Expand your Kubernetes knowledge with techniques to make your clusters dynamic and resilient.

DevOps for Developers

The world of DevOps has exploded with the help of technologies like Kubernetes and Docker.
In this course, you will learn about more advanced Kubernetes features such as monitoring, alerting, logging, and scaling - crucial concepts for any DevOps professional. You will learn how to monitor clusters, how to send alerts when potential issues arise, and how to query the whole system’s metrics and logs.
By the end of this course, you will have the required knowledge to make your clusters and applications truly dynamic and resilient, so that they require minimal manual involvement and are self-adaptive.

Kubernetes Monitoring, Logging and Auto-Scaling



 ***Change is the essential process of all existence.*** 

  ***- Spock*** 


# Kubernetes  system
By now, you probably understood that one of the critical aspects of a system based on Kubernetes is a high level of dynamism. Almost nothing is static. We define Deployments or StatefulSets, and Kubernetes distributes the Pods across the cluster. In most cases, those Pods are rarely sitting in one place for a long time. Rolling updates result in Pods being re-created and potentially moved to other nodes. Failure of any kind provokes the rescheduling of the affected resources. Many other events cause the Pods to move around. A Kubernetes cluster is like a beehive. It's full of life, and it's always in motion.

# Dynamic nature of Kubernetes

The dynamic nature of a Kubernetes cluster is not only due to our (human) actions or rescheduling caused by failures. Autoscaling is to be blamed as well. We should fully embrace Kubernetes' dynamic nature and move towards autonomous and self-sufficient clusters capable of serving the needs of our applications without (much) human involvement. To accomplish that, we need to provide sufficient information that will allow Kubernetes' to scale the applications as well as the nodes that constitute the cluster. In this chapter, we'll focus on the former case. We'll explore commonly used and basic ways to auto-scale Pods based on memory and CPU consumption. We'll accomplish that using `HorizontalPodAutoscaler`.

> `HorizontalPodAutoscaler's` only function is to automatically scale the number of Pods in a Deployment, a StatefulSet, or a few other types of resources. It accomplishes that by observing CPU and memory consumption of the Pods and acting when they reach pre-defined thresholds.


`HorizontalPodAutoscaler` is implemented as a Kubernetes API resource and a controller. The resource determines the behavior of the controller. The controller periodically adjusts the number of replicas in a StatefulSet or a Deployment to match the observed average CPU utilization to the target specified by a user.


This lesson focuses on the introduction of this chapter.

Introduction

Kubernetes system