uniqueN() is very slow compared to length(unique())

[shrektan]

I'm using the lastest dev version of data.table. Still uniqueN() is an order of magnitude slower than length(unique()) . So slow I think it should be tagged as a bug ... See the reprex example below.

Note, the below code uses 4 threads (on a Win7 computer). If we set the thread number to 1, the time cost will be reduced to a half but still significantly slower than length(unique()).

(In fact, the reason I notice this is because I have a daily routine script costs maybe 20 minutes... and trying to improve the speed leads me to the cause - uniqueN())

Character

library(data.table)
set.seed(1000)
mk_rd_words <- function(min = 4, max = 20) {
  n <- floor(runif(1, min, max))
  paste0(sample(c(letters, LETTERS), size = n, replace = TRUE), collapse = '')
}
words <- vapply(1:1000, function(x) mk_rd_words(4, 50), FUN.VALUE = 'a')

n <- 1e4
tbl <- data.table(
  a = sample(words, size = n, replace = TRUE),
  b = sample(words, size = n, replace = TRUE)
)
microbenchmark::microbenchmark(
  times = 100,
  tbl[, .(N = uniqueN(b)), keyby = a],
  tbl[, .(N = length(unique(b))), keyby = a]
)
#> Unit: milliseconds
#>                                        expr       min         lq
#>         tbl[, .(N = uniqueN(b)), keyby = a] 169.13260 171.651133
#>  tbl[, .(N = length(unique(b))), keyby = a]   8.12066   8.607032
#>        mean     median         uq       max neval
#>  176.649940 173.972808 181.373316 201.70846   100
#>    9.233874   8.738746   9.208014  16.28779   100

^{Created on 2019-08-02 by the reprex package (v0.2.1)}

Double

library(data.table)
set.seed(1000)
n <- 1e4
tbl <- data.table(
  a = sample(1:1e3, size = n, replace = TRUE),
  b = sample(1:1e3, size = n, replace = TRUE)
)
microbenchmark::microbenchmark(
  times = 100,
  tbl[, .(N = uniqueN(b)), keyby = a],
  tbl[, .(N = length(unique(b))), keyby = a]
)
#> Unit: milliseconds
#>                                        expr        min         lq
#>         tbl[, .(N = uniqueN(b)), keyby = a] 107.329319 111.531912
#>  tbl[, .(N = length(unique(b))), keyby = a]   5.777745   5.980306
#>        mean     median         uq       max neval
#>  119.497038 115.412347 124.303334 158.04780   100
#>    6.992722   6.314294   7.678619  13.08759   100

^{Created on 2019-08-02 by the reprex package (v0.2.1)}

[shrektan]

Well, I see. For very large vectors, uniqueN() is faster. The problem is if uniqueN() is supposed to be called millions of times (e.g. there're many subgroups), the performance's slowing down is significant.

Maybe for small atomic vector, we just use length(unique()) instead?

(Uses 4 thread on Mac OSX)

library(data.table)
set.seed(1000)
# small vector, uniqueN() is slower
x <- sample(1:1e5, size = 1e2, replace = TRUE)
microbenchmark::microbenchmark(
  uniqueN(x),
  length(unique(x))
)
#> Unit: microseconds
#>               expr    min      lq     mean  median     uq      max neval
#>         uniqueN(x) 20.744 21.3460 32.05373 21.7050 22.226 1010.481   100
#>  length(unique(x))  4.757  5.3565  6.18967  5.9215  6.698   18.199   100
# big vector, uniqueN() is faster
x <- sample(1:1e5, size = 1e6, replace = TRUE)
microbenchmark::microbenchmark(
  uniqueN(x),
  length(unique(x))
)
#> Unit: milliseconds
#>               expr       min       lq     mean   median       uq      max
#>         uniqueN(x)  9.176062 10.31124 11.55733 10.80676 12.44610 16.72396
#>  length(unique(x)) 20.834950 22.74216 26.23369 25.77416 27.84571 43.23249
#>  neval
#>    100
#>    100

^{Created on 2019-08-02 by the reprex package (v0.2.1)}

[jangorecki]

it make sense to include number of cores in those timings

[shrektan]

Thanks for the remind. It uses 4 threads and I've included the thread number in my original post above.

[r2evans]

Another example, perhaps also needing "n-cores" expansion: https://stackoverflow.com/questions/60623235/r-why-dplyr-counts-unique-values-n-distinct-by-groups-faster-than-data-table

[skanskan]

The same happens to me. These two lines produce the same result on my data (with 100000 different IDs and around 5 rows within each ID).

DT[,if(.N==uniqueN(test)) .SD ,by=ID] DT[,c(.SD,.N),by=.(ID,test)][,if(!any(N>1)) .SD, by=ID][,-"N"]

But the former is 10 times slower.

[MichaelChirico]

@skanskan can you confirm that timing's on current master? Since there was just a related commit yesterday. My understanding is the timing should be better (though probably not yet equivalent)

[skanskan]

Right now I'm using the stable version because I'm doing calculations for an article I didn't want to mess with versions. But I'm going to try the master version.

[skanskan]

OK, I have just compared data.table 1.12.8 vs 1.12.9 Windows 10 x64. i7 7700HQ 8 cores. 16GB RAM.

DT[,if(.N==uniqueN(test)) .SD ,by=ID] improved from 44.3s to 17.5s but DT[,c(.SD,.N),by=.(ID,test)][,if(!any(N>1)) .SD, by=ID][,-"N"] now is much slower, went from 1.9s to 6.2s. The slow part seems to be [,if(!any(N>1)) .SD, by=ID].

I have also tried DT[,if(!anyDuplicated(test)) .SD ,by=ID] which was 1.3s and now is 1.4s. DT[,if(.N==length(unique(test))) .SD ,by=ID] is 1.6s.

[jangorecki]

@shrektan any idea what was the version of DT when you reported this issue?

@skanskan j = if (...) .SD looks to be a different issue. Could you please fill up a new issue presenting regression?

[jangorecki]

1.12.8

Unit: milliseconds
                                       expr       min        lq      mean
        tbl[, .(N = uniqueN(b)), keyby = a] 22.487731 23.279140 26.204391
 tbl[, .(N = length(unique(b))), keyby = a]  6.958176  7.077496  8.084379
    median        uq     max neval
 24.351137 25.483656 54.6224   100
  7.321736  8.482808 21.8727   100

master

Unit: milliseconds
                                       expr       min        lq      mean
        tbl[, .(N = uniqueN(b)), keyby = a] 18.444103 19.182902 20.200975
 tbl[, .(N = length(unique(b))), keyby = a]  6.995607  7.304626  7.820315
   median        uq      max neval
 19.83011 20.730743 40.06141   100
  7.48119  8.519765 10.77755   100

4 cores / 50%

[shrektan]

@jangorecki I think I was using the dev version of data.table at time of writing. So it should be version 1.12.3 https://github.com/Rdatatable/data.table/commit/2986736264ac4d776133b19b9a9872519b58879a

BTW, I get basically the same result as you do using version 1.13.0 - still slower (2-3 times) but not that much (10 - 30 times) as it was in 1.12.3 .