[jira] [Resolved] (SPARK-38580) Requesting Restful API can cause NullPointerException
[
https://issues.apache.org/jira/browse/SPARK-38580?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yimin Yang resolved SPARK-38580.
Resolution: Duplicate
> Requesting Restful API can cause NullPointerException
> -
>
> Key: SPARK-38580
> URL: https://issues.apache.org/jira/browse/SPARK-38580
> Project: Spark
> Issue Type: Bug
> Components: Web UI
>Affects Versions: 3.1.0, 3.1.1, 3.1.2, 3.2.0, 3.2.1
>Reporter: Yimin Yang
>Priority: Major
>
> When requesting Restful API
> \{baseURL}/api/v1/applications/$appId/sql/$executionId which is introduced by
> this PR [https://github.com/apache/spark/pull/28208,] it can cause
> NullPointerException. The root cause is, when calling method doUpdate() of
> `LiveExecutionData`, `metricsValues` can be null. Then, when statement
> `printableMetrics(graph.allNodes, exec.metricValues)` is executed, it will
> throw NullPointerException.
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[jira] [Created] (SPARK-38580) Requesting Restful API can cause NullPointerException
Yimin Yang created SPARK-38580:
--
Summary: Requesting Restful API can cause NullPointerException
Key: SPARK-38580
URL: https://issues.apache.org/jira/browse/SPARK-38580
Project: Spark
Issue Type: Bug
Components: Web UI
Affects Versions: 3.2.1, 3.2.0, 3.1.2, 3.1.1, 3.1.0
Reporter: Yimin Yang
When requesting Restful API
\{baseURL}/api/v1/applications/$appId/sql/$executionId which is introduced by
this PR [https://github.com/apache/spark/pull/28208,] it can cause
NullPointerException. The root cause is, when calling method doUpdate() of
`LiveExecutionData`, `metricsValues` can be null. Then, when statement
`printableMetrics(graph.allNodes, exec.metricValues)` is executed, it will
throw NullPointerException.
--
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-
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
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[jira] [Created] (SPARK-38579) Requesting Restful API can cause NullPointerException
Yimin Yang created SPARK-38579:
--
Summary: Requesting Restful API can cause NullPointerException
Key: SPARK-38579
URL: https://issues.apache.org/jira/browse/SPARK-38579
Project: Spark
Issue Type: Bug
Components: Web UI
Affects Versions: 3.2.1, 3.2.0, 3.1.2, 3.1.1, 3.1.0
Reporter: Yimin Yang
When requesting Restful API
\{baseURL}/api/v1/applications/$appId/sql/$executionId which is introduced by
this PR [https://github.com/apache/spark/pull/28208,] it can cause
NullPointerException. The root cause is, when calling method doUpdate() of
`LiveExecutionData`, `metricsValues` can be null. Then, when statement
`printableMetrics(graph.allNodes, exec.metricValues)` is executed, it will
throw NullPointerException.
