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https://issues.apache.org/jira/browse/SPARK-3561?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14158571#comment-14158571
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Sandy Ryza commented on SPARK-3561:
-----------------------------------
I think there may be somewhat of a misunderstanding about the relationship
between Spark and YARN. YARN is not an "execution environment", but a cluster
resource manager that has the ability to start processes on behalf of execution
engines like Spark. Spark already supports YARN as a cluster resource manager,
but YARN doesn't provide its own execution engine. YARN doesn't provide a
stateless shuffle (although execution engines built atop it like MR and Tez
do).
If I understand, the broader intent is to decouple the Spark API from the
execution engine it runs on top of. Changing the title to reflect this. That,
the Spark API is currently very tightly integrated with its execution engine,
and frankly, decoupling the two so that Spark would be able to run on top of
execution engines with similar properties seems more trouble than its worth.
> 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: 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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