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!

How I Taught the D Programming Language at a Russian University

This article was originally published in Russian by Grigorii Smorkalov. It was translated to English for the D Blog by Georgy Markov and lightly revised from the original by Michael Parker.

This is the fourth year I’m teaching my D Programming Language course at a very real university in Russia. It’s a full-term course with lectures, practical lessons, and exams, although it’s all remote now. This is the story about how I got there, the challenges I encountered, and how students sometimes surpass their teachers.

What’s in D for university students

The job market for D is very small. As I always say during the first lesson, it’s unlikely that students are going to write D code for a salary, but that doesn’t mean that learning D is useless. Firstly, it’s much easier to learn how to program with D than with C or C++. This is important because many students don’t know how to program even after a full C/C++ course. Secondly, a broader outlook makes for better code. My familiarity with D improved my C++ skills and made it much easier to learn Python, especially its iterators. Most importantly, D is the future—of C++ and beyond. Many C++11/17/20 novelties were first battle-tested in D, and even today D is a much more modern and feature-rich language than C++.

Who am I and what’s in D for me

For simplicity, I would say that I am a C++ programmer. This is my main line of work. Before this course, I’d never been a teacher of any sort. Even on my job, I’m not involved much in mentoring. After earning my master’s degree in Computational Mathematics and Cybernetics at UNN (Lobachevsky State University of Nizhny Novgorod) in 2014, I had no relation to academics at all.

In my first months of university, I entertained the thought of being a school teacher. Now I realize that this was just a call for justice of sorts. There is a stark difference between a high school and a university, and for me, the latter was a much better experience. It felt like in just one month at university I learned more than in one term in high school, and that was so cool. Why couldn’t they explain it this way back in high school, was my common thought. If only educators applied the same educational methods in a regular school, it would make the experience much better. My desire to become a teacher vanished the second I received my first paycheck as a programmer (in Russia, ‘programmer’ is a very well-paid job and ‘school teacher’ is the total opposite), but some memory of that desire remained.

This was also the time when I became interested in D. Compared to C++ it looked like a perfect programming language. You can write code that would be as fast, but without all those C atavisms. I used D for my master’s thesis, and I loved it. My program was twice as small and simple as the older C++ version while performing better. Implementing complex and more efficient algorithms in D was much easier; doing the same in C++ would be too much work, and, like any student, I always struggled with my deadlines.

Since then I’ve followed D and used it for my little pet projects. I’ve always wanted to help the D community in some way, but I couldn’t write any useful library, nor could I find the motivation for contributing to open source.

Beginnings

In 2018, an unusual offer surfaced on the D mailing list: does anyone by any chance want to teach students in Moscow, Russia?

The initiator was Dmitry Olshansky, a well-known member of the D community, the creator of std.regex and more. I contacted him and said I was interested. I didn’t see myself as a full-fledged lecturer and expected to be just an assistant. At the beginning that was the plan, with someone else acting as a lecturer. He was going to give lectures remotely via Skype, and I would assist him on site.

To my surprise, the university in question was RSUH, Russian State University for the Humanities. As it turned out, they do have technical faculty there, and the students do actually code. I checked their program: D was introduced for third-term students, and during the first two terms they learned C, C++, Prolog, and even some Lisp, I think (a bit too much, but why not). Their math course was solid, too (yes, I am among those who think that math is important for programmers).

Preparations

I was introduced to the department staff. They explained everyone’s responsibilities and even offered the opportunity to join in scientific work as a programmer. We started working on the course program, although I barely included myself in that “we”. That was a mistake. With one month left until the classes started, the lecturer was suddenly leaving us. The news took me by surprise, but… there was still plenty of time, right?

This was happening when it was time for me to complete all the formalities and start work. Though I knew they were hiring only me, for some reason I was still under the impression that I wouldn’t be alone. I can’t say why I thought so. Everything was saying that there would be no help and that I had to do the whole course by myself, but my impression was hard to shake. The grave realization only came one week before Day One. Only then did I start to prepare for real.

Bureaucracy

This is supposed to be the part about the trials and tribulations of the endless bureaucracy awaiting a poor programmer’s soul. The amount of paperwork required to sign the contract was indeed an entertaining story to tell to my peers. And everyone was, as they say, “rolling on the floor laughing” when I described applying for a Mir payroll card (Mir is the Russian national payment system mandated in the state-funded sector). But that’s it, actually.

The next bureaucratic task was composing the formal program, an official paper including the course program and the materials. There were indeed a lot of formalities there, but I got some help: they showed me the paper for a very similar course. At the end of the day, it was easier than I expected. For this, I should thank the university staff. It was a one-year contract. Renewing it every year only takes one piece of paper and a couple of pen strokes.

The first class

The schedule was set up such that my first class happened to be a seminar (a.k.a. a practical lesson) instead of a lecture. This is only on paper, though with only one group consisting of just 14 people it didn’t make any difference. The schedule was adjusted later. I got two classes in a row and could decide which was a lecture and which was a seminar. I came up with the following arrangement: in the first class, I would lecture and answer general questions, and in the second, the students would program and ask practical questions.

For the first lesson, I prepared a brief description of the language, a syntax overview, a compiler, and several problems to solve: calculating the area of a triangle, solving a quadratic equation, and similar problems that yield a simple output for simple input. The idea was to immerse students in the language and immediately give them something to do with it. The plan was a success. By the end of the class, most of the students had gotten the hang of using a command-line compiler, written some code in a text editor, and solved some problems.

I bet many people would take issue with the command line and text editor part. Seriously? No IDE? IDEs for D exist, but I left it up to the students if they wanted to use them. The reason was my own experience in learning C++ and programming in general. Knowing how a compiler works and how to link several files into a project is integral to understanding the language as a whole.

This is especially true for C and C++. Things like the difference between declaration and definition lose their meaning without understanding the build process. In D, such nuances are fewer. For example, header files are not required. Still, I meant to teach how to program in D, not how to use an IDE. Things like the principles of import are easier to grok when using a command-line compiler rather than some “intuitive interface”. And you learn the syntax faster if you write the code by hand without autocomplete doing it for you.

Further development

Lifted by the success of the first seminar, I was slammed to the ground by the first lecture. It contained the full theoretical explanation of type systems and their various types, compared D with other languages, brought out the problems of C and C++, and demonstrated what makes D different. It was a total failure.

I expected the lecture to take the whole 80-minute class, including questions, but it finished in less than an hour without a single question during or after the speech. I even asked if the students couldn’t hear or understand me, or if there was a problem with my diction. But the problem was with the lecture itself. First, it was too much for one class. Second, the lecture was based on the talks I did for my job that were intended for seasoned programmers. I realized that everything that I’d prepared for my future lectures must be tossed aside and rewritten from scratch.

The new program

The first candidate for simplification, and by that I mean expunging, was metaprogramming. Getting rid of templates was impossible since even the most basic language features and algorithms are tied to them, but code generation of all sorts was the first to be removed. Following that was anything that required external libraries. What was left were things that D code can’t be written without.

Since I had better success with live communication during actual programming, I decided to focus on that. Rewriting the course on the fly was tricky, but I came up with a solid plan: throughout the semester we would write a complex calculating program, during lectures we would study language features required for a given task, and then during seminars the ideas would be transformed into code.

The semester assignment

If you’re a nerd like me, you’ve probably heard of the 10,958 problem. The gist of it is that you need to put signs and parentheses between numbers composed of the digits 1 to 9 so that the result would be exactly 10,958. The solution exists for any number up to 11,000, except 10,958. There’s no proof that it doesn’t exist either, hence the 10,958 problem.

