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new 16bc416be9 [VL] Prepare shim API for breaking change in SPARK-48610
(#7445)
16bc416be9 is described below
commit 16bc416be9999f43a329ceaade208c866ad7f2df
Author: Hongze Zhang <[email protected]>
AuthorDate: Thu Oct 10 08:04:54 2024 +0800
[VL] Prepare shim API for breaking change in SPARK-48610 (#7445)
---
.../spark/sql/execution/GlutenExplainUtils.scala | 154 +++++++++++----------
.../spark/sql/execution/GlutenImplicits.scala | 22 +--
.../org/apache/gluten/sql/shims/SparkShims.scala | 15 ++
.../gluten/sql/shims/spark32/Spark32Shims.scala | 13 ++
.../gluten/sql/shims/spark33/Spark33Shims.scala | 13 ++
.../gluten/sql/shims/spark34/Spark34Shims.scala | 13 ++
.../gluten/sql/shims/spark35/Spark35Shims.scala | 13 ++
7 files changed, 160 insertions(+), 83 deletions(-)
diff --git
a/gluten-substrait/src/main/scala/org/apache/spark/sql/execution/GlutenExplainUtils.scala
b/gluten-substrait/src/main/scala/org/apache/spark/sql/execution/GlutenExplainUtils.scala
index 43b74c8836..fa697789c8 100644
---
a/gluten-substrait/src/main/scala/org/apache/spark/sql/execution/GlutenExplainUtils.scala
+++
b/gluten-substrait/src/main/scala/org/apache/spark/sql/execution/GlutenExplainUtils.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.execution
import org.apache.gluten.execution.WholeStageTransformer
import org.apache.gluten.extension.GlutenPlan
import org.apache.gluten.extension.columnar.FallbackTags
+import org.apache.gluten.sql.shims.SparkShimLoader
import org.apache.gluten.utils.PlanUtil
import org.apache.spark.sql.AnalysisException
@@ -49,7 +50,7 @@ object GlutenExplainUtils extends AdaptiveSparkPlanHelper {
p: SparkPlan,
reason: String,
fallbackNodeToReason: mutable.HashMap[String, String]): Unit = {
- p.getTagValue(QueryPlan.OP_ID_TAG).foreach {
+ SparkShimLoader.getSparkShims.getOperatorId(p).foreach {
opId =>
// e.g., 002 project, it is used to help analysis by `substring(4)`
val formattedNodeName = f"$opId%03d ${p.nodeName}"
@@ -150,94 +151,99 @@ object GlutenExplainUtils extends AdaptiveSparkPlanHelper
{
// scalastyle:off
/**
* Given a input physical plan, performs the following tasks.
- * 1. Generates the explain output for the input plan excluding the
subquery plans.
- * 2. Generates the explain output for each subquery referenced in the
plan.
+ * 1. Generates the explain output for the input plan excluding the
subquery plans. 2. Generates
+ * the explain output for each subquery referenced in the plan.
