Acme-CPANModules-OrderedHash
view release on metacpan or search on metacpan
0.004 2025-04-15 Released-By: PERLANCAR; Urgency: medium
- Add participant: Tree::RB::XS (thanks NERDVANA).
0.003 2023-10-06 Released-By: PERLANCAR; Urgency: medium
- Add dataset: iterating; add benchmark notes.
0.002 2023-10-05 Released-By: PERLANCAR; Urgency: low
- No functional changes.
- [build] Forgot to add dzil plugin to show images.
0.001 2023-10-05 Released-By: PERLANCAR
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SYNOPSIS
To run benchmark with default option:
% bencher --cpanmodules-module OrderedHash
To run module startup overhead benchmark:
% bencher --module-startup --cpanmodules-module OrderedHash
For more options (dump scenario, list/include/exclude/add participants,
list/include/exclude/add datasets, etc), see bencher or run "bencher
--help".
DESCRIPTION
When you ask a Perl's hash for the list of keys, the answer comes back
unordered. In fact, Perl explicitly randomizes the order of keys it
returns everytime. The random ordering is a (security) feature, not a
bug. However, sometimes you want to know the order of insertion. These
modules provide you with an ordered hash; most of them implement it by
recording the order of insertion of keys in an additional array.
(6 cores)*, OS: *GNU/Linux Ubuntu version 24.10*, OS kernel: *Linux
version 6.11.0-8-generic*.
Benchmark command (default options):
% bencher --cpanmodules-module OrderedHash
Result formatted as table (split, part 1 of 4):
#table1#
{dataset=>"insert 1000 pairs"}
+----------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
| participant | rate (/s) | time (ms) | pct_faster_vs_slowest | pct_slower_vs_fastest | errors | samples |
+----------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
| Tie::StoredOrderHash | 539 | 1.85 | 0.00% | 528.45% | 1.4e-06 | 22 |
| Tie::LLHash | 640 | 1.6 | 19.19% | 427.28% | 3.4e-06 | 20 |
| Array::OrdHash | 889 | 1.12 | 64.84% | 281.24% | 9.6e-07 | 20 |
| Tie::IxHash | 1080 | 0.928 | 99.73% | 214.65% | 6.1e-07 | 20 |
| Hash::Ordered | 1460 | 0.684 | 170.98% | 131.92% | 4.1e-07 | 20 |
| Tie::Hash::Indexed | 1600 | 0.62 | 196.91% | 111.67% | 9.6e-07 | 20 |
| Tree::RB::XS | 3400 | 0.3 | 528.45% | 0.00% | 5.4e-07 | 21 |
T:L: participant=Tie::LLHash
T:S: participant=Tie::StoredOrderHash
TH:I: participant=Tie::Hash::Indexed
TR:X: participant=Tree::RB::XS
The above result presented as chart:
Result formatted as table (split, part 2 of 4):
#table2#
{dataset=>"insert 1000 pairs + delete"}
+----------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
| participant | rate (/s) | time (ms) | pct_faster_vs_slowest | pct_slower_vs_fastest | errors | samples |
+----------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
| Tie::IxHash | 31 | 32 | 0.00% | 5838.76% | 4.8e-05 | 21 |
| Tie::StoredOrderHash | 310 | 3.3 | 875.00% | 509.10% | 8.6e-06 | 21 |
| Tie::LLHash | 376 | 2.66 | 1098.31% | 395.59% | 2.5e-06 | 20 |
| Array::OrdHash | 440 | 2.3 | 1289.81% | 327.31% | 6.1e-06 | 20 |
| Hash::Ordered | 610 | 1.6 | 1854.01% | 203.93% | 1.9e-06 | 20 |
| Tie::Hash::Indexed | 1060 | 0.946 | 3272.21% | 76.11% | 5.7e-07 | 20 |
| Tree::RB::XS | 1900 | 0.54 | 5838.76% | 0.00% | 6.3e-07 | 20 |
T:L: participant=Tie::LLHash
T:S: participant=Tie::StoredOrderHash
TH:I: participant=Tie::Hash::Indexed
TR:X: participant=Tree::RB::XS
The above result presented as chart:
Result formatted as table (split, part 3 of 4):
#table3#
{dataset=>"insert 1000 pairs + iterate 10 times"}
+----------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
