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template <typename... Args> struct count;
 
template <typename T, typename... Args> struct count<T, Args...> {
        static const int num = count<Args...>::num + 1;
};
 
template <> struct count<> { static const int num = 0; };
 
// let's use the struct
const int numArgs = count<1, 2, 3, 4, 5, 6, 7, 8, 9, 10>; // numArgs will be equal to 10 because there are 10 parameters given to the count struct

Line 1, is pretty much the root of all template metaprogramming trickery. The fundamentals of it all relies on recursive partial specialization, and line 1 just sets up a forward declaration to the count struct, which never actually gets used. There’s some complicated rules to how partial specializations are chosen by a C++ compiler, but the basic rule of thumb is that the most specialized version of the template that applies is what’s chosen. So although our instantiation at line 10 could match our blank forward declaration, it doesn’t. The meat of our tiny template metaprogram gets called instead: the recursive partial specialization at line 3.

And this partial specialization brings us to the key behind using variadic templates in the real-world. Since it’s not possible to do random access on the pack elements, the only choice is to “unroll” the packs recursively. The trick is to define the partial specialization such that the first argument is just a single parameter, and the next is a parameter pack:

template <typename FirstArg, typename... RemainingArgs> struct count<T, Args...>;

When the above struct is instantiated with a parameter pack, the first element gets pulled out of the pack into FirstArg, and the remaining parameters are placed in RemainingArgs. So, if the count struct makes an instantiation of itself with RemainingArgs... as the only instantiation parameter, again the first parameter will get pulled out and become FirstArg, and the rest will get placed in RemainingArgs. Thus, we can now easily unroll any parameter pack!

Let’s go back to the first code listing at line 4. This is where the recursion happens, and we can see exactly what we were talking about above happening. The count struct calls itself with the remaining arguments, and simply keeps track of the number of times the recursion happens by incrementing a counter along the way. When the instantiation finishes count::num will be set to the number of parameters in the parameter pack as of the initial instantiation plus 1. We add one because technically the recursion only counts the number of items in the parameter pack which, due to our partial specialization, is going to be one less than the number of parameters we pass to count (remember the first element gets picked out of the parameter pack).

The next thing we need to do, like in all forms of recursion, is to define the termination condition of the recursion. There are a number of ways to do this, but in this first example, we employ the “empty partial specialization” trick:

template <> struct count<> { static const int num = 0; };

This terminates the recursion not because num is implicitly set to zero, but because this specialization does not call itself. Therefore when the recursion has completely unrolled the parameter pack we hit this empty specialization and since there is no recursive call, nothing else happens. We only bother to define num here so that if someone explicitly instantiates a count struct with no arguments, they can still call count::num and get a value of zero rather than a compilation error.

Alright, so that was fun, now let’s look at something a little more complex, and a lot more useful:

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template <int N, typename... Args> struct elementType;
 
template <int N, typename T, typename... Args>
struct elementType<N, T, Args...> {
        static_assert(N < sizeof...(Args) + 1, "overflow!");
        typedef typename elementType<N - 1, Args...>::type type;
};
 
template <typename T, typename... Args>
struct elementType<0, T, Args...> {
        typedef T type;
};
 
// now let's use our elementType struct
elementType<0, int, char, std::string, double>::type var0 = 1; // var0 is an int
elementType<1, int, char, std::string, double>::type var1 = '2'; // var1 is a char
elementType<2, int, char, std::string, double>::type var2 = ";P"; // var2 is a string
elementType<3, int, char, std::string, double>::type var3 = 3.14159; // var3 is a double

The basic idea behind the elementType struct is that we give the struct an index and a variable number of types, and we can then use the struct’s type definition as a custom data type that maps back to whatever parameter the index value is associated with. The above listing at lines 15 through 18 do exactly that.

