ddd

Domain-driven Design (ddd)

Hurry please, I have a short attention span, what is ddd?

Sorry, most likely ddd is not for you.

Who is ddd for?

Here are a few assumptions about you:

• You are an R user

• You are an experienced Data Scientist

• You have seen and created clunkers, that is, analytic applications with an abundance of cruft

• You have a passion for producing analytic applications of high-quality every day

If you belong to the intersection of all the sets above, then ddd may benefit you.

What ddd is (but first, what it is not)

Because ddd is an R package that caters for Data Scientists, it is natural to think it may contain a new learning algorithm, statistical method, a visualisation tool, or data processing toolkit. ddd offers none of these things. Instead, ddd automates repetitive tasks that arise during software development in R. In that sense, ddd is akin to the usethis package.

ddd is an acronym of a software development approach called domain-driven design, or DDD for short. ddd facilitates the implementation of key DDD patterns including Entity, Value Object and Domain Services. Together these building blocks form a domain model.

Before getting into specifics about domain-driven design, lets first discuss the importance of software design and how it is inevitable.

Why should I use software design?

When you develop software to solve a new business problem, what guides your software design? Obviously, you strive to eliminate unintended behaviours (bugs) from the software. Perhaps you are using tests and even a Test-Driven Development (TDD) approach. However, guiding your design with tests is analogous to driving a car by using the guardrails. It keeps you safe from the bar ditches while you drive, though it has no navigation utility.

All too often, Data Scientists produce software without thoughtful design. On the one hand, Data Scientists are typically not Software Engineers, nor are they taught good practices of software engineering. On the other hand, Data Scientists often take the role of Software Engineers when they develop analytic applications. Perhaps, the lack of software engineering knowledge, leads Data Scientists to overlook the significance of good design.

No design is a fallacy. This is because design still flows from the brains of the Data Scientists through their fingertips as they wrangle with the code. Thus, questions about whether a design is necessary or affordable are quite beside the point: design is inevitable. The alternative to good design is bad design, not no design at all.

Installation

You can install ddd by using:

install.packages("remotes")
remotes::install_github("tidylab/ddd")

Optionally, you can make a project independent of the ddd package by calling use_ddd(). ddd is needed for software development and has no added value in production. use_ddd() will copy the necessary R classes and functions into your project.

References

Evans, Eric. 2004. Domain-driven design: tackling complexity in the heart of software. Addison-Wesley Professional.

Percival, Harry, and Bob Gregory. 2020. Architecture Patterns with Python Enabling Test-Driven Development, Domain-Driven Design, and Event-Driven Microservice. Edited by 1 Edition. O’Reilly Media, Inc.

Vernon, Vaughn. 2013. Implementing Domain-Driven Design. 1st Editio. Addison-Wesley Professional.

Wlaschin, Scott. 2018. Domain Modeling Made Functional. Pragmatic Bookshelf.