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https://issues.apache.org/jira/browse/SPARK-15280?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Yin Huai resolved SPARK-15280.
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Resolution: Fixed
Fix Version/s: 2.0.0
Issue resolved by pull request 13066
[https://github.com/apache/spark/pull/13066]
> Extract ORC serialization logic from OrcOutputWriter for reusability
> ---------------------------------------------------------------------
>
> Key: SPARK-15280
> URL: https://issues.apache.org/jira/browse/SPARK-15280
> Project: Spark
> Issue Type: Improvement
> Components: Input/Output
> Reporter: Ergin Seyfe
> Priority: Minor
> Fix For: 2.0.0
>
>
> Summary:
> This is a proposal to move ORC serialization logic from OrcOutputWriter to a
> new public class (OrcSerializer) which can be re-used to serialize an
> InternalRow to a Writable object so it can be written to a ORC file via
> RecordWriter.
> Details:
> Since Spark doesn't support SMB join yet, we would like to do SMB join at
> Spark application side. Using DataFrame for reading and writing to a ORC file
> is easier but we also wanted to parallelize it so we can have 1 task per each
> Hive bucket. This approach didn't work because nested RDD's are not supported
> (cannot create/read a DF at executor).
> The workaround is creating a ORC reader & writer rather than DataFrame at
> each executor. For reading ORC file OrcFile.createReader works fine. In order
> to write to a ORC file OrcOutputFormat().getRecordWriter would do the trick.
> However the missing part is serialization of InternalRow to a Writable
> object. In order to reuse the serialization part, I am proposing to split the
> OrcOutputWriter into OrcOutputWriter and OrcSerializer (public) so we can
> reuse the serialization logic.
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