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Mastering Kubernetes

You're reading from   Mastering Kubernetes Large scale container deployment and management

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Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781786461001
Length 426 pages
Edition 1st Edition
Tools
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Author (1):
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Gigi Sayfan Gigi Sayfan
Author Profile Icon Gigi Sayfan
Gigi Sayfan
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Table of Contents (16) Chapters Close

Preface 1. Understanding Kubernetes Architecture FREE CHAPTER 2. Creating Kubernetes Clusters 3. Monitoring, Logging, and Troubleshooting 4. High Availability and Reliability 5. Configuring Kubernetes Security, Limits, and Accounts 6. Using Critical Kubernetes Resources 7. Handling Kubernetes Storage 8. Running Stateful Applications with Kubernetes 9. Rolling Updates, Scalability, and Quotas 10. Advanced Kubernetes Networking 11. Running Kubernetes on Multiple Clouds and Cluster Federation 12. Customizing Kubernetes - API and Plugins 13. Handling the Kubernetes Package Manager 14. The Future of Kubernetes Index

InfluxDB backend

InfluxDB is a modern and robust distributed time-series database. It is very well-suited and used broadly for centralized metrics and logging. It is also the preferred Heapster backend (outside the Google Cloud Platform). The only thing is InfluxDB clustering; high availability is part of enterprise offering.

The storage schema

The InfluxDB storage schema defines the information that Heapster stores in InfluxDB and is available for querying and graphing later. The metrics are divided into multiple categories, called measurements. You can treat and query each metric separately, or you can query a whole category as one measurement and receive the individual metrics as fields. The naming convention is <category>/<metrics name> (except for uptime, which has a single metric). If you have a SQL background you can think of measurements as tables. Each metrics are stored per container. Each metric is labeled with the following information:

  • pod_id: Unique ID of a pod
  • pod_name...
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