_SELECT) => subQueryExpression
-> ^(TOK_SUBQUERY_EXPR ^(TOK_SUBQUERY_OP) subQueryExpression)
| LPAREN! expression RPAREN!
;

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Original file line number Diff line number Diff line change
Expand Up @@ -667,6 +667,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
UnresolvedAttribute(nameParts :+ cleanIdentifier(attr))
case other => UnresolvedExtractValue(other, Literal(cleanIdentifier(attr)))
}
case Token("TOK_SUBQUERY_EXPR", Token("TOK_SUBQUERY_OP", Nil) :: subquery :: Nil) =>
ScalarSubquery(nodeToPlan(subquery))
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This might sound excedingly dumb but I cannot find ScalarSubquery or SubqueryExpression. Are they already in the code base? Or did you create branch on top of another branch?

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Nevermind I just found the other PR...

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I missed a file, sorry


/* Stars (*) */
case Token("TOK_ALLCOLREF", Nil) => UnresolvedStar(None)
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Original file line number Diff line number Diff line change
Expand Up @@ -80,6 +80,7 @@ class Analyzer(
ResolveGenerate ::
ResolveFunctions ::
ResolveAliases ::
ResolveSubquery ::
ResolveWindowOrder ::
ResolveWindowFrame ::
ResolveNaturalJoin ::
Expand Down Expand Up @@ -120,7 +121,14 @@ class Analyzer(
withAlias.getOrElse(relation)
}
substituted.getOrElse(u)
case other =>
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quick comment on why this isn't in ResolveSubquery

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done

// This can't be done in ResolveSubquery because that does not know the CTE.
other transformExpressions {
case e: SubqueryExpression =>
e.withNewPlan(substituteCTE(e.query, cteRelations))
}
}

}
}

Expand Down Expand Up @@ -693,6 +701,30 @@ class Analyzer(
}
}

/**
* This rule resolve subqueries inside expressions.
*
* Note: CTE are handled in CTESubstitution.
*/
object ResolveSubquery extends Rule[LogicalPlan] with PredicateHelper {

private def hasSubquery(e: Expression): Boolean = {
e.find(_.isInstanceOf[SubqueryExpression]).isDefined
}

private def hasSubquery(q: LogicalPlan): Boolean = {
q.expressions.exists(hasSubquery)
}

def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
case q: LogicalPlan if q.childrenResolved && hasSubquery(q) =>
q transformExpressions {
case e: SubqueryExpression if !e.query.resolved =>
e.withNewPlan(execute(e.query))
}
}
}

/**
* Turns projections that contain aggregate expressions into aggregations.
*/
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Original file line number Diff line number Diff line change
@@ -0,0 +1,81 @@
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
import org.apache.spark.sql.catalyst.plans.QueryPlan
import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Subquery}
import org.apache.spark.sql.types.DataType

/**
* An interface for subquery that is used in expressions.
*/
abstract class SubqueryExpression extends LeafExpression{
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add a space after LeafExpression


/**
* The logical plan of the query.
*/
def query: LogicalPlan
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why is this needed?

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This is an helper function used in Analyzer and Optimizer, or we need to do type conversion.

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This is the base class for both logical plan and physical plan, kind of weird. This is to make the generateTreeString works in QueryPlan

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Analyzer and Optimizer only applies to logical plan right?

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yes


/**
* The underline plan for the query, could be logical plan or physical plan.
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"Either a logical plan or a physical plan. The generated tree string (explain output) uses this field to explain the subquery."

*
* This is used to generate tree string.
*/
def plan: QueryPlan[_]

/**
* Updates the query with new logical plan.
*/
def withNewPlan(plan: LogicalPlan): SubqueryExpression
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scala doc

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can't this be just in the logical plan itself?

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This should be copy(), but I did not figure out how to make copy() work for different kind of SubqueryExpression.

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i think you can just remove this and move it into the logical subquery expression, since it's only used for logical plan anyway?

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Then should we have LogicalSubqueryExpression ?

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I meant ScalarSubquery. That's already the one isn't it?

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We will have ExistsSubquery, InSubquery shortly (or next release).

