doc/benchmark.R
In twang2218/pmap: Process Map Visualization
# Provision
# docker-machine create -d digitalocean --digitalocean-region=nyc3 --digitalocean-size=4gb d1
# apt install -y r-base libxml2-dev libssl-dev libcurl4-openssl-dev htop
# curl -sSL https://agent.digitalocean.com/install.sh | sh
install.packages("pmap")
install.packages("microbenchmark")
install.packages("devtools")
clean <- function() {
if ("pmap" %in% installed.packages()[,"Package"]) {
# 'pmap' has been installed, remove first
library(pmap)
detach("package:pmap")
remove.packages("pmap")
# .rs.restartR()
}
}
setup <- function(version) {
devtools::install_github("twang2218/pmap", ref = version)
pkgdb <- installed.packages()
if ("pmap" %in% pkgdb[,"Package"]) {
print(paste("Installed pmap version ", pkgdb["pmap", "Version"]))
library(pmap)
} else {
stop(paste("Unable to install pmap of version:", version))
}
}
library(dplyr)
library(microbenchmark)
library(pmap)
clean()
setup("master")
el_10k <- generate_eventlog(10000, 2000, c("campaign", "sale"), c(10, 3))
el_100k <- generate_eventlog(100000, 20000, c("campaign", "sale"), c(10, 3))
el_1000k <- generate_eventlog(1000000, 200000, c("campaign", "sale"), c(10, 3))
# 0.2.0
clean()
setup("v0.2.0")
el_10k <- generate_random_eventlog(10000, 2000, c("campaign", "sale"), c(10, 3))
el_100k <- generate_random_eventlog(100000, 20000, c("campaign", "sale"), c(10, 3))
el_1000k <- generate_random_eventlog(1000000, 200000, c("campaign", "sale"), c(10, 3))
microbenchmark(
create_pmap_from_eventlog(el_10k),
create_pmap_from_eventlog(el_100k),
# create_pmap_from_eventlog(el_1000k),
times = 10
)
# Unit: seconds
# expr min lq mean median uq max neval
# create_pmap_from_eventlog(el_10k) 1.56275 1.600056 1.637137 1.643266 1.671124 1.73218 10
# create_pmap_from_eventlog(el_100k) 47.74824 48.093884 50.321483 50.637030 51.098359 54.20957 10
# system.time(create_pmap_from_eventlog(el_1000k)) => 130 mins (156x)
# 0.3.2
clean()
setup("v0.3.2")
microbenchmark(
create_pmap(el_10k),
create_pmap(el_100k),
# create_pmap(el_1000k),
times = 10
)
# Unit: seconds
# expr min lq mean median uq max neval
# create_pmap(el_10k) 1.727415 1.799521 1.86058 1.828059 1.888508 2.13194 10
# create_pmap(el_100k) 55.219807 56.539817 57.64809 57.757177 58.450986 59.80957 10
# 0.4.0
clean()
setup("v0.4.0")
microbenchmark(
create_pmap(el_10k),
create_pmap(el_100k),
# create_pmap(el_1000k),
times = 10
)
# Unit: seconds
# expr min lq mean median uq max neval
# create_pmap(el_10k) 1.685579 1.773115 1.865584 1.826707 1.973622 2.11452 10
# create_pmap(el_100k) 57.925665 59.242784 61.246433 60.450505 62.296957 65.79441 10
# master
clean()
setup("master")
library(data.table)
el_10k <- generate_eventlog(10000, 2000, c("campaign", "sale"), c(10, 3))
el_100k <- generate_eventlog(100000, 20000, c("campaign", "sale"), c(10, 3))
el_1m <- generate_eventlog(1000000, 200000, c("campaign", "sale"), c(10, 3))
el_10m <- generate_eventlog(10000000, 2000000, c("campaign", "sale"), c(10, 3))
