rebar3 bench
Rebar3 microbenchmark plugin
Was inspired by Haskell criterion and Rust's criterion.rs. Parts of implementation were borrowed from rebar3 proper plugin. It relies on eministat for statistical calculations.
Use
Add the plugin to your rebar config:
{project_plugins, [rebar3_bench]}.
Add bench_* modules to "test" directory of your application:
my_app/
├── README.md
├── rebar.config
├── src
└── test
├── bench_codec.erl
├── bench_kv.erl
└── bench_calc.erl
Each module should have one or more exported bench_* functions accompanied by optional benchmark
options functions:
-export([reverse/1,
bench_reverse/2,
sort/1,
bench_sort/2]).
reverse({input, _}) ->
lists:seq(1, 1000).
bench_reverse(List, _) ->
lists:reverse(List).
sort({input, _}) ->
lists:seq(1, 1000).
bench_sort(List, _) ->
lists:sort(List).
server(init) ->
% init is called only once in the same process where benchmark will be running
% at the very beginning of benchmark.
% Value returned from this callback will be passed to server({input, State}),
% bench_server(_, State) and server({stop, State})
my_server:start();
server({input, _Server}) ->
% This callback is called after `init` to generate benchmark input data.
% Returned value will be passed to bench_server(Input, _)
binary:copy(<<1>>, 1024);
server({stop, Server}) ->
% Called only once at the very end of benchmark
my_server:stop(Server).
bench_server(BinaryInput, Server) ->
my_server:send(BinaryInput, Server),
BinaryInput = my_server:recv(Server).
Options function will be normally called once before and after benchmark and bench_ function will be
called lots of times.
Each benchmark is executed in a separate process with {priority, high}, {min_heap_size, 5mb}.
Garbage-collection is forced before time measurement is started, but if your benchmark
function produces a lot of heap data and benchmark duration (-t option) is high enough, it's
still possible that garbage collection will be triggered during benchmark execution.
It's importand that input and benchmark are as deterministic as possible. Try to not depend on use of random generators, time or other side effects.
Then just call your plugin directly in an existing application (it will auto-discover your benchmarks):
$ rebar3 bench
===> Testing bench_kv:bench_maps()
===> Stats for wall_time
Min: 133.00ns
25 percentile: 133.00ns
Median: 134.00ns
75 percentile: 135.00ns
Max: 174.00ns
Outliers: Lo: 0; Hi: 8; Sum: 8
Outlier variance: 0.316545 (moderate)
> Bootstrapped
Mean: 135.00ns
Std deviation: 5.00ns
Please, refer to eministat README to find out what does the output mean.
This will collect 100 samples, dump them to special directory under _build and calculates
statistics over them.
If you make your optimizations and run the same command again, it will again collect 100 samples using your new code, calculates statistics, comparing results with previous dump and will try to statistically prove that there exists significant difference between results of two runs - if there is an improvement or regression:
$ rebar3 bench
===> Testing bench_kv:bench_maps()
===> Stats for wall_time
Min: 133.00ns (+ 0.00ns / 0.3%)
25 percentile: 134.00ns (+ 1.00ns / 0.4%)
Median: 134.00ns (- 0.00ns / 0.1%)
75 percentile: 135.00ns (- 7.00ns / 4.9%)
Max: 146.00ns (- 0.00ns / 0.1%)
Outliers: Lo: 0; Hi: 5; Sum: 5
Outlier variance: 9.90000e-3 (unaffected)
> Bootstrapped
Mean: 135.00ns (- 2.00ns / 1.6%)
Std deviation: 2.00ns
> Relative
Difference at 95.0 confidence
-2.00ns ± 1.00ns
-1.60% ± 0.77%
(Student's t, pooled s = 3.21558)
The most important field to pay attention is Mean: 135.00ns (- 2.00ns / 1.6%) means
that the runtime of the single call to benchmarked function decreased by 2ns (1.6%), which
is a very minor improvement.
However, in the section below >Relative the statistical proof says, that this improvement is
not caused by random fluctuations, but is quite stable - "proven".
Another important data point is Outlier variance. If the value is more than ~0.5, it means
that the runtime of benchmarked function varies a lot between calls. So you need to either
simplify this function, remove side-effects from it or increase the benchmark runtime to
smoothen the influence of side-effects.
Example workflow
You can use --dump-baseline and --baseline to manage benchmark samples dumps.
git checkout master
# run benchmark and store results in "master" file
rebar3 bench --save-baseline master
git checkout optimizations
# run benchmark and compare results with data from "master" file
rebar3 bench --baseline master
# Do some optimizations
# run again and compare with "master" data to see if optimizations do work
rebar3 bench --baseline master
Cover mode
It might be interesting to see which lines of code your benchmark actually covers and what are
the most "hot" code paths. You can run your benchmark with "cover" enabled:
rebar3 bench --cover. It will run your benchmarks and will write coverage data to rebar3
standard destination, you can use rebar3 cover to see the report.
We don't do any measurements calculate statistics or write baseline files in cover mode,
because cover-compiled code is much slower.
Available options
$ rebar3 help bench
Run microbenchmarks from callback modules
Usage: rebar3 bench [-d [<dir>]] [-m <module>] [-b <benches>]
[-t [<duration>]] [-n [<samples>]]
[--confidence [<confidence>]] [-c [<cover>]]
[-p [<parameter>]]
[--save-baseline [<save_baseline>]]
[--baseline [<baseline>]]
-d, --dir directory where the benchmark tests are located
[default: test]
-m, --module name of one or more modules to run (comma-separated)
-b, --bench name of benchmark to run within a specified module
(comma-separated)
-t, --duration duration of single benchmark, in seconds [default: 10]
-n, --num-samples number of samples to collect and analyze [default:
100]
--confidence confidence level: 80, 90, 95, 98, 99 [default: 95]
-c, --cover run benchmarks in coverage mode (no measurements are
made), generate cover data [default: false]
-p, --parameter which parameter to measure: wall_time, memory,
reductions [default: wall_time]
--save-baseline save benchmark data to file with this name [default:
_tip]
--baseline use data stored in file as baseline [default: _tip]