--
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(v8.20.1#820001)
-
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-37728) reading nested columns with ORC vectorized reader can cause ArrayIndexOutOfBoundsException
[
https://issues.apache.org/jira/browse/SPARK-37728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yimin Yang updated SPARK-37728:
---
Affects Version/s: (was: 3.1.2)
(was: 3.0.3)
> reading nested columns with ORC vectorized reader can cause
> ArrayIndexOutOfBoundsException
> --
>
> Key: SPARK-37728
> URL: https://issues.apache.org/jira/browse/SPARK-37728
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.2.0
>Reporter: Yimin Yang
>Assignee: Yimin Yang
>Priority: Major
> Fix For: 3.3.0
>
>
> When spark.sql.orc.enableNestedColumnVectorizedReader is set to true, reading
> nested columns of ORC files can cause ArrayIndexOutOfBoundsException. Here is
> a simple reproduction:
> 1) create an ORC file which contains records of type Array>:
> {code:java}
> ./bin/spark-shell {code}
> {code:java}
> case class Item(record: Array[Array[String]])
> val data = new Array[Array[Array[String]]](100)
> for (i <- 0 to 99) {
> val temp = new Array[Array[String]](50)
> for (j <- 0 to 49) {
> temp(j) = new Array[String](1000)
> for (k <- 0 to 999) {
> temp(j)(k) = k.toString
> }
> }
> data(i) = temp
> }
> val rdd = spark.sparkContext.parallelize(data, 1)
> val df = rdd.map(x => Item(x)).toDF
> df.write.orc("file:///home/user_name/data") {code}
>
> 2) read the orc with spark.sql.orc.enableNestedColumnVectorizedReader=true
> {code:java}
> ./bin/spark-shell --conf spark.sql.orc.enableVectorizedReader=true --conf
> spark.sql.codegen.wholeStage=true --conf
> spark.sql.orc.enableNestedColumnVectorizedReader=true --conf
> spark.sql.orc.columnarReaderBatchSize=4096 {code}
> {code:java}
> val df = spark.read.orc("file:///home/user_name/data")
> df.show(100) {code}
>
> Then Spark threw ArrayIndexOutOfBoundsException:
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2455)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2404)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2403)
> at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
> at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2403)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1162)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1162)
> at scala.Option.foreach(Option.scala:407)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1162)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2643)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2585)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2574)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
> at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:940)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2227)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2248)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2267)
> at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:490)
> at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:443)
> at
> org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:48)
> at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3833)
> at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2832)
> at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3824)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:109)
> at
> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:169)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:95)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3822)
> at org.apache.spark.sql.Dataset.head(Dataset.scala:2832)
> at org.apache.spark.sql.Dataset.take(Dataset.scala:3053)
> at org.apache.spark.sql.Dataset.getRows(Dataset.scala:288)
>
[jira] [Updated] (SPARK-37728) reading nested columns with ORC vectorized reader can cause ArrayIndexOutOfBoundsException
[
https://issues.apache.org/jira/browse/SPARK-37728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yimin Yang updated SPARK-37728:
---
Description:
When spark.sql.orc.enableNestedColumnVectorizedReader is set to true, reading
nested columns of ORC files can cause ArrayIndexOutOfBoundsException. Here is a
simple reproduction:
1) create an ORC file which contains records of type Array>:
{code:java}
./bin/spark-shell {code}
{code:java}
case class Item(record: Array[Array[String]])
val data = new Array[Array[Array[String]]](100)
for (i <- 0 to 99) {
val temp = new Array[Array[String]](50)
for (j <- 0 to 49) {
temp(j) = new Array[String](1000)
for (k <- 0 to 999) {
temp(j)(k) = k.toString
}
}
data(i) = temp
}
val rdd = spark.sparkContext.parallelize(data, 1)
val df = rdd.map(x => Item(x)).toDF
df.write.orc("file:///home/user_name/data") {code}
2) read the orc with spark.sql.orc.enableNestedColumnVectorizedReader=true
{code:java}
./bin/spark-shell --conf spark.sql.orc.enableVectorizedReader=true --conf
spark.sql.codegen.wholeStage=true --conf
spark.sql.orc.enableNestedColumnVectorizedReader=true --conf
spark.sql.orc.columnarReaderBatchSize=4096 {code}
{code:java}
val df = spark.read.orc("file:///home/user_name/data")
df.show(100) {code}
Then Spark threw ArrayIndexOutOfBoundsException:
Driver stacktrace:
at
org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2455)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2404)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2403)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2403)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1162)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1162)
at scala.Option.foreach(Option.scala:407)
at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1162)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2643)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2585)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2574)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:940)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2227)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2248)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2267)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:490)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:443)
