| Overview |  |
Version: 2.0.0
Authors: Maxim Fedorov, (maximfca@gmail.com).
Erlang Performance & Benchmarking Suite. Simple way to say "this code is faster than that one".
Build:
$ rebar3 as prod escriptize
Find out how many times per sample (second) a function can be run (beware of shell escaping your code!):
$ ./erlperf 'rand:uniform().'
Code || QPS Time
rand:uniform(). 1 13942 Ki 71 ns
Run four processes executing rand:uniform() in a tight loop, and see that code is indeed
concurrent:
$ ./erlperf 'rand:uniform().' -c 4
Code || QPS Time
rand:uniform(). 4 39489 Ki 100 ns
Benchmark one function vs another, taking average of 10 seconds and skipping first second:
$ ./erlperf 'rand:uniform().' 'crypto:strong_rand_bytes(2).' --samples 10 --warmup 1
Code || QPS Time Rel
rand:uniform(). 1 15073 Ki 66 ns 100%
crypto:strong_rand_bytes(2). 1 1136 Ki 880 ns 7%
Run a function passing the state into the next iteration. This code demonstrates performance difference
between rand:uniform_s with state passed explicitly, and rand:uniform reading state from the process
dictionary:
$ ./erlperf 'r(_Init, S) -> {_, NS} = rand:uniform_s(S), NS.' --init_runner 'rand:seed(exsss).' 'r() -> rand:uniform().'
Code || QPS Time Rel
r(_Init, S) -> {_, NS} = rand:uniform_s(S), NS. 1 20272 Ki 49 ns 100%
r() -> rand:uniform(). 1 15081 Ki 66 ns 74%
Squeeze mode:
measure how concurrent your code is. In the example below, code:is_loaded/1 is implemented as
gen_server:call, and all calculations are done in a single process. It is still possible to
squeeze a bit more from a single process by putting work into the queue from multiple runners,
therefore the example may show higher concurrency.
$ ./erlperf 'code:is_loaded(local_udp).' --init 'code:ensure_loaded(local_udp).' --squeeze
Code || QPS Time
code:is_loaded(local_udp). 5 927 Ki 5390 ns
Start a server (pg scope in this example), use it in benchmark, and shut down after:
$ ./erlperf 'pg:join(scope, group, self()), pg:leave(scope, group, self()).' --init 'pg:start_link(scope).' --done 'gen_server:stop(scope).'
Code || QPS Time
pg:join(scope, group, self()), pg:leave(scope, group, self()). 1 336 Ki 2978 ns
Run the same code with different arguments, returned from init_runner function:
$ ./erlperf 'runner(X) -> timer:sleep(X).' --init_runner '1.' 'runner(X) -> timer:sleep(X).' --init_runner '2.'
Code || QPS Time Rel
runner(X) -> timer:sleep(X). 1 498 2008 us 100%
runner(X) -> timer:sleep(X). 1 332 3012 us 66%
Determine how many times a process can join/leave pg2 group on a single node:
$ ./erlperf 'ok = pg2:join(g, self()), ok = pg2:leave(g, self()).' --init 'pg2:create(g).'
Code || QPS Time
ok = pg2:join(g, self()), ok = pg2:leave(g, self()). 1 64021 15619 ns
Compare pg with pg2 running two nodes (note the -i argument spawning an extra node to
run benchmark in):
./erlperf 'ok = pg2:join(g, self()), ok = pg2:leave(g, self()).' --init 'pg2:create(g).' 'ok = pg:join(g, self()), ok = pg:leave(g, self()).' --init 'pg:start(pg).' -i
Code || QPS Time Rel
ok = pg:join(g, self()), ok = pg:leave(g, self()). 1 241 Ki 4147 ns 100%
ok = pg2:join(g, self()), ok = pg2:leave(g, self()). 1 1415 707 us 0%
Watch the progress of your test running (use -v option) with extra information: scheduler utilisation, dirty CPU & IO schedulers, number of running processes, ports, ETS tables, and memory consumption. Last column is the job throughput. When there are multiple jobs, multiple columns are printed.
$ ./erlperf 'rand:uniform().' -q -v
YYYY-MM-DDTHH:MM:SS-oo:oo Sched DCPU DIO Procs Ports ETS Mem Total Mem Proc Mem Bin Mem ETS <0.80.0>
2022-04-08T22:42:55-07:00 3.03 0.00 0.32 42 3 20 30936 Kb 5114 Kb 185 Kb 423 Kb 13110 Ki
2022-04-08T22:42:56-07:00 3.24 0.00 0.00 42 3 20 31829 Kb 5575 Kb 211 Kb 424 Kb 15382 Ki
2022-04-08T22:42:57-07:00 3.14 0.00 0.00 42 3 20 32079 Kb 5849 Kb 211 Kb 424 Kb 15404 Ki
<...>
2022-04-08T22:43:29-07:00 37.50 0.00 0.00 53 3 20 32147 Kb 6469 Kb 212 Kb 424 Kb 49162 Ki
2022-04-08T22:43:30-07:00 37.50 0.00 0.00 53 3 20 32677 Kb 6643 Kb 212 Kb 424 Kb 50217 Ki
Code || QPS Time
rand:uniform(). 8 54372 Ki 144 ns
Command-line benchmarking does not save results anywhere. It is designed to provide a quick answer to the question "is that piece of code faster".
