Re: Cassandra Doesn't Get Linear Performance Increment in Stress Test on Amazon EC2
Thanks Chris. I run a *client on a separate* AWS *instance from* the Cassandra cluster servers. At the client side, I create 40 or 50 threads for sending requests to each Cassandra node. I create one thrift client for each of the threads. And at the beginning, all the created thrift clients connect to the corresponding Cassandra nodes and keep connecting during the whole process(I did not close all the transports until the end of the test process). So I use very simple load balancing, since the same number of thrift clients connect to each node. And my source code is here: https://github.com/kongjialin/Cassandra/blob/master/cassandra_client.cpp It's very nice of you to help me improve my code. As I increase the number of threads, the latency gets longer. I'm using C++, so if I want to use native binary + prepared statements, the only way is to use C++ driver? Thanks very much. 2014-12-08 12:51 GMT+08:00 Chris Lohfink clohfin...@gmail.com: 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
Re: Cassandra Doesn't Get Linear Performance Increment in Stress Test on Amazon EC2
So I would -expect- an increase of ~20k qps per node with m3.xlarge so there may be something up with your client (I am not a c++ person however but hopefully someone on list will take notice). Latency does not decreases linearly as you add nodes. What you are likely seeing with latency since so few nodes is side effect of an optimization. When you read/write from a table the node you request will act as the coordinator. If the data exists on the coordinator and using rf=1 or cl=1 it will not have to send the request to another node, just service it locally: +-+ +--+ | node0 | +--|node1 | |-| |--| | client | --+| coordinator | +-+ +--+ In this case the write latency is dominated by the network between coordinator and client. A second case is where the coordinator actually has to send the request to another node: +-+ +--+ +---+ | node0 | +--|node1 |+-- |node2 | |-| |--| |---| | client | --+| coordinator |---+| data replica | +-+ +--+ +---+ As your adding nodes your increasing the probability of hitting this second scenario where the coordinator has to make an additional network hop. This possibly why your seeing an increase (aside from client issues). To get an idea on how the latency is affected when you increase nodes you really need to go higher then 4 (ie graph the same rf for 5, 10, 15, 25 nodes. below 5 isn't really the recommended way to run Cassandra anyway) nodes since the latency will approach that of the 2nd scenario (plus some spike outliers for GCs) and then it should settle down until you overwork the node. May want to give https://github.com/datastax/cpp-driver a go (not cpp guy take with grain of salt). I would still highly recommend using cassandra-stress instead of own stuff if you want to test cassandra and not your code. === Chris Lohfink On Mon, Dec 8, 2014 at 4:57 AM, 孔嘉林 kongjiali...@gmail.com wrote: Thanks Chris. I run a *client on a separate* AWS *instance from* the Cassandra cluster servers. At the client side, I create 40 or 50 threads for sending requests to each Cassandra node. I create one thrift client for each of the threads. And at the beginning, all the created thrift clients connect to the corresponding Cassandra nodes and keep connecting during the whole process(I did not close all the transports until the end of the test process). So I use very simple load balancing, since the same number of thrift clients connect to each node. And my source code is here: https://github.com/kongjialin/Cassandra/blob/master/cassandra_client.cpp It's very nice of you to help me improve my code. As I increase the number of threads, the latency gets longer. I'm using C++, so if I want to use native binary + prepared statements, the only way is to use C++ driver? Thanks very much. 2014-12-08 12:51 GMT+08:00 Chris Lohfink clohfin...@gmail.com: 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
Re: Cassandra Doesn't Get Linear Performance Increment in Stress Test on Amazon EC2
