\n
# We are using a docker based setup that can run this project ootb\n# it's available here: https:\/\/github.com\/retowyss\/rstudio-server-griddb-docker\n\n# You can set this up without docker, the containers use:\n\n# Latest version griddb https:\/\/docs.griddb.net\n# Latest version of R and tidyverse R >= 4.0.0\n# Latest version of JDBC https:\/\/github.com\/griddb\/jdbc\n\n# For general setup without docker you can follow this post:\n# https:\/\/griddb.net\/en\/blog\/analyzing-nba-play-by-play-data-using-r-and-griddb\/\n\nlibrary(RJDBC)\ndrv <- JDBC(\n driverClass = \"com.toshiba.mwcloud.gs.sql.Driver\",\n # Point this to your gridstore jar\n classPath = \"\/jdbc\/bin\/gridstore-jdbc.jar\"\n)\n\n# IP and port depend on your setup\ngriddb <- dbConnect(\n drv, \n \"jdbc:gs:\/\/172.20.0.42:20001\/dockerGridDB\/public\", \n \"admin\", \n \"admin\"\n)\n\n# System info table: cfx_info\ndbSendUpdate(griddb, paste(\n \"CREATE TABLE IF NOT EXISTS cfx_info\", \n \"(id INTEGER, Processor STRING, GPU STRING, SystemRam STRING,\", \n \"GameName STRING, OS STRING, Motherboard STRING);\"\n))\n\n# Frame time capture data: cfx_capture\ndbSendUpdate(griddb, paste(\n \"CREATE TABLE IF NOT EXISTS cfx_capture\", \n \"(info_id INTEGER, TimeInSeconds FLOAT, MsBetweenPresents FLOAT);\"\n))\n\n# Sensor data: cfx_sensors\ndbSendUpdate(griddb, paste(\n \"CREATE TABLE IF NOT EXISTS cfx_sensors\", \n \"(info_id INTEGER, MeasureTime FLOAT, BetweenMeasureTime FLOAT,\", \n \"cpu_power FLOAT, cpu_temperature FLOAT, gpu_clock FLOAT, gpu_power FLOAT,\", \n \"gpu_temperature FLOAT);\"\n))\n\ndbInsertTable <- function(conn, name, df, append = TRUE) {\n for (i in seq_len(nrow(df))) {\n dbWriteTable(conn, name, df[i, ], append = append)\n }\n invisible(TRUE)\n}\n\n# This is not a \"production\" ready approach of doing this, but it's sufficient\n# for this demo :)\n# Inserts dataset (cfx_data) into database (conn) using ID (uid)\ninsert_cfx_data <- function(conn, cfx_data, uid) {\n \n dbInsertTable(conn, \"cfx_info\", \n cfx_data$info %>% \n transmute(id = uid, Processor, GPU, SystemRam, GameName, OS, Motherboard)\n )\n \n dbInsertTable(conn, \"cfx_capture\", \n cfx_data$capture %>% \n transmute(info_id = uid, TimeInSeconds, MsBetweenPresents)\n )\n \n dbInsertTable(conn, \"cfx_sensors\",\n cfx_data$sensors %>% \n transmute(\n info_id = uid, \n MeasureTime, \n BetweenMeasureTime,\n cpu_power = amdcpu_0_power_0,\n cpu_temperature = amdcpu_0_temperature_0,\n gpu_clock = nvidiagpu_0_clock_0, \n gpu_power = nvidiagpu_0_power_0, \n gpu_temperature = nvidiagpu_0_temperature_0\n ) \n )\n invisible(TRUE)\n}<\/code><\/pre>\n<\/div>\n\n
