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https://issues.apache.org/jira/browse/HIVE-1641?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12918374#action_12918374
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Liyin Tang commented on HIVE-1641:
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The previously assumption is not always true. There might be multiple map join
operations in one local work.
No matter how many map join operators in one Map Red Task, for each map join
operator, there will be one parent operator from big table branch and other
operators from small table branches.
For big table branch, just leave it alone.
For small table branch, create a new JDBMSinkOperator to replace the current
MapJoin Operator. Now the local work has no common operators shared with the
MapredWork.
And create a JDBMDummyOperator to replace original parent operator for the
MapJoinOperator.
This JDBMDummyOperator will help MapJoinOperator generate correctly input
object inspector during the run time.
In the execution time, the LocalTask will process all the local work and
generate the JDBM file for each small tables.
When the MapRedTask starts to process the first row for MapJoinOperator, it
will load the JDBM file to generate the in-memory hash table.
If in the local mode, the JDBM files will be just stored in local directory. If
not, the jdbm files will be added into Distributed Cache.
This patch is just tested on Local Mode. I will submit another patch after
testing against the clusters.
> add map joined table to distributed cache
> -----------------------------------------
>
> Key: HIVE-1641
> URL: https://issues.apache.org/jira/browse/HIVE-1641
> Project: Hadoop Hive
> Issue Type: Improvement
> Components: Query Processor
> Reporter: Namit Jain
> Assignee: Liyin Tang
> Fix For: 0.7.0
>
>
> Currently, the mappers directly read the map-joined table from HDFS, which
> makes it difficult to scale.
> We end up getting lots of timeouts once the number of mappers are beyond a
> few thousand, due to
> concurrent mappers.
> It would be good idea to put the mapped file into distributed cache and read
> from there instead.
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