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 cld
##  base64url   512   590.5    971.53   769.0   884.5    19845   100   a
##  base64enc  1074  1245.0 108005.33  1647.5  1823.5 10529387   100   a
##    openssl 12793 13099.5  15090.23 13359.5 13767.0    97667   100   a

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 cld
##  base64url   493   577   929.30   726.5  1035.5  13151   100 a  
##  base64enc  3381  3585  4843.14  4236.0  4561.5  70327   100  b 
##    openssl 19140 19594 21121.08 19912.0 20212.0 102854   100   c

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   192.792   199.242   233.7204   226.315   259.414    387.039
##  base64enc  5490.085  5641.812  5921.4435  5700.369  6048.102   8864.857
##    openssl 32889.338 33628.666 35417.5657 34204.795 35348.879 119960.617
##  neval cld
##    100 a  
##    100  b 
##    100   c