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/*
* 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.execution
import java.util.Locale
import java.util.function.Supplier
import scala.collection.mutable
import org.apache.spark.broadcast
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.expressions.codegen._
import org.apache.spark.sql.catalyst.plans.physical.Partitioning
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.execution.aggregate.HashAggregateExec
import org.apache.spark.sql.execution.joins.{BroadcastHashJoinExec, SortMergeJoinExec}
import org.apache.spark.sql.execution.metric.SQLMetrics
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types._
import org.apache.spark.util.Utils
/**
* An interface for those physical operators that support codegen.
*/
trait CodegenSupport extends SparkPlan {
/** Prefix used in the current operator's variable names. */
private def variablePrefix: String = this match {
case _: HashAggregateExec => "agg"
case _: BroadcastHashJoinExec => "bhj"
case _: SortMergeJoinExec => "smj"
case _: RDDScanExec => "rdd"
case _: DataSourceScanExec => "scan"
case _ => nodeName.toLowerCase(Locale.ROOT)
}
/**
* Creates a metric using the specified name.
*
* @return name of the variable representing the metric
*/
def metricTerm(ctx: CodegenContext, name: String): String = {
ctx.addReferenceObj(name, longMetric(name))
}
/**
* Whether this SparkPlan supports whole stage codegen or not.
*/
def supportCodegen: Boolean = true
/**
* Which SparkPlan is calling produce() of this one. It's itself for the first SparkPlan.
*/
protected var parent: CodegenSupport = null
/**
* Returns all the RDDs of InternalRow which generates the input rows.
*
* @note Right now we support up to two RDDs
*/
def inputRDDs(): Seq[RDD[InternalRow]]
/**
* Returns Java source code to process the rows from input RDD.
*/
final def produce(ctx: CodegenContext, parent: CodegenSupport): String = executeQuery {
this.parent = parent
ctx.freshNamePrefix = variablePrefix
s"""
|${ctx.registerComment(s"PRODUCE: ${this.simpleString}")}
|${doProduce(ctx)}
""".stripMargin
}
/**
* Generate the Java source code to process, should be overridden by subclass to support codegen.
*
* doProduce() usually generate the framework, for example, aggregation could generate this:
*
* if (!initialized) {
* # create a hash map, then build the aggregation hash map
* # call child.produce()
* initialized = true;
* }
* while (hashmap.hasNext()) {
* row = hashmap.next();
* # build the aggregation results
* # create variables for results
* # call consume(), which will call parent.doConsume()
* if (shouldStop()) return;
* }
*/
protected def doProduce(ctx: CodegenContext): String
private def prepareRowVar(ctx: CodegenContext, row: String, colVars: Seq[ExprCode]): ExprCode = {
if (row != null) {
ExprCode("", "false", row)
} else {
if (colVars.nonEmpty) {
val colExprs = output.zipWithIndex.map { case (attr, i) =>
BoundReference(i, attr.dataType, attr.nullable)
}
val evaluateInputs = evaluateVariables(colVars)
// generate the code to create a UnsafeRow
ctx.INPUT_ROW = row
ctx.currentVars = colVars
val ev = GenerateUnsafeProjection.createCode(ctx, colExprs, false)
val code = s"""
|$evaluateInputs
|${ev.code.trim}
""".stripMargin.trim
ExprCode(code, "false", ev.value)
} else {
// There is no columns
ExprCode("", "false", "unsafeRow")
}
}
}
/**
* Consume the generated columns or row from current SparkPlan, call its parent's `doConsume()`.
*
* Note that `outputVars` and `row` can't both be null.
