Domain-Driven Design: where to find inspiration for Supple Design? [part1]

2 March 20105 March 2010 cyrille 1 Comment

Domain-Driven Design encourages to analyse the domain deeply in a process called Supple Design. In his book (the blue book) and in his talks Eric Evans gives some examples of this process, and in this blog I suggest some sources of inspirations and some recommendations drawn from my practice in order to help about this process.

When a common formalism fits the domain well, you can factor it out and adapt its rules to the domain.

A known formalism can be reused as a ready-made, well understood model.

Obvious sources of inspiration

Analysis patterns

It is quite obvious in the book, DDD builds clearly on top of Martin Fowler analysis patterns. The patterns Knowledge Level (aka Meta-Model), and Specification (a Strategy used as a predicate) are from Fowler, and Eric Evans mentions using and drawing insight from analysis patterns many times in the book.

Reading analysis patterns helps to appreciate good design; when you’ve read enough analysis patterns, you don’t even have to remember them to be able to improve your modelling skills. In my own experience, I have learnt to look for specific design qualities such as explicitness and traceability in my design as a result of getting used to analysis patterns such as Phenomenon or Observation.

Design patterns

Design patterns are another source of inspiration, but usually less relevant to domain modelling. Evans mentions the Strategy pattern, also named Policy (I rather like using an alternative name to make it clear that we are talking about the domain, not about a technical concerns), and the pattern Composite. Evans suggests considering other patterns, not just the GoF patterns, and to see whether they make sense at the domain level.

Programming paradigms

Eric Evans also mentions that sometimes the domain is naturally well-suited for particular approaches (or paradigms) such as state machines, predicate logic and rules engines. Now the DDD community has already expanded to include event-driven as a favourite paradigm, with the Event-Sourcing and CQRS approaches in particular.

On paradigms, my design style has also been strongly influenced by elements of functional programming, that I originally learnt from using Apache Commons Collections, together with a increasingly pronounced taste for immutability.

Maths

It is in fact the core job of mathematicians to factor out formal models of everything we experience in the world. As a result it is no surprise we can draw on their work to build deeper models.

Graph theory

The great benefit of any mathematical model is that it is highly formal, ready with plenty of useful theorems that depend on the set of axioms you can assume. In short, all the body of maths is just work already done for you, ready for you to reuse. To start with a well-known example, used extensively by Eric Evans, let’s consider a bit of graph theory.

If you recognize that your problem is similar (mathematicians would say isomorphic or something like that) to a graph, then you can jump in graph theory and reuse plenty of exciting results, such as how to compute a shortest-path using a Dijkstra or A* algorithm. Going further, the more you know or read about your theory, the more you can reuse: in other words the more lazy you can be!

In his classical example of modelling cargo shipping using Legs or using Stops, Eric Evans, could also refer to the concept of Line Graph, (aka edge-to-vertex dual) which comes with interesting results such as how to convert a graph into its edge-to-vertex dual.

Trees and nested sets

Other maths concepts common enough include trees and DAG, which come with useful concepts such as the topological sort. Hierarchy containment is another useful concept that appear for instance in every e-commerce catalog. Again, if you recognize the mathematical concept hidden behind your domain, then you can then search for prior knowledge and techniques already devised to manipulate the concept in an easy and correct way, such as how to store that kind of hierarchy into a SQL database.

Don’t miss the next part: part 2

Maths continued
General principles

Principles for using annotations

14 December 200914 December 2009 cyrille 1 Comment

Deciding where and how to place the annotations is not innocent. The last thing we want is to create extra maintenance effort because of the annotations. In other words, we want annotations that are stable, or that change for the same reasons and at the same time than the elements they annotate. This article suggests some good practices on how to design annotations.

Annotations are location-based

Language annotations or even good-old xDoclet tags enable to augment program elements with additional semantics, which can be used to configure tools, frameworks or containers.

Configuration is now increasingly done through annotations spread all over the project elements. The key advantage is that the location of the annotation directly references the program element (interface, class etc.), as opposed to configuration files that must reference program elements using awkward and error-prone qualified names: “com.mycompany.somepackage.MyClass”, that are also fragile to refactoring.

For example, we can annotate an entity to declare how it must be persisted, we can annotate a class to declare how it must be instantiated by a Dependency Injection framework, and we can annotate test methods to declare their purpose.

If not placed and thought carefully, annotations can make your code harder to maintain. This happens when annotations are placed at the “wrong” place, or when they introduce undesirable coupling, as we will see.

