Hi, Nick Can your perf_blockcache performance testing script can be applied to hbase cluster directly? If not, what kind of things should I take care?
2014-08-22 7:06 GMT+08:00 Nick Dimiduk <[email protected]>: > I'm familiar with Stack's work too, but thanks for pointing it out :) > > On Wed, Aug 20, 2014 at 8:19 PM, 牛兆捷 <[email protected]> wrote: > > > Hi Nick: > > > > Yes, I am interested in it. I will try first. > > > > Btw, this site (http://people.apache.org/~stack/bc/) also does the > similar > > performance evaluation. > > You can have a look if you are interested in. > > > > > > 2014-08-21 1:48 GMT+08:00 Nick Dimiduk <[email protected]>: > > > > > Hi Zhaojie, > > > > > > I'm responsible for this particular bit of work. One thing to note in > > these > > > experiments is that I did not control explicitly for OS caching. I ran > > > "warmup" workloads before collecting measurements, but because the > amount > > > of RAM on the machine is fixed, it's impact of OS cache is different > with > > > different based on the amount of memory used by HBase. Another, as Todd > > > pointed out on an earlier thread, is that my trend lines are probably > > > optimistic/misleading. > > > > > > Something I was driving for was to understand how well the different > > > implementations before as they're managing more and more memory. I'd > like > > > to get some insight into how we might be able to take advantage of > 100's > > or > > > even 1000's of GB of memory when the time comes. That's part of why > > there's > > > so many variables. > > > > > > I scripted out the running of the tests, all of my configurations are > > > available in the associated github repo [0], and all of the data points > > are > > > available as a csv. If you're interested in experimenting yourself, > > please > > > let me know how I can help. > > > > > > Cheers, > > > Nick > > > > > > [0]: https://github.com/ndimiduk/perf_blockcache > > > > > > > > > On Wed, Aug 20, 2014 at 6:00 AM, 牛兆捷 <[email protected]> wrote: > > > > > > > the complete blog link is: > > > > http://zh.hortonworks.com/blog/blockcache-showdown-hbase/ > > > > > > > > > > > > 2014-08-20 11:41 GMT+08:00 牛兆捷 <[email protected]>: > > > > > > > > > Hi all: > > > > > > > > > > I saw some interesting results from Hortonworks blog (block cache > > > > > < > > > > > > > > > > http://zh.hortonworks.com/wp-content/uploads/2014/03/perfeval_blockcache_v2.pdf > > > > > > > > > > ). > > > > > > > > > > In this result, the ratio of memory footprint to database size is > > held > > > > > fixed while > > > > > the absolute values are increased. > > > > > > > > > > In my mind, the performance should becomes worse for larger ratio > as > > > the > > > > > increase > > > > > of the absolute value. For example BucketCache#(tmpfs), the > > difference > > > > > between ratio (DB"1.5":"RAM"1.0) and ratio (DB"4.5":"RAM"1.0) > becomes > > > > > larger as the increase of memory. > > > > > Actually, the result of ratio ( DB"1.5":"RAM"1.0) increase > linearly, > > > and > > > > > the result of ratio (DB"1.5":"RAM"1.0) exponentially. > > > > > > > > > > However, for BucketCache#(heap) and LruBlockCache, the result is > out > > of > > > > my > > > > > expectation. > > > > > The curves of ratio (DB"1.5":"RAM"1.0) and ratio (DB"4.5":"RAM"1.0) > > > both > > > > > increase exponentially, but the relative differences as the > increase > > of > > > > > memory are not consistent. > > > > > Take LruBlockCache as an example, the difference of ratio > > > > > (DB"1.5":"RAM"1.0) and ratio (DB"4.5":"RAM"1.0) becomes smaller > from > > 20 > > > > GB > > > > > to 50 GB, but becomes larger from 50 GB to 60 GB. > > > > > > > > > > How to analysis the cause of this result, any ideas? > > > > > > > > > > -- > > > > > *Regards,* > > > > > *Zhaojie* > > > > > > > > > > > > > > > > > > > > > > -- > > > > *Regards,* > > > > *Zhaojie* > > > > > > > > > > > > > > > -- > > *Regards,* > > *Zhaojie* > > > -- *Regards,* *Zhaojie*
