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https://issues.apache.org/jira/browse/HBASE-7667?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13575995#comment-13575995
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Matt Corgan commented on HBASE-7667:
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{quote}Open Times : I think this will be an issue, specifically on server
start. Need to be careful here.{quote}Hopefully could be mitigated by making
regions larger, like doubling region size and setting max 2 stripes/region.
Theoretically should be able to have the same overall number of files as normal
regions, or are there other factors at play?
{quote}Wouldn't all the load always be on last region if you have TS keys? Or,
if you have artificial partitioning but query by TS, wouldn't all queries go to
all servers?{quote}An easy strategy for P partitions is to
* prepend a single byte to each key where prefix=hash(row)%P
* pre-split the table into P regions
* tweak the balancer to evenly spread the tail partitions for each region
* writes get sprayed evenly to all tail partitions
* a single Get query will only hit one region since you know hash(row)%P
beforehand
* you scan all P partitions using a P-way collating iterator
** so yes, scans go to all servers but presumably they are huge and would hit
lots of data anyway
** because they are huge, a client that scans the partitions concurrently will
be faster
* a big multi-Get will spray to the exact servers necessary, possibly all of
them, but like scans may be faster because done in parallel
I'm not sure what most people are doing with time series data but this seems
like a good approach to me. You basically just choose arbitrarily large P. An
MD5 prefix is essentially P=2^128 (I wouldn't recommend pre-splitting at that
granularity).
> 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
>
> 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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