# Memory Safety in a Systems Programming Language Part 3

The first entry in this series shows how to use the new DIP1000 rules to have slices and pointers refer to the stack, all while being memory safe. The second entry in this series teaches about the ref storage class and how DIP1000 works with aggregate types (classes, structs, and unions).

So far the series has deliberately avoided templates and auto functions. This kept the first two posts simpler in that they did not have to deal with function attribute inference, which I have referred to as “attribute auto inference” in earlier posts. However, both auto functions and templates are very common in D code, so a series on DIP1000 can’t be complete without explaining how those features work with the language changes. Function attribute inference is our most important tool in avoiding so-called “attribute soup”, where a function is decorated with several attributes, which arguably decreases readability.

We will also dig deeper into unsafe code. The previous two posts in this series focused on the scope attribute, but this post is more focused on attributes and memory safety in general. Since DIP1000 is ultimately about memory safety, we can’t get around discussing those topics.

## Avoiding repetition with attributes

Function attribute inference means that the language will analyze the body of a function and will automatically add the @safe, pure, nothrow, and @nogc attributes where applicable. It will also attempt to add scope or return scope attributes to parameters and return ref to ref parameters that can’t otherwise be compiled. Some attributes are never inferred. For instance, the compiler will not insert any ref, lazy, out or @trusted attributes, because very likely they are explicitly not wanted where they are left out.

There are many ways to turn on function attribute inference. One is by omitting the return type in the function signature. Note that the auto keyword is not required for this. auto is a placeholder keyword used when no return type, storage class, or attribute is specified. For example, the declaration half(int x) { return x/2; } does not parse, so we use auto half(int x) { return x/2; } instead. But we could just as well write @safe half(int x) { return x/2; } and the rest of the attributes (pure, nothrow, and @nogc) will be inferred just as they are with the auto keyword.

The second way to enable attribute inference is to templatize the function. With our half example, it can be done this way:

int divide(int denominator)(int x) { return x/denominator; }
alias half = divide!2;

The D spec does not say that a template must have any parameters. An empty parameter list can be used to turn attribute inference on: int half()(int x) { return x/2; }. Calling this function doesn’t even require the template instantiation syntax at the call site, e.g., half!()(12) is not required as half(12) will compile.

Another means to turn on attribute inference is to store the function inside another function. These are called nested functions. Inference is enabled not only on functions nested directly inside another function but also on most things nested in a type or a template inside the function. Example:

@safe void parentFun()
{
// This is auto-inferred.
int half(int x){ return x/2; }

class NestedType
{
// This is auto inferred
final int half1(int x) { return x/2; }

// This is not auto inferred; it's a
// virtual function and the compiler
// can't know if it has an unsafe override
// in a derived class.
int half2(int x) { return x/2; }
}

int a = half(12); // Works. Inferred as @safe.
auto cl = new NestedType;
int b = cl.half1(18); // Works. Inferred as @safe.
int c = cl.half2(26); // Error.
}

A downside of nested functions is that they can only be used in lexical order (the call site must be below the function declaration) unless both the nested function and the call are inside the same struct, class, union, or template that is in turn inside the parent function. Another downside is that they don’t work with Uniform Function Call Syntax.

Finally, attribute inference is always enabled for function literals (a.k.a. lambda functions). The halving function would be defined as enum half = (int x) => x/2; and called exactly as normal. However, the language does not consider this declaration a function. It considers it a function pointer. This means that in global scope it’s important to use enum or immutable instead of auto. Otherwise, the lambda can be changed to something else from anywhere in the program and cannot be accessed from pure functions. In rare cases, such mutability can be desirable, but most often it is an antipattern (like global variables in general).

### Limits of inference

Aiming for minimal manual typing isn’t always wise. Neither is aiming for maximal attribute bloat.

The primary problem of auto inference is that subtle changes in the code can lead to inferred attributes turning on and off in an uncontrolled manner. To see when it matters, we need to have an idea of what will be inferred and what will not.

The compiler in general will go to great lengths to infer @safe, pure, nothrow, and @nogc attributes. If your function can have those, it almost always will. The specification says that recursion is an exception: a function calling itself should not be @safe, pure, or nothrow unless explicitly specified as such. But in my testing, I found those attributes actually are inferred for recursive functions. It turns out, there is an ongoing effort to get recursive attribute inference working, and it partially works already.

Inference of scope and return on function parameters is less reliable. In the most mundane cases, it’ll work, but the compiler gives up pretty quickly. The smarter the inference engine is, the more time it takes to compile, so the current design decision is to infer those attributes in only the simplest of cases.

### Where to let the compiler infer?

A D programmer should get into the habit of asking, “What will happen if I mistakenly do something that makes this function unsafe, impure, throwing, garbage-collecting, or escaping?” If the answer is “immediate compiler error”, auto inference is probably fine. On the other hand, the answer could be “user code will break when updating this library I’m maintaining”. In that case, annotate manually.

In addition to the potential of losing attributes the author intends to apply, there is also another risk:

@safe pure nothrow @nogc firstNewline(string from)
{
foreach(i; 0 .. from.length) switch(from[i])
{
case '\r':
if(from.length > i+1 && from[i+1] == '\n')
{
return "\r\n";
}
else return "\r";

case '\n': return "\n";

default: break;
}

return "";
}


You might think that since the author is manually specifying the attributes, there’s no problem. Unfortunately, that’s wrong. Suppose the author decides to rewrite the function such that all the return values are slices of the from parameter rather than string literals:

@safe pure nothrow @nogc firstNewline(string from)
{
foreach(i; 0 .. from.length) switch(from[i])
{
case '\r':
if (from.length > i + 1 && from[i + 1] == '\n')
{
return from[i .. i + 2];
}
else return from[i .. i + 1];

case '\n': return from[i .. i + 1];

default: break;
}

return "";
}

Surprise! The parameter from was previously inferred as scope, and a library user was relying on that, but now it’s inferred as return scope instead, breaking client code.

Still, for internal functions, auto inference is a great way to save both our fingers when writing and our eyes when reading. Note that it’s perfectly fine to rely on auto inference of the @safe attribute as long as the function is used in explicitly in @safe functions or unit tests. If something potentially unsafe is done inside the auto-inferred function, it gets inferred as @system, not @trusted. Calling a @system function from a @safe function results in a compiler error, meaning auto inference is safe to rely on in this case.

It still sometimes makes sense to manually apply attributes to internal functions, because the error messages generated when they are violated tend to be better with manual attributes.

Auto inference is always enabled for templated functions. What if a library interface needs to expose one? There is a way to block the inference, albeit an ugly one:

private template FunContainer(T)
{
// Not auto inferred
// (only eponymous template functions are)
@safe T fun(T arg){return arg + 3;}
}

// Auto inferred per se, but since the function it calls
// is not, only @safe is inferred.
auto addThree(T)(T arg){return FunContainer!T.fun(arg);}

However, which attributes a template should have often depends on its compile-time parameters. It would be possible to use metaprogramming to designate attributes depending on the template parameters, but that would be a lot of work, hard to read, and easily as error-prone as relying on auto inference.

It’s more practical to just test that the function template infers the wanted attributes. Such testing doesn’t have to, and probably shouldn’t, be done manually each time the function is changed. Instead:

float multiplyResults(alias fun)(float[] arr)
if (is(typeof(fun(new float)) : float))
{
float result = 1.0f;
foreach (ref e; arr) result *= fun(&e);
return result;
}

@safe pure nothrow unittest
{
float fun(float* x){return *x+1;}
// Using a static array makes sure
// arr argument is inferred as scope or
// return scope
float[5] elements = [1.0f, 2.0f, 3.0f, 4.0f, 5.0f];

// No need to actually do anything with
// the result. The idea is that since this
// compiles, multiplyResults is proven @safe
// pure nothrow, and its argument is scope or
// return scope.
multiplyResults!fun(elements);
}

Thanks to D’s compile-time introspection powers, testing against unwanted attributes is also covered:

@safe unittest
{
import std.traits : attr = FunctionAttribute,
functionAttributes, isSafe;

float fun(float* x)
{
// Makes the function both throwing
// and garbage collector dependant.
if (*x > 5) throw new Exception("");
static float* impureVar;

// Makes the function impure.
auto result = impureVar? *impureVar: 5;

// Makes the argument unscoped.
impureVar = x;
return result;
}

enum attrs = functionAttributes!(multiplyResults!fun);

assert(!(attrs & attr.nothrow_));
assert(!(attrs & attr.nogc));

// Checks against accepting scope arguments.
// Note that this check does not work with
// @system functions.
assert(!isSafe!(
{
float[5] stackFloats;
multiplyResults!fun(stackFloats[]);
}));

// It's a good idea to do positive tests with
// similar methods to make sure the tests above
// would fail if the tested function had the
// wrong attributes.
assert(attrs & attr.safe);
assert(isSafe!(
{
float[] heapFloats;
multiplyResults!fun(heapFloats[]);
}));
}

If assertion failures are wanted at compile time before the unit tests are run, adding the static keyword before each of those asserts will get the job done. Those compiler errors can even be had in non-unittest builds by converting that unit test to a regular function, e.g., by replacing @safe unittest with, say, private @safe testAttrs().

## The live fire exercise: @system

Let’s not forget that D is a systems programming language. As this series has shown, in most D code the programmer is well protected from memory errors, but D would not be D if it didn’t allow going low-level and bypassing the type system in the same manner as C or C++: bit arithmetic on pointers, writing and reading directly to hardware ports, executing a struct destructor on a raw byte blob… D is designed to do all of that.

The difference is that in C and C++ it takes only one mistake to break the type system and cause undefined behavior anywhere in the code. A D programmer is only at risk when not in a @safe function, or when using dangerous compiler switches such as -release or -check=assert=off (failing a disabled assertion is undefined behavior), and even then the semantics tend to be less UB-prone. For example:

float cube(float arg)
{
float result;
result *= arg;
result *= arg;
return result;
}

This is a language-agnostic function that compiles in C, C++, and D. Someone intended to calculate the cube of arg but forgot to initialize result with arg. In D, nothing dangerous happens despite this being a @system function. No initialization value means result is default initialized to NaN (not-a-number), which leads to the result also being NaN, which is a glaringly obvious “error” value when using this function the first time.

However, in C and C++, not initializing a local variable means reading it is (sans a few narrow exceptions) undefined behavior. This function does not even handle pointers, yet according to the standard, calling this function could just as well have *(int*) rand() = 0XDEADBEEF; in it, all due to a trivial mistake. While many compilers with enabled warnings will catch this one, not all do, and these languages are full of similar examples where even warnings don’t help.

In D, even if you explicitly requested no default initialization with float result = void, it’d just mean the return value of the function is undefined, not anything and everything that happens if the function is called. Consequently, that function could be annotated @safe even with such an initializer.

Still, for anyone who cares about memory safety, as they probably should for anything intended for a wide audience, it’s a bad idea to assume that D @system code is safe enough to be the default mode. Two examples will demonstrate what can happen.

### What undefined behavior can do

Some people assume that “Undefined Behavior” simply means “erroneous behavior” or crashing at runtime. While that is often what ultimately happens, undefined behavior is far more dangerous than, say, an uncaught exception or an infinite loop. The difference is that with undefined behavior, you have no guarantees at all about what happens. This might not sound any worse than an infinite loop, but an accidental infinite loop is discovered the first time it’s entered. Code with undefined behavior, on the other hand, might do what was intended when it’s tested, but then do something completely different in production. Even if the code is tested with the same flags it’s compiled with in production, the behavior may change from one compiler version to another, or when making completely unrelated changes to the code. Time for an example:

// return whether the exception itself is in the array
bool replaceExceptions(Object[] arr, ref Exception e)
{
bool result;
foreach (ref o; arr)
{
if (&o is &e) result = true;
if (cast(Exception) o) o = e;
}

return result;
}

The idea here is that the function replaces all exceptions in the array with e. If e itself is in the array, it returns true, otherwise false. And indeed, testing confirms it works. The function is used like this:

auto arr = [new Exception("a"), null, null, new Exception("c")];
auto result = replaceExceptions
(
cast(Object[]) arr,
arr[3]
);

This cast is not a problem, right? Object references are always of the same size regardless of their type, and we’re casting the exceptions to the parent type, Object. It’s not like the array contains anything other than object references.

Unfortunately, that’s not how the D specification views it. Having two class references (or any references, for that matter) in the same memory location but with different types, and then assigning one of them to the other, is undefined behavior. That’s exactly what happens in

if (cast(Exception) o) o = e;

if the array does contain the e argument. Since true can only be returned when undefined behavior is triggered, it means that any compiler would be free to optimize replaceExceptions to always return false. This is a dormant bug no amount of testing will find, but that might, years later, completely mess up the application when compiled with the powerful optimizations of an advanced compiler.

It may seem that requiring a cast to use a function is an obvious warning sign that a good D programmer would not ignore. I wouldn’t be so sure. Casts aren’t that rare even in fine high-level code. Even if you disagree, other cases are provably bad enough to bite anyone. Last summer, this case appeared in the D forums:

string foo(in string s)
{
return s;
}

void main()
{
import std.stdio;
string[] result;
foreach(c; "hello")
{
result ~= foo([c]);
}
writeln(result);
}

This problem was encountered by Steven Schveighoffer, a long-time D veteran who has himself lectured about @safe and @system on more than one occasion. Anything that can burn him can burn any of us.

Normally, this works just as one would think and is fine according to the spec. However, if one enables another soon-to-be-default language feature with the -preview=in compiler switch along with DIP1000, the program starts malfunctioning. The old semantics for in are the same as const, but the new semantics make it const scope.

Since the argument of foo is scope, the compiler assumes that foo will copy [c] before returning it, or return something else, and therefore it allocates [c] on the same stack position for each of the “hello” letters. The result is that the program prints ["o", "o, "o", "o", "o"]. At least for me, it’s already somewhat hard to understand what’s happening in this simple example. Hunting down this sort of bug in a complex codebase could be a nightmare.

(With my nightly DMD version somewhere between 2.100 and 2.101 a compile-time error is printed instead. With 2.100.2, the example runs as described above.)

The fundamental problem in both of these examples is the same: @safe is not used. Had it been, both of these undefined behaviors would have resulted in compilation errors (the replaceExceptions function itself can be @safe, but the cast at the usage site cannot). By now it should be clear that @system code should be used sparingly.

### When to proceed anyway

Sooner or later, though, the time comes when the guard rail has to be temporarily lowered. Here’s an example of a good use case:

/// Undefined behavior: Passing a non-null pointer
/// to a standalone character other than '\0', or
/// to an array without '\0' at or after the
/// pointed character, as utf8Stringz
extern(C) @system pure
bool phobosValidateUTF8(const char* utf8Stringz)
{
import std.string, std.utf;

try utf8Stringz.fromStringz.validate();
catch (UTFException) return false;

return true;
}

This function lets code written in another language validate a UTF-8 string using Phobos. C being C, it tends to use zero-terminated strings, so the function accepts a pointer to one as the argument instead of a D array. This is why the function has to be unsafe. There is no way to safely check that utf8Stringz is pointing to either null or a valid C string. If the character being pointed to is not '\0', meaning the next character has to be read, the function has no way of knowing whether that character belongs to the memory allocated for the string. It can only trust that the calling code got it right.

Still, this function is a good use of the @system attribute. First, it is presumably called primarily from C or C++. Those languages do not get any safety guarantees anyway. Even a @safe function is safe only if it gets only those parameters that can be created in @safe D code. Passing cast(const char*) 0xFE0DA1 as an argument to a function is unsafe no matter what the attribute says, and nothing in C or C++ verifies what arguments are passed.

Second, the function clearly documents the cases that would trigger undefined behavior. However, it does not mention that passing an invalid pointer, such as the aforementioned cast(const char*) 0xFE0DA1, is UB, because UB is always the default assumption with @system-only values unless it can be shown otherwise.

