Kernel / Apache Cassandra benchmark with MGLRU TLDR ==== With the MGLRU, Apache Cassandra achieved 95% CIs [1.06, 4.10]%, [1.94, 5.43]% and [4.11, 7.50]% more operations per second (OPS), respectively, for exponential (distribution) access, random access and Zipfian access, when swap was off; 95% CIs [0.50, 2.60]%, [6.51, 8.77]% and [3.29, 6.75]% more OPS, respectively, for exponential access, random access and Zipfian access, when swap was set to minimum (vm.swappiness=1). Background ========== Memory overcommit can increase utilization and, if carried out properly, can also increase throughput. The challenges are to improve working set estimation and to optimize page reclaim. The risks are performance degradation and OOM kills. Short of overcoming the challenges, the only way to reduce the risks is to underutilize memory. Apache Cassandra is one of the most popular open-source NoSQL databases. YCSB is the leading open-source NoSQL database benchmarking software that supports multiple access distributions. Swap can have a negative effect, as Apache Cassandra cautions "Do never allow your system to swap" [1]. [1]: https://github.com/apache/cassandra/blob/trunk/conf/cassandra.yaml#L394 Matrix ====== Kernels: version [+ patchset] * Baseline: 5.15 * Patched: 5.15 + MGLRU Swap configurations: * Off * Minimum (vm.swappiness=1) Concurrency: average # of users per CPU * Medium: 3 Access distributions (2kB objects, 10% update): * Exponential * Uniform random * Zipfian Total configurations: 12 Data points per configuration: 10 Total run duration (minutes) per data point: ~40 Note that Apache Cassandra reached the peak performance for this benchmark with 2-3 users per CPU, i.e., its performance started degrading with fewer or more users. Procedure ========= The latest MGLRU patchset for the 5.15 kernel is available at git fetch https://linux-mm.googlesource.com/page-reclaim \ refs/changes/30/1430/2 Baseline and patched 5.15 kernel images are available at https://drive.google.com/drive/folders/1eMkQleAFGkP2vzM_JyRA21oKE0ESHBqp <install and configure OS> ycsb_load.sh systemctl stop cassandra e2image <backup /mnt/data> <for each kernel> grub-set-default <baseline, patched> <for each swap configuration> <swapoff, swapon> <for each access distribution> <update ycsb_run.sh> <for each data point> systemctl stop cassandra e2image <restore /mnt/data> reboot ycsb_run.sh <collect OPS> Hardware ======== Memory (GB): 256 CPU (total #): 48 NVMe SSD (GB): 1024 OS == $ cat /etc/lsb-release DISTRIB_ID=Ubuntu DISTRIB_RELEASE=21.10 DISTRIB_CODENAME=impish DISTRIB_DESCRIPTION="Ubuntu 21.10" $ cat /proc/swaps Filename Type Size Used Priority /dev/nvme0n1p3 partition 32970748 0 -2 $ cat /sys/fs/cgroup/user.slice/memory.min 4294967296 $ cat /proc/sys/vm/overcommit_memory 1 $ cat /proc/sys/vm/swappiness 1 $ cat /proc/sys/vm/max_map_count 1048575 Apache Cassandra ================ $ nodetool version ReleaseVersion: 4.0.1 $ cat jvm8-server.options <existing parameters> #-XX:+UseParNewGC #-XX:+UseConcMarkSweepGC #-XX:+CMSParallelRemarkEnabled #-XX:SurvivorRatio=8 #-XX:MaxTenuringThreshold=1 #-XX:CMSInitiatingOccupancyFraction=75 #-XX:+UseCMSInitiatingOccupancyOnly #-XX:CMSWaitDuration=10000 #-XX:+CMSParallelInitialMarkEnabled #-XX:+CMSEdenChunksRecordAlways #-XX:+CMSClassUnloadingEnabled -XX:+UseG1GC -XX:+ParallelRefProcEnabled -XX:MaxGCPauseMillis=400 <existing parameters> $ cat cassandra.yaml <existing parameters> data_file_directories: /mnt/data/ key_cache_size_in_mb: 5000 file_cache_enabled: true file_cache_size_in_mb: 10000 buffer_pool_use_heap_if_exhausted: false memtable_offheap_space_in_mb: 10000 memtable_allocation_type: offheap_buffers <existing parameters> YCSB ==== $ git log commit ce3eb9ce51c84ee9e236998cdd2cefaeb96798a8 (HEAD -> master, origin/master, origin/HEAD) Author: Ivan <john.koepi@xxxxxxxxx> Date: Tue Feb 16 17:38:00 2021 +0200 [scylla] enable token aware LB by default, improve the docs (#1507) $ cat ycsb_load.sh # load objects cqlsh -e "create keyspace ycsb WITH REPLICATION = {'class' : \ 'SimpleStrategy', 'replication_factor': 1};" cqlsh -k ycsb -e "create table