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https://issues.apache.org/jira/browse/AMBARI-15267?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15176613#comment-15176613
 ] 

Hadoop QA commented on AMBARI-15267:
------------------------------------

{color:red}-1 overall{color}.  Here are the results of testing the latest 
attachment 
  http://issues.apache.org/jira/secure/attachment/12790979/AMBARI-15267.patch
  against trunk revision .

    {color:red}-1 patch{color}.  The patch command could not apply the patch.

Console output: 
https://builds.apache.org/job/Ambari-trunk-test-patch/5685//console

This message is automatically generated.

> Metrics aggregate times should be tied to aggregation period instead of AMS 
> start time
> --------------------------------------------------------------------------------------
>
>                 Key: AMBARI-15267
>                 URL: https://issues.apache.org/jira/browse/AMBARI-15267
>             Project: Ambari
>          Issue Type: Task
>          Components: ambari-metrics
>    Affects Versions: 2.2.1
>            Reporter: Aravindan Vijayan
>            Assignee: Aravindan Vijayan
>             Fix For: 2.2.2
>
>         Attachments: AMBARI-15267.patch
>
>
> The timestamp of aggregated metrics is tied to service start time. For 
> example if the AMS service was started at 10:21, all hourly aggregated metric 
> will have timestamps like 10:21, 11:21, 12:21 and so on.
> If AMS was restarted at 1:47, the subsequent hourly aggregates will have 
> timestamps like 1:47, 2:47, 3:47 and so on.
> This creates inconsistency and difficulty in using the metrics. All aggregate 
> timestamps should have definitive boundaries. For example, irrespective of 
> when the AMS was started, the hourly aggregate should always be timestamped 
> to top of hour (eg. all aggregated metrics having timestamp >= 10 AM and < 
> 11:00 AM should be timestamped to 11:00 AM ), and similarly 5 minute 
> aggregates should be timestamped to 0th, 5th, 10th, 15th..... minute
> This will enable SmartSense to use this data reliably for trend analysis



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