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The DevOps 2.5 Toolkit

You're reading from   The DevOps 2.5 Toolkit Monitoring, Logging, and Auto-Scaling Kubernetes: Making Resilient, Self-Adaptive, And Autonomous Kubernetes Clusters

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781838647513
Length 322 pages
Edition 1st Edition
Concepts
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Author (1):
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Viktor Farcic Viktor Farcic
Author Profile Icon Viktor Farcic
Viktor Farcic
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Table of Contents (9) Chapters Close

1. Autoscaling Deployments and StatefulSets Based on Resource Usage FREE CHAPTER 2. Auto-scaling Nodes of a Kubernetes Cluster 3. Collecting and Querying Metrics and Sending Alerts 4. Debugging Issues Discovered Through Metrics and Alerts 5. Extending HorizontalPodAutoscaler with Custom Metrics 6. Visualizing Metrics and Alerts 7. Collecting and Querying Logs 8. What Did We Do? 9. Other Books You May Enjoy

The complete HorizontalPodAutoscaler 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.

Once scaling criteria is 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.

Figure 5-3: HPA using a combination of...
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