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From "Sergey Shelukhin (JIRA)" <j...@apache.org>
Subject [jira] [Created] (HBASE-7667) Support stripe compaction
Date Fri, 25 Jan 2013 01:32:11 GMT
Sergey Shelukhin created HBASE-7667:

             Summary: Support stripe compaction
                 Key: HBASE-7667
                 URL: https://issues.apache.org/jira/browse/HBASE-7667
             Project: HBase
          Issue Type: New Feature
            Reporter: Sergey Shelukhin
            Assignee: Sergey Shelukhin

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 key space is separated into configurable number of fixed-boundary stripes.
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 the entire 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
It's exactly like the 2nd, except it takes 2+ adjacent stripes and writes the results out
with different boundary.
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.

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