# Use `copy()` to avoid `data.table::setorder()` modify the original dataset.
microbenchmark(
create_pmap(data.table::copy(el_10k)),
create_pmap(data.table::copy(el_100k)),
create_pmap(data.table::copy(el_1m)),
create_pmap(data.table::copy(el_10m)),
times = 10
)
system.time(data.table::copy(el_10m))
el_100k <- generate_eventlog(100000, 20000, c("campaign", "sale"), c(10, 3))
create_pmap(data.table::copy(el_100k))
el_10m <- generate_eventlog(10000000, 2000000, c("campaign", "sale"), c(10, 3))
create_pmap(data.table::copy(el_1m))
# macbook pro
# Unit: milliseconds
# expr min lq mean median uq max neval
# create_pmap(el_10k) 392.4646 411.3784 452.0301 421.8778 426.4990 602.4456 10
# create_pmap(el_100k) 719.7945 732.0896 741.9988 744.0928 752.5453 767.8442 10
# create_pmap(el_1000k) 4934.7507 5007.0168 5077.1100 5052.3182 5173.4299 5224.5544 10
# d1 (Digital Ocean - 4GB)
# Unit: milliseconds
# expr min lq mean median uq max neval
# create_pmap(el_10k) 507.7968 524.3655 564.0430 542.9571 572.6458 685.7234 10
# create_pmap(el_100k) 807.3958 841.5750 857.1228 859.4381 880.2861 884.7447 10
# create_pmap(el_1000k) 4746.3864 4827.8861 5020.3630 5057.1328 5192.0509 5321.1395 10
# d2 (Digital Ocean - 4GB)
# Unit: milliseconds
# expr min lq mean median uq max neval
# create_pmap(el_10k) 559.0618 581.3116 626.3841 611.7513 622.4935 755.7317 10
# create_pmap(el_100k) 864.9402 885.6535 919.1273 894.4643 934.1724 1095.2642 10
# create_pmap(el_1000k) 4804.6721 5142.7089 5173.3926 5178.5153 5245.1480 5451.2313 10
# d3 (Digital Ocean - 4GB)
# Unit: milliseconds
# expr min lq mean median uq max neval
# create_pmap(el_10k) 558.3585 564.0215 601.3523 568.4247 583.9266 851.9249 10
# create_pmap(el_100k) 867.2925 898.8099 937.5693 929.7195 1001.6153 1016.4914 10
# create_pmap(el_1000k) 4921.7149 4945.8692 5116.3571 5119.2399 5278.9299 5317.3816 10
# d2
el_10m <- generate_eventlog(10000000, 2000000, c("campaign", "sale"), c(10, 3))
system.time(create_pmap(el_10m))
# 10 Millions records:
# user system elapsed
# 68.376 0.872 69.329
# dplyr::arrange
# Unit: milliseconds
# expr min lq mean median uq max neval
# create_pmap(el_10k) 519.5634 532.7716 669.306 626.399 764.8286 1070.190 10
# create_pmap(el_100k) 851.8473 963.7521 1354.263 1121.218 1391.0232 2527.904 10
# create_pmap(el_1m) 4758.2447 4799.4362 4959.207 4945.269 5066.6584 5308.487 10
# create_pmap(el_10m) 59258.2326 59877.8646 60903.077 60678.284 62170.0964 63480.491 10
# data.table:setorder()
# Unit: milliseconds
# expr min lq mean median uq max neval
# create_pmap(copy(el_10k)) 331.3813 430.4172 1004.1038 486.5171 518.0146 5945.3911 10
# create_pmap(copy(el_100k)) 417.2192 452.8110 544.5129 591.1354 607.8555 641.2991 10
# create_pmap(copy(el_1m)) 2057.1037 2202.2641 4609.8769 2631.9217 7847.6545 8466.5252 10
# create_pmap(copy(el_10m)) 23082.6826 28037.9299 31128.4557 29865.4468 33355.8728 42216.4454 10
el_1m <- generate_eventlog(1000000, 200000, c("campaign", "sale"), c(10, 3))
dt_1m <- as.data.table(el_1m)
microbenchmark(
# data.frame
head(el_1m, nrow(el_1m) - 1),
el_1m[-nrow(el_1m),],
tail(el_1m, nrow(el_1m) - 1),
el_1m[-1,],
# data.table
head(dt_1m, nrow(dt_1m) - 1),
