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  1. HBase
  2. HBASE-7667

Support stripe compaction



    • New Feature
    • Status: Closed
    • Major
    • Resolution: Fixed
    • None
    • 0.98.0, 0.99.0
    • Compaction
    • None


      So I was thinking about having many regions as the way to make compactions more manageable, and writing the level db doc about how level db range overlap and data mixing breaks seqNum sorting, and discussing it with Jimmy, Matteo and Ted, and thinking about how to avoid Level DB I/O multiplication factor.

      And I suggest the following idea, let's call it stripe compactions. It's a mix between level db ideas and having many small regions.
      It allows us to have a subset of benefits of many regions (wrt reads and compactions) without many of the drawbacks (managing and current memstore/etc. limitation).
      It also doesn't break seqNum-based file sorting for any one key.
      It works like this.
      The region key space is separated into configurable number of fixed-boundary stripes (determined the first time we stripe the data, see below).
      All the data from memstores is written to normal files with all keys present (not striped), similar to L0 in LevelDb, or current files.
      Compaction policy does 3 types of compactions.
      First is L0 compaction, which takes all L0 files and breaks them down by stripe. It may be optimized by adding more small files from different stripes, but the main logical outcome is that there are no more L0 files and all data is striped.
      Second is exactly similar to current compaction, but compacting one single stripe. In future, nothing prevents us from applying compaction rules and compacting part of the stripe (e.g. similar to current policy with rations and stuff, tiers, whatever), but for the first cut I'd argue let it "major compact" the entire stripe. Or just have the ratio and no more complexity.
      Finally, the third addresses the concern of the fixed boundaries causing stripes to be very unbalanced.
      It's exactly like the 2nd, except it takes 2+ adjacent stripes and writes the results out with different boundaries.
      There's a tradeoff here - if we always take 2 adjacent stripes, compactions will be smaller but rebalancing will take ridiculous amount of I/O.
      If we take many stripes we are essentially getting into the epic-major-compaction problem again. Some heuristics will have to be in place.
      In general, if, before stripes are determined, we initially let L0 grow before determining the stripes, we will get better boundaries.
      Also, unless unbalancing is really large we don't need to rebalance really.
      Obviously this scheme (as well as level) is not applicable for all scenarios, e.g. if timestamp is your key it completely falls apart.

      The end result:

      • many small compactions that can be spread out in time.
      • reads still read from a small number of files (one stripe + L0).
      • region splits become marvelously simple (if we could move files between regions, no references would be needed).
        Main advantage over Level (for HBase) is that default store can still open the files and get correct results - there are no range overlap shenanigans.
        It also needs no metadata, although we may record some for convenience.
        It also would appear to not cause as much I/O.


        1. stripe-cdf.pdf
          23 kB
          Sergey Shelukhin
        2. Stripe compaction perf evaluation.pdf
          339 kB
          Sergey Shelukhin
        3. Stripe compaction perf evaluation.pdf
          214 kB
          Sergey Shelukhin
        4. Stripe compaction perf evaluation.pdf
          206 kB
          Sergey Shelukhin
        5. Stripe compactions.pdf
          129 kB
          Sergey Shelukhin
        6. Stripe compactions.pdf
          129 kB
          Sergey Shelukhin
        7. Stripe compactions.pdf
          127 kB
          Sergey Shelukhin
        8. Stripe compactions.pdf
          114 kB
          Sergey Shelukhin
        9. Using stripe compactions.pdf
          141 kB
          Sergey Shelukhin
        10. Using stripe compactions.pdf
          138 kB
          Sergey Shelukhin
        11. Using stripe compactions.pdf
          135 kB
          Sergey Shelukhin

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              sershe Sergey Shelukhin
              sershe Sergey Shelukhin
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