Third, the function is small and easy to review manually. No function should be needlessly big, but it’s many times more important than usual to keep @system and @trusted functions small and simple to review. @safe functions can be debugged to pretty good shape by testing, but as we saw earlier, undefined behavior can be immune to testing. Analyzing the code is the only general answer to UB.

There is a reason why the parameter does not have a scope attribute. It could have it, no pointers to the string are escaped. However, it would not provide many benefits. Any code calling the function has to be @system, @trusted, or in a foreign language, meaning they can pass a pointer to the stack in any case. scope could potentially improve the performance of D client code in exchange for the increased potential for undefined behavior if this function is erroneously refactored. Such a tradeoff is unwanted in general unless it can be shown that the attribute helps with a performance problem. On the other hand, the attribute would make it clearer for the reader that the string is not supposed to escape. It’s a difficult judgment call whether scope would be a wise addition here.

### Further improvements

It should be documented why a @system function is @system when it’s not obvious. Often there is a safer alternative—our example function could have taken a D array or the CString struct from the previous post in this series. Why was an alternative not taken? In our case, we could write that the ABI would be different for either of those options, complicating matters on the C side, and the intended client (C code) is unsafe anyway.

@trusted functions are like @system functions, except they can be called from @safe functions, whereas @system functions cannot. When something is declared @trusted, it means the authors have verified that it’s just as safe to use as an actual @safe function with any arguments that can be created within safe code. They need to be just as carefully reviewed, if not more so, as @system functions.

In these situations, it should be documented (for other developers, not users) how the function was deemed to be safe in all situations. Or, if the function is not fully safe to use and the attribute is just a temporary hack, it should have a big ugly warning about that.

Such greenwashing is of course highly discouraged, but if there’s a codebase full of @system code that’s just too difficult to make @safe otherwise, it’s better than giving up. Even as we often talk about the dangers of UB and memory corruption, in our actual work our attitudes tend to be much more carefree, meaning such codebases are unfortunately common.

It might be tempting to define a small @trusted function inside a bigger @safe function to do something unsafe without disabling checks for the whole function:

extern(C) @safe pure
bool phobosValidateUTF8(const char* utf8Stringz)
{
import std.string, std.utf;

try (() @trusted => utf8Stringz.fromStringz)()
.validate();
catch (UTFException) return false;

return true;
}

Keep in mind though, that the parent function needs to be documented and reviewed like an overt @trusted function because the encapsulated @trusted function can let the parent function do anything. In addition, since the function is marked @safe, it isn’t obvious on a first look that it’s a function that needs special care. Thus, a visible warning comment is needed if you elect to use @trusted like this.

Most importantly, don’t trust yourself! Just like any codebase of non-trivial size has bugs, more than a handful of @system functions will include latent UB at some point. The remaining hardening features of D, meaning asserts, contracts, invariants, and bounds checking should be used aggressively and kept enabled in production. This is recommended even if the program is fully @safe. In addition, a project with a considerable amount of unsafe code should use external tools like LLVM address sanitizer and Valgrind to at least some extent.

Note that the idea in many of these hardening tools, both those in the language and those of the external tools, is to crash as soon as any fault is detected. It decreases the chance of any surprise from undefined behavior doing more serious damage.

This requires that the program is designed to accept a crash at any moment. The program must never hold such amounts of unsaved data that there would be any hesitation in crashing it. If it controls anything important, it must be able to regain control after being restarted by a user or another process, or it must have another backup program. Any program that “can’t afford” to run potentially crashing checks is in no business to be trusted with systems programming either.

## Conclusion

That concludes this blog series on DIP1000. There are some topics related to DIP1000 we have left up to the readers to experiment with themselves, such as associative arrays. Still, this should be enough to get them going.

Though we have uncovered some practical tips in addition to language rules, there surely is a lot more that could be said. Tell us your memory safety tips in the D forums!

Thanks to Walter Bright and Dennis Korpel for providing feedback on this article.

# DIP1000: Memory Safety in a Modern System Programming Language Pt. 2

The previous entry in this series shows how to use the new DIP1000 rules to have slices and pointers refer to the stack, all while being memory safe. But D can refer to the stack in other ways, too, and that’s the topic of this article.

## Object-oriented instances are the easiest case

In Part 1, I said that if you understand how DIP1000 works with pointers, then you understand how it works with classes. An example is worth more than mere words:

@safe Object ifNull(return scope Object a, return scope Object b)
{
return a? a: b;
}

The return scope in the above example works exactly as it does in the following:

@safe int* ifNull(return scope int* a, return scope int* b)
{
return a? a: b;
}

The principle is: if the scope or return scope storage class is applied to an object in a parameter list, the address of the object instance is protected just as if the parameter were a pointer to the instance. From the perspective of machine code, it is a pointer to the instance.

From the point of view of regular functions, that’s all there is to it. What about member functions of a class or an interface? This is how it’s done:

interface Talkative
{
@safe const(char)[] saySomething() scope;
}

class Duck : Talkative
{
char[8] favoriteWord;
@safe const(char)[] saySomething() scope
{
import std.random : dice;

// This wouldn't work
// return favoriteWord[];

// This does
return favoriteWord[].dup;

// Also returning something totally
// different works. This
// returns the first entry 40% of the time,
// The second entry 40% of the time, and
// the third entry the rest of the time.
return
[
"quack!",
"Quack!!",
"QUAAACK!!!"
][dice(2,2,1)];
}
}

scope positioned either before or after the member function name marks the this reference as scope, preventing it from leaking out of the function. Because the address of the instance is protected, nothing that refers directly to the address of the fields is allowed to escape either. That’s why return favoriteWord[] is disallowed; it’s a static array stored inside the class instance, so the returned slice would refer directly to it. favoriteWord[].dup on the other hand returns a copy of the data that isn’t located in the class instance, which is why it’s okay.

Alternatively one could replace the scope attributes of both Talkative.saySomething and Duck.saySomething with return scope, allowing the return of favoriteWord without duplication.

### DIP1000 and Liskov Substitution Principle

The Liskov substitution principle states, in simplified terms, that an inherited function can give the caller more guarantees than its parent function, but never fewer. DIP1000-related attributes fall in that category. The rule works like this:

• if a parameter (including the implicit this reference) in the parent functions has no DIP1000 attributes, the child function may designate it scope or return scope
• if a parameter is designated scope in the parent, it must be designated scope in the child
• if a parameter is return scope in the parent, it must be either scope or return scope in the child

If there is no attribute, the caller can not assume anything; the function might store the address of the argument somewhere. If return scope is present, the caller can assume the address of the argument is not stored other than in the return value. With scope, the guarantee is that the address is not stored anywhere, which is an even stronger guarantee. Example:

class C1
{   double*[] incomeLog;
@safe double* imposeTax(double* pIncome)
{
incomeLog ~= pIncome;
return new double(*pIncome * .15);
}
}

class C2 : C1
{
// Okay from language perspective (but maybe not fair
// for the taxpayer)
override @safe double* imposeTax
(return scope double* pIncome)
{
return pIncome;
}
}

class C3 : C2
{
// Also okay.
override @safe double* imposeTax
(scope double* pIncome)
{
return new double(*pIncome * .18);
}
}

class C4: C3
{
// Not okay. The pIncome parameter of C3.imposeTax
// is scope, and this tries to relax the restriction.
override @safe double* imposeTax
(double* pIncome)
{
incomeLog ~= pIncome;
return new double(*pIncome * .16);
}
}

## The special pointer, ref

We still have not uncovered how to use structs and unions with DIP1000. Well, obviously we’ve uncovered pointers and arrays. When referring to a struct or a union, they work the same as they do when referring to any other type. But pointers and arrays are not the canonical way to use structs in D. They are most often passed around by value, or by reference when bound to ref parameters. Now is a good time to explain how ref works with DIP1000.

They don’t work like just any pointer. Once you understand ref, you can use DIP1000 in many ways you otherwise could not.

### A simple ref int parameter

The simplest possible way to use ref is probably this:

@safe void fun(ref int arg) {
arg = 5;
}

What does this mean? ref is internally a pointer—think int* pArg—but is used like a value in the source code. arg = 5 works internally like *pArg = 5. Also, the client calls the function as if the argument were passed by value:

auto anArray = [1,2];
fun(anArray[1]); // or, via UFCS: anArray[1].fun;
// anArray is now [1, 5]

instead of fun(&anArray[1]). Unlike C++ references, D references can be null, but the application will instantly terminate with a segmentation fault if a null ref is used for something other than reading the address with the & operator. So this:

int* ptr = null;
fun(*ptr);

…compiles, but crashes at runtime because the assignment inside fun lands at the null address.

The address of a ref variable is always guarded against escape. In this sense @safe void fun(ref int arg){arg = 5;} is like @safe void fun(scope int* pArg){*pArg = 5;}. For example, @safe int* fun(ref int arg){return &arg;} will not compile, just like @safe int* fun(scope int* pArg){return pArg;} will not.

There is a return ref storage class, however, that allows returning the address of the parameter but no other form of escape, just like return scope. This means that @safe int* fun(return ref int arg){return &arg;} works.

### reference to a reference

reference to an int or similar type already allows much nicer syntax than one can get with pointers. But the real power of ref shows when it refers to a type that is a reference itself—a pointer or a class, for instance. scope or return scope can be applied to a reference that is referenced to by ref. For example:

@safe float[] mergeSort(ref return scope float[] arr)
{
import std.algorithm: merge;
import std.array : Appender;

if(arr.length < 2) return arr;

auto firstHalf = arr[0 .. $/2]; auto secondHalf = arr[$/2 .. $]; Appender!(float[]) output; output.reserve(arr.length); foreach ( el; firstHalf.mergeSort .merge!floatLess(secondHalf.mergeSort) ) output ~= el; arr = output[]; return arr; } @safe bool floatLess(float a, float b) { import std.math: isNaN; return a.isNaN? false: b.isNaN? true: a<b; } mergeSort here guarantees it won’t leak the address of the floats in arr except in the return value. This is the same guarantee that would be had from a return scope float[] arr parameter. But at the same time, because arr is a ref parameter, mergeSort can mutate the array passed to it. Then the client can write: float[] values = [5, 1.5, 0, 19, 1.5, 1]; values.mergeSort; With a non-ref argument, the client would have to write values = values.sort instead (not using ref would be a perfectly reasonable API in this case, because we do not always want to mutate the original array). This is something that cannot be accomplished with pointers, because return scope float[]* arr would protect the address of the array’s metadata (the length and ptr fields of the array), not the address of it’s contents. It is also possible to have a returnable ref argument to a scope reference. Since this example has a unit test, remember to use the -unittest compile flag to include it in the compiled binary. @safe ref Exception nullify(return ref scope Exception obj) { obj = null; return obj; } @safe unittest { scope obj = new Exception("Error!"); assert(obj.msg == "Error!"); obj.nullify; assert(obj is null); // Since nullify returns by ref, we can assign // to it's return value. obj.nullify = new Exception("Fail!"); assert(obj.msg == "Fail!"); } Here we return the address of the argument passed to nullify, but still guard both the address of the object pointer and the address of the class instance against being leaked by other channels. return is a free keyword that does not mandate ref or scope to follow it. What does void* fun(ref scope return int*) mean then? The spec states that return without a trailing scope is always treated as ref return. This example thus is equivalent to void* fun(return ref scope int*). However, this only applies if there is reference to bind to. Writing void* fun(scope return int*) means void* fun(return scope int*). It’s even possible to write void* fun(return int*) with the latter meaning, but I leave it up to you to decide whether this qualifies as conciseness or obfuscation. ## Member functions and ref ref and return ref often require careful consideration to keep track of which address is protected and what can be returned. It takes some experience to get confortable with them. But once you do, understanding how structs and unions work with DIP1000 is pretty straightforward. The major difference to classes is that where the this reference is just a regular class reference in class member functions, this in a struct or union member function is ref StructOrUnionName. union Uni { int asInt; char[4] asCharArr; // Return value contains a reference to // this union, won't escape references // to it via any other channel @safe char[] latterHalf() return { return asCharArr[2 ..$];
}

// This argument is implicitly ref, so the
// following means the return value does
// not refer to this union, and also that
// we don't leak it in any other way.
@safe char[] latterHalfCopy()
{
return latterHalf.dup;
}
}

Note that return ref should not be used with the this argument. char[] latterHalf() return ref fails to parse. The language already has to understand what ref char[] latterHalf() return means: the return value is a reference. The “ref” in return ref would be redundant anyway.

Note that we did not use the scope keyword here. scope would be meaningless with this union, because it does not contain references to anything. Just like it is meaningless to have a scope ref int, or a scope int function argument. scope makes sense only for types that refer to memory elsewhere.

scope in a struct or union means the same thing as it means in a static array. It means that the memory its members refer to cannot be escaped. Example:

struct CString
{
// We need to put the pointer in an anonymous
// union with a dummy member, otherwise @safe user
// code could assign ptr to point to a character
// not in a C string.
union
{
// Empty string literals get optimised to null pointers by D
// compiler, we have to do this for the .init value to really point to
// a '\0'.
immutable(char)* ptr = &nullChar;
size_t dummy;
}

