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https://issues.apache.org/jira/browse/PARQUET-831?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17684187#comment-17684187
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ASF GitHub Bot commented on PARQUET-831:
----------------------------------------

wgtmac commented on PR #1022:
URL: https://github.com/apache/parquet-mr/pull/1022#issuecomment-1416791622

   Thanks for your contribution @jianchun 
   
   Could you please fix the CI check first?




> Corrupt Parquet Files
> ---------------------
>
>                 Key: PARQUET-831
>                 URL: https://issues.apache.org/jira/browse/PARQUET-831
>             Project: Parquet
>          Issue Type: Bug
>          Components: parquet-mr
>    Affects Versions: 1.7.0
>         Environment: HDP-2.5.3.0 Spark-2.0.2
>            Reporter: Steve Severance
>            Priority: Major
>
> 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.



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