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https://issues.apache.org/jira/browse/SPARK-3561?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14158614#comment-14158614
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Oleg Zhurakousky edited comment on SPARK-3561 at 10/3/14 10:34 PM:
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[~sandyr]
Indeed YARN is a _resource manager_ that supports multiple execution
environments by facilitating resource allocation and management. On the other
hand, Spark, Tez and many other (custom) execution environments are currently
run on YARN. (NOTE: Custom execution environments on YARN are becoming very
common in large enterprises). Such decoupling will ensure that Spark can
integrate with any and all (where applicable) in a pluggable and extensible
fashion.
was (Author: ozhurakousky):
[~sandyr]
Indeed YARN is a _resource manager_ that supports multiple execution
environments by helping with resource allocation and management. On the other
hand, Spark, Tez and many other (custom) execution environments are currently
run on YARN. (NOTE: Custom execution environments on YARN are becoming very
common in large enterprises). Such decoupling will ensure that Spark can
integrate with any and all (where applicable) in a pluggable and extensible
fashion.
> Decouple Spark's API from its execution engine
> ----------------------------------------------
>
> Key: SPARK-3561
> URL: https://issues.apache.org/jira/browse/SPARK-3561
> Project: Spark
> Issue Type: New Feature
> Components: Spark 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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