[erlang-questions] What is the fastest way to check if a function is defined in a module?

[Jesper Louis Andersen]

On Sun, Mar 4, 2018 at 8:09 AM Metin Akat <akat.metin@REDACTED> wrote:

> I haven't yet done rigorous testing with many processes, but what you are
> suggesting is the slowest so far (at least by the most naive way of
> measuring, while not under load) :
>
> 37> timer:tc(fun() -> lists:member({not_a_function, 1},
> Module:module_info(exports)) end).
> {514,false}
> 38> timer:tc(fun() -> try Module:not_a_function() catch error:undef ->
>  not_defined end end).
> {47,not_defined}
> 39> timer:tc(fun() -> erlang:function_exported(Module, not_a_function, 1)
> end).
> {8,false}
>
>
Having looked a bit at this, the function in (38) is interpreted, not
compiled. We can try this with eministat:

-module(function_exported).

-export([t/0, datasets/0]).

-define(ROUNDS, 10000).


v1() ->
    v1(?ROUNDS).

v1(0) -> ok;
v1(N) ->
    lists:member({not_a_function, 1}, ?MODULE:module_info(exports)),
    v1(N-1).

v2() ->
    v2(?ROUNDS).

v2(0) -> ok;
v2(N) ->
    try ?MODULE:not_a_function()
    catch
        error:undef ->
            not_defined
    end,
    v2(N-1).

v3() ->
    v3(?ROUNDS).

v3(0) -> ok;
v3(N) ->
    _ = erlang:function_exported(?MODULE, not_a_function, 1),
    v3(N-1).

datasets() ->
    [eministat:s("Variant 1",
                 fun v1/0, 50),
     eministat:s("Variant 2",
                 fun v2/0, 50),
     eministat:s("Variant 3",
                 fun v3/0, 50)].

t() ->
    [H|T] = datasets(),
    eministat:x(95.0, H, T).


Running this yields:

2> function_exported:t().
x Variant 1
+ Variant 2
* Variant 3
+--------------------------------------------------------------------------+
|*          ++++++++                         x xxxxxxxxxxxxxx             x|
|*           ++++++                            x xxxxxxxx x                |
|*           ++++                              x x xxxx x x                |
|*           ++++                                  xxxx x x                |
|*           +++                                   xxx    x                |
|*           +++                                    xx    x                |
|*           +++                                    x     x                |
|*           +++                                    x                      |
|*           +++                                                           |
|*            ++                                                           |
|*            ++                                                           |
|*            ++                                                           |
|*            ++                                                           |
|*            +                                                            |
|*            +                                                            |
|*            +                                                            |
|*            +                                                            |
|*            +                                                            |
|*                                                                         |
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|*                                                                         |
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|*                                                                         |
|*                                                                         |
|*                                                                         |
|*                                                                         |
|                                                |___MA___|                |
|            |MA|                                                          |
|A                                                                         |
+--------------------------------------------------------------------------+
Dataset: x N=50 CI=95.0000
Statistic     Value     [         Bias] (Bootstrapped LB‥UB)
Min:         1.46008e+5
1st Qu.      1.67652e+5
Median:      1.73901e+5
3rd Qu.      1.83833e+5
Max:         2.43860e+5
Average:     1.75521e+5 [      18.3119] (   1.71931e+5 ‥    1.80582e+5)
Std. Dev:    1.54033e+4 [     -419.011] (   1.12376e+4 ‥    2.45340e+4)

Outliers: 0/1 = 1 (μ=1.75539e+5, σ=1.49843e+4)
        Outlier variance:      0.604402 (severe, the data set is probably
unusable)

------

Dataset: + N=50 CI=95.0000
Statistic     Value     [         Bias] (Bootstrapped LB‥UB)
Min:         3.74350e+4
1st Qu.      4.25210e+4
Median:      4.50860e+4
3rd Qu.      4.73290e+4
Max:         5.90190e+4
Average:     4.55904e+4 [     -12.9348] (   4.44757e+4 ‥    4.70025e+4)
Std. Dev:       4608.27 [     -81.7125] (      3694.90 ‥       5833.19)

Outliers: 0/3 = 3 (μ=4.55775e+4, σ=4526.56)
        Outlier variance:      0.665857 (severe, the data set is probably
unusable)

Difference at 95.0% confidence
        -1.29931e+5 ± 4511.12
        -74.0257% ± 2.57013%
        (Student's t, pooled s = 1.13687e+4)
------

Dataset: * N=50 CI=95.0000
Statistic     Value     [         Bias] (Bootstrapped LB‥UB)
Min:            361.000
1st Qu.         365.000
Median:         367.000
3rd Qu.         369.000
Max:            848.000
Average:        386.060 [   1.53180e-2] (      372.960 ‥       423.160)
Std. Dev:       74.0183 [     -7.40628] (      26.8655 ‥       157.196)

Outliers: 0/6 = 6 (μ=386.075, σ=66.6120)
        Outlier variance:      0.873417 (severe, the data set is probably
unusable)

Difference at 95.0% confidence
        -1.75135e+5 ± 4321.90
        -99.7800% ± 2.46232%
        (Student's t, pooled s = 1.08919e+4)
------

ok

So variant 2 is roughly 74% faster than variant 1.
And variant 3 is roughly 99.8% faster than variant 2.

I'll recommend variant 3. But you also have to measure more than a quick
synthetic benchmark. Chances are that your other parts of the code is going
to dominate to the point where wondering about this corner is futile.
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