*/
+ // scalastyle:on
+ // spotless:on
def processPlan[T <: QueryPlan[T]](
plan: T,
append: String => Unit,
- collectFallbackFunc: Option[QueryPlan[_] => FallbackInfo] = None):
FallbackInfo = synchronized {
- try {
- // Initialize a reference-unique set of Operators to avoid accdiental
overwrites and to allow
- // intentional overwriting of IDs generated in previous AQE iteration
- val operators = newSetFromMap[QueryPlan[_]](new util.IdentityHashMap())
- // Initialize an array of ReusedExchanges to help find Adaptively
Optimized Out
- // Exchanges as part of SPARK-42753
- val reusedExchanges = ArrayBuffer.empty[ReusedExchangeExec]
+ collectFallbackFunc: Option[QueryPlan[_] => FallbackInfo] = None):
FallbackInfo =
+ synchronized {
+ SparkShimLoader.getSparkShims.withOperatorIdMap(
+ new java.util.IdentityHashMap[QueryPlan[_], Int]()) {
+ try {
+ // Initialize a reference-unique set of Operators to avoid
accdiental overwrites and to
+ // allow intentional overwriting of IDs generated in previous AQE
iteration
+ val operators = newSetFromMap[QueryPlan[_]](new
util.IdentityHashMap())
+ // Initialize an array of ReusedExchanges to help find Adaptively
Optimized Out
+ // Exchanges as part of SPARK-42753
+ val reusedExchanges = ArrayBuffer.empty[ReusedExchangeExec]
- var currentOperatorID = 0
- currentOperatorID =
- generateOperatorIDs(plan, currentOperatorID, operators,
reusedExchanges, true)
+ var currentOperatorID = 0
+ currentOperatorID =
+ generateOperatorIDs(plan, currentOperatorID, operators,
reusedExchanges, true)
- val subqueries = ArrayBuffer.empty[(SparkPlan, Expression,
BaseSubqueryExec)]
- getSubqueries(plan, subqueries)
+ val subqueries = ArrayBuffer.empty[(SparkPlan, Expression,
BaseSubqueryExec)]
+ getSubqueries(plan, subqueries)
- currentOperatorID = subqueries.foldLeft(currentOperatorID) {
- (curId, plan) => generateOperatorIDs(plan._3.child, curId, operators,
reusedExchanges, true)
- }
+ currentOperatorID = subqueries.foldLeft(currentOperatorID) {
+ (curId, plan) =>
+ generateOperatorIDs(plan._3.child, curId, operators,
reusedExchanges, true)
+ }
- // SPARK-42753: Process subtree for a ReusedExchange with unknown child
- val optimizedOutExchanges = ArrayBuffer.empty[Exchange]
- reusedExchanges.foreach {
- reused =>
- val child = reused.child
- if (!operators.contains(child)) {
- optimizedOutExchanges.append(child)
- currentOperatorID =
- generateOperatorIDs(child, currentOperatorID, operators,
reusedExchanges, false)
+ // SPARK-42753: Process subtree for a ReusedExchange with unknown
child
+ val optimizedOutExchanges = ArrayBuffer.empty[Exchange]
+ reusedExchanges.foreach {
+ reused =>
+ val child = reused.child
+ if (!operators.contains(child)) {
+ optimizedOutExchanges.append(child)
+ currentOperatorID =
+ generateOperatorIDs(child, currentOperatorID, operators,
reusedExchanges, false)
+ }
}
- }
- val collectedOperators = BitSet.empty
- processPlanSkippingSubqueries(plan, append, collectedOperators)
+ val collectedOperators = BitSet.empty
+ processPlanSkippingSubqueries(plan, append, collectedOperators)
- var i = 0
- for (sub <- subqueries) {
- if (i == 0) {
- append("\n===== Subqueries =====\n\n")
- }
- i = i + 1
- append(
- s"Subquery:$i Hosting operator id = " +
- s"${getOpId(sub._1)} Hosting Expression = ${sub._2}\n")
+ var i = 0
+ for (sub <- subqueries) {
+ if (i == 0) {
+ append("\n===== Subqueries =====\n\n")
+ }
+ i = i + 1
+ append(
+ s"Subquery:$i Hosting operator id = " +
+ s"${getOpId(sub._1)} Hosting Expression = ${sub._2}\n")
- // For each subquery expression in the parent plan, process its child
plan to compute
- // the explain output. In case of subquery reuse, we don't print
subquery plan more
- // than once. So we skip [[ReusedSubqueryExec]] here.
- if (!sub._3.isInstanceOf[ReusedSubqueryExec]) {
- processPlanSkippingSubqueries(sub._3.child, append,
collectedOperators)
- }
- append("\n")
- }
+ // For each subquery expression in the parent plan, process its
child plan to compute
+ // the explain output. In case of subquery reuse, we don't print
subquery plan more
+ // than once. So we skip [[ReusedSubqueryExec]] here.
