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https://issues.apache.org/jira/browse/PARQUET-251?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14494974#comment-14494974
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Alex Levenson commented on PARQUET-251:
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I think a Binary is supposed to just be a unifying API over byte[] and various
other byte[]-like types (slices of byte arrays, byte buffers, and so on).
So I think that should come with the same caveats as sharing a byte[] -- if
someone gives you a byte[] you might need to make a defensive copy, as in this
case for the statistics. But in cases where you just use the Binary to write to
a stream, no need for a copy.
I guess what I'm saying is, it's not Binary that should be responsible for
handling shared state, but rather the user of it, right?
> 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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