}

/**
* A subquery that will return only one row and one column.
*
* This is not evaluable, it should be converted into SparkScalaSubquery.
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This will be converted into [[execution.ScalarSubquery]] during physical planning.

(make sure it links properly)

*/
case class ScalarSubquery(
query: LogicalPlan,
exprId: ExprId = NamedExpression.newExprId)
extends SubqueryExpression with Unevaluable {

override def plan: LogicalPlan = Subquery(toString, query)

override lazy val resolved: Boolean = query.resolved

override def dataType: DataType = query.schema.fields.head.dataType

override def checkInputDataTypes(): TypeCheckResult = {
if (query.schema.length != 1) {
TypeCheckResult.TypeCheckFailure("Scalar subquery can only have 1 column, but got " +
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needs tests. Maybe, probably AnalysisErrorSuite

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done

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"Scalar subquery must return only one column, but got " ...

(postgres')

query.schema.length.toString)
} else {
TypeCheckResult.TypeCheckSuccess
}
}

override def foldable: Boolean = false
override def nullable: Boolean = true

override def withNewPlan(plan: LogicalPlan): ScalarSubquery = ScalarSubquery(plan, exprId)

override def toString: String = s"subquery#${exprId.id}"

// TODO: support sql()
}
Original file line number Diff line number Diff line change
Expand Up @@ -88,7 +88,19 @@ abstract class Optimizer extends RuleExecutor[LogicalPlan] {
Batch("Decimal Optimizations", FixedPoint(100),
DecimalAggregates) ::
Batch("LocalRelation", FixedPoint(100),
ConvertToLocalRelation) :: Nil
ConvertToLocalRelation) ::
Batch("Subquery", Once,
Subquery) :: Nil
}

/**
* Optimize all the subqueries inside expression.
*/
object Subquery extends Rule[LogicalPlan] {
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Subquery -> OptimizeSubqueries

def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
case subquery: SubqueryExpression =>
subquery.withNewPlan(execute(subquery.query))
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where is execute coming from?

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The parent class, Optimizer.

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Maybe replace it with "Optimizer.this.execute" so it is more clear. Thanks!

}
}
}

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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
package org.apache.spark.sql.catalyst.plans

import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans.logical.Subquery
import org.apache.spark.sql.catalyst.trees.TreeNode
import org.apache.spark.sql.types.{DataType, StructType}

Expand Down Expand Up @@ -226,4 +227,29 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy
protected def statePrefix = if (missingInput.nonEmpty && children.nonEmpty) "!" else ""

override def simpleString: String = statePrefix + super.simpleString

override def generateTreeString(
depth: Int, lastChildren: Seq[Boolean], builder: StringBuilder): StringBuilder = {
if (depth > 0) {
lastChildren.init.foreach { isLast =>
val prefixFragment = if (isLast) " " else ": "
builder.append(prefixFragment)
}

val branch = if (lastChildren.last) "+- " else ":- "
builder.append(branch)
}

builder.append(simpleString)
builder.append("\n")

val allSubqueries = expressions.flatMap(_.collect {case e: SubqueryExpression => e})
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Can't we do this in subquery itself? It is pretty odd to have this general base class depend on some specific expression

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How to do that?

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can you try? I don't have time to think/write this one.

It just feels bad to do it here. If it is impossible, then of course we have to do it here ...

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If I could figure out a way, would not copy this function here and ask you.

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Why not create a allChildren function in TreeNode (default returns children), make TreeNode work with that, and override this for QueryPlan?

val allChildren = children ++ allSubqueries.map(e => e.plan)
if (allChildren.nonEmpty) {
allChildren.init.foreach(_.generateTreeString(depth + 1, lastChildren :+ false, builder))
allChildren.last.generateTreeString(depth + 1, lastChildren :+ true, builder)
}

builder
}
}
Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@ import org.apache.spark.sql.catalyst.analysis._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans.PlanTest
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.types.BooleanType
import org.apache.spark.unsafe.types.CalendarInterval

class CatalystQlSuite extends PlanTest {
Expand Down Expand Up @@ -201,4 +202,49 @@ class CatalystQlSuite extends PlanTest {
parser.parsePlan("select sum(product + 1) over (partition by (product + (1)) order by 2) " +
"from windowData")
}

test("subquery") {
comparePlans(
parser.parsePlan("select (select max(b) from s) ss from t"),
Project(
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@hvanhovell I'm going to remove these plan checking, it's very easy to break. The details of plan does not mean much, we will have other tests to verify the correctness.