// In constructors, the "return value" is the
// constructed data object. Thus, the return scope
// here makes sure this struct won't live longer
// than the memory in arr.
@trusted this(return scope string arr)
{
// Note: Normal assert would not do! They may be
// removed from release builds, but this assert
// is necessary for memory safety so we need
// to use assert(0) instead which never gets
// removed.
if(arr[$-1] != '\0') assert(0, "not a C string!"); ptr = arr.ptr; } // The return value refers to the same memory as the // members in this struct, but we don't leak references // to it via any other way, so return scope. @trusted ref immutable(char) front() return scope { return *ptr; } // No references to the pointed-to array passed // anywhere. @trusted void popFront() scope { // Otherwise the user could pop past the // end of the string and then read it! if(empty) assert(0, "out of bounds!"); ptr++; } // Same. @safe bool empty() scope { return front == '\0'; } } immutable nullChar = '\0'; @safe unittest { import std.array : staticArray; auto localStr = "hello world!\0".staticArray; auto localCStr = localStr.CString; assert(localCStr.front == 'h'); static immutable(char)* staticPtr; // Error, escaping reference to local. // staticPtr = &localCStr.front(); // Fine. staticPtr = &CString("global\0").front(); localCStr.popFront; assert(localCStr.front == 'e'); assert(!localCStr.empty); }  Part One said that @trusted is a terrible footgun with DIP1000. This example demonstrates why. Imagine how easy it’d be to use a regular assert or forget about them totally, or overlook the need to use the anonymous union. I think this struct is safe to use, but it’s entirely possible I overlooked something. ## Finally We almost know all there is to know about using structs, unions, and classes with DIP1000. We have two final things to learn today. But before that, a short digression regarding the scope keyword. It is not used for just annotating parameters and local variables as illustrated. It is also used for scope classes and scope guard statements. This guide won’t be discussing those, because the former feature is deprecated, and the latter is not related to DIP1000 or control of variable lifetimes. The point of mentioning them is to dispel a potential misconception that scope always means limiting the lifetime of something. Learning about scope guard statements is still a good idea, as it’s a useful feature. Back to the topic. The first thing is not really specific to structs or classes. We discussed what return, return ref, and return scope usually mean, but there’s an alternative meaning to them. Consider: @safe void getFirstSpace ( ref scope string result, return scope string where ) { //... } The usual meaning of the return attribute makes no sense here, as the function has a void return type. A special rule applies in this case: if the return type is void, and the first argument is ref or out, any subsequent return [ref/scope] is assumed to be escaped by assigning to the first argument. With struct member functions, they are assumed to be assigned to the struct itself. @safe unittest { static string output; immutable(char)[8] input = "on stack"; //Trying to assign stack contents to a static //variable. Won't compile. getFirstSpace(output, input); } Since out came up, it should be said it would be a better choice for result here than ref. out works like ref, with the one difference that the referenced data is automatically default-initialized at the beginning of the function, meaning any data to which the out parameter refers is guaranteed to not affect the function. The second thing to learn is that scope is used by the compiler to optimize class allocations inside function bodies. If a new class is used to initialize a scope variable, the compiler can put it on the stack. Example: class C{int a, b, c;} @safe @nogc unittest { // Since this unittest is @nogc, this wouldn't // compile without the scope optimization. scope C c = new C(); } This feature requires using the scope keyword explicitly. Inference of scope does not work, because initializing a class this way does not normally (meaning, without the @nogc attribute) mandate limiting the lifetime of c. The feature currently works only with classes, but there is no reason it couldn’t work with newed struct pointers and array literals too. ### Until next time This is pretty much all that there is to manual DIP1000 usage. But this blog series shall not be over yet! DIP1000 is not intended to always be used explicitly—it works with attribute inference. That’s what the next post will cover. It will also cover some considerations when daring to use @trusted and @system code. The need for dangerous systems programming exists and is part of the D language domain. But even systems programming is a responsible affair when people do what they can to minimize risks. We will see that even there it’s possible to do a lot. Thanks to Walter Bright and Dennis Korpel for reviewing this article # Memory Safety in a Modern Systems Programming Language Part 1 ## Memory safety needs no checks D is both a garbage-collected programming language and an efficient raw memory access language. Modern high-level languages like D are memory safe, preventing users from accidently reading or writing to unused memory or breaking the type system of the language. As a systems programming language, not all of D can give such guarantees, but it does have a memory-safe subset that uses the garbage collector to take care of memory management much like Java, C#, or Go. A D codebase, even in a systems programming project, should aim to remain within that memory-safe subset where practical. D provides the @safe function attribute to verify that a function uses only memory-safe features of the language. For instance, try this. @safe string getBeginning(immutable(char)* cString) { return cString[0..3]; } The compiler will refuse to compile this code. There’s no way to know what will result from the three-character slice of cString, which could be referring to an empty string (i.e., cString[0] is \0), a string with a length of 1, or even one or two characters without the terminating NUL. The result in those cases would be a memory violation. ### @safe does not mean slow Note that I said above that even a low-level systems programming project should use @safe where practical. How is that possible, given that such projects sometimes cannot use the garbage collector, a major tool used in D to guarantee memory safety? Indeed, such projects must resort to memory-unsafe constructs every now and then. Even higher-level projects often have reasons to do so, as they want to create interfaces to C or C++ libraries, or avoid the garbage collector when indicated by runtime performance. But still, surprisingly large parts of code can be made @safe without using the garbage collector at all. D can do this because the memory safe subset does not prevent raw memory access per se. @safe void add(int* a, int* b, int* sum) { *sum = *a + *b; } This compiles and is fully memory safe, despite dereferencing those pointers in the same completely unchecked way they are dereferenced in C. This is memory safe because @safe D does not allow creating an int* that points to unallocated memory areas, or to a float**, for instance. int* can point to the null address, but this is generally not a memory safety problem because the null address is protected by the operating system. Any attempt to dereference it would crash the program before any memory corruption can happen. The garbage collector isn’t involved, because D’s GC can only run if more memory is requestend from it, or if the collection is explicitly called. D slices are similar. When indexed at runtime, they will check at runtime that the index is less than their length and that’s it. They will do no checking whatsoever on whether they are referring to a legal memory area. Memory safety is achieved by preventing creation of slices that could refer to illegal memory in the first place, as demonstrated in the first example of this article. And again, there’s no GC involved. This enables many patterns that are memory-safe, efficient, and independent of the garbage collector. struct Struct { int[] slice; int* pointer; int[10] staticArray; } @safe @nogc Struct examples(Struct arg) { arg.slice[5] = *arg.pointer; arg.staticArray[0..5] = arg.slice[5..10]; arg.pointer = &arg.slice[8]; return arg; } As demonstrated, D liberally lets one do unchecked memory handling in @safe code. The memory referred to by arg.slice and arg.pointer may be on the garbage collected heap, or it may be in the static program memory. There is no reason the language needs to care. The program will probably need to either call the garbage collector or do some unsafe memory management to allocate memory for the pointer and the slice, but handling already allocated memory does not need to do either. If this function needed the garbage collector, it would fail to compile because of the @nogc attribute. ### However… There’s a historical design flaw here in that the memory may also be on the stack. Consider what happens if we change our function a bit. @safe @nogc Struct examples(Struct arg) { arg.pointer = &arg.staticArray[8]; arg.slice = arg.staticArray[0..8]; return arg; } Struct arg is a value type. Its contents are copied to the stack when examples is called and can be ovewritten after the function returns. staticArray is also a value type. It’s copied along with the rest of the struct just as if there were ten integers in the struct instead. When we return arg, the contents of staticArray are copied to the return value, but ptr and slice continue to point to arg, not the returned copy! But we have a fix. It allows one to write code just as performant in @safe functions as before, including references to the stack. It even enables a few formerly @system (the opposite of @safe) tricks to be written in a safe way. That fix is DIP1000. It’s the reason why this example already causes a deprecation warning by default if it’s compiled with the latest nightly dmd. ## Born first, dead last DIP1000 is a set of enhancements to the language rules regarding pointers, slices, and other references. The name stands for D Improvement Proposal number 1000, as that document is what the new rules were initially based on. One can enable the new rules with the preview compiler switch, -preview=dip1000. Existing code may need some changes to work with the new rules, which is why the switch is not enabled by default. It’s going to be the default in the future, so it’s best to enable it where possible and work to make code compatible with it where not. The basic idea is to let people limit the lifetime of a reference (an array or pointer, for example). A pointer to the stack is not dangerous if it does not exist longer than the stack variable it is pointing to. Regular references continue to exist, but they can refer only to data with an unlimited lifetime—that is, garbage collected memory, or static or global variables. ### Let’s get started The simplest way to construct limited lifetime references is to assign to it something with a limited lifetime. @safe int* test(int arg1, int arg2) { int* notScope = new int(5); int* thisIsScope = &arg1; int* alsoScope; // Not initially scope... alsoScope = thisIsScope; // ...but this makes it so. // Error! The variable declared earlier is // considered to have a longer lifetime, // so disallowed. thisIsScope = alsoScope; return notScope; // ok return thisIsScope; // error return alsoScope; // error } When testing these examples, remember to use the compiler switch -preview=dip1000 and to mark the function @safe. The checks are not done for non-@safe functions. Alternatively, the scope keyword can be explicitly used to limit the lifetime of a reference. @safe int[] test() { int[] normalRef; scope int[] limitedRef; if(true) { int[5] stackData = [-1, -2, -3, -4, -5]; // Lifetime of stackData ends // before limitedRef, so this is // disallowed. limitedRef = stackData[]; //This is how you do it scope int[] evenMoreLimited = stackData[]; } return normalRef; // Okay. return limitedRef; // Forbidden. } If we can’t return limited lifetime references, how they are used at all? Easy. Remember, only the address of the data is protected, not the data itself. It means that we have many ways to pass scoped data out of the function. @safe int[] fun() { scope int[] dontReturnMe = [1,2,3]; int[] result = new int[](dontReturnMe.length); // This copies the data, instead of having // result refer to protected memory. result[] = dontReturnMe[]; return result; // Shorthand way of doing the same as above return dontReturnMe.dup; // Also you are not always interested // in the contents as a whole; you // might want to calculate something else // from them return [ dontReturnMe[0] * dontReturnMe[1], cast(int) dontReturnMe.length ]; } ### Getting interprocedural With the tricks discussed so far, DIP1000 would be restricted to language primitives when handling limited lifetime references, but the scope storage class can be applied to function parameters, too. Because this guarantees the memory won’t be used after the function exits, local data references can be used as arguments to scope parameters. @safe double average(scope int[] data) { double result = 0; foreach(el; data) result += el; return result / data.length; } @safe double use() { int[10] data = [1,2,3,4,5,6,7,8,9,10]; return data[].average; // works! } Initially, it’s probably best to keep attribute auto inference off. Auto inference in general is a good tool, but it silently adds scope attributes to all parameters it can, meaning it’s easy to lose track of what’s happening. That makes the learning process a lot harder. Avoid this by always explicitly specifying the return type (or lack thereof with void or noreturn): @safe const(char[]) fun(int* val) as opposed to @safe auto fun(int* val) or @safe const fun(int* val). The function also must not be a template or inside a template. We’ll dig deeper on scope auto inference in a future post. scope allows handling pointers and arrays that point to the stack, but forbids returning them. What if that’s the goal? Enter the return scope attribute: //Being character arrays, strings also work with DIP1000. @safe string latterHalf(return scope string arg) { return arg[$/2 .. $]; } @safe string test() { // allocated in static program memory auto hello1 = "Hello world!"; // allocated on the stack, copied from hello1 immutable(char)[12] hello2 = hello1; auto result1 = hello1.latterHalf; // ok return result1; // ok auto result2 = hello2[].latterHalf; // ok // Nice try! result2 is scope and can't // be returned. return result2; } return scope parameters work by checking if any of the arguments passed to them are scope. If so, the return value is treated as a scope value that may not outlive any of the return scope arguments. If none are scope, the return value is treated as a global reference that can be copied freely. Like scope, return scope is conservative. Even if one does not actually return the address protected by return scope, the compiler will still perform the call site lifetime checks just as if one did. ### scope is shallow @safe void test() { scope a = "first"; scope b = "second"; string[] arr = [a, b]; } In test, initializing arr does not compile. This may be surprising given that the language automatically adds scope to a variable on initialization if needed. However, consider what the scope on scope string[] arr would protect. There are two things it could potentially protect: the addresses of the strings in the array, or the addresses of the characters in the strings. For this assignment to be safe, scope would have to protect the characters in the strings, but it only protects the top-level reference, i.e., the strings in the array. Thus, the example does not work. Now change arr so that it’s a static array: @safe void test() { scope a = "first"; scope b = "second"; string[2] arr = [a, b]; } This works because static arrays are not references. Memory for all of their elements is allocated in place on the stack (i.e., they contain their elements), as opposed to dynamic arrays which contain a reference to elements stored elsewhere. When a static array is scope, its elements are treated as scope. And since the example would not compile were arr not scope, it follows that scope is inferred. ## Some practical tips Let’s face it, the DIP1000 rules take time to understand, and many would rather spend that time coding something useful. The first and most important tip is: avoid non-@safe code like the plague if doable. Of course, this advice is not new, but it appears even more important with DIP1000. In a nutshell, the language does not check the validity of scope and return scope in a non-@safe function, but when calling those functions the compiler assumes that the attributes are respected. This makes scope and return scope terrible footguns in unsafe code. But by resisting the temptation to mark code @trusted to avoid thinking, a D coder can hardly do damage. Misusing DIP1000 in @safe code can cause needless compilation errors, but it won’t corrupt memory and is unlikely to cause other bugs either. A second important point worth mentioning is that there is no need for scope and return scope for function attributes if they receive only static or GC-allocated data. Many langauges do not let coders refer to the stack at all; just because D programmers can do so does not mean they must. This way, they don’t have to spend any more time solving compiler errors than they did before DIP1000. And if a desire to work with the stack arises after all, the authors can then return to annotate the functions. Most likely they will accomplish this without breaking the interface. ## What’s next? This concludes today’s blog post. This is enough to know how to use arrays and pointers with DIP1000. In principle, it also enables readers to use DIP1000 with classes and interfaces. The only thing to learn is that a class reference, including the this pointer in member functions, works with DIP1000 just like a pointer would. Still, it’s hard to grasp what that means from one sentence, so later posts shall illustrate the subject. In any case, there is more to know. DIP1000 has some features for ref function parameters, structs, and unions that we didn’t cover here. We’ll also dig deeper on how DIP1000 plays with non-@safe functions and attribute auto inference. Currently, the plan is to do two more posts for this series. Do let us know in the comment section or the D forums if you have any useful DIP1000 tips that were not covered! Thanks to Walter Bright for reviewing this article. # Reducing Template Compile Times Templates have been enormously profitable for the D programming language. They allow the programmer to generate efficient and correct code at compile time. Long gone are the days of preprocessor macros or handwritten, per-type data structures. D templates, though designed in the shadow of C++ templates, were not made in their image. D makes templates cleaner and more expressive, and also enables patterns like “Design by Introspection”. Here is a simple example of a template that would require the use of preprocessor macros in C or C++: template sizeOfTypeByName(string name) { enum sizeOfTypeByName = mixin(name, ".sizeof"); } unittest { assert(sizeOfTypeByName!"int" == 4); } D’s templates are powerful tools but should not be used unthinkingly. Carelessness could result in long compile times or excessive code generation. In this blog post, I introduce some simple concepts that can help in writing templates that minimize resource usage. Deeper intuition can also lead to the discovery of new abstractions or increased confidence in existing ones. ## Read the memo The D compiler memoizes template instantiations: if I instantiate MyTemplate!int once, the compiler produces an AST for that instantiation; if I instantiate that exact template again, the previous computation is reused. As a demonstration, let’s write a generic addition function and use pragma(msg, ...) to print the number of instantiations at compile time. I’m going to use it twice with integers and twice with floating point numbers. auto genericAdd(T)(const T x, const T y) { pragma(msg, "genericAdd instantiated with ", T); return x + y; } // Instantiate with int writeln(genericAdd!int(4, 5)); writeln(genericAdd!int(6, 1)); // Now for the float type writeln(genericAdd!float(24.0, 32.0)); writeln(genericAdd!float(0.0, float.nan)); Now let’s compile the code and look at what our pragma(msg, ) says about template instantiations in the compiler. dmd -c generic_add.d This yields the following output during compilation: genericAdd instantiated with int genericAdd instantiated with float We can see int and float as we expected, but notice that each is only mentioned once. Newcomers to languages with templates or generics can sometimes mistakenly think that using a template requires a potentially expensive instantiation on every use in the source code. For the benefit of those new users of D, the above is categoric proof that this is not the case; you cannot pay twice for templates you have already asked the compiler to instantiate. (You can, however, convince yourself that you are asking the compiler to do something it’s already done when you in fact are not. We’ll go over contrived and real-world examples of this later in this article.) The benefit of this feature should be obvious, but what may not be obvious is how it can be employed in writing templates. Within the bounds of our desire for ergonomics, we should design the interfaces of our templates to maximize the number of identical instantiations. ## What’s in a name? The following example, adapted from a real-world change to a large D project, yielded a reduction in compile time of a few percent for unit-test builds. Let’s say we have an expensive template whose behavior we want to test over a simple type. Our type might be: struct Vector3 { float x; float y; float z; } To demonstrate the phenomenon, we don’t have to do anything fancy, so we’ll just declare a stub called send. // Let's say this sends a value of type T to a database. void send(T)(T x); A note on syntax: Given a variable val of type int, this template could be explicitly instantiated as send!(int)(val). However, the compiler can infer the type T, so we can instantiate it as if it were a normal function call as send(val). Using D’s Uniform Function Call Syntax, we could alternatively call it like a property or member, as val.send() (the approach used in the following example), or even val.send, since parentheses are optional in function calls when there are no arguments. Our test might then be something like: struct Vector3 { float x; float y; float z; } unittest { Vector3 value; value.send(); } This is reasonable so far. However, an issue arises when we start to write more than one test. Should we want to test different behaviors of a fancy template, but instantiate it with the same type, then we end up spending more time in compilation than we would have expected. A lot more time. And we see large growth in the number of symbols emitted in the resulting binary, resulting in a larger file size than one would expect. Why is that? Despite our intuition that the compiler should consider multiple declarations of a type like Vector3 in multiple unittest blocks as identical, it actually does not. We can demonstrate this effect with an extremely simple example. We’ll provide an implementation of send that prints at compile time the type of each instantiation. Then we’ll use static foreach to generate five distinct implementations of a single unit test. void send(T)(T x) { pragma(msg, T); } // Generate 5 unittest blocks static foreach(_; 0..5) { unittest { struct JustInt { int x; } JustInt value; value.send; } } This results in the following output from the compiler: JustInt JustInt JustInt JustInt JustInt Huh? Doesn’t this violate our “you can’t pay twice” rule? If you were to take this output from the compiler as gospel, then yes, but there’s a more subtle truth here. ## Fully qualified names The name of a type as you would write it in your editor is not the complete name of a type. Let’s amend the implementation of send to print the return value of a template called fullyQualifiedName rather than printing T directly. The rest of the example remains the same. void send(T)(T x) { import std.traits : fullyQualifiedName; pragma(msg, fullyQualifiedName!T); } Assuming the module is named example, this yields something like: example.__unittest_L13_C3_1.JustInt example.__unittest_L13_C3_2.JustInt example.__unittest_L13_C3_3.JustInt example.__unittest_L13_C3_4.JustInt example.__unittest_L13_C3_5.JustInt This explains our previous conundrum. By declaring the type locally in each test, we have actually declared a new type per test, each of which results in a new instantiation. A type’s fully qualified name includes the name of its enclosing scope ({package-name.}module-name.{scope-name(s).