romainfrancois commented on pull request #11225:
URL: https://github.com/apache/arrow/pull/11225#issuecomment-928981161
@jonkeane Getting this on master:
``` r
library(arrow, warn.conflicts = FALSE)
#> See arrow_info() for available features
library(dplyr, warn.conflicts = FALSE)
inject_na <- function(x) {
x[sample(seq_along(x), length(x) / 10)] <- NA
x
}
df <- data.frame(
x = runif(1e6),
y = sample(1:100, 1e6, replace = TRUE),
z = sample(month.abb, 1e6, replace = TRUE)
) %>%
mutate(
across(everything(), inject_na, .names = "{.col}_na")
)
a <- Array$create(df)
a$type
#> StructType
#> struct<x: double, y: int32, z: string, x_na: double, y_na: int32, z_na:
string>
bench::workout({
v <- a$as_vector()
v$x
v$y
v$z
v$x_na
v$y_na
v$z_na
})
#> # A tibble: 7 × 3
#> exprs process real
#> <bch:expr> <bch:tm> <bch:tm>
#> 1 v <- a$as_vector() 148ms 148.53ms
#> 2 v$x 49µs 50.43µs
#> 3 v$y 3µs 4.9µs
#> 4 v$z 4µs 4.24µs
#> 5 v$x_na 4µs 3.89µs
#> 6 v$y_na 3µs 3.63µs
#> 7 v$z_na 3µs 3.97µs
# get few values
bench::workout({
v$x[1:10]
v$y[1:10]
v$z[1:10]
v$x_na[1:10]
v$y_na[1:10]
v$z_na[1:10]
})
#> # A tibble: 6 × 3
#> exprs process real
#> <bch:expr> <bch:tm> <bch:tm>
#> 1 v$x[1:10] 11µs 12.71µs
#> 2 v$y[1:10] 5µs 5.96µs
#> 3 v$z[1:10] 4µs 5.16µs
#> 4 v$x_na[1:10] 4µs 4.79µs
#> 5 v$y_na[1:10] 4µs 4.48µs
#> 6 v$z_na[1:10] 4µs 4.82µs
# duplicate -> materialize
bench::workout({
v$x[]
v$y[]
v$z[]
v$x_na[]
v$y_na[]
v$z_na[]
})
#> # A tibble: 6 × 3
#> exprs process real
#> <bch:expr> <bch:tm> <bch:tm>
#> 1 v$x[] 3.71ms 3.71ms
#> 2 v$y[] 8.95ms 8.95ms
#> 3 v$z[] 9.43ms 9.43ms
#> 4 v$x_na[] 67.23ms 67.32ms
#> 5 v$y_na[] 505µs 508.25µs
#> 6 v$z_na[] 4.55ms 4.56ms
```
<sup>Created on 2021-09-28 by the [reprex
package](https://reprex.tidyverse.org) (v2.0.0)</sup>
vs this on, this on this pull request:
``` r
library(arrow, warn.conflicts = FALSE)
#> See arrow_info() for available features
library(dplyr, warn.conflicts = FALSE)
inject_na <- function(x) {
x[sample(seq_along(x), length(x) / 10)] <- NA
x
}
df <- data.frame(
x = runif(1e6),
y = sample(1:100, 1e6, replace = TRUE),
z = sample(month.abb, 1e6, replace = TRUE)
) %>%
mutate(
across(everything(), inject_na, .names = "{.col}_na")
)
a <- Array$create(df)
a$type
#> StructType
#> struct<x: double, y: int32, z: string, x_na: double, y_na: int32, z_na:
string>
bench::workout({
v <- a$as_vector()
v$x
v$y
v$z
v$x_na
v$y_na
v$z_na
})
#> # A tibble: 7 × 3
#> exprs process real
#> <bch:expr> <bch:tm> <bch:tm>
#> 1 v <- a$as_vector() 236µs 237.54µs
#> 2 v$x 43µs 45.07µs
#> 3 v$y 3µs 4.86µs
#> 4 v$z 3µs 3.95µs
#> 5 v$x_na 3µs 3.81µs
#> 6 v$y_na 4µs 3.71µs
#> 7 v$z_na 3µs 3.51µs
# get few values
bench::workout({
v$x[1:10]
v$y[1:10]
v$z[1:10]
v$x_na[1:10]
v$y_na[1:10]
v$z_na[1:10]
})
#> # A tibble: 6 × 3
#> exprs process real
#> <bch:expr> <bch:tm> <bch:tm>
#> 1 v$x[1:10] 14µs 16.55µs
#> 2 v$y[1:10] 13µs 13.92µs
#> 3 v$z[1:10] 40µs 41.81µs
#> 4 v$x_na[1:10] 6µs 7.38µs
#> 5 v$y_na[1:10] 6µs 6.56µs
#> 6 v$z_na[1:10] 30µs 30.25µs
# duplicate -> materialize
bench::workout({
v$x[]
v$y[]
v$z[]
v$x_na[]
v$y_na[]
v$z_na[]
})
#> # A tibble: 6 × 3
#> exprs process real
#> <bch:expr> <bch:tm> <bch:tm>
#> 1 v$x[] 4.17ms 4.17ms
#> 2 v$y[] 2.08ms 2.08ms
#> 3 v$z[] 111.07ms 111.13ms
#> 4 v$x_na[] 6.39ms 6.39ms
#> 5 v$y_na[] 4.12ms 4.12ms
#> 6 v$z_na[] 31.86ms 31.89ms
```
<sup>Created on 2021-09-28 by the [reprex
package](https://reprex.tidyverse.org) (v2.0.0)</sup>
I guess it's a tradeoff, this pr being more lazy, whereas the previous code
was greedy (materializing copies immediately) but also doing it in parallel.
I think it's fine to only pay for making standard R vectors when it becomes
necessary.
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