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Develop real-time search suggestions using PHP and Manticore Search
Develop real-time search suggestions using PHP and Manticore Search
Develop real-time search suggestion function using PHP and Manticore Search
Introduction:
In modern Internet applications, real-time search suggestion function has become very common. When users enter keywords in the search box, the system can automatically prompt relevant search suggestions, making it easier for users to find the content they want. This article will introduce how to develop real-time search suggestion functionality using PHP and Manticore Search.
1. What is Manticore Search?
Manticore Search is an open source full-text search engine developed based on Sphinx. Manticore Search provides high-performance full-text search and paging functions, and supports real-time index updates. In this article, we will leverage Manticore Search to implement real-time search suggestions.
2. Preparation
Before starting development, we need to install Manticore Search and PHP extension sphinx. For specific installation steps, please refer to the Manticore Search official documentation.
3. Create a Manticore Search index
First, we need to create a Manticore Search index to store our search suggestion data. Here we assume that we are searching for the user's name.
- Create index configuration file
First, create a new configuration file suggest.conf in the configuration folder of Manticore Search and enter the following content:
source suggest
{
type = mysql
sql_host = localhost
sql_user = your_username
sql_pass = your_password
sql_db = your_database
sql_port = 3306
sql_query_pre = SET NAMES utf8
sql_query =
SELECT name, weight() AS weight
FROM users
}
index suggest_index
{
source = suggest
path = /path/to/suggest_index
morphology = none
min_infix_len = 1
}Here you need to modify the corresponding database connection information and path.
- Create index
Run the following command to create an index:
$ indexer --config suggest.conf --all --rotate
4. Write PHP code
Next, we need to write PHP code to implement real-time search Suggested features.
- Connecting to Manticore Search
First, in the PHP code, we need to connect to Manticore Search. We can use the sphinxapi extension to achieve this:
<?php $host = 'localhost'; $port = 9306; $index = 'suggest_index'; $cl = new SphinxClient(); $cl->SetServer($host, $port); $cl->SetConnectTimeout(1); // 设置连接超时时间 $cl->SetArrayResult(true); // 将结果转换为数组
- Processing user input
Next, we need to get the keywords entered by the user in the search box and pass them to Manticore Search Search. We can use the GET or POST method to obtain user input:
<?php $input = $_GET['q']; // 获取用户输入的关键字
- Search
Use Manticore Search to search and get the search results:
<?php $res = $cl->Query($input, $index);
- Output search suggestions
Process the search results into search suggestions and output them to the front end in JSON format:
<?php
$data = [];
if ($res && $res['total']) {
foreach ($res['matches'] as $match) {
$data[] = ['name' => $match['attrs']['name']];
}
}
echo json_encode($data);
5. Front-end code
Finally, we need to write the front-end code to Send requests to the PHP backend and receive search suggestion results.
- HTML code
<!DOCTYPE html>
<html>
<head>
<title>实时搜索建议</title>
<script src="https://cdn.bootcdn.net/ajax/libs/jquery/3.6.0/jquery.min.js"></script>
</head>
<body>
<input type="text" id="search-input" placeholder="请输入搜索关键字">
<ul id="search-suggestions"></ul>
<script>
$(function() {
var timer;
$('#search-input').on('input', function() {
clearTimeout(timer);
var query = $(this).val();
timer = setTimeout(function() {
$.get('suggest.php', { q: query }, function(data) {
var suggestions = '';
$.each(data, function(_, item) {
suggestions += '<li>' + item.name + '</li>';
});
$('#search-suggestions').html(suggestions);
});
}, 300);
});
});
</script>
</body>
</html> 6. Summary
By using PHP and Manticore Search, we can easily implement the real-time search suggestion function. This can improve the user's search experience and help users find what they are looking for faster. Hope this article is helpful to you!
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