}TypeName). The compiler rewrites each unittest as a unique function with a generated name. We have five unique functions, each with its own local, distinct declaration of a JustInt type. And so we end up with five distinct types. We want to ensure that one instantiation is reused across unittest blocks. We do that by moving the declaration of JustInt to module scope, outside of the unit tests. struct JustInt { int x; } static foreach(_; 0..5) { unittest { JustInt value; value.send; } } The send template now prints: example.JustInt Much better. ### Some hard data To collect some anecdata about the usefulness of these changes, we’ll look at compilation times and the size of compiled binaries. Since this template is very trivial, let’s generate a hundred copies of the same unittest rather than five so we can see a trend. On my system, timing the compilation of our programs shows the locally declared types took 243ms to compile, but the version with a single global type declaration took 159ms to compile. A difference of 84ms is not all that much, sure, but in a large codebase, there may be a lot of these speedups waiting to be found. Any reduction in compile times is to be embraced, especially when it’s cumulative. As for binary size, I saw a savings of 69K on disk. The quantity of machine code generated by the compiler is worth keeping a close eye on. Larger binaries mean more work for the linker, which in turn means more time waiting for builds to complete. The easiest job is the one you don’t have to do. ## A more complex example The following example demonstrates a very simple but fundamental change to a template that yields an enormous improvement in compile times and other metrics. Let’s say we have a fairly simple interpreter, and we want to expose functions in our D code to the scripts executed by our interpreter. We can do that with some sort of registration function, which we’ll call register. ### The signature of the register function To prove the point I’m discussing, we don’t need to implement this function—its interface is what can cause a big slow down. Let’s say our register function looks like this: // Context is something our hypothetical interpreter works with void register(alias func, string registeredName)(Context x);  It’s pretty reasonable, right? It takes a template alias parameter that specifies the function to call (a common idiom in D) and a template value parameter of type string that represents the name of the function as it is exposed to scripts. The implementation of register will presumably map the value of registeredName to the func alias, and then scripts can call the function using that value. Functions can be registered with, e.g., the following: context = createAContext(); context.register!(writeln!string, "writeln")(); The scripts can call the Phobos writeln function template using the name writeln. ### The compile-time performance of the register template The interface for register looks harmless, but it turns out that it has a significant impact on compile time. We can test this by registering some random functions. The actual contents of the functions don’t matter—this article is about template compile times, so we just want a baseline figure for roughly how much time the infrastructure templates take to compile rather than the code they are hooking together. Although we will pull the functions out of a hat, the thing that will drive our intuition is to realize that a small number of interfaces will likely be reused many times. We could start with a basket of interface stubs like this: int stub1(string) { return 1; } int stub2(string) { return 2; } int stub3(string) { return 3; } // etc. More broadly, with a bunch of functions that have identical signatures and a bunch of functions with random parameter lists and return types, we can get a rough baseline. With the set of stubs I used, compile times ended up at roughly 5 seconds. So what happens if we move the compile-time parameters to run time? Since registeredName is a template value parameter, we can just move it into the function parameter list with no change. We have to handle the func parameter differently. Almost any symbol can bind at compile time to a template alias parameter, but symbols can’t bind at run time to function parameters. We have to use a function pointer instead. In that case, we can use the type of the referenced function as a template parameter. void register(FuncType)(Context x, FuncType ptr, string registeredName); With this signature, the compile time drops to roughly 1 second. ### What’s going on? D is a fairly fast language to compile. Good decisions have been made over the lifetime of the language to make that possible. It is also the case that one can happily write slow-to-compile D code. Although we are choosing to ignore the compilation speeds of the non-infrastructure code to simplify the point being made, this can actually (in a certain sense) be the case in real projects, too. As such it is worthwhile to pay attention not to the quantity of metaprogramming being done semantically but rather the quantity of metaprogramming being performed by the compiler. In this case, with the first interface used for register, the compiler had no opportunity to reuse any instantiations. Because it accepted the registered functions as symbols, each instantiation was unique. By shifting instead to take the type of the registered function as a template parameter and a pointer to the function as a function parameter, the compiler could reuse instantiations. stub1, stub2, and stub3 are distinct symbols, but they each have the same type of (int function(string)). To be clear, this is not an indictment of template alias parameters. There are good use cases for them (the Phobos algorithms API is an example). The point of this example is to show how the compile-time costs of unique template instantiations can be hidden. A decision about the trade-offs between compile-time and run-time performance can only be made if the programmer is aware there is a decision to make. So when implementing a template, consider how it will be used. If it’s going to end up creating many unique instantiations, then you can weigh the benefits of keeping that interface versus redesigning it to maximize reuse. ## A false friend In linguistics, a false friend is a pair of words from two different languages that look the same but have different meanings. I’m going to abuse this term by using it to refer to a pair of programming patterns that actually result in the same program behavior, but via different routes through the language implementation, i.e., one of these patterns has, say, worse performance or compile times than the other. ### A simple example: Let’s say we have a library that exposes a template as part of its API, like this one that prints a string when a module is loaded during run-time initialization: template FunTemplate(string op) { shared static this() { import std.stdio; writeln(op); } } // Use like this mixin FunTemplate!"Hello from DLang"; Now let’s say we want to refactor the library in some way that it’s desirable to distinguish the name FunTemplate and its implementation. How would you go about doing that? One way would be to tack Impl onto the implementation name, then declare an eponymous template that aliases the shortened name to the implementation name and forwards the template argument like this: template FunTemplate(string op) { alias FunTemplate = FunTemplateImpl!op; } This does the job, but it also creates an additional instantiation for each different value of op, one instance of FunTemplate, and one of FunTemplateImpl. So if we instantiate it with, e.g., five different values, we end up with ten unique instantiations. Now imagine doing that with a template that’s heavily used throughout a program. Since we only want to provide an alternate name for the implementation and aren’t doing anything to the parameter list, we can achieve the same result without adding another template into the mix: just use alias by itself. alias FunTemplate = FunTemplateImpl;  Since FunTemplate is no longer a template, FunTemplate!"Foo" only creates the one instance of FunTemplateImpl. ## Normalization of template arguments Once we know what we want a template to look like, and we’re satisfied with the interface we want it to have, there are sometimes subtle ways to separate the interface and implementation of a template such that we can minimize the total amount of work the compiler has to do. The definition of “work” in this context can be important to consider, as we can find ways to balance a tradeoff between compile times and the amount of object code generated for each instantiation. One technique to reduce these costs is by normalizing a given list of template arguments into something called a canonical form. ### Canonical Forms A canonical form, resulting from a process called canonicalization, is a mathematical structure that is intended to reduce multiple different-looking but identical objects into one form that we can then manipulate as we see fit. Using an automatic code formatter is an example of transforming input (in this case, source code) into a canonical form. ### Application to templates Consider a template like this one: template Expensive(Args...) { /* Some kind of expensive metaprogramming or code generation */ } If we can think of a useful canonical form that isn’t too hard to compute, we can then write a second template Reduce to implement it, then inject it like in the following example. template Expensive(Args...) { // Reduce to some kind of canonical form alias reduced = Reduce!(Args); // Where ExpensiveImpl is the same as above but renamed alias Expensive = ExpensiveImpl!(reduced); } To be worth doing, ExpensiveImpl must be significantly more expensive than the reduction operation (pay attention to differences in sys time when measuring this), where “significant” is meant statistically rather than informally, i.e., any win is good as long as you can rigorously prove it’s real. ### An example: sorting template arguments Take a templated aggregate like this: struct ExposeMethods(Types...) { /* Some kind of internal state dependant on Types but not their order */ static foreach(Type; Types) { bool test(Type x) { /* Something slow to compile depending on Type */ } } } If it’s instantiated with, e.g., five different types across a large codebase, we could spend a lot of time redoing semantically identical compilation. If all possible types are used as input many times, we could end up with a few permutations, and if not we will probably get a few identical subsets. A canonical form that might come to mind (i.e., a potential definition of Reduce) is simply sorting the arguments by their names. This can be achieved via the use of staticSort. ## Conclusion D has powerful metaprogramming and code generation features. But like anything in programming, their use isn’t free. If you want to avoid the situation where you find yourself making coffee while your project builds, then it’s imperative to be aware of the cost vs. benefits of the metaprogramming features you use. Then you can make intelligent decisions about your compile-time interfaces and implementations. ## Appendix – Tracing the D compiler to count template instantiations Here’s a simple lesson in Linux userspace tracing: you can use a tool like bpftrace or DTrace to spy on the D compiler compiling other things, so we can get basic figures about the compilation of other D programs without either hacking the compiler or changing their build process. You’ll need a bpftrace file like the following (saved as e.g., main.bt): BEGIN { printf("Tracing a D file\n"); } uprobe:/home/mhh/dlang/dmd-2.097.0/linux/bin64/dmd:_Dmain { printf("This is the main\n"); } uprobe:/home/mhh/dlang/dmd-2.097.0/linux/bin64/dmd:_D3dmd10dsymbolsem24templateInstanceSemanticFCQBs9dtemplate16TemplateInstancePSQCz6dscope5ScopePSQDr4root5array__T5ArrayTCQEq10expression10ExpressionZQBkZv { //We do nothing with the knowledge here but if you write some code you can get info about the templates relatively easily printf("Instantiating a template\n"); } What am I hooking here? That big mangled name in the middle of the script is templateInstanceSemantic in the dsymbolsem module in the DMD source. By hooking it, we can get a rough idea of when a template is being worked on. Running it with sudo bpftrace main.bt (eBPF tracing currently requires root) when building DMD, for example, I see there are about 50,800 template hits. You can use a more complicated script in a system like bcc to reconstruct the compiler’s internal data structures. With that, we can get output a bit more like 09:05:17 69310 b'/home/mhh/d_dev/dmd/src/dmd/errors.d:85' and actually reconstruct the source/line info (alongside a timestamp and PID). # Interfacing D with C: Strings Part One This post is part of an ongoing series on working with both D and C in the same project. The previous two posts looked into interfacing D and C arrays. Here, we focus on a special kind of array: strings. Readers are advised to read Arrays Part One and Arrays Part Two before continuing with this one. ### The same but different D strings and C strings are both implemented as arrays of character types, but they have nothing more in common. Even that one similarity is only superficial. We’ve seen in previous blog posts that D arrays and C arrays are different under the hood: a C array is effectively a pointer to the first element of the array (or, in C parlance, C arrays decay to pointers, except when they don’t); a D dynamic array is a fat pointer, i.e., a length and pointer pair. A D array does not decay to a pointer, i.e., it cannot be implicitly assigned to a pointer or bound to a pointer parameter in an argument list. Example: extern(C) void metamorphose(int* a, size_t len); void main() { int[] a = [8, 4, 30]; metamorphose(a, a.length); // Error - a is not int* metamorphose(a.ptr, a.length); // Okay } Beyond that, we’ve got further incompatibilities: • each of D’s three string types, string, wstring, and dstring, are encoded as Unicode: UTF-8, UTF-16, and UTF-32 respectively. The C char* can be encoded as UTF-8, but it isn’t required to be. Then there’s the C wchar_t*, which differs in bit size between implementations, never mind encoding. • all of D’s string types are dynamic arrays with immutable contents, i.e., string is an alias to immutable(char)[]. C strings are mutable by default. • the last character of every C string is required to be the NUL character (the escape character \0, which is encoded as 0 in most character sets); D strings are not required to be NUL-terminated. It may appear at first blush as if passing D and C strings back and forth can be a major headache. In practice, that isn’t the case at all. In this and subsequent posts, we’ll see how easy it can be. In this post, we start by looking at how we can deal with NUL termination and wrap up by digging deeper into the related topic of how string literals are stored in memory. ### NUL termination Let’s get this out of the way first: when passing a D string to C, the programmer must ensure it is terminated with \0. std.string.toStringz, a simple utility function in the D standard library (Phobos), can be employed for this: import core.stdc.stdio : puts; import std.string : toStringz; void main() { string s0 = "Hello C "; string s1 = s0 ~ "from D!"; puts(s1.toStringz()); } toStringz takes a single argument of type const(char)[] and returns immutable(char)* (there’s more about const vs. immutable in Part Two). The form s1.toStringz, known as UFCS (Uniform Function Call Syntax), is lowered by the compiler into toStringz(s1). toStringz is the idiomatic approach, but it’s also possible to append "\0" manually. In that case, puts can be supplied with the string’s pointer directly: import core.stdc.stdio : puts; void main() { string s0 = "Hello C "; string s1 = s0 ~ "from D!" ~ "\0"; puts(s1.ptr); } Forgetting to use .ptr will result in a compilation error, but forget to append the "\0" and who knows when someone will catch it (possibly after a crash in production and one of those marathon debugging sessions which can make some programmers wish they had never heard of programming). So prefer toStringz to avoid such headaches. However, because strings in D are immutable, toStringz does allocate memory from the GC heap. The same is true when manually appending "\0" with the append operator. If there’s a requirement to avoid garbage collection at the point where the C function is called, e.g., in a @nogc function or when -betterC is enabled, it will have to be done in the same manner as in C, e.g., by allocating/reallocating space with malloc/realloc (or some other allocator) and copying the NUL terminator. (Also note that, in some situations, passing pointers to GC-managed memory from D to C can result in unintended consequences. We’ll dig into what that means, and how to avoid it, in Part Two.) None of this applies when we’re dealing directly with string literals, as they get a bit of special treatment from the compiler that makes puts("Hello D from C!".toStringz) redundant. Let’s see why. #### String literals in D are special D programmers very often find themselves passing string literals to C functions. Walter Bright recognized early on how common this would be and decided that it needed to be just as seamless in D as it is in C. So he implemented string literals in a way that mitigates the two major incompatibilities that arise from NUL terminators and differences in array internals: 1. D string literals are implicitly NUL-terminated. 2. D string literals are implicitly convertible to const(char)*. These two features may seem minor, but they are quite major in terms of convenience. That’s why I didn’t pass a literal to puts in the toStringz example. With a literal, it would look like this: import core.stdc.stdio : puts; void main() { puts("Hello C from D!"); } No need for toStringz. No need for manual NUL termination or .ptr. It just works. I want to emphasize that this only applies to string literals (of type string, wstring, and dstring) and not to string variables; once a string literal is included in an expression, the NUL-termination guarantee goes out the window. Also, no other array literal type is implicitly convertible to a pointer, so the .ptr property must be used to bind them to a pointer function parameter, e.g., giveMeIntPointer([1, 2, 3].ptr). But there is a little more to this story. #### String literals in memory Normal array literals will usually trigger a GC allocation (unless the compiler can elide the allocation, such as when assigning the literal to a static array). Let’s do a bit of digging to see what happens with a D string literal: import std.stdio; void main() { writeln("Where am I?"); } To make use of a command-line tool particularly convenient for this example, I compiled the above on 64-bit Linux with all three major compilers using the following command lines: dmd -ofdmd-memloc memloc.d gdc -o gdc-memloc memloc.d ldc2 -ofldc-memloc memloc.d If we were compiling C or C++, we could expect to find string literals in the read-only data segment, .rodata, of the binary. So let’s look there via the readelf command, which allows us to extract specific data from binaries in the elf object file format, to see if the same thing happens with D. The following is abbreviated output for each binary: readelf -x .rodata ./dmd-memloc | less Hex dump of section '.rodata': 0x0008e000 01000200 00000000 00000000 00000000 ................ 0x0008e010 04100000 00000000 6d656d6c 6f630000 ........memloc.. 0x0008e020 57686572 6520616d 20493f00 2f757372 Where am I?./usr 0x0008e030 2f696e63 6c756465 2f646d64 2f70686f /include/dmd/pho ... readelf -x .rodata ./gdc-memloc | less Hex dump of section '.rodata': 0x00003000 01000200 00000000 57686572 6520616d ........Where am 0x00003010 20493f00 00000000 2f757372 2f6c6962 I?...../usr/lib ... readelf -x .rodata ./ldc-memloc | less Hex dump of section '.rodata': 0x00001e40 57686572 6520616d 20493f00 00000000 Where am I?..... 0x00001e50 2f757372 2f6c6962 2f6c6463 2f783836 /usr/lib/ldc/x86 In all three cases, the string is right there in the read-only data segment. The D spec explicitly avoids specifying where a string literal will be stored, but in practice, we can bank on the following: it might be in the binary’s read-only segment, or it might be in the normal data segment, but it won’t trigger a GC allocation, and it won’t be allocated on the stack. Wherever it is, there’s a positive consequence that we can sometimes take advantage of. Notice in the readelf output that there is a dot (.) immediately following the question mark at the end of each string. That represents the NUL terminator. It is not counted in the string’s .length (so "Where am I?".length is 11 and not 12), but it’s still there. So when we initialize a string variable with a string literal or assign a string literal to a variable, the lack of an allocation also means there’s no copying, which in turn means the variable is pointing to the literal’s location in memory. And that means we can safely do this: import core.stdc.stdio: puts; void main() { string s = "I'm NUL-terminated."; puts(s.ptr); s = "And so am I."; puts(s.ptr); } If you’ve read the GC series on this blog, you are aware that the GC can only have a chance to run a collection if an attempt is made to allocate from the GC heap. More allocations mean a higher chance to trigger a collection and more memory that needs to be scanned when a collection runs. Many applications may never notice, but it’s a good policy to avoid GC allocations when it’s easy to do so. The above is a good example of just that: toStringz allocates, we don’t need it in either call to puts because we can trust that s is NUL-terminated, so we don’t use it. To be very clear: this is only true for string variables that have been directly initialized with a string literal or assigned one. If the value of the variable was the result of any other operation, then it cannot be considered NUL-terminated. Examples: string s1 = s ~ "...I'm Unreliable!!"; string s2 = s ~ s1; string s3 = format("I'm %s!!", "Unreliable"); None of these strings can be considered NUL-terminated. Each case will trigger a GC allocation. The runtime pays no mind to the NUL terminator of any of the literals during the append operations or in the format function, so the programmer can’t trust it will be part of the result. Pass any one of these strings to C without first terminating it and trouble will eventually come knocking. #### But hold on… Given that you’re reading a D blog, you’re probably adventurous or like experimenting. That may lead you to discover another case that looks reliable: import core.stdc.stdio: puts; void main() { string s = "Am I " ~ "reliable?"; puts(s.ptr); } The above very much looks like appending multiple string literals in an initialization or assignment is just as reliable as using a single string literal. We can strengthen that assumption with the following: import std.stdio : writeln; void main() { writeln("Am I reliable?".ptr); string s = "Am I " ~ "reliable?"; writeln(s.ptr); } writeln is a templated function that recognizes when it’s being given a pointer; rather than treating it as a string and printing what it points to, it prints the pointer’s value. So we can print memory addresses in D without a format string. Compiling the above, again on 64-bit Linux: dmd -ofdmd-rely rely.d gdc -o gdc-rely rely.d ldc2 -ofldc-rely rely.d Now let’s execute them all: ./dmd-rely 562363F63010 562363F63030 ./gdc-rely 5566145E0008 5566145E0008 ./ldc-rely 55C63CFB461C 55C63CFB461C We see that dmd-rely prints two different addresses, but they’re very close together. Both gdc-rely and ldc-rely print a single address in both cases. And if we make use of readelf as we did with the memloc example above, we’ll find that, in every case, the literals are in the read-only data segment. Case closed! Well, not so fast. What’s happening is that all three compilers are performing an optimization known as constant folding. In short, they can recognize when all operands of an append expression are compile-time constants, so they can perform the append at compile-time to produce a single string literal. In this case, the net effect is the same as s = "Am I reliable?". LDC and GDC go further and recognize that the resulting literal is identical to the one used earlier, so they reuse the existing literal’s address (a.k.a. string interning). (Note that DMD also performs string interning, but currently it only kicks in when a string literal appears more than twice.) To be clear: this only works because all of the operands are string literals. No matter how many string literals are involved in an operation, if only one operand is a variable, then the operation triggers a GC allocation. Although we see that the result of an append operation involving string literals can be passed directly to C just fine, and we’ve proven that it’s stored in read-only memory alongside its NUL terminator, this is not something we should consider reliable. It’s an optimization that no compiler is required to perform. Though it’s unlikely that any of the three major D compilers will suddenly stop constant folding string literals, a future D compiler could possibly be released without this particular optimization and instead trigger a GC allocation. In short: rely on this at your own risk. Addendum: Compile rely.d on Windows with dmd and the binary will yield some very different output: dmd -m64 -ofwin-rely.exe rely.d ./win-rely 7FF76701D440 7FF76702BB30 There is a much bigger difference in the memory addresses here than in the dmd binary on Linux. We’re dealing with the PE/COFF format in this case, and I’m not familiar with anything similar to readelf for that format on Windows. But I do know a little something about Abner Fog’s objconv utility. Not only does it convert between object file formats, but it can also disassemble them: objconv -fasm win-rely.obj This produces a file, win-rely.asm. Open it in a text editor and search for a portion of the string, e.g., "I rel". You’ll find the two entries aren’t too far apart, but one is located in a block of text under this heading: rdata SEGMENT PARA ‘CONST’ ; section number 4 And the other under this heading: .data$B SEGMENT PARA ‘DATA’ ; section number 6