+ if (!sub._3.isInstanceOf[ReusedSubqueryExec]) {
+ processPlanSkippingSubqueries(sub._3.child, append,
collectedOperators)
+ }
+ append("\n")
+ }
- i = 0
- optimizedOutExchanges.foreach {
- exchange =>
- if (i == 0) {
- append("\n===== Adaptively Optimized Out Exchanges =====\n\n")
+ i = 0
+ optimizedOutExchanges.foreach {
+ exchange =>
+ if (i == 0) {
+ append("\n===== Adaptively Optimized Out Exchanges =====\n\n")
+ }
+ i = i + 1
+ append(s"Subplan:$i\n")
+ processPlanSkippingSubqueries[SparkPlan](exchange, append,
collectedOperators)
+ append("\n")
}
- i = i + 1
- append(s"Subplan:$i\n")
- processPlanSkippingSubqueries[SparkPlan](exchange, append,
collectedOperators)
- append("\n")
- }
-
(subqueries.filter(!_._3.isInstanceOf[ReusedSubqueryExec]).map(_._3.child) :+
plan)
- .map {
- plan =>
- if (collectFallbackFunc.isEmpty) {
- collectFallbackNodes(plan)
- } else {
- collectFallbackFunc.get.apply(plan)
+
(subqueries.filter(!_._3.isInstanceOf[ReusedSubqueryExec]).map(_._3.child) :+
plan)
+ .map {
+ plan =>
+ if (collectFallbackFunc.isEmpty) {
+ collectFallbackNodes(plan)
+ } else {
+ collectFallbackFunc.get.apply(plan)
+ }
}
+ .reduce((a, b) => (a._1 + b._1, a._2 ++ b._2))
+ } finally {
+ removeTags(plan)
}
- .reduce((a, b) => (a._1 + b._1, a._2 ++ b._2))
- } finally {
- removeTags(plan)
+ }
}
- }
- // scalastyle:on
- // spotless:on
/**
* Traverses the supplied input plan in a bottom-up fashion and records the
operator id via
@@ -288,7 +294,7 @@ object GlutenExplainUtils extends AdaptiveSparkPlanHelper {
}
visited.add(plan)
currentOperationID += 1
- plan.setTagValue(QueryPlan.OP_ID_TAG, currentOperationID)
+ SparkShimLoader.getSparkShims.setOperatorId(plan, currentOperationID)
}
plan.foreachUp {
@@ -358,12 +364,12 @@ object GlutenExplainUtils extends AdaptiveSparkPlanHelper
{
* value.
*/
private def getOpId(plan: QueryPlan[_]): String = {
- plan.getTagValue(QueryPlan.OP_ID_TAG).map(v => s"$v").getOrElse("unknown")
+ SparkShimLoader.getSparkShims.getOperatorId(plan).map(v =>
s"$v").getOrElse("unknown")
}
private def removeTags(plan: QueryPlan[_]): Unit = {
def remove(p: QueryPlan[_], children: Seq[QueryPlan[_]]): Unit = {
- p.unsetTagValue(QueryPlan.OP_ID_TAG)
+ SparkShimLoader.getSparkShims.unsetOperatorId(p)
children.foreach(removeTags)
}
diff --git
a/gluten-substrait/src/main/scala/org/apache/spark/sql/execution/GlutenImplicits.scala
b/gluten-substrait/src/main/scala/org/apache/spark/sql/execution/GlutenImplicits.scala
index 7f5f921501..4ecc674d4b 100644
---
a/gluten-substrait/src/main/scala/org/apache/spark/sql/execution/GlutenImplicits.scala
+++
b/gluten-substrait/src/main/scala/org/apache/spark/sql/execution/GlutenImplicits.scala
@@ -25,7 +25,6 @@ import org.apache.spark.sql.catalyst.plans.QueryPlan
import org.apache.spark.sql.catalyst.plans.logical.{CommandResult, LogicalPlan}
import org.apache.spark.sql.catalyst.util.StringUtils.PlanStringConcat
import org.apache.spark.sql.execution.ColumnarWriteFilesExec.NoopLeaf
-import org.apache.spark.sql.execution.GlutenExplainUtils._
import org.apache.spark.sql.execution.adaptive.{AdaptiveSparkPlanExec,
AQEShuffleReadExec, QueryStageExec}
import org.apache.spark.sql.execution.columnar.InMemoryTableScanExec
import org.apache.spark.sql.execution.command.{DataWritingCommandExec,
ExecutedCommandExec}
@@ -42,8 +41,8 @@ import scala.collection.mutable.ArrayBuffer
* A helper class to get the Gluten fallback summary from a Spark [[Dataset]].
