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From Ian Varley <ivar...@salesforce.com>
Subject Re: Of hbase key distribution and query scalability, again.
Date Fri, 25 May 2012 18:23:41 GMT
Yeah, I think you're right Dmitriy; there's nothing like that in HBase today as far as I know.
If it'd be useful for you, maybe it would be for others, too; work up a rough patch and see
what people think on the dev list.


On May 25, 2012, at 1:02 PM, Dmitriy Lyubimov wrote:

> Thanks, Ian.
> I am talking about situation when even when we have uniform keys, the
> query distribution over them is still non-uniform and impossible to
> predict without sampling query skewness, but skewness is surprisingly
> great. (as in least active/most active user may differ in activity 100
> times and there is no way one could now which users are going to be
> active and which are going to be not active). Assuming there are few
> very active users, but many low active users, if two active users get
> into the same region, it creates a hotspot which could have been
> avoided if region balancer took notions of number of hits the regions
> are getting recently.
> Like i pointed out before, such skewness balancer could be fairly
> easily implemented externally to hbase (as in TotalOrderPartitioner),
> with exception that it would be interfering with the Hbase's balancer
> itself so it must be integrated with the balancer in that case.
> Also another distinct problem is time parameters of such balance
> controller. The load may be changing fast enough or slow enough so
> that sampling must be time-weighted itself.
> All these tehchnicalities make it difficult to implement it outside
> hbase or use key manipulation (as dynamic nature makes it difficult to
> deal with key re-assigning to match newly discovered load
> distribution).
> Ok I guess there's nothing in HBase like that right now otherwise i
> would've seen it in the book i suppose...
> Thanks.
> -d
> On Fri, May 25, 2012 at 10:42 AM, Ian Varley <ivarley@salesforce.com> wrote:
>> Dmitriy,
>> If I understand you right, what you're asking about might be called "Read Hotspotting".
For an obvious example, if I distribute my data nicely over the cluster but then say:
>> for (int x = 0; x < 10000000000; x++) {
>>   htable.get(new Get(Bytes.toBytes("row1")));
>> }
>> Then naturally I'm only putting read load on the region server that hosts "row1".
That's contrived, of course, you'd never really do that. But I can imagine plenty of situations
where there's an imbalance in query load w/r/t the leading part of the row key of a table.
It's not fundamentally different from "write hotspotting", except that it's probably less
common (it happens frequently in writes because ascending data in a time series or number
sequence is a common thing to insert into a database).
>> I guess the simple answer is, if you know of non-even distribution of read patterns,
it might be something to consider in a custom partitioning of the data into regions. I don't
know of any other technique (short of some external caching mechanism) that'd alleviate this;
at base, you still have to ask exactly one RS for any given piece of data.
>> Ian
>> On May 25, 2012, at 12:31 PM, Dmitriy Lyubimov wrote:
>>> Hello,
>>> I'd like to collect opinions from HBase experts on the query
>>> uniformity and whether there's any advance technique currently exists
>>> in HBase to cope with the problems of query uniformity beyond just
>>> maintaining the key uniform distribution.
>>> I know we start with the statement that in order to scale queries, we
>>> need them uniformly distributed over key space. The next advice people
>>> get is to use uniformly distributed key. Then, the thinking goes, the
>>> query load will also be uniformly distributed among regions.
>>> For what seems to be an embarassingly long time i was missing the
>>> point however that using uniformly distributed keys does not equate
>>> uniform distribution of the queries since it doesn't account for
>>> skewness of queries over the key space itself. This skewness can be
>>> bad enough under some circumstances to create query hot spots in the
>>> cluster which could have been avoided should region splits were
>>> balanced based on query loads rather than on a data size per se. (sort
>>> of dynamic query distribution sampling in order to equalize the load
>>> similar to how TotalOrderPartitioner does random data sampling to
>>> build distribution of the key skewness in the incoming data).
>>> To cut a long story, is the region size the only current HBase
>>> technique to balance load, esp. w.r.t query load? Or perhaps there are
>>> some more advanced techniques to do that ?
>>> Thank you very much.
>>> -Dmitriy

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