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https://issues.apache.org/jira/browse/MAPREDUCE-4495?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13473434#comment-13473434
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Arun C Murthy commented on MAPREDUCE-4495:
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I'll ask the same question I asked you personally last week:
Why aren't we putting this in an incubator project rather than importing code
from Oozie etc. into MapReduce?
What is the need for the complex event system here? Why is that needed if we
only need MR jobs? Why aren't we using JobControl api?
I've also asked this same question before:
Why aren't we using JobControl api since it already exists?
> Workflow Application Master in YARN
> -----------------------------------
>
> Key: MAPREDUCE-4495
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-4495
> Project: Hadoop Map/Reduce
> Issue Type: New Feature
> Affects Versions: 2.0.0-alpha
> Reporter: Bo Wang
> Assignee: Bo Wang
> Attachments: MAPREDUCE-4495-v1.1.patch, MAPREDUCE-4495-v1.patch,
> MapReduceWorkflowAM.pdf
>
>
> It is useful to have a workflow application master, which will be capable of
> running a DAG of jobs. The workflow client submits a DAG request to the AM
> and then the AM will manage the life cycle of this application in terms of
> requesting the needed resources from the RM, and starting, monitoring and
> retrying the application's individual tasks.
> Compared to running Oozie with the current MapReduce Application Master,
> these are some of the advantages:
> - Less number of consumed resources, since only one application master will
> be spawned for the whole workflow.
> - Reuse of resources, since the same resources can be used by multiple
> consecutive jobs in the workflow (no need to request/wait for resources for
> every individual job from the central RM).
> - More optimization opportunities in terms of collective resource requests.
> - Optimization opportunities in terms of rewriting and composing jobs in the
> workflow (e.g. pushing down Mappers).
> - This Application Master can be reused/extended by higher systems like Pig
> and hive to provide an optimized way of running their workflows.
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