DataMapper

DataMapper is a powerful tool for transforming data structures. It provides a fluent API for mapping, filtering, and transforming data with templates, pipelines, and SQL-like queries.

Quick Example

use event4u\DataHelpers\DataMapper;

$source = [
    'profile' => [
        'firstName' => 'John',
        'lastName' => 'Doe',
        'contact' => [
            'email' => 'john@example.com',
            'phone' => '+1234567890',
        ],
    ],
    'settings' => [
        'theme' => 'dark',
        'language' => 'en',
    ],
];

// Simple template mapping
$result = DataMapper::source($source)
    ->template([
        'name' => 'profile.firstName',
        'email' => 'profile.contact.email',
        'theme' => 'settings.theme',
    ])
    ->map();

// Result:
// [
//     'name' => 'John',
//     'email' => 'john@example.com',
//     'theme' => 'dark',
// ]

Key Features

Template Expressions

Map data using simple dot-notation paths or complex expressions:

DataMapper::source($data)
    ->template([
        'fullName' => '{{ profile.firstName }} {{ profile.lastName }}',
        'email' => 'profile.contact.email',
        'isActive' => 'status.active',
    ])
    ->map();

Filtering Data

Filter data using array functions:

$users = [
    ['name' => 'John', 'age' => 25, 'status' => 'active'],
    ['name' => 'Jane', 'age' => 17, 'status' => 'active'],
    ['name' => 'Bob', 'age' => 30, 'status' => 'inactive'],
];

$filtered = array_filter($users, function($user) {
    return $user['age'] > 18 && $user['status'] === 'active';
});

$filtered = array_slice($filtered, 0, 10);

Transforming Data

Transform data using DataMapper:

$data = [
    ['name' => '  john  ', 'email' => 'john@example.com'],
    ['name' => '  jane  ', 'email' => 'jane@example.com'],
];

$result = DataMapper::source($data)
    ->template([
        'name' => '{{ name | trim | upper }}',
        'email' => '{{ email }}',
    ])
    ->map();

When to Use DataMapper

Use DataMapper when you need to:

Transform API responses - Map external API data to your internal structure
Aggregate data - Group and summarize data with SQL-like operations
Filter collections - Query data with complex conditions
Normalize data - Convert data from one format to another
Build ETL pipelines - Extract, transform, and load data

Performance

DataMapper is optimized for performance:

Lazy evaluation - Only processes data when needed
Efficient memory usage - Streams large datasets
Cached templates - Compiled templates for repeated use
Minimal overhead - Direct array access without unnecessary copies

Learn More

For detailed documentation and advanced features, see:

DataMapper Introduction - Complete guide
Template Expressions - Advanced templating
Query Builder - SQL-like queries
GROUP BY Operator - Aggregation and grouping
Pipelines - Transformation chains
Reverse Mapping - Bidirectional mapping