PHP and Manticore Search Development Guide: Aggregating Search Results with Suggestions

PHP and Manticore Search Development Guide: Utilizing Suggestions to Aggregate Search Results

Introduction:
With the continuous development of search technology, modern search engines have become a core component of many Web applications. In PHP development, we can use Manticore Search, an open source full-text search engine, to achieve fast and efficient search functions. This article explains how to develop with PHP and Manticore Search, and how to leverage its suggestion feature to aggregate search results.

I. Introduction to Manticore Search
Manticore Search is an upgraded version of the Sphinx full-text search engine, which provides higher performance and more features. It supports distributed indexing, real-time indexing, multiple query syntaxes, and highly customizable parameter configurations, allowing developers to flexibly meet various search needs.

II. Install and configure Manticore Search
First, we need to install Manticore Search on the server. It is recommended to use the PHP package manager Composer to install it. In the composer.json file in the project directory, add the following dependencies:

{
    "require": {
        "bobthecow/manticoresearch": "^2.0"
    }
}

Then execute composer install on the command line to install Manticore Search.

After the installation is complete, we need to configure it. Create a configuration file in the project root directory and name it manticore.conf. The configuration is as follows:

index main
{
    type = plain
    path = /path/to/index
    source = src
}
searchd
{
    listen = localhost:9306:mysql41
    binlog_path = /path/to/binlog
    log = /path/to/log/searchd.log
}

In the above configuration file, we define an index named main and a search daemon named searchd. In actual development, more detailed configuration can be performed according to needs.

III. Create index and import data
First, we need to create an index to store data. In PHP, this can be achieved by using the API provided by Manticore Search.

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

use ManticoresearchClient;
use ManticoresearchQueryBoolQuery;
use ManticoresearchQuery;
use ManticoresearchQueryBuilders;

$client = new Client();
$client->connect(['host' => 'localhost', 'port' => 9306]);

// 创建索引
$client->indexCreate('main');
$index = $client->getIndex('main');

// 创建字段
$index->fieldCreate([
    'name' => 'title',
    'type' => 'text',
    'indexed' => true,
]);

// 导入数据
$data = [
    ['id' => 1, 'title' => 'PHP 开发'],
    ['id' => 2, 'title' => 'Manticore Search'],
    ['id' => 3, 'title' => '全文搜索引擎'],
];

foreach ($data as $item) {
    $index->addDocument($item['id'], ['title' => $item['title']]);
}

// 等待索引刷新
$index->flush();
?>

IV. Implementing the suggestion function
Manticore Search provides a suggestion function, which can provide search suggestions based on the keywords entered by the user. Below is a simple example that demonstrates how to use the suggestions feature to aggregate search results.

<?php
// 获取用户输入的关键字
$keyword = $_GET['keyword'];

// 创建一个布尔查询
$query = new BoolQuery();

// 添加全文搜索子查询
$query->addMust(new QueryMatchPhrasePrefix(['title' => $keyword]));

// 添加建议子查询
$suggestions = new BuildersSuggest(Suggest::RELEVANT_TERMS);
$suggestions->setMatchPhrase('title', $keyword);
$query->setSuggest($suggestions);

// 执行查询
$search = $client->search($query);

// 获取搜索结果
$results = $search->getRawResponse()['hits']['hits'];

// 获取建议结果
$suggest = $search->getSuggest();

// 输出搜索结果
foreach ($results as $result) {
    echo $result['_source']['title'] . "<br>";
}

// 输出建议结果
foreach ($suggest as $term) {
    echo $term['text'] . "<br>";
}
?>

V. Summary
This article introduces how to use PHP and Manticore Search to develop efficient search functions and use its suggestion function to aggregate search results. By rationally using Manticore Search's APIs and functions, we can easily build a powerful search engine and provide a better user experience. It is hoped that readers can flexibly use these technologies in actual development to further improve the quality and performance of the search function.

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