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Soumabrata Chakraborty commented on SPARK-18650: ------------------------------------------------ I am facing the same issue while trying to load a csv. I am using org.apache.spark:spark-core_2.10:2.0.2 and using the following to read the csv Dataset<Row> dataset = sparkSession.read().schema(schema).options(options).csv(path) ; The options contain "header":"false" and "sep":"|" > race condition in FileScanRDD.scala > ----------------------------------- > > Key: SPARK-18650 > URL: https://issues.apache.org/jira/browse/SPARK-18650 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 2.0.2 > Environment: scala 2.11 > macos 10.11.6 > Reporter: Jay Goldman > > I am attempting to create a DataSet from a single CSV file : > val ss: SparkSession = .... > val ddr = ss.read.option("path", path) > ... (choose between xml vs csv parsing) > var df = ddr.option("sep", ",") > .option("quote", "\"") > .option("escape", "\"") // want to retain backslashes (\) ... > .option("delimiter", ",") > .option("comment", "#") > .option("header", "true") > .option("format", "csv") > ddr.csv(path) > df.count() returns 2 times the number of lines in the CSV file - i.e., each > line of the input file shows up as 2 rows in df. > moreover df.distinct.count has the correct rows. > There appears to be a problem in FileScanRDD.compute. I am using spark > version 2.0.1 with scala 2.11. I am not going to include the entire contents > of FileScanRDD.scala here. > In FileScanRDD.compute there is the following: > private[this] val files = split.asInstanceOf[FilePartition].files.toIterator > If i put a breakpoint in either FileScanRDD.compute or > FIleScanRDD.nextIterator the resulting dataset has the correct number of rows. > Moreover, the code in FileScanRDD.scala is: > private def nextIterator(): Boolean = { > updateBytesReadWithFileSize() > if (files.hasNext) { // breakpoint here => works > currentFile = files.next() // breakpoint here => fails > .... > } > else { .... } > .... > } > if i put a breakpoint on the files.hasNext line all is well; however, if i > put a breakpoint on the files.next() line the code will fail when i continue > because the files iterator has become empty (see stack trace below). > Disabling the breakpoint winds up creating a Dataset with each line of the > csv file duplicated. > So it appears that multiple threads are using the files iterator or the > underling split value (an RDDPartition) and timing wise on my system 2 > workers wind up processing the same file, with the resulting DataSet having 2 > copies of each of the input lines. > This code is not active when parsing an XML file. > here is stack trace: > java.util.NoSuchElementException: next on empty iterator > at scala.collection.Iterator$$anon$2.next(Iterator.scala:39) > at scala.collection.Iterator$$anon$2.next(Iterator.scala:37) > at > scala.collection.IndexedSeqLike$Elements.next(IndexedSeqLike.scala:63) > at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:111) > at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:91) > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithoutKey$(Unknown > Source) > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown > Source) > at > org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) > at > org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370) > at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408) > at > org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47) > at org.apache.spark.scheduler.Task.run(Task.scala:86) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) > at java.lang.Thread.run(Thread.java:745) > 16/11/30 09:31:07 ERROR TaskSetManager: Task 0 in stage 0.0 failed 1 times; > aborting job > Exception in thread "main" org.apache.spark.SparkException: Job aborted due > to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: > Lost task 0.0 in stage 0.0 (TID 0, localhost): > java.util.NoSuchElementException: next on empty iterator > at scala.collection.Iterator$$anon$2.next(Iterator.scala:39) > at scala.collection.Iterator$$anon$2.next(Iterator.scala:37) > at > scala.collection.IndexedSeqLike$Elements.next(IndexedSeqLike.scala:63) > at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:111) > at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:91) > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithoutKey$(Unknown > Source) > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown > Source) > at > org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) > at > org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370) > at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408) > at > org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47) > at org.apache.spark.scheduler.Task.run(Task.scala:86) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) > at java.lang.Thread.run(Thread.java:745) > Driver stacktrace: > at > org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1454) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1442) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1441) > at > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) > at > org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1441) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811) > at scala.Option.foreach(Option.scala:257) > at > org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1667) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1622) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1611) > at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) > at > org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1890) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1903) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1916) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1930) > at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:912) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) > at org.apache.spark.rdd.RDD.withScope(RDD.scala:358) > at org.apache.spark.rdd.RDD.collect(RDD.scala:911) > at > org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:290) > at > org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2193) > at > org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57) > at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2546) > at > org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2192) > at > org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2199) > at > org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2227) > at > org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2226) > at org.apache.spark.sql.Dataset.withCallback(Dataset.scala:2559) > at org.apache.spark.sql.Dataset.count(Dataset.scala:2226) > at > edu.mit.ll.bb.ingest.LogAsMaps$$anonfun$makeDataset$1.apply(LogAsMaps.scala:275) > at > edu.mit.ll.bb.ingest.LogAsMaps$$anonfun$makeDataset$1.apply(LogAsMaps.scala:232) > at > scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) > at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186) > at edu.mit.ll.bb.ingest.LogAsMaps$.makeDataset(LogAsMaps.scala:232) > at edu.mit.ll.bb.ingest.LogAsMaps$.apply(LogAsMaps.scala:179) > at edu.mit.ll.bb.ingest.LogAsMaps$.makeLog(LogAsMaps.scala:396) > at > edu.mit.ll.bb.app.BrowseApp$.delayedEndpoint$edu$mit$ll$bb$app$BrowseApp$1(BrowseApp.scala:103) > at > edu.mit.ll.bb.app.BrowseApp$delayedInit$body.apply(BrowseApp.scala:18) > at scala.Function0$class.apply$mcV$sp(Function0.scala:34) > at > scala.runtime.AbstractFunction0.apply$mcV$sp(AbstractFunction0.scala:12) > at scala.App$$anonfun$main$1.apply(App.scala:76) > at scala.App$$anonfun$main$1.apply(App.scala:76) > at scala.collection.immutable.List.foreach(List.scala:381) > at > scala.collection.generic.TraversableForwarder$class.foreach(TraversableForwarder.scala:35) > at scala.App$class.main(App.scala:76) > at edu.mit.ll.bb.app.SingleEventFileApp.main(SingleEventFileApp.scala:6) > at edu.mit.ll.bb.app.BrowseApp.main(BrowseApp.scala) > Caused by: java.util.NoSuchElementException: next on empty iterator > at scala.collection.Iterator$$anon$2.next(Iterator.scala:39) > at scala.collection.Iterator$$anon$2.next(Iterator.scala:37) > at > scala.collection.IndexedSeqLike$Elements.next(IndexedSeqLike.scala:63) > at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:111) > at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:91) > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithoutKey$(Unknown > Source) > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown > Source) > at > org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) > at > org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370) > at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408) > at > org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47) > at org.apache.spark.scheduler.Task.run(Task.scala:86) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) > 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) > Disconnected from the target VM, address: '127.0.0.1:64555', transport: > 'socket' -- This message 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