Since 2.0, erlperf includes "low overhead" mode. It cannot be used for continuous benchmarking. In this mode
runner code is executed specified amount of times in a tight loop:
./erlperf 'rand:uniform().' 'rand:uniform(1000).' -l 10M
Code || QPS Time Rel
rand:uniform(). 1 16319 Ki 61 ns 100%
rand:uniform(1000). 1 15899 Ki 62 ns 97%
This mode effectively runs following code: loop(0) -> ok; loop(Count) -> rand:uniform(), loop(Count - 1).
Continuous mode adds 1-2 ns to each iteration.
erlperf can be used to measure performance of your application running in production, or code that is stored
on disk.
-pa argument to add extra code path. Example:
$ ./erlperf 'argparse:parse([], #{}).' -pa _build/test/lib/argparse/ebin
Code || QPS Time
argparse:parse([], #{}). 1 955 Ki 1047 ns
If you need to add multiple released applications, supply ERL_LIBS environment variable instead:
$ ERL_LIBS="_build/test/lib" erlperf 'argparse:parse([], #{}).'
Code || QPS Time
argparse:parse([], #{}). 1 735 Ki 1361 ns
It is possible to use erlperf to benchmark a running application (even in production, assuming necessary safety
precautions). To achieve this, add erlperf as a dependency, and use remote shell:
# run a mock production node with `erl -sname production'
# connect a remote shell to the production node
erl -remsh production
(production@max-au)3> erlperf:run(timer, sleep, [1]).
488
You can run a job continuously, to examine performance gains or losses while doing hot code reload. This process is designed to help during development and testing stages, allowing to quickly notice performance regressions.
Example source code: -module(mymod).
-export([do/1]).
do(Arg) -> timer:sleep(Arg).
Example below assumes you have erlperf application started (e.g. in a rebar3 shell)
% start a logger that prints VM monitoring information
> {ok, Logger} = erlperf_file_log:start_link(group_leader()).
{ok,<0.235.0>}
% start a job that will continuously benchmark mymod:do(),
% with initial concurrency 2.
> JobPid = erlperf:start(#{init_runner => "rand:uniform(10).",
runner => "runner(Arg) -> mymod:do(Arg)."}, 2).
{ok,<0.291.0>}
% increase concurrency to 4
> erlperf_job:set_concurrency(JobPid, 4).
ok.
% watch your job performance
% modify your application code,
% set do(Arg) -> timer:sleep(2*Arg), do hot code reload
> c(mymod).
{module, mymod}.
% see that after hot code reload throughput halved!
* **runner**: code that gets continuously executed * **init**: code that runs one when the job starts (for example, start some registered process or create an ETS table) * **done**: code that runs when the job is about to stop (used for cleanup, e.g. stop some registered process) * **init_runner**: code that is executed in every runner process (e.g. add something to process dictionary) * **job**: single instance of the running benchmark (multiple runners) * **concurrency**: how many processes are running concurrently, executing *runner* code * **throughput**: total number of calls per sampling interval (for all concurrent processes) * **cv**: coefficient of variation, the ratio of the standard deviation to the mean. Used to stop the concurrency (squeeze) test, the lower the *cv*, the longer it will take to stabilise and complete the test
erlperf:run({rand, uniform, [1000]}). or erlperf:run(rand, uniform, []).* anonymous function:
erlperf:run(fun() -> rand:uniform(100) end).* anonymous function with an argument:
erlperf:run(fun(Init) -> io_lib:format("~tp", [Init]) end).
* source code: erlperf:run("runner() -> rand:uniform(20).").
* (experimental) call chain: erlperf:run([{rand, uniform, [10]}, {erlang, node, []}])., see [recording call chain](#recording-call-chain). Call chain may contain only complete MFA tuples and cannot be mixed with functions.
Startup and teardown * init, done and init_runner are supported (there is no done_runner, because it is never stopped in a graceful way) * init_runner and done may be defined with arity 0 and 1 (in the latter case, result of init/0 passed as an argument) * runner can be of arity 0, 1 (accepting init_runner return value) or 2 (first argument is init_runner return value, and second is state passed between runner invocations)
Example with mixed MFA: erlperf:run(
#{
runner => fun(Arg) -> rand:uniform(Arg) end,
init =>
{pg, start_link, []},
init_runner =>
fun ({ok, Pid}) ->
{total_heap_size, THS} = erlang:process_info(Pid, total_heap_size),
THS
end,
done => fun ({ok, Pid}) -> gen_server:stop(Pid) end
}
).