| participant | rate (/s) | time (ms) | pct_faster_vs_slowest | pct_slower_vs_fastest | errors | samples |
+----------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
| Tie::StoredOrderHash | 71 | 14 | 0.00% | 508.52% | 2e-05 | 20 |
| Tie::LLHash | 75.4 | 13.3 | 5.52% | 476.69% | 1.2e-05 | 24 |
| Array::OrdHash | 87.2 | 11.5 | 22.04% | 398.65% | 1e-05 | 20 |
| Tie::IxHash | 107 | 9.36 | 49.51% | 307.02% | 2.5e-06 | 20 |
| Tie::Hash::Indexed | 171 | 5.85 | 139.18% | 154.42% | 5e-06 | 21 |
| Hash::Ordered | 250 | 4 | 250.17% | 73.78% | 6.1e-06 | 20 |
| Tree::RB::XS | 435 | 2.3 | 508.52% | 0.00% | 8.2e-07 | 20 |
T:L: participant=Tie::LLHash
T:S: participant=Tie::StoredOrderHash
TH:I: participant=Tie::Hash::Indexed
TR:X: participant=Tree::RB::XS
The above result presented as chart:
Result formatted as table (split, part 4 of 4):
#table4#
{dataset=>"insert 1000 pairs + return keys 100 times"}
+----------------------+-----------+-----------+-----------------------+-----------------------+-----------+---------+
| participant | rate (/s) | time (ms) | pct_faster_vs_slowest | pct_slower_vs_fastest | errors | samples |
+----------------------+-----------+-----------+-----------------------+-----------------------+-----------+---------+
| Tie::StoredOrderHash | 17 | 58 | 0.00% | 1439.14% | 6.1e-05 | 20 |
| Tie::LLHash | 20 | 50 | 16.39% | 1222.37% | 7.3e-05 | 20 |
| Array::OrdHash | 25 | 40 | 44.54% | 964.81% | 0.00011 | 21 |
| Tie::IxHash | 26.8 | 37.3 | 54.99% | 893.08% | 3.3e-05 | 20 |
| Tie::Hash::Indexed | 44 | 23 | 154.54% | 504.67% | 2.7e-05 | 20 |
| Hash::Ordered | 135 | 7.43 | 678.48% | 97.71% | 7.1e-06 | 20 |
| Tree::RB::XS | 270 | 3.8 | 1439.14% | 0.00% | 4.3e-06 | 20 |
lib/Acme/CPANModules/OrderedHash.pm view on Meta::CPAN
}
},
},
{
module => 'Hash::Ordered',
bench_code => sub {
my ($op, $numkeys, $numrep) = @_;
my $hash = Hash::Ordered->new;
for (1..$numkeys) { $hash->set("key$_" => $_) }
if ($op eq 'delete') {
for (1..$numkeys) { $hash->delete("key$_") }
} elsif ($op eq 'keys') {
for (1..$numrep) { my @keys = $hash->keys }
} elsif ($op eq 'iterate') {
for (1..$numrep) { my $iter = $hash->iterator; while (my ($k,$v) = $iter->()) {} }
}
},
},
lib/Acme/CPANModules/OrderedHash.pm view on Meta::CPAN
for (1..$numkeys) { $tree->delete("key$_") }
} elsif ($op eq 'keys') {
for (1..$numrep) { my @keys= $tree->keys }
} elsif ($op eq 'iterate') {
for (1..$numrep) { my $iter = $tree->iter; while (my $v = $iter->next) {} }
}
},
},
],
bench_datasets => [
{name=>'insert 1000 pairs', argv => ['insert', 1000]},
{name=>'insert 1000 pairs + delete', argv => ['delete', 1000]},
{name=>'insert 1000 pairs + return keys 100 times', argv => ['keys', 1000, 100]},
{name=>'insert 1000 pairs + iterate 10 times', argv => ['iterate', 1000, 10], exclude_participant_tags => ['no_iterate']},
],
};
1;
# ABSTRACT: List of modules that provide ordered hash data type
lib/Acme/CPANModules/OrderedHash.pm view on Meta::CPAN
=head1 SYNOPSIS
To run benchmark with default option:
% bencher --cpanmodules-module OrderedHash
To run module startup overhead benchmark:
% bencher --module-startup --cpanmodules-module OrderedHash
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
When you ask a Perl's hash for the list of keys, the answer comes back
unordered. In fact, Perl explicitly randomizes the order of keys it returns
everytime. The random ordering is a (security) feature, not a bug. However,
sometimes you want to know the order of insertion. These modules provide you
with an ordered hash; most of them implement it by recording the order of
insertion of keys in an additional array.
