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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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Toc

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

Extending HorizontalPodAutoscaler with Custom Metrics

Computers make excellent and efficient servants, but I have no wish to serve under them.

- Spock

Adoption of HorizontalPodAutoscaler (HPA) usually goes through three phases.

The first phase is discovery. The first time we find out what it does, we usually end up utterly amazed. "Look at this. It scales our applications automatically. I don't need to worry about the number of replicas anymore."

The second phase is the usage. Once we start using HPA, we quickly realize that scaling applications based memory and CPU is not enough. Some apps do increase their memory and CPU usage with the increase in load, while many others don't. Or, to be more precise, not proportionally. HPA, for some applications, works well. For many others, it does not work at all, or it is not enough. Sooner or later, we'll need...

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