[jira] [Assigned] (SPARK-34682) Regression in "operating on canonicalized plan" check in CustomShuffleReaderExec
[
https://issues.apache.org/jira/browse/SPARK-34682?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hyukjin Kwon reassigned SPARK-34682:
Assignee: Andy Grove
> Regression in "operating on canonicalized plan" check in
> CustomShuffleReaderExec
>
>
> Key: SPARK-34682
> URL: https://issues.apache.org/jira/browse/SPARK-34682
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
>Affects Versions: 3.1.1
>Reporter: Andy Grove
>Assignee: Andy Grove
>Priority: Minor
> Fix For: 3.2.0, 3.1.2
>
>
> In Spark 3.0.2 if I attempt to execute on a canonicalized version of
> CustomShuffleReaderExec I get an error "operating on canonicalized plan", as
> expected.
> There is a regression in Spark 3.1.1 where this check can never be reached
> because of a new call to sendDriverMetrics that was added prior to the check.
> This method will fail if operating on a canonicalized plan because it assumes
> the existence of metrics that do not exist if this is a canonicalized plan.
> {code:java}
> private lazy val shuffleRDD: RDD[_] = {
> sendDriverMetrics()
> shuffleStage.map { stage =>
> stage.shuffle.getShuffleRDD(partitionSpecs.toArray)
> }.getOrElse {
> throw new IllegalStateException("operating on canonicalized plan")
> }
> }{code}
> The specific error looks like this:
> {code:java}
> java.util.NoSuchElementException: key not found: numPartitions
> at scala.collection.immutable.Map$EmptyMap$.apply(Map.scala:101)
> at scala.collection.immutable.Map$EmptyMap$.apply(Map.scala:99)
> at
> org.apache.spark.sql.execution.adaptive.CustomShuffleReaderExec.sendDriverMetrics(CustomShuffleReaderExec.scala:122)
> at
> org.apache.spark.sql.execution.adaptive.CustomShuffleReaderExec.shuffleRDD$lzycompute(CustomShuffleReaderExec.scala:182)
> at
> org.apache.spark.sql.execution.adaptive.CustomShuffleReaderExec.shuffleRDD(CustomShuffleReaderExec.scala:181)
> at
> org.apache.spark.sql.execution.adaptive.CustomShuffleReaderExec.doExecuteColumnar(CustomShuffleReaderExec.scala:196)
> {code}
> I think the fix is simply to avoid calling sendDriverMetrics if the plan is
> canonicalized and I am planning on creating a PR to fix this.
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[jira] [Assigned] (SPARK-34682) Regression in "operating on canonicalized plan" check in CustomShuffleReaderExec
[
https://issues.apache.org/jira/browse/SPARK-34682?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-34682:
Assignee: Apache Spark
> Regression in "operating on canonicalized plan" check in
> CustomShuffleReaderExec
>
>
> Key: SPARK-34682
> URL: https://issues.apache.org/jira/browse/SPARK-34682
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.1.1
>Reporter: Andy Grove
>Assignee: Apache Spark
>Priority: Minor
> Fix For: 3.2.0, 3.1.2
>
>
> In Spark 3.0.2 if I attempt to execute on a canonicalized version of
> CustomShuffleReaderExec I get an error "operating on canonicalized plan", as
> expected.
> There is a regression in Spark 3.1.1 where this check can never be reached
> because of a new call to sendDriverMetrics that was added prior to the check.
> This method will fail if operating on a canonicalized plan because it assumes
> the existence of metrics that do not exist if this is a canonicalized plan.
> {code:java}
> private lazy val shuffleRDD: RDD[_] = {
> sendDriverMetrics()
> shuffleStage.map { stage =>
> stage.shuffle.getShuffleRDD(partitionSpecs.toArray)
> }.getOrElse {
> throw new IllegalStateException("operating on canonicalized plan")
> }
> }{code}
> The specific error looks like this:
> {code:java}
> java.util.NoSuchElementException: key not found: numPartitions
> at scala.collection.immutable.Map$EmptyMap$.apply(Map.scala:101)
> at scala.collection.immutable.Map$EmptyMap$.apply(Map.scala:99)
> at
> org.apache.spark.sql.execution.adaptive.CustomShuffleReaderExec.sendDriverMetrics(CustomShuffleReaderExec.scala:122)
> at
> org.apache.spark.sql.execution.adaptive.CustomShuffleReaderExec.shuffleRDD$lzycompute(CustomShuffleReaderExec.scala:182)
> at
> org.apache.spark.sql.execution.adaptive.CustomShuffleReaderExec.shuffleRDD(CustomShuffleReaderExec.scala:181)
> at
> org.apache.spark.sql.execution.adaptive.CustomShuffleReaderExec.doExecuteColumnar(CustomShuffleReaderExec.scala:196)
> {code}
> I think the fix is simply to avoid calling sendDriverMetrics if the plan is
> canonicalized and I am planning on creating a PR to fix this.
--
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[jira] [Assigned] (SPARK-34682) Regression in "operating on canonicalized plan" check in CustomShuffleReaderExec
[
https://issues.apache.org/jira/browse/SPARK-34682?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-34682:
Assignee: (was: Apache Spark)
> Regression in "operating on canonicalized plan" check in
> CustomShuffleReaderExec
>
>
> Key: SPARK-34682
> URL: https://issues.apache.org/jira/browse/SPARK-34682
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.1.1
>Reporter: Andy Grove
>Priority: Minor
> Fix For: 3.2.0, 3.1.2
>
>
> In Spark 3.0.2 if I attempt to execute on a canonicalized version of
> CustomShuffleReaderExec I get an error "operating on canonicalized plan", as
> expected.
> There is a regression in Spark 3.1.1 where this check can never be reached
> because of a new call to sendDriverMetrics that was added prior to the check.
> This method will fail if operating on a canonicalized plan because it assumes
> the existence of metrics that do not exist if this is a canonicalized plan.
> {code:java}
> private lazy val shuffleRDD: RDD[_] = {
> sendDriverMetrics()
> shuffleStage.map { stage =>
> stage.shuffle.getShuffleRDD(partitionSpecs.toArray)
> }.getOrElse {
> throw new IllegalStateException("operating on canonicalized plan")
> }
> }{code}
> The specific error looks like this:
> {code:java}
> java.util.NoSuchElementException: key not found: numPartitions
> at scala.collection.immutable.Map$EmptyMap$.apply(Map.scala:101)
> at scala.collection.immutable.Map$EmptyMap$.apply(Map.scala:99)
> at
> org.apache.spark.sql.execution.adaptive.CustomShuffleReaderExec.sendDriverMetrics(CustomShuffleReaderExec.scala:122)
> at
> org.apache.spark.sql.execution.adaptive.CustomShuffleReaderExec.shuffleRDD$lzycompute(CustomShuffleReaderExec.scala:182)
> at
> org.apache.spark.sql.execution.adaptive.CustomShuffleReaderExec.shuffleRDD(CustomShuffleReaderExec.scala:181)
> at
> org.apache.spark.sql.execution.adaptive.CustomShuffleReaderExec.doExecuteColumnar(CustomShuffleReaderExec.scala:196)
> {code}
> I think the fix is simply to avoid calling sendDriverMetrics if the plan is
> canonicalized and I am planning on creating a PR to fix this.
--
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