Kernel / MongoDB benchmark with MGLRU TLDR ==== With the MGLRU, MongoDB achieved 95% CIs [2.23, 3.44]%, [6.97, 9.73]% and [2.16, 3.55]% more operations per second (OPS) respectively for exponential (distribution) access, random access and Zipfian access, when underutizling memory; 95% CIs [8.83, 10.03]%, [21.12, 23.14]% and [5.53, 6.46]% more OPS respectively for exponential access, random access and Zipfian access, when slightly overcommitting memory. 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. MongoDB 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. Matrix ====== Kernels: version [+ patchset] * Baseline: 5.14 * Patched: 5.14 + MGLRU Memory utilization: % of memory size * Underutilizing: ~15% on inactive file list * Overcommitting: ~5% swapped out Concurrency: average # of users per CPU * Medium: 2 Access distributions (1kB objects, 20% update): * Exponential * Uniform random * Zipfian Total configurations: 12 Data points per configuration: 10 Total run duration (minutes) per data point: ~20 Note that MongoDB reached the peak performance with the concurrency for this benchmark, i.e., its performance degraded with fewer or more users for this benchmark. Procedure ========= The latest MGLRU patchset for the 5.14 kernel is available at git fetch https://linux-mm.googlesource.com/page-reclaim \ refs/changes/30/1430/1 Baseline and patched 5.14 kernel images are available at https://drive.google.com/drive/folders/1eMkQleAFGkP2vzM_JyRA21oKE0ESHBqp <install and configure OS> ycsb_load.sh systemctl stop mongod e2image <backup /mnt/data> <for each kernel> grub2-set-default <baseline, patched> <for each memory utilization> <update /etc/mongod.conf> <for each access distribution> <update ycsb_run.sh> <for each data point> systemctl stop mongod e2image <restore /mnt/data> reboot ycsb_run.sh <collect OPS> Hardware ======== Memory (GB): 64 CPU (total #): 32 NVMe SSD (GB): 1024 OS == $ cat /etc/redhat-release Red Hat Enterprise Linux release 8.4 (Ootpa) $ cat /proc/swaps Filename Type Size Used Priority /dev/nvme0n1p3 partition 32970748 0 -2 $ cat /proc/cmdline <existing parameters> systemd.unified_cgroup_hierarchy=1 $ cat /sys/fs/cgroup/user.slice/memory.min 4294967296 $ cat /proc/sys/vm/overcommit_memory 1 $ cat /sys/kernel/mm/transparent_hugepage/enabled always madvise [never] MongoDB ======= $ mongod --version db version v5.0.3 Build Info: { "version": "5.0.3", "gitVersion": "657fea5a61a74d7a79df7aff8e4bcf0bc742b748", "openSSLVersion": "OpenSSL 1.1.1g FIPS 21 Apr 2020", "modules": [], "allocator": "tcmalloc", "environment": { "distmod": "rhel80", "distarch": "x86_64", "target_arch": "x86_64" } } $ cat /etc/mongod.conf # mongod.conf <existing parameters> # Where and how to store data. storage: dbPath: /mnt/data journal: enabled: true wiredTiger: engineConfig: cacheSizeGB: <50, 60> <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 ycsb load mongodb -s -threads 16 \ -p mongodb.url=mongodb://%2Ftmp%2Fmongodb-27017.sock \ -p workload=site.ycsb.workloads.CoreWorkload \ -p recordcount=80000000 $ cat ycsb_run.sh # run benchmark ycsb run mongodb -s -threads 64 \ -p mongodb.url=mongodb://%2Ftmp%2Fmongodb-27017.sock \ -p workload=site.ycsb.workloads.CoreWorkload \ -p recordcount=80000000 -p operationcount=80000000 \ -p readproportion=0.8 -p updateproportion=0.2 \ -p requestdistribution=<exponential, uniform, zipfian> Results ======= Comparing the patched with the baseline kernel, MongoDB achieved 95% CIs [2.23, 3.44]%, [6.97, 9.73]% and [2.16, 3.55]% more OPS respectively for exponential access, random access and Zipfian access, when underutizling memory; 95% CIs [8.83, 10.03]%, [21.12, 23.14]% and [5.53, 6.46]% more OPS respectively for exponential