Kubernetes 1.19: Accentuate the Paw-sitive

The 1.19 release was quite different from a regular release due to COVID-19, the George Floyd protests, and several other global events that we experienced as a release team. Due to these events, we made the decision to adjust our timeline and allow the SIGs, Working Groups, and contributors more time to get things done. The extra time also allowed for people to take time to focus on their lives outside of the Kubernetes project, and ensure their mental wellbeing was in a good place.

Contributors are the heart of Kubernetes, not the other way around. The Kubernetes code of conduct asks that people be excellent to one another and despite the unrest in our world, we saw nothing but greatness and humility from the community.

Major Themes

Increase Kubernetes support window to one year

A survey conducted in early 2019 by the  Long Term Support (LTS) working group showed that a significant subset of Kubernetes end-users fail to upgrade within the current 9-month support period. This, and other responses from the survey, suggest that 30% of users would be able to keep their deployments on supported versions if the patch support period were extended to 12-14 months. This appears to be true regardless of whether the users are on self build or commercially vendored distributions. An extension would thus lead to more than 80% of users being on supported versions, instead of the 50-60% we have now. A yearly support period provides the cushion end-users appear to desire, and is more in harmony with familiar annual planning cycles. From Kubernetes version 1.19 on, the support window will be extended to one year.

Storage capacity tracking

Traditionally, the Kubernetes scheduler was based on the assumptions that additional persistent storage is available everywhere in the cluster and has infinite capacity. Topology constraints addressed the first point, but up to now pod scheduling was still done without considering that the remaining storage capacity may not be enough to start a new pod.  Storage capacity tracking, a new alpha feature, addresses that by adding an API for a CSI driver to report storage capacity and uses that information in the Kubernetes scheduler when choosing a node for a pod. This feature serves as a stepping stone for supporting dynamic provisioning for local volumes and other volume types that are more capacity constrained.

Generic ephemeral volumes

Kubernetes provides volume plugins whose lifecycle is tied to a pod and can be used as scratch space (e.g. the builtin emptydir volume type) or to load some data in to a pod (e.g. the builtin configmap and secret volume types, or “CSI inline volumes”). The new  generic ephemeral volumes alpha feature allows any existing storage driver that supports dynamic provisioning to be used as an ephemeral volume with the volume’s lifecycle bound to the Pod. It can be used to provide scratch storage that is different from the root disk, for example persistent memory, or a separate local disk on that node. All StorageClass parameters for volume provisioning are supported. All features supported with PersistentVolumeClaims are supported, such as storage capacity tracking, snapshots and restore, and volume resizing.

CSI Volume Health Monitoring 

kubernetes paw-sitive volumes

Our original Kubernetes tool list was so popular that we've curated another great list of tools to help you improve your functionality with the platform.

This article explains how you can leverage Kubernetes to reduce multi cloud complexities and improve stability, scalability, and velocity.

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In Kubernetes, a volume can be thought of as a directory which is accessible to the containers in a pod. We have different types of volumes in Kubernetes and the type defines how the volume is created and its content.