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Kubernetes in Production Best Practices

You're reading from   Kubernetes in Production Best Practices Build and manage highly available production-ready Kubernetes clusters

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781800202450
Length 292 pages
Edition 1st Edition
Tools
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Authors (2):
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Aly Saleh Aly Saleh
Author Profile Icon Aly Saleh
Aly Saleh
Murat Karslioglu Murat Karslioglu
Author Profile Icon Murat Karslioglu
Murat Karslioglu
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Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Introduction to Kubernetes Infrastructure and Production-Readiness 2. Chapter 2: Architecting Production-Grade Kubernetes Infrastructure FREE CHAPTER 3. Chapter 3: Provisioning Kubernetes Clusters Using AWS and Terraform 4. Chapter 4: Managing Cluster Configuration with Ansible 5. Chapter 5: Configuring and Enhancing Kubernetes Networking Services 6. Chapter 6: Securing Kubernetes Effectively 7. Chapter 7: Managing Storage and Stateful Applications 8. Chapter 8: Deploying Seamless and Reliable Applications 9. Chapter 9: Monitoring, Logging, and Observability 10. Chapter 10: Operating and Maintaining Efficient Kubernetes Clusters 11. Other Books You May Enjoy

Logging and tracing

In this section, we will learn about the popular logging solutions in the cloud-native ecosystem and how to get a logging stack quickly up and running.

Handling logs for applications running on Kubernetes is quite different than traditional application log handling. With monolithic applications, when a server or an application crashes, our server can still retain logs. In Kubernetes, a new pod is scheduled when a pod crashes, causing the old pod and its records to get wiped out. The main difference with containerized applications is how and where we ship and store our logs for future use.

Two cloud-native-focused popular logging stacks are the Elasticsearch, Fluentd, and Kibana (EFK) stack and the Promtail, Loki, and Grafana (PLG) stack. Both have fundamental design and architectural differences. The EFK stack uses Elasticsearch as an object store, Fluentd for log routing and aggregation, and Kibana for the visualization of logs. The PLG stack is based on...

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