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

# Review of the chapter

`Grafana` is relatively simple to use and intuitive. If you know how to write queries for the data source hooked to `Grafana` (e.g., `Prometheus`), you already learned the most challenging part. The rest is mostly about checking boxes, choosing panel types, and arranging things on the screen. The main difficulty is to avoid getting carried away by creating a bunch of flashy dashboards that do not provide much value. A common mistake is to create a graph for everything we can imagine. That only reduces the value of those that are truly important. Less is often more.

# Remove the previous resources

That's it. Destroy the cluster if it's dedicated to this chapter, or keep it if it's not or if you're planning to jump to the next chapter right away. If you're keeping it, please delete the *Grafana Chart* by executing the command that follows. If we need it in one of the next chapters, I'll make sure that it's included in the Gists.

```shell
helm delete grafana \
    --namespace metrics
```

In this lesson, we will revise and test the concepts we have learned in this chapter through a small quiz.

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

Revise and Test the Concepts

Review of the chapter #