[jira] [Updated] (SPARK-15280) Extract ORC serialization logic from OrcOutputWriter for reusability

["Yin Huai (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-15280?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Yin Huai updated SPARK-15280:
-----------------------------
    Assignee: Ergin Seyfe

>  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
>            Assignee: 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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