In other words, one of them is in the read-only data segment (rdata SEGMENT PARA 'CONST'), and the other is in the regular data segment. This goes back to what I mentioned earlier about the D spec being explicitly silent on where string literals are stored. Regardless, the behavior of the program on Windows is the same as it is on Linux; the second call to puts doesn’t blow anything up because the NUL terminator is still there, one slot past the last character. But it doesn’t change the fact that constant folding of appended string literals is an optimization and only to be relied upon at your own risk.

### Conclusion

This post provides all that’s needed for many of the use cases encountered with strings when interacting with C from D, but it’s not the complete picture. In Part Two, we’ll look at how mutability, immutability, and constness come into the picture, how to avoid a potential problem spot that can arise when passing GC-allocated D strings to C, and how to get D strings from C strings. We’ll save encoding for Part Three.

Thanks to Walter Bright, Ali Çehreli, and Iain Buclaw for their valuable feedback on this article.

# Symphony of Destruction: Structs, Classes and the GC (Part One)

This post is part of a broader series on garbage collection in D. The motivation is to explore how destructors and the GC interact. To do that, we first need a bit of background. We do not go into a broader discussion on the ins and outs of object destruction, only what is most relevant to the interaction of destructors and the GC.

I’ve split the discussion into two blog posts. Here in Part One, we look at how deterministic and non-deterministic destruction differ, consider the consequences of having a single destructor for both scenarios, and finally establish two simple guidelines that will help us avoid those consequences. In Part Two, we’ll go further and explore how we can still write solid destructors when circumstances dictate that the guidelines don’t apply.

## Deterministic destruction

Destruction is deterministic when it is predictable, meaning the programmer can, simply by following the flow of the code, point out where and when an object’s destructor is invoked. This is possible with struct instances allocated on the stack, as the compiler will insert calls to their destructors at well-defined points for automatic and deterministic destruction.

There are two basic rules for automatic destruction:

1. The destructors of all stack-allocated structs in a given scope are invoked when the scope exits.
2. Destructors are invoked in reverse lexical order (i.e., the opposite of the order in which the declarations appear in the source).

With these two rules in mind, we can examine the following example and accurately predict its output.

import std.stdio;
struct Predictable
{
int number;
this(int n)
{
writeln("Constructor #", n);
number = n;
}
~this()
{
writeln("Destructor #", number);
}
}

void main()
{
Predictable s0 = Predictable(0);
{
Predictable s1 = Predictable(1);
}
Predictable s2 = Predictable(2);
}

We see that both s0 and s2 are directly within the scope of the main function, so their destructors will run when main exits. Given that the declaration of s2 comes after that of s0, the destructor of s2 will run before that of s0.

We also see that s1 is declared in an anonymous inner scope between the declarations of s0 and s2. This scope exits before s2 is constructed, so the destructor of s1 will execute before the constructor of s2.

With that, we can expect the following output:

Constructor #0      // declaration of s0
Constructor #1      // declaration of s1
Destructor #1       // anonymous scope exits, s1 destroyed
Constructor #2      // declaration of s2
Destructor #2       // main exits, s2 then s0 destroyed
Destructor #0

Compiling and executing the example proves us accurate seers.

The programmer can implement deterministic destruction manually, as is necessary when destroying instances allocated on the non-GC heap, e.g., with malloc or std.experimental.allocator. In an earlier post, Go Your Own Way (Part Two: The Heap), I covered how to use std.conv.emplace to allocate instances on the non-GC heap and briefly mentioned that destructors can be invoked manually via destroy. That’s a function template declared in the automatically imported object module so that it’s always available. We won’t retread the allocation discussion, but an example of manual destruction isn’t out of bounds for this post.

In the following example, we’ll reuse the definition of the Predictable struct and a destroyPredictable function to manually invoke the destructors. For completeness, I’ve included functions for allocating and deallocating Predictable instances from the non-GC heap: allocatePredictable and deallocatePredictable. If it isn’t clear to you what these two functions are doing, please read the blog post I mentioned above.

void main()
{
Predictable* s0 = allocatePredictable(0);
scope(exit) { destroyPredictable(s0); }
{
Predictable* s1 = allocatePredictable(1);
scope(exit) { destroyPredictable(s1); }
}
Predictable* s2 = allocatePredictable(2);
scope(exit) { destroyPredictable(s2); }
}

void destroyPredictable(Predictable* p)
{
if(p) {
destroy(*p);
deallocatePredictable(p);
}
}

Predictable* allocatePredictable(int n)
{
import core.stdc.stdlib : malloc;
import std.conv : emplace;
auto p = cast(Predictable*)malloc(Predictable.sizeof);
return emplace!Predictable(p, n);
}

void deallocatePredictable(Predictable* p)
{
import core.stdc.stdlib : free;
free(p);
}

Running this program will result in precisely the same output as the previous example. In the destroyPredictable function, we dereference the struct pointer when calling destroy because there is no overload that takes a pointer. There are specializations for classes, interfaces, and structs passed by reference and a general catch-all that takes all other types by reference. Destructors are invoked on types that have them. Before exiting, the function sets the argument to its default .init value through the reference.

Note that if we were to give destroy a pointer without first dereferencing it, the code would still compile. The pointer would be accepted by reference and simply set to null, the default .init value for pointers, but the struct’s destructor would not be invoked (i.e., the pointer is “destroyed”, not the struct instance).

Inserting writeln(*p) immediately after destroy(*p) should print

Predictable(0)

for each destroyed instance. (The default .init state for a struct in D is the aggregate of the .init property of each of its members; in this case, the sole member, being of type int, has an .init property of 0, so the struct’s default .init state is Predictable(0). This can be changed in the struct definition, e.g., struct Predictable { int id = 1; }.)

destroy is not restricted to instances allocated on the non-GC heap. Any aggregate type instance (struct, class, or interface) is a valid argument no matter where it was allocated.

## Non-deterministic destruction

In languages with support for objects and a garbage collector, the responsibility for destroying object instances allocated on the GC heap falls to the GC. This is known as finalization. Before reclaiming an object’s memory, the GC finalizes the object by invoking its finalizer.

Finalization, though convenient, comes with a price. In Java’s particular circumstances, the price was determined to be so high that its maintainers deprecated the Object.finalize method and left a scary warning about its use in the documentation. It’s worth quoting here:

The finalization mechanism is inherently problematic. Finalization can lead to performance issues, deadlocks, and hangs. Errors in finalizers can lead to resource leaks; there is no way to cancel finalization if it is no longer necessary; and no ordering is specified among calls to finalize methods of different objects. Furthermore, there are no guarantees regarding the timing of finalization. The finalize method might be called on a finalizable object only after an indefinite delay, if at all.

Finalization in D isn’t quite the bugbear it is in Java, but we do see a less dramatic warning about it in the D documentation:

The garbage collector is not guaranteed to run the destructor for all unreferenced objects. Furthermore, the order in which the garbage collector calls destructors for unreferenced objects is not specified.

Although there’s no mention of “finalization” or “finalizers” here, that’s precisely what the text is referring to. The core message is the same in both warnings: finalization is non-deterministic and cannot be relied on.

Unlike structs, classes in D are reference types by default. Some consequences: the programmer never has direct access to the underlying class instance; instances declared uninitialized are null by default; the normal use case is to allocate instances via new. When a class is instantiated in D, it is usually going to be managed by the GC and its destructor will serve as a finalizer.

As an experiment, let’s change the definition of struct Predictable in our first example to class Unpredictable and use new to allocate the instances like so:

import std.stdio;
class Unpredictable
{
int number;
this(int n)
{
writeln("Constructor #", n);
number = n;
}
~this()
{
writeln("Destructor #", number);
}
}

void main()
{
Unpredictable s0 = new Unpredictable(0);
{
Unpredictable s1 = new Unpredictable(1);
}
Unpredictable s2 = new Unpredictable(2);
}

We’ll see that the output is drastically different:

Constructor #0
Constructor #1
Constructor #2
Destructor #0
Destructor #1
Destructor #2

Anyone familiar with the characteristics of the default DRuntime constructor can predict for this very simple program that all the destructors will be run when the GC’s cleanup function is executed as the D runtime shuts down, and that they will be executed in the order in which they were declared (an implementation detail; and note that destruction at shut down can be disabled via a command line argument). But in a more complex program, this ability to predict breaks down. Destructors can be invoked by the GC at almost any time and in any order.

To be clear, the GC will only perform its cleanup duties if and when it finds more memory is needed to fulfill a specific allocation request. In other words, it isn’t constantly running in the background, marking objects unreachable and calling destructors willy nilly. To that extent, we can predict when the GC has the possibility to perform its duties. Beyond that, all bets are off. We cannot predict with accuracy if any destructors will be invoked during any given allocation request or the order in which they will be invoked. This uncertainty has ramifications for how one implements destructors for any GC-managed type.

