Delve into advanced Kubernetes features like monitoring, alerting, logging, and auto-scaling. Gain insights into creating resilient, self-adaptive clusters and applications with minimal manual intervention.

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.

Advanced Kubernetes Techniques: Monitoring, Logging, Auto-Scaling

# No nodes with available resources



Usage of `HorizontalPodAutoscaler (HPA)` is one of the most critical aspects of making a resilient, fault-tolerant, and highly-available system. However, it is of no use if there are no nodes with available resources. When Kubernetes cannot schedule new Pods because there's not enough available memory or CPU, new Pods will be unschedulable and in the *pending* status. If we do not increase the capacity of our cluster, *pending* Pods might stay in that state indefinitely. To make things more complicated, Kubernetes might start removing other Pods to make room for those that are in the pending state. That, as you might have guessed, might lead to worse problems than the issue of our applications not having enough replicas to serve the demand.

Kubernetes solves the problem of scaling nodes through Cluster Autoscaler.

# Purpose of `Cluster Autoscaler`
 

`Cluster Autoscaler` has a single purpose: to adjust the size of the cluster by adding or removing worker nodes. It adds new nodes when Pods cannot be scheduled due to insufficient resources. Similarly, it eliminates nodes when they are underutilized for a period of time and when Pods running on one such node can be rescheduled somewhere else.

The logic behind `Cluster Autoscaler` is simple to grasp. We are yet to see whether it is simple to use as well.

This lesson points to the issue of no nodes with available resources and introduces Cluster Autoscaler to solve that issue.

Before Getting Started

Autoscaling Deployments and StatefulSets

Auto-Scaling Nodes Of A Kubernetes Cluster

Collecting and Querying Metrics and Sending Alerts

Debugging Issues Discovered Through Metrics and Alerts

Extending HorizontalPodAutoscaler With Custom Metrics

Visualizing Metrics And Alerts

Collecting And Querying Logs

Conclusion

Introduction

No nodes with available resources