Recently I ran accross a problem: The program I’m writing reads from hundreds of large data sets totaling in tens of gigabytes, and the memory on my or any potential user’s machine simply won’t fit.
Ideally, for caching objects on the disk, I would have wanted to use more sophisticated solusions such as Roger Peng’s filehash. Instead, I just wanted to simply dump everything onto the disk, and read & process each as needed, with the following solution:
- Write each data set as a binary file to the disk
- Make a function that reads from its own binary file when called
It goes as follows:
stash <- function(object, dir_path = tempdir()){
file_name <- paste0(paste0(sample(c(letters, LETTERS, 0:9), 20, TRUE), collapse = ""), ".RStash")
file_path <- file.path(dir_path, file_name)
saveRDS(object, file_path)
f <- function(){
if (!file.exists(file_path)){
stop("stash file missing.")
} else {
readRDS(file_path)
}
}
structure(f,
class = c("stash_pointer", class(f)),
file_path = file_path,
obj_size = format(object.size(object), unit = "MB", digits = 2),
obj_class = class(object)
)
}
So calling stash()
saves a binary of the object onto the disk, and returns a function that is going to read from this the file when called, with relevant metadata. I just call the cache on the disk “stash” and this function a “stash pointer”.
Let’s test it out.
mtcars2 <- stash(mtcars)
mtcars2()
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Perfect!
Let’s add some housekeeping funcitions, too.
print.stash_pointer <- function(x){
cat(paste0("<stash_pointer>", " `", attr(x, "obj_class")[1],"` ", attr(x, "obj_size"), "\n"))
cat("- ", paste(attr(x, "file_path")))
}
# delete the cache on disk
clear_stash <- function(stash_pointer){
file_path <- attr(stash_pointer, "file_path")
if (file.exists(file_path)){
file.remove(file_path)
}
}
stash_exists <- function(stash_pointer){
file.exists(attr(stash_pointer, "file_path"))
}
is_stash_pointer <- function(x){
inherits(x, "stash_pointer")
}
mtcars2
## <stash_pointer> `data.frame` 0.01 Mb
## - C:\Users\ELATI\AppData\Local\Temp\RtmpC2ecQK/frtPFqQUQyGw4FD9P0t1.RStash
is_stash_pointer(mtcars2)
## [1] TRUE
clear_stash(mtcars2)
## [1] TRUE
stash_exists(mtcars2)
## [1] FALSE
df_list <- list(mtcars, iris, chickwts, PlantGrowth, USArrests)
Let’s utilize purrr
’s functional programming interface and stash()
everything in df_list
:
require(purrr)
## Loading required package: purrr
stash_pointer_list <- map(df_list, stash)
The result is a list of stash_pointer
s:
stash_pointer_list
## [[1]]
## <stash_pointer> `data.frame` 0.01 Mb
## - C:\Users\ELATI\AppData\Local\Temp\RtmpC2ecQK/lBXoCPw8KfFLyhUEDQ8c.RStash
## [[2]]
## <stash_pointer> `data.frame` 0.01 Mb
## - C:\Users\ELATI\AppData\Local\Temp\RtmpC2ecQK/vqcoMazC0vMm2bh1KJWB.RStash
## [[3]]
## <stash_pointer> `data.frame` 0 Mb
## - C:\Users\ELATI\AppData\Local\Temp\RtmpC2ecQK/I08InLu4DOnWRyAdupho.RStash
## [[4]]
## <stash_pointer> `data.frame` 0 Mb
## - C:\Users\ELATI\AppData\Local\Temp\RtmpC2ecQK/KCKIOEQ994qOBjijQPoJ.RStash
## [[5]]
## <stash_pointer> `data.frame` 0.01 Mb
## - C:\Users\ELATI\AppData\Local\Temp\RtmpC2ecQK/hgGc332c70GpVXN3a3U7.RStash
See the column name of every data frame:
map(stash_pointer_list, ~ exec(.) %>% colnames())
## [[1]]
## [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
## [11] "carb"
##
## [[2]]
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
##
## [[3]]
## [1] "weight" "feed"
##
## [[4]]
## [1] "weight" "group"
##
## [[5]]
## [1] "Murder" "Assault" "UrbanPop" "Rape"
Cheers!
For real solutions on data that’s too big for memory, checkout fst
and bigmemory
, among others.