*/
final def consume(ctx: CodegenContext, outputVars: Seq[ExprCode], row: String = null): String = {
val inputVars =
if (outputVars != null) {
assert(outputVars.length == output.length)
// outputVars will be used to generate the code for UnsafeRow, so we should copy them
outputVars.map(_.copy())
} else {
assert(row != null, "outputVars and row cannot both be null.")
ctx.currentVars = null
ctx.INPUT_ROW = row
output.zipWithIndex.map { case (attr, i) =>
BoundReference(i, attr.dataType, attr.nullable).genCode(ctx)
}
}
val rowVar = prepareRowVar(ctx, row, outputVars)
// Set up the `currentVars` in the codegen context, as we generate the code of `inputVars`
// before calling `parent.doConsume`. We can't set up `INPUT_ROW`, because parent needs to
// generate code of `rowVar` manually.
ctx.currentVars = inputVars
ctx.INPUT_ROW = null
ctx.freshNamePrefix = parent.variablePrefix
val evaluated = evaluateRequiredVariables(output, inputVars, parent.usedInputs)
// Under certain conditions, we can put the logic to consume the rows of this operator into
// another function. So we can prevent a generated function too long to be optimized by JIT.
// The conditions:
// 1. The config "spark.sql.codegen.splitConsumeFuncByOperator" is enabled.
// 2. `inputVars` are all materialized. That is guaranteed to be true if the parent plan uses
// all variables in output (see `requireAllOutput`).
// 3. The number of output variables must less than maximum number of parameters in Java method
// declaration.
val confEnabled = SQLConf.get.wholeStageSplitConsumeFuncByOperator
val requireAllOutput = output.forall(parent.usedInputs.contains(_))
val paramLength = ctx.calculateParamLength(output) + (if (row != null) 1 else 0)
val consumeFunc = if (confEnabled && requireAllOutput && ctx.isValidParamLength(paramLength)) {
constructDoConsumeFunction(ctx, inputVars, row)
} else {
parent.doConsume(ctx, inputVars, rowVar)
}
s"""
|${ctx.registerComment(s"CONSUME: ${parent.simpleString}")}
|$evaluated
|$consumeFunc
""".stripMargin
}
/**
* To prevent concatenated function growing too long to be optimized by JIT. We can separate the
* parent's `doConsume` codes of a `CodegenSupport` operator into a function to call.
*/
private def constructDoConsumeFunction(
ctx: CodegenContext,
inputVars: Seq[ExprCode],
row: String): String = {
val (args, params, inputVarsInFunc) = constructConsumeParameters(ctx, output, inputVars, row)
val rowVar = prepareRowVar(ctx, row, inputVarsInFunc)
val doConsume = ctx.freshName("doConsume")
ctx.currentVars = inputVarsInFunc
ctx.INPUT_ROW = null
val doConsumeFuncName = ctx.addNewFunction(doConsume,
s"""
| private void $doConsume(${params.mkString(", ")}) throws java.io.IOException {
| ${parent.doConsume(ctx, inputVarsInFunc, rowVar)}
| }
""".stripMargin)
s"""
| $doConsumeFuncName(${args.mkString(", ")});
""".stripMargin
}
/**
* Returns arguments for calling method and method definition parameters of the consume function.
* And also returns the list of `ExprCode` for the parameters.
*/
private def constructConsumeParameters(
ctx: CodegenContext,
attributes: Seq[Attribute],
variables: Seq[ExprCode],
row: String): (Seq[String], Seq[String], Seq[ExprCode]) = {
val arguments = mutable.ArrayBuffer[String]()
val parameters = mutable.ArrayBuffer[String]()
val paramVars = mutable.ArrayBuffer[ExprCode]()
if (row != null) {
arguments += row
parameters += s"InternalRow $row"
}
variables.zipWithIndex.foreach { case (ev, i) =>
val paramName = ctx.freshName(s"expr_$i")
val paramType = ctx.javaType(attributes(i).dataType)
arguments += ev.value
parameters += s"$paramType $paramName"
val paramIsNull = if (!attributes(i).nullable) {
// Use constant `false` without passing `isNull` for non-nullable variable.
"false"
} else {
val isNull = ctx.freshName(s"exprIsNull_$i")
arguments += ev.isNull
parameters += s"boolean $isNull"
isNull
}
paramVars += ExprCode("", paramIsNull, paramName)
}
(arguments, parameters, paramVars)
}
/**
* Returns source code to evaluate all the variables, and clear the code of them, to prevent
* them to be evaluated twice.
*/
protected def evaluateVariables(variables: Seq[ExprCode]): String = {
val evaluate = variables.filter(_.code != "").map(_.code.trim).mkString("\n")
variables.foreach(_.code = "")
evaluate
}
/**
* Returns source code to evaluate the variables for required attributes, and clear the code
* of evaluated variables, to prevent them to be evaluated twice.
