10: Design patterns

“... describes a problem which occurs over and over again in our environment, and then describes the core of the solution to that problem, in such a way that you can use this solution a million times over, without ever doing it the same way twice” – Christopher Alexander

This chapter introduces the important and yet non-traditional “patterns” approach to program design.

[[ Much of the prose in this chapter still needs work, but the examples all compile. Also, more patterns and examples are forthcoming ]]

Probably the most important step forward in object-oriented design is the “design patterns” movement, chronicled in Design Patterns, by Gamma, Helm, Johnson & Vlissides (Addison-Wesley 1995).[26] That book shows 23 different solutions to particular classes of problems. In this chapter, the basic concepts of design patterns will be introduced along with examples. This should whet your appetite to read Design Patterns (a source of what has now become an essential, almost mandatory, vocabulary for OOP programmers).

The latter part of this chapter contains an example of the design evolution process, starting with an initial solution and moving through the logic and process of evolving the solution to more appropriate designs. The program shown (a trash recycling simulation) has evolved over time, and you can look at that evolution as a prototype for the way your own design can start as an adequate solution to a particular problem and evolve into a flexible approach to a class of problems.

Initially, you can think of a pattern as an especially clever and insightful way of solving a particular class of problems. That is, it looks like a lot of people have worked out all the angles of a problem and have come up with the most general, flexible solution for it. The problem could be one you have seen and solved before, but your solution probably didn’t have the kind of completeness you’ll see embodied in a pattern.

Although they’re called “design patterns,” they really aren’t tied to the realm of design. A pattern seems to stand apart from the traditional way of thinking about analysis, design, and implementation. Instead, a pattern embodies a complete idea within a program, and thus it can sometimes appear at the analysis phase or high-level design phase. This is interesting because a pattern has a direct implementation in code and so you might not expect it to show up before low-level design or implementation (and in fact you might not realize that you need a particular pattern until you get to those phases).

The basic concept of a pattern can also be seen as the basic concept of program design: adding layers of abstraction. Whenever you abstract something you’re isolating particular details, and one of the most compelling motivations behind this is to separate things that change from things that stay the same. Another way to put this is that once you find some part of your program that’s likely to change for one reason or another, you’ll want to keep those changes from propagating other modifications throughout your code. Not only does this make the code much cheaper to maintain, but it also turns out that it is usually simpler to understand (which results in lowered costs).

Often, the most difficult part of developing an elegant and cheap-to-maintain design is in discovering what I call “the vector of change.” (Here, “vector” refers to the maximum gradient and not a container class.) This means finding the most important thing that changes in your system, or put another way, discovering where your greatest cost is. Once you discover the vector of change, you have the focal point around which to structure your design.

So the goal of design patterns is to isolate changes in your code. If you look at it this way, you’ve been seeing some design patterns already in this book. For example, inheritance could be thought of as a design pattern (albeit one implemented by the compiler). It allows you to express differences in behavior (that’s the thing that changes) in objects that all have the same interface (that’s what stays the same). Composition could also be considered a pattern, since it allows you to change – dynamically or statically – the objects that implement your class, and thus the way that class works. Normally, however, features that are directly supported by a programming language are not classified as design patterns.

You’ve also already seen another pattern that appears in Design Patterns: the iterator. This is the fundamental tool used in the design of the STL; it hides the particular implementation of the container as you’re stepping through and selecting the elements one by one. The iterator allows you to write generic code that performs an operation on all of the elements in a range without regard to the container that holds the range. Thus your generic code can be used with any container that can produce iterators.

Possibly the simplest design pattern is the singleton, which is a way to provide one and only one instance of an object:

The key to creating a singleton is to prevent the client programmer from having any way to create an object except the ways you provide. To do this, you must declare all constructors as private, and you must create at least one constructor to prevent the compiler from synthesizing a default constructor for you.

At this point, you decide how you’re going to create your object. Here, it’s created statically, but you can also wait until the client programmer asks for one and create it on demand. In any case, the object should be stored privately. You provide access through public methods. Here, getHandle( ) produces a reference to the Singleton object. The rest of the interface (getValue( ) and setValue( )) is the regular class interface.

Note that you aren’t restricted to creating only one object. This technique easily supports the creation of a limited pool of objects. In that situation, however, you can be confronted with the problem of sharing objects in the pool. If this is an issue, you can create a solution involving a check-out and check-in of the shared objects.

Any static member object inside a class is an expression of singleton: one and only one will be made. So in a sense, the language has direct support for the idea; we certainly use it on a regular basis. However, there’s a problem associated with static objects (member or not), and that’s the order of initialization, as described in Volume 1 of this book. If one static object depends on another, it’s important that the order of initialization proceed correctly.

In Volume 1, you were shown how a static object defined inside a function can be used to control initialization order. This delays the initialization of the object until the first time the function is called. If the function returns a reference to the static object, it gives you the effect of a singleton while removing much of the worry of static initialization. For example, suppose you want to create a logfile upon the first call to a function which returns a reference to that logfile. This header file will do the trick:

The implementation must not be inlined, because that would mean that the whole function, including the static object definition within, could be duplicated in any translation unit where it’s included, and you’d end up with multiple copies of the static object. This would most certainly foil the attempts to control the order of initialization (but potentially in a very subtle and hard-to-detect fashion). So the implementation must be separate:

Now the log object will not be initialized until the first time logfile( ) is called. So if you use the function in one file:

And again in another file:

Then the log object doesn’t get created until the first call to f( ).

You can easily combine the creation of the static object inside a member function with the singleton class. SingletonPattern.cpp can be modified to use this approach:

An especially interesting case is if two of these singletons depend on each other, like this:

When Singleton2::ref( ) is called, it causes its sole Singleton2 object to be created. In the process of this creation, Singleton1::ref( ) is called, and that causes the sole Singleton1 object to be created. Because this technique doesn’t rely on the order of linking or loading, the programmer has much better control over initialization, leading to less problems.

You’ll see further examples of the singleton pattern in the rest of this chapter.

The Design Patterns book discusses 23 different patterns, classified under three purposes (all of which revolve around the particular aspect that can vary). The three purposes are:

The Design Patterns book has a section on each of its 23 patterns along with one or more examples for each, typically in C++ but sometimes in Smalltalk. This book will not repeat all the details of the patterns shown in Design Patterns since that book stands on its own and should be studied separately. The catalog and examples provided here are intended to rapidly give you a grasp of the patterns, so you can get a decent feel for what patterns are about and why they are so important.

[[ Describe different form of categorization, based on what you want to accomplish rather than the way the patterns look. More categories, but should result in easier-to-understand, faster selection ]]]

How things have gotten confused; conflicting pattern descriptions, naïve “patterns,” patterns are not trivial nor are they represented by features that are built into the language, nor are they things that you do almost all the time. Constructors and destructors, for example, could be called the “guaranteed initialization and cleanup design pattern.” This is an important and essential idea, but it’s built into the language.

Another example comes from various forms of aggregation. Aggregation is a completely fundamental principle in object-oriented programming: you make objects out of other objects [[ make reference to basic tenets of OO ]]. Yet sometimes this idea is classified as a pattern, which tends to confuse the issue. This is unfortunate because it pollutes the idea of the design pattern and suggest that anything that surprises you the first time you see it should be a design pattern.

Another misguided example is found in the Java language; the designers of the “JavaBeans” specification decided to refer to a simple naming convention as a design pattern (you say getInfo( ) for a member function that returns an Info property and setInfo( ) for one that changes the internal Info property; the use of the “get” and “set” strings is what they decided constituted calling it a design pattern).

You’ll often find that messy code can be cleaned up by putting it inside a class. This is more than fastidiousness – if nothing else, it aids readability and therefore maintainability, and it can often lead to reusability.

