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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

Exploring centralized logging through Elasticsearch, Fluentd, and Kibana

Elasticsearch is probably the most commonly used in-memory database. At least, if we narrow the scope to self-hosted databases. It is designed for many other scenarios, and it can be used to store (almost) any type of data. As such, it is almost perfect for storing logs which could come in many different formats. Given its flexibility, some use it for metrics as well and, as such, Elasticsearch competes with Prometheus. We'll leave metrics aside, for now, and focus only on logs.

The EFK (Elasticsearch, Fluentd, and Kibana) stack consists of three components. Data is stored in Elasticsearch, logs are collected, transformed, and pushed to the DB by Fluentd, and Kibana is used as UI through which we can explore data stored in Elasticsearch. If you are used to ELK (Logstash instead of Fluentd), the setup...

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