# Insert (this takes a while, don't run while knitting the doc)\njson_files <- list.files(\"data\")\nwalk2(json_files, 1:length(json_files), function(x, i) {\n insert_cfx_data(griddb, read_capframex_json(paste0(\"data\/\", x)), i)\n})<\/code><\/pre>\n<\/div>\n\u5358\u4e00\u306e\u30b7\u30b9\u30c6\u30e0\uff08Ryzen 9 5900X\u300116GB RAM\u3001Windows 11\uff09\u3092\u4f7f\u3063\u3066\u30c7\u30fc\u30bf\u3092\u53ce\u96c6\u3057\u307e\u3057\u305f\u304c\u3001\u8907\u6570\u306e\u30b2\u30fc\u30e0\u3092\u30c6\u30b9\u30c8\u3057\u3001GTX 1650\u3068GTX 1080 Ti\u3068\u3044\u30462\u7a2e\u985e\u306e\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u30ab\u30fc\u30c9\uff08GPU\uff09\u3092\u30c6\u30b9\u30c8\u3057\u307e\u3057\u305f\u3002<\/p>\n
\n
# Check\ndbGetQuery(griddb,\"SELECT * FROM cfx_info;\") %>% \n knitr::kable()<\/code><\/pre>\n<\/div>\n\n\n\n| id<\/th>\n | Processor<\/th>\n | GPU<\/th>\n | SystemRam<\/th>\n | GameName<\/th>\n | OS<\/th>\n | Motherboard<\/th>\n<\/tr>\n<\/thead>\n |
\n\n| 1<\/td>\n | AMD Ryzen 9 5900X<\/td>\n | NVIDIA GeForce GTX 1080 Ti<\/td>\n | 16GB (2x8GB) 3200MT\/s<\/td>\n | Assassin’s Creed Valhalla<\/td>\n | Microsoft Windows 11 Pro Build 22000<\/td>\n | Gigabyte Technology Co. Ltd. B550I AORUS PRO AX<\/td>\n<\/tr>\n |
\n| 2<\/td>\n | AMD Ryzen 9 5900X<\/td>\n | NVIDIA GeForce GTX 1650<\/td>\n | 16GB (2x8GB) 3200MT\/s<\/td>\n | Cyberpunk 2077<\/td>\n | Microsoft Windows 11 Pro Build 22000<\/td>\n | Gigabyte Technology Co. Ltd. B550I AORUS PRO AX<\/td>\n<\/tr>\n |
\n| 3<\/td>\n | AMD Ryzen 9 5900X<\/td>\n | NVIDIA GeForce GTX 1650<\/td>\n | 16GB (2x8GB) 3200MT\/s<\/td>\n | Doom Eternal<\/td>\n | Microsoft Windows 11 Pro Build 22000<\/td>\n | Gigabyte Technology Co. Ltd. B550I AORUS PRO AX<\/td>\n<\/tr>\n |
\n| 4<\/td>\n | AMD Ryzen 9 5900X<\/td>\n | NVIDIA GeForce GTX 1080 Ti<\/td>\n | 16GB (2x8GB) 3200MT\/s<\/td>\n | Doom Eternal<\/td>\n | Microsoft Windows 11 Pro Build 22000<\/td>\n | Gigabyte Technology Co. Ltd. B550I AORUS PRO AX<\/td>\n<\/tr>\n |
\n| 5<\/td>\n | AMD Ryzen 9 5900X<\/td>\n | NVIDIA GeForce GTX 1650<\/td>\n | 16GB (2x8GB) 3200MT\/s<\/td>\n | ROLLERDROME<\/td>\n | Microsoft Windows 11 Pro Build 22000<\/td>\n | Gigabyte Technology Co. Ltd. B550I AORUS PRO AX<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n GridDB\u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u306f\u6570\u30d5\u30a1\u30a4\u30eb\u3057\u304b\u5165\u308c\u3066\u3044\u307e\u305b\u3093\u304c\u3001\u3059\u3067\u306b\u7d0480\u4e07\u884c\u306e\u30ad\u30e3\u30d7\u30c1\u30e3\u30c7\u30fc\u30bf\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n \n # Check\ndbGetQuery(griddb,\"SELECT COUNT(*) FROM