Data modeling and index design of Elasticsearch in PHP development

Data modeling and index design of Elasticsearch in PHP development

Elasticsearch is an open source distributed search and analysis engine that is widely used in enterprises of all sizes. project. Its fast, scalable, and powerful search and analysis capabilities make it ideal for processing large-scale data. In PHP development, combining Elasticsearch can greatly improve search performance and user experience.

Before using Elasticsearch, we need to perform data modeling and index design. This article will introduce how to use PHP for data modeling and index design of Elasticsearch, and attach specific code examples for reference.

1. Data Modeling

In Elasticsearch, data is stored in the form of Document. Each document consists of a set of fields (Field), each field contains a name and a value.

First, we need to determine the data structure to be stored. Suppose we have a product search requirement and need to store product name, description, category, price and other information. Based on this information, we can design the following data structure:

{
  "name": "iPhone 12",
  "description": "Apple 最新发布的手机",
  "category": "手机",
  "price": 6999
}

Next, we need to create an index (Index) to store these documents. Indexes are similar to tables in a database, and each index contains multiple documents.

The sample code for using PHP to create an index is as follows:

$client = new ElasticsearchClient();

$params = [
    'index' => 'products',
    'body' => [
        'mappings' => [
            'properties' => [
                'name' => ['type' => 'text'],
                'description' => ['type' => 'text'],
                'category' => ['type' => 'keyword'],
                'price' => ['type' => 'integer']
            ]
        ]
    ]
];

$response = $client->indices()->create($params);

In the above code, we first create an Elasticsearch client object, and then use indices()->create( ) method creates an index named "products". In the body parameter we define the fields of the document and their types.

2. Index design

Index design is a key task in Elasticsearch, which determines the performance and accuracy of search. The following are several commonly used index design techniques:

1. Word Breaker (Analyzer)

Elasticsearch uses a word breaker to segment text so that it can be searched more accurately. . During index design, we can specify a tokenizer that suits specific needs. For example, for English text, we can use the english tokenizer, and for Chinese text, we can use the ik_smart or ik_max_word tokenizer.

Sample code:

$params = [
    'index' => 'products',
    'body' => [
        'settings' => [
            'analysis' => [

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