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From "Stavros Kontopoulos (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (SPARK-19700) Design an API for pluggable scheduler implementations
Date Wed, 11 Oct 2017 09:06:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-19700?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16199989#comment-16199989
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Stavros Kontopoulos edited comment on SPARK-19700 at 10/11/17 9:05 AM:
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Anyone working on this? Any design document or plans? 


was (Author: skonto):
Anyone working on this any design document or plans? 

> Design an API for pluggable scheduler implementations
> -----------------------------------------------------
>
>                 Key: SPARK-19700
>                 URL: https://issues.apache.org/jira/browse/SPARK-19700
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 2.1.0
>            Reporter: Matt Cheah
>
> One point that was brought up in discussing SPARK-18278 was that schedulers cannot easily
be added to Spark without forking the whole project. The main reason is that much of the scheduler's
behavior fundamentally depends on the CoarseGrainedSchedulerBackend class, which is not part
of the public API of Spark and is in fact quite a complex module. As resource management and
allocation continues evolves, Spark will need to be integrated with more cluster managers,
but maintaining support for all possible allocators in the Spark project would be untenable.
Furthermore, it would be impossible for Spark to support proprietary frameworks that are developed
by specific users for their other particular use cases.
> Therefore, this ticket proposes making scheduler implementations fully pluggable. The
idea is that Spark will provide a Java/Scala interface that is to be implemented by a scheduler
that is backed by the cluster manager of interest. The user can compile their scheduler's
code into a JAR that is placed on the driver's classpath. Finally, as is the case in the current
world, the scheduler implementation is selected and dynamically loaded depending on the user's
provided master URL.
> Determining the correct API is the most challenging problem. The current CoarseGrainedSchedulerBackend
handles many responsibilities, some of which will be common across all cluster managers, and
some which will be specific to a particular cluster manager. For example, the particular mechanism
for creating the executor processes will differ between YARN and Mesos, but, once these executors
have started running, the means to submit tasks to them over the Netty RPC is identical across
the board.
> We must also consider a plugin model and interface for submitting the application as
well, because different cluster managers support different configuration options, and thus
the driver must be bootstrapped accordingly. For example, in YARN mode the application and
Hadoop configuration must be packaged and shipped to the distributed cache prior to launching
the job. A prototype of a Kubernetes implementation starts a Kubernetes pod that runs the
driver in cluster mode.



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