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Cloud-Native Applications in Java

Cloud-Native Applications in Java

By : Olsson, Mahajan, Kumar Gupta, S
4.2 (51)
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Cloud-Native Applications in Java

Cloud-Native Applications in Java

4.2 (51)
By: Olsson, Mahajan, Kumar Gupta, S

Overview of this book

Businesses today are evolving so rapidly that they are resorting to the elasticity of the cloud to provide a platform to build and deploy their highly scalable applications. This means developers now are faced with the challenge of building build applications that are native to the cloud. For this, they need to be aware of the environment, tools, and resources they’re coding against. If you’re a Java developer who wants to build secure, resilient, robust, and scalable applications that are targeted for cloud-based deployment, this is the book for you. It will be your one stop guide to building cloud-native applications in Java Spring that are hosted in On-prem or cloud providers - AWS and Azure The book begins by explaining the driving factors for cloud adoption and shows you how cloud deployment is different from regular application deployment on a standard data centre. You will learn about design patterns specific to applications running in the cloud and find out how you can build a microservice in Java Spring using REST APIs You will then take a deep dive into the lifecycle of building, testing, and deploying applications with maximum automation to reduce the deployment cycle time. Gradually, you will move on to configuring the AWS and Azure platforms and working with their APIs to deploy your application. Finally, you’ll take a look at API design concerns and their best practices. You’ll also learn how to migrate an existing monolithic application into distributed cloud native applications. By the end, you will understand how to build and monitor a scalable, resilient, and robust cloud native application that is always available and fault tolerant.
Table of Contents (14 chapters)
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Summary

In this chapter, we covered a lot of core concepts, starting with adding a regular relational database to back our get requests. We enhanced its performance with a local cache and then a distributed cache, Hazelcast. We also looked at a CQRS pattern, replacing our relational databases with a MongoDB for flexible schema and Elasticsearch for flexible search and query capabilities.

We added insert, update, and delete operations to our product service and ensured that the necessary cache invalidation happens in the case of the relational project. We added input validations and proper error messages to our APIs. We covered eventing to ensure that the query model stays up to date with the command model. This is achieved by command model services sending a broadcast of changes, and query model services listening to the changes and updating their data model.

Up next, we will...

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