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https://issues.apache.org/jira/browse/HIVE-15104?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Rui Li updated HIVE-15104:
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Attachment: HIVE-15104.1.patch
Spark needs the hash code on reducer side for the groupBy shuffling. Since
groupBy does no ordering, reducer needs to put the shuffled data into a map to
combine values by key, thus needing the hash code. We just need to keep the
hash code during SerDe if groupBy shuffle is used.
Upload a PoC patch to demonstrate the idea. It disables kryo relocation which
should not be acceptable.
Also did simple test to see the improvement. The test is to run a query:
{{select key, count ( * ) from A group by key order by key;}}, where A contains
40000000 records with 20 distinct keys. The measurement is the number of bytes
written during shuffle. I tested optimize HiveKey alone, as well as optimize
HiveKey and BytesWritable. We can see even for simple classes like
BytesWritable, the custom SerDe does better than a generic one.
|| ||Opt(N)||Opt(Y, Key)||Opt(Y, Key + Value)||
||GBY(Y)|2269|1953|1699|
||GBY(N)|2269|1713|1460|
> Hive on Spark generate more shuffle data than hive on mr
> --------------------------------------------------------
>
> Key: HIVE-15104
> URL: https://issues.apache.org/jira/browse/HIVE-15104
> Project: Hive
> Issue Type: Bug
> Components: Spark
> Affects Versions: 1.2.1
> Reporter: wangwenli
> Assignee: Rui Li
> Attachments: HIVE-15104.1.patch
>
>
> the same sql, running on spark and mr engine, will generate different size
> of shuffle data.
> i think it is because of hive on mr just serialize part of HiveKey, but hive
> on spark which using kryo will serialize full of Hivekey object.
> what is your opionion?
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