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

* For any but the smallest systems, going from one resource to another and from one node to another to find the cause of an issue is anything but practical, reliable, or fast.

* More often than not, the `kubectl logs` command does not provide us with enough options to perform anything but the simplest retrieval of logs.
* Elasticsearch is excellent, but it does too much. Its lack of focus makes it inferior to Prometheus for storing and querying metrics, as well as sending alerts based on such data.
* Logs themselves are too expensive to parse, and most of the time they do not provide enough data to act as metrics.
* We need logs centralized in a single location so that we can explore logs from any part of the system.
* We need a query language that allows us to filter log messages.
* We need the solution to be fast.
* Use a service provided by your Cloud provider, unless it is more expensive than alternatives. If your cluster is on-prem, use a third-party service like Papertrail, unless there are rules that prevent you from sending logs outside your internal network. If everything else fails, use EFK.
* We should start exploring logs only when metrics reveal the culprit but not the cause of the issue.


# Remove the previous resources

You know what to do. Destroy the cluster if you created it specifically for this chapter.

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 #