Corrupt Parquet Files

I am getting corrupt parquet files as the result of a spark job. The write job completes with no errors but when I read the data again I get the following error:

org.apache.parquet.io.ParquetDecodingException: Can not read value at 0 in block -1 in file hdfs://MYPATH/part-r-00004-b5c93a19-2f75-4c04-b798-de9cb463f02f.gz.parquet
at org.apache.parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:228)
at org.apache.parquet.hadoop.ParquetRecordReader.nextKeyValue(ParquetRecordReader.java:201)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:91)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:128)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$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$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.NegativeArraySizeException
at org.apache.parquet.hadoop.ParquetFileReader$ConsecutiveChunkList.readAll(ParquetFileReader.java:755)
at org.apache.parquet.hadoop.ParquetFileReader.readNextRowGroup(ParquetFileReader.java:494)
at org.apache.parquet.hadoop.InternalParquetRecordReader.checkRead(InternalParquetRecordReader.java:127)
at org.apache.parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:208)

The job that generates this data partitions and sorts the data in a particular way to achieve better compression. If I don't partition and sort I have not been able to reproduce its behavior. It also only has this behavior on say 25% of the data. Most of the time simply rerunning the write job would cause the read error to go away but I have now run across cases where that was not the case. I am happy to give what data I can, or work with someone to run this down.

I know this is a sub-optimal report, but I have not been able to randomly generate data to reproduce this issue. The data that trips this bug is typically 5GB+ post compression files.

**Environment**: HDP-2.5.3.0 Spark-2.0.2
**Reporter**: [Steve Severance](https://issues.apache.org/jira/secure/ViewProfile.jspa?name=sseveran)

_{**Note**: *This issue was originally created as [PARQUET-831](https://issues.apache.org/jira/browse/PARQUET-831). Please see the [migration documentation](https://issues.apache.org/jira/browse/PARQUET-2502) for further details.*}

Inizia dai punti di ingresso dello stack trace ParquetFileReader.ConsecutiveChunkList.readAll e InternalParquetRecordReader, quindi analizza le scritture che coinvolgono il partizionamento e l’ordinamento di Spark. Usa l’ambiente indicato HDP-2.5.3.0 e Spark 2.0.2 e file compressi di grandi dimensioni per cercare un caso riproducibile. Il lavoro è completato quando viene identificata la causa e viene verificato che i file Parquet generati possano essere letti senza l’eccezione segnalata.

Scritto dal modello di indicizzazione a partire dal testo della issue.