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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

Switching to Elasticsearch for storing metrics

Now that we had Elasticsearch running in our cluster and knowing that it can handle almost any data type, a logical question could be whether we can use it to store our metrics besides logs. If you explore elastic.co (https://www.elastic.co/), you'll see that metrics are indeed something they advertise. If it could replace Prometheus, it would undoubtedly be beneficial to have a single tool that can handle not only logs but also metrics. On top of that, we could ditch Grafana and keep Kibana as a single UI for both data types.

Nevertheless, I would strongly advise against using Elasticsearch for metrics. It is a general-purpose free-text no-SQL database. That means that it can handle almost any data but, at the same time, it does not excel at any specific format. Prometheus, on the other hand, is designed to store time-series...

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