Glossary of Terms

Familiarity with the following terms will make it easier to understand the labels and symbols in the Lightbend Console.

Kubernetes Terms

To visualize Kubernetes components, you might find this overview and this blog useful. It also can help to see how the Kubernetes Dashboard represents them.

  • Cluster: a group of nodes that run Kubernetes components.
  • Controller: creates and manages pods, handles replication and rollout and provides self-healing capabilities for the cluster.
  • Container: a running Docker image. A Docker image is a lightweight, standalone, executable package of software that includes everything needed to run an application: code, runtime, system tools, system libraries and settings. Kubernetes organizes containers in pods, which you can manage manually or with controllers.
  • Deployment: a deployment object declares in a .yaml file the desired state of a pod, for example how many replicas should be running. When you create a deployment, the deployment controller creates the specified pod(s) and manages their lifecycle and they display in the Console as a workload.
  • Namespace: virtual clusters on top of a physical cluster that allow different teams to work with different resources in the same cluster.
  • Node: a physical or virtual machine.
  • Pod: a unit of deployment, the environment in which a single instance of an application runs. A single container or a small number of containers that are tightly coupled and that share resources can run in a pod.
  • Service: an abstraction defining a logical set of Pods and a policy by which to access them - sometimes called a microservice.
  • Volume: storage for the applications running as pods.
  • Workload: a group of pods which represents a single application. Typically a workload will map one-to-one with the pods managed by a Deployment, DaemonSet, or StatefulSet. Workloads are the main entities visualized by the Lightbend Console.

Console Terms

  • Metric: is a value that tells us something. Examples are CPU or memory usage for an application. We rely on Prometheus for scraping and storing metrics.
  • Time series: values of a metric stored over time, sampled at periodic intervals (called scrape intervals in Prometheus). The values of a specific metric over time is called a time series.
  • Monitor: health criteria for a specific metric. An example would be a monitor on the memory usage of an application that becomes unhealthy when too much memory is used. Monitors are the primary data model in Console and drive most of its functionality.