Benchmark

Encoding of a single string

library(base64url)
library(base64enc)
library(openssl)
library(microbenchmark)

x = "plain text"
microbenchmark(
  base64url = base64_urlencode(x),
  base64enc = base64encode(charToRaw(x)),
  openssl = base64_encode(x)
)

## Unit: nanoseconds
##       expr  min     lq     mean  median      uq    max neval
##  base64url  598  769.5  1540.96   943.5  1018.5  30277   100
##  base64enc 3264 3516.0  6035.51  4011.5  4323.5 120576   100
##    openssl 9329 9845.5 11977.05 10171.0 10801.5 110423   100

Decoding of a single string

x = "N0JBLlRaUTp1bi5KOW4xWStNWEJoLHRQaDZ3"
microbenchmark(
  base64url = base64_urldecode(x),
  base64enc = rawToChar(base64decode(x)),
  openssl = rawToChar(base64_decode(x))
)

## Unit: nanoseconds
##       expr   min      lq     mean  median      uq    max neval
##  base64url   712   862.5  1425.16  1102.5  1272.5  22033   100
##  base64enc  4728  4996.5  7176.57  5781.5  6137.5  63003   100
##    openssl 17927 18564.0 21402.33 18947.5 20211.0 128580   100

Encoding and decoding of character vectors

Here, the task has changed from encoding/decoding a single string to processing multiple strings stored inside a character vector. First, we create a small utility function which returns n random strings with a random number of characters (between 1 and 32) each.

rand = function(n, min = 1, max = 32) {
  chars = c(letters, LETTERS, as.character(0:9), c(".", ":", ",", "+", "-", "*", "/"))
  replicate(n, paste0(sample(chars, sample(min:max, 1), replace = TRUE), collapse = ""))
}
set.seed(1)
rand(10)

##  [1] "zN.n9+TRe"                     "mVA1IX/"                      
##  [3] "1,oSisAaA8xHP"                 "m5U2hXC4S2MK2bGY"             
##  [5] "G7EqegvJTC.uFwSrH0f8x5x"       "G97A1-DXBw0"                  
##  [7] "XiqjqeS"                       "13FC3PTys/RoiG:P*YyDkaXhES/IH"
##  [9] "0FJopP"                        "fcS,PMK*JVPqrYFmZh7"

Only base64url is vectorized for string input, the alternative implementations need wrappers to process character vectors:

base64enc_encode = function(x) {
  vapply(x, function(x) base64encode(charToRaw(x)), NA_character_, USE.NAMES = FALSE)
}

openssl_encode = function(x) {
  vapply(x, function(x) base64_encode(x), NA_character_, USE.NAMES = FALSE)
}

base64enc_decode = function(x) {
  vapply(x, function(x) rawToChar(base64decode(x)), NA_character_, USE.NAMES = FALSE)
}

openssl_decode = function(x) {
  vapply(x, function(x) rawToChar(base64_decode(x)), NA_character_, USE.NAMES = FALSE)
}

The following benchmark measures the runtime to encode 1000 random strings and then decode them again:

set.seed(1)
x = rand(1000)
microbenchmark(
  base64url = base64_urldecode(base64_urlencode(x)),
  base64enc = base64enc_decode(base64enc_encode(x)),
  openssl = openssl_decode(openssl_encode(x))
)

## Unit: microseconds
##       expr       min        lq       mean     median        uq       max
##  base64url   216.926   267.953   351.5029   348.9325   421.912   594.132
##  base64enc  7139.334  8248.340  9306.8484  9067.0465 10182.337 13086.887
##    openssl 22105.490 24446.610 27098.2167 25850.6455 29033.659 60137.814
##  neval
##    100
##    100
##    100