是的,分组在 v 1.12.0 中是并行的
你的基准有点转移注意力。你想要一个fast f(x, y)
如果你想隔离分组的速度。使用示例的基数,但使用一个简单的函数,我们得到:
library(data.table)
packageVersion("data.table")
#> [1] '1.12.0'
n = 5e6
N <- n
k = 1e4
print(getDTthreads())
#> [1] 12
DT = data.table(x = rep_len(runif(n), N),
y = rep_len(runif(n), N),
grp = rep_len(sample(1:k, n, TRUE), N))
bench::system_time(DT[, .(a = 1L), by = "grp"])
#> process real
#> 250.000ms 72.029ms
setDTthreads(1)
bench::system_time(DT[, .(a = 1L), by = "grp"])
#> process real
#> 125.000ms 126.385ms
Created on 2019-02-01 by the reprex package https://reprex.tidyverse.org (v0.2.1)
也就是说,我们在并行情况下稍微快一点,但仅快了大约 50 毫秒——与您的函数的 3 秒相比,可以忽略不计。
如果我们缩小 DT 的大小,我们可以看到更显着的差异:
library(data.table)
packageVersion("data.table")
#> [1] '1.12.0'
n = 5e6
N <- 1e9
k = 1e4
print(getDTthreads())
#> [1] 12
DT = data.table(x = rep_len(runif(n), N),
y = rep_len(runif(n), N),
grp = rep_len(sample(1:k, n, TRUE), N))
bench::system_time(DT[, .(a = 1L), by = "grp"])
#> process real
#> 45.719s 14.485s
setDTthreads(1)
bench::system_time(DT[, .(a = 1L), by = "grp"])
#> process real
#> 24.859s 24.890s
sessioninfo::session_info()
#> - Session info ----------------------------------------------------------
#> setting value
#> version R version 3.5.2 (2018-12-20)
#> os Windows 10 x64
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate English_Australia.1252
#> ctype English_Australia.1252
#> tz Australia/Sydney
#> date 2019-02-01
#>
Created on 2019-02-01 by the reprex package https://reprex.tidyverse.org (v0.2.1)