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

# Pulling the code

You know the drill. We'll move into the directory with the [vfarcic/k8s-specs](https://github.com/vfarcic/k8s-specs) repository, we'll pull the latest version of the code just in case I pushed something recently, and we'll create a new cluster unless you already have one at hand.

> 🔍 All the commands from this chapter are available in the <a href="https://gist.github.com/594a4a2e922f4722d4c83c9595b3d6eb">07-logging.sh</a> Gist.

```shell
cd k8s-specs

git pull
```

# Gists and specifications

Choose the flavor you want and run the commands from its `.sh` file to create the cluster and the required specifications needed in this chapter. 

> **NOTE:** In the end, you will see a command to `DELETE` the cluster too. Don't execute that command. Use the `DELETE` command only when you need to delete the cluster, preferably at the end of the chapter. 

This time, the requirements for the cluster changed. We need much more memory than before. The main culprit is ElasticSearch which is very resource hungry.

If you're using **Docker For Desktop** or **minikube**, you'll need to increase the memory dedicated to the cluster to **10 GB**. If that's too much for your laptop, you might choose to read the [Exploring Centralized Logging Through](https://www.educative.io/courses/advanced-kubernetes-techniques/explore-centralized-logging) without running the examples or you might have to switch to one of the Cloud providers (AWS, GCP, or Azure).

In the case of **EKS** and **AKS**, we'll need bigger nodes. For EKS we'll use **t2.large** and for AKS **Standard_B2ms**. Both are based on **2 CPUs** and **8 GB RAM**.

**GKE** requirements are the same as before.

On top of new requirements, it should be noted that we do NOT need `Prometheus` in this chapter, so I removed it from the Gists.

Feel free to use one of the Gists that follow to create a new cluster, or to validate that the one you're planning to use meets the requirements.



This lesson focuses on creating a cluster and the necessary requirements and gists for this chapter.

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

Create a Cluster

Pulling the code #