Benchee Hex Version docs Build Status Travis/Linux Build status AppVeyor/Windows Coverage Status Inline docs

Library for easy and nice (micro) benchmarking in Elixir. It allows you to compare the performance of different pieces of code at a glance. Benchee is also versatile and extensible, relying only on functions! There are also a bunch of plugins to draw pretty graphs and more!

Benchee runs each of your functions for a given amount of time after an initial warmup, it then measures their run time and optionally memory consumption. It then shows different statistical values like average, iterations per second and the standard deviation.

Benchee has a nice and concise main interface, its behavior can be altered through lots of configuration options:

list = Enum.to_list(1..10_000)
map_fun = fn(i) -> [i, i * i] end{
  "flat_map"    => fn -> Enum.flat_map(list, map_fun) end,
  "map.flatten" => fn -> list |> |> List.flatten end
}, time: 10, memory_time: 2)

Produces the following output on the console:

tobi@speedy:~/github/benchee(master)$ mix run samples/run.exs
Operating System: Linux"
CPU Information: Intel(R) Core(TM) i7-4790 CPU @ 3.60GHz
Number of Available Cores: 8
Available memory: 15.61 GB
Elixir 1.6.4
Erlang 20.3

Benchmark suite executing with the following configuration:
warmup: 2 s
time: 10 s
memory time: 2 s
parallel: 1
inputs: none specified
Estimated total run time: 28 s

Benchmarking flat_map...
Benchmarking map.flatten...

Name                  ips        average  deviation         median         99th %
flat_map           2.31 K      433.25 μs     ±8.64%         428 μs         729 μs
map.flatten        1.22 K      822.22 μs    ±16.43%         787 μs        1203 μs

flat_map           2.31 K
map.flatten        1.22 K - 1.90x slower

Memory usage statistics:

Name           Memory usage
flat_map          625.54 KB
map.flatten       781.85 KB - 1.25x memory usage

**All measurements for memory usage were the same**

The aforementioned plugins like benchee_html make it possible to generate nice looking html reports, where individual graphs can also be exported as PNG images:



  • first runs the functions for a given warmup time without recording the results, to simulate a “warm” running system
  • measures memory
  • provides you with lots of statistics - check the next list
  • plugin/extensible friendly architecture so you can use different formatters to generate CSV, HTML and more
  • nicely formatted console output with units scaled to appropriate units
  • hooks to execute something before/after a benchmark
  • execute benchmark jobs in parallel to gather more results in the same time, or simulate a system under load
  • well tested
  • well documented

Provides you with the following statistical data:

  • average - average execution time (the lower the better)
  • ips - iterations per second, aka how often can the given function be executed within one second (the higher the better)
  • deviation - standard deviation (how much do the results vary), given as a percentage of the average (raw absolute values also available)
  • median - when all measured times are sorted, this is the middle value (or average of the two middle values when the number of samples is even). More stable than the average and somewhat more likely to be a typical value you see. (the lower the better)
  • 99th % - 99th percentile, 99% of all run times are less than this

In addition, you can optionally output an extended set of statistics:

  • minimum - the smallest (fastest) run time measured for the job
  • maximum - the biggest (slowest) run time measured for the job
  • sample size - the number of run time measurements taken
  • mode - the run time(s) that occur the most. Often one value, but can be multiple values if they occur the same amount of times. If no value occurs at least twice, this value will be nil.


Add benchee to your list of dependencies in mix.exs:

defp deps do
  [{:benchee, "~> 0.11", only: :dev}]

Install via mix deps.get and then happy benchmarking as described in Usage :)

Elixir versions supported are 1.4+.


After installing just write a little Elixir benchmarking script:

list = Enum.to_list(1..10_000)
map_fun = fn(i) -> [i, i * i] end{
  "flat_map"    => fn -> Enum.flat_map(list, map_fun) end,
  "map.flatten" => fn -> list |> |> List.flatten end

(names can also be specified as :atoms if you want to)

This produces the following output:

tobi@speedy ~/github/benchee $ mix run samples/run.exs
Elixir 1.4.0
Erlang 19.1
Benchmark suite executing with the following configuration:
warmup: 2.0s
time: 5.0s
parallel: 1
inputs: none specified
Estimated total run time: 14.0s

Benchmarking flat_map...
Benchmarking map.flatten...

Name                  ips        average  deviation         median
flat_map           2.28 K      438.07 μs    ±16.66%         419 μs
map.flatten        1.25 K      802.99 μs    ±13.40%         782 μs

flat_map           2.28 K
map.flatten        1.25 K - 1.83x slower

See Features for a description of the different statistical values and what they mean.

