Book Image

The Kubernetes Operator Framework Book

By : Michael Dame
1 (1)
Book Image

The Kubernetes Operator Framework Book

1 (1)
By: Michael Dame

Overview of this book

From incomplete collections of knowledge and varying design approaches to technical knowledge barriers, Kubernetes users face various challenges when developing their own operators. Knowing how to write, deploy, and pack operators makes cluster management automation much easier – and that's what this book is here to teach you. Beginning with operators and Operator Framework fundamentals, the book delves into how the different components of Operator Framework (such as the Operator SDK, Operator Lifecycle Manager, and OperatorHub.io) are used to build operators. You’ll learn how to write a basic operator, interact with a Kubernetes cluster in code, and distribute that operator to users. As you advance, you’ll be able to develop a sample operator in the Go programming language using Operator SDK tools before running it locally with Operator Lifecycle Manager, and also learn how to package an operator bundle for distribution. The book covers best practices as well as sample applications and case studies based on real-world operators to help you implement the concepts you’ve learned. By the end of this Kubernetes book, you’ll be able to build and add application-specific operational logic to a Kubernetes cluster, making it easier to automate complex applications and augment the platform.
Table of Contents (16 chapters)
1
Part 1: Essentials of Operators and the Operator Framework
4
Part 2: Designing and Developing an Operator
9
Part 3: Deploying and Distributing Operators for Public Use

Implementing metrics reporting

Metrics are a crucial aspect of any Kubernetes cluster. Metrics tools can provide detailed insights into almost any measurable data in the cluster. This is why metrics are a key part of graduating an Operator to Level IV in the Capability Model. In fact, most native Kubernetes controllers already report metrics about themselves, for example, kube-scheduler and kube-controller-manager. These components export data in metrics such as schedule_attempts_total, which reports the number of attempts the scheduler has made to schedule Pods onto Nodes.

The original design for the Kubernetes monitoring architecture (https://github.com/kubernetes/design-proposals-archive/blob/main/instrumentation/monitoring_architecture.md) defines metrics such as schedule_attempts_total as service metrics. The alternative to service metrics is core metrics, which are metrics that are generally available from all components. Core metrics currently include information about CPU...