*
* Note that, if AQE is enabled, but the query is not materialized, then this
method will re-plan
- * the query execution with disabled AQE. It is a workaround to get the final
plan, and it may
- * cause the inconsistent results with a materialized query. However, we have
no choice.
+ * the query execution with disabled AQE. It is a workaround to get the final
plan, and it may cause
+ * the inconsistent results with a materialized query. However, we have no
choice.
*
* For example:
*
@@ -96,7 +95,9 @@ object GlutenImplicits {
args.substring(index + "isFinalPlan=".length).trim.toBoolean
}
- private def collectFallbackNodes(spark: SparkSession, plan: QueryPlan[_]):
FallbackInfo = {
+ private def collectFallbackNodes(
+ spark: SparkSession,
+ plan: QueryPlan[_]): GlutenExplainUtils.FallbackInfo = {
var numGlutenNodes = 0
val fallbackNodeToReason = new mutable.HashMap[String, String]
@@ -131,7 +132,7 @@ object GlutenImplicits {
spark,
newSparkPlan
)
- processPlan(
+ GlutenExplainUtils.processPlan(
newExecutedPlan,
new PlanStringConcat().append,
Some(plan => collectFallbackNodes(spark, plan)))
@@ -146,12 +147,15 @@ object GlutenImplicits {
if (PlanUtil.isGlutenTableCache(i)) {
numGlutenNodes += 1
} else {
- addFallbackNodeWithReason(i, "Columnar table cache is disabled",
fallbackNodeToReason)
+ GlutenExplainUtils.addFallbackNodeWithReason(
+ i,
+ "Columnar table cache is disabled",
+ fallbackNodeToReason)
}
collect(i.relation.cachedPlan)
case _: AQEShuffleReadExec => // Ignore
case p: SparkPlan =>
- handleVanillaSparkPlan(p, fallbackNodeToReason)
+ GlutenExplainUtils.handleVanillaSparkPlan(p, fallbackNodeToReason)
p.innerChildren.foreach(collect)
case _ =>
}
@@ -181,10 +185,10 @@ object GlutenImplicits {
// AQE is not materialized, so the columnar rules are not applied.
// For this case, We apply columnar rules manually with disable AQE.
val qe = spark.sessionState.executePlan(logicalPlan,
CommandExecutionMode.SKIP)
- processPlan(qe.executedPlan, concat.append, collectFallbackFunc)
+ GlutenExplainUtils.processPlan(qe.executedPlan, concat.append,
collectFallbackFunc)
}
} else {
- processPlan(plan, concat.append, collectFallbackFunc)
+ GlutenExplainUtils.processPlan(plan, concat.append,
collectFallbackFunc)
}
totalNumGlutenNodes += numGlutenNodes
totalNumFallbackNodes += fallbackNodeToReason.size
diff --git
a/shims/common/src/main/scala/org/apache/gluten/sql/shims/SparkShims.scala
b/shims/common/src/main/scala/org/apache/gluten/sql/shims/SparkShims.scala
index 7671f236c9..fba6a4a5a4 100644
--- a/shims/common/src/main/scala/org/apache/gluten/sql/shims/SparkShims.scala
+++ b/shims/common/src/main/scala/org/apache/gluten/sql/shims/SparkShims.scala
@@ -29,6 +29,7 @@ import org.apache.spark.sql.catalyst.catalog.BucketSpec
import org.apache.spark.sql.catalyst.csv.CSVOptions
import org.apache.spark.sql.catalyst.expressions.{Attribute, BinaryExpression,
Expression}
import
org.apache.spark.sql.catalyst.expressions.aggregate.TypedImperativeAggregate
+import org.apache.spark.sql.catalyst.plans.QueryPlan
import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
import org.apache.spark.sql.catalyst.plans.physical.{Distribution,
Partitioning}
import org.apache.spark.sql.catalyst.rules.Rule
@@ -270,4 +271,18 @@ trait SparkShims {
def extractExpressionArrayInsert(arrayInsert: Expression): Seq[Expression] =
{
throw new UnsupportedOperationException("ArrayInsert not supported.")