*/
protected def evaluateRequiredVariables(
attributes: Seq[Attribute],
variables: Seq[ExprCode],
required: AttributeSet): String = {
val evaluateVars = new StringBuilder
variables.zipWithIndex.foreach { case (ev, i) =>
if (ev.code != "" && required.contains(attributes(i))) {
evaluateVars.append(ev.code.trim + "\n")
ev.code = ""
}
}
evaluateVars.toString()
}
/**
* The subset of inputSet those should be evaluated before this plan.
*
* We will use this to insert some code to access those columns that are actually used by current
* plan before calling doConsume().
*/
def usedInputs: AttributeSet = references
/**
* Generate the Java source code to process the rows from child SparkPlan. This should only be
* called from `consume`.
*
* This should be override by subclass to support codegen.
*
* Note: The operator should not assume the existence of an outer processing loop,
* which it can jump from with "continue;"!
*
* For example, filter could generate this:
* # code to evaluate the predicate expression, result is isNull1 and value2
* if (!isNull1 && value2) {
* # call consume(), which will call parent.doConsume()
* }
*
* Note: A plan can either consume the rows as UnsafeRow (row), or a list of variables (input).
* When consuming as a listing of variables, the code to produce the input is already
* generated and `CodegenContext.currentVars` is already set. When consuming as UnsafeRow,
* implementations need to put `row.code` in the generated code and set
* `CodegenContext.INPUT_ROW` manually. Some plans may need more tweaks as they have
* different inputs(join build side, aggregate buffer, etc.), or other special cases.
*/
def doConsume(ctx: CodegenContext, input: Seq[ExprCode], row: ExprCode): String = {
throw new UnsupportedOperationException
}
/**
* Whether or not the result rows of this operator should be copied before putting into a buffer.
*
* If any operator inside WholeStageCodegen generate multiple rows from a single row (for
* example, Join), this should be true.
*
* If an operator starts a new pipeline, this should be false.
*/
def needCopyResult: Boolean = {
if (children.isEmpty) {
false
} else if (children.length == 1) {
children.head.asInstanceOf[CodegenSupport].needCopyResult
} else {
throw new UnsupportedOperationException
}
}
/**
* Whether or not the children of this operator should generate a stop check when consuming input
* rows. This is used to suppress shouldStop() in a loop of WholeStageCodegen.
*
* This should be false if an operator starts a new pipeline, which means it consumes all rows
* produced by children but doesn't output row to buffer by calling append(), so the children
* don't require shouldStop() in the loop of producing rows.
*/
def needStopCheck: Boolean = parent.needStopCheck
}
/**
* InputAdapter is used to hide a SparkPlan from a subtree that supports codegen.
*
* This is the leaf node of a tree with WholeStageCodegen that is used to generate code
* that consumes an RDD iterator of InternalRow.
*/
case class InputAdapter(child: SparkPlan) extends UnaryExecNode with CodegenSupport {
override def output: Seq[Attribute] = child.output
override def outputPartitioning: Partitioning = child.outputPartitioning
override def outputOrdering: Seq[SortOrder] = child.outputOrdering
override def doExecute(): RDD[InternalRow] = {
child.execute()
}
override def doExecuteBroadcast[T](): broadcast.Broadcast[T] = {
child.doExecuteBroadcast()
}
override def inputRDDs(): Seq[RDD[InternalRow]] = {
child.execute() :: Nil
}
override def doProduce(ctx: CodegenContext): String = {
// Right now, InputAdapter is only used when there is one input RDD.
