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https://issues.apache.org/jira/browse/YARN-5814?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Shilun Fan updated YARN-5814:
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    Target Version/s: 3.5.0  (was: 3.4.0)

>  Add druid as storage backend in YARN Timeline Service
> ------------------------------------------------------
>
>                 Key: YARN-5814
>                 URL: https://issues.apache.org/jira/browse/YARN-5814
>             Project: Hadoop YARN
>          Issue Type: New Feature
>          Components: ATSv2
>    Affects Versions: 3.0.0-alpha2
>            Reporter: Bingxue Qiu
>            Priority: Major
>         Attachments: Add-Druid-in-YARN-Timeline-Service.pdf
>
>
> h3. Introduction
> I propose to add druid as storage backend in YARN Timeline Service.
> We run more than 6000 applications and generate 450 million metrics daily in 
> Alibaba Clusters with thousands of nodes. We need to collect and store 
> meta/events/metrics data, online analyze the utilization reports of various 
> dimensions and display the trends of allocation/usage resources for cluster 
> by joining and aggregating data. It helps us to manage and optimize the 
> cluster by tracking resource utilization.
> To achieve our goal we have changed to use druid as the storage instead of 
> HBase and have achieved sub-second OLAP performance in our production 
> environment for few months. 
> h3. Analysis
> Currently YARN Timeline Service only supports aggregating metrics at a) flow 
> level by FlowRunCoprocessor and b) application level metrics aggregating by 
> AppLevelTimelineCollector, offline (time-based periodic) aggregation for 
> flows/users/queues for reporting and analysis is planned but not yet 
> implemented. YARN Timeline Service chooses Apache HBase as the primary 
> storage backend. As we all know that HBase doesn't fit for OLAP.
>  For arbitrary exploration of data,such as online analyze the utilization 
> reports of various dimensions(Queue,Flow,Users,Application,CPU,Memory) by 
> joining and aggregating data, Druid's custom column format enables ad-hoc 
> queries without pre-computation. The format also enables fast scans on 
> columns, which is important for good aggregation performance.
> To achieve our goal that support to online analyze the utilization reports of 
> various dimensions, display the variation trends of allocation/usage 
> resources for cluster, and arbitrary exploration of data, we propose to add 
> druid storage and implement DruidWriter /DruidReader in YARN Timeline Service.



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