do.eval = requireNamespace("microbenchmark", quietly = TRUE)
This small benchmark compares the performance of the base64 encoding/decoding in package base64url
with the implementations in the packages base64enc
and openssl
.
library(base64url) library(base64enc) library(openssl) library(microbenchmark) x = "plain text" microbenchmark( base64url = base64_urlencode(x), base64enc = base64encode(charToRaw(x)), openssl = base64_encode(x) )
x = "N0JBLlRaUTp1bi5KOW4xWStNWEJoLHRQaDZ3" microbenchmark( base64url = base64_urldecode(x), base64enc = rawToChar(base64decode(x)), openssl = rawToChar(base64_decode(x)) )
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)
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)) )
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