If you’re looking to see how to make something specific work, please refer to the samples directory. Also, especially when wanting to extend benchee check out the hexdocs.


Benchee takes a wealth of configuration options, however those are entirely optional. Benchee ships with sensible defaults for all of these.

In the most common interface configuration options are passed as the second argument in the form of an optional keyword list:{"some function" => fn -> magic end}, print: [benchmarking: false])

The available options are the following (also documented in hexdocs).

  • warmup - the time in seconds for which a benchmarking job should be run without measuring times before “real” measurements start. This simulates a “warm” running system. Defaults to 2.
  • time - the time in seconds for how long each individual benchmarking job should be run for measuring the execution times (run time performance). Defaults to 5.
  • memory_time - the time in seconds for how long memory measurements should be conducted. Defaults to 0 (turned off).
  • inputs - a map from descriptive input names to some different input, your benchmarking jobs will then be run with each of these inputs. For this to work your benchmarking function gets the current input passed in as an argument into the function. Defaults to nil, aka no input specified and functions are called without an argument. See Inputs.
  • formatters - list of formatters either as module implementing the formatter behaviour or formatter functions. They are run when using Functions need to accept one argument (which is the benchmarking suite with all data) and then use that to produce output. Used for plugins. Defaults to the builtin console formatter Benchee.Formatters.Console. See Formatters.
  • pre_check - whether or not to run each job with each input - including all given before or after scenario or each hooks - before the benchmarks are measured to ensure that your code executes without error. This can save time while developing your suites. Defaults to false.
  • parallel - the function of each benchmarking job will be executed in parallel number processes. If parallel: 4 then 4 processes will be spawned that all execute the same function for the given time. When these finish/the time is up 4 new processes will be spawned for the next job/function. This gives you more data in the same time, but also puts a load on the system interfering with benchmark results. For more on the pros and cons of parallel benchmarking check the wiki. Defaults to 1 (no parallel execution).
  • save - specify a path where to store the results of the current benchmarking suite, tagged with the specified tag. See Saving & Loading.
  • load - load saved suit or suits to compare your current benchmarks against. Can be a string or a list of strings or patterns. See Saving & Loading.
  • print - a map from atoms to true or false to configure if the output identified by the atom will be printed during the standard Benchee benchmarking process. All options are enabled by default (true). Options are:

    • :benchmarking - print when Benchee starts benchmarking a new job (Benchmarking name ..)
    • :configuration - a summary of configured benchmarking options including estimated total run time is printed before benchmarking starts
    • :fast_warning - warnings are displayed if functions are executed too fast leading to inaccurate measures
  • console - options for the built-in console formatter:

    • :comparison - if the comparison of the different benchmarking jobs (x times slower than) is shown. Enabled by default.
    • extended_statistics - display more statistics, aka minimum, maximum, sample_size and mode. Disabled by default.
  • :unit_scaling - the strategy for choosing a unit for durations and counts. May or may not be implemented by a given formatter (The console formatter implements it). When scaling a value, Benchee finds the “best fit” unit (the largest unit for which the result is at least 1). For example, 1_200_000 scales to 1.2 M, while 800_000 scales to 800 K. The unit_scaling strategy determines how Benchee chooses the best fit unit for an entire list of values, when the individual values in the list may have different best fit units. There are four strategies, defaulting to :best:

    • :best - the most frequent best fit unit will be used, a tie will result in the larger unit being selected.
    • :largest - the largest best fit unit will be used (i.e. thousand and seconds if values are large enough).
    • :smallest - the smallest best fit unit will be used (i.e. millisecond and one)
    • :none - no unit scaling will occur. Durations will be displayed in microseconds, and counts will be displayed in ones (this is equivalent to the behaviour Benchee had pre 0.5.0)
  • :before_scenario/after_scenario/before_each/after_each - read up on them in the hooks section

Measuring memory consumption

Starting with version 0.13, users can now get measurements of how much memory their benchmarks use. This measurement is not the actual effect on the size of the BEAM VM size, but the total amount of memory that was allocated during the execution of a given scenario. This includes all memory that was garbage collected during the execution of that scenario. It does not include any memory used in any process other than the original one in which the scenario is run.

This measurement of memory does not affect the measurement of run times.

In cases where all measurements of memory consumption are identical, which happens very frequently, the full statistics will be omitted from the standard console formatter. If your function is deterministic, this will always be the case. Only in functions with some amount of randomness will there be variation in memory usage.

