This document describes ephemeral volumes in Kubernetes. Familiarity with volumes is suggested, in particular PersistentVolumeClaim and PersistentVolume. Some applications need additional storage but don't care whether that data is stored persistently across restarts. For example, caching services are often limited by memory size and can move infrequently used data into storage that is slower than memory with little impact on overall performance. Other applications expect some read-only input data to be present in files, like configuration data or secret keys.

You can constrain a Pod so that it is restricted to run on particular node(s), or to prefer to run on particular nodes. There are several ways to do this and the recommended approaches all use label selectors to facilitate the selection. Often, you do not need to set any such constraints; the scheduler will automatically do a reasonable placement (for example, spreading your Pods across nodes so as not place Pods on a node with insufficient free resources).

System component metrics can give a better look into what is happening inside them. Metrics are particularly useful for building dashboards and alerts. Kubernetes components emit metrics in Prometheus format. This format is structured plain text, designed so that people and machines can both read it. Metrics in Kubernetes In most cases metrics are available on /metrics endpoint of the HTTP server. For components that don't expose endpoint by default, it can be enabled using --bind-address flag.

Restrict how many Pods you can create within a namespace.

FEATURE STATE: Kubernetes v1.19 [stable] The scheduling framework is a pluggable architecture for the Kubernetes scheduler. It consists of a set of "plugin" APIs that are compiled directly into the scheduler. These APIs allow most scheduling features to be implemented as plugins, while keeping the scheduling "core" lightweight and maintainable. Refer to the design proposal of the scheduling framework for more technical information on the design of the framework.

Lower-level detail relevant to creating or administering a Kubernetes cluster.

Define a range of valid CPU resource limits for a namespace, so that every new Pod in that namespace falls within the range you configure.

Operators are software extensions to Kubernetes that make use of custom resources to manage applications and their components. Operators follow Kubernetes principles, notably the control loop. Motivation The operator pattern aims to capture the key aim of a human operator who is managing a service or set of services. Human operators who look after specific applications and services have deep knowledge of how the system ought to behave, how to deploy it, and how to react if there are problems.

Production-Grade Container Orchestration

Note: This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information. Add-ons extend the functionality of Kubernetes. This page lists some of the available add-ons and links to their respective installation instructions. The list does not try to be exhaustive.