Simple Veneer (façade, Adapter (existing system), Bridge (designed in),

Hiding types (polymorphism, iterators, proxy)

Hiding connections (mediator,)

When you discover that you need to add new types to a system, the most sensible first step to take is to use polymorphism to create a common interface to those new types. This separates the rest of the code in your system from the knowledge of the specific types that you are adding. New types may be added without disturbing existing code ... or so it seems. At first it would appear that the only place you need to change the code in such a design is the place where you inherit a new type, but this is not quite true. You must still create an object of your new type, and at the point of creation you must specify the exact constructor to use. Thus, if the code that creates objects is distributed throughout your application, you have the same problem when adding new types – you must still chase down all the points of your code where type matters. It happens to be the creation of the type that matters in this case rather than the use of the type (which is taken care of by polymorphism), but the effect is the same: adding a new type can cause problems.

The solution is to force the creation of objects to occur through a common factory rather than to allow the creational code to be spread throughout your system. If all the code in your program must go through this factory whenever it needs to create one of your objects, then all you must do when you add a new object is to modify the factory.

Since every object-oriented program creates objects, and since it’s very likely you will extend your program by adding new types, I suspect that factories may be the most universally useful kinds of design patterns.

As an example, let’s revisit the Shape system. One approach is to make the factory a static method of the base class:

The factory( ) takes an argument that allows it to determine what type of Shape to create; it happens to be a string in this case but it could be any set of data. The factory( ) is now the only other code in the system that needs to be changed when a new type of Shape is added (the initialization data for the objects will presumably come from somewhere outside the system, and not be a hard-coded array as in the above example).

To ensure that the creation can only happen in the factory( ), the constructors for the specific types of Shape are made private, and Shape is declared a friend so that factory( ) has access to the constructors (you could also declare only Shape::factory( ) to be a friend, but it seems reasonably harmless to declare the entire base class as a friend).

The static factory( ) method in the previous example forces all the creation operations to be focused in one spot, to that’s the only place you need to change the code. This is certainly a reasonable solution, as it throws a box around the process of creating objects. However, the Design Patterns book emphasizes that the reason for the Factory Method pattern is so that different types of factories can be subclassed from the basic factory (the above design is mentioned as a special case). However, the book does not provide an example, but instead just repeats the example used for the Abstract Factory. Here is ShapeFactory1.cpp modified so the factory methods are in a separate class as virtual functions:

Now the factory method appears in its own class, ShapeFactory, as the virtual create( ). This is a private method which means it cannot be called directly, but it can be overridden. The subclasses of Shape must each create their own subclasses of ShapeFactory and override the create( ) method to create an object of their own type. The actual creation of shapes is performed by calling ShapeFactory::createShape( ), which is a static method that uses the map in ShapeFactory to find the appropriate factory object based on an identifier that you pass it. The factory is immediately used to create the shape object, but you could imagine a more complex problem where the appropriate factory object is returned and then used by the caller to create an object in a more sophisticated way. However, it seems that much of the time you don’t need the intricacies of the polymorphic factory method, and a single static method in the base class (as shown in ShapeFactory1.cpp) will work fine.

Notice that the ShapeFactory must be initialized by loading its map with factory objects, which takes place in the singleton ShapeFactoryInizializer. So to add a new type to this design you must inherit the type, create a factory, and modify ShapeFactoryInizializer so that an instance of your factory is inserted in the map. This extra complexity again suggests the use of a static factory method if you don’t need to create individual factory objects.

The Abstract Factory pattern looks like the factory objects we’ve seen previously, with not one but several factory methods. Each of the factory methods creates a different kind of object. The idea is that at the point of creation of the factory object, you decide how all the objects created by that factory will be used. The example given in Design Patterns implements portability across various graphical user interfaces (GUIs): you create a factory object appropriate to the GUI that you’re working with, and from then on when you ask it for a menu, button, slider, etc. it will automatically create the appropriate version of that item for the GUI. Thus you’re able to isolate, in one place, the effect of changing from one GUI to another.

As another example suppose you are creating a general-purpose gaming environment and you want to be able to support different types of games. Here’s how it might look using an abstract factory:

In this environment, Player objects interact with Obstacle objects, but there are different types of players and obstacles depending on what kind of game you’re playing. You determine the kind of game by choosing a particular GameElementFactory, and then the GameEnvironment controls the setup and play of the game. In this example, the setup and play is very simple, but those activities (the initial conditions and the state change) can determine much of the game’s outcome. Here, GameEnvironment is not designed to be inherited, although it could very possibly make sense to do that.

This also contains examples of Double Dispatching and the Factory Method, both of which will be explained later.

Show simpler version of virtual constructor scheme, letting the user create the object with new. Probably make constructor for objects private and use a maker function to force all objects on the heap.

One of the primary goals of using a factory is so that you can organize your code so you don’t have to select an exact type of constructor when creating an object. That is, you can say, “I don’t know precisely what type of object you are, but here’s the information: Create yourself.”

In addition, during a constructor call the virtual mechanism does not operate (early binding occurs). Sometimes this is awkward. For example, in the Shape program it seems logical that inside the constructor for a Shape object, you would want to set everything up and then draw( ) the Shape. draw( ) should be a virtual function, a message to the Shape that it should draw itself appropriately, depending on whether it is a circle, square, line, and so on. However, this doesn’t work inside the constructor, for the reasons given in Chapter XX: Virtual functions resolve to the “local” function bodies when called in constructors.

If you want to be able to call a virtual function inside the constructor and have it do the right thing, you must use a technique to simulate a virtual constructor (which is a variation of the Factory Method). This is a conundrum. Remember the idea of a virtual function is that you send a message to an object and let the object figure out the right thing to do. But a constructor builds an object. So a virtual constructor would be like saying, “I don’t know exactly what type of object you are, but build yourself anyway.” In an ordinary constructor, the compiler must know which VTABLE address to bind to the VPTR, and if it existed, a virtual constructor couldn’t do this because it doesn’t know all the type information at compile-time. It makes sense that a constructor can’t be virtual because it is the one function that absolutely must know everything about the type of the object.

And yet there are times when you want something approximating the behavior of a virtual constructor.

In the Shape example, it would be nice to hand the Shape constructor some specific information in the argument list and let the constructor create a specific type of Shape (a Circle, Square) with no further intervention. Ordinarily, you’d have to make an explicit call to the Circle, Square constructor yourself.

Coplien[27] calls his solution to this problem “envelope and letter classes.” The “envelope” class is the base class, a shell that contains a pointer to an object of the base class. The constructor for the “envelope” determines (at runtime, when the constructor is called, not at compile-time, when the type checking is normally done) what specific type to make, then creates an object of that specific type (on the heap) and assigns the object to its pointer. All the function calls are then handled by the base class through its pointer. So the base class is acting as a proxy for the derived class:

The base class Shape contains a pointer to an object of type Shape as its only data member. When you build a “virtual constructor” scheme, you must exercise special care to ensure this pointer is always initialized to a live object.

Each time you derive a new subtype from Shape, you must go back and add the creation for that type in one place, inside the “virtual constructor” in the Shape base class. This is not too onerous a task, but the disadvantage is you now have a dependency between the Shape class and all classes derived from it (a reasonable trade-off, it seems). Also, because it is a proxy, the base-class interface is truly the only thing the user sees.

In this example, the information you must hand the virtual constructor about what type to create is very explicit: It’s a string that names the type. However, your scheme may use other information – for example, in a parser the output of the scanner may be handed to the virtual constructor, which then uses that information to determine which token to create.

The virtual constructor Shape(type) can only be declared inside the class; it cannot be defined until after all the derived classes have been declared. However, the default constructor can be defined inside class Shape, but it should be made protected so temporary Shape objects cannot be created. This default constructor is only called by the constructors of derived-class objects. You are forced to explicitly create a default constructor because the compiler will create one for you automatically only if there are no constructors defined. Because you must define Shape(type), you must also define Shape( ).

