This page shows how to configure a Key Management Service (KMS) provider and plugin to enable secret data encryption. In Kubernetes 1.35 there are two versions of KMS at-rest encryption. You should use KMS v2 if feasible because KMS v1 is deprecated (since Kubernetes v1.28) and disabled by default (since Kubernetes v1.29). KMS v2 offers significantly better performance characteristics than KMS v1. Caution:This documentation is for the generally available implementation of KMS v2 (and for the deprecated version 1 implementation).

In Kubernetes, a HorizontalPodAutoscaler automatically updates a workload resource (such as a Deployment or StatefulSet), with the aim of automatically scaling capacity to match demand. Horizontal scaling means that the response to increased load is to deploy more Pods. This is different from vertical scaling, which for Kubernetes would mean assigning more resources (for example: memory or CPU) to the Pods that are already running for the workload. If the load decreases, and the number of Pods is above the configured minimum, the HorizontalPodAutoscaler instructs the workload resource (the Deployment, StatefulSet, or other similar resource) to scale back down.

By default, containers run with unbounded compute resources on a Kubernetes cluster. Using Kubernetes resource quotas, administrators (also termed cluster operators) can restrict consumption and creation of cluster resources (such as CPU time, memory, and persistent storage) within a specified namespace. Within a namespace, a Pod can consume as much CPU and memory as is allowed by the ResourceQuotas that apply to that namespace. As a cluster operator, or as a namespace-level administrator, you might also be concerned about making sure that a single object cannot monopolize all available resources within a namespace.

You can use topology spread constraints to control how Pods are spread across your cluster among failure-domains such as regions, zones, nodes, and other user-defined topology domains. This can help to achieve high availability as well as efficient resource utilization. You can set cluster-level constraints as a default, or configure topology spread constraints for individual workloads. Motivation Imagine that you have a cluster of up to twenty nodes, and you want to run a workload that automatically scales how many replicas it uses.

When you specify a Pod, you can optionally specify how much of each resource a container needs. The most common resources to specify are CPU and memory (RAM); there are others. When you specify the resource request for containers in a Pod, the kube-scheduler uses this information to decide which node to place the Pod on. When you specify a resource limit for a container, the kubelet enforces those limits so that the running container is not allowed to use more of that resource than the limit you set.

This task demonstrates running multiple Jobs based on a common template. You can use this approach to process batches of work in parallel. For this example there are only three items: apple, banana, and cherry. The sample Jobs process each item by printing a string then pausing. See using Jobs in real workloads to learn about how this pattern fits more realistic use cases. Before you begin You should be familiar with the basic, non-parallel, use of Job.

This page guides you through configuring Node overprovisioning in your Kubernetes cluster. Node overprovisioning is a strategy that proactively reserves a portion of your cluster's compute resources. This reservation helps reduce the time required to schedule new pods during scaling events, enhancing your cluster's responsiveness to sudden spikes in traffic or workload demands. By maintaining some unused capacity, you ensure that resources are immediately available when new pods are created, preventing them from entering a pending state while the cluster scales up.

This page explains how to upgrade a Kubernetes cluster created with kubeadm from version 1.34.x to version 1.35.x, and from version 1.35.x to 1.35.y (where y > x). Skipping MINOR versions when upgrading is unsupported. For more details, please visit Version Skew Policy. To see information about upgrading clusters created using older versions of kubeadm, please refer to following pages instead: Upgrading a kubeadm cluster from 1.33 to 1.34 Upgrading a kubeadm cluster from 1.

Kubernetes objects can be created, updated, and deleted by storing multiple object configuration files in a directory and using kubectl apply to recursively create and update those objects as needed. This method retains writes made to live objects without merging the changes back into the object configuration files. kubectl diff also gives you a preview of what changes apply will make. Before you begin Install kubectl. You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster.

This tutorial shows you how to deploy a WordPress site and a MySQL database using Minikube. Both applications use PersistentVolumes and PersistentVolumeClaims to store data. A PersistentVolume (PV) is a piece of storage in the cluster that has been manually provisioned by an administrator, or dynamically provisioned by Kubernetes using a StorageClass. A PersistentVolumeClaim (PVC) is a request for storage by a user that can be fulfilled by a PV.