// Inline mutable state since an InputAdapter is used once in a task for WholeStageCodegen
val input = ctx.addMutableState("scala.collection.Iterator", "input", v => s"$v = inputs[0];",
forceInline = true)
val row = ctx.freshName("row")
s"""
| while ($input.hasNext() && !stopEarly()) {
| InternalRow $row = (InternalRow) $input.next();
| ${consume(ctx, null, row).trim}
| if (shouldStop()) return;
| }
""".stripMargin
}
override def generateTreeString(
depth: Int,
lastChildren: Seq[Boolean],
builder: StringBuilder,
verbose: Boolean,
prefix: String = "",
addSuffix: Boolean = false): StringBuilder = {
child.generateTreeString(depth, lastChildren, builder, verbose, "")
}
override def needCopyResult: Boolean = false
}
object WholeStageCodegenExec {
val PIPELINE_DURATION_METRIC = "duration"
private def numOfNestedFields(dataType: DataType): Int = dataType match {
case dt: StructType => dt.fields.map(f => numOfNestedFields(f.dataType)).sum
case m: MapType => numOfNestedFields(m.keyType) + numOfNestedFields(m.valueType)
case a: ArrayType => numOfNestedFields(a.elementType)
case u: UserDefinedType[_] => numOfNestedFields(u.sqlType)
case _ => 1
}
def isTooManyFields(conf: SQLConf, dataType: DataType): Boolean = {
numOfNestedFields(dataType) > conf.wholeStageMaxNumFields
}
}
object WholeStageCodegenId {
// codegenStageId: ID for codegen stages within a query plan.
// It does not affect equality, nor does it participate in destructuring pattern matching
// of WholeStageCodegenExec.
//
// This ID is used to help differentiate between codegen stages. It is included as a part
// of the explain output for physical plans, e.g.
//
// == Physical Plan ==
// *(5) SortMergeJoin [x#3L], [y#9L], Inner
// :- *(2) Sort [x#3L ASC NULLS FIRST], false, 0
// : +- Exchange hashpartitioning(x#3L, 200)
// : +- *(1) Project [(id#0L % 2) AS x#3L]
// : +- *(1) Filter isnotnull((id#0L % 2))
// : +- *(1) Range (0, 5, step=1, splits=8)
// +- *(4) Sort [y#9L ASC NULLS FIRST], false, 0
// +- Exchange hashpartitioning(y#9L, 200)
// +- *(3) Project [(id#6L % 2) AS y#9L]
// +- *(3) Filter isnotnull((id#6L % 2))
// +- *(3) Range (0, 5, step=1, splits=8)
//
// where the ID makes it obvious that not all adjacent codegen'd plan operators are of the
// same codegen stage.
//
// The codegen stage ID is also optionally included in the name of the generated classes as
// a suffix, so that it's easier to associate a generated class back to the physical operator.
// This is controlled by SQLConf: spark.sql.codegen.useIdInClassName
//
// The ID is also included in various log messages.
//
// Within a query, a codegen stage in a plan starts counting from 1, in "insertion order".
// WholeStageCodegenExec operators are inserted into a plan in depth-first post-order.
// See CollapseCodegenStages.insertWholeStageCodegen for the definition of insertion order.
//
// 0 is reserved as a special ID value to indicate a temporary WholeStageCodegenExec object
// is created, e.g. for special fallback handling when an existing WholeStageCodegenExec
// failed to generate/compile code.
private val codegenStageCounter = ThreadLocal.withInitial(new Supplier[Integer] {
override def get() = 1 // TODO: change to Scala lambda syntax when upgraded to Scala 2.12+
})
def resetPerQuery(): Unit = codegenStageCounter.set(1)
def getNextStageId(): Int = {
val counter = codegenStageCounter
val id = counter.get()
counter.set(id + 1)
id
}
}
/**
* WholeStageCodegen compiles a subtree of plans that support codegen together into single Java
* function.
*
* Here is the call graph of to generate Java source (plan A supports codegen, but plan B does not):
*
* WholeStageCodegen Plan A FakeInput Plan B
* =========================================================================
*
* -> execute()
* |
* doExecute() ---------> inputRDDs() -------> inputRDDs() ------> execute()
* |
* +-----------------> produce()
* |
* doProduce() -------> produce()
* |
* doProduce()
* |
* doConsume() <--------- consume()
* |
* doConsume() <-------- consume()
*
* SparkPlan A should override `doProduce()` and `doConsume()`.
*
* `doCodeGen()` will create a `CodeGenContext`, which will hold a list of variables for input,
* used to generated code for [[BoundReference]].
