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Server cluster implementation method in Workerman documentation

王林
Release: 2023-11-08 20:09:18
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Server cluster implementation method in Workerman documentation

Workerman is a high-performance PHP Socket framework that allows PHP to handle asynchronous network communications more efficiently. In Workerman's documentation, there are detailed instructions and code examples on how to implement a server cluster.

In order to implement a server cluster, we first need to clarify the concept of a server cluster. A server cluster connects multiple servers to a network to improve system performance, reliability and scalability by sharing loads and resources. In Workerman, server clustering can be implemented in two ways: using a central load balancer and using distributed shared memory.

  1. Using a central load balancer (Load Balancer)
    The central load balancer is one of the key components in a distributed system. It receives requests from clients and distributes them to various servers in the cluster. In Workerman, this functionality can be achieved by creating a separate PHP script that acts as a central load balancer.

First, we need to install Workerman. You can install it through Composer, or download the source code directly and introduce the Autoloader.php file. Next, create a PHP script named balancer.php. In the script, we first need to introduce Workerman's Autoloader file and load the relevant class libraries.

<?php
require_once '/path/to/your/workerman/Autoloader.php';
use WorkermanWorker;
use WorkermanProtocolsHttp;
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Next, create a Worker instance to listen for client requests and distribute the requests to the servers in the cluster.

$balancer = new Worker('tcp://0.0.0.0:8080');
$balancer->name = 'LoadBalancer';
$balancer->count = 4;

$balancer->onConnect = function($connection) {
    // 连接到达时,选择一个服务器进行负载均衡
    $servers = array('tcp://server1.com:8888', 'tcp://server2.com:8888', 'tcp://server3.com:8888');
    $connection->backendConnection = new Connection($servers[array_rand($servers)]);
};

$balancer->onMessage = function($connection, $data) {
    // 接收到消息时,将消息发送给后端服务器
    $connection->backendConnection->send($data);
};

$balancer->onClose = function($connection) {
    // 连接关闭时,关闭后端服务器的连接
    $connection->backendConnection->close();
};
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The above code creates a Worker instance named LoadBalancer and listens to port 8080. As each connection arrives, the connection is distributed to the backend servers by randomly selecting a server. When a message is received, the message is sent to the backend server. When the connection is closed, close the connection to the backend server.

Finally, run the balancer.php script and execute the following command in the terminal:

php balancer.php start
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After starting the load balancer, the client's request can be distributed to each server in the cluster.

  1. Using Distributed Shared Memory

Distributed shared memory is a technology that stores data shared among multiple servers. In Workerman, you can use Redis as distributed shared memory. Redis is an open source in-memory database that supports persistent storage and provides rich data structures and operation commands.

Using distributed shared memory requires installing and configuring the Redis server first. Then, in Workerman's script, you can use the Redis connection to share data.

<?php
require_once '/path/to/your/workerman/Autoloader.php';
use WorkermanWorker;
use WorkermanProtocolsHttp;
use WorkermanConnectionAsyncTcpConnection;

$worker = new Worker('tcp://0.0.0.0:8888');
$worker->name = 'Server';
$worker->onWorkerStart = function($worker) {
    // 连接Redis服务器
    $redis_connection = new AsyncTcpConnection('tcp://redis.server:6379');
    $redis_connection->connect();
    
    // 将服务器的信息保存到Redis
    $worker->addListener = function($connection) use($redis_connection) {
        $redis_connection->lPush('servers', $connection->getRemoteAddress());
    };
    
    // 从Redis获取服务器列表,用于负载均衡
    $worker->onMessage = function($connection, $data) use($redis_connection) {
        $redis_connection->lRange('servers', 0, -1, function($result) use($connection, $data) {
            // 根据负载均衡策略选择一个服务器
            $server = $result[array_rand($result)];
            
            // 将消息发送给选定的服务器
            $backend_connection = new AsyncTcpConnection('tcp://' . $server);
            $backend_connection->send($data);
            
            // 接收后端服务器的响应,并发送给客户端
            $backend_connection->onMessage = function($connection, $backend_data) use($connection) {
                $connection->send($backend_data);
            };
            
            // 关闭后端服务器的连接
            $backend_connection->onClose = function($connection) {
                $connection->close();
            };
        });
    };
    
    // 在服务器关闭时,从Redis中移除服务器的信息
    $worker->onClose = function($connection) use($redis_connection) {
        $remote_address = $connection->getRemoteAddress();
        $redis_connection->lRem('servers', $remote_address, 1);
    };
};
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The above code creates a Worker instance named Server and listens to port 8888. In the onWorkerStart callback function of the Worker instance, first connect to the Redis server, and then every time a client request is heard, the server list is obtained through the Redis connection, a server is selected according to the load balancing policy, and the request is forwarded to the server. After receiving the response from the backend server, return the response to the client. When the server is shut down, remove the server information from Redis.

Finally, run the server.php script and execute the following command in the terminal:

php server.php start
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After starting the server, you can connect to the server through the client and achieve load balancing.

Through the above two methods, we can use the Workerman framework to implement server clusters. Whether using a central load balancer or distributed shared memory, the performance and reliability of the system can be improved to meet the needs of large-scale applications. Of course, in actual applications, we can further optimize and expand the implementation of server clusters based on specific scenarios and needs.

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