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https://issues.apache.org/jira/browse/PARQUET-251?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14495488#comment-14495488
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Alex Levenson commented on PARQUET-251:
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Yes, my point was that Binary is just a wrapper, and the decision to copy / not
copy seems like it should be handled outside the wrapper.
So maybe all that is needed is to add a helper method to Binary called
copyToByteArray()?
Though toByteArray() usually make a copy, so maybe we should just make that the
contract?
Or just some consistency around ownership? If you pass a byte[] to a Binary,
should we say the contract is that ownership has passed to the Binary and you
shouldn't mutate it any more?
> 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
> 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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