Domain-Driven Design Read online

  Rob Mee read some of my earliest explorations of the material, and brainstormed ideas with me when I was groping for some way to communicate this style of design. He then pored over a much later draft with me.

  Josh Kerievsky is responsible for one of the major turning points in the book’s development: He persuaded me to try out the “Alexandrian” pattern format, which became so central to the book’s organization. He also helped me to bring together some of the material now in Part II into a coherent form for the first time, during the intensive “shepherding” process preceding the PLoP conference in 1999. This became a seed around which much of the rest of the book formed.

  Also I thank Awad Faddoul for the hundreds of hours I sat writing in his wonderful café. That retreat, along with a lot of windsurfing, helped me keep going.

  And I’m very grateful to Martine Jousset, Richard Paselk, and Ross Venables for creating some beautiful photographs to illustrate a few key concepts (see photo credits on page 517).

  Before I could have conceived of this book, I had to form my view and understanding of software development. That formation owed a lot to the generosity of a few brilliant people who acted as informal mentors to me, as well as friends. David Siegel, Eric Gold, and Iseult White, each in a different way, helped me develop my way of thinking about software design. Meanwhile, Bruce Gordon, Richard Freyberg, and Dr. Judith Segal, also in very different ways, helped me find my way in the world of successful project work.

  My own notions naturally grew out of a body of ideas in the air at that time. Some of those contributions will be clear in the main text and referenced where possible. Others are so fundamental that I don’t even realize their influence on me.

  My master’s thesis advisor, Dr. Bala Subramanium, turned me on to mathematical modeling, which we applied to chemical reaction kinetics. Modeling is modeling, and that work was part of the path that led to this book.

  Even before that, my way of thinking was shaped by my parents, Carol and Gary Evans. And a few special teachers awakened my interest or helped me lay foundations, especially Dale Currier (a high school math teacher), Mary Brown (a high school English composition teacher), and Josephine McGlamery (a sixth-grade science teacher).

  Finally, I thank my friends and family, and Fernando De Leon, for their encouragement all along the way.

  I: Putting the Domain Model to Work

  This eighteenth-century Chinese map represents the whole world. In the center and taking up most of the space is China, surrounded by perfunctory representations of other countries. This was a model of the world appropriate to that society, which had intentionally turned inward. The worldview that the map represents must not have been helpful in dealing with foreigners. Certainly it would not serve modern China at all. Maps are models, and every model represents some aspect of reality or an idea that is of interest. A model is a simplification. It is an interpretation of reality that abstracts the aspects relevant to solving the problem at hand and ignores extraneous detail.

  Every software program relates to some activity or interest of its user. That subject area to which the user applies the program is the domain of the software. Some domains involve the physical world: The domain of an airline-booking program involves real people getting on real aircraft. Some domains are intangible: The domain of an accounting program is money and finance. Software domains usually have little to do with computers, though there are exceptions: The domain of a source-code control system is software development itself.

  To create software that is valuably involved in users’ activities, a development team must bring to bear a body of knowledge related to those activities. The breadth of knowledge required can be daunting. The volume and complexity of information can be overwhelming. Models are tools for grappling with this overload. A model is a selectively simplified and consciously structured form of knowledge. An appropriate model makes sense of information and focuses it on a problem.

  A domain model is not a particular diagram; it is the idea that the diagram is intended to convey. It is not just the knowledge in a domain expert’s head; it is a rigorously organized and selective abstraction of that knowledge. A diagram can represent and communicate a model, as can carefully written code, as can an English sentence.

  Domain modeling is not a matter of making as “realistic” a model as possible. Even in a domain of tangible real-world things, our model is an artificial creation. Nor is it just the construction of a software mechanism that gives the necessary results. It is more like moviemaking, loosely representing reality to a particular purpose. Even a documentary film does not show unedited real life. Just as a moviemaker selects aspects of experience and presents them in an idiosyncratic way to tell a story or make a point, a domain modeler chooses a particular model for its utility.

  The Utility of a Model in Domain-Driven Design

  In domain-driven design, three basic uses determine the choice of a model.

  1. The model and the heart of the design shape each other. It is the intimate link between the model and the implementation that makes the model relevant and ensures that the analysis that went into it applies to the final product, a running program. This binding of model and implementation also helps during maintenance and continuing development, because the code can be interpreted based on understanding the model. (See Chapter 3.)

  2. The model is the backbone of a language used by all team members. Because of the binding of model and implementation, developers can talk about the program in this language. They can communicate with domain experts without translation. And because the language is based on the model, our natural linguistic abilities can be turned to refining the model itself. (See Chapter 2.)

