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The kubeadm tool now supports etcd learner mode, which allows you to enhance the resilience and stability of your Kubernetes clusters by leveraging the learner mode feature introduced in etcd version 3.4. This guide will walk you through using etcd learner mode with kubeadm. By default, kubeadm runs a local etcd instance on each control plane node. In v1.27, kubeadm introduced a new feature gate EtcdLearnerMode. With this feature gate enabled, when joining a new control plane node, a new etcd member will be created as a learner and promoted to a voting member only after the etcd data are fully aligned... View the full article

Years before Amazon Elastic Kubernetes Service (EKS) was released, our customers told us they wanted a service that would simplify Kubernetes management. Many of them were running self-managed clusters on Amazon Elastic Computer Cloud (EC2) and were having challenges upgrading, scaling, and maintaining the Kubernetes control plane. When EKS launched in 2018, it aimed to reduce our customers’ operational burden by offering a managed control plane for Kubernetes. This initially included automated upgrades, patching, and backups, which we often refer to as “undifferentiated heavy lifting.” We analyzed volumes of data to create a control plane that would work for the vast majority of our customers. However, as usage of EKS grew, we discovered there were customers who occasionally exceeded the provisioned capacity of the cluster. When this happened, they had to file a ticket with AWS support to have their cluster control plane resized. This was not an ideal user experience. Today, the control plane is scaled automatically when certain metrics are exceeded. At first, we used basic metrics such as CPU/memory for scaling. As we learned how the control plane behaved under different conditions, we adjusted our metrics to make scaling more responsive. Now we use a variety of metrics to scale the control plane, including the number of worker nodes and the size of the etcd database. These enhancements are a great example of the flywheel effect where AWS releases a feature in response to customer feedback, solicits feedback from end users about its impact, and uses that feedback to continue improving the customer experience... View the full article