• Sub-task
    • Status: Resolved
    • Major
    • Resolution: Fixed
    • None
    • 3.0.0-alpha4
    • namenode
    • None
    • Reviewed


      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.

      Thanks arpitagarwal and szetszwo for the offline discussion.


        1. HDFS-11535.004.patch
          19 kB
          Chen Liang
        2. HDFS-11535.003.patch
          17 kB
          Chen Liang
        3. HDFS-11535.002.patch
          18 kB
          Yiqun Lin
        4. HDFS-11535.001.patch
          16 kB
          Chen Liang
        5. PerfTest.pdf
          192 kB
          Chen Liang

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              vagarychen Chen Liang
              vagarychen Chen Liang
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