Li Lu commented on YARN-3816:

bq. I'm still very confused by the usage of the word "aggregate". In this 
patch, "aggregate" really means accumulating values of a metric along the time 
dimension, which is completely different than the notion of aggregation we have 
used all along. The aggregation has always been about rolling up values from 
children to parents.

I have a similar concern with regard to the dimensions of "aggregations", too. 
If I understand the problem correctly, we have two dimensions in a flow/user 
level aggregation: one dimension for all entities belong to this flow/user, 
another dimension for time. If we aggregate in the flow/user dimension, one 
typical problem we will hit is aligning times. Suppose entity E1 and E2 both 
belong to flow F1. In an aggregation, we would like to aggregate E1 and E2. 
However, if a metric M is a time series, how do we align the times in E1.M and 
E2.M? Normally the two time series may have slightly different sample times, so 
I believe we need to decide the semantic on this situation? 

> [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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