For starters, destructors of GC-managed objects should never perform any operation that can potentially result in a GC allocation request. Attempting to do so can result in an InvalidMemoryOperationError at run time. I use the word “potentially” because some operations can indirectly cause the error in certain circumstances, but not in others. Some examples: attempting to index an associative array can trigger an attempt to allocate a RangeError if the key is not present; a failed assert will result in allocation of an AssertError; calling any function not annotated with @nogc means GC operations are always possible in the call stack. These and any such operations should be avoided in the destructors of GC-managed objects. (The first seven items on the list of operations disallowed in @nogc functions are collectively a good guide.)

A larger issue is that one cannot rely on any resource still being valid when a destructor is called by the GC. Consider a class that attempts to close a socket handle in its destructor; it’s quite possible that the destructor won’t be called until after the program has already shutdown the network interface. There is no scenario in which the runtime can catch this. In the best case, such circumstances will result in a silent failure, but they could also result in crashes during program shutdown or even sooner.

What it comes down to is that GC-allocated objects should never be used to manage any resource that depends upon deterministic destruction for cleanup.

## Designing for destruction

For the D neophyte, it can appear as if destructors in D are useless. Given that both struct and class instances can be allocated from memory that may or may not be managed by the GC, that destructors of GC-managed objects are not guaranteed to run, and that destructors are forbidden to perform GC operations during finalization, how can we ever rely on them?

In practice, it’s not as bad as it may seem. Issues do arise for the unwary, but armed with a basic awareness of the nature of D destructors, it turns out that it’s pretty easy to avoid having problems. This is especially true if the programmer adopts two fairly simple rules.

### 1. Pretend class destructors don’t exist

Class instances will nearly always be allocated with new. That means their destructors will nearly always be non-deterministic. Much of what one would want to do in a destructor is somehow dependent on program state: either the destructor itself expects a certain state (like writing to a log file that is expected to be open), or the program expects the destructor to have modified the program state in a specific manner (like releasing a resource handle).

Non-deterministic destruction means that all expectations about program state are thrown out the window. That log file might have already been closed, so the message will never be written (I hope it wasn’t important). That system resource handle may never be released until the program ends (I hope that particular resource isn’t scarce). Even if it seems through testing that a class destructor is working the way it’s intended, it’s quite likely down to the fact that the testing has not uncovered the case where it breaks. In a long-running program, that case will inevitably pop up at some point. Have fun debugging when your production game server starts randomly crashing.

So when using classes in D, pretend they have no destructors. Pretend that they are Java classes with a deprecated finalize method.

### 2. Don’t allocate structs on the GC heap when they have destructors

Since we’re pretending classes have no destructors, then we’re going to turn to structs for all of our destructive needs. Allocating structs as value objects on the stack will cover many use cases, but sometimes we may need to allocate them on the heap. When that situation arises, do not allocate any destructor-bearing struct with new. If we allocate a struct that has a destructor on the GC heap, it completely defeats our purpose of avoiding class destructors in the first place. That destructor we intended to be deterministic is now non-deterministic, so we may as well have just used a class.

As we have seen, struct instances can be allocated on the non-GC heap (e.g., with malloc) and their destructors manually invoked with the destroy function. If we need deterministic destruction and we absolutely must have a heap-allocated struct, then we cannot allocate that struct on the GC heap.

## Guidelines schmuidelines

I’m sure someone is reading the above guidelines and thinking, “If I have to pretend that classes have no destructors, then why do classes have destructors?” Well, you don’t have to.

There is no One True Path to follow when deciding if an object should be implemented as a class or struct. Personally, I will always prefer structs over classes, and I will only reach for a class if I need something structs can’t give me easily (like a hierarchy) or efficiently. Other people will consider if the object they need to represent has an identity, e.g., an Actor in a simulation versus the Vertex that defines its 3D coordinates. POD (Plain Old Data) types should always be structs, but beyond that it’s largely a matter of preference.

My two guidelines are based on my experience and that of others with whom I’ve spoken. They are intended to help you keep the full implications of D’s distinction between classes and structs at the forefront of your thoughts when architecting your program. They are not commandments that every D programmer must follow.

Realistically, most D programmers will encounter circumstances at one time or another in which the guidelines do not apply. For example, when mixing GC-managed memory and manually-managed memory in the same program, it’s quite possible for a struct intended for stack use to wind up on the GC heap if the programmer is unaware of the circumstances. And some D programmers will always prefer classes over structs because that’s just the way they want it, and so will simply choose to ignore the guidelines. That’s no problem as long as they fully understand the consequences.

So what does that mean? How do you get over the non-deterministic nature of class destructors if your Actor class absolutely must have a destructor, or if you prefer to always follow The Way of the Class? How do you prevent structs intended for the stack from being GC-allocated? These are things we’ll be looking at in Part Two. See you there.

Thanks to Ali Çehreli and Max Haughton for their feedback. And to Adam D. Ruppe for his conversation on the topic in Discord and the title suggestion (it fits in more nicely with the series than the ‘Appetite For Destruction’ I had intended to go with)

# The ABC’s of Templates in D

D was not supposed to have templates.

Several months before Walter Bright released the first alpha version of the DMD compiler in December 2001, he completed a draft language specification in which he wrote:

Templates. A great idea in theory, but in practice leads to numbingly complex code to implement trivial things like a “next” pointer in a singly linked list.

The (freely available) HOPL IV paper, Origins of the D Programming Language, expands on this:

[Walter] initially objected to templates on the grounds that they added disproportionate complexity to the front end, they could lead to overly complex user code, and, as he had heard many C++ users complain, they had a syntax that was difficult to understand. He would later be convinced to change his mind.

It did take some convincing. As activity grew in the (then singular) D newsgroup, requests that templates be added to the language became more frequent. Eventually, Walter started mulling over how to approach templates in a way that was less confusing for both the programmer and the compiler. Then he had an epiphany: the difference between template parameters and function parameters is one of compile time vs. run time.

From this perspective, there’s no need to introduce a special template syntax (like the C++ style <T>) when there’s already a syntax for parameter lists in the form of (T). So a template declaration in D could look like this:

template foo(T, U) {
// template members here
}

From there, the basic features fell into place and were expanded and enhanced over time.

In this article, we’re going to lay the foundation for future articles by introducing the most basic concepts and terminology of D’s template implementation. If you’ve never used templates before in any language, this may be confusing. That’s not unexpected. Even though many find D’s templates easier to understand than other implementations, the concept itself can still be confusing. You’ll find links at the end to some tutorial resources to help build a better understanding.

## Template declarations

Inside a template declaration, one can nest any other valid D declaration:

template foo(T, U) {
int x;
T y;

struct Bar {
U thing;
}

void doSomething(T t, U u) {
...
}
}

In the above example, the parameters T and U are template type parameters, meaning they are generic substitutes for concrete types, which might be built-in types like int, float, or any type the programmer might implement with a class or struct declaration. By declaring a template, it’s possible to, for example, write a single implementation of a function like doSomething that can accept multiple types for the same parameters. The compiler will generate as many copies of the function as it needs to accomodate the concrete types used in each unique template instantiation.

Other kinds of parameters are supported: value parameters, alias parameters, sequence (or variadic) parameters, and this parameters, all of which we’ll explore in future blog posts.

### One name to rule them all

In practice, it’s not very common to implement templates with multiple members. By far, the most common form of template declaration is the single-member eponymous template. Consider the following:

template max(T) {
T max(T a, T b) { ... }
}

An eponymous template can have multiple members that share the template name, but when there is only one, D provides us with an alternate template declaration syntax. In this example, we can opt for a normal function declaration that has the template parameter list in front of the function parameter list:

T max(T)(T a, T b) { ... }

The same holds for eponymous templates that declare an aggregate type:

// Instead of the longhand template declaration...
/*
template MyStruct(T, U) {
struct MyStruct {
T t;
U u;
}
}
*/

// ...just declare a struct with a type parameter list
struct MyStruct(T, U) {
T t;
U u;
}

Eponymous templates also offer a shortcut for instantiation, as we’ll see in the next section.

## Instantiating templates

In relation to templates, the term instantiate means essentially the same as it does in relation to classes and structs: create an instance of something. A template instance is, essentially, a concrete implementation of the template in which the template parameters have been replaced by the arguments provided in the instantiation. For a template function, that means a new copy of the function is generated, just as if the programmer had written it. For a type declaration, a new copy of the declaration is generated, just as if the programmer had written it.

We’ll see an example, but first we need to see the syntax.

### Explicit instantiations

An explicit instantiation is a template instance created by the programmer using the template instantiation syntax. To easily disambiguate template instantiations from function calls, D requires the template instantiation operator, !, to be present in explicit instantiations. If the template has multiple members, they can be accessed in the same manner that members of aggregates are accessed: using dot notation.

import std;

template Temp(T, U) {
T x;
struct Pair {
T t;
U u;
}
}

void main()
{
Temp!(int, float).x = 10;
Temp!(int, float).Pair p;
p.t = 4;
p.u = 3.2;
writeln(Temp!(int, float).x);
writeln(p);
}

Run it online at run.dlang.io

There is one template instantiation in this example: Temp!(int, float). Although it appears three times, it refers to the same instance of Temp, one in which T == int and U == float. The compiler will generate declarations of x and the Pair type as if the programmer had written the following:

int x;
struct Pair {
int t;
float u;
}

However, we can’t just refer to x and Pair by themselves. We might have other instantiations of the same template, like Temp!(double, long), or Temp(MyStruct, short). To avoid conflict, the template members must be accessed through a namespace unique to each instantiation. In that regard, Temp!(int, float) is like a class or struct with static members; just as you would access a static x member variable in a struct Foo using the struct name, Foo.x, you access a template member using the template name, Temp!(int, float).x.

There is only ever one instance of the variable x for the instantiation Temp!(int float), so no matter where you use it in a code base, you will always be reading and writing the same x. Hence, the first line of main isn’t declaring and initializing the variable x, but is assigning to the already declared variable. Temp!(int, float).Pair is a struct type, so that after the declaration Temp!(int, float).Pair p, we can refer to p by itself. Unlike x, p is not a member of the template. The type Pair is a member, so we can’t refer to it without the prefix.

### Aliased instantiations

It’s possible to simplify the syntax by using an alias declaration for the instantiation:

import std;

template Temp(T, U) {
T x;
struct Pair {
T t;
U u;
}
}
alias TempIF = Temp!(int, float);

void main()
{
TempIF.x = 10;
TempIF.Pair p = TempIF.Pair(4, 3.2);
writeln(TempIF.x);
writeln(p);
}

Run it online at run.dlang.io

Since we no longer need to type the template argument list, using a struct literal to initialize p, in this case TempIF.Pair(3, 3.2), looks cleaner than it would with the template arguments. So I opted to use that here rather than first declare p and then initialize its members. We can trim it down still more using D’s auto attribute, but whether this is cleaner is a matter of preference:

auto p = TempIF.Pair(4, 3.2);

Run it online at run.dlang.io

### Instantiating eponymous templates

Not only do eponymous templates have a shorthand declaration syntax, they also allow for a shorthand instantiation syntax. Let’s take the x out of Temp and rename the template to Pair. We’re left with a Pair template that provides a declaration struct Pair. Then we can take advantage of both the shorthand declaration and instantiation syntaxes:

import std;

struct Pair(T, U) {
T t;
U u;
}

// We can instantiate Pair without the dot operator, but still use
// the alias to avoid writing the argument list every time
alias PairIF = Pair!(int, float);

void main()
{
PairIF p = PairIF(4, 3.2);
writeln(p);
}

Run it online at run.dlang.io

The shorthand instantiation syntax means we don’t have to use the dot operator to access the Pair type.

### Even shorter syntax

When a template instantiation is passed only one argument, and the argument’s symbol is a single token (e.g., int as opposed to int[] or int*), the parentheses can be dropped from the template argument list. Take the standard library template function std.conv.to as an example:

void main() {
import std.stdio : writeln;
import std.conv : to;
writeln(to!(int)("42"));
}

Run it online at run.dlang.io

std.conv.to is an eponymous template, so we can use the shortened instantiation syntax. But the fact that we’ve instantiated it as an argument to the writeln function means we’ve got three pairs of parentheses in close proximity. That sort of thing can impair readability if it pops up too often. We could move it out and store it in a variable if we really care about it, but since we’ve only got one template argument, this is a good place to drop the parentheses from the template argument list.

writeln(to!int("42"));

Whether that looks better is another case where it’s up to preference, but it’s fairly idiomatic these days to drop the parentheses for a single template argument no matter where the instantiation appears.

### Not done with the short stuff yet

std.conv.to is an interesting example because it’s an eponymous template with multiple members that share the template name. That means that it must be declared using the longform syntax (as you can see in the source code), but we can still instantiate it without the dot notation. It’s also interesting because, even though it accepts two template arguments, it is generally only instantiated with one. This is because the second template argument can be deduced by the compiler based on the function argument.

For a somewhat simpler example, take a look at std.utf.toUTF8:

void main()
{
import std.stdio : writeln;
import std.utf : toUTF8;
string s1 = toUTF8("This was a UTF-16 string."w);
string s2 = toUTF8("This was a UTF-32 string."d);
writeln(s1);
writeln(s2);
}

Run it online at run.dlang.io

Unlike std.conv.to, toUTF8 takes exactly one parameter. The signature of the declaration looks like this:

string toUTF8(S)(S s)

But in the example, we aren’t passing a type as a template argument. Just as the compiler was able to deduce to’s second argument, it’s able to deduce toUTF8’s sole argument.

toUTF8 is an eponymous function template with a template parameter S and a function parameter s of type S. There are two things we can say about this: 1) the return type is independent of the template parameter and 2) the template parameter is the type of the function parameter. Because of 2), the compiler has all the information it needs from the function call itself and has no need for the template argument in the instantiation.

Take the first call to the toUTF8 function in the declaration of s1. In long form, it would be toUTF8!(wstring)("blah"w). The w at the end of the string literal indicates it is of type wstring, with UTF-16 encoding, as opposed to string, with UTF-8 encoding (the default for string literals). In this situation, having to specify !(wstring) in the template instantiation is completely redundant. The compiler already knows that the argument to the function is a wstring. It doesn’t need the programmer to tell it that. So we can drop the template instantiation operator and the template argument list, leaving what looks like a simple function call. The compiler knows that toUTF8 is a template, knows that the template is declared with one type parameter, and knows that the type should be wstring.

Similarly, the d suffix on the literal in the initialization of s2 indicates a UTF-32 encoded dstring, and the compiler knows all it needs to know for that instantiation. So in this case also, we drop the template argument and make the instantiation appear as a function call.

It does seem silly to convert a wstring or dstring literal to a string when we could just drop the w and d prefixes and have string literals that we can directly assign to s1 and s2. Contrived examples and all that. But the syntax the examples are demonstrating really shines when we work with variables.

wstring ws = "This is a UTF-16 string"w;
string s = ws.toUTF8;
writeln(s);

Run it online at run.dlang.io

Take a close look at the initialization of s. This combines the shorthand template instantiation syntax with Uniform Function Call Syntax (UFCS) and D’s shorthand function call syntax. We’ve already seen the template syntax in this post. As for the other two:

• UFCS allows using dot notation on the first argument of any function call so that it appears as if a member function is being called. By itself, it doesn’t offer much benefit aside from, some believe, readability. Generally, it’s a matter of preference. But this feature can seriously simplify the implementation of generic templates that operate on aggregate types and built-in types.
• The parentheses in a function call can be dropped when the function takes no arguments, so that foo() becomes foo. In this case, the function takes one argument, but we’ve taken it out of the argument list using UFCS, so the argument list is now empty and the parentheses can be dropped. (The compiler will lower this to a normal function call, toUTF8(ws)—it’s purely programmer convenience.) When and whether to do this in the general case is a matter of preference. The big win, again, is in the simplification of template implementations: a template can be implemented to accept a type T with a member variable foo or a member function foo, or a free function foo for which the first argument is of type T.

