[jira] [Resolved] (HBASE-11482) Optimize HBase TableInput/OutputFormats for exposing tables and snapshots as Spark RDDs

["Lars Hofhansl (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/HBASE-11482?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Lars Hofhansl resolved HBASE-11482.
-----------------------------------
       Resolution: Duplicate
    Fix Version/s:     (was: 2.0.0)

Closing as dupe of HBASE-13992.

> 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
>          Components: mapreduce, spark
>            Reporter: Andrew Purtell
>            Assignee: Ted Malaska
>
> 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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