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. > >> >> 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 >> >