Memory measurement is disabled by default, and you can choose to enable it by passing memory_time: your_seconds option to{
  "something_great" => fn -> cool_stuff end
}, memory_time: 2)

Memory time can be specified separately as it will often be constant - so it might not need as much measuring time.

A full example, including an example of the console output, can be found here.


:inputs is a very useful configuration that allows you to run the same benchmarking jobs with different inputs. You specify the inputs as a map from name (String or atom) to the actual input value. Functions can have different performance characteristics on differently shaped inputs - be that structure or input size.

One of such cases is comparing tail-recursive and body-recursive implementations of map. More information in the repository with the benchmark and the blog post.

map_fun = fn(i) -> i + 1 end
inputs = %{
  "Small (1 Thousand)"    => Enum.to_list(1..1_000),
  "Middle (100 Thousand)" => Enum.to_list(1..100_000),
  "Big (10 Million)"      => Enum.to_list(1..10_000_000),
} %{
  "map tail-recursive" =>
    fn(list) -> MyMap.map_tco(list, map_fun) end,
  "stdlib map" =>
    fn(list) ->, map_fun) end,
  "map simple body-recursive" =>
    fn(list) -> MyMap.map_body(list, map_fun) end,
  "map tail-recursive different argument order" =>
    fn(list) -> MyMap.map_tco_arg_order(list, map_fun) end
}, time: 15, warmup: 5, inputs: inputs

This means each function will be benchmarked with each input that is specified in the inputs. Then you’ll get the output divided by input so you can see which function is fastest for which input.

Therefore, I highly recommend using this feature and checking different realistically structured and sized inputs for the functions you benchmark!


Among all the configuration options, one that you probably want to use are the formatters. They format and print out the results of the benchmarking suite.

The :formatters option is specified a list of:

  • modules implementing the Benchee.Formatter behaviour, or…
  • functions that take one argument (the benchmarking suite with all its results) and then do whatever you want them to

So if you are using the HTML plugin and you want to run both the console formatter and the HTML formatter this looks like this (after you installed it of course):

list = Enum.to_list(1..10_000)
map_fun = fn(i) -> [i, i * i] end{
  "flat_map"    => fn -> Enum.flat_map(list, map_fun) end,
  "map.flatten" => fn -> list |> |> List.flatten end
  formatters: [
  formatter_options: [html: [file: "samples_output/my.html"]]

Extended Console Formatter Statistics

Showing more statistics such as minimum, maximum, sample_size and mode is as simple as passing extended_statistics: true to the console formatter.

list = Enum.to_list(1..10_000)
map_fun = fn(i) -> [i, i * i] end{
  "flat_map"    => fn -> Enum.flat_map(list, map_fun) end,
  "map.flatten" => fn -> list |> |> List.flatten end
}, time: 10, formatter_options: %{console: %{extended_statistics: true}})

Which produces:

# your normal output...

Extended statistics:
Name                minimum        maximum    sample size                     mode
flat_map             365 μs        1371 μs        22.88 K                   430 μs
map.flatten          514 μs        1926 μs        13.01 K                   517 μs

(Btw. notice how the modes of both are much closer and for map.flatten much less than the average of 766.99, see samples/run_extended_statistics)

Saving, loading and comparing previous runs

Benchee can store the results of previous runs in a file and then load them again to compare them. For example this is useful to compare what was recorded on the master branch against a branch with performance improvements.

Saving is done through the save configuration option. You can specify a path where results are saved, or you can use the default option of"benchmark.benchee" if you don’t pass a path. You can also pass a tag option which annotates these results (for instance with a branch name). The default option for the tag is a timestamp of when the benchmark was run.

Loading is done through the load option specifying a path to the file to load (for instance benchmark.benchee). You can also specify multiple files to load through a list of paths (["my.benchee", "master_save.benchee"]) - each one of those can also be a glob expression to match even more files glob ("save_number*.benchee").{
  "something_great" => fn -> cool_stuff end
  save: [path: "save.benchee", tag: "first-try"]
){}, load: "save.benchee")

In the more verbose API this is triggered via Benchee.load/1.

Hooks (Setup, Teardown etc.)

Most of the time, it’s best to keep your benchmarks as simple as possible: plain old immutable functions work best. But sometimes you need other things to happen. When you want to add before or after hooks to your benchmarks, we’ve got you covered! Before you dig into this section, you usually don’t need hooks.

