I’m excited to announce the release of Rancher 2.2 Preview 2, which contains a number of powerful features for day two operations on Kubernetes clusters. Please visit our release page or the release notes to learn more about all of the features we shipped today. In this article I introduce one of the features: multi-cluster applications. Read on to learn how this will dramatically reduce your workload and increase the reliability of multi-cluster operations.

In a previous post, we explained the concept of configuration management and presented three of the most popular tools: Chef, Puppet, and Ansible. We also briefly explored the impact that containerization is having on configuration management, and how the two can be used in combination. This article takes a more in-depth look at this relationship by presenting different techniques for using Chef, Puppet, and Ansible to deploy and manage a Kubernetes cluster.

When you are using Rancher to manage your Kubernetes clusters, at some point you will encounter the terms Rancher, RKE, and custom cluster. If you are new to Rancher, it can be difficult to understand the difference between and purpose of each of these concepts. In this post, I will go over what each component is used for and how they are used together in parts of the system.

We are excited about Calico v3.5 release. This release provides more control and allows much finer-grained dynamic IP management vs the static allocation of a fixed set of addresses to each node in native Kubernetes. Here are the details.

Etcd is an open-source distributed key-value store created by the CoreOS team, now managed by the Cloud Native Computing Foundation. It is pronounced “et-cee-dee”, making reference to distributing the Unix “/etc” directory, where most global configuration files live, across multiple machines. It serves as the backbone of many distributed systems, providing a reliable way for storing data across a cluster of servers.

One of the key Kubernetes security concepts is that workload identity is tied back to information that the orchestrator has. The orchestrator is actually the authoritative entity for what the actual workloads are in the platform. Kubernetes uses labels to select objects and to identify collections of objects that satisfy certain conditions. We, and others in the Kubernetes networking space, often talk about using Kubernetes ‘labels’ as identity bearers.

OpsRamp, the service-centric AIOps software-as-a-service (SaaS) platform for the hybrid enterprise, today announced new topology maps, enhanced artificial intelligence for IT operatzions (AIOps) features and new monitoring capabilities for cloud native workloads.

Kubernetes is a fantastic tool for building large containerised software systems in a manner that is both resilient and scalable. But the architecture and design of Kubernetes has evolved over time, and there are some areas that could do with tweaking or rethinking. This post digs into some issues related to how image tags are handled in Kubernetes and how they are treated differently in plain Docker.

Kubernetes clusters can manage large numbers of unrelated workloads concurrently and organizations often choose to deploy projects created by separate teams to shared clusters. Even with relatively light use, the number of deployed objects can quickly become unmanageable, slowing down operational responsiveness and increasing the chance of dangerous mistakes.

In part 1 of this series, I discussed the rise of microservice architecture and the reliance on Kubernetes and Docker for container orchestration and management. I also shared some of the challenges these new technologies present and what sources of data we need in order to effectively monitor our Kubernetes environments.