A visualizer for Docker Swarm Mode that displays services and tasks running across swarm nodes in a real-time diagram.
Docker Swarm Visualizer is a real-time visualization tool for Docker Swarm Mode that displays services, tasks, and nodes in a dynamic diagram. It helps users monitor and understand the state of their swarm clusters by connecting to the Docker Remote API. The tool updates automatically as services scale, fail, or move across nodes.
DevOps engineers, system administrators, and developers who manage or learn Docker Swarm clusters and need a visual overview of their containerized services.
It provides an intuitive, real-time graphical interface for swarm monitoring without complex setup, and it's open-source with multi-architecture support for flexibility in different environments.
A visualizer for Docker Swarm Mode using the Docker Remote API, Node.JS, and D3
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Displays live updates of Docker services and tasks across swarm nodes with dynamic diagram changes, helping users immediately see scaling, failures, and node health.
Offers pre-built images for Linux/amd64, ARM (Raspberry Pi), and Windows via separate Dockerfiles, enabling flexible deployment on diverse hardware.
Can be deployed with a single docker run command or as a swarm service, making it accessible for demonstrations and educational purposes without complex setup.
Originally a DockerCon demo, now maintained by contributors with regular improvements, such as ARM and Windows support, ensuring ongoing relevance.
The README explicitly warns that running it in production is insecure and requires manual SSL configuration to protect the Docker daemon socket, posing a vulnerability if not hardened.
The TODO section notes CSS is optimized for only 3 nodes on a big screen, meaning the interface may become cluttered or unusable for larger clusters.
The README admits the Docker Remote API occasionally returns incomplete data, such as missing node names, leading to temporary inaccuracies in the visualization.
For Windows containers, setup requires configuring the Docker engine to listen on TCP and setting environment variables, which is more involved and error-prone than the Linux version.