usertable (y_id varchar primary key, \ field0 varchar, field1 varchar, field2 varchar, field3 varchar, \ field4 varchar, field5 varchar ,field6 varchar, field7 varchar, \ field8 varchar, field9 varchar);" ycsb load cassandra-cql -s -threads 24 -p hosts=localhost \ -p workload=site.ycsb.workloads.CoreWorkload -p fieldlength=200 \ -p recordcount=130000000 $ cat ycsb_run.sh # run benchmark ycsb run cassandra-cql -s -threads 144 -p hosts=localhost \ -p workload=site.ycsb.workloads.CoreWorkload \ -p recordcount=130000000 -p operationcount=130000000 \ -p readproportion=0.9 -p updateproportion=0.1 \ -p maxexecutiontime=1800 \ -p requestdistribution=<exponential, uniform, zipfian> Results ======= Comparing the patched with the baseline kernel, Apache Cassandra achieved 95% CIs [1.06, 4.10]%, [1.94, 5.43]% and [4.11, 7.50]% more OPS, respectively, for exponential access, random access and Zipfian access, when swap was off; 95% CIs [0.50, 2.60]%, [6.51, 8.77]% and [3.29, 6.75]% more OPS, respectively, for exponential access, random access and Zipfian access, when swap was set to minimum (vm.swappiness=1). +--------------------+--------------------+---------------------+ | Mean OPS [95% CI] | No swap | Minimum swap | +--------------------+--------------------+---------------------+ | Exponential access | 71084.9 / 72917.5 | 71499.6 / 72607.9 | | | [751.42, 2913.77] | [358.40, 1858.19] | +--------------------+--------------------+---------------------+ | Random access | 47127.2 / 48862.8 | 47585.4 / 51220.1 | | | [912.68, 2558.51] | [3097.39, 4172.00] | +--------------------+--------------------+---------------------+ | Zipfian access | 70271.5 / 74348.8 | 70698.2 / 74248.3 | | | [2887.20, 5267.39] | [2326.69, 4773.50] | +--------------------+--------------------+---------------------+ Table 1. Comparison between the baseline and the patched kernels Comparing minimum swap with no swap, Apache Cassandra achieved 95% CIs [4.05, 5.60]% more OPS for random access, when using the patched kernel. There were no statistically significant changes in OPS under other conditions. +--------------------+--------------------+---------------------+ | Mean OPS [95% CI] | Baseline kernel | Patched kernel | +--------------------+--------------------+---------------------+ | Exponential access | 71084.9 / 71499.6 | 72917.5 / 72607.9 | | | [-358.97, 1188.37] | [-1376.93, 757.73] | +--------------------+--------------------+---------------------+ | Random access | 47127.2 / 47585.4 | 48862.8 / 51220.1 | | | [-424.55, 1340.95] | [1977.09, 2737.50] | +--------------------+--------------------+---------------------+ | Zipfian access | 70271.5 / 70698.2 | 74348.8 / 74248.3 | | | [-749.39, 1602.79] | [-1337.07, 1136.07] | +--------------------+--------------------+---------------------+ Table 2. Comparison between no swap and minimum swap Metrics collected during each run are available at https://github.com/ediworks/KernelPerf/tree/master/mglru/cassandra/5.15 Appendix ======== $ cat raw_data_cassandra.r v <- c( # baseline swapoff exp 69952, 70274, 70286, 70818, 70946, 71202, 71244, 71615, 71787, 72725, # baseline swapoff uni 45309, 46056, 46086, 46188, 47275, 47524, 47797, 48243, 48329, 48465, # baseline swapoff zip 69096, 69194, 69386, 69408, 69412, 70795, 70890, 71170, 71232, 72132, # baseline swapon exp 69836, 70783, 70951, 71188, 71521, 71764, 72035, 72166, 72287, 72465, # baseline swapon uni 46089, 46963, 47308, 47599, 47776, 47822, 47952, 48042, 48092, 48211, # baseline swapon zip 68986, 69279, 69290, 69805, 70146, 70913, 71462, 71978, 72370, 72753, # patched swapoff exp 70701, 71328, 71458, 72846, 72885, 73078, 73702, 74077, 74415, 74685, # patched swapoff uni 48275, 48460, 48735, 48813, 48902, 48969, 48996, 49007, 49213, 49258, # patched swapoff zip 71829, 72909, 73259, 73835, 74200, 74544, 75318, 75514, 76031, 76049, # patched swapon exp 71169, 71968, 72208, 72374, 72401, 72755, 72861, 72942, 73469, 73932, # patched swapon uni 50292, 50529, 50981, 51224, 51414, 51420, 51480, 51608, 51625, 51628, # patched swapon zip 72032, 72325, 73834, 74366, 74482, 74573, 74810, 75044, 75371, 75646 ) a <- array(v, dim = c(10, 3, 2, 2)) # baseline vs patched for (swap in 1:2) { for (dist in 1:3) { r <- t.test(a[, dist, swap, 1], a[, dist, swap, 2]) print(r) p <- r$conf.int * 100 / r$estimate[1] if ((p[1] > 0 && p[2] < 0) || (p[1] < 0 && p[2] > 0)) { s <- sprintf("swap%d dist%d: no significance", swap, dist) } else { s <- sprintf("swap%d dist%d: [%.2f, %.2f]%%", swap, dist, -p[2], -p[1]) } print(s) } } # swapoff vs swapon for (kern in 1:2) { for (dist in 1:3) { r <- t.test(a[, dist, 1, kern], a[, dist, 2, kern]) print(r) p <- r$conf.int * 100 / r$estimate[1] if ((p[1] > 0 && p[2] < 0) || (p[1] < 0 && p[2] > 0)) { s <- sprintf("kern%d dist%d: no significance", kern, dist) } else { s <- sprintf("kern%d dist%d: [%.2f, %.2f]%%", kern, dist, -p[2], -p[1]) } print(s) } } $ R -q -s -f raw_data_cassandra.r Welch Two Sample t-test data: a[, dist, swap, 1] and a[, dist, swap, 2] t = -3.6172, df = 14.793, p-value = 0.002585 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -2913.7703 -751.4297 sample estimates: mean of x mean of y 71084.9 72917.5 [1] "swap1 dist1: [1.06, 4.10]%" Welch Two Sample t-test data: a[, dist, swap, 1] and a[, dist, swap, 2] t = -4.679, df = 10.331, p-value = 0.0007961 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -2558.5199 -912.6801 sample estimates: mean of x mean of y 47127.2 48862.8 [1] "swap1 dist2: [1.94, 5.43]%" Welch Two Sample t-test data: a[, dist, swap, 1] and a[, dist, swap, 2] t = -7.2315, df = 16.902, p-value = 1.452e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -5267.396 -2887.204 sample estimates: mean of x mean of y 70271.5 74348.8 [1] "swap1 dist3: [4.11, 7.50]%" Welch Two Sample t-test data: a[, dist, swap, 1] and a[, dist, swap, 2] t = -3.1057, df = 17.95, p-value = 0.006118 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -1858.191 -358.409 sample estimates: mean of x mean of y 71499.6 72607.9 [1] "swap2 dist1: [0.50, 2.60]%" Welch Two Sample t-test data: a[, dist, swap, 1] and a[, dist, swap, 2] t = -14.307, df = 16.479, p-value = 1.022e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -4172.006 -3097.394 sample estimates: mean of x mean of y 47585.4 51220.1 [1] "swap2 dist2: [6.51, 8.77]%" Welch Two Sample t-test data: a[, dist, swap, 1] and a[, dist, swap, 2] t = -6.1048, df = 17.664, p-value = 9.877e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -4773.504 -2326.696 sample estimates: mean of x mean of y 70698.2 74248.3 [1] "swap2 dist3: [3.29, 6.75]%" Welch Two Sample t-test data: a[, dist, 1, kern] and a[, dist, 2, kern] t = -1.1261, df = 17.998, p-value = 0.2749 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -1188.3785 358.9785 sample estimates: mean of x mean of y 71084.9 71499.6 [1] "kern1 dist1: no significance" Welch Two Sample t-test data: a[, dist, 1, kern] and a[, dist, 2, kern] t = -1.1108, df = 14.338, p-value = 0.2849 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -1340.9555 424.5555 sample estimates: mean of x mean of y 47127.2 47585.4 [1] "kern1 dist2: no significance" Welch Two Sample t-test data: a[, dist, 1, kern] and a[, dist, 2, kern] t = -0.76534, df = 17.035, p-value = 0.4545 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -1602.7926 749.3926 sample estimates: mean of x mean of y 70271.5 70698.2 [1] "kern1 dist3: no significance" Welch Two Sample t-test data: a[, dist, 1, kern] and a[, dist, 2, kern] t = 0.62117, df = 14.235, p-value = 0.5443 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -757.7355 1376.9355 sample estimates: mean of x mean of y 72917.5 72607.9 [1] "kern2 dist1: no significance" Welch Two Sample t-test data: a[, dist, 1, kern] and a[, dist, 2, kern] t = -13.18, df = 15.466, p-value = 8.07e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -2737.509 -1977.091 sample estimates: mean of x mean of y 48862.8 51220.1 [1] "kern2 dist2: [4.05, 5.60]%" Welch Two Sample t-test data: a[, dist, 1, kern] and a[, dist, 2, kern] t = 0.17104, df = 17.575, p-value = 0.8661 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -1136.076 1337.076 sample estimates: mean of x mean of y 74348.8 74248.3 [1] "kern2 dist3: no significance"