UnresolvedAlias(
Alias(
ScalarSubquery(
Project(
UnresolvedAlias(
UnresolvedFunction("max", UnresolvedAttribute("b") :: Nil, false)) :: Nil,
UnresolvedRelation(TableIdentifier("s")))),
"ss")(ExprId(0))) :: Nil,
UnresolvedRelation(TableIdentifier("t"))))
comparePlans(
parser.parsePlan("select * from t where a = (select b from s)"),
Project(
UnresolvedAlias(
UnresolvedStar(None)) :: Nil,
Filter(
EqualTo(
UnresolvedAttribute("a"),
ScalarSubquery(
Project(
UnresolvedAlias(
UnresolvedAttribute("b")) :: Nil,
UnresolvedRelation(TableIdentifier("s"))))),
UnresolvedRelation(TableIdentifier("t")))))
comparePlans(
parser.parsePlan("select * from t group by g having a > (select b from s)"),
Filter(
Cast(
GreaterThan(
UnresolvedAttribute("a"),
ScalarSubquery(
Project(
UnresolvedAlias(
UnresolvedAttribute("b")) :: Nil,
UnresolvedRelation(TableIdentifier("s"))))),
BooleanType),
Aggregate(
UnresolvedAttribute("g") :: Nil,
UnresolvedAlias(UnresolvedStar(None)) :: Nil,
UnresolvedRelation(TableIdentifier("t")))))
}
}
Original file line number Diff line number Diff line change
Expand Up @@ -113,6 +113,12 @@ class AnalysisErrorSuite extends AnalysisTest {

val dateLit = Literal.create(null, DateType)

errorTest(
"invalid scalar subquery",
testRelation.select(
(ScalarSubquery(testRelation.select('a, dateLit.as('b))) + Literal(1)).as('a)),
"Scalar subquery can only have 1 column, but got 2" :: Nil)
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add a test case for 0 column if possible


errorTest(
"single invalid type, single arg",
testRelation.select(TestFunction(dateLit :: Nil, IntegerType :: Nil).as('a)),
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -884,6 +884,7 @@ class SQLContext private[sql](
@transient
protected[sql] val prepareForExecution = new RuleExecutor[SparkPlan] {
val batches = Seq(
Batch("Subquery", Once, ConvertSubquery(self)),
Batch("Add exchange", Once, EnsureRequirements(self)),
Batch("Whole stage codegen", Once, CollapseCodegenStages(self))
)
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,8 @@ package org.apache.spark.sql.execution
import java.util.concurrent.atomic.AtomicBoolean

import scala.collection.mutable.ArrayBuffer
import scala.concurrent.{Await, ExecutionContext, Future}
import scala.concurrent.duration._

import org.apache.spark.Logging
import org.apache.spark.rdd.{RDD, RDDOperationScope}
Expand All @@ -31,6 +33,7 @@ import org.apache.spark.sql.catalyst.plans.QueryPlan
import org.apache.spark.sql.catalyst.plans.physical._
import org.apache.spark.sql.execution.metric.{LongSQLMetric, SQLMetric}
import org.apache.spark.sql.types.DataType
import org.apache.spark.util.ThreadUtils

/**
* The base class for physical operators.
Expand Down Expand Up @@ -122,7 +125,32 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
final def prepare(): Unit = {
if (prepareCalled.compareAndSet(false, true)) {
doPrepare()

// collect all the subqueries and submit jobs to execute them in background
val queryResults = ArrayBuffer[(SparkScalarSubquery, Future[Array[InternalRow]])]()
val allSubqueries = expressions.flatMap(_.collect {case e: SparkScalarSubquery => e})
allSubqueries.foreach { e =>
val futureResult = Future {
e.plan.executeCollect()
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e.executedPlan rather than e.plan, if you want to keep executedPlan

}(SparkPlan.subqueryExecutionContext)
queryResults += e -> futureResult
}

children.foreach(_.prepare())

// fill in the result of subqueries
queryResults.foreach {
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we should move the blocking phase into execute, otherwise if multiple nodes have subqueries, it becomes blocking.