What’s so great about that, and why would such a thing be useful? While it may not seem like such an awesome thing to do at first, what we’ve done is found a way to pick elements out of a parameter pack by an index. Essentially we’ve given ourselves a way to allow random access to packs. The technique used is similar to the count struct, but not quite the same. We’re still using recursion, but this time we need to stop the recursion at the right element.

Take a look at line 6 in the above listing. This is where all the magic happens. The elementType struct calls itself using the same variadic unrolling trick that count used, except we decrement N at each step of the way, until N reaches zero, and we hit the termination condition (the partial specialization for when N is zero at lines 9 through 12). This means if we give an index of 5, we’ll get 5 recursions and the typedef that ends up getting defined is the one for the 6th parameter in the pack.

No problem right! So let’s put all this together for something actually useful — a variadic tuple class:

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template <typename... Args> 
struct tuple {
        tuple(const Args&... args) {}
};
 
tuple<int, char> t1(1, '2');

The idea is we want to be able to use our tuple class like line 6 shows. This is the example from Part I, and as I previously mentioned, this isn’t so useful. Sure it lets us declare a tuple like we want, but it doesn’t actually store the tuple’s constructor arguments, nor does it give us any way to retrieve them. So how are we going to store the elements?

Like all things variadic, we’re going to use a recursive template definition! The tuple class will instantiate itself recursively, storing the arguments as it goes. Basically the tuple class will become a compile-time linked list of sorts. This means we’ll also have to use more metaprogramming to get access to the elements, but for now let’s take a look at the tuple class definition itself:

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template <typename... Args> struct tuple;
 
// recursion termination condition
template <> struct tuple<> {};
 
// recursive tuple template
template <typename Head, typename... TailArgs>
struct tuple<Head, TailArgs...> : private tuple<TailArgs...> {
        /**
         * \brief  Recursive constructor
        **/
        tuple(const Head& head, const TailArgs&... tailArgs) :
                tuple<TailArgs...>(tailArgs...),
                mHead(head)
        {
        }
 
private:
        Head mHead; ///< Tuple head
};

Although this might look a bit weird at first, there’s not actually that much that’s new here. We’re still just recursively defining a template class, and unrolling a parameter pack as we do it, but we’re doing it via inheritance this time. The tuple class’ base class is actually itself. We use the same trick we’ve used before to extract the first parameter from the parameter pack, but now we pass the template’s TailArgs to the base class so that the base class’ Head becomes the first element in its super class’ TailArgs.

When the instantiation of this class finishes it will be a tuple class that has an mHead member variable set to the value of the first argument in the parameter pack given to the tuple’s constructor as the head variable (which you can see being set at line 14). That tuple class will have a base class that itself has an mHead member variable which is set to be the second parameter in the initial instantiation’s parameter pack (or the first parameter given to the base class instantiation). This process repeats until all the parameters in the initial pack are exhausted and the termination condition is hit.

Okay, so that’s pretty cool, but now what? Basically we’ve got this weirdly constructed tuple class with a strange inheritance hierarchy that stores all the elements given to the constructor — we still need some method for extracting them! This is where all our previous metaprogramming fun comes into play. We’ve already built much of the framework we need to make this happen:

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template <typename Head, typename... TailArgs>
struct tuple<Head, TailArgs...> : private tuple<TailArgs...> {
        /**
         * \brief  Recursive constructor
        **/
        tuple(const Head& head, const TailArgs&... tailArgs) :
                tuple<TailArgs...>(tailArgs...),
                mHead(head)
        {
        }
 
        /**
         * \brief  Get a tuple argument
        **/
        template <int N>
        typename elementType<N, Head, TailArgs...>::type
        get() const {
                return getValueFromTuple<N>::template getValue<typename elementType<N, Head, TailArgs...>::type, Head, TailArgs...>(*this);
        }
 
        /**
         * \brief  Get the head value
        **/
        Head                            head() const { return mHead; }
 
        /**
         * \brief  Get the next argument in the chain
        **/
        const tuple<TailArgs...>&       next() const { return *this; }
 
private:
        Head                            mHead;                                  ///< Tuple head
};
 
// let's use our new get function!
tuple<int, char, bool, double> t1(1, '2', true, 3.14159);
char c = t1.get<1>(); // template get function where the index specified returns that parameter of the tuple class

The first function we’ve added is the tuple’s get function, which can be seen being used at line 37. So how the %@*! does that work anyway?

