We've also noticed very good read and write latencies with the hi1.4xls compared to our previous instance classes. We actually ran a mixed cluster of hi1.4xls and m2.4xls to watch side-by-side comparison.
Despite the significant improvement in underlying hardware, we've noticed that streaming performance with 1.2.6+vnodes is a lot slower than we would expect. Bootstrapping a node into a ring with large storage loads can take 6+ hours. We have a JIRA open that describes our current config: https://issues.apache.org/jira/browse/CASSANDRA-5726 Aiman: We also use tablesnap for our backups. We're using a slightly modified version [1]. We currently backup every sst as soon as they hit disk (tablesnap's inotify), but we're considering moving to a periodic snapshot approach as the sst churn after going from 24 nodes -> 6 nodes is quite high. Mike [1]: https://github.com/librato/tablesnap On Thu, Jul 11, 2013 at 7:33 AM, Aiman Parvaiz <ai...@grapheffect.com>wrote: > Hi, > We also recently migrated to 3 hi.4xlarge boxes(Raid0 SSD) and the disk IO > performance is definitely better than the earlier non SSD servers, we are > serving up to 14k reads/s with a latency of 3-3.5 ms/op. > I wanted to share our config options and ask about the data back up > strategy for Raid0. > > We are using C* 1.2.6 with > > key_chache and row_cache of 300MB > I have not changed/ modified any other parameter except for going with > multithreaded GC. I will be playing around with other factors and update > everyone if I find something interesting. > > Also, just wanted to share backup strategy and see if I can get something > useful from how others are taking backup of their raid0. I am using > tablesnap to upload SSTables to s3 and I have attached a separate EBS > volume to every box and have set up rsync to mirror Cassandra data from > Raid0 to EBS. I would really appreciate if you guys can share how you > taking backups. > > Thanks > > > On Jul 9, 2013, at 7:11 AM, Alain RODRIGUEZ <arodr...@gmail.com> wrote: > > > Hi, > > > > Using C*1.2.2. > > > > We recently dropped our 18 m1.xLarge (4CPU, 15GB RAM, 4 Raid-0 Disks) > servers to get 3 hi1.4xLarge (16CPU, 60GB RAM, 2 Raid-0 SSD) servers > instead, for about the same price. > > > > We tried it after reading some benchmark published by Netflix. > > > > It is awesome and I recommend it to anyone who is using more than 18 > xLarge server or can afford these high cost / high performance EC2 > instances. SSD gives a very good throughput with an awesome latency. > > > > Yet, we had about 200 GB data per server and now about 1 TB. > > > > To alleviate memory pressure inside the heap I had to reduce the index > sampling. I changed the index_interval value from 128 to 512, with no > visible impact on latency, but a great improvement inside the heap which > doesn't complain about any pressure anymore. > > > > Is there some more tuning I could use, more tricks that could be useful > while using big servers, with a lot of data per node and relatively high > throughput ? > > > > SSD are at 20-40 % of their throughput capacity (according to > OpsCenter), CPU almost never reach a bigger load than 5 or 6 (with 16 CPU), > 15 GB RAM used out of 60GB. > > > > At this point I have kept my previous configuration, which is almost the > default one from the Datastax community AMI. There is a part of it, you can > consider that any property that is not in here is configured as default : > > > > cassandra.yaml > > > > key_cache_size_in_mb: (empty) - so default - 100MB (hit rate between 88 > % and 92 %, good enough ?) > > row_cache_size_in_mb: 0 (not usable in our use case, a lot of different > and random reads) > > flush_largest_memtables_at: 0.80 > > reduce_cache_sizes_at: 0.90 > > > > concurrent_reads: 32 (I am thinking to increase this to 64 or more since > I have just a few servers to handle more concurrence) > > concurrent_writes: 32 (I am thinking to increase this to 64 or more too) > > memtable_total_space_in_mb: 1024 (to avoid having a full heap, shoul I > use bigger value, why for ?) > > > > rpc_server_type: sync (I tried hsha and had the "ERROR 12:02:18,971 Read > an invalid frame size of 0. Are you using TFramedTransport on the client > side?" error). No idea how to fix this, and I use 5 different clients for > different purpose (Hector, Cassie, phpCassa, Astyanax, Helenus)... > > > > multithreaded_compaction: false (Should I try enabling this since I now > use SSD ?) > > compaction_throughput_mb_per_sec: 16 (I will definitely up this to 32 or > even more) > > > > cross_node_timeout: true > > endpoint_snitch: Ec2MultiRegionSnitch > > > > index_interval: 512 > > > > cassandra-env.sh > > > > I am not sure about how to tune the heap, so I mainly use defaults > > > > MAX_HEAP_SIZE="8G" > > HEAP_NEWSIZE="400M" (I tried with higher values, and it produced bigger > GC times (1600 ms instead of < 200 ms now with 400M) > > > > -XX:+UseParNewGC > > -XX:+UseConcMarkSweepGC > > -XX:+CMSParallelRemarkEnabled > > -XX:SurvivorRatio=8 > > -XX:MaxTenuringThreshold=1 > > -XX:CMSInitiatingOccupancyFraction=70 > > -XX:+UseCMSInitiatingOccupancyOnly > > > > Does this configuration seems coherent ? Right now, performance are > correct, latency < 5ms almost all the time. What can I do to handle more > data per node and keep these performances or get even better once ? > > > > I know this is a long message but if you have any comment or insight > even on part of it, don't hesitate to share it. I guess this kind of > comment on configuration is usable by the entire community. > > > > Alain > > > > -- Mike Heffner <m...@librato.com> Librato, Inc.
