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HBase >> mail # user >> Of hbase key distribution and query scalability, again.


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Re: Of hbase key distribution and query scalability, again.
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.

Ian

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 <[EMAIL PROTECTED]> 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
NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB