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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
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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

Combining Metric Server data with custom metrics

So far, the few HPA examples used a single custom metric to decide whether to scale the Deployment. You already know from the Chapter 1, Autoscaling Deployments and StatefulSets Based on Resource Usage, that we can combine multiple metrics in an HPA. However, all the examples in that chapter used data from the Metrics Server. We learned that in many cases memory and CPU metrics from the Metrics Server are not enough, so we introduced Prometheus Adapter that feeds custom metrics to the Metrics Aggregator. We successfully configured an HPA to use those custom metrics. Still, more often than not, we'll need a combination of both types of metrics in our HPA definitions. While memory and CPU metrics are not enough by themselves, they are still essential. Can we combine both?

Let's take a look at yet another HPA definition...

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