The default constructor in this scheme has at least one very important chore – it must set the value of the s pointer to zero. This sounds strange at first, but remember that the default constructor will be called as part of the construction of the actual object – in Coplien’s terms, the “letter,” not the “envelope.” However, the “letter” is derived from the “envelope,” so it also inherits the data member s. In the “envelope,” s is important because it points to the actual object, but in the “letter,” s is simply excess baggage. Even excess baggage should be initialized, however, and if s is not set to zero by the default constructor called for the “letter,” bad things happen (as you’ll see later).

The virtual constructor takes as its argument information that completely determines the type of the object. Notice, though, that this type information isn’t read and acted upon until runtime, whereas normally the compiler must know the exact type at compile-time (one other reason this system effectively imitates virtual constructors).

Inside the virtual constructor there’s a switch statement that uses the argument to construct the actual (“letter”) object, which is then assigned to the pointer inside the “envelope.” At that point, the construction of the “letter” has been completed, so any virtual calls will be properly directed.

As an example, consider the call to draw( ) inside the virtual constructor. If you trace this call (either by hand or with a debugger), you can see that it starts in the draw( ) function in the base class, Shape. This function calls draw( ) for the “envelope” s pointer to its “letter.” All types derived from Shape share the same interface, so this virtual call is properly executed, even though it seems to be in the constructor. (Actually, the constructor for the “letter” has already completed.) As long as all virtual calls in the base class simply make calls to identical virtual function through the pointer to the “letter,” the system operates properly.

To understand how it works, consider the code in main( ). To fill the vector shapes, “virtual constructor” calls are made to Shape. Ordinarily in a situation like this, you would call the constructor for the actual type, and the VPTR for that type would be installed in the object. Here, however, the VPTR used in each case is the one for Shape, not the one for the specific Circle, Square, or Triangle.

In the for loop where the draw( ) and erase( ) functions are called for each Shape, the virtual function call resolves, through the VPTR, to the corresponding type. However, this is Shape in each case. In fact, you might wonder why draw( ) and erase( ) were made virtual at all. The reason shows up in the next step: The base-class version of draw( ) makes a call, through the “letter” pointer s, to the virtual function draw( ) for the “letter.” This time the call resolves to the actual type of the object, not just the base class Shape. Thus the runtime cost of using virtual constructors is one more virtual call every time you make a virtual function call.

In order to create any function that is overridden, such as draw( ), erase( ) or test( ), you must proxy all calls to the s pointer in the base class implementation, as shown above. This is because, when the call is made, the call to the envelope’s member function will resolve as being to Shape, and not to a derived type of Shape. Only when you make the proxy call to s will the virtual behavior take place. In main( ), you can see that everything works correctly, even when calls are made inside constructors and destructors.

The activities of destruction in this scheme are also tricky. To understand, let’s verbally walk through what happens when you call delete for a pointer to a Shape object – specifically, a Square – created on the heap. (This is more complicated than an object created on the stack.) This will be a delete through the polymorphic interface, as in the statement delete shapes[i] in main( ).

The type of the pointer shapes[i] is of the base class Shape, so the compiler makes the call through Shape. Normally, you might say that it’s a virtual call, so Square’s destructor will be called. But with the virtual constructor scheme, the compiler is creating actual Shape objects, even though the constructor initializes the letter pointer to a specific type of Shape. The virtual mechanism is used, but the VPTR inside the Shape object is Shape’s VPTR, not Square’s. This resolves to Shape’s destructor, which calls delete for the letter pointer s, which actually points to a Square object. This is again a virtual call, but this time it resolves to Square’s destructor.

With a destructor, however, C++ guarantees, via the compiler, that all destructors in the hierarchy are called. Square’s destructor is called first, followed by any intermediate destructors, in order, until finally the base-class destructor is called. This base-class destructor has code that says delete s. When this destructor was called originally, it was for the “envelope” s, but now it’s for the “letter” s, which is there because the “letter” was inherited from the “envelope,” and not because it contains anything. So this call to delete should do nothing.

The solution to the problem is to make the “letter” s pointer zero. Then when the “letter” base-class destructor is called, you get delete 0, which by definition does nothing. Because the default constructor is protected, it will be called only during the construction of a “letter,” so that’s the only situation where s is set to zero.

Your most common tool for hiding construction will probably be ordinary factory methods rather than the more complex approaches. The idea of adding new types with minimal effect on the rest of the system will be further explored later in this chapter.

Decoupling code behavior

Like the other forms of callback, this contains a hook point where you can change code. The difference is in the observer’s completely dynamic nature. It is often used for the specific case of changes based on other object’s change of state, but is also the basis of event management. Anytime you want to decouple the source of the call from the called code in a completely dynamic way.

The observer pattern solves a fairly common problem: What if a group of objects needs to update themselves when some other object changes state? This can be seen in the “model-view” aspect of Smalltalk’s MVC (model-view-controller), or the almost-equivalent “Document-View Architecture.” Suppose that you have some data (the “document”) and more than one view, say a plot and a textual view. When you change the data, the two views must know to update themselves, and that’s what the observer facilitates.

There are two types of objects used to implement the observer pattern in the following code. The Observable class keeps track of everybody who wants to be informed when a change happens, whether the “state” has changed or not. When someone says “OK, everybody should check and potentially update themselves,” the Observable class performs this task by calling the notifyObservers( ) member function for each observer on the list. The notifyObservers( ) member function is part of the base class Observable.

There are actually two “things that change” in the observer pattern: the quantity of observing objects and the way an update occurs. That is, the observer pattern allows you to modify both of these without affecting the surrounding code.

There are a number of ways to implement the observer pattern, but the code shown here will create a framework from which you can build your own observer code, following the example. First, this interface describes what an observer looks like:

Since Observer interacts with Observable in this approach, Observable must be declared first. In addition, the Argument class is empty and only acts as a base class for any type of argument you wish to pass during an update. If you want, you can simply pass the extra argument as a void*; you’ll have to downcast in either case but some folks find void* objectionable.

Observer is an “interface” class that only has one member function, update( ). This function is called by the object that’s being observed, when that object decides its time to update all it’s observers. The arguments are optional; you could have an update( ) with no arguments and that would still fit the observer pattern; however this is more general – it allows the observed object to pass the object that caused the update (since an Observer may be registered with more than one observed object) and any extra information if that’s helpful, rather than forcing the Observer object to hunt around to see who is updating and to fetch any other information it needs.

The “observed object” that decides when and how to do the updating will be called the Observable:

Again, the design here is more elaborate than is necessary; as long as there’s a way to register an Observer with an Observable and for the Observable to update its Observers, the set of member functions doesn’t matter. However, this design is intended to be reusable (it was lifted from the design used in the Java standard library). As mentioned elsewhere in the book, there is no support for multithreading in the Standard C++ libraries, so this design would need to be modified in a multithreaded environment.

Observable has a flag to indicate whether it’s been changed. In a simpler design, there would be no flag; if something happened, everyone would be notified. The flag allows you to wait, and only notify the Observers when you decide the time is right. Notice, however, that the control of the flag’s state is protected, so that only an inheritor can decide what constitutes a change, and not the end user of the resulting derived Observer class.

The collection of Observer objects is kept in a set<Observer*> to prevent duplicates; the set insert( ), erase( ), clear( ) and size( ) functions are exposed to allow Observers to be added and removed at any time, thus providing runtime flexibility.

Most of the work is done in notifyObservers( ). If the changed flag has not been set, this does nothing. Otherwise, it first clears the changed flag so repeated calls to notifyObservers( ) won’t waste time. This is done before notifying the observers in case the calls to update( ) do anything that causes a change back to this Observable object. Then it moves through the set and calls back to the update( ) member function of each Observer.

