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From "Yin Huai (JIRA)" <>
Subject [jira] [Commented] (SPARK-15280) Extract ORC serialization logic from OrcOutputWriter for reusability
Date Sat, 21 May 2016 23:16:12 GMT


Yin Huai commented on SPARK-15280:
---------------------------------- extracts the ORC serialization logic to a
new internal class (private[orc]) called {{OrcSerializer}}.

>  Extract ORC serialization logic from OrcOutputWriter for reusability
> ---------------------------------------------------------------------
>                 Key: SPARK-15280
>                 URL:
>             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 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 so we can reuse the serialization logic.

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