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https://issues.apache.org/jira/browse/HBASE-2000?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Andrew Purtell resolved HBASE-2000.
-----------------------------------
Resolution: Fixed
Fix Version/s: 0.92.0
Assignee: (was: Andrew Purtell)
Hadoop Flags: [Reviewed]
> Coprocessors
> ------------
>
> Key: HBASE-2000
> URL: https://issues.apache.org/jira/browse/HBASE-2000
> Project: HBase
> Issue Type: New Feature
> Components: coprocessors
> Reporter: Andrew Purtell
> Fix For: 0.92.0
>
>
> From Google's Jeff Dean, in a keynote to LADIS 2009
> (http://www.scribd.com/doc/21631448/Dean-Keynote-Ladis2009, slides 66 - 67):
> BigTable Coprocessors (New Since OSDI'06)
> * Arbitrary code that runs run next to each tablet in table
> ** As tablets split and move, coprocessor code automatically splits/moves
> too
> * High-level call interface for clients
> ** Unlike RPC, calls addressed to rows or ranges of rows
> * coprocessor client library resolves to actual locations
> ** Calls across multiple rows automatically split into multiple
> parallelized RPCs
> * Very flexible model for building distributed services
> ** Automatic scaling, load balancing, request routing for apps
> Example Coprocessor Uses
> * Scalable metadata management for Colossus (next gen GFS-like file system)
> * Distributed language model serving for machine translation system
> * Distributed query processing for full-text indexing support
> * Regular expression search support for code repository
> For HBase, adding a coprocessor framework will allow for pluggable
> incremental addition of functionality. No more need to subclass the
> regionserver interface and implementation classes and set
> {{hbase.regionserver.class}} and {{hbase.regionserver.impl}} in
> hbase-site.xml. That mechanism allows for extension but at the exclusion of
> all others.
> Also in HBASE-2001 currently there is a in-process map reduce framework for
> the regionservers. Coprocessors can optionally implement a 'MapReduce'
> interface which clients will be able to invoke concurrently on all regions of
> the table. Note this is not MapReduce on the table; this is MapReduce on each
> region, concurrently. One can implement MapReduce in a manner very similar to
> Hadoop's MR framework, or use shared variables to avoid the overhead of
> generating (and processing) a lot of intermediates. An initial application of
> this could be support for rapid calculation of aggregates over data stored in
> HBase.
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