At first it may appear that you can use an ordinary Observable object to manage the updates. But this doesn’t work; to get an effect, you must inherit from Observable and somewhere in your derived-class code call setChanged( ). This is the member function that sets the “changed” flag, which means that when you call notifyObservers( ) all of the observers will, in fact, get notified. Where you call setChanged( ) depends on the logic of your program.

Now we encounter a dilemma. An object that should notify its observers about things that happen to it – events or changes in state – might have more than one such item of interest. For example, if you’re dealing with a graphical user interface (GUI) item – a button, say – the items of interest might be the mouse clicked the button, the mouse moved over the button, and (for some reason) the button changed its color. So we’d like to be able to report all of these events to different observers, each of which is interested in a different type of event.

The problem is that we would normally reach for multiple inheritance in such a situation: “I’ll inherit from Observable to deal with mouse clicks, and I’ll ... er ... inherit from Observable to deal with mouse-overs, and, well, ... hmm, that doesn’t work.”

Here’s a situation where we do actually need to (in effect) upcast to more than one type, but in this case we need to provide several different implementations of the same base type. The solution is something I’ve lifted from Java, which takes C++’s nested class one step further. Java has a built-in feature called inner classes, which look like C++’s nested classes, but they do two other things:

[[ Insert the definition of a closure ]]. So to implement the inner class idiom in C++, we must do these things by hand. Here’s an example:

The example begins with the Poingable and Bingable interfaces, each of which contain a single member function. The services provided by callPoing( ) and callBing( ) require that the object they receive implement the Poingable and Bingable interfaces, respectively, but they put no other requirements on that object so as to maximize the flexibility of using callPoing( ) and callBing( ). Note the lack of virtual destructors in either interface – the intent is that you never perform object destruction via the interface.

Outer contains some private data (name) and it wishes to provide both a Poingable interface and a Bingable interface so it can be used with callPoing( ) and callBing( ). Of course, in this situation we could simply use multiple inheritance. This example is just intended to show the simplest syntax for the idiom; we’ll see a real use shortly. To provide a Poingable object without inheriting Outer from Poingable, the inner class idiom is used. First, the declaration class Inner says that, somewhere, there is a nested class of this name. This allows the friend declaration for the class, which follows. Finally, now that the nested class has been granted access to all the private elements of Outer, the class can be defined. Notice that it keeps a pointer to the Outer which created it, and this pointer must be initialized in the constructor. Finally, the poing( ) function from Poingable is implemented. The same process occurs for the second inner class which implements Bingable. Each inner class has a single private instance created, which is initialized in the Outer constructor. By creating the member objects and returning references to them, issues of object lifetime are eliminated.

Notice that both inner class definitions are private, and in fact the client programmer doesn’t have any access to details of the implementation, since the two access methods operator Poingable&( ) and operator Bingable&( ) only return a reference to the upcast interface, not to the object that implements it. In fact, since the two inner classes are private, the client programmer cannot even downcast to the implementation classes, thus providing complete isolation between interface and implementation.

Just to push a point, I’ve taken the extra liberty here of defining the automatic type conversion operators operator Poingable&( ) and operator Bingable&( ). In main( ), you can see that these actually allow a syntax that looks like Outer is multiply inherited from Poingable and Bingable. The difference is that the casts in this case are one way. You can get the effect of an upcast to Poingable or Bingable, but you cannot downcast back to an Outer. In the following example of observer, you’ll see the more typical approach: you provide access to the inner class objects using ordinary member functions, not automatic type conversion operations.

Armed with the Observer and Observable header files and the inner class idiom, we can look at an example of the observer pattern:

The events of interest are that a Flower can open or close. Because of the use of the inner class idiom, both these events can be separately-observable phenomena. OpenNotifier and CloseNotifier both inherit Observable, so they have access to setChanged( ) and can be handed to anything that needs an Observable. You’ll notice that, contrary to InnerClassIdiom.cpp, the Observable descendants are public. This is because some of their member functions must be available to the client programmer. There’s nothing that says that an inner class must be private; in InnerClassIdiom.cpp I was simply following the design guideline “make things as private as possible.” You could make the classes private and expose the appropriate methods by proxy in Flower, but it wouldn’t gain much.

The inner class idiom also comes in handy to define more than one kind of Observer, in Bee and Hummingbird, since both those classes may want to independently observe Flower openings and closings. Notice how the inner class idiom provides something that has most of the benefits of inheritance (the ability to access the private data in the outer class, for example) without the same restrictions.

In main( ), you can see one of the prime benefits of the observer pattern: the ability to change behavior at runtime by dynamically registering and un-registering Observers with Observables.

If you study the code above you’ll see that OpenNotifier and CloseNotifier use the basic Observable interface. This means that you could inherit other completely different Observer classes; the only connection the Observers have with Flowers is the Observer interface.

When dealing with multiple types which are interacting, a program can get particularly messy. For example, consider a system that parses and executes mathematical expressions. You want to be able to say Number + Number, Number * Number, etc., where Number is the base class for a family of numerical objects. But when you say a + b, and you don’t know the exact type of either a or b, so how can you get them to interact properly?

The answer starts with something you probably don’t think about: C++ performs only single dispatching. That is, if you are performing an operation on more than one object whose type is unknown, C++ can invoke the dynamic binding mechanism on only one of those types. This doesn’t solve the problem, so you end up detecting some types manually and effectively producing your own dynamic binding behavior.

The solution is called multiple dispatching. Remember that polymorphism can occur only via member function calls, so if you want double dispatching to occur, there must be two member function calls: the first to determine the first unknown type, and the second to determine the second unknown type. With multiple dispatching, you must have a virtual call to determine each of the types. Generally, you’ll set up a configuration such that a single member function call produces more than one dynamic member function call and thus determines more than one type in the process. To get this effect, you need to work with more than one virtual function: you’ll need a virtual function call for each dispatch. The virtual functions in the following example are called compete( ) and eval( ), and are both members of the same type. (In this case there will be only two dispatches, which is referred to as double dispatching). If you are working with two different type hierarchies that are interacting, then you’ll have to have a virtual call in each hierarchy.

Here’s an example of multiple dispatching:

The assumption is that you have a primary class hierarchy that is fixed; perhaps it’s from another vendor and you can’t make changes to that hierarchy. However, you’d like to add new polymorphic methods to that hierarchy, which means that normally you’d have to add something to the base class interface. So the dilemma is that you need to add methods to the base class, but you can’t touch the base class. How do you get around this?

The design pattern that solves this kind of problem is called a “visitor” (the final one in the Design Patterns book), and it builds on the double dispatching scheme shown in the last section.

The visitor pattern allows you to extend the interface of the primary type by creating a separate class hierarchy of type Visitor to virtualize the operations performed upon the primary type. The objects of the primary type simply “accept” the visitor, then call the visitor’s dynamically-bound member function.

The nature of this problem (modeling a trash recycling system) is that the trash is thrown unclassified into a single bin, so the specific type information is lost. But later, the specific type information must be recovered to properly sort the trash. In the initial solution, RTTI (described in Chapter XX) is used.

This is not a trivial design because it has an added constraint. That’s what makes it interesting – it’s more like the messy problems you’re likely to encounter in your work. The extra constraint is that the trash arrives at the trash recycling plant all mixed together. The program must model the sorting of that trash. This is where RTTI comes in: you have a bunch of anonymous pieces of trash, and the program figures out exactly what type they are.

One of the objectives of this program is to sum up the weight and value of the different types of trash. The trash will be kept in (potentially different types of) containers, so it makes sense to templatize the “summation” function on the container holding it (assuming that container exhibits basic STL-like behavior), so the function will be maximally flexible:

When you look at a piece of code like this, it can be initially disturbing because you might wonder “how can the compiler know that the member functions I’m calling here are valid?” But of course, all the template says is “generate this code on demand,” and so only when you call the function will type checking come into play. This enforces that *tally produces an object that has member functions weight( ) and value( ), and that out is a global ostream.

