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