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From "Alejandro Abdelnur (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MAPREDUCE-4495) Workflow Application Master in YARN
Date Thu, 11 Oct 2012 16:47:03 GMT

    [ https://issues.apache.org/jira/browse/MAPREDUCE-4495?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13474302#comment-13474302
] 

Alejandro Abdelnur commented on MAPREDUCE-4495:
-----------------------------------------------

Bobby, the code lines breakdown is:

* workflow lib: ~2400 lines
* workflow am: ~4000 lines
* tests: ~4300 lines


                
> 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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