All of this shorthand syntax is employed to great effect in D’s range API, which allows chained function calls on types that are completely hidden from the public-facing API (aka Voldemort types).

## More to come

In future articles, we’ll explore the different kinds of template parameters, introduce template constraints, see inside a template instantiation, and take a look at some of the ways people combine templates with D’s powerful compile-time features in the real world. In the meantime, here are some template tutorial resources to keep you busy:

• Ali Çehreli’s Programming in D is an excellent introduction to D in general, suitable even for those with little programming experience. The two chapters on templates (the first called ‘Templates’ and the second ‘More Templates’) provide a great introduction. (The online version of the book is free, but if you find it useful, please consider throwing Ali some love by buying the ebook or print version linked at the top of the TOC.)
• More experienced programmers may find Phillipe Sigaud’s ‘D Template Tutorial’ a good read. It’s almost a decade old, but still relevant (and still free!). This tutorial goes beyond the basics into some of the more advanced template features. It includes a look at D’s compile-time features, provides a number of examples, and sports an appendix detailing the is expression (a key component of template constraints). It can also serve as a great reference when reading open source D code until you learn your way around D templates.

There are other resources, though other than my book ‘Learning D’ (this is a referral link that will benefit the D Language Foundation), I’m not aware of any that provide as much detail as the above. (And my book is currently not freely available). Eventually, we’ll be able to add this blog series to the list.

# Interfacing D with C: Arrays and Functions (Arrays Part 2)

This post is part of an ongoing series on working with both D and C in the same project. The previous post explored the differences in array declaration and initialization. This post takes the next step: declaring and calling C functions that take arrays as parameters.

## Arrays and C function declarations

Using C libraries in D is extremely easy. Most of the time, things work exactly as one would expect, but as we saw in the previous article there can be subtle differences. When working with C functions that expect arrays, it’s necessary to fully understand these differences.

The most straightforward and common way of declaring a C function that accepts an array as a parameter is to to use a pointer in the parameter list. For example, this hypothetical C function:

void f0(int *arr);

In C, any array of int can be passed to this function no matter how it was declared. Given int a[], int b[3], or int *c, the function calls f0(a), f0(b), and f0(c) are all the same: a pointer to the first element of each array is passed to the function. Or using the lingo of C programmers, arrays decay to pointers

Typically, in a function like f0, the implementer will expect the array to have been terminated with a marker appropriate to the context. For example, strings in C are arrays of char that are terminated with the \0 character (we’ll look at D strings vs. C strings in a future post). This is necessary because, without that character, the implementation of f0 has no way to know which element in the array is the last one. Sometimes, a function is simply documented to expect a certain length, either in comments or in the function name, e.g., a vector3f_add(float *vec) will expect that vec points to exactly 3 elements. Another option is to require the length of the array as a separate argument:

void f1(int *arr, size_t len);

None of these approaches is foolproof. If f0 receives an array with no end marker or which is shorter than documented, or if f1 receives an array with an actual length shorter than len, then the door is open for memory corruption. D arrays take this possibility into account, making it much easier to avoid such problems. But again, even D’s safety features aren’t 100% foolproof when calling C functions from D.

There are other, less common, ways array parameters may be declared in C:

void f2(int arr[]);
void f3(int arr[9]);
void f4(int arr[static 9]);

Although these parameters are declared using C’s array syntax, they boil down to the exact same function signature as f0 because of the aforementioned pointer decay. The [9] in f3 triggers no special enforcement by the compiler; arr is still effectively a pointer to int with unknown length. The [9] serves as documentation of what the function expects, and the implementation cannot rely on the array having nine elements.

The only potential difference is in f4. The static added to the declaration tells the compiler that the function must take an array of, in this case, at least nine elements. It could have more than nine, but it can’t have fewer. That also rules out null pointers. The problem is, this isn’t necessarily enforced. Depending on which C compiler you use, if you shortchange the function and send it less than nine elements you might see warnings if they are enabled, but the compiler might not complain at all. (I haven’t tested current compilers for this article to see if any are actually reporting errors for this, or which ones provide warnings.)

The behavior of C compilers doesn’t matter from the D side. All we need be concerned with is declaring these functions appropriately so that we can call them from D such that there are no crashes or unexpected results. Because they are all effectively the same, we could declare them all in D like so:

extern(C):
void f0(int* arr);
void f1(int* arr, size_t len);
void f2(int* arr);
void f3(int* arr);
void f4(int* arr);

But just because we can do a thing doesn’t mean we should. Consider these alternative declarations of f2, f3, and f4:

extern(C):
void f2(int[] arr);
void f3(int[9] arr);
void f4(int[9] arr);

Are there any consequences of taking this approach? The answer is yes, but that doesn’t mean we should default to int* in each case. To understand why, we need first to explore the innards of D arrays.

## The anatomy of a D array

The previous article showed that D makes a distinction between dynamic and static arrays:

int[] a0;
int[9] a1;

a0 is a dynamic array and a1 is a static array. Both have the properties .ptr and .length. Both may be indexed using the same syntax. But there are some key differences between them.

### Dynamic arrays

Dynamic arrays are usually allocated on the heap (though that isn’t a requirement). In the above case, no memory for a0 has been allocated. It would need to be initialized with memory allocated via new or malloc, or some other allocator, or with an array literal. Because a0 is uninitialized, a0.ptr is null and a0.length is 0.

A dynamic array in D is an aggregate type that contains the two properties as members. Something like this:

struct DynamicArray {
size_t length;
size_t ptr;
}

In other words, a dynamic array is essentially a reference type, with the pointer/length pair serving as a handle that refers to the elements in the memory address contained in the ptr member. Every built-in D type has a .sizeof property, so if we take a0.sizeof, we’ll find it to be 8 on 32-bit systems, where size_t is a 4-byte uint, and 16 on 64-bit systems, where size_t is an 8-byte ulong. In short, it’s the size of the handle and not the cumulative size of the array elements.

### Static arrays

Static arrays are generally allocated on the stack. In the declaration of a1, stack space is allocated for nine int values, all of which are initialized to int.init (which is 0) by default. Because a1 is initialized, a1.ptr points to the allocated space and a1.length is 9. Although these two properties are the same as those of the dynamic array, the implementation details differ.

A static array is a value type, with the value being all of its elements. So given the declaration of a1 above, its nine int elements indicate that a1.sizeof is 9 * int.sizeof, or 36. The .length property is a compile-time constant that never changes, and the .ptr property, though not readable at compile time, is also a constant that never changes (it’s not even an lvalue, which means it’s impossible to make it point somewhere else).

These implementation details are why we must pay attention when we cut and paste C array declarations into D source modules.

## Passing D arrays to C

Let’s go back to the declaration of f2 in C and give it an implementation:

void f2(int arr[]) {
for(int i=0; i<3; ++i)
printf("%d\n", arr[i]);
}

A naïve declaration in D:

extern(C) void f2(int[]);

void main() {
int[] a = [10, 20, 30];
f2(a);
}

I say naïve because this is never the right answer. Compiling f2.c with df2.d on Windows (cl /c f2.c in the “x64 Native Tools” command prompt for Visual Studio, followed by dmd -m64 df2.d f2.obj), then running df2.exe, shows me the following output:

3
0
1970470928

There is no compiler error because the declaration of f2 is pefectly valid D. The extern(C) indicates that this function uses the cdecl calling convention. Calling conventions affect the way arguments are passed to functions and how the function’s symbol is mangled. In this case, the symbol will be either _f2 or f2 (other calling conventions, like stdcallextern(Windows) in D—have different mangling schemes). The declaration still has to be valid D. (In fact, any D function can be marked as extern(C), something which is necessary when creating a D library that will be called from other languages.)

There is also no linker error. DMD is calling out to the system linker (in this case, Microsoft’s link.exe), the same linker used by the system’s C and C++ compilers. That means the linker has no special knowledge about D functions. All it knows is that there is a call to a symbol, f2 or _f2, that needs to be linked with the implementation. Since the type and number of parameters are not mangled into the symbol name, the linker will happily link with any matching symbol it finds (which, by the way, is the same thing it would do if a C program tried to call a C function which was declared with an incorrect parameter list).

The C function is expecting a single pointer as an argument, but it’s instead receiving two values: the array length followed by the array pointer.

The moral of this story is that any C function with array parameters declared using array syntax, like int[], should be declared to accept pointers in D. Change the D source to the following and recompile using the same command line as before (there’s no need to recompile the C file):

extern(C) void f2(int*);

void main() {
int[] a = [10, 20, 30];
f2(a.ptr);
}

Note the use of a.ptr. It’s an error to try to pass a D array argument where a pointer is expected (with one very special exception, string literals, which I’ll cover in the next article in this series), so the array’s .ptr property must be used instead.

The story for f3 and f4 is similar:

void f3(int arr[9]);
void f4(int arr[static 9]);

Remember, int[9] in D is a static array, not a dynamic array. The following do not match the C declarations:

void f3(int[9]);
void f4(int[9]);

Try it yourself. The C implementation:

void f3(int arr[9]) {
for(int i=0; i<9; ++i)
printf("%d\n", arr[i]);
}

And the D implementation:

extern(C) void f3(int[9]);

void main() {
int[9] a = [10, 20, 30, 40, 50, 60, 70, 80, 90];
f3(a);
}

This is likely to crash, depending on the system. Rather than passing a pointer to the array, this code is instead passing all nine array elements by value! Now consider a C library that does something like this:

typedef float[16] mat4f;
void do_stuff(mat4f mat);

Generally, when writing D bindings to C libraries, it’s a good idea to keep the same interface as the C library. But if the above is translated like the following in D:

alias mat4f = float[16];
extern(C) void do_stuff(mat4f);

The sixteen floats will be passed to do_stuff every time it’s called. The same for all functions that take a mat4f parameter. One solution is just to do the same as in the int[] case and declare the function to take a pointer. However, that’s no better than C, as it allows the function to be called with an array that has fewer elements than expected. We can’t do anything about that in the int[] case, but that will usually be accompanied by a length parameter on the C side anyway. C functions that take typedef’d types like mat4f usually don’t have a length parameter and rely on the caller to get it right.

In D, we can do better:

void do_stuff(ref mat4f);

Not only does this match the API implementor’s intent, the compiler will guarantee that any arrays passed to do_stuff are static float arrays with 16 elements. Since a ref parameter is just a pointer under the hood, all is as it should be on the C side.

With that, we can rewrite the f3 example:

extern(C) void f3(ref int[9]);

void main() {
int[9] a = [10, 20, 30, 40, 50, 60, 70, 80, 90];
f3(a);
}

### Conclusion

Most of the time, when interfacing with C from D, the C API declarations and any example code can be copied verbatim in D. But most of the time is not all of the time, so care must be taken to account for those exceptional cases. As we saw in the previous article, carelessness when declaring array variables can usually be caught by the compiler. As this article shows, the same is not the case for C function declarations. Interfacing D with C requires the same care as when writing C code.

In the next article in this series, we’ll look at mixing D strings and C strings in the same program and some of the pitfalls that may arise. In the meantime, Steven Schveighoffer’s excellent article, “D Slices”, is a great place to start for more details about D arrays.

Thanks to Walter Bright and Átila Neves for their valuable feedback on this article.

# Tracing D Applications

At one time or another during application development you need to make a decision: does your application work like it should and, if not, what is wrong with it? There are different techniques to help you decide, some of which are logging, tracing, and profiling. How are they different? One way to look at it is like this:

• when you know exactly the events you are interested in to make the decision, you use logging
• when you don’t know exactly the events you need to make a decision and you are forced to collect as many events as you can, you use tracing
• when you need to collect some events and analyze them to derive new information, you use profiling

When developing an application, you can use tracing to monitor its characteristics at run time to, for example, estimate its performance or memory consumption. There are several options to do so, and some of them are:

• means provided by the programming language (for example, using D’s writef, a.k.a. printf debugging)
• debuggers (using scripts or remote tracing)
• OS-specific tracing frameworks (linux {k|u}probes and usdt probes, linux kernel event, performance events in windows etc)

In this article, the following contrived D example is used to help illustrate all three cases. We’ll be focusing on Linux. All example code in this article can be found in the GitHub repository at https://github.com/drug007/tracing_post.

foreach(counter; 0..total_cycles)
{
// randomly generate one of three kinds of event
Event event = cast(Event) uniform(0, 3);

// "perform" the job and benchmark its CPU execution time
switch (event)
{
case Event.One:

doSomeWork;

break;
case Event.Two:

doSomeWork;

break;
case Event.Three:

doSomeWork;

break;
default:
assert(0);
}
}

doSomeWork simulates a CPU-intensive job by using DRuntime’s Thread.sleep method. This is a very common pattern where an application runs in cycles and, on every iteration, performs a job depending on the application state. Here we can see that the application has three code paths (CaseOne, CaseTwo, and CaseThree). We need to trace the application at run time and collect information about its timings.

## The writef-Based Approach

Using writef/ln from Phobos, D’s standard library, to trace the application is naive, but can be very useful nevertheless. The code from tracing_writef.d:

    case Event.One:
auto sw = StopWatch(AutoStart.no);
sw.start();

doSomeWork;

sw.stop();
writefln("%d:\tEvent %s took: %s", counter, event, sw.peek);
break;


With this trivial approach, StopWatch from the standard library is used to measure the execution time of the code block of interest. Compile and run the application with the command dub tracing_writef.d and look at its output:

Running ./example-writef
0:      Event One took:   584 ms, 53 μs, and 5 hnsecs
1:      Event One took:   922 ms, 72 μs, and 6 hnsecs
2:      Event Two took:   1 sec, 191 ms, 73 μs, and 8 hnsecs
3:      Event Two took:   974 ms, 73 μs, and 7 hnsecs
...

There is a price for this—we need to compile tracing code into our binary, we need to manually implement the collection of tracing output, disable it when we need to, and so on—and this means the size of the binary increases. To summarize:

Pros

• all the might of Phobos is available to employ (except when in BetterC mode)
• tracing output can be displayed in a human readable format (look at the nice output of Duration above; thanks to Jonathan M. Davis for the std.datetime package)
• source code is portable
• easy to use
• no third-party tools required

Cons

• the application must be rebuilt and restarted in order to make any changes, which is inappropriate for some applications (such as servers)
• noise in the code due to the addition of tracing code
• can be unusable due to a lot of debug output
• can be hard to use in production

This approach is very suitable in the early stages of development and less useful in the final product. Although, if the tracing logic is fixed and well defined, this approach can be used in production-ready applications/libraries. For example, this approach was suggest by Stefan Koch for tracing the DMD frontend to profile performance and memory consumption.

## Debugger-Based Approach

The debugger, in this case GDB, is a more advanced means to trace applications. There is no need to modify the application to change the tracing methodology, making it very useful in production. Instead of compiling tracing logic into the application, breakpoints are set. When the debugger stops execution on a breakpoint, the developer can use the large arsenal of GDB functionality to inspect the internal state of the inferior (which, in GDB terms, usually refers to the process being debugged). It is not possible in this case to use Phobos directly, but helpers are available and, moreover, you have access to registers and the stack—options which are unavailable in the case of writef debugging.

