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From "Chao (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (HIVE-7526) Research to use groupby transformation to replace Hive existing partitionByKey and SparkCollector combination
Date Wed, 30 Jul 2014 19:30:39 GMT

     [ https://issues.apache.org/jira/browse/HIVE-7526?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Chao updated HIVE-7526:
-----------------------

    Attachment: HIVE-7526.3.patch

An attempt to fix the last patch by moving groupBy op to ShuffleTran.
Also, since now SparkTran::transform may have input/output value types other than BytesWritable,
we need to make it generic as well..

Also added a CompTran class, which is basically a composition of transformations. It offers
better type compatibility than ChainedTran.

This is NOT the perfect solution, and may subject to further change.

> Research to use groupby transformation to replace Hive existing partitionByKey and SparkCollector
combination
> -------------------------------------------------------------------------------------------------------------
>
>                 Key: HIVE-7526
>                 URL: https://issues.apache.org/jira/browse/HIVE-7526
>             Project: Hive
>          Issue Type: Task
>          Components: Spark
>            Reporter: Xuefu Zhang
>            Assignee: Chao
>         Attachments: HIVE-7526.2.patch, HIVE-7526.3.patch, HIVE-7526.patch
>
>
> Currently SparkClient shuffles data by calling paritionByKey(). This transformation outputs
<key, value> tuples. However, Hive's ExecMapper expects <key, iterator<value>>
tuples, and Spark's groupByKey() seems outputing this directly. Thus, using groupByKey, we
may be able to avoid its own key clustering mechanism (in HiveReduceFunction). This research
is to have a try.



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