Description: Glances is a cross-platform monitoring tool that provides a quick overview of system performance. Designed to be lightweight and user-friendly, Glances allows users to obtain real-time information about various system aspects, such as CPU, memory, disk, and network usage. Its interface is intuitive and presents data clearly, making it easy to identify performance issues. Glances can run on multiple operating systems, including Linux, Windows, and macOS, making it a versatile option for system administrators and advanced users. Additionally, it can be run in web mode, allowing remote monitoring through a browser. This tool is based on the Python library ‘psutil’, which provides access to system information and allows for constant data updates. Glances is useful for monitoring servers and workstations alike, helping to optimize performance and detect resource bottlenecks. Its focus on simplicity and efficiency has made it a popular tool among IT professionals looking for a quick and effective solution for system monitoring.
History: Glances was created by Franck Nijhof in 2013 as an open-source project. Since its release, it has significantly evolved, incorporating new features and improvements based on user community feedback. Over the years, Glances has been adopted by a wide variety of users, from system administrators to developers, due to its flexibility and ease of use.
Uses: Glances is primarily used to monitor the performance of systems and servers in real-time. It is especially useful in production environments where constant resource monitoring is crucial for maintaining stability and performance. It can also be used to conduct performance audits on various systems and to identify resource issues in development environments.
Examples: A practical example of Glances is its use on a web server to monitor CPU and memory usage during traffic spikes. Administrators can use Glances to quickly identify if the server is reaching its resource limits and make informed decisions about scalability. Another example is its implementation on workstations to help developers optimize the performance of their applications during development.