I gave the students an assignment to write a program that would find the solution, brute-forcing any possible combination of signs and parenthesis. All calculations were done with double instead of some sort of bignum, so it’s not a real solution, but it’s simpler this way.

I had several reasons to think that this was a well-fitting problem. First, I could write a solution I expected to see from the students in five hours or two evenings. Compensating for the students’ level, it looked like a good project for a semester-long assignment. Second, the solution isn’t too straightforward. Simple brute force would take too much time so you need to cut off the equal variants in the beginning. And third, I was fascinated by this problem myself, so I thought the students would feel the same. I couldn’t be more wrong.

Not only were a majority of students not into such mathematics, most of them couldn’t even grasp what the problem was about and what this 10,958 was for. I failed to get them interested.

When I realized the problem, it was too late to change anything. So the first semester wasn’t very engaging. Only a few students could finish the assignment to its fullest. For the rest, it was impossible.

The second semester

Since it was a full-term course, I had time to make up for it. For the second semester, I tried to come up with something practical and interactive. I gave the students an assignment to write a game. They were learning networking, so it was to be a multiplayer game. I recalled playing “tic-tac-toe on an infinite field” with my peers during boring lectures. The smarter name for this game is Gomoku. Two players can play online, and the game logic is simple. I was hoping to spark students’ interest. This assignment turned out much better than the previous one, but I still can’t call it a full success.

I really wanted to show them how coroutines (fibers, as they are called in D) make async programming much more manageable, how nice the vibe.d framework is, and how easy it is to use external libraries with the dub package manager. Lesson One: don’t try to sell coroutines to those who don’t know what callback hell is. Lesson Two: always keep hardware limitations in mind.

Problems out of nowhere

I would never have thought that single-threaded compilation could be thwarted by the memory limit. It happened because the computers at the university were equipped with only 2 GB of memory, and some students’ netbooks worked on a mere 1 GB. Simple programs build just fine even on weak machines—D is much better than C++ in terms of compilation time—but a large framework like vibe.d requires a lot of memory for its compilation. Now imagine, I was just telling them how everything is so easy-peasy and then half the students couldn’t even compile a networked version of Hello World.

In my defense, I checked everything in advance. I set up a virtual machine with 2 GB of memory and made sure that it worked with the special compiler flags. Theoretically, I was prepared. But dealing with a new library and a new build system and new compiler flags all at once was just too much for the students. Their brains were getting DoS’d and shut down. So even though I demonstrated how to compile a program on a low-memory machine, I still had to explain to them individually why the usual method of compilation wasn’t working. For those who had only 1 GB of memory, I didn’t even have a ready solution.

But still, the results of the second semester were much better: absolutely everyone could write the client side of the game. Some had problems with coroutines on the server side, but in general, they got this part just fine. For me, that wasn’t enough. So I gave them a supertask: write a simple AI for the game. This would help them understand the advantages of the client-server architecture. They had to realize that an AI is just another client, so the server code should be left as it is, and on the client side, the only required modification was move polling. This was a good problem on architecture design, suitable for students who already had gotten a grip on programming.

We also had a little contest. I invited the students to play some code golf, solving a simple problem with the smallest program possible. For encouragement, I promised to free the best-achieving students from writing a semester report. And if anyone could beat my solution, they would pass the semester examination automatically (in Russian universities, teachers are allowed to set arbitrary conditions, like participating in side projects, for passing the semester without the usual examination).

As I expected, nobody could beat me, though a couple of students came up with some interesting tricks, like writing 10 instead of "\0" to save two symbols. Some would say this is an abuse of the type system, but clever hacks like this speak to a student’s knowledge.

The results of the first year

I was asking too much for a single semester; only one student could write an AI that kind of worked (she said that the first time it made a sensible move, it made her really happy). Another student who complained the whole year that programming was not her thing and that she couldn’t understand anything managed to write her own client and server. At the end of the day, all was not for nothing.

The second try

Everything described until this point happened during the 2018/2019 academic year. The higher-ups had no issues with the course, and I was able to continue on the following year. New students, new possibilities, a new program. This time I was much better prepared. I had materials for most of the lectures, no more need to redo everything on the fly, and during the summer I had time to fix the problems with the course.

The 10,958 problem was expelled on the charge of being boring. Instead, the Gomoku game was expanded, now lasting almost the whole term. “Almost” because the first assignment was a problem regarding OOP: students had to implement various geometrical shapes as classes and draw them on screen with some customization. I made this the first step after Hello World so everyone could learn how to use the tools, and so I could judge their level.

It’s not surprising that students are very different. Some are already working as programmers and attend the course out of curiosity, while some still struggle with variables and loops. From the beginning, I decided that my course would be not just for everyone, but for those who are interested. However, disregarding the others would be wrong, so we needed assignments for different levels.

To jump-start the network study, I set up a server with the reference implementation, and everyone could connect to it and play. Even telnet would do. Allotting more time for an assignment and providing better explanations served students well. Many could finish the supertask. We even had a little contest between AIs. A human could still beat them with ease, but it was still a big improvement over the previous year.

A pleasant surprise

I had the same code golf contest again. And again, I promised to give a free pass to whoever could beat me. I’m sure you can see where this is going, but first I must explain the problem in detail. The task was to implement a function, challenge, defined as follows:

import std;

/**
* Params:
* s = Multiline string, each line containing positive integers separated by whitespace.
* Returns: An array containing the sums of each line and the grand total as the last element.
*/
uint[] challenge(string s);

unittest {
assert(challenge("0") == [0, 0]);
assert(challenge("1\n1") == [1, 1, 2]);
assert(challenge("2\n2\n3") == [2, 2, 3, 7]);
assert(challenge("2\n2 0 3\n3 1 1 4") == [2, 5, 9, 16]);
}

Only the symbols inside the function body count. Using any Phobos functionality is allowed, but no renamed imports.

My solution from the previous year was pretty straightforward:

auto r=s.split('\n').map!(a=>a.split.map!(i=>i.to!uint).sum); return r.array~r.sum; // 84 characters

I didn’t show them my solution, I just told them its character count. I was absolutely sure that, just like the previous year, nobody could beat it. Imagine my shock when I was outdone in a mere week:

auto r=split(src,"\n").map!"sum(map!(to!uint)(a.split))".array;return r~[sum(r)]; // 82 characters

Even with the unneeded brackets, this solution was better, thanks to the shortened map syntax: passing a lambda as a string in the first case and passing to directly in the second. The latter was just my oversight, but the former was a typical teacher’s mistake. You see, back when I first wrote my solution, it wasn’t possible to pass a string to map like that. I don’t remember well what the problem was, something about scopes. I missed that it was fixed at some point. That’s how in just one year you become an old geezer teacher who can’t keep up with the times.

I stayed true to my word and granted a free pass to the resourceful student at the end of the semester. I was concerned that securing the pass in the middle of the semester would demotivate him to attend classes, but fortunately, I was wrong.

COVID-19 strikes

Of course, I can’t omit how the pandemic impacted the educational system. In the spring of 2020, all classes were moved online. There are some pros and cons to this.

Giving lectures remotely turned out to be very convenient. You can mutually agree on a suitable time, and you don’t need to find a free lecture room. Another very good thing is that students can either ask questions vocally—as when they raise their hands offline—or write them in the chat so that the teacher can answer them when it’s most appropriate. I really think that this system works very well.

