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From "Xuefu Zhang (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (HIVE-8621) Dump small table join data for map-join [Spark Branch]
Date Mon, 10 Nov 2014 13:55:34 GMT

     [ https://issues.apache.org/jira/browse/HIVE-8621?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Xuefu Zhang updated HIVE-8621:
------------------------------
    Resolution: Fixed
        Status: Resolved  (was: Patch Available)

Committed to Spark branch. Thanks, Jimmy!

> Dump small table join data for map-join [Spark Branch]
> ------------------------------------------------------
>
>                 Key: HIVE-8621
>                 URL: https://issues.apache.org/jira/browse/HIVE-8621
>             Project: Hive
>          Issue Type: Sub-task
>            Reporter: Suhas Satish
>            Assignee: Jimmy Xiang
>             Fix For: spark-branch
>
>         Attachments: HIVE-8621.1-spark.patch, HIVE-8621.2-spark.patch
>
>
> This jira aims to re-use a slightly modified approach of map-reduce distributed cache
in spark to dump map-joined small tables as hash tables onto spark DFS cluster. 
> This is a sub-task of map-join for spark 
> https://issues.apache.org/jira/browse/HIVE-7613
> This can use the baseline patch for map-join
> https://issues.apache.org/jira/browse/HIVE-8616
> The original thought process was to use broadcast variable concept in spark, for the
small tables. 
> The number of broadcast variables that must be created is m x n where
> 'm' is  the number of small tables in the (m+1) way join and n is the number of buckets
of tables. If unbucketed, n=1
> But it was discovered that objects compressed with kryo serialization on disk, can occupy
20X or more when deserialized in-memory. For bucket join, the spark Driver has to hold all
the buckets (for bucketed tables) in-memory (to provide for fault-tolerance against Executor
failures) although the executors only need individual buckets in their memory. So the broadcast
variable approach may not be the right approach. 



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