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ah ok you can't have a general execute.

I guess this is why some query engines have init and then prepare.

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or a subquery is now blocking broadcasting ...

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This is called after doPrepare(), and after prepare() of it's children, so it will NOT block broadcasting (will happen in the same time).

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what if there is a broadcast join after this?

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Broadcast will be issued before this.

case (e, futureResult) =>
val rows = Await.result(futureResult, Duration.Inf)
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Do we really want to wait that long?

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will use sqlContext.conf.broadcastTimeout

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After offline discussion with @rxin and @marmbrus , we decided to not have a timeout here, we can see the spark job in UI and could cancel it anytime if it's blocked.

if (rows.length > 1) {
sys.error(s"Scalar subquery should return at most one row, but got ${rows.length}: " +
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we can use postgres' error message: "more than one row returned by a subquery used as an expression"

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I have never thought we should match the exactly error message with PostgreSQL, that's great.

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The current error message has more information than postgres', should we change?

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not 100% sure. maybe it's better to just say more than one, so we don't need to run the whole plan (e..g i'm thinking maybe we should inject a limit of 2 to subquery)

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Good idea, changed to call executeTake(2)

s"${e.query.treeString}")
}
// Analyzer will make sure that it only return on column
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"Analyzer should make sure this only returns one column"

and add an assert after this.

if (rows.length > 0) {
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can rows.length ever be 0 here? if it can only be 1, why we are testing > 0 here?

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the length could be zero, then the value is null.

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How do we write a query with 0 column? The comment above said the analyzer would make sure there's only one column.

If it is possible to have 0 column, then I'd make it explicitly here to set the value to null.

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Also if it is possible to have 0 column, we also need to add a test case.

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rows.length means number of rows, not number of columns.

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ok makes sense.

please change the check to rows.length == 1

it's pretty confusing to first check it's greater than 1, and then check it is greater than 0, when you are just expecting 1.

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and same thing applies - the test coverage for this is pretty bad. add a test case where the subquery returns 0 or more than 1 rows.

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Repeated: rows.length could be 0, then the value will be null, will add a comment for that.

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Yea, it'd be better to make it more explicit, e.g.

if (rows.length == 0) {
  e.updateResult(null)
} else {
  assert(rows.length == 1)
  e.updateResult(rows(0).get(0, e.dataType))
}

e.updateResult(rows(0).get(0, e.dataType))
}
}
}
}

Expand Down Expand Up @@ -231,6 +259,11 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
}
}

object SparkPlan {
private[execution] val subqueryExecutionContext = ExecutionContext.fromExecutorService(
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What threadpool are broadcasts done on? Should it be the same?

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This could be refactored later, use the same thread pool for all of them.

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BroadcastHashJoin defines a ThreadPool for broadcasting. I am moving that as part of #11083 into exchange.scala. We could use that one.

ThreadUtils.newDaemonCachedThreadPool("subquery", 16))
}

private[sql] trait LeafNode extends SparkPlan {
override def children: Seq[SparkPlan] = Nil
override def producedAttributes: AttributeSet = outputSet
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -343,3 +343,18 @@ case class OutputFaker(output: Seq[Attribute], child: SparkPlan) extends SparkPl

protected override def doExecute(): RDD[InternalRow] = child.execute()
}

/**
* A plan as subquery.
*
* This is used to generate tree string for SparkScalarSubquery.
*/
case class Subquery(name: String, child: SparkPlan) extends UnaryNode {
override def output: Seq[Attribute] = child.output
override def outputPartitioning: Partitioning = child.outputPartitioning
override def outputOrdering: Seq[SortOrder] = child.outputOrdering

protected override def doExecute(): RDD[InternalRow] = {
child.execute()
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throw unsupported exception?

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Maybe it could be used some day, I'd like to leave it as it is, since it's correct.

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But by then we can just implement it? I think once execute is called, this violates your design that this is only used in a subquery, and we might have subtle other problems ..

}
}
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