Same example with source code:
erlperf:run(
#{
runner => "runner(Max) -> rand:uniform(Max).",
init => "init() -> pg:start_link().",
init_runner => "init_runner({ok, Pid}) ->
{total_heap_size, THS} = erlang:process_info(Pid, total_heap_size),
THS.",
done => "done({ok, Pid}) -> gen_server:stop(Pid)."
}
).
erlperf:run({erlang, node, []}, #{concurrency => 2, samples => 10, warmup => 1}).
Next example takes 10 samples with 100 ms duration. Note that throughput is reported per *sample_duration*: if you shorten duration in half, throughput report will also be halved:
$ ./erlperf 'rand:uniform().' -d 100 -s 20
Code || QPS Time
rand:uniform(). 1 1480 Ki 67 ns
$ ./erlperf 'rand:uniform().' -d 200 -s 20
Code || QPS Time
rand:uniform(). 1 2771 Ki 72 ns
Sometimes it's necessary to measure code running multiple concurrent processes, and find out when it saturates the node. It can be used to detect bottlenecks, e.g. lock contention, single dispatcher process bottleneck etc.. Example (with maximum concurrency limited to 50):
> erlperf:run({code, is_loaded, [local_udp]}, #{warmup => 1}, #{max => 50}).
{1284971,7}
In this example, 7 concurrent processes were able to squeeze 1284971 calls per second
for code:is_loaded(local_udp).
Benchmarking overhead varies depending on ERTS version and the way runner code is supplied. See the example:
(erlperf@max-au)7> erlperf:benchmark([
#{runner => "runner(X) -> is_float(X).", init_runner=>"2."},
#{runner => {erlang, is_float, [2]}},
#{runner => fun (X) -> is_float(X) end, init_runner => "2."}],
#{}, undefined).
[105824351,66424280,5057372]
This difference is caused by the ERTS itself: running compiled code (first variant) with OTP 25 is
two times faster than applying a function, and 20 times faster than repeatedly calling anonymous fun. Use
the same invocation method to get a relevant result.
Absolute benchmarking overhead may be significant for very fast functions taking just a few nanoseconds. Use "low overhead mode" for such occasions.
This experimental feature allows capturing a sequence of calls as a list of
{Module, Function, [Args]}. The trace can be supplied as a runner argument
to erlperf for benchmarking purposes:
> f(Trace), Trace = erlperf:record(pg, '_', '_', 1000).
...
% for things working with ETS, isolation is recommended
> erlperf:run(#{runner => Trace}, #{isolation => #{}}).
...
% Trace can be saved to file before executing:
> file:write("pg.trace", term_to_binary(Trace)).
% run the saved trace
> {ok, Bin} = file:read_file("pg.trace"),
> erlperf:run(#{runner => binary_to_term(Trace)}).
It's possible to create a Common Test testcase using recorded samples.
Just put the recorded file into xxx_SUITE_data:
benchmark_check(Config) ->
{ok, Bin} = file:read_file(filename:join(?config(data_dir, Config), "pg.trace")),
QPS = erlperf:run(#{runner => binary_to_term(Bin)}),
?assert(QPS > 500). % catches regression for QPS falling below 500
It's possible to run a job on a separate node in the cluster.
% watch the entire cluster (printed to console)
(node1@host)> {ok, _} = erlperf_history:start_link().
{ok,<0.213.0>}
(node1@host)> {ok, ClusterLogger} = erlperf_cluster_monitor:start_link(group_leader(), [sched_util, jobs]).
{ok, <0.216.0>}
% also log cluster-wide reports to file (jobs & sched_util)
(node1@host)> {ok, FileLogger} = erlperf_cluster_monitor:start_link("/tmp/cluster", [time, sched_util, jobs]).
{ok, <0.223.0>}
% run the benchmarking process in a different node of your cluster
(node1@host)> rpc:call('node2@host', erlperf, run, [#{runner => {rand, uniform, []}}]).
Cluster-wide monitoring will reflect changes accordingly.
= Implementation details =
Starting with 2.0, erlperf uses call counting for continuous benchmarking purposes. This allows
the tightest possible loop without extra runtime calls. Running
erlperfrand:uniform().' --init '1'. --done '2.' --init_runner '3.'' results in creating,
compiling and loading a module with this source code:
-module(unique_name).
-export([init/0, init_runner/0, done/0, run/0]).
init() ->
1.
init_runner() ->
3.
done() ->
2.
run() ->
runner(),
run().
runner() ->
rand:uniform().
Number of run/0 calls per second is reported as throughput. Before 2.0, erlperf
used atomics to maintain a counter shared between all runner processes, introducing
unnecessary BIF call overhead.
runner(0) ->
ok;
runner(Count) ->
rand:uniform(),
runner(Count - 1).
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