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From "Ted Malaska (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HBASE-11482) Optimize HBase TableInput/OutputFormats for exposing tables and snapshots as Spark RDDs
Date Wed, 16 Jul 2014 18:40:06 GMT

    [ https://issues.apache.org/jira/browse/HBASE-11482?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14063880#comment-14063880
] 

Ted Malaska commented on HBASE-11482:
-------------------------------------

Can I take this.  I'm working on Spark-2447 and I've got a first cut at interacting with HBase
at https://github.com/tmalaska/SparkOnHBase 

Next on my list was to add support for tables and even bulk loads.   Adding snapshots shouldn't
be that hard.

Let me know
Thanks

> Optimize HBase TableInput/OutputFormats for exposing tables and snapshots as Spark RDDs
> ---------------------------------------------------------------------------------------
>
>                 Key: HBASE-11482
>                 URL: https://issues.apache.org/jira/browse/HBASE-11482
>             Project: HBase
>          Issue Type: New Feature
>            Reporter: Andrew Purtell
>
> A core concept of Apache Spark is the resilient distributed dataset (RDD), a "fault-tolerant
collection of elements that can be operated on in parallel". One can create a RDDs referencing
a dataset in any external storage system offering a Hadoop InputFormat, like HBase's TableInputFormat
and TableSnapshotInputFormat. 
> Insure the integration is reasonable and provides good performance. 
> Add the ability to save RDDs back to HBase with a {{saveAsHBaseTable}} action, implicitly
creating necessary schema on demand.
> Add support for {{filter}} transformations that push predicates down to the server as
HBase filters. 
> Consider supporting conversions between Scala and Java types and HBase data using the
HBase types library.
> Consider an option to lazily and automatically produce a snapshot only when needed, in
a coordinated way. (Concurrently executing workers may want to materialize a table snapshot
RDD at the same time.)



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