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https://issues.apache.org/jira/browse/HDFS-11535?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Chen Liang updated HDFS-11535:
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Attachment: PerfTest.pdf
> Performance analysis of new DFSNetworkTopology#chooseRandom
> -----------------------------------------------------------
>
> Key: HDFS-11535
> URL: https://issues.apache.org/jira/browse/HDFS-11535
> Project: Hadoop HDFS
> Issue Type: Sub-task
> Components: namenode
> Reporter: Chen Liang
> Assignee: Chen Liang
> Attachments: PerfTest.pdf
>
>
> This JIRA is created to post the results of some performance experiments we
> did. For those who are interested, please the attached .pdf file for more
> detail. The attached patch file includes the experiment code we ran.
> The key insights we got from these tests is that: although *the new method
> outperforms the current one in most cases*. There is still *one case where
> the current one is better*. Which is when there is only one storage type in
> the cluster, and we also always look for this storage type. In this case, it
> is simply a waste of time to perform storage-type-based pruning, blindly
> picking up a random node (current methods) would suffice.
> Therefore, based on the analysis, we propose to use a *combination of both
> the old and the new methods*:
> say, we search for a node of type X, since now inner node all keep storage
> type info, we can *just check root node to see if X is the only type it has*.
> If yes, blindly picking a random leaf will work, so we simply call the old
> method, otherwise we call the new method.
> There is still at least one missing piece in this performance test, which is
> garbage collection. The new method does a few more object creation when doing
> the search, which adds overhead to GC. I'm still thinking of any potential
> optimization but this seems tricky, also I'm not sure whether this
> optimization worth doing at all. Please feel free to leave any
> comments/suggestions.
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