at
org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:48)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3833)
at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2832)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3824)
at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:109)
at
org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:169)
at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:95)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3822)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2832)
at org.apache.spark.sql.Dataset.take(Dataset.scala:3053)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:288)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:327)
at org.apache.spark.sql.Dataset.show(Dataset.scala:807)
at org.apache.spark.sql.Dataset.show(Dataset.scala:766)
... 47 elided
Caused by: java.lang.ArrayIndexOutOfBoundsException: 4096
at
org.apache.spark.sql.execution.datasources.orc.OrcArrayColumnVector.getArray(OrcArrayColumnVector.java:53)
at
org.apache.spark.sql.vectorized.ColumnarArray.getArray(ColumnarArray.java:170)
at
org.apache.spark.sql.vectorized.ColumnarArray.getArray(ColumnarArray.java:31)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
Source)
at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
[jira] [Updated] (SPARK-37728) reading nested columns with ORC vectorized reader can cause ArrayIndexOutOfBoundsException
A0 at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2403) at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62) at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2403) at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1162) at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1162) at scala.Option.foreach(Option.scala:407) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1162) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2643) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2585) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2574) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:940) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2227) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2248) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2267) at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:490) at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:443) at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:48) at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3833) at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2832) at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3824) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:109) at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:169) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:95) at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3822) at org.apache.spark.sql.Dataset.head(Dataset.scala:2832) at org.apache.spark.sql.Dataset.take(Dataset.scala:3053) at org.apache.spark.sql.Dataset.getRows(Dataset.scala:288) at org.apache.spark.sql.Dataset.showString(Dataset.scala:327) at org.apache.spark.sql.Dataset.show(Dataset.scala:807) at org.apache.spark.sql.Dataset.show(Dataset.scala:766) ... 47 elided Caused by: java.lang.ArrayIndexOutOfBoundsException: 4096 at org.apache.spark.sql.execution.datasources.orc.OrcArrayColumnVector.getArray(OrcArrayColumnVector.java:53) at org.apache.spark.sql.vectorized.ColumnarArray.getArray(ColumnarArray.java:170) at org.apache.spark.sql.vectorized.ColumnarArray.getArray(ColumnarArray.java:31) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:760) at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:363) at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:890) at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:890) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:365) at org.apache.spark.rdd.RDD.iterator(RDD.scala:329) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.run(Task.scala:136) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:507) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1468) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:510) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) > reading nested columns with ORC vectorized reader can cause > ArrayIndexOutOfBoundsException > -- > > Key: SPARK-37728 > URL: https://issues.apache.org/jira/browse/SPARK-37728 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 3.0.3, 3.1.2, 3.2.0 >Reporter: Yimin Yang >Priority: Major > > When
[jira] [Updated] (SPARK-37728) reading nested columns with ORC vectorized reader can cause ArrayIndexOutOfBoundsException
[
https://issues.apache.org/jira/browse/SPARK-37728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yimin Yang updated SPARK-37728:
---
Description:
When spark.sql.orc.enableNestedColumnVectorizedReader is set to true, reading
nested columns of ORC files can cause ArrayIndexOutOfBoundsException. Here is a
simple reproduction:
1) create an ORC file which contains records of type Array>:
{code:java}
./bin/spark-shell {code}
{code:java}
case class Item(record: Array[Array[String]])
val data = new Array[Array[Array[String]]](100)
for (i <- 0 to 99) {
val temp = new Array[Array[String]](50)
for (j <- 0 to 49) {
temp(j) = new Array[String](1000)
for (k <- 0 to 999) {
temp(j)(k) = k.toString
}
}
data(i) = temp
}
val rdd = spark.sparkContext.parallelize(data, 1)
val df = rdd.map(x => Item(x)).toDF
df.write.orc("file:///home/user_name/data") {code}
2) read the orc with spark.sql.orc.enableNestedColumnVectorizedReader=true
{code:java}
./bin/spark-shell --conf spark.sql.orc.enableVectorizedReader=true --conf
spark.sql.codegen.wholeStage=true --conf
spark.sql.orc.enableNestedColumnVectorizedReader=true --conf
spark.sql.orc.columnarReaderBatchSize=4096 {code}
{code:java}
val df = spark.read.orc("file:///home/user_name/data")
df.show(100) {code}
