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Posted to issues@spark.apache.org by "Josh Rosen (JIRA)" <ji...@apache.org> on 2019/07/10 00:22:00 UTC
[jira] [Commented] (SPARK-25966) "EOF Reached the end of stream
with bytes left to read" while reading/writing to Parquets
[ https://issues.apache.org/jira/browse/SPARK-25966?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16881625#comment-16881625 ]
Josh Rosen commented on SPARK-25966:
------------------------------------
Cross-post: there's discussion of a similar issue at https://issues.apache.org/jira/browse/SPARK-27570. Based on that, I suspect that https://issues.apache.org/jira/browse/HADOOP-16109 may fix this problem.
> "EOF Reached the end of stream with bytes left to read" while reading/writing to Parquets
> -----------------------------------------------------------------------------------------
>
> Key: SPARK-25966
> URL: https://issues.apache.org/jira/browse/SPARK-25966
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.4.0
> Environment: Spark 2.4.0 (built from RC5 tag) running Hadoop 3.1.1 on top of a Mesos cluster. Both input and output Parquet files are on S3.
> Reporter: Alessandro Andrioni
> Priority: Major
>
> I was persistently getting the following exception while trying to run one Spark job we have using Spark 2.4.0. It went away after I regenerated from scratch all the input Parquet files (generated by another Spark job also using Spark 2.4.0).
> Is there a chance that Spark is writing (quite rarely) corrupted Parquet files?
> {code:java}
> org.apache.spark.SparkException: Job aborted.
> at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:196)
> at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:159)
> at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
> at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)
> at org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
> at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
> at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
> at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:668)
> at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:668)
> at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
> at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
> at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
> at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:668)
> at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:276)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:270)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:228)
> at org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:557)
> (...)
> Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 312 in stage 682.0 failed 4 times, most recent failure: Lost task 312.3 in stage 682.0 (TID 235229, 10.130.29.78, executor 77): java.io.EOFException: Reached the end of stream with 996 bytes left to read
> at org.apache.parquet.io.DelegatingSeekableInputStream.readFully(DelegatingSeekableInputStream.java:104)
> at org.apache.parquet.io.DelegatingSeekableInputStream.readFullyHeapBuffer(DelegatingSeekableInputStream.java:127)
> at org.apache.parquet.io.DelegatingSeekableInputStream.readFully(DelegatingSeekableInputStream.java:91)
> at org.apache.parquet.hadoop.ParquetFileReader$ConsecutiveChunkList.readAll(ParquetFileReader.java:1174)
> at org.apache.parquet.hadoop.ParquetFileReader.readNextRowGroup(ParquetFileReader.java:805)
> at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.checkEndOfRowGroup(VectorizedParquetRecordReader.java:301)
> at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:256)
> at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextKeyValue(VectorizedParquetRecordReader.java:159)
> at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
> at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
> at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:181)
> at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage109.scan_nextBatch_0$(Unknown Source)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage109.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
> at org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:187)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
> at org.apache.spark.scheduler.Task.run(Task.scala:121)
> at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:748)
> {code}
> This job used to work fine with Spark 2.2.1, and succeeded once we regenerated the inputs. This is also one of three jobs that had this issue out of the 6000+ we tested.
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