*/
case class WholeStageCodegenExec(child: SparkPlan)(val codegenStageId: Int)
extends UnaryExecNode with CodegenSupport {
override def output: Seq[Attribute] = child.output
override def outputPartitioning: Partitioning = child.outputPartitioning
override def outputOrdering: Seq[SortOrder] = child.outputOrdering
override lazy val metrics = Map(
"pipelineTime" -> SQLMetrics.createTimingMetric(sparkContext,
WholeStageCodegenExec.PIPELINE_DURATION_METRIC))
def generatedClassName(): String = if (conf.wholeStageUseIdInClassName) {
s"GeneratedIteratorForCodegenStage$codegenStageId"
} else {
"GeneratedIterator"
}
/**
* Generates code for this subtree.
*
* @return the tuple of the codegen context and the actual generated source.
*/
def doCodeGen(): (CodegenContext, CodeAndComment) = {
val ctx = new CodegenContext
val code = child.asInstanceOf[CodegenSupport].produce(ctx, this)
// main next function.
ctx.addNewFunction("processNext",
s"""
protected void processNext() throws java.io.IOException {
${code.trim}
}
""", inlineToOuterClass = true)
val className = generatedClassName()
val source = s"""
public Object generate(Object[] references) {
return new $className(references);
}
${ctx.registerComment(
s"""Codegend pipeline for stage (id=$codegenStageId)
|${this.treeString.trim}""".stripMargin)}
final class $className extends ${classOf[BufferedRowIterator].getName} {
private Object[] references;
private scala.collection.Iterator[] inputs;
${ctx.declareMutableStates()}
public $className(Object[] references) {
this.references = references;
}
public void init(int index, scala.collection.Iterator[] inputs) {
partitionIndex = index;
this.inputs = inputs;
${ctx.initMutableStates()}
${ctx.initPartition()}
}
${ctx.emitExtraCode()}
${ctx.declareAddedFunctions()}
}
""".trim
// try to compile, helpful for debug
val cleanedSource = CodeFormatter.stripOverlappingComments(
new CodeAndComment(CodeFormatter.stripExtraNewLines(source), ctx.getPlaceHolderToComments()))
logDebug(s"\n${CodeFormatter.format(cleanedSource)}")
(ctx, cleanedSource)
}
override def doExecute(): RDD[InternalRow] = {
val (ctx, cleanedSource) = doCodeGen()
// try to compile and fallback if it failed
val (_, maxCodeSize) = try {
CodeGenerator.compile(cleanedSource)
} catch {
case _: Exception if !Utils.isTesting && sqlContext.conf.codegenFallback =>
// We should already saw the error message
logWarning(s"Whole-stage codegen disabled for plan (id=$codegenStageId):\n $treeString")
return child.execute()
}
// Check if compiled code has a too large function
if (maxCodeSize > sqlContext.conf.hugeMethodLimit) {
logInfo(s"Found too long generated codes and JIT optimization might not work: " +
s"the bytecode size ($maxCodeSize) is above the limit " +
s"${sqlContext.conf.hugeMethodLimit}, and the whole-stage codegen was disabled " +
s"for this plan (id=$codegenStageId). To avoid this, you can raise the limit " +
s"`${SQLConf.WHOLESTAGE_HUGE_METHOD_LIMIT.key}`:\n$treeString")
child match {
// The fallback solution of batch file source scan still uses WholeStageCodegenExec
case f: FileSourceScanExec if f.supportsBatch => // do nothing
case _ => return child.execute()
}
}
val references = ctx.references.toArray
val durationMs = longMetric("pipelineTime")
val rdds = child.asInstanceOf[CodegenSupport].inputRDDs()
assert(rdds.size <= 2, "Up to two input RDDs can be supported")
if (rdds.length == 1) {
rdds.head.mapPartitionsWithIndex { (index, iter) =>
val (clazz, _) = CodeGenerator.compile(cleanedSource)
val buffer = clazz.generate(references).asInstanceOf[BufferedRowIterator]
buffer.init(index, Array(iter))
new Iterator[InternalRow] {
override def hasNext: Boolean = {
val v = buffer.hasNext
if (!v) durationMs += buffer.durationMs()
v
}
override def next: InternalRow = buffer.next()
}
}
} else {
// Right now, we support up to two input RDDs.
