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https://issues.apache.org/jira/browse/PHOENIX-3073?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15376491#comment-15376491
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Junegunn Choi commented on PHOENIX-3073:
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I also found that the client overhead for this particular query was greatly
increased recently due to the following change (PHOENIX-3040):
https://github.com/apache/phoenix/commit/f9420e6fb8d635572a7049527db0cc513dbeebe6#diff-8c3d3f644c66ef36d5bc604f017fabfcL144
With the change, the query started to use guideposts (isPointLookup = true,
plan.isSerial() = false) and it turns out to be quite costly on the client
side. The client CPU usage for the same workload before PHOENIX-3040 is 22.3%.
> Fast path for single-key point lookups
> --------------------------------------
>
> Key: PHOENIX-3073
> URL: https://issues.apache.org/jira/browse/PHOENIX-3073
> Project: Phoenix
> Issue Type: Improvement
> Reporter: Junegunn Choi
> Assignee: Junegunn Choi
> Attachments: PHOENIX-3073.patch
>
>
> While comparing Phoenix JDBC client to the native HBase Java client, I
> noticed that Phoenix client uses significantly more CPU time on the client
> machine. Profiling revealed that the majority of the time was spent on
> {{BaseResultIterators.getParallelScans()}}. This was surprising to me as I
> was only testing with simple point lookup queries.
> Here's how I tested:
> - {{SELECT /*+ SMALL SERIAL */ ID, DOCID FROM IMAGE WHERE ID = ?}}
> - {{IMAGE}} is a salted table with 100 salt buckets
> - {{ID}}, the primary key, was randomly selected in a small range so that
> the requests are served without disk I/O
> - 20K/sec concurrent requests using 128 threads
> {{getParallelScans()}} is quite expensive as it iterates over all regions of
> the table which can be many, only to return a single Scan object for this
> query. Since such a single-key point lookup is one of the most frequent type
> of requests in a typical OLTP application, I believe it makes sense to have a
> fast path for it. With the patch, the average CPU usage of the client during
> the workload dropped to 18.8% from 56.7% before the patch.
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