Dependencies still matter

The question of coupling between elements of the code base is also relevant for annotations. That the coupling is done via an annotation rather than plain code does not make it more acceptable.

We want to group together things that change together. As a consequence, put your annotations on the elements that change with the annotations.

In particular, when the annotation is used to declare a dependency:

Only annotate the dependent element, not the element depended on

If you use Dependency Injection and you want the class MyServiceImpl to be injected everywhere the interface MyService is used, then Guice offers the annotation @ImplementedBy:

@ImplementedBy(MyServiceImpl.class)
interface MyService {... }

This annotation is a direct violation of the advice above, since it makes a pure abstraction (an interface) aware of an implementation, whereas the regular dependency should be the other way round: only the implementation must depend on the interface.

I must however acknowledge that the annotation @ImplementedBy is quite convenient for unit tests anyway, to declare a default implementation for the interface. And it was done just for that, as described in the Guice documentation along with a warning:

Use @ImplementedBy carefully; it adds a compile-time dependency from the interface to its implementation.

Favor intrinsic annotations

annotation2 — Another annotation on the wall in Paris

If you want to declare that a service is stateless, you cannot get it wrong: just put the annotation @Stateless on its interface. This is straightforward because being stateless is a truly intrinsic property. It also makes perfect sense to annotate a method argument with the @Nullable annotation, as the capability to expect null or not is really intrinsic to the method.

On the other hand, a service interface does not really care about how it is called. It can be called by another object (local call) or remotely, through some remote proxy. The object is not intrinsically local or remote in itself.

The point is that the decision to consume the service locally or remotely does not belong to the service, in itself, but depends on each client. It actually belongs to each use-case considered.

Said another way, specifying @Remotable or @Local directly on the service would require the developer of the service to guess how it will be used!

Intrinsic properties really belong to the element and therefore are stable, as opposed to use-case-specific properties that vary with the particular case of use. Hence, if we want stable annotations:

Only annotate an element about its intrinsic properties, and avoid annotating about use-case-specific properties.

Annotations as pointcuts

Let’s consider an example of an accounting service in a bank. Only selected categories of staff can access this service. We can use annotations to declare its security configuration:

@RolesAllowed({"auditor", "bankmanager", "admin"})

The problem with that approach is that it couples the service directly to the user roles defined elsewhere; as a consequence, if we want to change the user roles (we now need to add the user role “externalauditor”), we will have to review every security annotation and change them. On the other hand, if we want to change the access policy (which happen every time a new senior management comes into place), we will also have to change annotations all over the code. How can we improve that?

We can improve the situation by going back to the business analysis on the topic and separate what’s intrinsic and what’s not. In other words, we want to find out how did a BA came up with the security roles for the service.

Rather than specifying the need for security in terms of allowed user roles, we can instead declare the facts: the service is “sensitive” and is about “accounting”:

@Domain(Accounting)
@Confidentiality(Sensitive)

Then we can define expressions that use the declared annotations (which are now stable because they are intrinsic) to select elements (here services) and associate them to allowed user roles. These rules should be defined outside of the elements they apply to, typically in configuration files.

Thanks to the annotations that already define half of the security knowledge, expressing the rules becomes much simpler that doing it method by method. So next time the senior management changes and decides that from now on, “every service that is both Confidentiality(Sensitive) and Domain(Accounting) is only allowed to corporate-officer roles”, you just have to update a few rules expressed in terms of domain and confidentiality level, rather than by listing many method.

The mindset is very similar to AOP where we first define pointcuts, and then attach advices to them. Here we use annotations as an alternative way to declare the pointcuts.

Conclusion

Annotations are very efficient to declare properties about program elements directly on the elements. They are robust versus refactoring and are easier to use than specifying long qualified names in XML files.

To get the best of annotations, we still need to consider the coupling they can introduce, in particular with respect to dependencies. If a class should not know about another, its annotations should not either.

Annotations are much more stable (less likely to change) when they only relate to intrinsic properties of the elements they are located on. When we need to configure cross-cutting concerns (security, transactions etc.) annotations can be used to declare the half of the knowledge that is really intrinsic to the elements, in the same spirit than pointcuts in AOP.

All that leads to the acknowledgement that even though annotations can be of huge value, in practice there is still a case for configuration files to complement them. In this approach, annotations on elements declare what belongs to the elements, while each use-case-specific configuration file makes use of the annotations and as a result is much simpler.

Design your value objects carefully and your life will get better!

14 November 200919 February 2010 cyrille 2 Comments

Many concepts look obvious because they are used often, not because they are really simple.

