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https://issues.apache.org/jira/browse/PARQUET-251?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14546930#comment-14546930
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Ashish K Singh commented on PARQUET-251:
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Submitted PR, https://github.com/apache/parquet-mr/pull/197.
There are a few places which do not need a copy of the backing byte array, as
they are not modifying it. I am wondering if it makes sense to add
{{getBackingBytes()}} to Binary. Having the method will help in avoiding
copies, usages of {{getBytes()}}, done at places where there is no need.
However, the method only makes sense for {{ByteArrayBackedBinary}}. If you guys
think there is value in adding the method, then I can update the PR.
> Binary column statistics error when reuse byte[] among rows
> -----------------------------------------------------------
>
> Key: PARQUET-251
> URL: https://issues.apache.org/jira/browse/PARQUET-251
> Project: Parquet
> Issue Type: Bug
> Components: parquet-mr
> Affects Versions: 1.6.0
> Reporter: Yijie Shen
> Assignee: Ashish K Singh
> Priority: Blocker
>
> I think it is a common practice when inserting table data as parquet file,
> one would always reuse the same object among rows, and if a column is byte[]
> of fixed length, the byte[] would also be reused.
> If I use ByteArrayBackedBinary for my byte[], the bug occurs: All of the row
> groups created by a single task would have the same max & min binary value,
> just as the last row's binary content.
> The reason is BinaryStatistic just keep max & min as parquet.io.api.Binary
> references, since I use ByteArrayBackedBinary for byte[], the real content of
> max & min would always point to the reused byte[], therefore the latest row's
> content.
> Does parquet declare somewhere that the user shouldn't reuse byte[] for
> Binary type? If it doesn't, I think it's a bug and can be reproduced by
> [Spark SQL's RowWriteSupport
> |https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala#L353-354]
> The related Spark JIRA ticket:
> [SPARK-6859|https://issues.apache.org/jira/browse/SPARK-6859]
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