The perfect combination of Swoole and ElasticSearch: building a high-performance full-text search engine

With the continuous development of cloud computing and big data technology, full-text search engines are increasingly used and have become an indispensable part in data analysis, intelligent search, information management and other fields. In the implementation of full-text search engines, Swoole and ElasticSearch are undoubtedly two powerful tools that can be combined to build a high-performance full-text search engine.

Swoole is a high-performance network communication framework based on PHP language. It supports multi-process, coroutine, asynchronous, concurrency and other features. ElasticSearch is an open source full-text search engine with the advantages of distribution, high availability and horizontal scalability. By combining Swoole and ElasticSearch, we can build a high-performance, scalable full-text search engine to realize the full-text search function.

Before implementing the full-text search engine, we need to understand the principle of full-text search. Full-text retrieval is a retrieval technology based on inverted index, which uses all words in the text data as index items to create an index table. When the user enters a search term, the search term is compared with the words in the index table, all documents that meet the conditions are found, sorted according to relevance, and the search results are finally presented to the user.

Next, we will introduce in detail how to use Swoole and ElasticSearch to build a full-text search engine.

Step 1: Install Swoole and ElasticSearch

Swoole can be installed through the source package or composer tool provided by the official website. ElasticSearch can be installed through the installation package provided on the official website, or can be quickly installed through container technology such as Docker.

Step 2: Build the index table

In ElasticSearch, we use mapping to define the index table, and the document data is stored in JSON format in the index table. When building an index table, you need to specify parameters such as index name, document type, and mapping. The specific code is as follows:

use ElasticsearchClientBuilder;

$client = ClientBuilder::create()->build();
$params = [
    'index' => 'my_index',
    'body' => [
        'mappings' => [
            'my_mapping' => [
                'properties' => [
                    'title' => [
                        'type' => 'text'
                    ],
                    'content' => [
                        'type' => 'text'
                    ]
                ]
            ]
        ]
    ]
];

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

Through the above code, we created an index table named my_index and defined the document type as my_mapping, which contains two fields: title and content.

Step 3: Insert document data

In ElasticSearch, we store and retrieve data through documents, which are stored in JSON format. The code example for inserting a document is as follows:

$params = [
    'index' => 'my_index',
    'type' => 'my_mapping',
    'id' => '1',
    'body' => [
        'title' => '标题',
        'content' => '文本内容'
    ]
];

$response = $client->index($params);

Through the above code, we insert a piece of document data with an id of 1, a title of "title", and a content of "text content".

Step 4: Perform full-text retrieval

In ElasticSearch, we perform full-text retrieval through query, which is also defined in JSON format. The code example of full-text search is as follows:

$params = [
    'index' => 'my_index',
    'type' => 'my_mapping',
    'body' => [
        'query' => [
            'match' => [
                'title' => '关键词'
            ]
        ]
    ]
];

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

In the above code, we use match to perform full-text search and match the search keyword "keyword" with the title field.

Step 5: Use Swoole to implement asynchronous network communication

In the full-text search engine, network communication is a very important part. Swoole provides a rich asynchronous network communication API, which can implement asynchronous operations such as HTTP requests and network I/O. By using Swoole's asynchronous network communication function, we can implement a high-performance full-text search engine and improve the response speed and stability of full-text search.

The following is a code example of using Swoole to implement asynchronous network communication:

$client = new SwooleClient(SWOOLE_SOCK_TCP, SWOOLE_SOCK_ASYNC);
$client->on("connect", function(SwooleClient $cli) {
    $cli->send("GET / HTTP/1.1
Host: www.example.com

");
});
$client->on("receive", function(SwooleClient $cli, $data){
    echo "Received: ".$data."
";
    $cli->close();
});
$client->on("error", function(SwooleClient $cli){
    echo "Connect failed
";
});
$client->on("close", function(SwooleClient $cli){
    echo "Connection close
";
});
$client->connect('127.0.0.1', 80, 0.5);

Through the above code, we can use Swoole to implement asynchronous network communication and improve the performance and stability of the full-text search engine.

To sum up, by combining Swoole and ElasticSearch, we can build a high-performance full-text search engine to achieve fast and accurate full-text search functions. At the same time, we can also use Swoole's asynchronous network communication function to improve the performance and stability of the full-text search engine. In practical applications, other technologies can also be combined to further optimize the performance and scalability of the full-text search engine.

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