  3. The model is distilled knowledge. The model is the team’s agreed-upon way of structuring domain knowledge and distinguishing the elements of most interest. A model captures how we choose to think about the domain as we select terms, break down concepts, and relate them. The shared language allows developers and domain experts to collaborate effectively as they wrestle information into this form. The binding of model and implementation makes experience with early versions of the software applicable as feedback into the modeling process. (See Chapter 1.)

  The next three chapters set out to examine the meaning and value of each of these contributions in turn, and the ways they are intertwined. Using a model in these ways can support the development of software with rich functionality that would otherwise take a massive investment of ad hoc development.

  The Heart of Software

  The heart of software is its ability to solve domain-related problems for its user. All other features, vital though they may be, support this basic purpose. When the domain is complex, this is a difficult task, calling for the concentrated effort of talented and skilled people. Developers have to steep themselves in the domain to build up knowledge of the business. They must hone their modeling skills and master domain design.

  Yet these are not the priorities on most software projects. Most talented developers do not have much interest in learning about the specific domain in which they are working, much less making a major commitment to expand their domain-modeling skills. Technical people enjoy quantifiable problems that exercise their technical skills. Domain work is messy and demands a lot of complicated new knowledge that doesn’t seem to add to a computer scientist’s capabilities.

  Instead, the technical talent goes to work on elaborate frameworks, trying to solve domain problems with technology. Learning about and modeling the domain is left to others. Complexity in the heart of software has to be tackled head-on. To do otherwise is to risk irrelevance.

  In a TV talk show interview, comedian John Cleese told a story of an event during the filming of Monty Python and the Holy Grail. They had been shooting a particular scene over and over, but somehow it wasn’t funny. Finally, he took a break and consulted with fellow comedian Michael Palin (the other actor in the scene), and they came up with a slight variation. They shot on
e more take, and it turned out funny, so they called it a day.

  The next morning, Cleese was looking at the rough cut the film editor had put together of the previous day’s work. Coming to the scene they had struggled with, Cleese found that it wasn’t funny; one of the earlier takes had been used.

  He asked the film editor why he hadn’t used the last take, as directed. “Couldn’t use it. Someone walked in-shot,” the editor replied. Cleese watched the scene again, and then again. Still he could see nothing wrong. Finally, the editor stopped the film and pointed out a coat sleeve that was visible for a moment at the edge of the picture.

  The film editor was focused on the precise execution of his own specialty. He was concerned that other film editors who saw the movie would judge his work based on its technical perfection. In the process, the heart of the scene had been lost (“The Late Late Show with Craig Kilborn,” CBS, September 2001).

  Fortunately, the funny scene was restored by a director who understood comedy. In just the same way, leaders within a team who understand the centrality of the domain can put their software project back on course when development of a model that reflects deep understanding gets lost in the shuffle.

  This book will show that domain development holds opportunities to cultivate very sophisticated design skills. The messiness of most software domains is actually an interesting technical challenge. In fact, in many scientific disciplines, “complexity” is one of the most exciting current topics, as researchers attempt to tackle the messiness of the real world. A software developer has that same prospect when facing a complicated domain that has never been formalized. Creating a lucid model that cuts through that complexity is exciting.

  There are systematic ways of thinking that developers can employ to search for insight and produce effective models. There are design techniques that can bring order to a sprawling software application. Cultivation of these skills makes a developer much more valuable, even in an initially unfamiliar domain.

  One. Crunching Knowledge

  A few years ago, I set out to design a specialized software tool for printed-circuit board (PCB) design. One catch: I didn’t know anything about electronic hardware. I had access to some PCB designers, of course, but they typically got my head spinning in three minutes. How was I going to understand enough to write this software? I certainly wasn’t going to become an electrical engineer before the delivery deadline!

  We tried having the PCB designers tell me exactly what the software should do. Bad idea. They were great circuit designers, but their software ideas usually involved reading in an ASCII file, sorting it, writing it back out with some annotation, and producing a report. This was clearly not going to lead to the leap forward in productivity that they were looking for.

  The first few meetings were discouraging, but there was a glimmer of hope in the reports they asked for. They always involved “nets” and various details about them. A net, in this domain, is essentially a wire conductor that can connect any number of components on a PCB and carry an electrical signal to everything it is connected to. We had the first element of the domain model.

  Figure 1.1

  I started drawing diagrams for them as we discussed the things they wanted the software to do. I used an informal variant of object interaction diagrams to walk through scenarios.

  Figure 1.2

  PCB Expert 1: The components wouldn’t have to be chips.

  Developer (Me): So I should just call them “components”?

  Expert 1: We call them “component instances.” There could be many of the same component.

  Expert 2: The “net” box looks just like a component instance.