Practical lessons are problematic though, especially with those students who aren’t involved enough. When they sit in a classroom they at least do something. Why show up at all if you’re not going to do anything? When it’s online, they just skip class. The chemistry of a group coding session with a teacher ready to help won’t kick in. I encouraged them to ask questions not only during class but at any time, hoping that this would allow me to assist them whenever they are coding. But this only worked for a couple of people.

Lessons of the second year

The second year was better than the first but still less than ideal. I saw no need to modify the course, but its presentation had to be addressed.

We need to tear down this wall in communication. Mutual trust between the teacher and the student makes for a better educational process. It’s difficult to work with those who are wary of the teacher even when doing alright. I don’t yet know a robust solution; each case is tackled individually.

Even those who are doing well need some control. I used to think that attendance scrutiny is for those students who don’t really want to learn, to intimidate them to show up. When I was a student, the best teachers never bothered about absentees. So I too was liberal with these things, even when half the group was missing. But everything has its limits. Bad students will skip classes anyway, but lazy B-graders could benefit from a little scolding.

Fast forward to the present

I don’t have much to say about the last year-and-a-half. Teaching D is now part of my life. Due to the pandemic, we’ve had to move online completely. I couldn’t even meet my students face-to-face. Aside from what I said earlier about how this affects the educational process, this also disrupted one of the ideas I had.

I need some way to track students’ progress to be sure that they actually follow the course and don’t just show up for classes. The way I handled this during the previous two years worked for some students: if they had a problem, they asked questions during a lecture or a practical lesson. But some students just keep quiet when they have problems, so I need some means to identify them. I couldn’t do written tests for programming; that would be nonsense. So during the summer break, I came up with the idea of doing some small quizzes if COVID restrictions were to be lifted. These quizzes would affect the final grade, but not much, as the intention behind them was to help students who are having problems, not to be the final straw that breaks the camel’s back. Unfortunately, remote lessons made this nearly impossible.

My students keep surprising me, coming up with newer and better solutions to the code golf puzzle. Unfortunately, I don’t have the exact code of the student who won in the third year (it appears that she deleted her Github account), but it was something like:

auto r=s.split('\n').map!(a=>a.split.to!(uint[]).sum); return r.array~r.sum; // 74 characters

Again, it was a feature I didn’t know about: to converts between arrays, too.

I thought that this problem was done and that there was no room for further improvement. Oh, how wrong I was. This is what they’ve brought me this year:

return(s.split('\n')~s).map!(a=>a.split.to!(uint[]).sum).array; // 63 characters

This completely destroys my solution with all the improvements! Note that the trick is not in the syntax, but in the logic. This works by concatenating the split lines with the original string, making it unnecessary to declare an intermediate array, which allows implementing this function as a single statement. I would never have thought to do that! Algorithms win over micro-optimizations, even in code golf.

The biggest change this year is a new, formalized evaluation system. In the semester, students earn points for doing assignments and writing reports on them. The first and simplest assignment is worth 5 points, and the last and hardest is worth 30. The maximum number of points a student can score without participating in code golf is 100. Code golf is scored by the formula 110 − length. The code golf winner this year got 47 points for his solution which earned him an exemption from writing any reports. We have a table listing every student’s points so everybody knows how many points they need to score. Everything is very transparent, so I don’t need to worry about not being objective when evaluating students.

A Gas Dynamics Toolkit in D

The Eilmer flow simulation code is the main simulation program in our collection of gas dynamics simulation tools. An example of its application is shown here with the simulation of the hypersonic flow over the BoLT-II research vehicle that is to be flown in 2022.

Some history

This simulation program, originally called cns4u, started as a relatively small C program that ran on the Cray-Y/MP supercomputer at NASA Langley Research Center in 1991. A PDF of an early report with the title ‘Single-Block Navier-Stokes Integrator’ can be found here. It describes the simple finite-volume formulation of the code that allows simulation of a nonreacting gas on a single, structured grid. Thirty years on, many capabilities have been added through the efforts of a number of academic staff and students. These capabilities include high-temperature thermochemical effects with reacting gases and distributed-memory parallel simulations on cluster computers. The language in which the program was written changed from C to C++, with connections to Tcl, Python and Lua.

The motivation for using C++ in combination with the scripting languages was to allow many code variations and user programmability so that we could tackle any number of initially unimagined gas-dynamic processes as new PhD students arrived to do their studies. By 2010, the Eilmer3 code (as it was called by then) was sitting at about 100k lines of code and was growing. We were, and still are, mechanical engineers and students of gas-dynamics first and programmers second. C++ was a lot of trouble for us. Over the next 4 years, C++ became even more trouble for us as the Eilmer3 code grew to about 250k lines of code and many PhD students used it to do all manner of simulations for their thesis studies.

Also in 2010, a couple of us (PAJ and RJG) living in different parts of the world (Queensland, Australia and Virginia, USA) came across the D programming language and took note of Andrei Alexandrescu’s promise of stability into the future. Here was the promise of a C++ replacement that we could use to rebuild our code and remain somewhat sane. We each bought a copy of Andrei’s book and experimented with the D language to see if it really was the C++-done-right that we wished for. One of us still has the copy of the initial printing of Andrei’s book without his name on the front cover.

Rebuilding in D

In 2014 we got serious about using D for the next iteration of Eilmer and started porting the core gas dynamics code from C++ to D. Over the next four years, in between university teaching activities, we reimplemented much of the Eilmer3 C++ code in D and extended it. We think that this was done to good effect. This conference paper, from late 2015, documents our effort at the initial port of the structured grid solver. (A preprint is hosted on our site.) The Eilmer4 program is as fast as the earlier C++ program but is far more versatile while being implemented in fewer lines of code. It now works with unstructured as well as structured grids and has a new flexible boundary condition model, a high-temperature thermochemistry module, and in the past two years we have added the Newton-Krylov-accelerated steady-state solver that was used to do the simulation shown above. And importantly for us, with the code now being in D, we now have have many fewer WTF moments.

If you want more details on our development of the Eilmer4 code in D, we have the slides from a number of presentations given to the Centre for Hypersonics over the past six years.

Features of D that have been of benefit to us include:

• Template programming that other Mechanical Engineers can understand (thanks Walter!). Many of our numerical routines are defined to work with numbers that we define as an alias to either double or Complex!double values. This has been important to us because we can use the same basic update code and get the sensitivity coefficients via finite differences in the complex direction. We think this saved us a large number of lines of code.

• String mixins have replaced our use of the M4 preprocessor to generate C++ code in Eilmer3. We still have to do a bit of head-scratching while building the code with mixins, but we have retained most of our hair—something that we did not expect to do if we continued to work with C++.

• Good error messages from the compiler. We often used to be overwhelmed by the C++ template error messages that could run to hundreds of lines. The D compilers have been much nicer to us and we have found the “did you mean” suggestions to be quite useful.

• A comprehensive standard library in combination with language features such as delegates and closures that allow us to write less code and instead concentrate on our gas dynamics calculations. I think that having to use C++ Functors was about the tipping point in our 25-year adventure with C++.

• Ranges and the foreach loops make our D code so much tidier than our equivalent C++ code.

• Low-barrier shared-memory parallelism. We do many of the flow update calculations in parallel over blocks of cells and we like to take advantage of the many cores that are available on a typical workstation.

• Simple and direct linkage to C libraries. We make extensive use of Lua for our configuration and do large simulations by using many processors in parallel via the OpenMPI library.

