Book Image

Edge Computing Systems with Kubernetes

By : Sergio Méndez
Book Image

Edge Computing Systems with Kubernetes

By: Sergio Méndez

Overview of this book

Edge computing is a way of processing information near the source of data instead of processing it on data centers in the cloud. In this way, edge computing can reduce latency when data is processed, improving the user experience on real-time data visualization for your applications. Using K3s, a light-weight Kubernetes and k3OS, a K3s-based Linux distribution along with other open source cloud native technologies, you can build reliable edge computing systems without spending a lot of money. In this book, you will learn how to design edge computing systems with containers and edge devices using sensors, GPS modules, WiFi, LoRa communication and so on. You will also get to grips with different use cases and examples covered in this book, how to solve common use cases for edge computing such as updating your applications using GitOps, reading data from sensors and storing it on SQL and NoSQL databases. Later chapters will show you how to connect hardware to your edge clusters, predict using machine learning, and analyze images with computer vision. All the examples and use cases in this book are designed to run on devices using 64-bit ARM processors, using Raspberry Pi devices as an example. By the end of this book, you will be able to use the content of these chapters as small pieces to create your own edge computing system.
Table of Contents (21 chapters)
1
Part 1: Edge Computing Basics
7
Part 2: Cloud Native Applications at the Edge
13
Part 3: Edge Computing Use Cases in Practice

CAP theorem for SQL and NoSQL databases

CAP theorem was defined by Eric Brewer in 1999 and presented at the 19th Annual ACM Symposium on Principles of Distributed Computing (PODC) in 2000. This theorem states that a distributed data store can only provide two of the following guarantees:

  • Consistency: This means when reading information, the data store returns the most recent written data or returns an error if it fails. This refers to regular SQL databases that use atomic operations to guarantee that data is written. If not, the system automatically rolls back to a previous data state.
  • Availability: This means that all reads contain data, but it might not be the most recent.
  • Partition tolerance: This is the most desired feature in a distributed system. It means that data is distributed in several nodes, helping to reduce downtime for the database. This means that if a node is down, just a small portion of data will be inaccessible:

Figure...