The Complete HorizontalPodAutoscaler Flow of Events

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

We'll cover the following

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.

🔍 HPA is periodically evaluating metrics defined as scaling criteria. It’s fetching data from Metrics Aggregator, and it does not really care whether they’re coming from Metrics Server, Prometheus Adapter, 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.

HPA using a combination of metrics from Metrics Server and those provided by Prometheus Adapter (arrows show the flow of data)
HPA using a combination of metrics from Metrics Server and those provided by Prometheus Adapter (arrows show the flow of data)
Q

Data from both Metrics Server and Prometheus Adapter is combined in Metrics Aggregator.

A)

True

B)

False

In the next lesson, we will discuss reaching Nirvana.

Combine Metric Server Data with Custom Metrics
Reaching Nirvana
Mark as Completed
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