Small quantities that we encounter all the time in most projects, such as date, time, price, quantity of items, a min and a max… hardly are a subject of interest from developers “as it is obvious that we can represent them with primitives”.

Yes you can, but you should not.

I have experienced through different projects that every decision to use a new value value object or to improve an existing value object, even very small and trivial, was probably the design decision that yields the most benefit in every further development, maintenance or evolution. This comes from the simple fact that using something simpler “all the time” just makes our life simpler all the time.

We often dont pay attention to the usual chair, however it was carefully designed.

As a reminder, value objects are objects with an identity solely defined by their data. Using value objects brings many typical benefits of Object Orientation, such as:

One value object often gathers several primitives together, and one thing is always simpler that a few things
The value object can be fully unit-tested: this gives a tremendous return, because once it is trusted there will simply be no bug about it any more
Methods that encapsulate even the simplest expression with a good name tells exactly what it does, instead of having to interpret the expression every time (think of isEmpty() instead of “size() == 0”, it does save some time each time)
The value object is a place to put documentation such as the corresponding unit and other conventions, and have them enforced. Static creators makes creation easy, self-explanatory, can enforce the validations and help select the right unit (percent, meter, currency)
The method toString() can just tell in a pretty-formatted fashion what it is, and this is precious!
The value object can also define various abilities, such as being Comparable, being immutable, be reused in a Flyweight fashion etc.

And of course, when needed it can be changed to evolve, without breaking the code using it. I could not imagine a team claiming to be really agile without the ability at least to evolve its most basic concepts in an easy way.

The first time it takes some extra time to build a value object, as opposed to jumping to a few primitives; but being lazy actually means doing less in the long run, not just right now.

Pattern grammar for the variant problem

1 November 20092 November 2009 cyrille

For tools to be aware of patterns, the patterns must be formalized, at least partially. At this point I must quote Gregor Hohpe to clarify my thoughts, as I strongly agree with his skipticism:

Typically, when people ask me about “codifying” or “toolifying” patterns my first reaction is one of skepticism. Patterns are meant to be a human-to-human communication mechanism, not a human-to-machine mechanism. After all, I have pointed many people to the fact that a pattern is not just the piece of code in the example section. It’s the context-problem-forces-solution combination that makes patterns so useful.

Patterns link together a problem part to a solution part. This is expressed within the limits a stated context outside of which it is no more applicable. Patterns also emphasize the forces involved, that you must consider to decide how and whether or not to apply the pattern.

Patterns litterature usually describes examples of application of patterns. In your project, you will have to do more work to adapt the pattern solution to your exact need. A pattern solution may be stretched a lot, but the pattern remains as long as humans still recognize its presence. Every different way of applying a pattern is called a pattern variant.

Addressing the variant problem

Formal descriptions of anything human is too restrictive, and this is especially true for patterns which are the product of human analysis, in that they resist simple formalization. However if we focus on sub-parts of patterns, it becomes easier to formalize them, at least for their solution part.

For example a design pattern that uses (in its solution part) some form of inheritance admits several variants. At first, the pattern solution seems to resist against its formalization. However if we now focus on the inheritance part only, we can enumerate every possible alternative for it. For example we can use:

interface and classes that implement it
abstract class and classes that extend it
concrete class provided it is not final (assume we’re in Java), so that it can be extended

Notice that each alternative is a solution to the same problem “How to realize some

Tree structure in volume (Milano International Fair 2009)

form of inheritance”. We can say that each alternative is indeed a pattern, and by the way Mark Grand already described them in Patterns in Java Volume 1. These patterns are easy to formalize, as they can be precisely described in terms of programming language elements.

How can we split a pattern into parts? The idea is to identify the areas that are fragile with respect to the variant problem in the solution part of a pattern, and to consider them as lower-level problems embedded inside the bigger pattern.

In the example before, the problem was to achieve “some form of inheritance”, and we listed three patterns that address this problem.

Provided it can be split into sub-parts (hence into smaller problems), any pattern solution can be formalized by recursively formalizing its sub-parts. If a sub-part cannot be easily made formal, then it can be split again into sub-parts, and so on until each sub-part can.

Given a pattern solution that we want to formalize:

If it can be described formally directly, then we are done (terminal)
If it cannot be fully described formally, then extract the problematic sub-parts into sub-problems, then find every pattern that addresses each sub-problem, and formalize their solution part

We can then represent a pattern as a tree of smaller patterns, where the solution part of each patter is connected to the problem part of another pattern.

