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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

Alerting on saturation-related issues

Saturation measures fullness of our services and the system. We should be aware if replicas of our services are processing too many requests and being forced to queue some of them. We should also monitor whether usage of our CPUs, memory, disks, and other resources reaches critical limits.

For now, we'll focus on CPU usage. We'll start by opening the Prometheus' graph screen.

 1  open "http://$PROM_ADDR/graph"

Let's see if we can get the rate of used CPU by node (instance). We can use node_cpu_seconds_total metric for that. However, it is split into different modes, and we'll have to exclude a few of them to get the "real" CPU usage. Those will be idle, iowait, and any type of guest cycles.

Please type the expression that follows, and press the Execute button.

 1  sum(rate(
 2    node_cpu_seconds_total...
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