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https://issues.apache.org/jira/browse/SPARK-3561?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14159573#comment-14159573
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Sean Owen commented on SPARK-3561:
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I'd be interested to see a more specific motivating use case. Is this about
using Tez for example, and where does it help to stack Spark on Tez on YARN? or
MR2, etc. Spark Core and Tez overlap, to be sure, and I'm not sure how much
value it adds to run one on the other. Kind of like running Oracle on MySQL or
something. For whatever it is: is it maybe not more natural to integrate the
feature into Spark itself?
It would be great if it this were all just a matter of one extra trait and
interface. In practice I suspect there are a number of hidden assumptions
throughout the code that may leak through attempts at this abstraction.
I am definitely asking rather than asserting, curious to see more specifics
about the upside.
> 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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