From pattern language to pattern grammar

In the car, some parts can be replaced by other alternate parts that play the same contract (e.g. the wheels)

When the pattern is applied into the code, at each node in the tree there is actually a selection of which variant of the sub pattern to use. As such, each selected pattern represents an atom of decision in the design process.

It then appears that we have a form of grammar for patterns, where there are terminal patterns solutions T (easier to formalize in term of programming language elements), non-terminal parts of patterns solutions N (that cannot be easily formalized but that can formally ask for help to solve their sub-problems), and where the production rules P are nothing but the patterns themselves:

Patterns are production rules that link:
elements of N (the problems) to elements of (N Union T)

Conclusion

I have suggested quickly a way to formalize patterns solutions in spite of their fragility with respect to their variants. This approach identifies patterns as production rules in some grammar over the set of patterns considered.

This perspective is well-suited for tools to work on patterns in real-world projects, where the patterns are indeed applied in many variant forms. The problem of this approach is that every known pattern that is variant-fragile must be reconsidered and have its solution split into a formal part and one or several sub problems to be addressed by specific, lower-level patterns, which themselves must be formalised in the same way.

It is essential that for every problem (“intent”) we can enumerate every pattern that addresses it. Intents can be also classified as a taxonomy, where some intents are specialized versions of more generic intents.

This approach does not claim to formalize the full potential of patterns, it only aims at enabling tools to understand patterns that are already there so that to assist the developers for various tasks.

Patterns express intents

6 September 200929 September 2009 cyrille

Patterns represent a couple (intent, solution); sometime they refer to a solution, more often they essentially represents an intent, independently of its solution.

Sometimes the solution part of patterns includes a trick or a workaround to overcome the limits of a language, but patterns cannot be reduced to that trick. Indeed, a very important role of patterns (not only design patterns but patterns in general ) is that they represent stereotypes of intents.

A matter of intent

Therefore, it does not really matter if the Strategy pattern can be expressed using a Java interface, a C++ functor or a first-class function: it remains a Strategy because this is just what we want: “Strategy lets the algorithm vary independently from clients that use it“.

Is the intent of this book holder clear?

Another similar pattern is the Command pattern, which intent is:” Encapsulate a request as an object, thereby letting you parameterize clients with different requests, queue or log requests, and support undoable operations.” Here the intent talks about ‘object’ because it was written for an object-oriented context, however it can easily be made generic if you think ‘handle on function’ (or closure etc.) instead of object. Again, even if first class functions such as delegates in C# can achieve this goal, they do not replace the need to declare the precise intent: “you want to parameterize clients with different requests, queue or log requests, and support undoable operations.” So in some sense, just using a functor without declaring that the intent is to do a Strategy or a Command is like using untyped variables: you are supposed to know what you are doing, but it is implicit.*

Yet another example with the Visitor pattern and its intent: “Represent an operation to be performed on the elements of an object structure. Visitor lets you define a new operation without changing the classes of the elements on which it operates.” This is typically achieved through double-dispatch in languages lacking multimethods, but regardless of how it is implemented the intent remains, and this is what matters most.

Generic Vs. specialized intents

For example, the intent of the generic Proxy pattern defined in a paper from James Noble:

The Proxy pattern is used to “Provide a surrogate or placeholder for another object the Subject to control access to it”. A Proxy object provides the same interface as the original Subject object, but intercepts any messages directed to the Subject. A Proxy object can therefore be used in place of the Subject by a client which is designed to access the Subject, without the client being aware the Subject has been replaced by a Proxy.

This intent can then be specialized for various purposes, leading to several specialized patterns:

What's your intent if you buy that? (yes this is brand new furniture for sale)

Remote Proxy: provides a local representative to an object that is only available on a remote machine
Protection Proxy: checks the access rights before directing to the original object
Virtual Proxy: creates expensive object only on demand so that they are created only when necessary
Cache Proxy: an object representative that remembers the result of calling the methods of an object to avoid directing subsequent call again to this object
Counter Proxy (smart pointer management), etc.

We say that these patterns are specializations of the Proxy pattern. The main Proxy pattern introduces the common solution—providing a placeholder for an object. Every specialized proxy pattern is a special kind of Proxy: A “Protection Proxy” is a special kind of “Proxy”. The specialization here only deals with the Intent part of the patterns.

Conclusion

Now that functional languages are getting more attention, it becomes fashionable to question the usefulness of patterns: “Scala does that without the need for patterns”. I agree Scala is great, I disagree this argument. Patterns are first of all signs to denote intents, even if they can do more.