rdds.head.zipPartitions(rdds(1)) { (leftIter, rightIter) =>
Iterator((leftIter, rightIter))
// a small hack to obtain the correct partition index
}.mapPartitionsWithIndex { (index, zippedIter) =>
val (leftIter, rightIter) = zippedIter.next()
val (clazz, _) = CodeGenerator.compile(cleanedSource)
val buffer = clazz.generate(references).asInstanceOf[BufferedRowIterator]
buffer.init(index, Array(leftIter, rightIter))
new Iterator[InternalRow] {
override def hasNext: Boolean = {
val v = buffer.hasNext
if (!v) durationMs += buffer.durationMs()
v
}
override def next: InternalRow = buffer.next()
}
}
}
}
override def inputRDDs(): Seq[RDD[InternalRow]] = {
throw new UnsupportedOperationException
}
override def doProduce(ctx: CodegenContext): String = {
throw new UnsupportedOperationException
}
override def doConsume(ctx: CodegenContext, input: Seq[ExprCode], row: ExprCode): String = {
val doCopy = if (needCopyResult) {
".copy()"
} else {
""
}
s"""
|${row.code}
|append(${row.value}$doCopy);
""".stripMargin.trim
}
override def generateTreeString(
depth: Int,
lastChildren: Seq[Boolean],
builder: StringBuilder,
verbose: Boolean,
prefix: String = "",
addSuffix: Boolean = false): StringBuilder = {
child.generateTreeString(depth, lastChildren, builder, verbose, s"*($codegenStageId) ")
}
override def needStopCheck: Boolean = true
override protected def otherCopyArgs: Seq[AnyRef] = Seq(codegenStageId.asInstanceOf[Integer])
}
/**
* Find the chained plans that support codegen, collapse them together as WholeStageCodegen.
*/
case class CollapseCodegenStages(conf: SQLConf) extends Rule[SparkPlan] {
private def supportCodegen(e: Expression): Boolean = e match {
case e: LeafExpression => true
// CodegenFallback requires the input to be an InternalRow
case e: CodegenFallback => false
case _ => true
}
private def supportCodegen(plan: SparkPlan): Boolean = plan match {
case plan: CodegenSupport if plan.supportCodegen =>
val willFallback = plan.expressions.exists(_.find(e => !supportCodegen(e)).isDefined)
// the generated code will be huge if there are too many columns
val hasTooManyOutputFields =
WholeStageCodegenExec.isTooManyFields(conf, plan.schema)
val hasTooManyInputFields =
plan.children.exists(p => WholeStageCodegenExec.isTooManyFields(conf, p.schema))
!willFallback && !hasTooManyOutputFields && !hasTooManyInputFields
case _ => false
}
/**
* Inserts an InputAdapter on top of those that do not support codegen.
*/
private def insertInputAdapter(plan: SparkPlan): SparkPlan = plan match {
case p if !supportCodegen(p) =>
// collapse them recursively
InputAdapter(insertWholeStageCodegen(p))
case j: SortMergeJoinExec =>
// The children of SortMergeJoin should do codegen separately.
j.withNewChildren(j.children.map(child => InputAdapter(insertWholeStageCodegen(child))))
case p =>
p.withNewChildren(p.children.map(insertInputAdapter))
}
/**
* Inserts a WholeStageCodegen on top of those that support codegen.
*/
private def insertWholeStageCodegen(plan: SparkPlan): SparkPlan = plan match {
// For operators that will output domain object, do not insert WholeStageCodegen for it as
// domain object can not be written into unsafe row.
case plan if plan.output.length == 1 && plan.output.head.dataType.isInstanceOf[ObjectType] =>
plan.withNewChildren(plan.children.map(insertWholeStageCodegen))
case plan: CodegenSupport if supportCodegen(plan) =>
WholeStageCodegenExec(insertInputAdapter(plan))(WholeStageCodegenId.getNextStageId())
case other =>
other.withNewChildren(other.children.map(insertWholeStageCodegen))
}
def apply(plan: SparkPlan): SparkPlan = {
if (conf.wholeStageEnabled) {
WholeStageCodegenId.resetPerQuery()
insertWholeStageCodegen(plan)
} else {
plan
}
}
}