}
+
+ /** Shim method for usages from GlutenExplainUtils.scala. */
+ def withOperatorIdMap[T](idMap: java.util.Map[QueryPlan[_], Int])(body: =>
T): T = {
+ body
+ }
+
+ /** Shim method for usages from GlutenExplainUtils.scala. */
+ def getOperatorId(plan: QueryPlan[_]): Option[Int]
+
+ /** Shim method for usages from GlutenExplainUtils.scala. */
+ def setOperatorId(plan: QueryPlan[_], opId: Int): Unit
+
+ /** Shim method for usages from GlutenExplainUtils.scala. */
+ def unsetOperatorId(plan: QueryPlan[_]): Unit
}
diff --git
a/shims/spark32/src/main/scala/org/apache/gluten/sql/shims/spark32/Spark32Shims.scala
b/shims/spark32/src/main/scala/org/apache/gluten/sql/shims/spark32/Spark32Shims.scala
index f62f9031ce..973e675fa9 100644
---
a/shims/spark32/src/main/scala/org/apache/gluten/sql/shims/spark32/Spark32Shims.scala
+++
b/shims/spark32/src/main/scala/org/apache/gluten/sql/shims/spark32/Spark32Shims.scala
@@ -29,6 +29,7 @@ import org.apache.spark.sql.catalyst.catalog.BucketSpec
import org.apache.spark.sql.catalyst.csv.CSVOptions
import org.apache.spark.sql.catalyst.expressions.{Attribute,
AttributeReference, BinaryExpression, Expression, InputFileBlockLength,
InputFileBlockStart, InputFileName}
import
org.apache.spark.sql.catalyst.expressions.aggregate.TypedImperativeAggregate
+import org.apache.spark.sql.catalyst.plans.QueryPlan
import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
import org.apache.spark.sql.catalyst.plans.physical.{Distribution,
HashClusteredDistribution}
import org.apache.spark.sql.catalyst.rules.Rule
@@ -283,4 +284,16 @@ class Spark32Shims extends SparkShims {
val s = decimalType.scale
DecimalType(p, if (toScale > s) s else toScale)
}
+
+ override def getOperatorId(plan: QueryPlan[_]): Option[Int] = {
+ plan.getTagValue(QueryPlan.OP_ID_TAG)
+ }
+
+ override def setOperatorId(plan: QueryPlan[_], opId: Int): Unit = {
+ plan.setTagValue(QueryPlan.OP_ID_TAG, opId)
+ }
+
+ override def unsetOperatorId(plan: QueryPlan[_]): Unit = {
+ plan.unsetTagValue(QueryPlan.OP_ID_TAG)
+ }
}
diff --git
a/shims/spark33/src/main/scala/org/apache/gluten/sql/shims/spark33/Spark33Shims.scala
b/shims/spark33/src/main/scala/org/apache/gluten/sql/shims/spark33/Spark33Shims.scala
index 168b88275c..5cf7c5505a 100644
---
a/shims/spark33/src/main/scala/org/apache/gluten/sql/shims/spark33/Spark33Shims.scala
+++
b/shims/spark33/src/main/scala/org/apache/gluten/sql/shims/spark33/Spark33Shims.scala
@@ -30,6 +30,7 @@ import org.apache.spark.sql.catalyst.catalog.BucketSpec
import org.apache.spark.sql.catalyst.csv.CSVOptions
import org.apache.spark.sql.catalyst.expressions._
import
org.apache.spark.sql.catalyst.expressions.aggregate.{BloomFilterAggregate,
RegrR2, TypedImperativeAggregate}
+import org.apache.spark.sql.catalyst.plans.QueryPlan
import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution,
Distribution}
import org.apache.spark.sql.catalyst.rules.Rule
@@ -364,4 +365,16 @@ class Spark33Shims extends SparkShims {
RebaseSpec(LegacyBehaviorPolicy.CORRECTED)
)
}
+
