package Bencher::Scenario::StringFunctions::Indent;

our $AUTHORITY = 'cpan:PERLANCAR'; # AUTHORITY
our $DATE = '2021-07-31'; # DATE
our $DIST = 'Bencher-Scenarios-StringFunctions'; # DIST
our $VERSION = '0.005'; # VERSION

use 5.010001;
use strict;
use warnings;

our $scenario = {
    summary => "Benchmark string indenting (adding whitespace to lines of text)",
    participants => [
        {fcall_template=>'String::Nudge::nudge(<num_spaces>, <str>)'},
        {fcall_template=>'String::Indent::indent(<indent>, <str>)'},
        {fcall_template=>'String::Indent::Join::indent(<indent>, <str>)'},
        # TODO: Indent::String
        # TODO: Indent::Utils
        # TODO: Text::Indent
    ],
    datasets => [
        {name=>'empty'        , args=>{num_spaces=>4, indent=>'    ', str=>''}},
        {name=>'1line'        , args=>{num_spaces=>4, indent=>'    ', str=>join("", map {"line $_\n"} 1..1)}},
        {name=>'10line'       , args=>{num_spaces=>4, indent=>'    ', str=>join("", map {"line $_\n"} 1..10)}},
        {name=>'100line'      , args=>{num_spaces=>4, indent=>'    ', str=>join("", map {"line $_\n"} 1..100)}},
        {name=>'1000line'     , args=>{num_spaces=>4, indent=>'    ', str=>join("", map {"line $_\n"} 1..1000)}},
    ],
};

1;
# ABSTRACT: Benchmark string indenting (adding whitespace to lines of text)

__END__

=pod

=encoding UTF-8

=head1 NAME

Bencher::Scenario::StringFunctions::Indent - Benchmark string indenting (adding whitespace to lines of text)

=head1 VERSION

This document describes version 0.005 of Bencher::Scenario::StringFunctions::Indent (from Perl distribution Bencher-Scenarios-StringFunctions), released on 2021-07-31.

=head1 SYNOPSIS

To run benchmark with default option:

 % bencher -m StringFunctions::Indent

To run module startup overhead benchmark:

 % bencher --module-startup -m StringFunctions::Indent

For more options (dump scenario, list/include/exclude/add participants, list/include/exclude/add datasets, etc), see L<bencher> or run C<bencher --help>.

=head1 DESCRIPTION

Packaging a benchmark script as a Bencher scenario makes it convenient to include/exclude/add participants/datasets (either via CLI or Perl code), send the result to a central repository, among others . See L<Bencher> and L<bencher> (CLI) for more details.

=head1 BENCHMARKED MODULES

Version numbers shown below are the versions used when running the sample benchmark.

L<String::Nudge> 1.0002

L<String::Indent> 0.03

L<String::Indent::Join>

=head1 BENCHMARK PARTICIPANTS

=over

=item * String::Nudge::nudge (perl_code)

Function call template:

 String::Nudge::nudge(<num_spaces>, <str>)



=item * String::Indent::indent (perl_code)

Function call template:

 String::Indent::indent(<indent>, <str>)



=item * String::Indent::Join::indent (perl_code)

Function call template:

 String::Indent::Join::indent(<indent>, <str>)



=back

=head1 BENCHMARK DATASETS

=over

=item * empty

=item * 1line

=item * 10line

=item * 100line

=item * 1000line

=back

=head1 BENCHMARK SAMPLE RESULTS

Run on: perl: I<< v5.34.0 >>, CPU: I<< Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz (4 cores) >>, OS: I<< GNU/Linux LinuxMint version 19 >>, OS kernel: I<< Linux version 5.3.0-68-generic >>.

