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<Array<String>>: {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 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) -- This message was sent by Atlassian Jira (v8.20.1#820001) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org