On Fri, Nov 18, 2011 at 7:47 AM, Sylvain Lebresne <sylv...@datastax.com> wrote: > On Fri, Nov 18, 2011 at 4:23 PM, Mohit Anchlia <mohitanch...@gmail.com> wrote: >> On Fri, Nov 18, 2011 at 6:39 AM, Sylvain Lebresne <sylv...@datastax.com> >> wrote: >>> On Fri, Nov 18, 2011 at 1:53 AM, Todd Burruss <bburr...@expedia.com> wrote: >>>> I'm using cassandra 1.0. Been doing some testing on using cass's cache. >>>> When I turn it on (using the CLI) I see ParNew jump from 3-4ms to >>>> 200-300ms. This really screws with response times, which jump from >>>> ~25-30ms >>>> to 1300+ms. I've increase new gen and that helps, but still this is >>>> suprising to me, especially since 1.0 defaults to the >>>> SerializingCacheProvider – off heap. >>>> The interesting tid bit is that I have wide rows. 70k+ columns per row, >>>> ~50 >>>> bytes per column value. The cache only must be about 400 rows to catch all >>>> the data per node and JMX is reporting 100% cache hits. Nodetool ring >>>> reports < 2gb per node, my heap is 6gb and total RAM is 16gb. >>>> Thoughts? >>> >>> You're problem is the mix of wide rows and the serializing cache. >>> What happens with the serializing cache is that our data is stored >>> out of the heap. But that means that for each read to a row, we >>> 'deserialize' the row for the out-of-heap memory into the heap to >>> return it. The thing is, when we do that, we do the full row each >>> time. In other word, for each query we deserialize 70k+ columns >>> even if to return only one. I'm willing to bet this is what is killing >>> your response time. If you want to cache wide rows, I really >>> suggest you're using the ConcurrentLinkedHashCacheProvider >>> instead. >> >> What happens when using ConcurrentLinkedHashCache? What is the >> implementation like and why is it better? > > With ConcurrentLinkedHashCache, the cache is in the heap. So there > is no deserialization/copy during gets, so having wide rows is not a > problem. Outside of the fact that if you're enabling cache on a column > family with wide rows, you have to keep in mind that we always keep > full rows in cache. >
Wouldn't it move the problem to GC pauses from not being able to clean up old generation? I am using these rows in concurrenthashmap will get migrated to old gen. >> >>> >>> I'll also note that this explain the ParNew times too. Deserializing >>> all those columns from off-heap creates lots of short-lived object, >>> and since you deserialize 70k+ on each query, that's quite some >>> pressure on the new gen. Note that the serializing cache is >>> actually minimizing the use of old gen, because that is the one >>> that is the one that can create huge GC pauses with big heap, >>> but it actually put more pressure on the new gen. This is by >>> design and because new gen is much less of a problem than >>> old gen. >> >> In this scenario would it help if Young generation space is increased? > > That's a hard one to answer because GC tuning is a bit of a black > art, when testing and benchmarking is often key. Having a bigger > young generation means having young collection kicked less often > but on the other side it reduces the size for the old generation. > But again, I don't think the problem is really the GC here, at least not > primarily. > > -- > Sylvain > >> >>> >>> -- >>> Sylvain >>> >> >
