Book Image

Serverless Architectures with Kubernetes

By : Onur Yılmaz, Sathsara Sarathchandra
Book Image

Serverless Architectures with Kubernetes

By: Onur Yılmaz, Sathsara Sarathchandra

Overview of this book

Kubernetes has established itself as the standard platform for container management, orchestration, and deployment. By learning Kubernetes, you’ll be able to design your own serverless architecture by implementing the function-as-a-service (FaaS) model. After an accelerated, hands-on overview of the serverless architecture and various Kubernetes concepts, you’ll cover a wide range of real-world development challenges faced by real-world developers, and explore various techniques to overcome them. You’ll learn how to create production-ready Kubernetes clusters and run serverless applications on them. You'll see how Kubernetes platforms and serverless frameworks such as Kubeless, Apache OpenWhisk and OpenFaaS provide the tooling to help you develop serverless applications on Kubernetes. You'll also learn ways to select the appropriate framework for your upcoming project. By the end of this book, you’ll have the skills and confidence to design your own serverless applications using the power and flexibility of Kubernetes.
Table of Contents (11 chapters)
2. Introduction to Serverless in the Cloud


In this chapter, we described the evolution of cloud technology offerings, including how the cloud products have changed over the years and how responsibilities are distributed among organizations, starting with IaaS and PaaS and, finally, FaaS. Following that, criteria were presented for evaluating serverless cloud offerings.

Programming language support, function triggers, and the cost structure of serverless products were listed so that we could compare the various cloud providers, that is, AWS Lambda, Azure Functions, and GCF. In addition, we deployed a serverless function to all three cloud providers. This showed you how cloud functions can be integrated with other cloud services, such as the AWS API Gateway for REST API operations. Furthermore, a parameterized function was deployed to Azure Functions to show how we can process inputs from users or other systems. Finally, we deployed a scheduled function to GCF to show integration with other cloud services. At the end...