The sumValue( ) function is templatized on the type of container that’s holding the Trash pointers. Notice there’s nothing in the template signature that says “this container must behave like an STL container and must hold Trash*”; that is all implied in the code that’s generated which uses the container.

The first version of the example takes the straightforward approach: creating a vector<Trash*>, filling it with Trash objects, then using RTTI to sort them out:

This uses the classic structure of virtual functions in the base class that are redefined in the derived class. In addition, there are two static data members in the base class: _count to indicate the number of Trash objects that are created, and _dcount to keep track of the number that are destroyed. This verifies that proper memory management occurs. To support this, the operator= and copy-constructor are disallowed by declaring them private (no definitions are necessary; this simply prevents the compiler from synthesizing them). Those operations would cause problems with the count, and if they were allowed you’d have to define them properly.

The Trash objects are created, for the sake of this example, by the generator TrashGen, which uses the random number generator to choose the type of Trash, and also to provide it with a “weight” argument. The return value of the generator’s operator( ) is upcast to Trash*, so all the specific type information is lost. In main( ), a vector<Trash*> called bin is created and then filled using the STL algorithm generate_n( ). To perform the sorting, three vectors are created, each of which holds a different type of Trash*. An iterator moves through bin and RTTI is used to determine which specific type of Trash the iterator is currently selecting, placing each into the appropriate typed bin. Finally, sumValue( ) is applied to each of the containers, and the Trash objects are cleaned up using purge( ) (defined in Chapter XX). The creation and destruction counts ensure that things are properly cleaned up.

Of course, it seems silly to upcast the types of Trash into a container holding base type pointers, and then to turn around and downcast. Why not just put the trash into the appropriate receptacle in the first place? (indeed, this is the whole enigma of recycling). In this program it might be easy to repair, but sometimes a system’s structure and flexibility can benefit greatly from downcasting.

The program satisfies the design requirements: it works. This may be fine as long as it’s a one-shot solution. However, a good program will evolve over time, so you must ask: what if the situation changes? For example, cardboard is now a valuable recyclable commodity, so how will that be integrated into the system (especially if the program is large and complicated). Since the above type-check coding in the switch statement and in the RTTI statements could be scattered throughout the program, you’d have to go find all that code every time a new type was added, and if you miss one the compiler won’t help you.

The key to the misuse of RTTI here is that every type is tested. If you’re only looking for a subset of types because that subset needs special treatment, that’s probably fine. But if you’re hunting for every type inside a switch statement, then you’re probably missing an important point, and definitely making your code less maintainable. In the next section we’ll look at how this program evolved over several stages to become much more flexible. This should prove a valuable example in program design.

The solutions in Design Patterns are organized around the question “What will change as this program evolves?” This is usually the most important question that you can ask about any design. If you can build your system around the answer, the results will be two-pronged: not only will your system allow easy (and inexpensive) maintenance, but you might also produce components that are reusable, so that other systems can be built more cheaply. This is the promise of object-oriented programming, but it doesn’t happen automatically; it requires thought and insight on your part. In this section we’ll see how this process can happen during the refinement of a system.

The answer to the question “What will change?” for the recycling system is a common one: more types will be added to the system. The goal of the design, then, is to make this addition of types as painless as possible. In the recycling program, we’d like to encapsulate all places where specific type information is mentioned, so (if for no other reason) any changes can be localized inside those encapsulations. It turns out that this process also cleans up the rest of the code considerably.

This brings up a general object-oriented design principle that I first heard spoken by Grady Booch: “If the design is too complicated, make more objects.” This is simultaneously counterintuitive and ludicrously simple, and yet it’s the most useful guideline I’ve found. (You might observe that “make more objects” is often equivalent to “add another level of indirection.”) In general, if you find a place with messy code, consider what sort of class would clean things up. Often the side effect of cleaning up the code will be a system that has better structure and is more flexible.

Consider first the place where Trash objects are created. In the above example, we’re conveniently using a generator to create the objects. The generator nicely encapsulates the creation of the objects, but the neatness is an illusion because in general we’ll want to create the objects based on something more than a random number generator. Some information will be available which will determine what kind of Trash object this should be. Because you generally need to make your objects by examining some kind of information, if you’re not paying close attention you may end up with switch statements (as in TrashGen) or cascaded if statements scattered throughout your code. This is definitely messy, and also a place where you must change code whenever a new type is added. If new types are commonly added, a better solution is a single member function that takes all of the necessary information and produces an object of the correct type, already upcast to a Trash pointer. In Design Patterns this is broadly referred to as a creational pattern (of which there are several). The specific pattern that will be applied here is a variant of the Factory Method (“method” being a more OOPish way to refer to a member function). Here, the factory method will be a static member of Trash, but more commonly it is a member function that is overridden in the derived class.

The idea of the factory method is that you pass it the essential information it needs to know to create your object, then stand back and wait for the pointer (already upcast to the base type) to pop out as the return value. From then on, you treat the object polymorphically. Thus, you never even need to know the exact type of object that’s created. In fact, the factory method hides it from you to prevent accidental misuse. If you want to use the object without polymorphism, you must explicitly use RTTI and casting.

But there’s a little problem, especially when you use the more complicated approach (not shown here) of making the factory method in the base class and overriding it in the derived classes. What if the information required in the derived class requires more or different arguments? “Creating more objects” solves this problem. To implement the factory method, the Trash class gets a new member function called factory( ). To hide the creational data, there’s a new class called Info that contains all of the necessary information for the factory( ) method to create the appropriate Trash object. Here’s a simple implementation of Info:

An Info object’s only job is to hold information for the factory( ) method. Now, if there’s a situation in which factory( ) needs more or different information to create a new type of Trash object, the factory( ) interface doesn’t need to be changed. The Info class can be changed by adding new data and new constructors, or in the more typical object-oriented fashion of subclassing.

Here’s the second version of the program with the factory method added. The object-counting code has been removed; we’ll assume proper cleanup will take place in all the rest of the examples.

In the factory method Trash::factory( ), the determination of the exact type of object is simple, but you can imagine a more complicated system in which factory( ) uses an elaborate algorithm. The point is that it’s now hidden away in one place, and you know to come to this place to make changes when you add new types.

The creation of new objects is now more general in main( ), and depends on “real” data (albeit created by another generator, driven by random numbers). The generator object is created, telling it the maximum type number and the largest “data” value to produce. Each call to the generator creates an Info object which is passed into Trash::factory( ), which in turn produces some kind of Trash object and returns the pointer that’s added to the vector<Trash*> bin.

The constructor for the Info object is very specific and restrictive in this example. However, you could also imagine a vector of arguments into the Info constructor (or directly into a factory( ) call, for that matter). This requires that the arguments be parsed and checked at runtime, but it does provide the greatest flexibility.

You can see from this code what “vector of change” problem the factory is responsible for solving: if you add new types to the system (the change), the only code that must be modified is within the factory, so the factory isolates the effect of that change.

A problem with the above design is that it still requires a central location where all the types of the objects must be known: inside the factory( ) method. If new types are regularly being added to the system, the factory( ) method must be changed for each new type. When you discover something like this, it is useful to try to go one step further and move all of the activities involving that specific type – including its creation – into the class representing that type. This way, the only thing you need to do to add a new type to the system is to inherit a single class.

To move the information concerning type creation into each specific type of Trash, the “prototype” pattern will be used. The general idea is that you have a master container of objects, one of each type you’re interested in making. The “prototype objects” in this container are used only for making new objects. In this case, we’ll name the object-creation member function clone( ). When you’re ready to make a new object, presumably you have some sort of information that establishes the type of object you want to create. The factory( ) method (it’s not required that you use factory with prototype, but they commingle nicely) moves through the master container comparing your information with whatever appropriate information is in the prototype objects in the master container. When a match is found, factory( ) returns a clone of that object.

