To showcase the performance of diseasystore on different
database backends, we include this vignette that summarises a simple
benchmark: A sample dataset is created based on the
datasets::mtcars dataset. This data is repeated 1000 times
and given a unique ID (the row number of the data):
benchmark_data
#> # A tibble: 32,000 × 13
#> row_id car mpg cyl disp hp drat wt qsec vs am gear
#> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 Mazda RX4… 21 6 160 110 3.9 2.62 16.5 0 1 4
#> 2 2 Mazda RX4… 21 6 160 110 3.9 2.88 17.0 0 1 4
#> 3 3 Datsun 71… 22.8 4 108 93 3.85 2.32 18.6 1 1 4
#> 4 4 Hornet 4 … 21.4 6 258 110 3.08 3.22 19.4 1 0 3
#> 5 5 Hornet Sp… 18.7 8 360 175 3.15 3.44 17.0 0 0 3
#> 6 6 Valiant 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3
#> 7 7 Duster 36… 14.3 8 360 245 3.21 3.57 15.8 0 0 3
#> 8 8 Merc 240D… 24.4 4 147. 62 3.69 3.19 20 1 0 4
#> 9 9 Merc 230 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4
#> 10 10 Merc 280 … 19.2 6 168. 123 3.92 3.44 18.3 1 0 4
#> # ℹ 31,990 more rows
#> # ℹ 1 more variable: carb <dbl>A simple diseasystore is built around this data, with
two FeatureHandlers, one each for the cyl and
vs variables.
DiseasystoreMtcars <- R6::R6Class(
classname = "DiseasystoreBase",
inherit = DiseasystoreBase,
private = list(
.ds_map = list("n_cyl" = "mtcars_cyl", "vs" = "mtcars_vs"),
mtcars_cyl = FeatureHandler$new(
compute = function(start_date, end_date, slice_ts, source_conn) {
out <- benchmark_data |>
dplyr::transmute(
"key_car" = .data$car, "n_cyl" = .data$cyl,
"valid_from" = Sys.Date() - lubridate::days(2 * .data$row_id - 1),
"valid_until" = .data$valid_from + lubridate::days(2)
)
return(out)
},
key_join = key_join_sum
),
mtcars_vs = FeatureHandler$new(
compute = function(start_date, end_date, slice_ts, source_conn) {
out <- benchmark_data |>
dplyr::transmute(
"key_car" = .data$car, .data$vs,
"valid_from" = Sys.Date() - lubridate::days(2 * .data$row_id),
"valid_until" = .data$valid_from + lubridate::days(2)
)
return(out)
},
key_join = key_join_sum
)
)
)Two separate benchmark functions are created. The first benchmarking
function tests the computation time of
$get_feature() by computing first the
n_cyl feature then computing the vs feature,
before finally deleting the computations from the database.
The second benchmarking function tests the computation time of
$key_join_features() by joining the
vs feature to the n_cyl observation. Note that
the n_cyl and vs are re-computed before the
benchmarks are started and are not deleted by the benchmarking function
as was the case for the benchmark of
$get_feature(). In addition, we only use
the first 100 rows of the benchmark_data for this test to
reduce computation time.
The performance of these benchmark functions are timed with the
{{microbenchmark}} package using 10 replicates. All
benchmarks are run on the same machine.
