Book Image

End-to-End Automation with Kubernetes and Crossplane

By : Arun Ramakani
Book Image

End-to-End Automation with Kubernetes and Crossplane

By: Arun Ramakani

Overview of this book

In the last few years, countless organizations have taken advantage of the disruptive application deployment operating model provided by Kubernetes. With Crossplane, the same benefits are coming to the world of infrastructure provisioning and management. The limitations of Infrastructure as Code with respect to drift management, role-based access control, team collaboration, and weak contract make people move towards a control-plane-based infrastructure automation, but setting it up requires a lot of know-how and effort. This book will cover a detailed journey to building a control-plane-based infrastructure automation platform with Kubernetes and Crossplane. The cloud-native landscape has an overwhelming list of configuration management tools that can make it difficult to analyze and choose. This book will guide cloud-native practitioners to select the right tools for Kubernetes configuration management that best suit the use case. You'll learn about configuration management with hands-on modules built on popular configuration management tools such as Helm, Kustomize, Argo, and KubeVela. The hands-on examples will be patterns that one can directly use in their work. By the end of this book, you'll be well-versed with building a modern infrastructure automation platform to unify application and infrastructure automation.
Table of Contents (16 chapters)
1
Part 1: The Kubernetes Disruption
4
Part 2: Building a Modern Infrastructure Platform
10
Part 3:Configuration Management Tools and Recipes

Automating the application deployment environment

The complete Kubernetes cluster creation and configuring of the cross-cutting concerns will be automated using this step. We will develop an XR/claim API, which does the following:

  1. Provisions a remote GKE cluster
  2. Sets up Helm and the Kubernetes provider configuration for the GKE cluster
  3. Installs Argo CD using the Helm provider into the product-a GKE cluster

Let’s look at the XRD and composition to understand the API in detail (refer to the XRD and composition in the book’s GitHub repository). We will capture two mandatory parameters (node count and machine size). The size parameter takes either BIG or SMALL as an enum value. Inside the composition, we have composed five resources. The following is the list of resources and their purpose:

  • Cluster and NodePool: Cluster and NodePool are two relevant resources responsible for GKE cluster provisioning. It is very similar to the way we provisioned...