README.md

[Deprecated] Dedicated Mapper (Bundle*)

I present to You the fastest PHP Dedicated Mapper ever created!
It's even 31 times faster than JMS Serializer and even 43 times faster than Symfony Serializer in denormalization!
_{*The package supports Symfony Bundle system but not require to be used with Symfony.}

Usage

<?php

declare(strict_types=1);

namespace App;

use PBaszak\DedicatedMapper\Expression\Builder\ArrayExpressionBuilder as ArrayBuilder;
use PBaszak\DedicatedMapper\Expression\Builder\ReflectionClassExpressionBuilder as ClassBuilder;
use PBaszak\DedicatedMapper\MapperService;

class Test
{
    private string $name;
}

$data = [
    'name' => 'test';
];
$mapper = new MapperService('/app/var/mapper/');

/** @var Test $test */
$test = $mapper->map(
    $data, 
    Test::class,
    new ArrayBuilder(), # You have to specify $data type, available options: 'array', 'object', 'class object'. In this case it's a `array`
    new ClassBuilder(), # You have to specify output type, in this case it's `class object` based on the blueprint class `Test`.
);

How it works?

The Dedicated Mapper generates php file which looks like this one (it's only example for specific case):

<?php

declare(strict_types=1);

return function (array $data): PBaszak\DedicatedMapper\Tests\Performance\SimpleData {
    $ref_df1d1d13 = new ReflectionClass(PBaszak\DedicatedMapper\Tests\Performance\SimpleData::class);
    /** @var PBaszak\DedicatedMapper\Tests\Performance\SimpleData $output */
    $output = $ref_df1d1d13->newInstanceWithoutConstructor();

    if (array_key_exists('name', $data)) {
        $ref_df1d1d13->getProperty('name')->setValue($output, $data['name']);
    }

    return $output;
};

and using it in the mapping process.

Report

Environment Details

• PHP: 8.2.7 (from Dockerfile and without xdebug)
• Docker: Docker version 20.10.12, build 20.10.12-0ubuntu2~20.04.1
• OS: WSL2 - Ubuntu 20.04.5 LTS, Windows 10 Pro 22H2 19045.3324
• CPU: Intel(R) Core(TM) i5-10400 CPU @ 2.90GHz
• RAM: DDR4 32,0GB 2667MHz
• SSD: Samsung 970 EVO Plus 500GB M.2 2280 PCI-E x4 Gen3 NVMe

Note: Each test was run 100 times.
If you want to run the test yourself:

git clone https://github.com/patrykbaszak/dedicated-mapper.git
bash start.sh
docker exec php composer test:performance

Comparison Results

JMS Serializer vs Dedicated Mapper

Test Case	Metric	JMS Serializer	Dedicated Mapper	Performance Gain
Build & Use	avg	0.00073563575744629 s	0.00022567272186279 s	3.26x faster
	min	0.00061202049255371 s	0.0001990795135498 s	3.07x faster
	max	0.006666898727417 s	0.0012781620025635 s	5.22x faster
Use (one time)	avg	0.00064053297042847 s	0.00020017862319946 s	3.2x faster
	min	0.00055694580078125 s	0.00018501281738281 s	3.01x faster
	max	0.0044717788696289 s	0.0002751350402832 s	16.25x faster
Second Use (Same Data)	avg	0.00031213998794556 s	5.3181648254395E-5 s	5.87x faster
	min	0.00029683113098145 s	5.0067901611328E-5 s	5.93x faster
	max	0.0003669261932373 s	7.8916549682617E-5 s	4.65x faster
Second Use (Different Data)	avg	0.00062076330184937 s	5.8262348175049E-5 s	10.65x faster
	min	0.0005500316619873 s	5.4836273193359E-5 s	10.03x faster
	max	0.0029869079589844 s	9.5129013061523E-5 s	31.4x faster

Symfony Serializer vs Dedicated Mapper

Test Case	Metric	Symfony Serializer	Dedicated Mapper	Performance Gain
Build & Use	avg	0.0021615481376648 s	0.00021748304367065 s	9.94x faster
	min	0.0019149780273438 s	9.3221664428711E-5 s	20.54x faster
	max	0.01357889175415 s	0.00031518936157227 s	43.08x faster
Use (one time)	avg	0.0019077062606812 s	0.0002018141746521 s	9.45x faster
	min	0.0017900466918945 s	0.00018596649169922 s	9.63x faster
	max	0.0043408870697021 s	0.0002748966217041 s	15.79x faster
Second Use (Same Data)	avg	0.0011084413528442 s	6.0606002807617E-5 s	18.29x faster
	min	0.0010659694671631 s	5.5074691772461E-5 s	19.35x faster
	max	0.0017890930175781 s	0.0001060962677002 s	16.86x faster
Second Use (Different Data)	avg	0.0019117307662964 s	6.1674118041992E-5 s	31x faster
	min	0.0017828941345215 s	5.6028366088867E-5 s	31.82x faster
	max	0.0042397975921631 s	0.00010395050048828 s	40.79x faster
Build, Use & Validation	avg	0.0023907327651978 s	0.00046631574630737 s	5.13x faster
	min	0.002216100692749 s	0.00043201446533203 s	5.13x faster
	max	0.007519006729126 s	0.00083398818969727 s	9.02x faster
Use & Validation (one time)	avg	0.0022232341766357 s	0.00038869619369507 s	5.72x faster
	min	0.0020699501037598 s	0.0003659725189209 s	5.66x faster
	max	0.0052480697631836 s	0.0004730224609375 s	11.09x faster
Second Use & Validation (Same Data)	avg	0.0012864065170288 s	0.00018531322479248 s	6.94x faster
	min	0.0012338161468506 s	0.00017285346984863 s	7.14x faster
	max	0.0020201206207275 s	0.0002751350402832 s	7.34x faster
Second Use & Validation (Different Data)	avg	0.0021827912330627 s	0.00018846273422241 s	11.58x faster
	min	0.00205397605896 s	0.00017499923706055 s	11.74x faster
	max	0.0047998428344727 s	0.0003211498260498 s	14.95x faster

_{*Tables generated using Chat GPT-4 based on test data. This information is here because the chat is not a perfect tool and could mess up the measured times for example.}