Speed of glue • glue

library(glue)
library(ggplot2)
library(bench)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

Glue is advertised as

Fast, dependency free string literals

So what do we mean when we say that glue is fast? This does not mean glue is the fastest thing to use in all cases, however for the features it provides we can confidently say it is fast.

A good way to determine this is to compare its speed of execution to some alternatives.

base::paste0(), base::sprintf(): Functions in base R implemented in C that provide variable insertion (but not interpolation).
R.utils::gstring(): Provides a similar interface as glue, but uses ${} to delimit blocks to interpolate.
pystr::pystr_format()¹, rprintf::rprintf(): Provide an interface similar to python string formatters with variable replacement, but not arbitrary interpolation.

Note: stringr::str_interp() was previously included in this benchmark, but is now formally marked as “superseded”, in favor of stringr::str_glue(), which just calls glue::glue().

Simple concatenation

bar <- "baz"

simple <- bench::mark(
  glue       = as.character(glue::glue("foo{bar}")),
  gstring    = R.utils::gstring("foo${bar}"),
  paste0     = paste0("foo", bar),
  sprintf    = sprintf("foo%s", bar),
  rprintf    = rprintf::rprintf("foo$bar", bar = bar)
)

simple %>%
  select(expression:total_time) %>%
  arrange(median)
#> # A tibble: 5 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 sprintf     750.9ns 861.01ns  1113764.        0B      0  
#> 2 paste0        1.5µs   1.63µs   581551.        0B     58.2
#> 3 glue         97.7µs 103.77µs     9419.   139.5KB     32.0
#> 4 gstring     227.1µs 237.15µs     4122.    2.45MB     19.1
#> 5 rprintf     278.4µs  285.8µs     3442.   78.14KB     10.3

# plotting function defined in a hidden chunk
plot_comparison(simple)

While glue() is slower than paste0 and sprintf(), it is twice as fast as gstring(), and rprintf().

Although paste0() and sprintf() don’t do string interpolation and will likely always be significantly faster than glue, glue was never meant to be a direct replacement for them.

rprintf::rprintf() does only variable interpolation, not arbitrary expressions, which was one of the explicit goals of writing glue.

So glue is ~2x as fast as the function (gstring()), which has roughly equivalent functionality.

It also is still quite fast, with over 8000 evaluations per second on this machine.

Vectorized performance

Taking advantage of glue’s vectorization is the best way to improve performance. In a vectorized form of the previous benchmark, glue’s performance is much closer to that of paste0() and sprintf().

bar <- rep("bar", 1e5)

vectorized <- bench::mark(
  glue    = as.character(glue::glue("foo{bar}")),
  gstring = R.utils::gstring("foo${bar}"),
  paste0  = paste0("foo", bar),
  sprintf = sprintf("foo%s", bar),
  rprintf = rprintf::rprintf("foo$bar", bar = bar)
)

vectorized %>%
  select(expression:total_time) %>%
  arrange(median)
#> # A tibble: 5 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 paste0       8.32ms   8.34ms     119.    781.3KB     6.40
#> 2 sprintf      9.62ms   9.88ms     101.    781.3KB     4.21
#> 3 gstring     11.19ms  11.27ms      88.7    1.53MB     6.49
#> 4 glue        12.39ms  12.78ms      78.9    2.29MB     9.02
#> 5 rprintf     28.56ms  28.93ms      33.4    3.05MB     2.09

# plotting function defined in a hidden chunk
plot_comparison(vectorized)