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)
Part 1: Foundations
Part 2: Real-World DDD
Part 3: Evolution Patterns


In previous chapters, we saw how it is possible to break down an existing application along bounded context boundaries. We also saw how it is possible to split bounded contexts to be extremely fine-grained, often as physically disparate components. Failure in any of these components can cause disruptions in others that are dependent on them. Obviously, early detection and more importantly attribution to specific components through a combination of proactive and reactive monitoring can ideally prevent or, at the very least, minimize business disruption.

When it comes to monitoring, most teams seem to think of technology runtime metrics that we associate with components (such as CPU utilization, memory consumed, queue depths, exception count, and so on).

Lending Objectivity to Metrics

To make it more formal, we use the terms Service-Level Objectives (SLOs) and Service-Level Indicators (SLIs) specified within a Service-Level Agreement (SLA) to mean the following...