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? It certainly affects performance. Read performance suffers first, then write performance suffers eventually. For this synthetic test, if you want to compare like states then you should certainly wipe between. You may fall behind on compaction for the first run, then the second run pays the penalty for data grooming backlog generated during the first run. As for latency, which latency should I care about most? p(99) or p(999)? p(99) discards the worst 1% of results for reporting, p(999) discards the worst 0.1% of results for reporting. Which you prefer depends on your tolerance for response time jitter. I.E. do you need 99% of responses to be under a threshold, 99.9%? The more 9's, the more likely you are to fail your threshold due to an outlier. So, how did you test your cluster that can get 86k writes/sec? How many requests did you send to your cluster? I wrote the same data to each of 5 tables with similar columns, but different key configurations. I did 100 runs of 5,000 records (different records for each run). The data itself was 5 columns composed of a mix of bigint, text, and timestamp (so per record, fairly small data). I wrote records in asynchronous batches of 100 at a time, completing each of the 5,000 records for one table before moving on to the next table (the last write to table 1 needed to complete before I moved on to the first write of table 2, but within a table the operations were done in parallel). I used the Datastax Java Driver, which speaks the native protocol, and is faster and supports more parallelism than Thrift. Was it also 1 million? In total it was 500,000 records written to each of 5 tables - so 2.5 million records overall. 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. No, I measured throughput on my client only. I don't have much experience with OpsCenter, so I'm afraid I can't give you much insight into why you'd see inconsistent information compared to data you measured. Maybe you're just seeing information for a single node instead of the whole cluster? Again, the validity of this kind of test is highly suspect even though I happened to have set this up already. In my case I was trying to measure burst performance specifically. Cassandra will definitely accept bursts well, but if you sustain such a load, performance will degrade over time. Under sustained conditions you need to be certain you are staying on top of compaction - outstanding compaction tasks should rarely if ever exceed 2 or 3. Above 10, you need to reduce your write volume or your cluster will gradually fall over, and you'll struggle to bootstrap new nodes to expand. Do not size Cassandra for burst writes, size it for sustained writes. Write your sizing tests with that in mind - how much can you write and not fall behind on compaction over time, and accordingly your tests need to run for hours or days, not seconds or minutes. On Mon Dec 08 2014 at 3:58:35 AM 孔嘉林 kongjiali...@gmail.com wrote: Thanks Chris. I run a *client on a separate* AWS *instance from* the Cassandra cluster servers. At the client side, I create 40 or 50 threads for sending requests to each Cassandra node. I create one thrift client for each of the threads. And at the beginning, all the created thrift clients connect to the corresponding Cassandra nodes and keep connecting during the whole process(I did not close all the transports until the end of the test process). So I use very simple load balancing, since the same number of thrift clients connect to each node. And my source code is here: https://github.com/kongjialin/Cassandra/blob/master/cassandra_client.cpp It's very nice of you to help me improve my code. As I increase the number of threads, the latency gets longer. I'm using C++, so if I want to use native binary + prepared statements, the only way is to use C++ driver? Thanks very much. 2014-12-08 12:51 GMT+08:00 Chris Lohfink clohfin...@gmail.com: 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
Re: Cassandra Doesn't Get Linear Performance Increment in Stress Test on Amazon EC2
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
Re: Cassandra Doesn't Get Linear Performance Increment in Stress Test on Amazon EC2
I'm sorry, I meant to say 6 nodes rf=3. Also look at this performance over sustained periods of times, not burst writing. Run your test for several hours and watch memory and especially compaction stats. See if you can walk in what data volume you can write while keeping outstanding compaction tasks 5 (preferably 0 or 1) for sustained periods. Measuring just burst writes will definitely mask real world conditions, and Cassandra actually absorbs bursted writes really well (which in turn masks performance problems since by the time your write times suffer from overwhelming a cluster, you're probably already in insane and difficult to recover crisis mode). On Sun Dec 07 2014 at 8:55:47 AM Eric Stevens migh...@gmail.com wrote: 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
Re: Cassandra Doesn't Get Linear Performance Increment in Stress Test on Amazon EC2
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
Re: Cassandra Doesn't Get Linear Performance Increment in Stress Test on Amazon EC2
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
