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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-3561) Native Hadoop/YARN integration for batch/ETL workloads
Date Wed, 17 Sep 2014 03:11:33 GMT

    [ https://issues.apache.org/jira/browse/SPARK-3561?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14136706#comment-14136706
] 

Apache Spark commented on SPARK-3561:
-------------------------------------

User 'olegz' has created a pull request for this issue:
https://github.com/apache/spark/pull/2422

> Native Hadoop/YARN integration for batch/ETL workloads
> ------------------------------------------------------
>
>                 Key: SPARK-3561
>                 URL: https://issues.apache.org/jira/browse/SPARK-3561
>             Project: Spark
>          Issue Type: New Feature
>          Components: core
>    Affects Versions: 1.1.0
>            Reporter: Oleg Zhurakousky
>              Labels: features
>             Fix For: 1.2.0
>
>         Attachments: SPARK-3561.pdf
>
>
> Currently Spark provides integration with external resource-managers such as Apache Hadoop
YARN, Mesos etc. Specifically in the context of YARN, the current architecture of Spark-on-YARN
can be enhanced to provide significantly better utilization of cluster resources for large
scale, batch and/or ETL applications when run alongside other applications (Spark and others)
and services in YARN. 
> Proposal:
> The proposed approach would introduce a pluggable JobExecutionContext (trait) - a gateway
and a delegate to Hadoop execution environment - as a non-public api (@DeveloperAPI) not exposed
to end users of Spark.
> The trait will define 4 only operations:
> * hadoopFile
> * newAPIHadoopFile
> * broadcast
> * runJob
> Each method directly maps to the corresponding methods in current version of SparkContext.
JobExecutionContext implementation will be accessed by SparkContext via master URL as "execution-context:foo.bar.MyJobExecutionContext"
with default implementation containing the existing code from SparkContext, thus allowing
current (corresponding) methods of SparkContext to delegate to such implementation. An integrator
will now have an option to provide custom implementation of DefaultExecutionContext by either
implementing it from scratch or extending form DefaultExecutionContext.
> Please see the attached design doc for more details.
> Pull Request will be posted shortly as well



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