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https://issues.apache.org/jira/browse/SPARK-3561?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14159559#comment-14159559
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Oleg Zhurakousky commented on SPARK-3561:
-----------------------------------------
Sandy, one other thing:
While I understand the reasoning for changes to the title and the description
of the JIRA, it would probably be better to coordinate this with the original
submitter before making such changes in the future (similar to the way Patric
suggested in SPARK-3174). This would alleviate potential discrepancies in the
overall message and intentions of the JIRA.
Anyway, I’ve edited both the title and the description taking into
consideration your edits.
> Expose pluggable architecture to facilitate native integration with
> third-party execution environments.
> -------------------------------------------------------------------------------------------------------
>
> 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 _integrates with external resource-managing platforms_ such
> as Apache Hadoop YARN and Mesos to facilitate
> execution of Spark DAG in a distributed environment provided by those
> platforms.
> However, this integration is tightly coupled within Spark's implementation
> making it rather difficult to introduce integration points with
> other resource-managing platforms without constant modifications to Spark's
> core (see comments below for more details).
> In addition, Spark _does not provide any integration points to a third-party
> **DAG-like** and **DAG-capable** execution environments_ native
> to those platforms, thus limiting access to some of their native features
> (e.g., MR2/Tez stateless shuffle, YARN resource localization, YARN management
> and monitoring and more) as well as specialization aspects of
> such execution environments (open source and proprietary). As an example,
> inability to gain access to such features are starting to affect Spark's
> viability in large scale, batch
> and/or ETL applications.
> Introducing a pluggable architecture would solve both of the issues mentioned
> above ultimately benefitting Spark's technology and community by allowing it
> to
> venture into co-existence and collaboration with a variety of existing Big
> Data platforms as well as the once yet to come to the market.
> Proposal:
> The proposed approach would introduce a pluggable JobExecutionContext (trait)
> - as a non-public api (@DeveloperAPI).
> 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
> ensuring binary and source compatibility with older versions of Spark.
> 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 and pull request for more details.
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