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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 +1,82 @@
/*
* 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 {

/**
* 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


/**
* Either a logical plan or a physical plan. The generated tree string (explain output) uses this
* field to explain the subquery.
*/
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 will be converted into [[execution.ScalarSubquery]] during physical planning.
*
* Note: `exprId` is used to have unique name in explain string output.
*/
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 must return only one column, but got " +
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,
OptimizeSubqueries) :: Nil
}

/**
* Optimize all the subqueries inside expression.
*/
object OptimizeSubqueries extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
case subquery: SubqueryExpression =>
subquery.withNewPlan(Optimizer.this.execute(subquery.query))
}
}
}

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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 +202,10 @@ class CatalystQlSuite extends PlanTest {
parser.parsePlan("select sum(product + 1) over (partition by (product + (1)) order by 2) " +
"from windowData")
}

test("subquery") {
parser.parsePlan("select (select max(b) from s) ss from t")
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The only thing we are testing here is that things don't go really really wrong. I'd prefer it if we test the plan as well.

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Since plan checking is too easy to break, I added test for plan, finally remove them.

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Ok that makes sense.

parser.parsePlan("select * from t where a = (select b from s)")
parser.parsePlan("select * from t group by g having a > (select b from s)")
}
}
Original file line number Diff line number Diff line change
Expand Up @@ -113,6 +113,17 @@ class AnalysisErrorSuite extends AnalysisTest {

val dateLit = Literal.create(null, DateType)

errorTest(
"scalar subquery with 2 columns",
testRelation.select(
(ScalarSubquery(testRelation.select('a, dateLit.as('b))) + Literal(1)).as('a)),
"Scalar subquery must return only one column, but got 2" :: Nil)

errorTest(
"scalar subquery with no column",
testRelation.select(ScalarSubquery(LocalRelation()).as('a)),
"Scalar subquery must return only one column, but got 0" :: Nil)

errorTest(
"single invalid type, single arg",
testRelation.select(TestFunction(dateLit :: Nil, IntegerType :: Nil).as('a)),
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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, PlanSubqueries(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 +125,34 @@ 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[(ScalarSubquery, Future[Array[InternalRow]])]()
val allSubqueries = expressions.flatMap(_.collect {case e: ScalarSubquery => e})
allSubqueries.foreach { e =>
val futureResult = Future {
e.plan.executeTake(2)
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add some comment explaining the 2 here

}(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"more than one row returned by a subquery used as an expression:\n${e.plan}")
}
if (rows.length == 1) {
assert(rows(0).numFields == 1, "Analyzer should make sure this only returns one column")
e.updateResult(rows(0).get(0, e.dataType))
} else {
// There is no rows returned, the result should be null.
e.updateResult(null)
}
}
}
}

Expand Down Expand Up @@ -231,6 +261,11 @@ 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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