Hi, Jumping in on this late...
>>>> 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 ? So maybe I'm missing something but I don't see the problem. In terms of writing data to be evenly/randomly distributed, you would hash the key (md5 or SHA-1 as examples). This works well if you're doing get()s and not a lot of scan()s. But on reads, how do you get 'hot spotting' ? Should those rows be cached in memory? So what am I missing? Besides another cup of coffee? -Mike On May 25, 2012, at 1:23 PM, Ian Varley wrote: > 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 >>>> 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 >>> > >
