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