• The garbage collector is wonderful, even if other people complain about it. It makes life simpler for us. For the input and output, we take the comfortable path of letting the compiler manage the memory and then tell the garbage collector to tidy up after us. Of course, we don’t want to overuse it. @nogc is used in the core of the code to force us not to generate garbage. We allocate much of our data storage at the start of a simulation and then pass references to parts of it into the core functions.

• Fast compilation and good optimizing compilers. Nearly an hour’s build time was fairly common for our old C++ code, and now we would expect a DMD or LDC debug build in about a quarter of a minute. This builds a basic version of the main simulation code on a Lenovo ThinkPad with Core i7 processor. An optimized build can take a little over a minute but the benefit of the faster simulation is paid back by orders of magnitude when a simulation job is run for several hours over hundreds of processors.

• version(xxxx) { ... } has been a good way to have variants of the code. Some use complex numbers and others are just double numbers. Also, some variants of the code can have multiple chemical species and/or just work with a single-species nonreacting gas. This reduction in physical modelling allows us to reduce the memory required by the simulation. For big simulations of 3D flows, the required memory can be on the order of hundreds of gigabytes.

• debug { ... } gets used to hide IO code in @nogc functions. If a simulation fails, our first action is often to run the debug-flavor of the code to get more information and then, if needed, run the debug flavor of the code under the control of gdb to dig into the details.

We have a very specialized application and so don’t make use of much of the software ecosystem that has built up around the D language. For a build tool, we use make and for an IDE, we use emacs. The D major mode is very convenient.

There are lots of other features that just work together to make our programming lives a bit better. We are six years in on our adventure with the D programming language and we are still liking it.

Teaching D from Scratch: Is it a viable first language?

About two-and-a-half years ago, one of my son’s homeschooling friends asked if I would teach a coding class. Both of my kids have shown interest in coding via Scratch, and I also need to pull my weight when it comes to homeschooling them (my wife doing most of the instruction). So I thought, surely, this can’t be too hard. The challenges I would encounter over the course of the last few years surprised me more often than I would have expected!

First, a bit about my background. I’ve been writing software professionally for over 20 years, about half using system languages and half doing web development. I’ve dabbled in a lot of programming projects, including embedded microcontrollers with 256 bytes of RAM, collaborative video systems using high-end Unix workstations, and automating a factory testing rack-mounted appliances. What I haven’t ever done to any official degree is teach programming. For sure, this is a challenge that is novel to me, and also one that I have found very satisfying.

A bit about the kids. I had ten students, aged 9 – 15, all of whom had very little experience writing real software. Since this class was going to be a side project for me on top of my normal job, we went rather slowly, meeting once every two weeks.

One of the goals of my class was to try not to dumb down coding into an abstraction. I wanted the kids to experience real programming without too much of a pre-built hand-holding framework, and I believed they could do it. I’ve seen many (and purchased some) online resources for teaching to code that hide most of the real work and just focus on using custom libraries or simplified languages to learn the “basics”, while not actually teaching to be productive in a standard environment. I also witnessed firsthand my own kids getting bored when the material was presented too slowly or too abstractly.

Note: you can see some of my homework assignments and review pages on a dedicated website that I created for this class

First Try: Javascript and Lua

The first language we worked with, because many of the kids didn’t have actual full-blown computers to work on (this was pre-Covid so it was at my house), was Javascript. Javascript is possible to develop in a browser, using a tablet or a Chromebook, so all the kids I was teaching could participate. My focus in these early days was basic concepts like loops, branching, and basic statements. I think Javascript can be a great first language, though the things you can do with it as a beginner are a bit bland, and to do anything exciting you have to start learning the DOM (including how objects and methods work).

After half a year we moved on to our second language: Lua, specifically in the video game system Roblox. I almost wish we had skipped this one because it was a bit too advanced for these students, and I was not familiar with the language and the system to begin with. The kids did have fun with the model-building UI. However, I can see an experienced teacher finding good ways to instruct using this environment. Roblox, with its pre-built rendering and physics engines, has the nice benefit of allowing one to generate a really cool game with relatively little code—something new coders quite enjoy.

Just use D

And that leads us to the second year, where I decided to just try teaching what I know best: the D programming language. Since all the kids have a keen interest in building games, I searched for a simple library I could use to build games. I have to give a shout-out to the library I discovered, Raylib, and the YouTuber Ki Rill, whose very straightforward and well-made videos gave me the confidence that this also would be something I could teach.

For a quick demo of what Raylib looks like, here is a program I wrote in a few minutes that bounces a Raylib-drawn D-man around the window.

Everyone in the class is using Windows, so the toolchain that I am having them use is the stock DMD compiler and the Visual Studio Code development environment with the code-d extension. Throughout the last year-and-a-half, I have had the kids build such classics as hangman and tic-tac-toe using text-based “graphics”. After that came the introduction to 2D graphics and Raylib, where we built a brick-breaking game similar to Arkanoid and others.

Finally, I let the students pick what they wanted for the last project, and their pick was a rock-paper-scissors online tournament game. I’m not sure of the lasting power of that one…

Keep in mind that my goal was to teach them programming, not necessarily D. Working through all these projects allowed me to introduce a lot of the typical programming concepts (branching, looping, functions, data types, arrays, etc.). This year we are focusing on aggregates (specifically arrays and structs), and how they can be useful to encapsulate your coding solution into pieces that are easier to deal with. The game we are writing this year is a snake clone. I’m hoping to wrap things up on that by the end of the year, and then next year add in networking to have the game playable between the students (all the kids these days are into online tournament-style games).

My Take on Teaching With D

So how is D as a beginner’s programming language? I can say with confidence that the kids understand the flavor of D that I have taught just as well as the other languages. What flavor is that you may ask? Why vanilla of course! As D was the first typed language they were exposed to, I had to first explain types and why they are important. I also had to pick and choose which concepts in D I did not want to confuse them with. To that end, I’ve left out a lot of the advanced features (so far), such as:

• Templates
• Type inference
• __traits
• Classes/Interfaces/OOP
• Pointers/references (mostly)
• Memory management in general (thank you GC!)

This means that a lot of language features are missing from “Vanilla D”, and a lot of those are pieces I like. I had to force myself to avoid those things while teaching so as not to confuse them. I also try to avoid shortcuts if I can, as I want them to master the basic concepts before learning how to do it quicker or less verbosely. I tried hard to always use curly braces for scope blocks, for instance, instead of just writing single statements without them.

The features that I found the kids handled well were basic types like ints and strings (though what a funny name “string” is, that took a while to sink in), if statements and foreach/while loops, structs, and functions. They had a harder time understanding arrays and associative arrays, which might be due to my lack of experience teaching. I’ll also note that teaching virtually has some significant challenges—there’s just no substitute to being able to walk over to a student’s computer and observe and help their progress or draw out on a whiteboard how data is laid out. One surprising thing (to me anyway) that they handled completely in stride, was nested functions. That’s a nice D feature that is not available in C, barely in C++, and something that I personally took a while to get used to.

What Could Go Wrong?

The challenges were numerous. First I had to try and remember what it’s like to know nothing about programming. I did not take any classes on teaching programming and did not look up the “proper” way to teach it, I just went with what I thought would be the next best thing to do based on their skills and experience. Having class only once every two weeks is a big drawback since the students’ brains tend to garbage-collect some of the stuff we worked on last time. For sure, a daily or maybe twice-weekly class schedule would improve the situation, but that would mean I’d have to generate material to teach that many classes, which I unfortunately don’t have the time for.

Secondly, I previously had no experience with games programming. Raylib made this pretty easy, and as long as you can understand event loops, it’s pretty straightforward (and fun—I wish I had learned it sooner).

