tianshiyeben/wgcloud

Linux运维监控工具,支持系统硬件信息,内存,CPU,温度,磁盘空间及IO,硬盘smart,GPU,防火墙,网络流量速率等监控,服务接口监测,大屏展示,拓扑图,端口监控,进程监控,docker监控,日志监控,文件防篡改,数据库监控,指令批量下发执行,web...

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Summary Information

Updated 13 minutes ago
Added to GitGenius on September 13th, 2026
Created on January 15th, 2019
Open Issues & Pull Requests: 34 (+0)
GitHub issues: Enabled
Number of forks: 910
Total Stargazers: 5,184 (+0)
Total Subscribers: 79 (+0)

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Detailed Description

WGCLOUD is a distributed Linux operations monitoring system built on SpringBoot that collects and visualizes infrastructure metrics across servers and network devices with minimal configuration.

The tool addresses the need for rapid deployment of monitoring infrastructure without templates or scripts. It operates on a server-agent architecture where agents collect system metrics—CPU usage and temperature, memory, disk capacity and I/O performance, disk SMART health, network traffic, hardware information, running processes, and open ports—and report them to a central server every two minutes by default. The server processes this data and generates charts and dashboards. The system also monitors applications, files, databases, Docker containers, service APIs, and network devices like switches and routers, automatically generating network topology diagrams and large-screen visualizations. It includes web SSH functionality, batch command execution, alert notifications through email and messaging platforms, and asset management capabilities.

Organizations should choose this tool if they need straightforward infrastructure monitoring across multiple Linux servers without complex configuration overhead. It suits environments ranging from small deployments to thousands of simultaneously monitored hosts. The project explicitly distinguishes its open-source version from a commercial variant available on its website, noting that the commercial version offers more features, better performance, improved security, and more active support, though the commercial version can be used without cost.

Development activity shows consistent engagement with the codebase. The project maintains active issue tracking and pull request handling. Code commits demonstrate ongoing refinement of core monitoring components and feature additions. The team has made architectural decisions such as replacing the SIGAR library with OSHI for host metric collection. Documentation is actively maintained across the project website and repository, with examples and deployment guides regularly updated.