PHP implements open source ClickHouse real-time analysis database

With the rapid development of the Internet and the explosive growth of data volume, data analysis has gradually become an indispensable part of corporate decision-making. In this process, the choice of database is particularly important. Compared with traditional relational databases (such as MySQL, Oracle, etc.), ClickHouse has better performance in real-time analysis scenarios, and the construction cost is relatively low. This article will introduce how to use PHP to implement the open source ClickHouse real-time analysis database to help readers master this technology.

1. ClickHouse Overview

ClickHouse is an open source columnar database developed by Yandex (a Russian search engine company), which is particularly suitable for real-time analysis of large-scale data. It has the following characteristics:

(1) Fast response: It adopts columnar storage structure, has high reading and writing efficiency, and can respond to query requests of massive data in seconds.

(2) Scalability: Supports distributed deployment and can easily expand the ClickHouse cluster to meet the needs of large-scale data analysis scenarios.

(3) High performance: It has optimization features such as batch query and vectorized calculation, which greatly improves the query speed.

2. Development environment preparation

Before using PHP to operate ClickHouse, you need to make the following preparations:

(1) Install and start the ClickHouse database service;

(2) Create the required database and tables in ClickHouse. It can be created using ClickHouse's web interface or using command line tools.

(3) Install PHP related drivers. PHP officially provides ClickHouse driver, which can be installed through Composer.

3. PHP code operation ClickHouse

(1) Connect to the database

In PHP, you can use the official driver provided by ClickHouse to connect to the database. The following is a code example for connecting to the database:

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

use ClickHouseDBClient;

$client = new Client([
    'host' => 'localhost',
    'port' => '8123',
    'username' => 'default',
    'password' => '',
]);

//测试连接是否成功
$client->select('SELECT 1');
?>

Among them, $client is the client object connecting to ClickHouse. You need to specify the IP address, connection port, user name, password and other information of the ClickHouse server. After the connection is successful, you can check whether the connection is normal by executing the SELECT 1 statement.

(2) Insert data

Inserting data in ClickHouse requires the use of the INSERT INTO statement. The following is a code example for inserting data:

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

use ClickHouseDBClient;

$client = new Client([
    'host' => 'localhost',
    'port' => '8123',
    'username' => 'default',
    'password' => '',
]);

$data = [
    ['2019-01-01', 'user1', 100],
    ['2019-01-01', 'user2', 200],
    ['2019-01-02', 'user3', 300],
];

$client->insert('mydb.mytable', $data, ['date', 'name', 'value']);
?>

The above code inserts data into the mydb.mytable table and specifies the field names of the data: date, name, and value.

(3) Query data

Querying data in ClickHouse requires the use of the SELECT statement. The following is a code example for querying data:

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

use ClickHouseDBClient;

$client = new Client([
    'host' => 'localhost',
    'port' => '8123',
    'username' => 'default',
    'password' => '',
]);

$response = $client->select('SELECT * FROM mydb.mytable WHERE date >= '2019-01-01' AND date <= '2019-01-02'');

print_r($response->rows());
?>

The above code will query the mydb.mytable table Select the data from January 1 to 2, 2019, and print out the query results.

4. Summary

This article introduces how to use PHP to implement the open source ClickHouse real-time analysis database, including operations such as connecting to the database, inserting data, and querying data. As a fast-response, scalable, and high-performance columnar database, ClickHouse has great application prospects in big data analysis scenarios. I hope readers can master this technology and better apply it in actual development.

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