In this scheme there is no hard-coded information for creation. Each object knows how to expose appropriate information to allow matching, and how to clone itself. Thus, the factory( ) method doesn’t need to be changed when a new type is added to the system.

The prototypes will be contained in a static vector<Trash*> called prototypes. This is a private member of the base class Trash. The friend class TrashPrototypeInit is responsible for putting the Trash* prototypes into the prototype list.

You’ll also note that the Info class has changed. It now uses a string to act as type identification information. As you shall see, this will allow us to read object information from a file when creating Trash objects.

The basic part of the Trash class remains as before. The rest of the class supports the prototyping pattern. The id( ) member function returns a string that can be compared with the id( ) of an Info object to determine whether this is the prototype that should be cloned (of course, the evaluation can be much more sophisticated than that if you need it). Both id( ) and clone( ) are pure virtual functions so they must be overridden in derived classes.

The last two member functions, addToBin( ) and accept( ), are “stubs” which will be used in later versions of the trash sorting problem. It’s necessary to have these virtual functions in the base class, but in the early examples there’s no need for them, so they are not pure virtuals so as not to intrude.

The factory( ) member function has the same declaration, but the definition is what handles the prototyping. Here is the implementation file:

The string inside the Info object contains the type name of the Trash to be created; this string is compared to the id( ) values of the objects in prototypes. If there’s a match, then that’s the object to create.

Of course, the appropriate prototype object might not be in the prototypes list. In this case, the return in the inner loop is never executed and you’ll drop out at the end, where a default value is created. It might be more appropriate to throw an exception here.

As you can see from the code, there’s nothing that knows about specific types of Trash. The beauty of this design is that this code doesn’t need to be changed, regardless of the different situations it will be used in.

To fit into the prototyping scheme, each new subclass of Trash must follow some rules. First, it must create a protected default constructor, so that no one but TrashPrototypeInit may use it. TrashPrototypeInit is a singleton, creating one and only one prototype object for each subtype. This guarantees that the Trash subtype will be properly represented in the prototypes container.

After defining the “ordinary” member functions and data that the Trash object will actually use, the class must also override the id( ) member (which in this case returns a string for comparison) and the clone( ) function, which must know how to pull the appropriate information out of the Info object in order to create the object correctly.

Here are the different types of Trash, each in their own file.

And here’s a new type of Trash:

The static val data members must be defined and initialized in a separate code file:

There’s one other issue: initialization of the static data members. TrashPrototypeInit must create the prototype objects and add them to the static Trash::prototypes vector. So it’s very important that you control the order of initialization of the static objects, so the prototypes vector is created before any of the prototype objects, which depend on the prior existence of prototypes. The most straightforward way to do this is to put all the definitions in a single file, in the order in which you want them initialized.

TrashPrototypeInit must be defined separately because it inserts the actual prototypes into the vector, and throughout the chapter we’ll be inheriting new types of Trash from the existing types. By making this one class in a separate file, a different version can be created and linked in for the new situations, leaving the rest of the code in the system alone.

This is taken a step further by making TrashPrototypeInit a singleton (the constructor is private), even though the class definition is not available in a header file so it would seem safe enough to assume that no one could accidentally make a second instance.

Unfortunately, this is one more separate piece of code you must maintain whenever you add a new type to the system. However, it’s not too bad since the linker should give you an error message if you forget (since prototypes is defined in this file as well). The really difficult problems come when you don’t get any warnings or errors if you do something wrong.

The information about Trash objects will be read from an outside file. The file has all of the necessary information about each piece of trash in a single entry in the form Trash:weight. There are multiple entries on a line, separated by commas:

To parse this, the line is read and the string member function find( ) produces the index of the ‘:’. This is first used with the string member function substr( ) to extract the name of the trash type, and next to get the weight that is turned into a double with the atof( ) function (from <cstdlib>).

The Trash file parser is placed in a separate file since it will be reused throughout this chapter. To facilitate this reuse, the function fillBin( ) which does the work takes as its first argument the name of the file to open and read, and as its second argument a reference to an object of type Fillable. This uses what I’ve named the “interface” idiom at the beginning of the chapter, and the only attribute for this particular interface is that “it can be filled,” via a member function addTrash( ). Here’s the header file for Fillable:

Notice that it follows the interface idiom of having no non-static data members, and all pure virtual member functions.

This way, any class which implements this interface (typically using multiple inheritance) can be filled using fillBin( ). Here’s the header file:

The overloaded version will be discussed shortly. First, here is the implementation:

After the file is opened, each line is read and parsed into entries by looking for the separating comma, then each entry is parsed into its type and weight by looking for the separating colon. Note the convenience of using the trim( ) function from chapter 17 to remove the white space from both ends of a string. Once the type and weight are discovered, an Info object is created from that data and passed to the factory( ). The result of this call is a Trash* which is passed to the addTrash( ) function of the bin (which is the only function, remember, that a Fillable guarantees).

Anything that supports the Fillable interface can be used with fillBin( ). Of course, vector doesn’t implement Fillable, so it won’t work. Since vector is used in most of the examples, it makes sense to add the second overloaded fillBin( ) function that takes a vector, as seen previously in fillBin.h. But how to make a vector<Trash*> adapt to the Fillable interface, which says it must have an addTrash( ) member function? The key is in the word “adapt”; we use the adapter pattern to create a class that has a vector and is also Fillable.

By saying “is also Fillable,” the hint is strong (is-a) to inherit from Fillable. But what about the vector<Trash*>? Should this new class inherit from that? We don’t actually want to be making a new kind of vector, which would force everyone to only use our vector in this situation. Instead, we want someone to be able to have their own vector and say “please fill this.” So the new class should just keep a reference to that vector:

You can see that the only job of this class is to connect Fillable’s addTrash( ) member function to vector’s push_back( ) (that’s the “adapter” motivation). With this class in hand, the overloaded fillBin( ) member function can be used with a vector in fillBin.h:

Notice that the adapter object fv only exists for the duration of the function call, and it wraps bin in an interface that works with the other fillBin( ) function.

This approach works for any container class that’s used frequently. Alternatively, the container can multiply inherit from Fillable. (You’ll see this later, in DynaTrash.cpp.)

Now you can see the new version of the recycling solution using the prototyping technique:

The process of opening the data file containing Trash descriptions and the parsing of that file have been wrapped into fillBin( ), so now it’s no longer a part of our design focus. You will see that throughout the rest of the chapter, no matter what new classes are added, fillBin( ) will continue to work without change, which indicates a good design.

In terms of object creation, this design does indeed severely localize the changes you need to make to add a new type to the system. However, there’s a significant problem in the use of RTTI that shows up clearly here. The program seems to run fine, and yet it never detects any cardboard, even though there is cardboard in the list of trash data! This happens because of the use of RTTI, which looks for only the types that you tell it to look for. The clue that RTTI is being misused is that every type in the system is being tested, rather than a single type or subset of types. But if you forget to test for your new type, the compiler has nothing to say about it.

As you will see later, there are ways to use polymorphism instead when you’re testing for every type. But if you use RTTI a lot in this fashion, and you add a new type to your system, you can easily forget to make the necessary changes in your program and produce a difficult-to-find bug. So it’s worth trying to eliminate RTTI in this case, not just for aesthetic reasons – it produces more maintainable code.

With creation out of the way, it’s time to tackle the remainder of the design: where the classes are used. Since it’s the act of sorting into bins that’s particularly ugly and exposed, why not take that process and hide it inside a class? This is simple “complexity hiding,” the principle of “If you must do something ugly, at least localize the ugliness.” In an OOP language, the best place to hide complexity is inside a class. Here’s a first cut:

A TrashSorter object holds a vector that somehow connects to vectors holding specific types of Trash. The most convenient solution would be a vector<vector<Trash*>>, but it’s too early to tell if that would work out best.