Aside from my personal and environmental shortcomings, I have some suggestions for the D language and community that would make D a much better “first language”. In no particular order:

Error Messages

Error messages in D are sometimes esoteric, and I’m not just talking about templates. The most common error the students make is bad punctuation. This leads to some of the worst messages. Most of the time, I need to explain what is happening, but even when explaining it myself, I am sometimes thwarted by the compiler. For instance, if you forget a semicolon:

void main()
{
import std.stdio;
int x = 5
writeln(x); // line 5
}

The resulting error is:

foo.d(5): Error: semicolon expected, not writeln

But wait, there is a semicolon on line 5! So why is it having a problem? Technically, putting an extra semicolon just before the writeln call on line 5 would solve the error (after all, D doesn’t have significant whitespace). But really, the compiler shouldn’t be suggesting that.

I’d rather see something like:

foo.d(5): Error: previous statement not terminated, perhaps you need
a semicolon on line 4?

What if you are missing a brace or have an extra one? That spits out a lot of error vomit that is sometimes really difficult to decipher. I know this isn’t as easy a problem to solve, but since the compiler is not even to the tricky semantic parts, what if it used some other hints of the source to try and tease out a suggested solution? Like maybe taking cues from the indentation.

A great feature of the D compiler is the spelling correction suggestions. But sometimes it’s not easy to see what has changed in the suggested spelling correction (especially when it’s just different capitalization). Why not highlight the changed letters? Since D has gained colorized output for errors, things have become much clearer in my opinion.

I do have to relay a comment from one of my students, who said he appreciated the ability of VS Code to jump to the line that the compiler error is referencing. This helps put into perspective where their mindset is.

Debugger on VSCode for Windows

Another challenge I’ve faced is the lack of a good debugging experience for VS Code on Windows. The C debugger for VS Code displays, for example, a string as a length and a C string (which might have trailing garbage). I’d rather not confuse them with this. I have read that Visual D has a better debugging experience, but I need dub support for these projects, and Visual D focuses on Visual Studio integration, something I don’t necessarily want to deal with in teaching these kids. I’m hoping that a recent focus on debugging (e.g., the LLDB integration project) will help.

I think WebFreak has done a great job on the VS Code plugin, and for the most part, it works amazingly well. Thank you! I know debugging is a piece that is hard to get right.

I spend an inordinate amount of time helping the kids set up their build environments, and having this built-in to the IDE or dub would be immensely helpful for using D for learning.

Suggestions for the Future

I am confident that D can be a viable first language. I have students that, prior to my class, had never before written real code now creating 2D games using D and Raylib (with a lot of hand-holding, darn it!). What I think D needs for this success to be scalable is a set of resources that assumes no programming experience. Most of D’s learning resources are aimed at teaching experienced programmers how D works in terms of their existing knowledge. They answer the question, “How do I do <insert feature> from <insert language> in D?” What we need is to answer the question, “How do I write code?”, that just happens to be using D.

Editor’s Note:Some readers may be thinking at this point of Ali Çehreli’s Programming in D. Though written for beginner-level programmers, it’s focused more on teaching the language than teaching to program generally.

This shouldn’t be too hard to create, as there are a ton of resources on learning to program in C already. D is so similar you could almost just replace C with D and call it a day. In fact, I’m considering creating a YouTube series on the topic with the experience I’ve gained. One important tool that needs creating is a map of D features and where they fall on the difficulty scale. This allows one to write D tutorials that follow a gradual introduction of features. Broaching the subject of complex features is probably needed early, as they will experience some of them (you are using a template whenever you use writeln, or to), but explanation of the entire feature may be deferred to a later level.

And finally, such materials should be engaging! I can’t stress enough how important it is to be doing something fun when you are learning to program. The kids definitely are more engaged since we are creating games that they might actually want to play vs. simple hello world examples or even Javascript web pages. A nice way to proceed might be to write a game in Vanilla D and then introduce features that allow you to refine and improve the code.

I hope this becomes a reality because I think this area of development has been relegated to either frameworks that attempt to water down programming to get kids engaged or sterile hello world-style programming that is intended for rote instruction or reference. I think we can show the full potential of D and, at the same time, engage eager learners with interesting and fun projects to trick them into learning code with our favorite language!

I Wrote a High-Frequency Trading Platform In D

I’ve used the D programming language to implement a high-frequency trading (HFT) platform. I’ve been quite satisfied with the experience and thought I’d share how I got here. It wasn’t a direct path.

In 2008, I stumbled across a book on Amazon called Learn to Tango with D. That grabbed my curiosity, so I decided to research D further. That led me to Digital Mars and Walter Bright. I had first heard of Walter when I learned about Zortech C++, the first native C++ compiler. His work had been a huge influence on my C++ learning experience. So I was immediately interested in the language just because it was his, and excited to learn that he was working with Andrei Alexandrescu on version 2. Still, I decided to wait until they were further along with the new version before I dove in.

In 2010, I bought Andrei’s The D Programming Language as soon as it was published and started reading. At the time, I was working at BNP Paribas using C++ to optimize their HFT platform, so high performance was prevalent in my thoughts. When I saw that D’s classes were reference types, with functions that are virtual by default, I was disappointed. I didn’t see how this could be useful for low-latency programming. I became too busy with work to explore further at the time, so I put the book and the language aside.

In 2014, I began preparing for a new adventure. As part of that, I started working on a feed handler framework from scratch in C++, using my own long-maintained C++ library of low-level components useful in low-latency, high-performance applications. Andrei’s book came to my attention again, so I decided to give it another look.

This time, I read the book through to the end and learned that my initial impression had been misplaced. I found that I liked D’s metaprogramming features and its support for programming in a functional style. By the end of the book, I was ready to give D a try.

I started by porting my C++ library and feed handler to D. It wasn’t difficult. I use very little inheritance in my C++ code, preferring composition and concrete classes. I found myself quite productive with D’s structs, templates, and mixins. All the while, I kept a close eye on performance benchmarks. When D turned out to give me the same performance as my C++ code, I was sold. I found D to be much more elegant, cleaner, more readable, and easier to maintain. I made the switch and never looked back.

My goal was to develop a complete HFT system using D. The system would consist of different subsystems:

• Feed-Handler Framework: receives market data from exchanges; builds the books for all securities; publishes the updates to the other subsystems.
• Strategies Framework: receives market data updates from feed handlers; facilitates communications with the Order Management System; allows for plugging into it strategies that make decisions on stock trades.
• Order Management System: communicates with the exchange and the strategies framework; maintains a database of orders.
• Signal Generator: receives market data updates from feed handlers; generates different signals as indicator values, predictions of stock prices, etc.; sends the different signals to strategies.

Ultimately, I found a new data structure and better design for my feed-handler framework. I developed the new version completely in D. This implementation heavily uses templates. I like D’s template syntax and generally find the error messages clearer than the complex error messages I was used to from C++. I needed to drop down to assembly for some specific x86 instructions and it was straightforward to do in D.

Later, I needed to work with configuration files. I prefer to write my config files in Lua, a lightweight scripting language that is easy to integrate into a program as an extension via its C API. For this, I found a D Lua binding called DerelictLua. Using, again, D’s metaprogramming facilities, I developed a very easy and practical way to interface with Lua on top of DerelictLua. Editor’s Note: DerelictLua has since been discontinued; new projects should use its successor, bindbc-lua, instead.

