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



***It is curious how often you humans manage to obtain that which you do not want.***

***- Spock***

*Dashboards are useless! They are a waste of time. Get Netflix if you want to watch something. It's cheaper than any other option.*

# Why dashboards?
I repeated those words on many public occasions. I think that companies exaggerate the need for **dashboards**. They spend a lot of effort creating a bunch of graphs and put a lot of people in charge of staring at them. As if that's going to help anyone. The main advantage of **dashboards** is that they are colorful and full of lines, boxes, and labels. Those properties are always an easy sell to decision-makers like CTOs and heads of departments. When a software vendor comes to a meeting with decision-makers with the authority to write checks, they know that there is no sale without "pretty colors". It does not matter what that software does, but how it looks. That's why every software company focuses on **dashboards**.

Think about it. What is a **dashboard** good for? Are we going to look at graphs until a bar reaches a red line indicating that a critical threshold is reached? If that's the case, why not create an alert that will trigger under the same conditions and stop wasting time staring at screens and waiting until something happens. Instead, we can be doing something more useful (like staring Netflix).

## Dashboards with graphs

Is our "panic criteria" more complex than what can be expressed through alerts? I do think that it is more complex. However, that complexity cannot be reflected through pre-defined graphs. Sure, unexpected things happen, and we need to dig through data. However, the word "unexpected" defies what **dashboards** provide. They are all about the expected outcomes. Otherwise, how are we going to define a graph without knowing what to expect? "It can be anything" cannot be translated to a graph. **Dashboards** with graphs are our ways to assume what might go wrong and put those assumptions on a screen or, more often than not, on a lot of screens. However, unexpected can only be explored by querying metrics and going deeper and deeper until we find the cause of an issue. That's investigative work that does not translate well to **dashboards**. We use `Prometheus queries` for that.

And yet, here I am dedicating a chapter to **dashboards**.

## Conclusion

I do admit that **dashboards** are not (fully) useless. They are useful, sometimes. What I truly wanted to convey is that their usefulness is exaggerated and that we might require different construction and use dashboards differently than what many are used to.

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But, I'm jumping ahead of myself. We'll discuss the details of the **dashboards** a bit later. For now, we need to create a cluster that will allow us to experiment and take this conversation to a more practical level. Let's see that 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