Introduction to Elasticsearch and Its Integration with Laravel

Elasticsearch is built on the open‑source Lucene library and wraps it with a RESTful API, making it ready to use without writing low‑level Lucene code.

Its core concepts can be likened to MySQL tables, as illustrated by a comparison diagram (not shown here).

To achieve fast full‑text search, Elasticsearch uses an inverted index; the article first explains forward (or "positive") indexes as the basis for building inverted indexes.

For Laravel integration, the basemkhirat/elasticsearch package is recommended. Install it via Composer: $ composer require basemkhirat/elasticsearch Then publish the service provider:

$ php artisan vendor:publish --provider="Basemkhirat\Elasticsearch\ElasticsearchServiceProvider"

Configuration is added to the Laravel config file, defining connections, servers, and the default index:

'connections' => [
    'default' => [
        'servers' => [
            [
                "host" => env("ELASTIC_HOST", "127.0.0.1"),
                "port" => env("ELASTIC_PORT", 9200),
                'user' => env('ELASTIC_USER', ''),
                'pass' => env('ELASTIC_PASS', ''),
                'scheme' => env('ELASTIC_SCHEME', 'http'),
            ]
        ],
        'index' => env('ELASTIC_INDEX', 'my_index')
    ]
],

The plugin’s query methods mirror Laravel’s query builder, supporting where, whereIn, whereBetween, orderBy, first, get, and pagination via paginate.

Example to retrieve documents:

$documents = ES::connection("default")
    ->index("my_index")
    ->type("my_type")
    ->get(); // returns a collection of results

Field boosting can be set as follows:

ES::type("my_type")->search("hello", function($search){
    $search->boost(2)->fields(["title" => 2, "content" => 1]);
})->get();

For advanced queries, raw Elasticsearch DSL can be supplied directly:

$params['body'] = [
    'aggs' => [
        'all_created' => [
            'terms' => [
                'field' => 'element.raw',
                'size' => $this->_mAggsPageSize,
            ],
            'aggs' => [
                'sum_pv' => [
                    'sum' => ['field' => 'pv'],
                ],
            ],
        ],
    ],
];

In conclusion, Elasticsearch’s built‑in analyzers are not optimal for Chinese tokenization; the IK analyzer is recommended, and its version must match the Elasticsearch version used.