lib/Acme/CPANModules/OrderedHash.pm view on Meta::CPAN
Run on: perl: I<< v5.40.1 >>, CPU: I<< AMD Ryzen 5 7535HS with Radeon Graphics (6 cores) >>, OS: I<< GNU/Linux Ubuntu version 24.10 >>, OS kernel: I<< Linux version 6.11.0-8-generic >>.
Benchmark command (default options):
% bencher --cpanmodules-module OrderedHash
Result formatted as table (split, part 1 of 4):
#table1#
{dataset=>"insert 1000 pairs"}
+----------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
| participant | rate (/s) | time (ms) | pct_faster_vs_slowest | pct_slower_vs_fastest | errors | samples |
+----------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
| Tie::StoredOrderHash | 539 | 1.85 | 0.00% | 528.45% | 1.4e-06 | 22 |
| Tie::LLHash | 640 | 1.6 | 19.19% | 427.28% | 3.4e-06 | 20 |
| Array::OrdHash | 889 | 1.12 | 64.84% | 281.24% | 9.6e-07 | 20 |
| Tie::IxHash | 1080 | 0.928 | 99.73% | 214.65% | 6.1e-07 | 20 |
| Hash::Ordered | 1460 | 0.684 | 170.98% | 131.92% | 4.1e-07 | 20 |
| Tie::Hash::Indexed | 1600 | 0.62 | 196.91% | 111.67% | 9.6e-07 | 20 |
| Tree::RB::XS | 3400 | 0.3 | 528.45% | 0.00% | 5.4e-07 | 21 |
lib/Acme/CPANModules/OrderedHash.pm view on Meta::CPAN
=begin html
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAtAAAAH4CAMAAABUnipoAAAAIGNIUk0AAHomAACAhAAA+gAAAIDoAAB1MAAA6mAAADqYAAAXcJy6UTwAAADDUExURf///wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA...
=end html
Result formatted as table (split, part 2 of 4):
#table2#
{dataset=>"insert 1000 pairs + delete"}
+----------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
| participant | rate (/s) | time (ms) | pct_faster_vs_slowest | pct_slower_vs_fastest | errors | samples |
+----------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
| Tie::IxHash | 31 | 32 | 0.00% | 5838.76% | 4.8e-05 | 21 |
| Tie::StoredOrderHash | 310 | 3.3 | 875.00% | 509.10% | 8.6e-06 | 21 |
| Tie::LLHash | 376 | 2.66 | 1098.31% | 395.59% | 2.5e-06 | 20 |
| Array::OrdHash | 440 | 2.3 | 1289.81% | 327.31% | 6.1e-06 | 20 |
| Hash::Ordered | 610 | 1.6 | 1854.01% | 203.93% | 1.9e-06 | 20 |
| Tie::Hash::Indexed | 1060 | 0.946 | 3272.21% | 76.11% | 5.7e-07 | 20 |
| Tree::RB::XS | 1900 | 0.54 | 5838.76% | 0.00% | 6.3e-07 | 20 |
lib/Acme/CPANModules/OrderedHash.pm view on Meta::CPAN
=begin html
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAtAAAAH4CAMAAABUnipoAAAAIGNIUk0AAHomAACAhAAA+gAAAIDoAAB1MAAA6mAAADqYAAAXcJy6UTwAAADDUExURf///wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA...