The feed handler on the Bats market comes on 31 simultaneous channels, so it is more efficient to use multithreading. For this, I chose not to use the multithreading facilities provided by Phobos. I felt I needed more control in such a low-latency environment, particularly the ability to map each thread to a specific core. I opted to use the pthreads library and its affinity feature. D’s C ABI compatibility made it a straightforward thing to do.

I’m running on FreeBSD. For my intercommunication needs, I’m using kernel queues and sockets. The same functionality is available on macOS, my preferred development platform. D did not get in my way in using these APIs on either macOS or FreeBSD. It was as seamless as using kernel queues from C.

A few notes about problems and limitations:

• I encountered one compiler bug. I found a workaround, so it wasn’t a blocker. I was able to reproduce it with a few lines of code and contacted the D community. They solved the problem and had a fix in a later version of the compiler.
• I did not use D’s garbage collector. This is not a strike against D or its GC, though. In a low-latency system like this, even the use of malloc and free can be costly, so I’m not going to take a chance on a nondeterministic system with unpredictable latency. Instead, I used my library to handle allocation/deallocation via free lists, with memory preallocated upfront. As a consequence, I also decided not to use D’s standard library for anything.
• I had to work with fixed-size ASCII strings that are not NUL-terminated and are, instead, padded with spaces at the end. Without the standard library, I found it easier to manipulate them C-style via pointers.

I was the sole developer on this project but completed it successfully in a relatively short period. Big credit to D and its productivity, readability, and ease of modifications.

D Summer School v3

The third edition of the D Summer School, held at University POLITEHNICA of Bucharest, took place from the 5th to the 25th of July. It was three weeks of boot camping bachelor students into the basics of D across eight sessions of hands-on workshops and a hackathon. We will describe our experience in organizing the program, teaching the students, and trying to integrate them into the D community.

Recap from past editions

For the first two editions we had the following process:

• we started advertising the summer school two months before the event;
• we selected students from among the applicants;
• the students had to complete a project during the summer school;
• we helped students improve their projects during the hackathon;
• we collected feedback at the end.

For more information, we recommend our previous article on the first edition.

DSSv3

Pre-event actions

In contrast to previous years, this time we started marketing the event very early in February, five months before the start date. We used the most influential vectors we could to promote it: the most popular professors. We nagged them ceaselessly to promote DSSv3 during their courses. The results were spectacular: we received 86 student applications (as opposed to 25 in the previous years). Since this was an online version of the summer school, we decided to drop the selection process and open the door to everyone. This had the added benefit that we didn’t have to conduct interviews with everyone and a wider range of students had a chance to be introduced to D.

To cope with the larger number of participants, we had to grow our team. Former Symmetry Autumn of Code participants Robert Aron and Adela Vais, and Symmetry employee Alexandru Militaru, agreed to join us. As such, we were able to diversify our teaching materials and raise the overall quality of the presentations.

The teaching materials were mostly the same as in the previous years; we simply reshuffled the order to have an incremental level of complexity and added a lab, “WebApp Tutorial using Vibe.d”. Besides this, we also changed the project competition. During the previous editions of DSS we found that students were not very engaged with the project assignment, so this time we came up with something different. We created a Dlang Bug Fix Competition: the top three students who had the largest number of merged PRs that solved a Bugzilla issue would win Raspberry Pi 400 kits (you may have noticed the “DSSv3” labeled PRs on our three main repositories). You may think that contributing to a programming language is a scary task, however, the truth is there are dozens of approachable, easy-to-fix issues that give instant gratification to the contributor.

With all the planning into place, we were now ready to start DSSv3.

The teaching sessions

A teaching session is comprised of an hour-long lecture and two hours of hands-on exercises. This year, we abandoned the slides in favor of tutorial-like examples. Since everything was online, we simply shared our screen and highlighted the different aspects of D in a practical way by directly compiling and running examples. This made the lessons more interactive as students enthusiastically asked “what happens if…?” questions, and we could easily demonstrate the results.

For the exercises, we followed a team-play system where students were grouped in teams of four, and they worked together to solve their tasks. This made it easier for us to organize everything on the Teams platform (we would enter rooms of four students instead of talking students individually) and it encouraged them to help each other.

From among the 86 applicants, we had an average of 35 students attending the sessions, with a record high of 56 (“Introduction to D”) and a low of 25 (“C\C++ Interoperability and Tooling”). It seems that from the first lecture to the last, almost half of the students abandon the course. This may seem like a grim figure, but note that we had students from all types of backgrounds, some of them in their first year at university. Since we are teaching subjects like memory management and multithreading, it’s only natural that some of them will be lost along the way. Regardless, the lowest number of attendees was higher than the highest number from previous years.

The hackathon was attended by eight people. Again, a low figure, but that was not a surprise. Keep in mind that the majority of the participants had never made an open-source contribution. We expected that only the best students would manage to contribute. One other factor that may have influenced the low number was our uninspired decision to organize the hackathon on a Sunday; several people noted in our feedback form that they would have participated had they not had other plans. The result of the hackathon:

• 9 PRs submitted to Phobos: 5 were merged, 2 were closed but led to closing the associated Bugzilla entries, and 2 were rejected
• 1 PR submitted to DRuntime that was merged
• 4 PRs submitted to DMD: 2 were merged, 2 were rejected

We had hoped that students would submit PRs before the hackathon, but we were wrong. It seems that students should be assisted when making their first PR.

The winners of the hackathon were:

1. danandrei279 with 3 PRs merged
2. vladchicos with 2 PRs merged
3. lucica28 with 1 PR merged

Feedback

We asked the students to fill out a feedback form, and we received 15 responses. It is highly possible that the results are biased since the feedback form was available at the end of the hackathon; by that time some students had already dropped out. Although it would have been great to understand their perspective, we still had valuable feedback on what went well and what could be improved.

From aggregating the results, we have the following conclusions.

• The difficulty of DSS is perceived as being high. Those who are well prepared love it, but those who don’t have too much programming experience are lost along the way.
• The introductory courses are much more popular than advanced ones such as “C\C++ interop” and “Multithreading in D”.
• The hackathon was appreciated by hardcore programmers (a small percentage of the total number of attendees), but the rest were intimidated by it.
• Students appreciated the relaxed interactive atmosphere of the sessions, with some commenting: “The general feel of the summer school was a chill evening hanging out with your friends.”

Overall, DSSv3 generated enthusiasm among the programming geeks, but we still have some work to do to make it attractive to a less savvy audience.

Future plans

Now that our team has grown, we plan to do a bigger restructuring of the course. Given the high drop-out rate, we would like to make the course welcoming for any type of CS student regardless of background or experience. To that end, we are considering creating two tracks for the course: one at a beginner level, and one for more advanced students. That way we can accommodate any type of audience.

Another aspect to think about is the hackathon. We still haven’t found the most appealing project that would motivate students to commit to it. Experience has shown that creating a project from scratch in a language you’ve just learned doesn’t really work (or we haven’t found the adequate project) and contributing to the D language may be intimidating. We are still searching for better solutions, so if you have any ideas, please contact us.

Also, right now, UPB is going through a major redesign of its curriculum. Proposals for new courses are being accepted, and we will forward this course as our choice. There’s a long wait for acceptance, but we’re keeping our fingers crossed.

Conclusions

Overall, we are happy with this year’s edition. We managed to expand our team, grow our reach, and motivate some students to contribute to the language. Even though we still have some work to do to engage less passionate students, we think we are on the right track.

See you next time at DSSv4!

Driving with D

Here is what comes to mind when I think of D: fast, expressive, easy, and… driving? That’s right, I drive with D.