Then Spark threw ArrayIndexOutOfBoundsException:
Driver stacktrace:
at
org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2455)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2404)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2403)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2403)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1162)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1162)
at scala.Option.foreach(Option.scala:407)
at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1162)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2643)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2585)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2574)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:940)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2227)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2248)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2267)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:490)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:443)
at
org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:48)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3833)
at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2832)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3824)
at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:109)
at
org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:169)
at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:95)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3822)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2832)
at org.apache.spark.sql.Dataset.take(Dataset.scala:3053)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:288)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:327)
at org.apache.spark.sql.Dataset.show(Dataset.scala:807)
at org.apache.spark.sql.Dataset.show(Dataset.scala:766)
... 47 elided
Caused by: java.lang.ArrayIndexOutOfBoundsException: 4096
at
org.apache.spark.sql.execution.datasources.orc.OrcArrayColumnVector.getArray(OrcArrayColumnVector.java:53)
at
org.apache.spark.sql.vectorized.ColumnarArray.getArray(ColumnarArray.java:170)
at
org.apache.spark.sql.vectorized.ColumnarArray.getArray(ColumnarArray.java:31)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
Source)
at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
[jira] [Created] (SPARK-37728) reading nested columns with ORC vectorized reader can cause ArrayIndexOutOfBoundsException
Yimin Yang created SPARK-37728:
--
Summary: reading nested columns with ORC vectorized reader can
cause ArrayIndexOutOfBoundsException
Key: SPARK-37728
URL: https://issues.apache.org/jira/browse/SPARK-37728
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 3.2.0, 3.1.2, 3.0.3
Reporter: Yimin Yang
When spark.sql.orc.enableNestedColumnVectorizedReader is set to true, reading
nested columns of ORC files can cause ArrayIndexOutOfBoundsException. Here is a
simple reproduction:
1) create an ORC file which contains records of type Array>:
{code:java}
./bin/spark-shell {code}
{code:java}
case class Item(record: Array[Array[String]])
val data = new Array[Array[Array[String]]](100)
for (i <- 0 to 99) {
val temp = new Array[Array[String]](50)
for (j <- 0 to 49) {
temp(j) = new Array[String](1000)
for (k <- 0 to 999) {
temp(j)(k) = k.toString
}
}
data(i) = temp
}
val rdd = spark.sparkContext.parallelize(data, 1)
val df = rdd.map(x => Item(x)).toDF
df.write.orc("file:///home/user_name/data") {code}
2) read the orc with spark.sql.orc.enableNestedColumnVectorizedReader=true
{code:java}
./bin/spark-shell --conf spark.sql.orc.enableVectorizedReader=true --conf
spark.sql.codegen.wholeStage=true --conf
spark.sql.orc.enableNestedColumnVectorizedReader=true --conf
spark.sql.orc.columnarReaderBatchSize=4096 {code}
{code:java}
val df = spark.read.orc("file:///home/user_name/data")
df.show(100) {code}
Then Spark threw ArrayIndexOutOfBoundsException:
Driver stacktrace:
at
org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2455)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2404)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2403)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2403)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1162)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1162)
at scala.Option.foreach(Option.scala:407)
at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1162)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2643)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2585)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2574)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:940)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2227)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2248)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2267)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:490)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:443)
at
org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:48)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3833)
at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2832)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3824)
at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:109)
at
org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:169)
at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:95)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3822)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2832)
at org.apache.spark.sql.Dataset.take(Dataset.scala:3053)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:288)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:327)
at org.apache.spark.sql.Dataset.show(Dataset.scala:807)
at org.apache.spark.sql.Dataset.show(Dataset.scala:766)
... 47 elided
Caused by: java.lang.ArrayIndexOutOfBoundsException: 4096
at
org.apache.spark.sql.execution.datasources.orc.OrcArrayColumnVector.getArray(OrcArrayColumnVector.java:53)
at
org.apache.spark.sql.vectorized.ColumnarArray.getArray(ColumnarArray.java:170)
at
