PHP Elasticsearch and relational database integration practice guide

Practical Guide for the Integration of PHP Elasticsearch and Relational Database

Introduction:

With the advent of the Internet and big data era, data storage and Processing methods are also constantly evolving. Traditional relational databases have gradually shown some shortcomings when faced with scenarios such as massive data, high concurrent reading and writing, and full-text search. As a real-time distributed search and analysis engine, Elasticsearch has gradually attracted the attention and use of the industry through its high-performance full-text search, real-time analysis and data visualization functions.

However, in many practical application scenarios, we often need to integrate existing relational databases with Elasticsearch to take into account traditional data storage and processing requirements, as well as functions such as full-text search and intelligent recommendations. This article will introduce how to integrate Elasticsearch with a relational database in a PHP environment, and provide specific code examples.

Part One: Environment Preparation and Configuration

1. Installing Elasticsearch

First, we need to install and configure the Elasticsearch server. The corresponding installation package can be downloaded from the official website (https://www.elastic.co/downloads/elasticsearch). After the installation is complete, start the Elasticsearch service.

2. Install the PHP-Elasticsearch library

The interaction between PHP and Elasticsearch can be achieved through the officially provided PHP-Elasticsearch library. It can be installed through Composer. The command is as follows:

composer require elasticsearch/elasticsearch

After the installation is completed, we can use the relevant APIs of Elasticsearch by importing the corresponding namespace.

3. Database preparation and configuration

We need to prepare a relational database and create the corresponding table structure in it. Taking MySQL as an example, you can create a table named "users" through the following SQL statement:

CREATE TABLE users (
  id INT AUTO_INCREMENT PRIMARY KEY,
  name VARCHAR(50),
  age INT,
  email VARCHAR(50)
);

Next, we need to configure the relational database. You need to edit the config.php file to configure database connection related information, as shown below:

<?php
  $hostname = 'localhost';
  $username = 'your_username';
  $password = 'your_password';
  $database = 'your_database';
?>

Part 2: Data Synchronization and Index Creation

1. Data Synchronization

Before synchronizing the data in the database to Elasticsearch, we need to write a PHP script to implement this function. The following is a simple example:

<?php
  require 'vendor/autoload.php';
  require 'config.php';

  // 建立数据库连接
  $connection = new mysqli($hostname, $username, $password, $database);
  if ($connection->connect_error) {
    die("连接数据库失败：" . $connection->connect_error);
  }

  // 查询数据库数据
  $result = $connection->query("SELECT * FROM users");
  if (!$result) {
    die("查询数据失败：" . $connection->error);
  }

  // 将数据同步到Elasticsearch
  $client = ElasticsearchClientBuilder::create()->build();
  foreach ($result as $row) {
    $params = [
      'index' => 'users',
      'type' => 'user',
      'id' => $row['id'],
      'body' => [
        'name' => $row['name'],
        'age' => $row['age'],
        'email' => $row['email']
      ]
    ];
    $client->index($params);
  }

  echo "数据同步完成。";
?>

After running the script, the data in the database will be synchronized to the users index of Elasticsearch.

2. Index creation

Index is how data is organized in Elasticsearch, similar to tables in relational databases. We need to configure the index in Elasticsearch and define the corresponding field mapping.

The following is a sample code to create an index:

<?php
  $params = [
    'index' => 'users',
    'body' => [
      'mappings' => [
        'user' => [
          'properties' => [
            'name' => [
              'type' => 'text'
            ],
            'age' => [
              'type' => 'integer'
            ],
            'email' => [
              'type' => 'keyword'
            ]
          ]
        ]
      ]
    ]
  ];

  $client->indices()->create($params);
?>

In the above example, we define an index named users, which contains name# There are three fields: ##, age and email, and the corresponding field mapping is used.

Part Three: Data Search and Display

Data Search

Before conducting data search, we need to configure Elasticsearch and introduce the corresponding dependent libraries. The following is a simple example:

<?php
  require 'vendor/autoload.php';

  // 连接Elasticsearch
  $client = ElasticsearchClientBuilder::create()->build();

  // 查询用户信息
  $params = [
    'index' => 'users',
    'type' => 'user',
    'body' => [
      'query' => [
        'match' => [
          'name' => 'John'
        ]
      ]
    ]
  ];

  $response = $client->search($params);
  print_r($response);
?>

In the above example, we query the user information that contains "John" in the

name field.

Data display

After obtaining the search results, we can display and process the results according to needs. The following is a simple display code example:

<?php
  require 'vendor/autoload.php';

  // 连接Elasticsearch
  $client = ElasticsearchClientBuilder::create()->build();

  // 查询用户信息
  $params = [
    'index' => 'users',
    'type' => 'user',
    'body' => [
      'query' => [
        'match' => [
          'name' => 'John'
        ]
      ]
    ]
  ];

  $response = $client->search($params);
  
  echo "查询到" . $response['hits']['total']['value'] . "条用户信息：" . PHP_EOL;
  
  foreach ($response['hits']['hits'] as $hit) {
    echo "ID：" . $hit['_id'] . "，Name：" . $hit['_source']['name'] . "，Age：" . $hit['_source']['age'] . "，Email：" . $hit['_source']['email'] . PHP_EOL;
  }
?>

The above example will display the searched user information.

Conclusion:

This article introduces how to integrate Elasticsearch with a relational database in a PHP environment, and provides specific code examples. We hope that readers can successfully achieve seamless integration of the two through the guidance of this article, thereby fully utilizing the powerful functions and performance advantages of Elasticsearch and improving the efficiency and quality of data storage and processing.

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