Benchee has three types of hooks:

Of course, hooks are not included in the measured run times. So they are there especially if you want to do something and want it to not be included in the measured times. Sadly there is the notable exception of too_fast_functions (the ones that execute in less than 10 microseconds). As we need to measure their repeated invocations to get halfway good measurements before_each and after_each hooks are included there.

Suite hooks

It is very easy in benchee to do setup and teardown for the whole benchmarking suite (think: “before all” and “after all”). As benchee is just plain old functions, just do your setup and teardown before/after you call benchee:

your_setup() %{"Awesome stuff" => fn -> magic end }


When might this be useful?

  • Seeding the database with data to be used by all benchmarking functions
  • Starting/shutting down a server, process, other dependencies

Scenario hooks

For the following discussions, it’s important to know what benchee considers a “benchmarking scenario”.

What is a scenario?

A scenario is the combination of one benchmarking function and one input. So, given this benchmark: %{
  "foo" => fn(input) -> ... end,
  "bar" => fn(input) -> ... end
}, inputs: %{
  "input 1" => 1,
  "input 2" => 2

there are 4 scenarios:

  1. foo with input 1
  2. foo with input 2
  3. bar with input 1
  4. bar with input 2

A scenario includes warmup and actual run time.


Is executed before every scenario that it applies to (see hook configuration). Before scenario hooks take the input of the scenario as an argument.

Since the return value of a before_scenario becomes the input for next steps (see hook arguments and return values), there usually are 3 kinds of before scenarios:

  • you just want to invoke a side effect: in that case return the given input unchanged
  • you want to alter the given input: in that case alter the given input
  • you want to keep the given input but add some other data: in that case return a tuple like {original_input, new_fancy_data}

For before scenario hooks, the global hook is invoked first, then the local (see when does a hook happen?).

Usage: %{
  "foo" =>
      fn({input, resource}) -> foo(input, resource) end,
      before_scenario: fn({input, resource}) ->
        resource = alter_resource(resource)
        {input, resource}
  "bar" =>
    fn({input, resource}) -> bar(input, resource) end
}, inputs: %{"input 1" => 1},
   before_scenario: fn(input) ->
     resource = start_expensive_resource()
     {input, resource}

When might this be useful?

  • Starting a process to be used in your scenario
  • Recording the PID of self() for use in your benchmark (each scenario is executed in its own process, so scenario PIDs aren’t available in functions running before the suite)
  • Clearing the cache before a scenario

Is executed after a scenario has completed. After scenario hooks receive the return value of the last before_scenario that ran as an argument. The return value is discarded (see hook arguments and return values).

For after scenario hooks, the local hook is invoked first, then the global (see when does a hook happen?).

Usage: %{
  "bar" => fn -> bar() end
}, after_scenario: fn(_input) -> bust_my_cache() end

When might this be useful?

  • Busting caches after a scenario completed
  • Deleting all the records in the database that the scenario just created
  • Terminating whatever was setup by before_scenario

Benchmarking function hooks

You can also schedule hooks to run before and after each invocation of a benchmarking function.


Is executed before each invocation of the benchmarking function (before every measurement). Before each hooks receive the return value of their before_scenario hook as their argument. The return value of a before each hook becomes the input to the benchmarking function (see hook arguments and return values).

For before each hooks, the global hook is invoked first, then the local (see when does a hook happen?).

Usage: %{
  "bar" => fn(record) -> bar(record) end
}, inputs: %{ "record id" => 1},
   before_each: fn(input) -> get_from_db(input) end

When might this be useful?

  • Retrieving a record from the database and passing it on to the benchmarking function to do something(tm) without the retrieval from the database adding to the benchmark measurement
  • Busting caches so that all measurements are taken in an uncached state
  • Picking a random value from a collection and passing it to the benchmarking function for measuring performance with a wider spread of values

Is executed directly after the invocation of the benchmarking function. After each hooks receive the return value of the benchmarking function as their argument. The return value is discarded (see hook arguments and return values).

For after each hooks, the local hook is invoked first, then the global (see when does a hook happen?).

Usage: %{
  "bar" => fn(input) -> bar(input) end
}, inputs: %{ "input 1" => 1}.
   after_each: fn(result) -> assert result == 42 end

When might this be useful?

  • Running assertions that whatever your benchmarking function returns is truly what it should be (i.e. all “contestants” work as expected)
  • Busting caches/terminating processes setup in before_each or elsewhere
  • Deleting files created by the benchmarking function

Hook arguments and return values

Before hooks form a chain, where the return value of the previous hook becomes the argument for the next one. The first defined before hook receives the scenario input as an argument, and returns a value that becomes the argument of the next in the chain. The benchmarking function receives the value of the last before hook as its argument (or the scenario input if there are no before hooks).