dt_1m[-nrow(dt_1m),],
tail(dt_1m, nrow(dt_1m) - 1),
dt_1m[-1,],
# options
times = 10
)
# Unit: milliseconds
# expr min lq mean median uq max neval
# head(el_1m, nrow(el_1m) - 1) 210.21150 220.92995 498.46559 227.53659 351.69505 2609.82393 10
# el_1m[-nrow(el_1m), ] 221.22007 232.19248 522.66130 242.74177 772.59692 1577.07064 10
# tail(el_1m, nrow(el_1m) - 1) 199.24440 221.85880 359.02000 229.58408 293.01069 1116.23355 10
# el_1m[-1, ] 221.73122 256.88776 557.31452 582.11652 790.43861 915.05392 10
# head(dt_1m, nrow(dt_1m) - 1) 50.43247 60.00987 211.00094 64.51988 75.36679 1004.58526 10
# dt_1m[-nrow(dt_1m), ] 45.35805 57.66475 136.56469 62.09029 73.12450 778.36658 10
# tail(dt_1m, nrow(dt_1m) - 1) 60.19097 62.80756 110.81733 71.27415 102.08019 431.90562 10
# dt_1m[-1, ] 58.69383 61.06019 65.61778 63.77155 64.25639 93.41506 10
# `head()/tail()` ==> `data.table[]`
# Unit: milliseconds
# expr min lq mean median uq max neval
# create_pmap(copy(el_10k)) 339.9077 377.3973 642.3140 456.4188 971.7753 1240.7110 10
# create_pmap(copy(el_100k)) 411.8673 414.4656 535.7509 451.7886 633.8586 956.8731 10
# create_pmap(copy(el_1m)) 1181.9168 1206.9495 1629.7927 1542.5033 1881.3097 2672.0710 10
# create_pmap(copy(el_10m)) 16923.7892 16957.7548 19407.6900 18383.3316 19774.4897 29064.6801 10
el_1m_2 <- generate_eventlog(
size_of_eventlog = 1000 * 1000,
number_of_cases = 100 * 1000,
categories_size = c(30, 5, 20, 5)
)
library(data.table)
el_10m <- generate_eventlog(10000000, 2000000, c("campaign", "sale"), c(10, 3))
profvis::profvis(pmap::create_pmap(data.table::copy(el_10m)))
twang2218/pmap documentation built on Nov. 3, 2021, 11:25 p.m.
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# Provision
# docker-machine create -d digitalocean --digitalocean-region=nyc3 --digitalocean-size=4gb d1
# apt install -y r-base libxml2-dev libssl-dev libcurl4-openssl-dev htop
# curl -sSL https://agent.digitalocean.com/install.sh | sh
install.packages("pmap")
install.packages("microbenchmark")
install.packages("devtools")
clean <- function() {
if ("pmap" %in% installed.packages()[,"Package"]) {
# 'pmap' has been installed, remove first
library(pmap)
detach("package:pmap")
remove.packages("pmap")
# .rs.restartR()
}
}
setup <- function(version) {
devtools::install_github("twang2218/pmap", ref = version)
pkgdb <- installed.packages()
if ("pmap" %in% pkgdb[,"Package"]) {
print(paste("Installed pmap version ", pkgdb["pmap", "Version"]))
library(pmap)
} else {
stop(paste("Unable to install pmap of version:", version))
}
}
library(dplyr)
library(microbenchmark)
library(pmap)
clean()
setup("master")
el_10k <- generate_eventlog(10000, 2000, c("campaign", "sale"), c(10, 3))
el_100k <- generate_eventlog(100000, 20000, c("campaign", "sale"), c(10, 3))
el_1000k <- generate_eventlog(1000000, 200000, c("campaign", "sale"), c(10, 3))
# 0.2.0
clean()
setup("v0.2.0")
el_10k <- generate_random_eventlog(10000, 2000, c("campaign", "sale"), c(10, 3))
el_100k <- generate_random_eventlog(100000, 20000, c("campaign", "sale"), c(10, 3))
el_1000k <- generate_random_eventlog(1000000, 200000, c("campaign", "sale"), c(10, 3))
microbenchmark(
