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https://issues.apache.org/jira/browse/SPARK-3561?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Oleg Zhurakousky updated SPARK-3561:
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Description:
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
(@Experimental) not exposed to end users of Spark.
The trait will define 6 operations:
* hadoopFile
* newAPIHadoopFile
* broadcast
* runJob
* persist
* unpersist
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.
was:
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
(@Experimental) not exposed to end users of Spark.
The trait will define 6 operations:
* hadoopFile
* newAPIHadoopFile
* broadcast
* runJob
* persist
* unpersist
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
> 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 (@Experimental) not exposed to end users of Spark.
> The trait will define 6 operations:
> * hadoopFile
> * newAPIHadoopFile
> * broadcast
> * runJob
> * persist
> * unpersist
> 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.
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