Expand your Kubernetes knowledge with techniques to make your clusters dynamic and resilient.

DevOps for Developers

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.

Kubernetes Monitoring, Logging and Auto-Scaling

# Complete `HPA` flow of events

`Metrics Server` is fetching memory and CPU data from Kubelets running on the worker nodes. In parallel, `Prometheus Adapter` is fetching data from `Prometheus Server` which, as you already know, pulls data from different sources. Data from both **Metrics Server** and `Prometheus Adapter` is combined in *Metrics Aggregator*.

> 🔍 <code>HPA</code> is periodically evaluating metrics defined as scaling criteria. It's fetching data from <b>Metrics Aggregator</b>, and it does not really care whether they're coming from <code>Metrics Server</code>, <code>Prometheus Adapter</code>, or any other tool we could have used.

## `HPA` manipulation

Once scaling criteria are met, `HPA` manipulates Deployments and StatefulSets by changing their number of replicas. As a result, rolling updates are performed by creating and updating ReplicaSets which, in turn, create or remove Pods.


In this lesson, we will describe the complete flow of HPA events.

The Complete HorizontalPodAutoscaler Flow of Events

Complete HPA

Complete `HPA`