create_pmap_from_eventlog(el_10k),
create_pmap_from_eventlog(el_100k),
# create_pmap_from_eventlog(el_1000k),
times = 10
)
# Unit: seconds
# expr min lq mean median uq max neval
# create_pmap_from_eventlog(el_10k) 1.56275 1.600056 1.637137 1.643266 1.671124 1.73218 10
# create_pmap_from_eventlog(el_100k) 47.74824 48.093884 50.321483 50.637030 51.098359 54.20957 10
# system.time(create_pmap_from_eventlog(el_1000k)) => 130 mins (156x)
# 0.3.2
clean()
setup("v0.3.2")
microbenchmark(
create_pmap(el_10k),
create_pmap(el_100k),
# create_pmap(el_1000k),
times = 10
)
# Unit: seconds
# expr min lq mean median uq max neval
# create_pmap(el_10k) 1.727415 1.799521 1.86058 1.828059 1.888508 2.13194 10
# create_pmap(el_100k) 55.219807 56.539817 57.64809 57.757177 58.450986 59.80957 10
# 0.4.0
clean()
setup("v0.4.0")
microbenchmark(
create_pmap(el_10k),
create_pmap(el_100k),
# create_pmap(el_1000k),
times = 10
)
# Unit: seconds
# expr min lq mean median uq max neval
# create_pmap(el_10k) 1.685579 1.773115 1.865584 1.826707 1.973622 2.11452 10
# create_pmap(el_100k) 57.925665 59.242784 61.246433 60.450505 62.296957 65.79441 10
# master
clean()
setup("master")
library(data.table)
el_10k <- generate_eventlog(10000, 2000, c("campaign", "sale"), c(10, 3))
el_100k <- generate_eventlog(100000, 20000, c("campaign", "sale"), c(10, 3))
el_1m <- generate_eventlog(1000000, 200000, c("campaign", "sale"), c(10, 3))
el_10m <- generate_eventlog(10000000, 2000000, c("campaign", "sale"), c(10, 3))
# Use `copy()` to avoid `data.table::setorder()` modify the original dataset.
microbenchmark(
create_pmap(data.table::copy(el_10k)),
create_pmap(data.table::copy(el_100k)),
create_pmap(data.table::copy(el_1m)),
create_pmap(data.table::copy(el_10m)),
times = 10
)
system.time(data.table::copy(el_10m))
el_100k <- generate_eventlog(100000, 20000, c("campaign", "sale"), c(10, 3))
create_pmap(data.table::copy(el_100k))
el_10m <- generate_eventlog(10000000, 2000000, c("campaign", "sale"), c(10, 3))
create_pmap(data.table::copy(el_1m))
# macbook pro
# Unit: milliseconds
# expr min lq mean median uq max neval
# create_pmap(el_10k) 392.4646 411.3784 452.0301 421.8778 426.4990 602.4456 10
# create_pmap(el_100k) 719.7945 732.0896 741.9988 744.0928 752.5453 767.8442 10
# create_pmap(el_1000k) 4934.7507 5007.0168 5077.1100 5052.3182 5173.4299 5224.5544 10
# d1 (Digital Ocean - 4GB)
# Unit: milliseconds
# expr min lq mean median uq max neval
# create_pmap(el_10k) 507.7968 524.3655 564.0430 542.9571 572.6458 685.7234 10
# create_pmap(el_100k) 807.3958 841.5750 857.1228 859.4381 880.2861 884.7447 10
# create_pmap(el_1000k) 4746.3864 4827.8861 5020.3630 5057.1328 5192.0509 5321.1395 10
# d2 (Digital Ocean - 4GB)
# Unit: milliseconds
# expr min lq mean median uq max neval
# create_pmap(el_10k) 559.0618 581.3116 626.3841 611.7513 622.4935 755.7317 10
# create_pmap(el_100k) 864.9402 885.6535 919.1273 894.4643 934.1724 1095.2642 10
# create_pmap(el_1000k) 4804.6721 5142.7089 5173.3926 5178.5153 5245.1480 5451.2313 10
# d3 (Digital Ocean - 4GB)
# Unit: milliseconds
# expr min lq mean median uq max neval
# create_pmap(el_10k) 558.3585 564.0215 601.3523 568.4247 583.9266 851.9249 10
# create_pmap(el_100k) 867.2925 898.8099 937.5693 929.7195 1001.6153 1016.4914 10
# create_pmap(el_1000k) 4921.7149 4945.8692 5116.3571 5119.2399 5278.9299 5317.3816 10
# d2
el_10m <- generate_eventlog(10000000, 2000000, c("campaign", "sale"), c(10, 3))
