Book Image

Mastering Kubernetes - Second Edition

By : Gigi Sayfan
Book Image

Mastering Kubernetes - Second Edition

By: Gigi Sayfan

Overview of this book

Kubernetes is an open source system that is used to automate the deployment, scaling, and management of containerized applications. If you are running more containers or want automated management of your containers, you need Kubernetes at your disposal. To put things into perspective, Mastering Kubernetes walks you through the advanced management of Kubernetes clusters. To start with, you will learn the fundamentals of both Kubernetes architecture and Kubernetes design in detail. You will discover how to run complex stateful microservices on Kubernetes including advanced features such as horizontal pod autoscaling, rolling updates, resource quotas, and persistent storage backend. Using real-world use cases, you will explore the options for network configuration, and understand how to set up, operate, and troubleshoot various Kubernetes networking plugins. In addition to this, you will get to grips with custom resource development and utilization in automation and maintenance workflows. To scale up your knowledge of Kubernetes, you will encounter some additional concepts based on the Kubernetes 1.10 release, such as Promethus, Role-based access control, API aggregation, and more. By the end of this book, you’ll know everything you need to graduate from intermediate to advanced level of understanding Kubernetes.
Table of Contents (16 chapters)

Detecting node problems

In Kubernetes' conceptual model, the unit of work is the pod. However, pods are scheduled on nodes. When it comes to monitoring and reliability, the nodes are what require the most attention, because Kubernetes itself (the scheduler and replication controllers) takes care of the pods. Nodes can suffer from a variety of problems that Kubernetes is unaware of. As a result, it will keep scheduling pods to the bad nodes and the pods might fail to function properly. Here are some of the problems that nodes may suffer while still appearing functional:

  • Bad CPU
  • Bad memory
  • Bad disk
  • Kernel deadlock
  • Corrupt filesystem
  • Problems with the Docker daemon

The kubelet and cAdvisor don't detect these issues, another solution is needed. Enter the node problem detector.