Benchmark command (default options):

 % bencher -m StringFunctions::Indent

Result formatted as table (split, part 1 of 5):

 #table1#
 {dataset=>"1000line"}
 +------------------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
 | participant                  | rate (/s) | time (μs) | pct_faster_vs_slowest | pct_slower_vs_fastest |  errors | samples |
 +------------------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
 | String::Indent::indent       |  3000     |  333      |                 0.00% |                61.96% | 4.9e-08 |      24 |
 | String::Nudge::nudge         |  3990     |  251      |                33.01% |                21.76% | 2.1e-07 |      20 |
 | String::Indent::Join::indent |  4859.002 |  205.8036 |                61.96% |                 0.00% | 5.6e-12 |      21 |
 +------------------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+

The above result formatted in L<Benchmark.pm|Benchmark> style:

               Rate  SI:i  SN:n  SIJ:i 
  SI:i       3000/s    --  -24%   -38% 
  SN:n       3990/s   32%    --   -18% 
  SIJ:i  4859.002/s   61%   21%     -- 
 
 Legends:
   SI:i: participant=String::Indent::indent
   SIJ:i: participant=String::Indent::Join::indent
   SN:n: participant=String::Nudge::nudge

The above result presented as chart:

=begin html

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" />

=end html


Result formatted as table (split, part 2 of 5):

 #table2#
 {dataset=>"100line"}
 +------------------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
 | participant                  | rate (/s) | time (μs) | pct_faster_vs_slowest | pct_slower_vs_fastest |  errors | samples |
 +------------------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
 | String::Indent::indent       |   28400   |   35.3    |                 0.00% |                87.83% | 1.2e-08 |      26 |
 | String::Nudge::nudge         |   37437.5 |   26.7112 |                32.05% |                42.24% | 5.8e-12 |      20 |
 | String::Indent::Join::indent |   53300   |   18.8    |                87.83% |                 0.00% | 6.7e-09 |      20 |
 +------------------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+

The above result formatted in L<Benchmark.pm|Benchmark> style:

              Rate  SI:i  SN:n  SIJ:i 
  SI:i     28400/s    --  -24%   -46% 
  SN:n   37437.5/s   32%    --   -29% 
  SIJ:i    53300/s   87%   42%     -- 
 
 Legends:
   SI:i: participant=String::Indent::indent
   SIJ:i: participant=String::Indent::Join::indent
   SN:n: participant=String::Nudge::nudge

The above result presented as chart:

=begin html

<img src="data:image/png;base64,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/>

=end html


Result formatted as table (split, part 3 of 5):

 #table3#
 {dataset=>"10line"}
 +------------------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
 | participant                  | rate (/s) | time (μs) | pct_faster_vs_slowest | pct_slower_vs_fastest |  errors | samples |
 +------------------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
 | String::Indent::indent       |    218140 |    4.5843 |                 0.00% |               110.87% | 1.7e-11 |      20 |
 | String::Nudge::nudge         |    279260 |    3.5808 |                28.02% |                64.71% | 5.8e-12 |      20 |
 | String::Indent::Join::indent |    459980 |    2.174  |               110.87% |                 0.00% | 5.8e-12 |      20 |
 +------------------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+

The above result formatted in L<Benchmark.pm|Benchmark> style:

             Rate  SI:i  SN:n  SIJ:i 
  SI:i   218140/s    --  -21%   -52% 
  SN:n   279260/s   28%    --   -39% 
  SIJ:i  459980/s  110%   64%     -- 
 
 Legends:
   SI:i: participant=String::Indent::indent
   SIJ:i: participant=String::Indent::Join::indent
   SN:n: participant=String::Nudge::nudge

The above result presented as chart:

=begin html

<img 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HRwczpMZXZlbABBZG9iZS0yLjAKOZN0DQAAAABJRU5ErkJggg==" />

=end html


Result formatted as table (split, part 4 of 5):

 #table4#
 {dataset=>"1line"}
 +------------------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
 | participant                  | rate (/s) | time (ns) | pct_faster_vs_slowest | pct_slower_vs_fastest |  errors | samples |
 +------------------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
 | String::Indent::indent       |    822660 |    1215.6 |                 0.00% |               179.86% | 5.8e-12 |      20 |
 | String::Nudge::nudge         |   1040000 |     962   |                26.42% |               121.37% | 3.8e-10 |      24 |
 | String::Indent::Join::indent |   2302000 |     434.3 |               179.86% |                 0.00% | 5.6e-12 |      20 |
 +------------------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+

The above result formatted in L<Benchmark.pm|Benchmark> style:

              Rate  SI:i  SN:n  SIJ:i 
  SI:i    822660/s    --  -20%   -64% 
  SN:n   1040000/s   26%    --   -54% 
  SIJ:i  2302000/s  179%  121%     -- 
 