system.time(create_pmap(el_10m))
# 10 Millions records:
# user system elapsed
# 68.376 0.872 69.329
# dplyr::arrange
# Unit: milliseconds
# expr min lq mean median uq max neval
# create_pmap(el_10k) 519.5634 532.7716 669.306 626.399 764.8286 1070.190 10
# create_pmap(el_100k) 851.8473 963.7521 1354.263 1121.218 1391.0232 2527.904 10
# create_pmap(el_1m) 4758.2447 4799.4362 4959.207 4945.269 5066.6584 5308.487 10
# create_pmap(el_10m) 59258.2326 59877.8646 60903.077 60678.284 62170.0964 63480.491 10
# data.table:setorder()
# Unit: milliseconds
# expr min lq mean median uq max neval
# create_pmap(copy(el_10k)) 331.3813 430.4172 1004.1038 486.5171 518.0146 5945.3911 10
# create_pmap(copy(el_100k)) 417.2192 452.8110 544.5129 591.1354 607.8555 641.2991 10
# create_pmap(copy(el_1m)) 2057.1037 2202.2641 4609.8769 2631.9217 7847.6545 8466.5252 10
# create_pmap(copy(el_10m)) 23082.6826 28037.9299 31128.4557 29865.4468 33355.8728 42216.4454 10
el_1m <- generate_eventlog(1000000, 200000, c("campaign", "sale"), c(10, 3))
dt_1m <- as.data.table(el_1m)
microbenchmark(
# data.frame
head(el_1m, nrow(el_1m) - 1),
el_1m[-nrow(el_1m),],
tail(el_1m, nrow(el_1m) - 1),
el_1m[-1,],
# data.table
head(dt_1m, nrow(dt_1m) - 1),
dt_1m[-nrow(dt_1m),],
tail(dt_1m, nrow(dt_1m) - 1),
dt_1m[-1,],
# options
times = 10
)
# Unit: milliseconds
# expr min lq mean median uq max neval
# head(el_1m, nrow(el_1m) - 1) 210.21150 220.92995 498.46559 227.53659 351.69505 2609.82393 10
# el_1m[-nrow(el_1m), ] 221.22007 232.19248 522.66130 242.74177 772.59692 1577.07064 10
# tail(el_1m, nrow(el_1m) - 1) 199.24440 221.85880 359.02000 229.58408 293.01069 1116.23355 10
# el_1m[-1, ] 221.73122 256.88776 557.31452 582.11652 790.43861 915.05392 10
# head(dt_1m, nrow(dt_1m) - 1) 50.43247 60.00987 211.00094 64.51988 75.36679 1004.58526 10
# dt_1m[-nrow(dt_1m), ] 45.35805 57.66475 136.56469 62.09029 73.12450 778.36658 10
# tail(dt_1m, nrow(dt_1m) - 1) 60.19097 62.80756 110.81733 71.27415 102.08019 431.90562 10
# dt_1m[-1, ] 58.69383 61.06019 65.61778 63.77155 64.25639 93.41506 10
# `head()/tail()` ==> `data.table[]`
# Unit: milliseconds
# expr min lq mean median uq max neval
# create_pmap(copy(el_10k)) 339.9077 377.3973 642.3140 456.4188 971.7753 1240.7110 10
# create_pmap(copy(el_100k)) 411.8673 414.4656 535.7509 451.7886 633.8586 956.8731 10
# create_pmap(copy(el_1m)) 1181.9168 1206.9495 1629.7927 1542.5033 1881.3097 2672.0710 10
# create_pmap(copy(el_10m)) 16923.7892 16957.7548 19407.6900 18383.3316 19774.4897 29064.6801 10
el_1m_2 <- generate_eventlog(
size_of_eventlog = 1000 * 1000,
number_of_cases = 100 * 1000,
categories_size = c(30, 5, 20, 5)
)
library(data.table)
el_10m <- generate_eventlog(10000000, 2000000, c("campaign", "sale"), c(10, 3))
profvis::profvis(pmap::create_pmap(data.table::copy(el_10m)))
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