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



***Computers make excellent and efficient servants, but I have no wish to serve under them.***

***- Spock***

# `HorizontalPodAutoscaler (HPA)` phases
The adoption of `HorizontalPodAutoscaler (HPA)` usually goes through three phases.

## First phase
The first phase is **discovery**. The first time we find out what it does, we usually end up utterly amazed. "Look at this. It scales our applications automatically. I don't need to worry about the number of replicas anymore."

## Second phase
The second phase is the **usage**. Once we start using `HPA`, we quickly realize that scaling applications based on memory and CPU is not enough. Some apps do increase their memory and CPU usage with the increase in load, while many others don't. Or, to be more precise, not proportionally. `HPA`, for some applications, works well. For many others, it does not work at all, or it is not enough. Sooner or later, we'll need to extend `HPA` thresholds beyond those based on memory and CPU. This phase is characterized by *disappointment*. "It seemed like a good idea, but we cannot use it with most of our applications. We need to fall back to alerts based on metrics and manual changes to the number of replicas."

## Third phase
The third phase is **re-discovery**. Once we go through the `HPA` v2 documentation (still in beta at the time of this writing) we can see that it allows us to extend it to almost any type of metrics and expressions. We can hook `HPAs` to `Prometheus`, or nearly any other tool, though adapters. Once we master that, there is almost no limit to the conditions we can set as triggers of automatic scaling of our applications. The only restriction is our ability to transform data into Kubernetes' custom metrics.

---
Our next goal is to extend `HorizontalPodAutoscaler` definitions to include conditions based on data stored in `Prometheus`. But first, let's create a cluster in the next lesson.


This lesson provides an Introduction to this course.

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

`HorizontalPodAutoscaler (HPA)` phases

First phase

Introduction

HorizontalPodAutoscaler (HPA) phases

First phase

`HorizontalPodAutoscaler (HPA)` phases