Enter my venerable Holden VZ Ute daily driver. From the factory, it came with a rubbish four-speed automatic gearbox. During 18 months of ownership, I destroyed four gearboxes. I could not afford a new vehicle at the time (I’m a 20-year-old Australian computer science student at Monash University), so I had to get creative. I purchased a rock-solid, bulletproof, six-speed automatic gearbox from another car. But that’s where the solutions ended. To make it work, I had to build my own circuit board, computer system, and firmware to control the solenoids, hydraulics, and clutches inside the gearbox, handle user input, perform shifting decisions, and interface to my car by pretending to be the four-speed automatic.

I’m quite proud of my solution. It can perform a shift in 250 milliseconds, which is great for racing. It has a steep first gear, giving it a swift takeoff. It has given some more powerful cars a run for their money. It’s got flappy paddles, diagnostic data on the screen, and the ability to go ahead and change the way it works whenever I want.

Here’s a very old video of the system working. It’s not representative of the current system—that ghastly blue screen is gone, the speedo works, and shifting has improved.

The computer is split into two parts: the user interface board, which drives an OLED display and uses an STM32F042, and the mainboard, which handles everything else, utilizing an STM32F407. The two cooperate over a CAN bus (Controller Area Network). All the firmware to handle this is written in D.

I picked D (as -betterC) because of its ingenious Uniform Function Call Syntax (UFCS), design by contract, metaprogramming, ease of interfacing with C, unit testing, portability, shared, @safe, and codebase maintenance features. Another bonus is the helpful, welcoming community. It has genuinely been a joy discussing D on the forums, and with the founders and community leaders.

Uniform Function Call Syntax (UFCS)
This has made my code significantly clearer. My code can accurately follow the flow of data without polluting my stack with single-use variables, nesting many function calls, or other sorts of clutter.
Here is an example of how you could potentially use it in an ECU (Engine Control Unit):

immutable injectorTime = airStoich(100.kpa, 25.degCelsius)
.airMass
.fuelMass((14.7f).afr)
.fuelMol
.calculateInjectorWidth;

This is equivalent to:

immutable injectorTime =
calculateInjectorWidth(
fuelMol(
fuelMass(
airMass(
airStoich(kpa(100), degCelsius(25))
),
afr(14.7)
)
)
); // brackets have been expanded for reading clarity

Please note: the values in this example are hardcoded to simplify the code and demonstrate how UFCS can give a unit of measurement to a value.

Both representations are valid D code; you can use either.

With UFCS, there’s no need to read the code backward or count your brackets, no need to use a gazillion single-use variables. Function calls mirror the flow of data. It’s concise.

Design by Contract
D’s contract programming is quite similar to Ada’s. Functions can be marked with preconditions, designated by in, and postconditions, designated by out. Should a contract fail, an assertion is thrown.

// This demonstrates D’s contract programming for a function.
// It uses the short-hand expression based syntax.
int iHaveAContract(void* ptr)
in(ptr !is null) // this is a precondition, if ptr is null, an assertion is raised
in(ptr !is null, "ptr is null :(") // this is a precondition, if ptr is null, an assertion with the error message "ptr is null :(" is raised
out(result; result > 0) // this is a post condition, it captures the return value as result and tests it
out(result; result > 0, "result was too low") // this is a post condition, it captures the return value as result, tests it, and if it fails, raise an assertion with the message "result was too low"
{
// normal function code here
}

If you want to do something a bit more complex in your contracts, an alternative syntax is available:

int iHaveAContract(void* ptr)
in {
assert(ptr !is null); // this is equivalent to in(ptr !is null) from above.
MyStruct* ms = cast(MyStruct*)ptr; // we can introduce variables local to this contract
assert(ms.blah == 2, "MyStruct.blah must be 2!"); // test ms.blah, if it fails, raise an assert with an error message
}
out(result) { // captures the return value as result
int squareIt = result * result;
assert(squareIt == 4);
}
do { // this designates the function body
// normal function code here
}

void main(string[] args)
{
auto i = iHaveAContract(null); // this will violate the contract
}

Structs and classes can include contracts, called invariants, that sanity check the state of an instance for its whole lifetime. Invariants are checked after the constructor is run, before the destructor is run, and before and after public function calls.

struct MyStruct
{
int blah;

invariant {
assert(blah == 2, "blah must always be 2 for some reason!");
}
// shorthand:
invariant(blah == 2, "blah must be 2 for some reason!");

// The invariant is checked at function entry and exit. If value is anything other than 2, the invariant will fail when the function exits
void setBlah(int value)
{
blah = value;
}
}

void main(string[] args)
{
MyStruct s; // invariant is run after construction
s.setBlah(3); // invariant contract will be violated.
}

Metaprogramming
The Don’t Repeat Yourself (DRY) principle is often touted by programmers. D’s metaprogramming is an incredible tool to achieve that goal. I use it in my CAN bus implementation. For example:

struct CANPacket(ushort ID) {
enum id = ID;
ubyte[8] data;
}
alias HeartbeatPacket = CANPacket!10;
alias BeepHornPacket = CANPacket!140;

I’ve got specific aliased types as HeartbeatPacket and BeepHornPacket, but I haven’t needed to repeat any code. They all follow the same underlying structure, so if I modify CANPacket, every alias is also updated.

Maybe you want a more descriptive CAN packet? Mixin templates can help with that!

struct GenericCanPacket
{
ushort id;
ubyte[8] data; // 8 bytes to store the CAN packet payload
}

struct HeartbeatPacket
{
ubyte deviceID; // first byte is the device ID
ubyte statusID; // second byte is the status
}

To translate a GenericCanPacket to the descriptive HeartbeatPacket, we can use mixin templates.

mixin template CanPacketHelperFunctions(ushort ID)
{
enum id = ID;

// typeof(this) means that the return type of readFromGeneric will be the struct this template is instantiated in.
static typeof(this) readFromGeneric(const ref GenericCanPacket p)
in(p.id == ID, "Generic packet cannot be converted!")
{
// do some cast
}
}

struct HeartbeatPacket
{
// the stuff declared in CanPacketHelperFunctions is pretty much copy-pasted (not literally) into here
mixin CanPacketHelperFunctions!10;

ubyte deviceID; // first byte is the device ID
ubyte statusID; // second byte is the status
}

void main()
{
GenericCanPacket generic;
generic.id = 10;
generic.data = [ 2 /* deviceID */, 3 /* statusID */ ];

writeln(HeartbeatPacket.id); // the packet ID is 10
writeln(heartbeat.deviceID); // 2
writeln(heratbeat.statusID); // 3
}

The mixin template CanPacketHelperFunctions can be used over and over for all sorts of packet representations, and since it is only declared once, the implementation remains consistent across all types that use it.

Interfacing to C
I frequently must communicate with my microcontroller’s HAL and RTOS; D’s C interface made that a breeze. Just add an extern(C) and it’s good to go.

extern(C) c_setPwm(int solenoid, void* userData); // declaration
c_setPwm(4, null); // usage, pretty easy!

