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https://issues.apache.org/jira/browse/PHOENIX-1452?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14341233#comment-14341233
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James Taylor commented on PHOENIX-1452:
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Looks good, [~samarthjain]. My two cents would be to keep this dead simple. No 
StrictlyCovariance option, just keep counters/sums. Let the client calculate 
the average. I like Jan's idea for the thread pool info.

We'll want to see the impact of perf (through Pherf) with and without this 
metrics collection to make sure we're not impacting our customers.

> Add Phoenix client-side logging and capture resource utilization metrics
> ------------------------------------------------------------------------
>
>                 Key: PHOENIX-1452
>                 URL: https://issues.apache.org/jira/browse/PHOENIX-1452
>             Project: Phoenix
>          Issue Type: Improvement
>    Affects Versions: 5.0.0, 4.2
>            Reporter: Jan Fernando
>            Assignee: Samarth Jain
>         Attachments: wip.patch
>
>
> For performance testing and tuning of features that use Phoenix and for 
> production monitoring it would be really helpful to easily be able to extract 
> statistics about Phoenix's client-side Thread Pool and Queue Depth usage to 
> help with tuning and being able to correlate the impact of tuning these 2 
> parameters to query performance.
> For global per JVM logging one of the following would meet my needs, with a 
> preference for #2:
> 1. A simple log line that that logs the data in ThreadPoolExecutor.toString() 
> at a configurable interval
> 2. Exposing the ThreadPoolExecutor metrics in PhoenixRuntime or other global 
> client exposed class and allow client to do their own logging.
> In addition to this it would also be really valuable to have a single log 
> line per query that provides statistics about the level of parallelism i.e. 
> number of parallel scans being executed. I don't full explain plan level of 
> data but a good heuristic to be able to track over time how queries are 
> utilizing the thread pool as data size grows etc. 



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