access, random access and Zipfian access, when slightly overcommitting memory. +--------------------+-----------------------+-----------------------+ | Mean OPS [95% CI] | Underutilizing memory | Overcommitting memory | +--------------------+-----------------------+-----------------------+ | Exponential access | 76615.56 / 78788.76 | 73984.90 / 80961.66 | | | [1708.76, 2637.62] | [6533.94, 7419.58] | +--------------------+-----------------------+-----------------------+ | Random access | 62093.40 / 67276.01 | 55990.56 / 68379.91 | | | [4324.96, 6040.25] | [11824.09, 12954.62] | +--------------------+-----------------------+-----------------------+ | Zipfian access | 92532.25 / 95174.43 | 93545.62 / 99151.12 | | | [1997.20, 3287.17] | [5171.27, 6039.72] | +--------------------+-----------------------+-----------------------+ Table 1. Comparison between the baseline and patched kernels Comparing overcommitting with underutilizing memory, MongoDB achieved 95% CIs [-4.10, -2.77]%, [-11.20, -8.46]% and [0.36, 1.83]% more OPS respectively for exponential access, random access and Zipfian access, when using the baseline kernel; 95% CIs [2.27, 3.25]%, [0.78, 2.50]% and [3.81, 4.54]% more OPS respectively for exponential access, random access and Zipfian access, when using the patched kernel. +--------------------+-----------------------+-----------------------+ | Mean OPS [95% CI] | Baseline kernel | Patched kernel | +--------------------+-----------------------+-----------------------+ | Exponential access | 76615.56 / 73984.90 | 78788.76 / 80961.66 | | | [-3139.12, -2122.20] | [1786.70, 2559.09] | +--------------------+-----------------------+-----------------------+ | Random access | 62093.40 / 55990.56 | 67276.01 / 68379.91 | | | [-6953.44, -5252.23] | [525.42, 1682.38] | +--------------------+-----------------------+-----------------------+ | Zipfian access | 92532.25 / 93545.62 | 95174.43 / 99151.12 | | | [330.99, 1695.75] | [3628.31, 4325.06] | +--------------------+-----------------------+-----------------------+ Table 2. Comparison between underutilizing and overcommitting memory Metrics collected during each run are available at https://github.com/ediworks/KernelPerf/tree/master/mglru/mongodb/5.14 Appendix ======== $ cat raw_data_mongodb.r v <- c( # baseline 50g exp 75814.86, 75884.91, 76052.71, 76621.01, 76641.19, 76661.24, 76870.15, 77017.79, 77289.08, 77302.67, # baseline 50g uni 60638.17, 60968.91, 61128.61, 61548.40, 61779.30, 61917.58, 62152.28, 63440.15, 63625.47, 63735.11, # baseline 50g zip 91271.16, 91482.41, 91524.17, 92467.16, 92585.62, 92843.29, 92885.65, 93229.98, 93408.94, 93624.08, # baseline 60g exp 73183.67, 73191.30, 73527.58, 73831.79, 74047.95, 74056.24, 74401.23, 74418.53, 74547.58, 74643.08, # baseline 60g uni 55175.76, 55477.42, 55605.52, 55680.21, 55903.39, 56171.05, 56375.06, 56380.43, 56509.94, 56626.78, # baseline 60g zip 92653.82, 92775.02, 93100.44, 93290.21, 93593.74, 93775.64, 93868.72, 93915.12, 94194.77, 94288.69, # patched 50g exp 78349.95, 78385.64, 78392.33, 78419.91, 78726.59, 78738.68, 78930.72, 78948.25, 79404.38, 79591.14, # patched 50g uni 66622.91, 66667.33, 66951.43, 67104.80, 67117.30, 67196.90, 67389.75, 67406.62, 68131.43, 68171.61, # patched 50g zip 94261.14, 94822.34, 94914.70, 95114.89, 95156.75, 95205.90, 95383.78, 95612.00, 95624.00, 95648.81, # patched 60g exp 80272.04, 80612.33, 80679.23, 80717.74, 81011.18, 81029.64, 81146.68, 81371.84, 81379.13, 81396.76, # patched 60g uni 67559.52, 67600.11, 67718.90, 68062.57, 68278.78, 68446.56, 68452.82, 68853.86, 69278.34, 69547.67, # patched 60g zip 98706.81, 98864.41, 98903.77, 99044.10, 99155.68, 99162.94, 99165.64, 99482.31, 99484.91, 99540.62 ) a <- array(v, dim = c(10, 3, 2, 2)) # baseline vs patched for (mem in 1:2) { for (dist in 1:3) { r <- t.test(a[, dist, mem, 1], a[, dist, mem, 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("mem%d dist%d: no significance", mem, dist) } else { s <- sprintf("mem%d dist%d: [%.2f, %.2f]%%", mem, dist, -p[2], -p[1]) } print(s) } } # 50g vs 60g 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_mongodb.r Welch Two Sample t-test data: a[, dist, mem, 1] and a[, dist, mem, 2] t = -9.8624, df = 17.23, p-value = 1.671e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -2637.627 -1708.769 sample estimates: mean of x mean of y 76615.56 78788.76 [1] "mem1 dist1: [2.23, 3.44]%" Welch Two Sample t-test data: a[, dist, mem, 1] and a[, dist, mem, 2] t = -13.081, df = 12.744, p-value = 9.287e-09 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -6040.256 -4324.964 sample estimates: mean of x mean of y 62093.40 67276.01 [1] "mem1 dist2: [6.97, 9.73]%" Welch Two Sample t-test data: a[, dist, mem, 1] and a[, dist, mem, 2] t = -8.8194, df = 13.459, p-value = 5.833e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -3287.17 -1997.20 sample estimates: mean of x mean of y 92532.25 95174.43 [1] "mem1 dist3: [2.16, 3.55]%" Welch Two Sample t-test data: a[, dist, mem, 1] and a[, dist, mem, 2] t = -33.368, df = 16.192, p-value = 2.329e-16 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -7419.582 -6533.942 sample estimates: mean of x mean of y 73984.90 80961.66 [1] "mem2 dist1: [8.83, 10.03]%" Welch Two Sample t-test data: a[, dist, mem, 1] and a[, dist, mem, 2] t = -46.386, df = 16.338, p-value < 2.2e-16 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -12954.62 -11824.09 sample estimates: mean of x mean of y 55990.56 68379.91 [1] "mem2 dist2: [21.12, 23.14]%" Welch Two Sample t-test data: a[, dist, mem, 1] and a[, dist, mem, 2] t = -27.844, df = 13.209, p-value = 4.049e-13 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -6039.729 -5171.275 sample estimates: mean of x mean of y 93545.62 99151.12 [1] "mem2 dist3: [5.53, 6.46]%" Welch Two Sample t-test data: a[, dist, 1, kern] and a[, dist, 2, kern] t = 10.87, df = 18, p-value = 2.439e-09 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 2122.207 3139.125 sample estimates: mean of x mean of y 76615.56 73984.90 [1] "kern1 dist1: [-4.10, -2.77]%" Welch Two Sample t-test data: a[, dist, 1, kern] and a[, dist, 2, kern] t = 15.593, df = 12.276, p-value = 1.847e-09 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 5252.237 6953.447 sample estimates: mean of x mean of y 62093.40 55990.56 [1] "kern1 dist2: [-11.20, -8.46]%" Welch Two Sample t-test data: a[, dist, 1, kern] and a[, dist, 2, kern] t = -3.1512, df = 15.811, p-value = 0.006252 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -1695.7509 -330.9911 sample estimates: mean of x mean of y 92532.25 93545.62 [1] "kern1 dist3: [0.36, 1.83]%" Welch Two Sample t-test data: a[, dist, 1, kern] and a[, dist, 2, kern] t = -11.836, df = 17.672, p-value = 7.84e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -2559.092 -1786.704 sample estimates: mean of x mean of y 78788.76 80961.66 [1] "kern2 dist1: [2.27, 3.25]%" Welch Two Sample t-test data: a[, dist, 1, kern] and a[, dist, 2, kern] t = -4.0276, df = 16.921, p-value = 0.0008807 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -1682.3864 -525.4236 sample estimates: mean of x mean of y 67276.01 68379.91 [1] "kern2 dist2: [0.78, 2.50]%" Welch Two Sample t-test data: a[, dist, 1, kern] and a[, dist, 2, kern] t = -24.26, df = 15.517, p-value = 9.257e-14 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -4325.062 -3628.314 sample estimates: mean of x mean of y 95174.43 99151.12 [1] "kern2 dist3: [3.81, 4.54]%"