Our new class member template function works pretty much the same as Boost’s tuple library (although much simpler). All we’ve done is defined a template function that takes an integer as a template parameter, and we’ve used our elementType struct, which we’ve already seen from earlier in the article, to set the return type of the function to whatever type is at that index in the tuple’s parameter pack!

We are missing some pretty important code though. The get function then calls the getValue function, which we’ve yet to define:

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template <int N>
struct getValueFromTuple {
        template <typename ReturnType, typename Head, typename... TailArgs>
        static ReturnType getValue(tuple<Head, TailArgs...>& t) {                                                                          
                return getValueFromTuple<N - 1>::template getValue<ReturnType, TailArgs...>(t.next());
        }                                                                          
 
        template <typename ReturnType, typename Head, typename... TailArgs>
        static const ReturnType getValue(const tuple<Head, TailArgs...>& t) {                                                                          
                return getValueFromTuple<N - 1>::template getValue<ReturnType, TailArgs...>(t.next());
        }                                                                          
};                                                                           
 
// termination condition for when getValueFromTuple is instantiated with zero
template <>
struct getValueFromTuple<0> {
        template <typename ReturnType, typename... Args>
        static ReturnType getValue(tuple<Args...>& t) {                                                                         
                return t.head();
        }                                                                         
 
        template <typename ReturnType, typename... Args>
        static const ReturnType getValue(const tuple<Args...>& t) {                                                                         
                return t.head();
        }                                                                         
};

This might look a bit complicated, but there’s absolutely nothing here we haven’t seen before. We define a template struct called getValueFromTuple that takes an integer as a template parameter. This is done so that we can partially specialize the struct so we have a termination condition for the function we actually need (getValue). C++ function templates are a bit more limited than class templates in how we can partially specialize them — specific specializations by constants are not allowed, so we use this struct wrapping trick to give us what we need.

The getValue function is also nothing new. It does the exact same recursive unrolling that the elementType struct uses to extract a specific parameter by index. The function recursively instantiates itself until N reaches zero and the termination condition is called. The trick here is that getValue calls the tuple class’ next() function during each instantiation, which passes the tuple’s base class to the next instantiation of the function. All we’re doing here is walking the “list” of parameters passed to the tuple’s constructor, we just happen to be doing it by walking the class hierarchy.

Let’s take a quick look at the tuple’s next() function to see how this works:

const tuple<TailArgs...>&       next() const { return *this; }

This is how we trick the compiler into returning its base class — which has an mHead that contains the next parameter in the pack. The return type is the same type that the base class has, so when we return ourselves through the this pointer the compiler does the right thing and casts down to the base class.

So there we go: a fully functioning variadic tuple class that stores its constructor parameters and gives us a way to get at them by their index! We can now do this:

tuple<int, char, string, double> t1(1, '2', "variadics rule", 3.14159);
 
int    var0 = t1.get<0>(); // var0 is now 1
char   var1 = t1.get<1>(); // var1 is '2'
string var2 = t1.get<2>(); // var2 is now "variadics rule"
double var3 = t1.get<3>(); // var3 is now 3.14159

Sweet! Of course there are many improvements that could be made such as allowing access to elements by reference (for efficiently storing and retrieving large structures), adding iterators, copy constructors, operator overrides, etc, but I’ll leave that stuff as reader exercises .