After hooks do not form a chain, and their return values are discarded. An after_each hook receives the return value of the benchmarking function as its argument. An after_scenario function receives the return value of the last before_scenario that ran (or the scenario’s input if there is no before_scenario hook).

If you haven’t defined any inputs, the hook chain is started with the special input argument returned by Benchee.Benchmark.no_input().

Hook configuration: global versus local

Hooks can be defined either globally as part of the configuration or locally for a specific benchmarking function. Global hooks will be executed for every scenario in the suite. Local hooks will only be executed for scenarios involving that benchmarking function.

Global hooks

Global hooks are specified as part of the general benchee configuration: %{
  "foo" => fn(input) -> ... end,
  "bar" => fn(input) -> ... end
}, inputs: %{
     "input 1" => 1,
     "input 2" => 2,
  before_scenario: fn(input) -> ... end

Here the before_scenario function will be executed for all 4 scenarios present in this benchmarking suite.

Local hooks

Local hooks are defined alongside the benchmarking function. To define a local hook, pass a tuple in the initial map, instead of just a single function. The benchmarking function comes first, followed by a keyword list specifying the hooks to run: %{
  "foo" => {fn(input) -> ... end, before_scenario: fn(input) -> ... end},
  "bar" => fn(input) -> ... end
}, inputs: %{
  "input 1" => 1,
  "input 2" => 2

Here before_scenario is only run for the 2 scenarios associated with "foo", i.e. foo with input 1 and foo with input 2. It is not run for any "bar" benchmarks.

When does a hook happen? (Complete Example)

Yes the whole hooks system is quite a lot to take in. Here is an overview showing the order of hook execution, along with the argument each hook receives (see hook arguments and return values). The guiding principle whether local or global is run first is that local always executes closer to the benchmarking function.

Given the following code:

suite_set_up() %{
  "foo" =>
      fn(input) -> foo(input) end,
      before_scenario: fn(input) ->
        input + 1
      before_each: fn(input) ->
        input + 1
      after_each: fn(value) ->
      after_scenario: fn(input) ->
  "bar" =>
    fn(input) -> bar(input) end
}, inputs: %{"input 1" => 1},
   before_scenario: fn(input) ->
     input + 1
   before_each: fn(input) ->
     input + 1
   after_each: fn(value) ->
   after_scenario: fn(input) ->


Keeping in mind that the order of foo and bar could be different, here is how the hooks are called:


# starting with the foo scenario
local_before_scenario(2) # as before_scenario incremented it

foo(5) # let's say foo(5) returns 6

foo(5) # let's say foo(5) returns 6

# ... this block is repeated as many times as benchee has time


# now it's time for the bar scenario, it has no hooks specified for itself
# so only the global hooks are run


bar(3) # let's say foo(3) returns 4

bar(3) # let's say foo(3) returns 4

# ... this block is repeated as many times as benchee has time



More verbose usage

It is important to note that the benchmarking code shown in the beginning is the convenience interface. The same benchmark in its more verbose form looks like this:

list = Enum.to_list(1..10_000)
map_fun = fn(i) -> [i, i * i] end

Benchee.init(time: 3)
|> Benchee.system
|> Benchee.benchmark("flat_map", fn -> Enum.flat_map(list, map_fun) end)
|> Benchee.benchmark("map.flatten",
                     fn -> list |> |> List.flatten end)
|> Benchee.measure
|> Benchee.statistics
|> Benchee.load # can be omitted when you don't want to/need to load scenarios
|> Benchee.Formatters.Console.output

This is a take on the functional transformation of data applied to benchmarks:

  1. Configure the benchmarking suite to be run
  2. Gather System data
  3. Define the functions to be benchmarked
  4. Run benchmarks with the given configuration gathering raw run times per function
  5. Generate statistics based on the raw run times
  6. Format the statistics in a suitable way and print them out

This is also part of the official API and allows for more fine grained control. (It’s also what benchee does internally when you use

Do you just want to have all the raw run times? Just work with the result of Benchee.measure/1! Just want to have the calculated statistics and use your own formatting? Grab the result of Benchee.statistics/1! Or, maybe you want to write to a file or send an HTTP post to some online service? Just grab the complete suite after statistics were generated.

