Book Image

Domain-Driven Design with Java - A Practitioner's Guide

By : Premanand Chandrasekaran, Karthik Krishnan
Book Image

Domain-Driven Design with Java - A Practitioner's Guide

By: Premanand Chandrasekaran, Karthik Krishnan

Overview of this book

Domain-Driven Design (DDD) makes available a set of techniques and patterns that enable domain experts, architects, and developers to work together to decompose complex business problems into a set of well-factored, collaborating, and loosely coupled subsystems. This practical guide will help you as a developer and architect to put your knowledge to work in order to create elegant software designs that are enjoyable to work with and easy to reason about. You'll begin with an introduction to the concepts of domain-driven design and discover various ways to apply them in real-world scenarios. You'll also appreciate how DDD is extremely relevant when creating cloud native solutions that employ modern techniques such as event-driven microservices and fine-grained architectures. As you advance through the chapters, you'll get acquainted with core DDD’s strategic design concepts such as the ubiquitous language, context maps, bounded contexts, and tactical design elements like aggregates and domain models and events. You'll understand how to apply modern, lightweight modeling techniques such as business value canvas, Wardley mapping, domain storytelling, and event storming, while also learning how to test-drive the system to create solutions that exhibit high degrees of internal quality. By the end of this software design book, you'll be able to architect, design, and implement robust, resilient, and performant distributed software solutions.
Table of Contents (17 chapters)
1
Part 1: Foundations
4
Part 2: Real-World DDD
12
Part 3: Evolution Patterns

Persistence technology choices

If you are using a state store to persist your aggregates, using your usual evaluation process for choosing your persistence technology should suffice. However, if you are looking at event-sourced aggregates, the decision can be a bit more nuanced. In our experience, even a simple relational database can do the trick. Indeed, we once made use of a relational database to act as an event store for a high-volume transactional application with billions of events. This setup worked just fine for us. It is worth noting that we were only using the event store to insert new events and loading events for a given aggregate in sequential order. However, there is a multitude of specialized technologies that have been purpose-built to act as an event store that supports several other value-added features, such as time travel, full event replay, event payload introspection, and so on. If you have such requirements, it might be worth considering other options, such...