  Expert 1: He’s not using our notation. Everything is a box for them, I guess.

  Developer: Sorry to say, yes. I guess I’d better explain this notation a little more.

  They constantly corrected me, and as they did I started to learn. We ironed out collisions and ambiguities in their terminology and differences between their technical opinions, and they learned. They began to explain things more precisely and consistently, and we started to develop a model together.

  Expert 1: It isn’t enough to say a signal arrives at a ref-des, we have to know the pin.

  Developer: Ref-des?

  Expert 2: Same thing as a component instance. Ref-des is what it’s called in a particular tool we use.

  Expert 1: Anyhow, a net connects a particular pin of one instance to a particular pin of another.

  Developer: Are you saying that a pin belongs to only one component instance and connects to only one net?

  Expert 1: Yes, that’s right.

  Expert 2: Also, every net has a topology, an arrangement that determines the way the elements of the net connect.

  Developer: OK, how about this?

  Figure 1.3

  To focus our exploration, we limited ourselves, for a while, to studying one particular feature. A “probe simulation” would trace the propagation of a signal to detect likely sites of certain kinds of problems in the design.

  Developer: I understand how the signal gets carried by the Net to all the Pins attached, but how does it go any further than that? Does the Topology have something to do with it?

  Expert 2: No. The component pushes the signal through.

  Developer: We certainly can’t model the internal behavior of a chip. That’s way too complicated.

  Expert 2: We don’t have to. We can use a simplification. Just a list of pushes through the component from certain Pins to certain others.

  Developer: Something like this?

  [With considerable trial-and-error, together we sketched out a scenario.]

  Figure 1.4

  Developer: But what exactly do you need to know from this computation?

  Expert 2: We’d be looking for long signal delays—say, any signal path that was more than two or three hops. It’s a rule of thumb. If the path is too long, the signal may not arrive during the clock cycle.

  Developer: More than three hops.... So we need to calculate the path lengths. And what counts as a hop?

  Expert 2: Each time the signal goes over a Net, that’s one hop.

  Developer: So we could pass the number of hops along, and a Net could increment it, like this.

  Figure 1.5

  Developer: The only part that isn’t clear to me is where the “pushes” come from. Do we store that data for every Component Instance?

  Expert 2: The pushes would be the same for all the instances of a component.

  Developer: So the type of component determines the pushes. They’ll be the same for every instance?

  Figure 1.6

  Expert 2: I’m not sure exactly what some of this means, but I would imagine storing push-throughs for each component would look something like that.

  Developer: Sorry, I got a little too detailed there. I was just thinking it through. . . . So, now, where does the Topology come into it?

  Expert 1: That’s not used for the probe simulation.

  Developer: Then I’m going to drop it out for now, OK? We can bring it back when we get to those features.

  And so it went (with much more stumbling than is shown here). Brainstorming and refining; questioning and explaining. The model developed along with my understanding of the domain and their understanding of how the model would play into the solution. A class diagram representing that early model looks something like this.

  Figure 1.7

  After a couple more part-time days of this, I felt I understood enough to attempt some code. I wrote a very simple prototype, driven by an automated test framework. I avoided all infrastructure. There was no persistence, and no user interface (UI). This allowed me to concentrate on the behavior. I was able to demonstrate a simple probe simulation in just a few more days. Although it used dummy data and wrote raw text to the console, it was nonetheless doing the actual computation of path lengths using Java objects. Those Java objects reflected a model shared by the domain experts and myself.
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  The concreteness of this prototype made clearer to the domain experts what the model meant and how it related to the functioning software. From that point, our model discussions became more interactive, as they could see how I incorporated my newly acquired knowledge into the model and then into the software. And they had concrete feedback from the prototype to evaluate their own thoughts.

  Embedded in that model, which naturally became much more complicated than the one shown here, was knowledge about the domain of PCB relevant to the problems we were solving. It consolidated many synonyms and slight variations in descriptions. It excluded hundreds of facts that the engineers understood but that were not directly relevant, such as the actual digital features of the components. A software specialist like me could look at the diagrams and in minutes start to get a grip on what the software was about. He or she would have a framework to organize new information and learn faster, to make better guesses about what was important and what was not, and to communicate better with the PCB engineers.

  As the engineers described new features they needed, I made them walk me through scenarios of how the objects interacted. When the model objects couldn’t carry us through an important scenario, we brainstormed new ones or changed old ones, crunching their knowledge. We refined the model; the code coevolved. A few months later the PCB engineers had a rich tool that exceeded their expectations.

  Ingredients of Effective Modeling

  Certain things we did led to the success I just described.

  1. Binding the model and the implementation. That crude prototype forged the essential link early, and it was maintained through all subsequent iterations.