cfx_capture;\")<\/code><\/pre>\n<\/div>\n\n ## \n## 1 778780<\/code><\/pre>\n<\/div>\nPC\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u30b5\u30de\u30ea\u30fc\u3092\u4f5c\u6210\u3059\u308b<\/h2>\n\u79c1\u305f\u3061\u306f\u4eca\u3001\u7279\u5b9a\u306eCPU\u3001GPU\u3001\u30b2\u30fc\u30e0\u306e\u7d44\u307f\u5408\u308f\u305b\u306b\u5bfe\u3059\u308b\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u306e\u30b5\u30de\u30ea\u30fc\u3092\u6c42\u3081\u3066\u3044\u307e\u3059\u3002PC\u30e1\u30fc\u30ab\u30fc\u304c\u3053\u306e\u30a2\u30a4\u30c7\u30a2\u3092\u767a\u5c55\u3055\u305b\u3001\u540c\u3058\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2\u30b3\u30f3\u30dd\u30fc\u30cd\u30f3\u30c8\u3092\u4f7f\u7528\u3057\u305f\u5225\u306e\u30b7\u30b9\u30c6\u30e0\u3068\u30c7\u30fc\u30bf\u3092\u6bd4\u8f03\u3059\u308b\u3053\u3068\u3082\u53ef\u80fd\u3067\u3059\u3002 \u305d\u3046\u3059\u308b\u3053\u3068\u3067\u3001\u69cb\u7bc9\u3057\u305f\u30b7\u30b9\u30c6\u30e0\u304c\u671f\u5f85\u901a\u308a\u306e\u6027\u80fd\u3092\u767a\u63ee\u3059\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u3066\u304b\u3089\u3001\u304a\u5ba2\u69d8\u306b\u304a\u5c4a\u3051\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u308b\u306e\u3067\u3059\u3002<\/p>\n \n # We use paste and str_interp to build up our SQL in a readable and easy to\n# understand way\nget_performance_summary <- function(conn, cpu, gpu, game) {\n system_info <- paste(\n \"SELECT ID\",\n \"FROM cfx_info\",\n \"WHERE Processor = '${cpu}' AND GPU = '${gpu}' AND GameName = '${game}'\"\n )\n \n capture <- paste(\n \"SELECT AVG(MsBetweenPresents) AS frame_time,\",\n \"MAX(TimeInSeconds) AS session_time,\",\n \"COUNT(*) AS frame_count\",\n \"FROM (\", system_info, \") info\",\n \"LEFT JOIN (SELECT * FROM cfx_capture) capture\",\n \"ON info.id = capture.info_id\"\n )\n\n sensors <- paste(\n \"SELECT AVG(cpu_power) AS cpu_power, \",\n \"AVG(cpu_temperature) AS cpu_temperature,\",\n \"AVG(gpu_clock) AS gpu_clock, AVG(gpu_power) AS gpu_power,\",\n \"AVG(gpu_temperature) AS gpu_temperature\",\n \"FROM (\", system_info, \") info\",\n \"LEFT JOIN (SELECT * FROM cfx_sensors) sensors\",\n \"ON info.id = sensors.info_id\"\n )\n \n capture_res <- dbGetQuery(conn, str_interp(capture))\n sensors_res <- dbGetQuery(conn, str_interp(sensors))\n \n as_tibble(bind_cols(\n tibble(cpu = cpu, gpu = gpu, game = game),\n capture_res %>% \n mutate(fps = 1000 \/ frame_time), \n sensors_res\n ))\n}\n\ngtx_1650_rollerdrome <- get_performance_summary(griddb, \n cpu = \"AMD Ryzen 9 5900X\", \n gpu = \"NVIDIA GeForce GTX 1650\", \n game = \"ROLLERDROME\"\n)\n\nglimpse(gtx_1650_rollerdrome)<\/code><\/pre>\n<\/div>\n\n ## Rows: 1\n## Columns: 12\n## $ cpu <chr> \"AMD Ryzen 9 5900X\"\n## $ gpu <chr> \"NVIDIA GeForce GTX 1650\"\n## $ game <chr> \"ROLLERDROME\"\n## $ frame_time <dbl> 2.660829\n## $ session_time <dbl> 400.8515\n## $ frame_count <dbl> 150650\n## $ fps <dbl> 375.8227\n## $ cpu_power <dbl> 95.65372\n## $ cpu_temperature <dbl> 56.58357\n## $ gpu_clock <dbl> 1848.533\n## $ gpu_power <dbl> 74.72834\n## $ gpu_temperature <dbl> 60.41905<\/code><\/pre>\n<\/div>\n\u307e\u305f\u3001\u305d\u308c\u3092\u30c6\u30ad\u30b9\u30c8\u30d1\u30e9\u30b0\u30e9\u30d5\u306b\u3057\u3066\u3001\u30b7\u30b9\u30c6\u30e0\u30ec\u30dd\u30fc\u30c8\u306b\u8a18\u8f09\u3059\u308b\u3053\u3068\u3082\u51fa\u6765\u307e\u3059\u3002<\/p>\n \n summary2text <- function(summary_data) {\n spinable <- paste0(\n \"Your system featuring a ${cpu} and ${gpu} performance in \", \n \"a ${round(session_time\/60)} minutes session of ${game} \",\n \"was ${round(fps)} FPS.\",\n \"The graphics card used ${round(gpu_power)}W and \",\n \"ran at ${round(gpu_temperature)}\u00b0C, \",\n \"while the CPU used ${round(cpu_power)}W and \", \n \"ran at ${round(cpu_temperature)}\u00b0C\"\n )\n \n str_interp(spinable, env = summary_data)\n}\n\nsummary2text(gtx_1650_rollerdrome)<\/code><\/pre>\n<\/div>\n\n ## [1] \"Your system featuring a AMD Ryzen 9 5900X and NVIDIA GeForce GTX 1650 performance in a 7 minutes session of ROLLERDROME was 376 FPS.The graphics card used 75W and ran at 60\u00b0C, while the CPU used 96W and ran at 57\u00b0C\"<\/code><\/pre>\n<\/div>\n\u6642\u9593\u8ef8\u306e\u89e3\u6790<\/h2>\n\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u4e0a\u3067\u76f4\u63a5\u3001\u96c6\u8a08\u306a\u3069\u306e\u8a08\u7b97\u3092\u3059\u308b\u3053\u3068\u3082\u51fa\u6765\u307e\u3059\u304c\u3001\u6642\u306b\u306f\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u304b\u3089\u30c7\u30fc\u30bf\u3092\u53d6\u308a\u51fa\u3057\u3066\u3001\u8a73\u7d30\u306b\u5206\u6790\u3059\u308b\u3053\u3068\u3082\u3042\u308a\u307e\u3059\u3002<\/p>\n \u30c7\u30d0\u30a4\u30b9\u304c\u3088\u308a\u591a\u304f\u306e\u96fb\u529b\u3092\u4f7f\u7528\u3059\u308b\u5834\u5408\u3001\u305d\u306e\u6e29\u5ea6\u306f\u4e0a\u6607\u3059\u308b\u306f\u305a\u3067\u3059\u3002\u3064\u307e\u308a\u3001gpu_power\u306fgpu_temperature\u3068\u76f8\u95a2\u304c\u3042\u308b\u3053\u3068\u304c\u5206\u304b\u308b\u306f\u305a\u3067\u3059\u3002<\/p>\n \n get_sensor_data <- function(conn, cpu, gpu, game) {\n system_info <- paste(\n \"SELECT ID\",\n \"FROM cfx_info\",\n \"WHERE Processor = '${cpu}' AND GPU = '${gpu}' AND GameName = '${game}'\"\n )\n\n sensors <- paste(\n \"SELECT *\",\n \"FROM (\", system_info, \") info\",\n \"LEFT JOIN (SELECT * FROM cfx_sensors) sensors\",\n \"ON info.id = sensors.info_id\"\n )\n\n sensors_res <- dbGetQuery(conn, str_interp(sensors))\n \n cbind(tibble(cpu = cpu, gpu = gpu, game = game), as_tibble(sensors_res))\n}\n\ngtx_1080_ti_doom <- get_sensor_data(griddb, \n cpu = \"AMD Ryzen 9 5900X\", \n gpu = \"NVIDIA GeForce GTX 1080 Ti\", \n game = \"Doom Eternal\" \n) \n\ngtx_1650_doom <- get_sensor_data(griddb, \n cpu = \"AMD Ryzen 9 5900X\", \n gpu = \"NVIDIA GeForce GTX 1650\", \n game = \"Doom Eternal\" \n) \n\ndoom_data <- bind_rows(gtx_1080_ti_doom, gtx_1650_doom)\n\nggplot(doom_data, aes(x = MeasureTime, y = gpu_power, color = gpu)) +\n geom_line()<\/code><\/pre>\n<\/div>\n![\"\"](\"https:\/\/griddb.net\/wp-content\/uploads\/2023\/02\/unnamed-chunk-11-1.png\") <\/p>\n
\u6025\u306b\u30d1\u30ef\u30fc\u304c\u843d\u3061\u308b\u306e\u306f\u9762\u767d\u3044\u3067\u3059\u304c\u3001\u305d\u308c\u306f\u8aa4\u308a\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u3002 \u30c1\u30a7\u30c3\u30af\u30dd\u30a4\u30f3\u30c8\u306e\u30ed\u30fc\u30c9\u3001\u5d16\u304b\u3089\u306e\u8ee2\u843d\u3001\u30b7\u30cd\u30de\u30c6\u30a3\u30c3\u30af\u30b9\u306e\u30ed\u30fc\u30c9\u306a\u3069\u306b\u5bfe\u5fdc\u3059\u308b\u3082\u306e\u3067\u3059\u3002\u3053\u306e\u3088\u3046\u306a\u77ed\u3044\u6642\u9593\u3067\u306f\u3001\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u30ab\u30fc\u30c9\u306f\u30cf\u30fc\u30c9\u306b\u52d5\u4f5c\u3059\u308b\u5fc5\u8981\u304c\u306a\u3044\u305f\u3081\u3001\u305d\u308c\u307b\u3069\u591a\u304f\u306e\u96fb\u529b\u3092\u5fc5\u8981\u3068\u3057\u306a\u3044\u306e\u3067\u3059\u3002<\/p>\n \n ggplot(doom_data, aes(x = MeasureTime, y = gpu_temperature, color = gpu)) +\n geom_line()<\/code><\/pre>\n<\/div>\n![\"\"](\"https:\/\/griddb.net\/wp-content\/uploads\/2023\/02\/unnamed-chunk-12-1.png\") <\/p>\n
\u6027\u80fd\u306e\u4f4e\u3044\u30ab\u30fc\u30c9\u3067\u3042\u308bGTX 1650\u306f\u3001\u30c1\u30a7\u30c3\u30af\u30dd\u30a4\u30f3\u30c8\u306e\u8aad\u307f\u8fbc\u307f\u306b\u5bfe\u5fdc\u3059\u308b\u6e29\u5ea6\u306e\u4f4e\u4e0b\u304c\u5927\u304d\u304f\u306a\u3063\u3066\u3044\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u30d2\u30fc\u30c8\u30b7\u30f3\u30af\u306e\u71b1\u5bb9\u91cf\u304c\u4f4e\u304f\u3001GTX 1080 Ti\u306b\u642d\u8f09\u3055\u308c\u305f\u30a2\u30eb\u30df\u30cb\u30a6\u30e0\u306e\u30d8\u30c3\u30c8\u30b8\u30e3\u30f3\u30af\u3088\u308a\u3082\u305a\u3063\u3068\u65e9\u304f\u51b7\u5374\u3055\u308c\u308b\u305f\u3081\u3068\u601d\u308f\u308c\u307e\u3059\u3002<\/p>\n \n # Technically there may be a small delay until the gpu-die heats up and\n# the current