Simple Benchmarks on logger Performance • logger

Although this has not been an important feature in the early development and overall design of this logger implementation, but with the default ?layout_simple and ?formatter_glue, it seems to perform pretty well when comparing with futile.logger and logging packages:

library(microbenchmark)

## fl
library(futile.logger)
t1 <- tempfile()
flog.appender(appender.file(t1))
#> NULL

## lg
library(logging)
t2 <- tempfile()
addHandler(writeToFile, file = t2)

## lr
library(logger)
#> The following objects are masked from ‘package:futile.logger’: DEBUG, ERROR, FATAL, INFO, TRACE, WARN
t3 <- tempfile()
log_appender(appender_file(t3))

string_fl <- function() flog.info('hi')
string_lg <- function() loginfo('hi')
string_lr <- function() log_info('hi')
dynamic_fl <- function() flog.info('hi %s', 42)
dynamic_lg <- function() loginfo('hi %s', 42)
dynamic_lr <- function() log_info('hi {42}')
vector_fl <- function() flog.info(paste('hi', 1:5))
vector_lg <- function() loginfo(paste('hi', 1:5))
vector_lr <- function() log_info('hi {1:5}')

microbenchmark(
    string_fl(), string_lg(), string_lr(),
    vector_fl(), vector_lg(), vector_lr(),
    dynamic_fl(), dynamic_lg(), dynamic_lr(),
    times = 1e3)
#> Unit: microseconds
#>          expr      min        lq      mean    median        uq        max neval
#>   string_fl() 1533.379 1650.7915 2510.5517 1759.9345 2885.4465  20835.425  1000
#>   string_lg()  172.963  206.7615  315.6177  237.3150  335.3010  12738.735  1000
#>   string_lr()  227.981  263.4715  390.7139  301.9045  409.0400  11926.974  1000
#>   vector_fl() 1552.706 1661.7030 2434.0460 1766.7485 2819.5525  40892.197  1000
#>   vector_lg()  198.338  234.2355  330.3268  266.7695  358.2510   9969.333  1000
#>   vector_lr()  290.169  337.4730  592.0041  382.4335  537.5485 101946.435  1000
#>  dynamic_fl() 1538.985 1663.7890 2564.6668 1782.1160 2932.7555  46039.686  1000
#>  dynamic_lg()  188.213  226.5370  387.2470  255.1745  350.2015  60737.562  1000
#>  dynamic_lr()  271.478  317.3350  486.1123  360.5815  483.5830  12070.936  1000

paste(t1, length(readLines(t1)))
#> [1] "/tmp/Rtmp3Fp6qa/file7a8919485a36 7000"
paste(t2, length(readLines(t2)))
#> [1] "/tmp/Rtmp3Fp6qa/file7a89b17929f 7000"
paste(t3, length(readLines(t3)))
#> [1] "/tmp/Rtmp3Fp6qa/file289f24c88c41 7000"

So based on the above, non-comprehensive benchmark, it seems that when it comes to using the very base functionality of a logging engine, logging comes first, then logger performs with a bit of overhead due to using glue by default, then comes a bit slower futile.logger.

On the other hand, there are some low-hanging fruits to improve performance, eg caching the logger function in the namespace, or using much faster message formatters (eg paste0 or sprintf instead of glue) if needed – like what futile.logger and logging are using instead of glue, so a quick logger comparison:

log_formatter(formatter_sprintf)
string <- function() log_info('hi')
dynamic <- function() log_info('hi %s', 42)
vector <- function() log_info(paste('hi', 1:5))

microbenchmark(string(), vector(), dynamic(), times = 1e3)
#> Unit: microseconds
#>       expr     min       lq     mean   median       uq      max neval cld
#>   string() 110.192 118.4850 148.5848 137.1825 152.7275 1312.903  1000  a
#>   vector() 129.111 136.8245 168.9274 155.5840 172.6795 3230.528  1000   b
#>  dynamic() 116.347 124.7620 159.1570 143.2140 160.5040 4397.640  1000  ab

Which suggests that logger is a pretty well-performing log framework.

If you need even more performance with slower appenders, then asynchronous logging is your friend: passing the log messages to a reliable message queue, and a background process delivering those to the actual log destination in the background – without blocking the main R process. This can be easily achieved in logger by wrapping any appender function in the appender_async function, such as:

## demo log appender that's pretty slow
appender_file_slow <- function(file) {
  force(file)
  function(lines) {
    Sys.sleep(1)
    cat(lines, sep = '\n', file = file, append = TRUE)
  }
}

## create an async appender and start using it right away
log_appender(appender_async(appender_file_slow(file = tempfile())))

async <- function() log_info('Was this slow?')
microbenchmark(async(), times = 1e3)
#> Unit: microseconds
#>     expr     min      lq     mean   median     uq      max neval
#>  async() 511.329 528.247 614.8694 558.2535 616.14 5018.731  1000

Please note that although this ~0.6 ms is significantly higher than the ~0.15 ms we achieved above with the sprintf formatter, but this time we are calling an appender that would take 1 full second to deliver the log message (and not just printing to the console), so bringing that down to less than 1 millisecond is not too bad. If you need even higher throughput, then a custom appender_async without checking on the background process and potentially a faster message queue can bring this even below to 200 µs.