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From Jean-Daniel Cryans <jdcry...@apache.org>
Subject Re: Region number and allocation advice
Date Fri, 06 Jul 2012 21:35:49 GMT
Yeah that's a good point. Something I haven't looked at, and it seems
to be what the FB guys are counting on, is that compacting less
aggressively and relying in the compaction selection algorithm to
merge small files plus having bloom filters on (so that you can skip
looking at every file all the time) would effectively let you have
very big regions without taking big IO hits when compactions happen.
It does require 0.92

Good Stuff(tm),


On Fri, Jul 6, 2012 at 2:27 PM, Jack Levin <magnito@gmail.com> wrote:
> Please note that if you have fast growing datastore, you may end up
> with very large region files - if you limit the number of regions.  If
> that happens (and you can tell by simply examining your HDFS), your
> compactions (which you can't avoid) will end up rewriting a lot of
> data.  In our case (we have 600TB hbase cluster for storing photos),
> we keep our region size at about 10-15GB and no more. Anything else
> will overdrive your disk IO during compactions.
> On Fri, Jul 6, 2012 at 2:22 PM, Jean-Daniel Cryans <jdcryans@apache.org> wrote:
>> Hey Nick,
>> I'd say it has nothing to do with spindles/CPUs. The reason is that
>> locking happens only at the row level thus having 1 or 100 regions
>> doesn't change anything, then you only have 1 HLog to write to
>> (currently at least) so spindles don't really count either. It might
>> influence the number of threads that can compact tho.
>> I gave some explanations at the HBase BoF we did after the Hadoop
>> Summit[1] and, from the writes POV, you want to have as much MemStore
>> potential as you have dedicated heap for it and the HLog need to match
>> that too. One region can make sense for that except for the issues I
>> demonstrated and also it's harder to balance. Instead, having a few
>> regions will give you more agility. For the sake of giving a number
>> I'd say 10-20 but then you need to tune your memstore size
>> accordingly. This really is over-optimization tho and it's not even
>> something we put in practice here since we have so many different use
>> cases.
>> The cluster size in my opinion doesn't influence the number of regions
>> you should have per machine, as long as you keep it low it will be
>> fine.
>> Hope this helps,
>> J-D
>> 1. http://www.slideshare.net/jdcryans/performance-bof12
>> On Fri, Jul 6, 2012 at 1:53 PM, Nick Dimiduk <ndimiduk@gmail.com> wrote:
>>> Heya,
>>> I'm looking for more detailed advice about how many regions a table should
>>> run. Disabling automatic splits (often hand-in-hand with disabling
>>> automatic compactions) is often described as advanced practice, at least
>>> when guaranteeing latency SLAs. Which begs the question: how many regions
>>> should I have? Surely this depends on both the shape of your data and
>>> expected workload. I've seen "10-20 Regions per RS" thrown around as a
>>> stock answer. My question is: why? Presumably that's 10-20 regions per RS
>>> for all tables rather than per-table. That advice is centered around a
>>> regular region size, but surely distribution of ops/load matters more. But
>>> still, where does 10-20 come from? Is it a calculation vs the number of
>>> cores on the RS, like advice given around parallelizing builds? If so, how
>>> many cores are we assuming the RS has? Is it a calculation vs the amount of
>>> RAM available? Is 20 regions based on a trade-off between static
>>> allocations and per-region memory overhead? Does 10-20 become 5-15 in a
>>> memory-restricted environment and bump to 20-40 when more RAM is available?
>>> Does it have to do with the number of spindles available on the machine?
>>> Threads like this one [0] give some hint about how the big players work.
>>> However, that advice looks heavily influenced by concerns when there are
>>> 1000's of regions to manage. How does advice for larger clusters (>50
>>> nodes) differ from smaller clusters (<20 nodes)?
>>> Thanks,
>>> -n
>>> [0]: http://thread.gmane.org/gmane.comp.java.hadoop.hbase.user/22451

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