


Advanced Workerman development: realizing distributed multi-process communication
Workerman Development Advanced: Implementing Distributed Multi-Process Communication
With the development of Internet applications, the needs of Web back-end development are becoming more and more diverse and complex. The traditional single-process single-thread development model can no longer meet the needs of large traffic and high concurrency. In order to improve the performance and scalability of the system, distributed multi-process communication has become a key technology.
In this article, we will introduce how to use the Workerman framework to implement distributed multi-process communication. Workerman is a simple and easy-to-use PHP multi-process network programming framework that supports high-performance TCP/UDP server and client programming. By leveraging the power of Workerman, we can easily build a high-performance, scalable distributed application.
First, let us understand the basic usage of the Workerman framework. The following is a simple TCP server sample code based on Workerman:
<?php require_once __DIR__ . '/vendor/autoload.php'; use WorkermanWorker; $worker = new Worker('tcp://0.0.0.0:8888'); $worker->count = 4; $worker->onWorkerStart = function($worker) { echo "Worker {$worker->id} started "; }; $worker->onConnect = function($connection) { echo "New connection from {$connection->getRemoteIp()}:{$connection->getRemotePort()} "; }; $worker->onMessage = function($connection, $data) { echo "Received message: {$data} "; $connection->send("Hello, {$data}! "); }; Worker::runAll();
In the above sample code, we created a Worker object based on the TCP protocol, which listens to the local 8888 port and set up 4 processes to Handle connections. Each process will execute the onWorkerStart callback function when it starts, which is used to output the Worker number. When a new connection is established, the onConnect callback function is triggered and connection-related information is output on the console. When a message from the client is received, the onMessage callback function is triggered, the received message is output on the console, and then Hello and message content are sent back to the client.
The above examples are only the basic usage of Workerman. Next, we will introduce how to use Workerman to achieve distributed multi-process communication. Suppose we have an application that needs to handle a large number of image uploads. In order to improve performance, we want to distribute the image upload task to multiple processes for processing. The following is a sample code that implements distributed multi-process communication:
<?php require_once __DIR__ . '/vendor/autoload.php'; use WorkermanWorker; use WorkermanLibTimer; $taskWorkerCount = 4; $uploadWorkerCount = 2; $taskWorker = new Worker(); $taskWorker->count = $taskWorkerCount; $uploadWorker = new Worker(); $uploadWorker->count = $uploadWorkerCount; $taskWorker->onWorkerStart = function($worker) { $uploadWorker = new Worker(); $uploadWorker->count = $GLOBALS['uploadWorkerCount']; $uploadWorker->onMessage = function($connection, $data) { echo "TaskWorker {$worker->id} received upload message: {$data} "; $connection->send("TaskWorker {$worker->id} received upload message: {$data} "); }; $uploadWorker->listen('tcp://127.0.0.1:5678'); echo "TaskWorker {$worker->id} started "; }; $uploadWorker->onWorkerStart = function($worker) { Timer::add(1, function() use($worker) { $taskWorkerId = rand(0, $GLOBALS['taskWorkerCount'] - 1); $taskWorker = $worker->getWorkerById($taskWorkerId); $taskWorker->send("Upload message"); }); echo "UploadWorker {$worker->id} started "; }; Worker::runAll();
In the above sample code, we created a TaskWorker and an UploadWorker. TaskWorker is responsible for receiving messages from UploadWorker and outputting the received messages on the console. UploadWorker is responsible for sending a message to TaskWorker every 1 second. For convenience, each TaskWorker also creates an UploadWorker when it starts and listens to the local port 5678 to receive messages from the UploadWorker.
Through the above sample code, we can see how to use Workerman to implement simple distributed multi-process communication. By properly allocating tasks and taking advantage of multiple processes, we can achieve high-performance and scalable applications.
In summary, the Workerman framework is a very suitable tool for distributed multi-process communication. By flexibly using Workerman's functions, we can easily build a high-performance, scalable distributed application. I hope this article will be helpful to everyone's work and study.
The above is the detailed content of Advanced Workerman development: realizing distributed multi-process communication. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



How to use Redis to achieve distributed data synchronization With the development of Internet technology and the increasingly complex application scenarios, the concept of distributed systems is increasingly widely adopted. In distributed systems, data synchronization is an important issue. As a high-performance in-memory database, Redis can not only be used to store data, but can also be used to achieve distributed data synchronization. For distributed data synchronization, there are generally two common modes: publish/subscribe (Publish/Subscribe) mode and master-slave replication (Master-slave).

How Redis implements distributed session management requires specific code examples. Distributed session management is one of the hot topics on the Internet today. In the face of high concurrency and large data volumes, traditional session management methods are gradually becoming inadequate. As a high-performance key-value database, Redis provides a distributed session management solution. This article will introduce how to use Redis to implement distributed session management and give specific code examples. 1. Introduction to Redis as a distributed session storage. The traditional session management method is to store session information.

MongoDB is an open source NoSQL database with high performance, scalability and flexibility. In distributed systems, task scheduling and execution are a key issue. By utilizing the characteristics of MongoDB, distributed task scheduling and execution solutions can be realized. 1. Requirements Analysis for Distributed Task Scheduling In a distributed system, task scheduling is the process of allocating tasks to different nodes for execution. Common task scheduling requirements include: 1. Task request distribution: Send task requests to available execution nodes.

Using Redis to achieve distributed cache consistency In modern distributed systems, cache plays a very important role. It can greatly reduce the frequency of system access to the database and improve system performance and throughput. In a distributed system, in order to ensure cache consistency, we need to solve the problem of data synchronization between multiple nodes. In this article, we will introduce how to use Redis to achieve distributed cache consistency and give specific code examples. Redis is a high-performance key-value database that supports persistence, replication, and collection

How to use Swoole to implement distributed scheduled task scheduling Introduction: In traditional PHP development, we often use cron to implement scheduled task scheduling, but cron can only execute tasks on a single server and cannot cope with high concurrency scenarios. Swoole is a high-performance asynchronous concurrency framework based on PHP. It provides complete network communication capabilities and multi-process support, allowing us to easily implement distributed scheduled task scheduling. This article will introduce how to use Swoole to implement distributed scheduled task scheduling

Sharing practical experience in Java development: Building a distributed log collection function Introduction: With the rapid development of the Internet and the emergence of large-scale data, the application of distributed systems is becoming more and more widespread. In distributed systems, log collection and analysis are very important. This article will share the experience of building distributed log collection function in Java development, hoping to be helpful to readers. 1. Background introduction In a distributed system, each node generates a large amount of log information. These log information are useful for system performance monitoring, troubleshooting and data analysis.

Using Redis to implement distributed task scheduling With the expansion of business and the development of the system, many businesses need to implement distributed task scheduling to ensure that tasks can be executed on multiple nodes at the same time, thereby improving the stability and availability of the system. As a high-performance memory data storage product, Redis has the characteristics of distribution, high availability, and high performance, and is very suitable for implementing distributed task scheduling. This article will introduce how to use Redis to implement distributed task scheduling and provide corresponding code examples. 1. Redis base

Details, techniques, and best practices for implementing distributed log collection and analysis with Golang and RabbitMQ. In recent years, with the popularity of microservice architecture and the complexity of large-scale systems, log collection and analysis have become more and more important. In a distributed system, the logs of each microservice are often scattered in different places. How to efficiently collect and analyze these logs becomes a challenge. This article will introduce the details, techniques, and best practices on how to use Golang and RabbitMQ to implement distributed log collection and analysis. Ra