+ override def getOperatorId(plan: QueryPlan[_]): Option[Int] = {
+ plan.getTagValue(QueryPlan.OP_ID_TAG)
+ }
+
+ override def setOperatorId(plan: QueryPlan[_], opId: Int): Unit = {
+ plan.setTagValue(QueryPlan.OP_ID_TAG, opId)
+ }
+
+ override def unsetOperatorId(plan: QueryPlan[_]): Unit = {
+ plan.unsetTagValue(QueryPlan.OP_ID_TAG)
+ }
}
diff --git
a/shims/spark34/src/main/scala/org/apache/gluten/sql/shims/spark34/Spark34Shims.scala
b/shims/spark34/src/main/scala/org/apache/gluten/sql/shims/spark34/Spark34Shims.scala
index 558d7f60d5..bedad4c017 100644
---
a/shims/spark34/src/main/scala/org/apache/gluten/sql/shims/spark34/Spark34Shims.scala
+++
b/shims/spark34/src/main/scala/org/apache/gluten/sql/shims/spark34/Spark34Shims.scala
@@ -32,6 +32,7 @@ import org.apache.spark.sql.catalyst.catalog.BucketSpec
import org.apache.spark.sql.catalyst.csv.CSVOptions
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.catalyst.plans.QueryPlan
import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution,
Distribution, KeyGroupedPartitioning, Partitioning}
import org.apache.spark.sql.catalyst.rules.Rule
@@ -499,4 +500,16 @@ class Spark34Shims extends SparkShims {
val expr = arrayInsert.asInstanceOf[ArrayInsert]
Seq(expr.srcArrayExpr, expr.posExpr, expr.itemExpr,
Literal(expr.legacyNegativeIndex))
}
+
+ override def getOperatorId(plan: QueryPlan[_]): Option[Int] = {
+ plan.getTagValue(QueryPlan.OP_ID_TAG)
+ }
+
+ override def setOperatorId(plan: QueryPlan[_], opId: Int): Unit = {
+ plan.setTagValue(QueryPlan.OP_ID_TAG, opId)
+ }
+
+ override def unsetOperatorId(plan: QueryPlan[_]): Unit = {
+ plan.unsetTagValue(QueryPlan.OP_ID_TAG)
+ }
}
diff --git
a/shims/spark35/src/main/scala/org/apache/gluten/sql/shims/spark35/Spark35Shims.scala
b/shims/spark35/src/main/scala/org/apache/gluten/sql/shims/spark35/Spark35Shims.scala
index 4a6590161c..d130864a9f 100644
---
a/shims/spark35/src/main/scala/org/apache/gluten/sql/shims/spark35/Spark35Shims.scala
+++
b/shims/spark35/src/main/scala/org/apache/gluten/sql/shims/spark35/Spark35Shims.scala
@@ -31,6 +31,7 @@ import org.apache.spark.sql.catalyst.catalog.BucketSpec
import org.apache.spark.sql.catalyst.csv.CSVOptions
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.catalyst.plans.QueryPlan
import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution,
Distribution, KeyGroupedPartitioning, Partitioning}
import org.apache.spark.sql.catalyst.rules.Rule
@@ -524,4 +525,16 @@ class Spark35Shims extends SparkShims {
val expr = arrayInsert.asInstanceOf[ArrayInsert]
Seq(expr.srcArrayExpr, expr.posExpr, expr.itemExpr,
Literal(expr.legacyNegativeIndex))
}
+
+ override def getOperatorId(plan: QueryPlan[_]): Option[Int] = {
+ plan.getTagValue(QueryPlan.OP_ID_TAG)
+ }
+
+ override def setOperatorId(plan: QueryPlan[_], opId: Int): Unit = {
+ plan.setTagValue(QueryPlan.OP_ID_TAG, opId)
+ }
+
+ override def unsetOperatorId(plan: QueryPlan[_]): Unit = {
+ plan.unsetTagValue(QueryPlan.OP_ID_TAG)
+ }
}
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