 Legends:
   SI:i: participant=String::Indent::indent
   SIJ:i: participant=String::Indent::Join::indent
   SN:n: participant=String::Nudge::nudge

The above result presented as chart:

=begin html

<img 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" />

=end html


Result formatted as table (split, part 5 of 5):

 #table5#
 {dataset=>"empty"}
 +------------------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
 | participant                  | rate (/s) | time (ns) | pct_faster_vs_slowest | pct_slower_vs_fastest |  errors | samples |
 +------------------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
 | String::Indent::indent       |    867600 |    1153   |                 0.00% |               591.98% | 1.7e-11 |      20 |
 | String::Nudge::nudge         |   1200000 |     850   |                35.15% |               412.01% | 1.2e-09 |      21 |
 | String::Indent::Join::indent |   6004000 |     166.6 |               591.98% |                 0.00% | 5.7e-12 |      20 |
 +------------------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+

The above result formatted in L<Benchmark.pm|Benchmark> style:

              Rate  SI:i  SN:n  SIJ:i 
  SI:i    867600/s    --  -26%   -85% 
  SN:n   1200000/s   35%    --   -80% 
  SIJ:i  6004000/s  592%  410%     -- 
 
 Legends:
   SI:i: participant=String::Indent::indent
   SIJ:i: participant=String::Indent::Join::indent
   SN:n: participant=String::Nudge::nudge

The above result presented as chart:

=begin html

<img 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/>

=end html


Benchmark module startup overhead (C<< bencher -m StringFunctions::Indent --module-startup >>):

Result formatted as table:

 #table6#
 +----------------------+-----------+-------------------+-----------------------+-----------------------+---------+---------+
 | participant          | time (ms) | mod_overhead_time | pct_faster_vs_slowest | pct_slower_vs_fastest |  errors | samples |
 +----------------------+-----------+-------------------+-----------------------+-----------------------+---------+---------+
 | String::Nudge        |         8 |                 2 |                 0.00% |                42.10% | 0.00013 |      20 |
 | String::Indent::Join |         8 |                 2 |                 4.35% |                36.19% | 0.00017 |      20 |
 | String::Indent       |         8 |                 2 |                 7.06% |                32.74% | 0.00013 |      21 |
 | perl -e1 (baseline)  |         6 |                 0 |                42.10% |                 0.00% | 0.00012 |      20 |
 +----------------------+-----------+-------------------+-----------------------+-----------------------+---------+---------+


The above result formatted in L<Benchmark.pm|Benchmark> style:

                Rate  S:N  SI:J  S:I  :perl -e1 ( 
  S:N          0.1/s   --    0%   0%         -25% 
  SI:J         0.1/s   0%    --   0%         -25% 
  S:I          0.1/s   0%    0%   --         -25% 
  :perl -e1 (  0.2/s  33%   33%  33%           -- 
 
 Legends:
   :perl -e1 (: mod_overhead_time=0 participant=perl -e1 (baseline)
   S:I: mod_overhead_time=2 participant=String::Indent
   S:N: mod_overhead_time=2 participant=String::Nudge
   SI:J: mod_overhead_time=2 participant=String::Indent::Join

The above result presented as chart:

=begin html

<img src="data:image/png;base64,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" />

=end html


To display as an interactive HTML table on a browser, you can add option C<--format html+datatables>.

=head1 BENCHMARK NOTES

Joining is faster than regex substitution for the datasets tested (0-1000
lines of short text).

=head1 HOMEPAGE

Please visit the project's homepage at L<https://metacpan.org/release/Bencher-Scenarios-StringFunctions>.

=head1 SOURCE

Source repository is at L<https://github.com/perlancar/perl-Bencher-Scenarios-StringFunctions>.

=head1 BUGS

Please report any bugs or feature requests on the bugtracker website L<https://rt.cpan.org/Public/Dist/Display.html?Name=Bencher-Scenarios-StringFunctions>

When submitting a bug or request, please include a test-file or a
patch to an existing test-file that illustrates the bug or desired
feature.

=head1 AUTHOR

perlancar <perlancar@cpan.org>

=head1 COPYRIGHT AND LICENSE

This software is copyright (c) 2021, 2018 by perlancar@cpan.org.

This is free software; you can redistribute it and/or modify it under
the same terms as the Perl 5 programming language system itself.

=cut