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https://issues.apache.org/jira/browse/PARQUET-251?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14533289#comment-14533289
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Alex Levenson commented on PARQUET-251:
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[~singhashish] I think that sounds good, but we will now be doing more copies
than we used to, and only some of them will be necessary -- I think we need to
watch out for whether this is a big performance regression or not.
Another approach is to split Binary into two -- ImmutableBinary and
MutableBinary, and use these two types to signal who has ownership of the
backing byte arrays (eg, ImmutableBinary is guaranteed to never be mutated
underneath you, but MutableBinary require a copy to safely be held on to).
> 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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