For the next installment the plan is to go even deeper down the the metaprogramming hole, and see what kind of compile-time fun we can have. Template metaprogramming is a really useful skill to have; the more you can do at compile time, the fewer instructions need to get executed at runtime, plus, as we’ve just seen in this article, metaprogramming trickery can make other code more readable and, in turn, easier to maintain. Not only that but playing around with template metaprogramming is a great way to get to know your compiler, and the C++ language!

The same goes for C++’s templates. Have you ever wondered how Boost’s Tuple library works? Well, Boost does a lot of cool complex things, like defining variadic templates using preprocessor and template metaprogramming, but in the end it’s actually pretty simple:

struct NullArg {};
template<typename Type1 = NullArg, typename Type2 = NullArg, typename Type3 = NullArg> 
class tuple {
        tuple(T0 t0) {}
        tuple(T0 t0, T1 t1) {}
        tuple(T0 t0, T1 t1, T2 t2) {}
};
 
tuple<int, char> t(1, '2');

Of course this tuple class doesn’t store or let you retrieve arguments, only allows for up to three parameters (adding more arguments, while monotonous without some preprocessor fun, is easy), and there’s some definite metaprogramming magics that go into tuple’s get<X>() template function, but it’s all still possible.

Getting back to the point: alright so we can, in effect, do variadic templates already, even without fancy C++0x. So why are they good? Well, all we’re really able to do is emulate variadic templates. The compiler always sees those “variadic” structs as having the maximum number of parameters, which means extremely long mangled names (increased binary size), slower compilation time, stupidly complex compiler diagnostic output, and did I mention slower compilation time? Not to mention that it’s an ugly and hacky way to achieve something that could be much more elegant. It’s fine for libraries to define handy structs with variadic emulation, but do you really want your production code nastied up like that? Likely not.

So, in come’s C++0x’s variadic templates. They’re easy, fun, and the whole family can use them! The syntax looks like this:

template<typename... Args> 
class tuple {};
 
tuple<char, int, bool> t;

Not so useful yet, but as you can see the ellipsis operator is given new purpose, and in this case it’s meant to denote a template parameter pack. It can also be used to denote a parameter pack expansion, like this:

template<typename... Args> 
class tuple {
        tuple(Args...) {};
};
 
tuple<char, int, bool> t('1', 2, false);

Here we’ve created a variadic tuple template and expanded the Args parameter pack in the constructor, thus letting us pass a variable number of arguments to the tuple’s constructor.

You can use the pack expansion pretty much anywhere it makes sense: base classes, constructor initializers, you name it, but in my opinion the real power of variadics comes once you start using them in conjunction with template functions and we begin to utilize C++’s powers of argument deduction. Take a look at this:

template<typename... Args>
struct tuple {
        tuple(Args...) {};
};
 
template<typename... Args>
tuple<Args...> make_tuple(Args... args) {
        return tuple<Args...>(args...);
}
 
int main() {
        auto t1 = make_tuple(1, 2, 3, 4, 5);
        return 0;
}

Nice and clean! There’s no need to specify all the template arguments to make_tuple because the compiler deduces them from the function arguments! Also note the use of the C++0x auto keyword there. Auto variables are still statically typed, they just get their type set to whatever initializes them.

So there’s the ultra-basics. Up next I’m going to start exploring what you can really do with variadic templates — like how you can extract an arbitrary element from a pack, pack searching, and other fun compile time trickery. You’d think Bjarne might have wanted to allow random access to variadic parameter lists, but that just wouldn’t be any fun. Why do things with random access when you can do it all with recursion!

Ready for more? Head to Part II!

I, personally, am in the camp that loves design. I have seen some beautifully elegant code written by crazy geniuses <cough>John Carmack</cough> that was… less than well designed, but most of the programs I admire (of which I also have access to the source code) generally have equally admirable design philosophies.

