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https://issues.apache.org/jira/browse/YARN-3816?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14643504#comment-14643504
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Li Lu commented on YARN-3816:
-----------------------------

Thanks for the clarification [~vrushalic]! Yes the problem is with time series 
metrics. I think your approach works here, but maybe we'd like to change the 
scale of round-ups according to the scale of the aggregation? For example, if 
we aggregate the data for one whole day, we can merge the data in the same 
minute. If we aggregate the data in a week, maybe we can merge the data in the 
same hour? 

> [Aggregation] App-level Aggregation for YARN system metrics
> -----------------------------------------------------------
>
>                 Key: YARN-3816
>                 URL: https://issues.apache.org/jira/browse/YARN-3816
>             Project: Hadoop YARN
>          Issue Type: Sub-task
>          Components: timelineserver
>            Reporter: Junping Du
>            Assignee: Junping Du
>         Attachments: Application Level Aggregation of Timeline Data.pdf, 
> YARN-3816-poc-v1.patch, YARN-3816-poc-v2.patch
>
>
> We need application level aggregation of Timeline data:
> - To present end user aggregated states for each application, include: 
> resource (CPU, Memory) consumption across all containers, number of 
> containers launched/completed/failed, etc. We need this for apps while they 
> are running as well as when they are done.
> - Also, framework specific metrics, e.g. HDFS_BYTES_READ, should be 
> aggregated to show details of states in framework level.
> - Other level (Flow/User/Queue) aggregation can be more efficient to be based 
> on Application-level aggregations rather than raw entity-level data as much 
> less raws need to scan (with filter out non-aggregated entities, like: 
> events, configurations, etc.).



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