It also allows you to alter behaviour, normally Benchee.load/1 is called right before the formatters so that neither the benchmarks are run again or statistics are computed again. However, you might want to run the benchmarks again or recompute the statistics. Then you can call Benchee.load/1 right at the start.

This way Benchee should be flexible enough to suit your needs and be extended at will. Have a look at the available plugins.

Usage from Erlang

Before you dig deep into this, it is inherently easier to setup a small elixir project, add a dependency to your erlang project and then run the benchmarks from elixir. The reason is easy - mix knows about rebar3 and knows how to work with it. The reverse isn’t true so the road ahead is somewhat bumpy.

There is an example project to check out.

You can use the rebar3_elixir_compile plugin. In your rebar.config you can do the following which should get you started:

deps, [
  {benchee, {elixir, "benchee", "0.9.0"}}

{plugins, [
    { rebar3_elixir_compile, ".*", {git, "", {branch, "master"}}}

{provider_hooks, [
  {pre, [{compile, {ex, compile}}]},
  {pre, [{release, {ex, compile}}]}

    {env, dev}

Then benchee already provides a :benchee interface for erlang compatibility which you can use. Sadly couldn’t get it to work in an escript yet.

You can then invoke it like this (for instance):

tobi@comfy ~/github/benchee_erlang_try $ rebar3 shell
===> dependencies etc.
Erlang/OTP 18 [erts-7.3] [source] [64-bit] [smp:4:4] [async-threads:0] [hipe] [kernel-poll:false]

Eshell V7.3  (abort with ^G)
1> benchee:run(#{myFunc => fun() -> lists:sort([8, 2, 3, 4, 2, 1, 3, 4, 9, 10, 11, 12, 13, 20, 1000, -4, -5]) end}, [{warmup, 0}, {time, 2}]).
Operating System: Linux
CPU Information: Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz
Number of Available Cores: 4
Available memory: 8.05 GB
Elixir 1.3.4
Erlang 18.3
Benchmark suite executing with the following configuration:
warmup: 0.0 μs
time: 2 s
parallel: 1
inputs: none specified
Estimated total run time: 2 s

Benchmarking myFunc...

Name             ips        average  deviation         median
myFunc      289.71 K        3.45 μs   ±250.31%           3 μs

This doesn’t seem to be too reliable right now, so suggestions and input are very welcome :)


Benchee only has small runtime dependencies that were initially extracted from it. Further functionality is provided through plugins that then pull in dependencies, such as HTML generation and CSV export. They help provide excellent visualization or interoperability.

  • benchee_html - generate HTML including a data table and many different graphs with the possibility to export individual graphs as PNG :)
  • benchee_csv - generate CSV from your Benchee benchmark results so you can import them into your favorite spreadsheet tool and make fancy graphs
  • benchee_json - export suite results as JSON to feed anywhere or feed it to your JavaScript and make magic happen :)

With the HTML plugin for instance you can get fancy graphs like this boxplot:


Of course there also are normal bar charts including standard deviation:


Presentation + general benchmarking advice

If you’re into watching videos of conference talks and also want to learn more about benchmarking in general I can recommend watching my talk from ElixirLive 2016. Slides can be found here, video - click the washed out image below ;)

Benchee Video

Contributing Open Source Helpers

Contributions to benchee are very welcome! Bug reports, documentation, spelling corrections, whole features, feature ideas, bugfixes, new plugins, fancy graphics… all of those (and probably more) are much appreciated contributions!

Keep in mind that the plugins live in their own repositories with their own issue tracker and they also like to get contributions :)

Please respect the Code of Conduct.

In addition to contributing code, you can help to triage issues. This can include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to subscribe to pragtob/benchee on CodeTriage.

You can also look directly at the open issues. There are help wanted and good first issue labels - those are meant as guidance, of course other issues can be tackled :)

A couple of (hopefully) helpful points:

  • Feel free to ask for help and guidance on an issue/PR (“How can I implement this?”, “How could I test this?”, …)
  • Feel free to open early/not yet complete pull requests to get some early feedback
  • When in doubt if something is a good idea open an issue first to discuss it
  • In case I don’t respond feel free to bump the issue/PR or ping me in other places

If you’re on the elixir-lang slack also feel free to drop by in #benchee and say hi!


  • mix deps.get to install dependencies
  • mix test to run tests
  • mix dialyzer to run dialyzer for type checking, might take a while on the first invocation (try building plts first with mix dialyzer --plt)
  • mix credo --strict to find code style problems
  • or run mix guard to run all of them continuously on file change