In addition, we’d like to have a sort( ) function as part of the TrashSorter class. But, keeping in mind that the goal is easy addition of new types of Trash, how would the statically-coded sort( ) function deal with the fact that a new type has been added? To solve this, the type information must be removed from sort( ) so all it needs to do is call a generic function which takes care of the details of type. This, of course, is another way to describe a virtual function. So sort( ) will simply move through the vector of Trash bins and call a virtual function for each. I’ll call the function grab(Trash*), so the structure now looks like this:

However, TrashSorter needs to call grab( ) polymorphically, through a common base class for all the vectors. This base class is very simple, since it only needs to establish the interface for the grab( ) function.

Now there’s a choice. Following the above diagram, you could put a vector of trash pointers as a member object of each subclassed Tbin. However, you will want to treat each Tbin as a vector, and perform all the vector operations on it. You could create a new interface and forward all those operations, but that produces work and potential bugs. The type we’re creating is really a Tbin and a vector, which suggests multiple inheritance. However, it turns out that’s not quite necessary, for the following reason.

Each time a new type is added to the system the programmer will have to go in and derive a new class for the vector that holds the new type of Trash, along with its grab( ) function. The code the programmer writes will actually be identical code except for the type it’s working with. That last phrase is the key to introduce a template, which will do all the work of adding a new type. Now the diagram looks more complicated, although the process of adding a new type to the system will be simple. Here, TrashBin can inherit from TBin, which inherits from vector<Trash*> like this (the multiple-lined arrows indicated template instantiation):

The reason TrashBin must be a template is so it can automatically generate the grab( ) function. A further templatization will allow the vectors to hold specific types.

That said, we can look at the whole program to see how all this is implemented.

TrashSorter needs to call each grab( ) member function and get a different result depending on what type of Trash the current vector is holding. That is, each vector must be aware of the type it holds. This “awareness” is accomplished with a virtual function, the grab( ) function, which thus eliminates at least the outward appearance of the use of RTTI. The implementation of grab( ) does use RTTI, but it’s templatized so as long as you put a new TrashBin in the TrashSorter when you add a type, everything else is taken care of.

Memory is managed by denoting bin as the “master container,” the one responsible for cleanup. With this rule in place, calling purge( ) for bin cleans up all the Trash objects. In addition, TrashSorter assumes that it “owns” the pointers it holds, and cleans up all the TrashBin objects during destruction.

A basic OOP design principle is “Use data members for variation in state, use polymorphism for variation in behavior.” Your first thought might be that the grab( ) member function certainly behaves differently for a vector that holds Paper than for one that holds Glass. But what it does is strictly dependent on the type, and nothing else.

The above design is certainly satisfactory. Adding new types to the system consists of adding or modifying distinct classes without causing code changes to be propagated throughout the system. In addition, RTTI is not as “misused” as it was in Recycle1.cpp. However, it’s possible to go one step further and eliminate RTTI altogether from the operation of sorting the trash into bins.

To accomplish this, you must first take the perspective that all type-dependent activities – such as detecting the type of a piece of trash and putting it into the appropriate bin – should be controlled through polymorphism and dynamic binding.

The previous examples first sorted by type, then acted on sequences of elements that were all of a particular type. But whenever you find yourself picking out particular types, stop and think. The whole idea of polymorphism (dynamically-bound member function calls) is to handle type-specific information for you. So why are you hunting for types?

The multiple-dispatch pattern demonstrated at the beginning of this chapter uses virtual functions to determine all type information, thus eliminating RTTI.

In the Trash hierarchy we will now make use of the “stub” virtual function addToBin( ) that was added to the base class Trash but unused up until now. This takes an argument of a container of TypedBin. A Trash object uses addToBin( ) with this container to step through and try to add itself to the appropriate bin, and this is where you’ll see the double dispatch.

The new hierarchy is TypedBin, and it contains its own member function called add( ) that is also used polymorphically. But here’s an additional twist: add( ) is overloaded to take arguments of the different types of Trash. So an essential part of the double dispatching scheme also involves overloading (or at least having a group of virtual functions to call; overloading happens to be particularly convenient here).

In each particular subtype of Aluminum, Paper, Glass, and Cardboard, the addToBin( ) member function is implemented, but it looks like the code is exactly the same in each case. The code in each addToBin( ) calls add( ) for each TypedBin object in the array. But notice the argument: this. The type of this is different for each subclass of Trash, so the code is different. So this is the first part of the double dispatch, because once you’re inside this member function you know you’re Aluminum, or Paper, etc. During the call to add( ), this information is passed via the type of this. The compiler resolves the call to the proper overloaded version of add( ). But since tb[i] produces a pointer to the base type TypedBin, this call will end up calling a different member function depending on the type of TypedBin that’s currently selected. That is the second dispatch.

You can see that the overloaded add( ) methods all return false. If the member function is not overloaded in a derived class, it will continue to return false, and the caller (addToBin( ), in this case) will assume that the current Trash object has not been added successfully to a container, and continue searching for the right container.

In each of the subclasses of TypedBin, only one overloaded member function is overridden, according to the type of bin that’s being created. For example, CardboardBin overrides add(DD<Cardboard>). The overridden member function adds the Trash pointer to its container and returns true, while all the rest of the add( ) methods in CardboardBin continue to return false, since they haven’t been overridden. With C++ templates, you don’t have to explicitly write the subclasses or place the addToBin( ) member function in Trash.

To set up for prototyping the new types of trash, there must be a different initializer file:

Here’s the rest of the program:

TrashBinSet encapsulates all of the different types of TypedBins, along with the sortIntoBins( ) member function, which is where all the double dispatching takes place. You can see that once the structure is set up, sorting into the various TypedBins is remarkably easy. In addition, the efficiency of two virtual calls and the double dispatch is probably better than any other way you could sort.

Notice the ease of use of this system in main( ), as well as the complete independence of any specific type information within main( ). All other methods that talk only to the Trash base-class interface will be equally invulnerable to changes in Trash types.

The changes necessary to add a new type are relatively isolated: you inherit the new type of Trash with its addToBin( ) member function, then make a small modification to TypedBin, and finally you add a new type into the vector in TrashBinSet and modify DDTrashPrototypeInit.cpp.

Now consider applying a design pattern with an entirely different goal to the trash-sorting problem. As demonstrated earlier in this chapter, the visitor pattern’s goal is to allow the addition of new polymorphic operations to a frozen inheritance hierarchy.

For this pattern, we are no longer concerned with optimizing the addition of new types of Trash to the system. Indeed, this pattern makes adding a new type of Trash more complicated. It looks like this:

Now, if t is a Trash pointer to an Aluminum object, the code:

causes two polymorphic member function calls: the first one to select Aluminum’s version of accept( ), and the second one within accept( ) when the specific version of visit( ) is called dynamically using the base-class Visitor pointer v.

This configuration means that new functionality can be added to the system in the form of new subclasses of Visitor. The Trash hierarchy doesn’t need to be touched. This is the prime benefit of the visitor pattern: you can add new polymorphic functionality to a class hierarchy without touching that hierarchy (once the accept( ) methods have been installed). Note that the benefit is helpful here but not exactly what we started out to accomplish, so at first blush you might decide that this isn’t the desired solution.

But look at one thing that’s been accomplished: the visitor solution avoids sorting from the master Trash sequence into individual typed sequences. Thus, you can leave everything in the single master sequence and simply pass through that sequence using the appropriate visitor to accomplish the goal. Although this behavior seems to be a side effect of visitor, it does give us what we want (avoiding RTTI).

The double dispatching in the visitor pattern takes care of determining both the type of Trash and the type of Visitor. In the following example, there are two implementations of Visitor: PriceVisitor to both determine and sum the price, and WeightVisitor to keep track of the weights.

You can see all of this implemented in the new, improved version of the recycling program. As with DoubleDispatch.cpp, the Trash class has had an extra member function stub (accept( )) inserted in it to allow for this example.

