I think your client could use improvements. How many threads do you have running in your test? With a thrift call like that you only can do one request at a time per connection. For example, assuming C* takes 0ms, a 10ms network latency/driver overhead will mean 20ms RTT and a max throughput of ~50 QPS per thread (native binary doesn't behave like this). Are you running client on its own system or shared with a node? how are you load balancing your requests? Source code would help since theres a lot that can become a bottleneck.
Generally you will see a bit of a dip in latency from N=RF=1 and N=2, RF=2 etc since there are optimizations on the coordinator node when it doesn't need to send the request to the replicas. The impact of the network overhead decreases in significance as cluster grows. Typically; latency wise, RF=N=1 is going to be fastest possible for smaller loads (ie when a client cannot fully saturate a single node). Main thing to expect is that latency will plateau and remain fairly constant as load/nodes increase while throughput potential will linearly (empirically at least) increase. You should really attempt it with the native binary + prepared statements, running cql over thrift is far from optimal. I would recommend using the cassandra-stress tool if you want to stress test Cassandra (and not your code) http://www.datastax.com/dev/blog/improved-cassandra-2-1-stress-tool-benchmark-any-schema === Chris Lohfink On Sun, Dec 7, 2014 at 9:48 PM, 孔嘉林 <kongjiali...@gmail.com> wrote: > Hi Eric, > Thank you very much for your reply! > Do you mean that I should clear my table after each run? Indeed, I can see > several times of compaction during my test, but could only a few times > compaction affect the performance that much? Also, I can see from the > OpsCenter some ParNew GC happen but no CMS GC happen. > > I run my test on EC2 cluster, I think the network could be of high speed > with in it. Each Cassandra server has 4 units CPU, 15 GiB memory and 80 SSD > storage, which is of m3.xlarge type. > > As for latency, which latency should I care about most? p(99) or p(999)? I > want to get the max QPS under a certain limited latency. > > I know my testing scenario are not the common case in production, I just > want to know how much burden my cluster can bear under stress. > > So, how did you test your cluster that can get 86k writes/sec? How many > requests did you send to your cluster? Was it also 1 million? Did you also > use OpsCenter to monitor the real time performance? I also wonder why the > write and read QPS OpsCenter provide are much lower than what I calculate. > Could you please describe in detail about your test deployment? > > Thank you very much, > Joy > > 2014-12-07 23:55 GMT+08:00 Eric Stevens <migh...@gmail.com>: > >> Hi Joy, >> >> Are you resetting your data after each test run? I wonder if your tests >> are actually causing you to fall behind on data grooming tasks such as >> compaction, and so performance suffers for your later tests. >> >> There are *so many* factors which can affect performance, without >> reviewing test methodology in great detail, it's really hard to say whether >> there are flaws which might uncover an antipattern cause atypical number of >> cache hits or misses, and so forth. You may also be producing gc pressure >> in the write path, and so forth. >> >> I *can* say that 28k writes per second looks just a little low, but it >> depends a lot on your network, hardware, and write patterns (eg, data >> size). For a little performance test suite I wrote, with parallel batched >> writes, on a 3 node rf=3 cluster test cluster, I got about 86k writes per >> second. >> >> Also focusing exclusively on max latency is going to cause you some >> troubles especially in the case of magnetic media as you're using. Between >> ill-timed GC and inconsistent performance characteristics from magnetic >> media, your max numbers will often look significantly worse than your p(99) >> or p(999) numbers. >> >> All this said, one node will often look better than several nodes for >> certain patterns because it completely eliminates proxy (coordinator) write >> times. All writes are local writes. It's an over-simple case that doesn't >> reflect any practical production use of Cassandra, so it's probably not >> worth even including in your tests. I would recommend start at 3 nodes >> rf=3, and compare against 6 nodes rf=6. Make sure you're staying on top of >> compaction and aren't seeing garbage collections in the logs (either of >> those will be polluting your results with variability you can't account for >> with small sample sizes of ~1 million). >> >> If you expect to sustain write volumes like this, you'll find these >> clusters are sized too small (on that hardware you won't keep up with >> compaction), and your tests are again testing scenarios you wouldn't >> actually see in production. >> >> On Sat Dec 06 2014 at 7:09:18 AM kong <kongjiali...