Thanks Oleg. Another caveat from our side is, we have a very large data space (imaging picking 100 items out of 3 million, the chance of having 2 items from the same bin is pretty low). We will experiment with row cache, and hopefully it will help, not the opposite (the tuning guide says row cache could be detrimental in some circumstances).
-- Y. On Wed, May 16, 2012 at 4:25 PM, Oleg Dulin <oleg.du...@gmail.com> wrote: > Indeed. This is how we are trying to solve this problem. > > Our application has a built-in cache that resembles a supercolumn or > standardcolumn data structure and has API that resembles a combination of > Pelops selector and mutator. You can do something like that for Hector. > > The cache is constrained and uses LRU to purge unused items and keep > memory usage steady. > > It is not perfect and we have bugs still but it cuts down on 90% of > cassandra reads. > > > On 2012-05-16 20:07:11 +0000, Mike Peters said: > > Hi Yiming, >> >> Cassandra is optimized for write-heavy environments. >> >> If you have a read-heavy application, you shouldn't be running your reads >> through Cassandra. >> >> On the bright side - Cassandra read throughput will remain consistent, >> regardless of your volume. But you are going to have to "wrap" your reads >> with memcache (or redis), so that the bulk of your reads can be served from >> memory. >> >> >> Thanks, >> Mike Peters >> >> On 5/16/2012 3:59 PM, Yiming Sun wrote: >> Hello, >> >> I asked the question as a follow-up under a different thread, so I figure >> I should ask here instead in case the other one gets buried, and besides, I >> have a little more information. >> >> "We find the lack of performance disturbing" as we are only able to get >> about 3-4MB/sec read performance out of Cassandra. >> >> We are using cassandra as the backend for an IR repository of digital >> texts. It is a read-mostly repository with occasional writes. Each row >> represents a book volume, and each column of a row represents a page of the >> volume. Granted the data size is small -- the average size of a column >> text is 2-3KB, and each row has about 250 columns (varies quite a bit from >> one volume to another). >> >> Currently we are running a 3-node cluster, and will soon be upgraded to a >> 6-node setup. Each node is a VM with 4 cores and 16GB of memory. All VMs >> use SAN as disk storage. >> >> To retrieve a volume, a slice query is used via Hector that specifies the >> row key (the volume), and a list of column keys (pages), and the >> consistency level is set to ONE. It is typical to retrieve multiple >> volumes per request. >> >> The read rate that I have been seeing is about 3-4 MB/sec, and that is >> reading the raw bytes... using string serializer the rate is even lower, >> about 2.2MB/sec. >> >> The server log shows the GC ParNew frequently gets longer than 200ms, >> often in the range of 4-5seconds. But nowhere near 15 seconds (which is an >> indication that JVM heap is being swapped out). >> >> Currently we have not added JNA. From a blog post, it seems JNA is able >> to increase the performance by 13%, and we are hoping to increase the >> performance by something more like 1300% (3-4 MB/sec is just disturbingly >> low). And we are hesitant to disable swap entirely since one of the nodes >> is running a couple other services >> >> Do you have any suggestions on how we may boost the performance? Thanks! >> >> -- Y. >> > > > >