References

Patterns as Signs, James Noble and Robert Biddle, Victoria University of Wellington, New Zealand

Classifying Relationships Between Object-Oriented Design Patterns, James Noble, Microsoft Research Institute, Macquarie University

* By the way, how to achieve “undoable operations” by using first class functions in an elegant way? this would require passing two functions always together: do() and undo()

Patterns as another kind of types

3 September 20096 September 2009 cyrille 1 Comment

Patterns represent a couple (intent, solution), where the intent matters most. Based on that intents, that can be generic or specialized, I propose to consider patterns like types in languages with strong typing, for the compiler to enforce their constraints.

Declaring patterns: what for?

Consider the very simple Quantity pattern from Analysis Patterns (Fowler):

Represent dimensioned values with both their amount and their unit

By simply declaring this pattern, we immediately express many things:

We wish to represent a quantity with its unit
Quantity is a special kind of ValueObject (as in Fowler or DDD), hence:
1. It is Immutable, therefore it has no setter, only a valued constructor
2. Identity is based solely on its values, two value objects are equal if they share the same values
In Java, the hashcode() and equals() methods must be implemented solely on the values of the immutable fields, and the hashcode can be cached internally; the toString() result can be cached as well
In the spirit of Domain Driven Design, it also probably follows Closure of Operation and Side -Effect-Free Functions
It must not depend on any heavyweight dependencies such as middleware, database or gui classes
It must not depend on Entities and Services

If we had an explicit way to mark the use of the pattern in the code, and if the compiler was able to know about this pattern, then it could enforce all the above. Moreover, it could also generate corresponding unit tests to assert the dynamic and runtime aspects constrained by the pattern.

This approach can be considered similar to the typing used in programming languages, where the compiler can enforce many constraints according to the type declarations given by the developer.

In practice

In current languages there is no explicit way to declare the use of patterns in the source code. The reader of the source must pay attention to various hints and compare to his/her prior knowledge to decide whether or not a pattern is used. Typical hints include naming conventions, where the class or interface names ends with the name of the pattern participant: an interface TradeFactory is probably a Factory that creates Trade instances.

Since many years I have been using a custom Javadoc tag @pattern to explicitly mark the patterns I use in my source code, hoping that a tool could use it in the future. Over time I have noticed several benefits from doing that, the biggest one being that I am forced to be 100% clear about what I want. Many times I noticed that asking myself to explicitly declare in patterns what I was doing required me to think deeper. Though I know what I am doing, when trying to express that intent using patterns from the books I am forced to clarify my intent to decide which exact pattern I am applying. This is fully in the spirit of the Pragmatic Programmer‘s “Programming by Coincidence“, and also in the spirit of Test Driven Development where the unit test is forcing you to clarify what you really want.

In other words, we could call that Pattern Driven Development. PDD, yet another acronym!

Yet another strangely typed object (the disco vaccum cleaner)

Discussion

It may not sound intuitive that there exists a documented pattern for each and every possible design decision, but I can confirm that the panel of existing patterns is very wide and already covers a lot of common design decisions. Such patterns are far from sophisticated solutions or tricks, they usually just name intents and known practices. But this is exactly what we need. Dirk Riehle, in a recent paper entitled Design Pattern Density Defined, found that in the case of open-source frameworks, and considering only classic design patterns, the density of such patterns was between 40 and 70%, where density is defined by “The design pattern density of an object-oriented framework is the percentage of its collaborations that are design pattern instances.”

In addition, it becomes increasingly common for software teams to use custom patterns as a convenient way of documenting their common design practices. Actually this is how the pattern story began when Kent Beck and Ralph Johnson decided to document the design of HotDraw in the now classic paper: Patterns generate architectures. To illustrate that, the Drupal team is using custom patterns in addition to standard patterns to document their design in an efficient way, as described in the program of the DrupalCon Paris 2009 conference:

This session will cover the how and why of design patterns, and review the most common patterns used in Drupal as well as a number of common patterns we could be using. Some are Drupal-specific patterns and some more more general software design patterns.

Another major example of patterns dedicated for a particular project is the the Eclipse IDE. Erich Gamma, the main Eclipse designer -one of the Gang of Four- wrote the book Contributing to Eclipse: principles, patterns, and plug-ins (together with Kent Beck) where Eclipse-specific and standard patterns are used together as the primary mean of documenting the design.

Conclusion

Patterns are signs to denote intents, and I propose to promote the use of patterns (patterns already existing or to be defined in the context of a particular project) to trace these intents explicitly, and to enable tools to enforce pattern-specific constraints.

Pictures taken at la Biennale Internationale Design, Saint Etienne 2008