@gmail.com> wrote: >> >>> Hi, >>> >>> I am doing stress test on Datastax Cassandra Community 2.1.2, not using >>> the provided stress test tool, but use my own stress-test client code >>> instead(I write some C++ stress test code). My Cassandra cluster is >>> deployed on Amazon EC2, using the provided Datastax Community AMI( HVM >>> instances ) in the Datastax document, and I am not using EBS, just using >>> the ephemeral storage by default. The EC2 type of Cassandra servers are >>> m3.xlarge. I use another EC2 instance for my stress test client, which is >>> of type r3.8xlarge. Both the Cassandra sever nodes and stress test client >>> node are in us-east. I test the Cassandra cluster which is made up of 1 >>> node, 2 nodes, and 4 nodes separately. I just do INSERT test and SELECT >>> test separately, but the performance doesn’t get linear increment when new >>> nodes are added. Also I get some weird results. My test results are as >>> follows(*I do 1 million operations and I try to get the best QPS when >>> the max latency is no more than 200ms, and the latencies are measured from >>> the client side. The QPS is calculated by total_operations/total_time).* >>> >>> >>> >>> *INSERT(write):* >>> >>> Node count >>> >>> Replication factor >>> >>> QPS >>> >>> Average latency(ms) >>> >>> Min latency(ms) >>> >>> .95 latency(ms) >>> >>> .99 latency(ms) >>> >>> .999 latency(ms) >>> >>> Max latency(ms) >>> >>> 1 >>> >>> 1 >>> >>> 18687 >>> >>> 2.08 >>> >>> 1.48 >>> >>> 2.95 >>> >>> 5.74 >>> >>> 52.8 >>> >>> 205.4 >>> >>> 2 >>> >>> 1 >>> >>> 20793 >>> >>> 3.15 >>> >>> 0.84 >>> >>> 7.71 >>> >>> 41.35 >>> >>> 88.7 >>> >>> 232.7 >>> >>> 2 >>> >>> 2 >>> >>> 22498 >>> >>> 3.37 >>> >>> 0.86 >>> >>> 6.04 >>> >>> 36.1 >>> >>> 221.5 >>> >>> 649.3 >>> >>> 4 >>> >>> 1 >>> >>> 28348 >>> >>> 4.38 >>> >>> 0.85 >>> >>> 8.19 >>> >>> 64.51 >>> >>> 169.4 >>> >>> 251.9 >>> >>> 4 >>> >>> 3 >>> >>> 28631 >>> >>> 5.22 >>> >>> 0.87 >>> >>> 18.68 >>> >>> 68.35 >>> >>> 167.2 >>> >>> 288 >>> >>> >>> >>> *SELECT(read):* >>> >>> Node count >>> >>> Replication factor >>> >>> QPS >>> >>> Average latency(ms) >>> >>> Min latency(ms) >>> >>> .95 latency(ms) >>> >>> .99 latency(ms) >>> >>> .999 latency(ms) >>> >>> Max latency(ms) >>> >>> 1 >>> >>> 1 >>> >>> 24498 >>> >>> 4.01 >>> >>> 1.51 >>> >>> 7.6 >>> >>> 12.51 >>> >>> 31.5 >>> >>> 129.6 >>> >>> 2 >>> >>> 1 >>> >>> 28219 >>> >>> 3.38 >>> >>> 0.85 >>> >>> 9.5 >>> >>> 17.71 >>> >>> 39.2 >>> >>> 152.2 >>> >>> 2 >>> >>> 2 >>> >>> 35383 >>> >>> 4.06 >>> >>> 0.87 >>> >>> 9.71 >>> >>> 21.25 >>> >>> 70.3 >>> >>> 215.9 >>> >>> 4 >>> >>> 1 >>> >>> 34648 >>> >>> 2.78 >>> >>> 0.86 >>> >>> 6.07 >>> >>> 14.94 >>> >>> 30.8 >>> >>> 134.6 >>> >>> 4 >>> >>> 3 >>> >>> 52932 >>> >>> 3.45 >>> >>> 0.86 >>> >>> 10.81 >>> >>> 21.05 >>> >>> 37.4 >>> >>> 189.1 >>> >>> >>> >>> The test data I use is generated randomly, and the schema I use is like >>> (I use the cqlsh to create the columnfamily/table): >>> >>> CREATE TABLE table( >>> >>> id1 varchar, >>> >>> ts varchar, >>> >>> id2 varchar, >>> >>> msg varchar, >>> >>> PRIMARY KEY(id1, ts, id2)); >>> >>> So the fields are all string and I generate each character of the string >>> randomly, using srand(time(0)) and rand() in C++, so I think my test data >>> could be uniformly distributed into the Cassandra cluster. And, in my >>> client stress test code, I use thrift C++ interface, and the basic >>> operation I do is like: >>> >>> thrift_client.execute_cql3_query(“INSERT INTO table WHERE id1=xxx, >>> ts=xxx, id2=xxx, msg=xxx”); and thrift_client.execute_cql3_query(“SELECT >>> FROM table WHERE id1=xxx”); >>> >>> Each data entry I INSERT of SELECT is of around 100 characters. >>> >>> On my stress test client, I create several threads to send the read and >>> write requests, each thread having its own thrift client, and at the >>> beginning all the thrift clients connect to the Cassandra servers evenly. >>> For example, I create 160 thrift clients, and each 40 clients of them >>> connect to one server node, in a 4 node cluster. >>> >>> >>> >>> *So, * >>> >>> *1. **Could anyone help me explain my test results? Why does the >>> performance ( QPS ) just get a little increment when new nodes are added? * >>> >>> *2. **I learn from the materials that, Cassandra has better write >>> performance than read. But why in my case the read performance is better?* >>> >>> *3. **I also use the OpsCenter to monitor the real-time >>> performance of my cluster. But when I get the average QPS above, the >>> operations/s provided by OpsCenter is around 10000+ for write peak and >>> 5000+ for read peak. Why is my result inconsistent with that from >>> OpsCenter?* >>> >>> *4. **Are there any unreasonable things in my test method, such >>> as test data and QPS calculation?* >>> >>> >>> >>> *Thank you very much,* >>> >>> *Joy* >>> >> >