=end html
Result formatted as table (split, part 3 of 4):
#table3#
{dataset=>"insert 1000 pairs + iterate 10 times"}
+----------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
| participant | rate (/s) | time (ms) | pct_faster_vs_slowest | pct_slower_vs_fastest | errors | samples |
+----------------------+-----------+-----------+-----------------------+-----------------------+---------+---------+
| Tie::StoredOrderHash | 71 | 14 | 0.00% | 508.52% | 2e-05 | 20 |
| Tie::LLHash | 75.4 | 13.3 | 5.52% | 476.69% | 1.2e-05 | 24 |
| Array::OrdHash | 87.2 | 11.5 | 22.04% | 398.65% | 1e-05 | 20 |
| Tie::IxHash | 107 | 9.36 | 49.51% | 307.02% | 2.5e-06 | 20 |
| Tie::Hash::Indexed | 171 | 5.85 | 139.18% | 154.42% | 5e-06 | 21 |
| Hash::Ordered | 250 | 4 | 250.17% | 73.78% | 6.1e-06 | 20 |
| Tree::RB::XS | 435 | 2.3 | 508.52% | 0.00% | 8.2e-07 | 20 |
lib/Acme/CPANModules/OrderedHash.pm view on Meta::CPAN
=begin html
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAtAAAAH4CAMAAABUnipoAAAAIGNIUk0AAHomAACAhAAA+gAAAIDoAAB1MAAA6mAAADqYAAAXcJy6UTwAAADJUExURf///wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA...
=end html
Result formatted as table (split, part 4 of 4):
#table4#
{dataset=>"insert 1000 pairs + return keys 100 times"}
+----------------------+-----------+-----------+-----------------------+-----------------------+-----------+---------+
| participant | rate (/s) | time (ms) | pct_faster_vs_slowest | pct_slower_vs_fastest | errors | samples |
+----------------------+-----------+-----------+-----------------------+-----------------------+-----------+---------+
| Tie::StoredOrderHash | 17 | 58 | 0.00% | 1439.14% | 6.1e-05 | 20 |
| Tie::LLHash | 20 | 50 | 16.39% | 1222.37% | 7.3e-05 | 20 |
| Array::OrdHash | 25 | 40 | 44.54% | 964.81% | 0.00011 | 21 |
| Tie::IxHash | 26.8 | 37.3 | 54.99% | 893.08% | 3.3e-05 | 20 |
| Tie::Hash::Indexed | 44 | 23 | 154.54% | 504.67% | 2.7e-05 | 20 |
| Hash::Ordered | 135 | 7.43 | 678.48% | 97.71% | 7.1e-06 | 20 |
| Tree::RB::XS | 270 | 3.8 | 1439.14% | 0.00% | 4.3e-06 | 20 |
lib/Acme/CPANModules_ScenarioR/OrderedHash.pm view on Meta::CPAN
## no critic
package Acme::CPANModules_ScenarioR::OrderedHash;
our $VERSION = 0.004; # VERSION
our $results = do{my$var=[[200,"OK",[{_name=>"participant=Tie::StoredOrderHash",_succinct_name=>"T:S",errors=>1.4e-06,participant=>"Tie::StoredOrderHash",pct_faster_vs_slowest=>0,pct_slower_vs_fastest=>5.16666666666667,rate=>539,samples=>22,time=>1.8...
1;
# ABSTRACT: List of modules that provide ordered hash data type
=head1 DESCRIPTION
This module is automatically generated by Pod::Weaver::Plugin::Bencher::Scenario during distribution build.
A Acme::CPANModules_ScenarioR::* module contains the raw result of sample benchmark and might be useful for some stuffs later.
t/author-critic.t view on Meta::CPAN
unless ($ENV{AUTHOR_TESTING}) {
print qq{1..0 # SKIP these tests are for testing by the author\n};
exit
}
}
use strict;
use warnings;
# this test was generated with Dist::Zilla::Plugin::Test::Perl::Critic::Subset 3.001.006
use Test::Perl::Critic (-profile => "") x!! -e "";
my $filenames = ['lib/Acme/CPANModules/OrderedHash.pm','lib/Acme/CPANModules_ScenarioR/OrderedHash.pm'];
unless ($filenames && @$filenames) {
$filenames = -d "blib" ? ["blib"] : ["lib"];
}
all_critic_ok(@$filenames);
[@Author::PERLANCAR]
[-Acme::CPANModules]
[-Bencher::Scenario]
result_split_fields=dataset
chart=1
( run in 0.802 second using v1.01-cache-2.11-cpan-49f99fa48dc )