Let’s take a look the code from tracing_gdb.d for the first event:

    case Case.One:

doSomeWork;

break;

As you can see, now there is no tracing code and it is much cleaner. The tracing logic is placed in a separate file called trace.gdb. It consists of a series of command blocks configured to execute on specific breakpoints, like this:

set pagination off

break app.d:53
commands
set $EventOne = currClock() continue end break app.d:54 commands set$EventOne = currClock() - $EventOne printf "%d:\tEvent One took: %s\n", counter, printClock($EventOne)
continue
end

...

run
quit

In the first line, pagination is switched off. This enables scrolling so that there is no need to press “Enter” or “Q” to continue script execution when the current console fills up. The second line disables showing the address of the current breakpoint in order to make the output less verbose. Then breakpoints are set on lines 53 and 54, each followed by a list of commands (between the commands and end labels) that will be executed when GDB stops on these breakpoints. The breakpoint on line 53 is configured to fetch the current timestamp (using a helper) before doSomeWork is called, and the one on line 54 to get the current timestamp after doSomeWork has been executed. In fact, line 54 is an empty line in the source code, but GDB is smart enough to set the breakpoint on the next available line. $EventOne is a convenience variable where we store the timestamps to calculate code execution time. currClock() and printClock(long) are helpers to let us prettify the formatting by means of Phobos. The last two commands in the script initiate the debugging and quit the debugger when it’s finished. To build and run this tracing session, use the following commands: dub build tracing_gdb.d --single gdb --command=trace.gdb ./tracing-gdb | grep Event trace.gdb is the name of the GDB script and tracing-gdb is the name of the binary. We use grep to make the GDB output look like writefln output for easier comparison. Pros • the code is clean and contains no tracing code • there is no need to recompile the application to change the tracing methodology—in many cases, it’s enough to simply change the GDB script • there is no need to restart the application • it can be used in production (sort of) • there is no overhead if/when not tracing and little when tracing • watchpoints and catchpoints can be used instead of breakpoints Cons • using breakpoints in some cases may be inconvenient, annoying, or impossible. • GDB’s pretty-printing provides “less pretty” output because of the lack of full Phobos support compared to the writef approach • sometimes GDB is not available in production The point about setting breakpoints in GDB being inconvenient is based on the fact that you can use only an address, a line number, or a function name (see the gdb manual). Using an address is too low level and inconvenient. Line numbers are ephemeral and can easily change when the source file is edited, so the scripts will be broken (this is annoying). Using function names is convenient and stable, but is useless if you need to place a tracing probe inside a function. A good example of using GDB for tracing is Vladimir Panteleev’s dmdprof. ## The USDT-Based Approach So far we have two ways to trace our application that are complimentary, but is there a way to unify all the advantages of these two approaches and avoid their drawbacks? Of course, the answer is yes. In fact there are several ways to achieve this, but hereafter only one will be discussed: USDT (Userland Statically Defined Tracing). Unfortunately, due to historical reasons, the Linux tracing ecosystem is fragmented and rather confusing. There is no plain and simple introduction. Get ready to invest much more time if you want to understand this domain. The first well-known, full-fledged tracing framework was DTrace, developed by Sun Microsystems (now it is open source and licensed under the GPL). Yes, strace and ltrace have been around for a long time, but they are limited, e.g., they do not let you trace what happens inside a function call. Today, DTrace is available on Solaris, FreeBSD, macOS, and Oracle Linux. DTrace is not available in other Linux distributions because it was initially licensed under the CDDL. In 2018, it was relicensed under the GPL, but by then Linux had its own tracing ecosystem. As with everything, Open Source has disadvantages. In this case, it resulted in fragmentation. There are now several tools/frameworks/etc. that are able to solve the same problems in different ways but somehow and sometimes can interoperate with each other. We will be using bpftrace, a high level tracing language for Linux eBPF. In D, USDT probes are provided by the usdt library. Let’s start from the code in tracing_usdt.d:  case Case.One: mixin(USDT_PROBE!("dlang", "CaseOne", kind)); doSomeWork; mixin(USDT_PROBE!("dlang", "CaseOne_return", kind)); break; Here we mixed in two probes at the start and the end of the code of interest. It looks similar to the first example using writef, but a huge difference is that there is no logic here. We only defined two probes that are NOP instructions. That means that these probes have almost zero overhead and we can use them in production. The second great advantage is that we can change the logic while the application is running. That is just impossible when using the writef approach. Since we are using bpftrace, we need to write a script, called bpftrace.bt, to define actions that should be performed on the probes: usdt:./tracing-usdt:dlang:CaseOne { @last["CaseOne"] = nsecs; } usdt:./tracing-usdt:dlang:CaseOne_return { if (@last["CaseOne"] != 0) {$tmp = nsecs;
$period = ($tmp - @last["CaseOne"]) / 1000000;
printf("%d:\tEvent CaseOne   took: %d ms\n", @counter++, $period); @last["CaseOne"] =$tmp;
@timing = hist(\$period);
}
}
...

The first statement is the action block. It triggers on the USDT probe that is compiled in the ./tracing-usdt executable (it includes the path to the executable) with the dlang provider name and the CaseOne probe name. When this probe is hit, then the global (indicated by the @ sign) associative array last updates the current timestamp for its element CaseOne. This stores the time of the moment the code starts running. The second action block defines actions performed when the CaseOne_return probe is hit. It first checks if corresponding element in the @last associative array is already initialized. This is needed because the application may already be running when the script is executed, in which case the CaseOne_return probe can be fired before CaseOne. Then we calculate how much time code execution took, output it, and store it in a histogram called timing.

The BEGIN and END blocks at the top of bpftrace.bt define actions that should be performed at the beginning and the end of script execution. This is nothing more than initialization and finalization. Build and run the example with:

dub build tracing_usdt.d   --single --compiler=ldmd2 # or gdc
./tracing-usdt &                                     # run the example in background
sudo bpftrace bpftrace.bt                            # start tracing session

Output:

Attaching 8 probes...
0:	Event CaseThree took: 552 ms
1:	Event CaseThree took: 779 ms
2:	Event CaseTwo   took: 958 ms
3:	Event CaseOne   took: 1174 ms
4:	Event CaseOne   took: 1059 ms
5:	Event CaseThree took: 481 ms
6:	Event CaseTwo   took: 1044 ms
7:	Event CaseThree took: 611 ms
8:	Event CaseOne   took: 545 ms
9:	Event CaseTwo   took: 1038 ms
10:	Event CaseOne   took: 913 ms
11:	Event CaseThree took: 989 ms
12:	Event CaseOne   took: 1149 ms
13:	Event CaseThree took: 541 ms
14:	Event CaseTwo   took: 1072 ms
15:	Event CaseOne   took: 633 ms
16:	Event CaseTwo   took: 832 ms
17:	Event CaseTwo   took: 1120 ms
^C

@timing:
[256, 512)             1 |@@@@@                                               |
[512, 1K)             10 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@|
[1K, 2K)               7 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@                |

In the session output above there are only 18 lines instead of 20; it’s because tracing-usdt was started before the bpftrace script so the first two events were lost. Also, it’s necessary to kill the example by typing Ctrl-C after tracing-usdt completes. After the bpftrace script stops execution, it ouputs the contents of the timing map as a histogram. The histogram says that one-time code execution takes between 256 and 512 ms, ten times between 512 and 1024 ms, and seven times more between 1024 and 2048 ms. These builtin statistics make using bpftrace easy.

Pros

• minimal noise in the code
• no need to recompile or restart when changing the tracing logic
• can be effectively used in production

Cons

• learning USDT can be hard, particularly considering the state of the Linux tracing ecosystem
• requires external tools (frontends)
• OS specific

Note: GDB has had support for USDT probes since version 7.5. To use it, modify the trace.gdb script to set breakpoints using USDT probes instead of line numbers. That eases development because it eliminates the need to synchronize line numbers during source code modification.

## Conclusion

Feature writef gdb usdt
pretty
printing
by means of Phobos
and other libs
by means of
pretty-printing
limited builtins
low-level no yes yes
clean code no yes sort of
recompilation yes no no
restart yes no no
usage
complexity
third-party
tools
no only debugger tracing system front end
cross platform yes sorta of OS specific
overhead can be large none can be ignored
even in production
production ready sometimes possible sometimes impossible yes

Feature descriptions:

• pretty printing is important if the tracing output should be read by humans (and can be ignored in the case of inter-machine data exchange)
• low-level means access to low-level details of the traced binary, e.g., registers or memory
• clean code characterizes whether additional tracing code which is unrelated to the applications’s business logic would be required.
• recompilation determines if it is necessary to recompile when changing the tracing methodology
• restart determines if it is necessary to restart the application when changing the tracing methodology
• usage complexity indicates the level of development experience that may be required to utilize this technology
• third-party tools describes tools not provided by standard D language distributions are required to use this technology
• cross platform indicates if this technology can be used on different platforms without changes
• overhead – the cost of using this technology
• production ready – indicates if this technology may be used in a production system without consequences

# wc in D: 712 Characters Without a Single Branch

After reading “Beating C With 80 Lines Of Haskell: Wc”, which I found on Hacker News, I thought D could do better. So I wrote a wc in D.

## The Program

It consists of one file and has 34 lines and 712 characters.

import std.stdio : writefln, File;
import std.algorithm : map, fold, splitter;
import std.range : walkLength;
import std.typecons : Yes;
import std.uni : byCodePoint;

struct Line {
size_t chars;
size_t words;
}

struct Output {
size_t lines;
size_t words;
size_t chars;
}

Output combine(Output a, Line b) pure nothrow {
return Output(a.lines + 1, a.words + b.words, a.chars + b.chars);
}

Line toLine(char[] l) pure {
return Line(l.byCodePoint.walkLength, l.splitter.walkLength);
}

void main(string[] args) {
auto f = File(args[1]);
Output o = f
.byLine(Yes.keepTerminator)
.map!(l => toLine(l))
.fold!(combine)(Output(0, 0, 0));

writefln!"%u %u %u %s"(o.lines, o.words, o.chars, args[1]);
}

Sure, it is using Phobos, D’s standard library, but then why wouldn’t it? Phobos is awesome and ships with every D compiler. The program itself does not contain a single if statement. The Haskell wc implementation has several if statements. The D program, apart from the main function, contains three tiny functions. I could have easily put all the functionally in one range chain, but then it probably would have exceeded 80 characters per line. That’s a major code-smell.

## The Performance

Is the D wc faster than the coreutils wc? No, but it took me 15 minutes to write mine (I had to search for walkLength, because I forgot its name).

file lines bytes coreutils haskell D
app.d 46 906 3.5 ms +- 1.9 ms 39.6 ms +- 7.8 ms 8.9 ms +- 2.1 ms
big.txt 862 64k 4.7 ms +- 2.0 ms 39.6 ms +- 7.8 ms 9.8 ms +- 2.1 ms
vbig.txt 1.7M 96M 658.6ms +- 24.5ms 226.4 ms +- 29.5 ms 1.102 s +- 0.022 s
vbig2.txt 12.1M 671M 4.4 s +- 0.058 s 1.1 s +- 0.039 s 7.4 s +- 0.085 s

Memory:

app.d 2052K 7228K 7708K
big.txt 2112K 7512K 7616K
vbig.txt 2288K 42620K 7712K
vbig2.txt 2360K 50860K 7736K

Is the Haskell wc faster? For big files, absolutely, but then it is using threads. For small files, GNU’s coreutils still beats the competition. At this stage my version is very likely IO bound, and it’s fast enough anyway.

I’ll not claim that one language is faster than another. If you spend a chunk of time on optimizing a micro-benchmark, you are likely going to beat the competition. That’s not real life. But I will claim that functional programming in D gives functional programming in Haskell a run for its money.

A range is an abstraction that you can consume through iteration without consuming the underlying collection (if there is one). Technically, a range can be a struct or a class that adheres to one of a handful of Range interfaces. The most basic form, the InputRange, requires the function

void popFront();

and two members or properties:

T front;
bool empty;

T is the generic type of the elements the range iterates.

In D, ranges are special in a way that other objects are not. When a range is given to a foreach statement, the compiler does a little rewrite.

foreach (e; range) { ... }

is rewritten to

for (auto __r = range; !__r.empty; __r.popFront()) {
auto e = __r.front;
...
}

auto e = infers the type and is equivalent to T e =.

Given this knowledge, building a range that can be used by foreach is easy.

struct Iota {
int front;
int end;

@property bool empty() const {
return this.front == this.end;
}

void popFront() {
++this.front;
}
}

unittest {
import std.stdio;
foreach(it; Iota(0, 10)) {
writeln(it);
}
}

Iota is a very simple range. It functions as a generator, having no underlying collection. It iterates integers from front to end in steps of one. This snippet introduces a little bit of D syntax.

@property bool empty() const {

The @property attribute allows us to use the function empty the same way as a member variable (calling the function without the parenthesis). The trailing const means that we don’t modify any data of the instance we call empty on. The built-in unit test prints the numbers 0 to 10.

Another small feature is the lack of an explicit constructor. The struct Iota has two member variables of type int. In the foreach statement in the test, we create an Iota instance as if it had a constructor that takes two ints. This is a struct literal. When the D compiler sees this, and the struct has no matching constructor, the ints will be assigned to the struct’s member variables from top to bottom in the order of declaration.

The relation between the three members is really simple. If empty is false, front is guaranteed to return a different element, the next one in the iteration, after a call to popFront. After calling popFront the value of empty might have changed. If it is true, this means there are no more elements to iterate and any further calls to front are not valid. According to the InputRange documentation:

• front can be legally evaluated if and only if evaluating empty has, or would have, equaled false.
• front can be evaluated multiple times without calling popFront or otherwise mutating the range object or the underlying data, and it yields the same result for every evaluation.

Now, using foreach statements, or loops in general, is not really functional in my book. Lets say we want to filter all uneven numbers of the Iota range. We could put an if inside the foreach block, but that would only make it worse. It would be nicer if we had a range that takes a range and a predicate that can decide if an element is okay to pass along or not.

struct Filter {
Iota input;
bool function(int) predicate;

this(Iota input, bool function(int) predicate) {
this.input = input;
this.predicate = predicate;
this.testAndIterate();
}

void testAndIterate() {
while(!this.input.empty
&& !this.predicate(this.input.front))
{
this.input.popFront();
}
}

void popFront() {
this.input.popFront();
this.testAndIterate();
}

@property int front() {
return this.input.front;
}

@property bool empty() const {
return this.input.empty;
}
}

bool isEven(int a) {
return a % 2 == 0;
}

unittest {
foreach(it; Filter(Iota(0,10), &isEven)) {
writeln(it);
}
}

Filter is again really simple: it takes one Iota and a function pointer. On construction of Filter, we call testAndIterate, which pops elements from Iota until it is either empty or the predicate returns false. The idea is that the passed predicate decides what to filter out and what to keep. The properties front and empty just forward to Iota. The only thing that actually does any work is popFront. It pops the current element and calls testAndIterate. That’s it. That’s an implementation of filter.

Sure, there is a new while loop in testAndIterate, but rewriting that with recursion is just silly, in my opinion. What makes D great is that you can use the right tool for the job. Functional programming is fine and dandy a lot of the time, but sometimes it’s not. If a bit of inline assembly would be necessary or nicer, use that.

The call to Filter still does not look very nice. Assuming, you are used to reading from left to right, Filter comes before Iota, even though it is executed after Iota. D has no pipe operator, but it does have Uniform Function Call Syntax (UFCS). If an expression can be implicitly converted to the first parameter of a function, the function can be called like it is a member function of the type of the expression. That’s a lot of words, I know. An example helps:

string foo(string a) {
return a ~ "World";
}

unittest {
string a = foo("Hello ");
string b = "Hello ".foo();
assert(a == b);
}

The above example shows two calls to the function foo. As the assert indicates, both calls are equivalent. What does that mean for our Iota Filter example? UFCS allows us to rewrite the unit test to:

unittest {
foreach(it; Iota(1,10).Filter(&isEven)) {
writeln(it);
}
}

Implementing a map/transform range should now be possible for every reader. Sure, Filter can be made more abstract through the use of templates, but that’s just work, nothing conceptually new.

Of course, there are different kinds of ranges, like a bidirectional range. Guess what that allows you to do. A small tip: a bidirectional range has two new primitives called back and popBack. There are other range types as well, but after you understand the input range demonstrated twice above, you pretty much know them all.

P.S. Just to be clear, do not implement your own filter, map, or fold; the D standard library Phobos has everything you every need. Have a look at std.algorithm and std.range`.