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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

What should we expect from centralized logging?

We explored several products that can be used to centralize logging. As you saw, all are very similar, and we can assume that most of the other solutions follow the same principles. We need to collect logs across the cluster. We used Fluentd for that, which is the most widely accepted solution that you will likely use no matter which database receives those logs (Azure being an exception).

Log entries collected with Fluentd are shipped to a database which, in our case, is Papertrail, Elasticsearch, or one of the solutions provided by hosting vendors. Finally, all solutions offer a UI that allows us to explore the logs.

I usually provide a single solution for a problem but, in this case, there are quite a few candidates for your need for centralized logging. Which one should you choose? Will it be Papertrail, Elasticsearch-Fluentd...

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