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On Disk Data Storage for Cross-Session Access in R
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README.md

odds

The goal of {odds} is to provide an on-disk data-storage of native R object, for cross-session data access.

Installation

You can install the dev version of {odds} from GitHub with:

remotes::install_github("colinfay/odds")

Basic use

The main goal of {odds} is to create a data storage architecture, on disk, so that you can access the values from one session to another.

How it works

By default, the storage is done at ~/.odds, but it can be changed when creating the storage object.

Note that the path is passed through fs::path_norm(), which doesn’t treat ~ the same way as base R on Windows.

library(odds)
st <- Storage$new()

There are two main methods: set() and get(). The first saves a value under a name on the disk, the second retrieve this value from the storage.

st$set(head(iris), "a")
st$get("a")
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1          5.1         3.5          1.4         0.2  setosa
#> 2          4.9         3.0          1.4         0.2  setosa
#> 3          4.7         3.2          1.3         0.2  setosa
#> 4          4.6         3.1          1.5         0.2  setosa
#> 5          5.0         3.6          1.4         0.2  setosa
#> 6          5.4         3.9          1.7         0.4  setosa

Storages can be namespaced, and the default is “global”,

nsp <- paste(sample(letters, 3), collapse = "")

st$set(mtcars, "a", namespace = nsp)
st$get("a", namespace = nsp)
#>                      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

Cross session access

Let’s create an object in another R session:

library(callr)
rx <- r_bg(
  function(){
    library(odds)
    st <- Storage$new()
    st$set(head(airquality), "ping", namespace = "blop")
  }
)

It’s now accessible in the first session:

st$get("ping", namespace = "blop")
#>   Ozone Solar.R Wind Temp Month Day
#> 1    41     190  7.4   67     5   1
#> 2    36     118  8.0   72     5   2
#> 3    12     149 12.6   74     5   3
#> 4    18     313 11.5   62     5   4
#> 5    NA      NA 14.3   56     5   5
#> 6    28      NA 14.9   66     5   6

Values can be deleted:

st$rm("ping", namespace = "blop")

Namespaces can be deleted:

st$remove_namespace(nsp)
st$remove_namespace("blop")

Overhead

Of course, reading from disk adds some overhead, but for small to medium size objects, the cost of getting from disk instead of reading for RAM is pretty small.

library(dplyr, warn.conflicts = FALSE)
library(ggplot2)
st$set(diamonds, "dm", "bench")
bench::mark(
  ram = {
   diamonds %>% filter(cut == "Ideal")
  }, 
  disk = {
    st$get("dm", "bench") %>% 
      filter(cut == "Ideal")
  }
)
#> # A tibble: 2 x 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 ram           1.5ms   3.01ms     287.     3.07MB     21.7
#> 2 disk         12.7ms  14.55ms      66.7    6.05MB     12.8

set() and get() are powered by {qs} qread() and qwrite() and take the same arguments, so you can use parameters to these functions to speed up the read and write timing.

Read the {qs} benchmark online.

Acknowledgment

This package heavily relies on the {qs} package. Thanks to the package authors for their work.

Coc

Please note that the ‘odds’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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