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https://issues.apache.org/jira/browse/HIVE-14919?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15935686#comment-15935686
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Rui Li commented on HIVE-14919:
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[~stakiar], [~kellyzly] we're using RDD with type like <HiveKey,
BytesWritable>. That's inline with MR (probably Tez too). And I think that's
what Hive operators and SerDe expect.
I don't know much about the DataFrame/DataSet API, but from the discussion
above, it seems to require a lot of work. [~xuefuz] could you share your
thoughts?
> Improve the performance of Hive on Spark 2.0.0
> ----------------------------------------------
>
> Key: HIVE-14919
> URL: https://issues.apache.org/jira/browse/HIVE-14919
> Project: Hive
> Issue Type: Improvement
> Reporter: Ferdinand Xu
> Assignee: Ferdinand Xu
>
> In HIVE-14029, we have updated Spark dependency to 2.0.0. We use Intel
> BigBench[1] to run benchmark with Spark 2.0 over 1 TB data set comparing with
> Spark 1.6. We can see performance improvments about 5.4% in general and 45%
> for the best case. However, some queries doesn't have significant performance
> improvements. This JIRA is the umbrella ticket addressing those performance
> issues.
> [1] https://github.com/intel-hadoop/Big-Data-Benchmark-for-Big-Bench
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