But, this isn’t a perfect world. It’s a bit sad that we still haven’t figured out a way to hook computers directly to our brains, so we’re left with using terribly inefficient and somewhat cumbersome programming languages to wrangle these computation machines into doing something close to what we wish them to. Of course they have the irritating habit of doing exactly what we tell them, rather than what we mean.

I bring this point up because although programmer skill is definitely one issue, languages themselves suffer from design issues, and I’d say a good majority of the time that’s spent struggling with design is actually spent struggling with a language’s design. It’s the same battle artists have fought for years: I’ve got a beautiful image in my head, now how do I get this paint brush to paint that image on this canvas?

For this series of of articles I’m going to pick on C++. Love it or hate it’s so prevalent that even if you’re one who spends most of your time with more modern languages like Ruby, Python, and Haskell, you’re still likely forced to bust out some C++ now and again. And for those of us who spend 40 hours a week (or more) with C++ and love it, I’m sure we’ve all got a fairly lengthy laundry list of problems that don’t necessary come out as clean as we’d like when we put them into the crazy kitchen sink that is C++. So basically, I’m going to pick on C++ because it’s easy — that and the fact that I’m a C++ compiler developer by day, so I may have a somewhat unique perspective on the language.

For this first Effective C++ installment I’m going to start off slow. I’ve got plans to dive into some more advanced things like template metaprogramming and some of the upcoming C++0x features down the road, but I thought first I’d tackle a problem that’s plagued me more than once, and in more languages than just C++ over the years. It’s also an issue that I’ve seen some fairly… wacky (I’m being nice)… solutions to by students and even in some open source code in the past: it’s the joyous design problem known as the cyclomatic dependency.

But before I begin let me just say: there’s no judging here. Say what you want, but I know as well as the next, given certain circumstances sometimes a goto, or a Duff’s device can actually be an elegant solution, so I’m not going to sit here and say “rethink your design”, I’m going to trust that you have, and move on. Fact is, you’ve run into an issue where you need to have class A depend on class B, and class B on A. Let’s look at an example:

Here is NetworkServer.hpp:

#include "NetworkConnection.hpp"
 
class NetworkServer {
        list<NetworkConnection> mConnections;
};

And here’s NetworkConnection.hpp:

#include "NetworkServer.hpp"
 
class NetworkConnection {
public:
	NetworkConnection(const NetworkServer& server) : mServer(server);
private:
	NetworkServer& mServer;
};

You can immediately spot the issue. Now, of course, this simplistic problem could be reworked in any number of ways to avoid the cyclical dependency, but real world problems are often much more complex and thus harder to refactor, yet often come down to some variation of the above.

Fortunately C++ actually has a fairly decent solution to this problem: the forward declaration. It’s simple, and it works well, but before looking at it, let me say that I’ve seen enough work arounds to know there’s plenty of C++ programmers out there who don’t know what a forward declaration is, or if they do, why it’s useful, or what the C++ compiler actually does with it.

One of the solutions I’ve seen to this is to use generic programming, or in C++ speak: templates. We could define NetworkConnection like so:

template <typename T>
class NetworkConnection {
public:
	NetworkConnection(const T& server) : mServer(server);
private:
	T& mServer;
};

This works, but what if someone instantiates NetworkConnection with a type that doesn’t provide the same methods as NetworkServer — broken design! Not only that but if you’re using NetworkConnection as a base class it might not make sense to have it templated because you might need to cast from it to a super class, and you might not know what it was instantiated with at casting time. This is actually a design flaw with C++, and is being addressed with the upcoming C++0x language feature known as concepts (which I intend to look at in detail in a future installment).