temperature of the heatsink may also affect the gpu temperature \ncor.test(gtx_1080_ti_doom$gpu_power, gtx_1080_ti_doom$gpu_temperature)<\/code><\/pre>\n<\/div>\n\n ## \n## Pearson's product-moment correlation\n## \n## data: gtx_1080_ti_doom$gpu_power and gtx_1080_ti_doom$gpu_temperature\n## t = 48.622, df = 7071, p-value < 2.2e-16\n## alternative hypothesis: true correlation is not equal to 0\n## 95 percent confidence interval:\n## 0.4828882 0.5178252\n## sample estimates:\n## cor \n## 0.5005605<\/code><\/pre>\n<\/div>\n\u305d\u3057\u3066\u3001\u305d\u306e\u76f8\u95a2\u95a2\u4fc2\u3092\u898b\u51fa\u3059\u306e\u3067\u3059\u3002<\/p>\n \u307e\u3068\u3081<\/h2>\n\n- R\u3092\u4f7f\u3063\u3066CapFrameX\u304c\u4fdd\u5b58\u3057\u3066\u3044\u308bJSON\u3092\u62bd\u51fa\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u898b\u3066\u304d\u307e\u3057\u305f\u3002<\/li>\n
- GridDB\u306b\u30c6\u30fc\u30d6\u30eb\u30b9\u30ad\u30fc\u30de\u3092\u4f5c\u6210\u3057\u3001JSON\u30d5\u30a1\u30a4\u30eb\u304b\u3089\u30c7\u30fc\u30bf\u3092\u633f\u5165\u3057\u307e\u3057\u305f\u3002<\/li>\n
- R\u3067GridDB\u306b\u5bfe\u3057\u3066\u30d1\u30e9\u30e1\u30fc\u30bf\u4ed8\u304d\u306e\u30af\u30a8\u30ea\u3092\u4f5c\u6210\u3059\u308b\u305f\u3081\u306b\u3001
paste<\/code>\u3068str_interp<\/code>\u3092\u4f7f\u7528\u3057\u307e\u3057\u305f\u3002<\/li>\n- GridDB\u306e\u30a6\u30a3\u30f3\u30c9\u30a6\u95a2\u6570\u3092\u4f7f\u3046\u3053\u3068\u3067\u3001\u30ef\u30fc\u30af\u30b9\u30c6\u30fc\u30b7\u30e7\u30f3\u306b\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u307e\u306a\u3044\u3088\u3046\u306b\u3057\u307e\u3057\u305f\u3002<\/li>\n
- GridDB\u304b\u3089\u30c7\u30fc\u30bf\u3092\u53d6\u308a\u51fa\u3057\u3066R\u3067\u89e3\u6790\u3057\u3066\u307f\u307e\u3057\u305f\u3002<\/li>\n<\/ul>\n
\u72ec\u81ea\u306e\u30c7\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u305f\u3044\u5834\u5408\u306f\u3001\u30c7\u30fc\u30bf\u62bd\u51fa\u306e\u904e\u7a0b\u3067\u4e00\u90e8\u8abf\u6574\u304c\u5fc5\u8981\u306a\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u3001\u300camd_cpu\u300d\u3068\u300cnvidia_gpu\u300d\u306b\u7279\u5316\u3057\u3066\u3044\u308b\u306e\u3067\u3001\u3082\u3057\u9055\u3046\u7d44\u307f\u5408\u308f\u305b\u306e\u5834\u5408\u306f\u3001\u30bb\u30ec\u30af\u30bf\u306e\u540d\u524d\u3092\u5909\u66f4\u3059\u308b\u304b\u3001\u4efb\u610f\u306e\u7d44\u307f\u5408\u308f\u305b\u3067\u52d5\u4f5c\u3059\u308b\u3088\u3046\u306b\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002 \u305d\u3057\u3066\u3001Github\u3067\u5171\u6709\u3057\u307e\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":" 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