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Mastering Kubernetes

Mastering Kubernetes

By : Gigi Sayfan
4 (9)
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Mastering Kubernetes

Mastering Kubernetes

4 (9)
By: Gigi Sayfan

Overview of this book

Kubernetes is an open source system to automate the deployment, scaling, and management of containerized applications. If you are running more than just a few containers or want automated management of your containers, you need Kubernetes. This book mainly focuses on the advanced management of Kubernetes clusters. It covers problems that arise when you start using container orchestration in production. We start by giving you an overview of the guiding principles in Kubernetes design and show you the best practises in the fields of security, high availability, and cluster federation. You will discover how to run complex stateful microservices on Kubernetes including advanced features as horizontal pod autoscaling, rolling updates, resource quotas, and persistent storage back ends. Using real-world use cases, we explain the options for network configuration and provides guidelines on how to set up, operate, and troubleshoot various Kubernetes networking plugins. Finally, we cover custom resource development and utilization in automation and maintenance workflows. By the end of this book, you’ll know everything you need to know to go from intermediate to advanced level.
Table of Contents (16 chapters)
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15
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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