Since there’s nothing concrete in the Visitor base class, it can be created as an interface:

As before, a different version of the initialization file is necessary:

The rest of the program creates specific Visitor types and sends them through a single list of Trash objects:

Note that the shape of main( ) has changed again. Now there’s only a single Trash bin. The two Visitor objects are accepted into every element in the sequence, and they perform their operations. The visitors keep their own internal data to tally the total weights and prices.

Finally, there’s no run-time type identification other than the inevitable cast to Trash when pulling things out of the sequence.

One way you can distinguish this solution from the double dispatching solution described previously is to note that, in the double dispatching solution, only one of the overloaded methods, add( ), was overridden when each subclass was created, while here each one of the overloaded visit( ) methods is overridden in every subclass of Visitor.

There’s a lot more code here, and there’s definite coupling between the Trash hierarchy and the Visitor hierarchy. However, there’s also high cohesion within the respective sets of classes: they each do only one thing (Trash describes trash, while Visitor describes actions performed on Trash), which is an indicator of a good design. Of course, in this case it works well only if you’re adding new Visitors, but it gets in the way when you add new types of Trash.

Low coupling between classes and high cohesion within a class is definitely an important design goal. Applied mindlessly, though, it can prevent you from achieving a more elegant design. It seems that some classes inevitably have a certain intimacy with each other. These often occur in pairs that could perhaps be called couplets, for example, containers and iterators. The Trash-Visitor pair above appears to be another such couplet.

Various designs in this chapter attempt to remove RTTI, which might give you the impression that it’s “considered harmful” (the condemnation used for poor goto). This isn’t true; it is the misuse of RTTI that is the problem. The reason our designs removed RTTI is because the misapplication of that feature prevented extensibility, which contravened the stated goal of adding a new type to the system with as little impact on surrounding code as possible. Since RTTI is often misused by having it look for every single type in your system, it causes code to be non-extensible: when you add a new type, you have to go hunting for all the code in which RTTI is used, and if you miss any you won’t get help from the compiler.

However, RTTI doesn’t automatically create non-extensible code. Let’s revisit the trash recycler once more. This time, a new tool will be introduced, which I call a TypeMap. It inherits from a map that holds a variant of type_info object as the key, and vector<Trash*> as the value. The interface is simple: you call addTrash( ) to add a new Trash pointer, and the map class provides the rest of the interface. The keys represent the types contained in the associated vector. The beauty of this design (suggested by Larry O’Brien) is that the TypeMap dynamically adds a new key-value pair whenever it encounters a new type, so whenever you add a new type to the system (even if you add the new type at runtime), it adapts.

The example will again build on the structure of the Trash types, and will use fillBin( ) to parse and insert the values into the TypeMap. However, TypeMap is not a vector<Trash*>, and so it must be adapted to work with fillBin( ) by multiply inheriting from Fillable. In addition, the Standard C++ type_info class is too restrictive to be used as a key, so a kind of wrapper class TypeInfo is created, which simply extracts and stores the type_info char* representation of the type (making the assumption that, within the realm of a single compiler, this representation will be unique for each type).

TypeInfo is templatized because typeid( ) does not allow the use of void*, which would be the most general way to solve the problem. So you are required to work with some specific class, but this class should be the most base of all the classes in your hierarchy. TypeInfo must define an operator< because a map needs it to order its keys.

Although powerful, the definition for TypeMap is simple; the addTrash( ) member function does most of the work. When you add a new Trash pointer, the a TypeInfo<Trash> object for that type is generated. This is used as a key to determine whether a vector holding objects of that type is already present in the map. If so, the Trash pointer is added to that vector. If not, the TypeInfo object and a new vector are added as a key-value pair.

An iterator to the map, when dereferenced, produces a pair object where the key (TypeInfo) is the first member, and the value (Vector<Trash*>) is the second member. And that’s all there is to it.

The TypeMap takes advantage of the design of fillBin( ), which doesn’t just try to fill a vector but instead anything that implements the Fillable interface with its addTrash( ) member function. Since TypeMap is multiply inherited from Fillable, it can be used as an argument to fillBin( ) like this:

An interesting thing about this design is that even though it wasn’t created to handle the sorting, fillBin( ) is performing a sort every time it inserts a Trash pointer into bin. When the Trash is thrown into bin it’s immediately sorted by TypeMap’s internal sorting mechanism. Stepping through the TypeMap and operating on each individual vector becomes a simple matter, and uses ordinary STL syntax.

As you can see, adding a new type to the system won’t affect this code at all, nor the code in TypeMap. This is certainly the smallest solution to the problem, and arguably the most elegant as well. It does rely heavily on RTTI, but notice that each key-value pair in the map is looking for only one type. In addition, there’s no way you can “forget” to add the proper code to this system when you add a new type, since there isn’t any code you need to add, other than that which supports the prototyping process (and you’ll find out right away if you forget that).

Coming up with a design such as TrashVisitor.cpp that contains a larger amount of code than the earlier designs can seem at first to be counterproductive. It pays to notice what you’re trying to accomplish with various designs. Design patterns in general strive to separate the things that change from the things that stay the same. The “things that change” can refer to many different kinds of changes. Perhaps the change occurs because the program is placed into a new environment or because something in the current environment changes (this could be: “The user wants to add a new shape to the diagram currently on the screen”). Or, as in this case, the change could be the evolution of the code body. While previous versions of the trash-sorting example emphasized the addition of new types of Trash to the system, TrashVisitor.cpp allows you to easily add new functionality without disturbing the Trash hierarchy. There’s more code in TrashVisitor.cpp, but adding new functionality to Visitor is cheap. If this is something that happens a lot, then it’s worth the extra effort and code to make it happen more easily.

The discovery of the vector of change is no trivial matter; it’s not something that an analyst can usually detect before the program sees its initial design. The necessary information will probably not appear until later phases in the project: sometimes only at the design or implementation phases do you discover a deeper or more subtle need in your system. In the case of adding new types (which was the focus of most of the “recycle” examples) you might realize that you need a particular inheritance hierarchy only when you are in the maintenance phase and you begin extending the system!

One of the most important things that you’ll learn by studying design patterns seems to be an about-face from what has been promoted so far in this book. That is: “OOP is all about polymorphism.” This statement can produce the “two-year-old with a hammer” syndrome (everything looks like a nail). Put another way, it’s hard enough to “get” polymorphism, and once you do, you try to cast all your designs into that one particular mold.

What design patterns say is that OOP isn’t just about polymorphism. It’s about “separating the things that change from the things that stay the same.” Polymorphism is an especially important way to do this, and it turns out to be helpful if the programming language directly supports polymorphism (so you don’t have to wire it in yourself, which would tend to make it prohibitively expensive). But design patterns in general show other ways to accomplish the basic goal, and once your eyes have been opened to this you will begin to search for more creative designs.

Since the Design Patterns book came out and made such an impact, people have been searching for other patterns. You can expect to see more of these appear as time goes on. Here are some sites recommended by Jim Coplien, of C++ fame (http://www.bell-labs.com/~cope), who is one of the main proponents of the patterns movement:

http://st-www.cs.uiuc.edu/users/patterns
http://c2.com/cgi/wiki
http://c2.com/ppr
http://www.bell-labs.com/people/cope/Patterns/Process/index.html
http://www.bell-labs.com/cgi-user/OrgPatterns/OrgPatterns
http://st-www.cs.uiuc.edu/cgi-bin/wikic/wikic
http://www.cs.wustl.edu/~schmidt/patterns.html
http://www.espinc.com/patterns/overview.html

Also note there has been a yearly conference on design patterns, called PLOP, that produces a published proceedings. The third one of these proceedings came out in late 1997 (all published by Addison-Wesley).

[26] Conveniently, the examples are in C++.

[27]James O. Coplien, Advanced C++ Programming Styles and Idioms, Addison-Wesley, 1992.