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https://issues.apache.org/jira/browse/HIVE-7526?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14084450#comment-14084450
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Rui Li commented on HIVE-7526:
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Hi [~xuefuz] [~csun], it seems in SparkShuffler, we lost the # of partitions
when applying the shuffle transformations. It may be useful if user can specify
it (e.g. HIVE-7540). Should we add that to the "shuffle" method?
> 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
> Fix For: spark-branch
>
> Attachments: HIVE-7526.2.patch, HIVE-7526.3.patch,
> HIVE-7526.4-spark.patch, HIVE-7526.5-spark.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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