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https://issues.apache.org/jira/browse/HBASE-7667?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13612653#comment-13612653
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M. C. Srivas commented on HBASE-7667:
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@mcorgan and @stack:
The total i/o in terms of i/o bandwidth consumed is the same. But the disk iops
are much, much worse. And disk iops are at a premium, and "bg activity" like
compactions should consume as few as possible.
Let's say we split a region into a 100 sub-regions, such that each sub-region
is in the few 10's of MB. If the data is written uniformly randomly, each
sub-region will write out a store at approx the same time. That is, a RS will
write 100x more files into HDFS (100x more random i/o on the local
file-system). Next, all sub-regions will do a compaction at almost the same
time, which is again 100x more read iops to read the old stores for merging.
One can try to stagger the compactions to avoid the sudden burst by
incorporating, say, a queue of to-be-compacted-subregions. But while the
sub-regions at the head of the queue will compact "in time", the ones at the
end of the queue will have many more store files to merge, and will use much
more than their "fair-share" of iops (not to mention that the
read-amplification in these sub-regions will be higher too). The iops profile
will be worse than just 100x.
> Support stripe compaction
> -------------------------
>
> Key: HBASE-7667
> URL: https://issues.apache.org/jira/browse/HBASE-7667
> Project: HBase
> Issue Type: New Feature
> Components: Compaction
> Reporter: Sergey Shelukhin
> Assignee: Sergey Shelukhin
> Attachments: Stripe compactions.pdf
>
>
> 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.
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