There’s also a non-template way to deal with this issue, however, and it’s something I’ve seen used a lot, especially by students (who are, through no fault of their own, ingrained in the java-single-inheritance-heavy-on-the-interface style of design). You simply make class A inherit from an abstract base class that provides pure virtual function interfaces to the methods needed by the other class caught in the cyclical loop. Then pass the abstract class around rather than its implementation and voila, dependency avoided:

NetworkServerInterface.hpp:

struct NetworkServerInterface {
	virtual void someFuncA() = 0;
	virtual bool someFuncB() = 0;
	...
};

NetworkServer.hpp:

#include "NetworkServerInterface.hpp"
#include "NetworkConnection.hpp"
 
class NetworkServer : public NetworkServerInterface {
public:
	void someFuncA();
	bool SomeFuncB();
private:
	list<NetworkConnection> mConnections;
};

NetworkConnection.hpp:

#include "NetworkServerInterface.hpp"
 
class NetworkConnection {
public:
	NetworkConnection(const NetworkServerInterface& server) : mServer(server) { mServer.someFuncA(); }
private:
	NetworkServerInterface& mServer;
};

This works, and can be a good technique to enforece certain design principles (it’s easy to only allow specific objects to use the NetworkServer in exactly the way you want by only providing a subset of functions in the abstract class definition), but it’s not without its shortcomings. Firstly, significant development effort is wasted by requiring the interface class and main class be kept in sync. Then there’s the overhead of virtual function calls that should be considered. For a time critical section of code this scenario should be avoided like the plague.

Another possible solution is the dreaded void *. It’s a holdover from the days of C programming, and much like the goto, is generally frowned upon in modern OO design. The problem with using a void pointer is if you need to use the member variable as its actual type then you have to cast it back and you’re left with the same cyclical dependency because then you need to include the casted type’s header file again.

Fortunately with C++, since the headers no longer include one another, and the translation unit is often broken into separate header and source files, you’re free to include any headers you wish in the parts of the translation unit that contain the implementation definitions (the cpp files). So, you could store the variable as a void pointer and cast from it whenever you need to access its actual type’s methods, ie:

NetworkConnection.hpp

class NetworkConnection {
public:
	NetworkConnection(void* server);
private:
	void* mpServer;
};

NetworkConnection.cpp:

#include "NetworkConnection.hpp"
#include "NetworkServer.hpp"
 
NetworkConnection::NetworkConnection(void* server) {
	mpServer = server;
	static_cast<NetworkServer*>(server)->someFuncA();
}

Yep, this works, but it’s nasty ugly, and kills proper design because you can’t really return the void pointer from public facing functions in any safe way. Normally I wouldn’t even bring this up, but it is an option, and it’s also a precursor to the forward declaration.

Essentially with forward declarations you’re telling the compiler that you want to use a class without knowing anything about it. In compiler terminology basically you’re doing namelookup, and that’s about it. You can’t use any objects of that type until you give the compiler the details it needs. This is fine though, because you can make sure to only use the variable after its class definition is available (ie in out of line member functions, or in the implementation part of the translation unit). Here’s an example:

NetworkServer.hpp:

#include "NetworkConnection.hpp"
 
class NetworkServer {
public:
	int someFuncA() { return 0; }
private:
	list<NetworkConnection> mConnections;
};

NetworkConnection.hpp:

class NetworkServer; // Foward declaration
 
class NetworkConnection {
public:
	NetworkConnection(const NetworkServer& server);
private:
	NetworkServer& mServer;
};

NetworkConnection.cpp:

#include "NetworkConnection.hpp"
#include "NetworkServer.hpp"
 
NetworkConnection::NetworkConnection(const NetworkServer& server) : mServer(server) {
	mServer.someFuncA();
}

Not only does this solve the cyclical dependency issue, but it speeds up compilation times as well. I’d even go as far as to say it’s good practice to use forward declarations wherever possible, which is in most header files if you’re writing a traditional application or a library that’s not meant to be completely header based.

So there you have it, a look at how to deal with cyclical dependencies in C++.

I’m thinking that the next topic will be a multi-part series on the upcoming variadic templates feature of C++0x since you can already play with them via gcc >= 4.3. Plus my goal is to eventually focus on more metaprogramming type stuff, and variadics are going to make that a lot more fun.