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https://issues.apache.org/jira/browse/SPARK-3561?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14166705#comment-14166705
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Oleg Zhurakousky commented on SPARK-3561:
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Patrick, I think there is misunderstanding about the mechanics of this
proposal, so I'd like to clarify. The proposal here is certainly not to
introduce any new dependencies to Spark Core and
existing pull request (https://github.com/apache/spark/pull/2422) clearly shows
it.
What I am proposing is to expose an integration point in Spark by means of
extracting *existing* Spark operations into a *configurable and @Experimental*
strategy, allowing Spark not only to integrate with other execution
environments, but it would also be very useful in unit-testing as it would
provide a clear separation between _assembly_ and _execution_ layer allowing
them to be tested in isolation.
I think this feature would benefit Spark tremendously; particularly given how
several folks have already expressed their interest in this feature/direction.
Appreciate your help and advise in helping to get this contribution into Spark.
Thanks!
> Allow for pluggable execution contexts in Spark
> -----------------------------------------------
>
> 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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