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

# Limitations of using `HPA` without metrics adapter

If we do not create a `Metrics Adapter`, *Metrics Aggregator* only knows about CPU and memory usage related to containers and nodes. To make things more complicated, that information is limited only to the last few minutes. Since `HorizontalPodAutoscaler (HPA)` is just concerned about Pods and containers inside them, we are limited to only two metrics. When we create an `HPA`, it will scale or descale our Pods if memory or CPU consumption of the containers that constitute those Pods is above or below predefined thresholds.

`Metrics Server` periodically fetches information (CPU and memory) from Kubelets running inside worker nodes. Those metrics are passed to *Metrics Aggregator* which, in this scenario, does not add any additional value. From there on, `HPAs` periodically consult the data in the *Metrics Aggregator* (through its API endpoint). When there is a discrepancy between target values defined in an `HPA` and the actual values, an `HPA` will manipulate the number of replicas of a Deployment or a StatefulSet. As we already know, any change to those controllers results in rolling updates executed through the creation and manipulation of ReplicaSets, (which create and delete Pods), which are converted into containers by a Kubelet running on a node where a Pod is scheduled.


In this lesson, we will see what are the limitations of using HPA without metrics adapter and the data available in Metrics Aggregator.

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

Using HorizontalPodAutoscaler Without Metrics Adapter

Limitations of using `HPA` without metrics adapter #

Using HorizontalPodAutoscaler Without Metrics Adapter

Limitations of using HPA without metrics adapter #

Limitations of using `HPA` without metrics adapter #