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https://issues.apache.org/jira/browse/AMBARI-20392?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15905130#comment-15905130
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Hadoop QA commented on AMBARI-20392:
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{color:red}-1 overall{color}. Here are the results of testing the latest
attachment
http://issues.apache.org/jira/secure/attachment/12857320/AggregateMetric.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/10967//console
This message is automatically generated.
> Get aggregate metric records from HBase encounters performance issues
> ---------------------------------------------------------------------
>
> Key: AMBARI-20392
> URL: https://issues.apache.org/jira/browse/AMBARI-20392
> Project: Ambari
> Issue Type: Improvement
> Components: ambari-metrics
> Affects Versions: 2.4.2
> Reporter: Chuan Jin
> Attachments: AggregateMetric.patch
>
>
> I have a mini cluster ( ~6 nodes) managed by Ambari, and use a distributed
> HBase (~3 nodes) to hold metrics collected from these nodes. After I deploy
> YARN serivce, then I notice that some widgets (Cluster Memory,Cluster
> Disk,...) cannot display properly in the YARN service dashboard page. And
> Ambari Server has continuous timeout exceptions, which complains that it
> doesn't get timeline metrics for connection refused.
> The request timeout parameter is 5s, which means the query of getting metrics
> from HBase takes more time than that. Then I use Phoenix shell to login and
> perform the same query in the HBase , and it takes nearly 30s to finish. But
> If I split the big query into small pieces , i mean, use less values in the
> "metric_name" field in the where ... in clause , then the result return in 1s
> after several small queries.
> The query performance in HBase is highly based on the design of rowkey and
> the proper usage for it. In the method of getting aggregate metrics, AMS
> collector query the METRIC_AGGREGATE table in a way that may cause the
> co-processor to scan several regions across different RS. If we add more
> metrics in the service dashboard, this situation will be worse.
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