Unit Testing
D’s built-in unit testing has saved me from blowing my foot off a few times. I can run all my unit tests on Windows to guarantee logical correctness, and then build a final target for my microcontroller. Here is an example:

struct MyStruct
{
int x;
int squareIt() { return x * x; }
}

unittest
{
MyStruct s;
s.x = 9;
assert(s.squareIt == 9 * 9); // If for some reason the implementation breaks, then the unit test fails
}

Deprecation
Codebases can be ever-changing, and sometimes certain functionality may no longer be considered good practice but must be retained for legacy reasons. D provides a way to explicitly mark this in code. Any use of such deprecated code will trigger a deprecation warning from the compiler. Example:

deprecated struct Example
{
// ...
}

// This deprecation includes a reason
deprecated("This is the reason for deprecation..") struct ExampleWithMessage
{
// ...
}

void main()
{
// This will generate the following compiler warning:
// "Deprecation: struct Example is deprecated"
Example e;

// This will generate the following compiler warning:
// "Deprecation: struct ExampleWithMessage is deprecated - This is the reason for deprecation.."
ExampleWithMessage ewm;
}

Of course, deprecated can be applied to all sorts of things, not just structs.

Portability
Following on from above, D supports a surprisingly large number of targets via GDC and LDC. If it weren’t for D’s portability, I would have had to write my project in C++ (ugh). I use LDC, and cross-compiling can be performed by simply adjusting my command line arguments.

Shared
shared is D’s way of guarding against multi-threaded access of code. It’s not perfect, but I use it as-is, and I think it works well. I do have multiple threads in my codebase, and they need to synchronize data. I mark certain variables as shared, which means I must take special care accessing that data. It works with system locks and mutexes. While locked, I can cast shared away and use it like a normal variable. This is handy with structs and classes.

shared int sensorValue;
sensorValue = 4; // using it like a single-thread variable, error
atomicStore(sensorValue, 4); // works with atomics

SafeD
@safe exists to prohibit sketchy memory activities and enforce best behavior. I haven’t had to fight @safe much yet because I don’t do anything wicked with my memory, but it is comfortable knowing that if I am going to make a mistake, the compiler can assist me in stopping it.

Mental Friction
Adam D. Ruppe puts it succinctly: D has low mental friction. The flexibility and expressiveness of the language make it easy to translate one’s thoughts into written code and maintain productivity. I don’t have to fight D much. This is my personal opinion, but I feel like D is the language in which I’m most productive.

Final Thoughts

D is the perfect fit for this sort of project— I think it’s going to have a bright future ahead in the embedded world. I’m going to continue using D for my projects. I’ve got another D-powered automotive project in the works which I hope to show off in the future. Even if D isn’t yet suitable for your project, keep an eye on it. D has been making enormous strides in the past few years, especially in regards to memory safety.

The examples shown in this article are purely meant to demonstrate how D’s features can be used in the real world. Do not take them as gospel as to how you should program.

Function Generation in D: The Good, the Bad, the Ugly, and the Bolt

Introduction

A while ago, Andrei Alexandrescu started a thread in the D Programming Language forums, titled “Perfect forwarding”, about a challenge which came up during the July 2020 beerconf:

Write an idiomatic template forward that takes an alias fun and defines (generates) one overload for each overload of fun.

Several people proposed solutions. In the course of the discussion, it arose that there is sometimes a need to alter a function’s properties, like adding, removing, or hanging user-defined attributes or parameter storage classes. This was exactly the problem I struggled with while trying to support all the bells and whistles of D functions in my openmethods library.

Eventually, I created a module that helps with this problem and contributed it to the bolts meta-programming library. But before we get to that, let’s first take a closer look at function generation in D.

The Good

I speculate that many a programmer who is a moderately advanced beginner in D would quickly come up with a mostly correct solution to the “Perfect forwarding” challenge, especially those with a C++ background who have an interest in performing all sorts of magic tricks by means of template meta-programming. The solution will probably look like this:

template forward(alias fun)
{
import std.traits: Parameters;
static foreach (
ovl; __traits(getOverloads, __traits(parent, fun), __traits(identifier, fun))) {
auto forward(Parameters!ovl args)
{
return ovl(args);
}
}
}

...

int plus(int a, int b) { return a + b; }
string plus(string a, string b) { return a ~ b; }

assert(forward!plus(1, 2) == 3);        // pass
assert(forward!plus("a", "b") == "ab"); // pass

This solution is not perfect, as we shall see, but it is not far off either. It covers many cases, including some that a beginner may not even be aware of. For example, forward handles the following function without dropping function attributes or parameter storage classes:

class Matrix { ... }

Matrix times(scope const Matrix a, scope const Matrix b) pure @safe
{
return ...;
}

pragma(msg, typeof(times));
// pure @safe Matrix(scope const(Matrix) a, scope const(Matrix) b)

pragma(msg, typeof(forward!times));
// pure @safe Matrix(scope const(Matrix) _param_0, scope const(Matrix) _param_1)

It even handles user-defined attributes (UDAs) on parameters:

struct testParameter;

void testPlus(@testParameter int a, @testParameter int b);

pragma(msg, typeof(testPlus));
// void(@(testParameter) int a, @(testParameter) int b)

pragma(msg, typeof(forward!testPlus));
// void(@(testParameter) int a, @(testParameter) int b)

Speaking of UDAs, that’s one of the issues with the solution above: it doesn’t carry function UDAs. It also doesn’t work with functions that return a reference. Both issues are easy to fix:

template forward(alias fun)
{
import std.traits: Parameters;
static foreach (ovl; __traits(getOverloads, __traits(parent, fun), __traits(identifier, fun)))
{
@(__traits(getAttributes, fun)) // copy function UDAs
auto ref forward(Parameters!ovl args)
{
return ovl(args);
}
}
}

This solution is still not 100% correct though. If the forwardee is @trusted, the forwarder will be @safe:

@trusted void useSysCall() { ... }

pragma(msg, typeof(&useSysCall));         // void function() @trusted
pragma(msg, typeof(&forward!useSysCall)); // void function() @safe

This happens because the body of the forwarder consists of a single statement calling the useSysCall function. Since calling a trusted function is safe, the forwarder is automatically deemed safe by the compiler.

However, Andrei’s challenge was not exactly what we discussed in the previous section. It came with a bit of pseudocode that suggested the template should not be eponymous. In other words, I believe that the exact task was to write a template that would be used like this: forward!fun.fun(...). Here is the pseudocode:

// the instantiation of forward!myfun would be (stylized):

template forward!myfun
{
void myfun(int a, ref double b, out string c)
{
return myfun(a, b, c);
}
int myfun(in string a, inout double b)
{
return myfun(a, b);
}
}

Though this looks like a small difference, if we want to implement exactly this, a complication arises. In the eponymous forward, we did not need to create a new identifier; we simply used the template name as the function name. Thus, the function name was fixed. Now we need to create a function with a name that depends on the forwardee’s name. And the only way to do this is with a string mixin.

The first time I had to do this, I tried the following:

template forward(alias fun)
{
import std.format : format;
import std.traits: Parameters;
enum name = __traits(identifier, fun);
static foreach (ovl; __traits(getOverloads, __traits(parent, fun), name)) {
@(__traits(getAttributes, fun))
auto ref mixin(name)(Parameters!ovl args)
{
return ovl(args);
}
}
}

This doesn’t work because a string mixin can only be used to create expressions or statements. Therefore, the solution is to simply expand the mixin to encompass the entire function definition. The token-quote operator q{} is very handy for this:

template forward(alias fun)
{
import std.format : format;
import std.traits: Parameters;
enum name = __traits(identifier, fun);
static foreach (ovl; __traits(getOverloads, __traits(parent, fun), name)) {
mixin(q{
@(__traits(getAttributes, fun))
auto ref %s(Parameters!ovl args)
{
return ovl(args);
}
}.format(name));
}
}

Though string mixins are powerful, they are essentially C macros. For many D programmers, resorting to a string mixin can feel like a defeat.

Let us now move on to a similar, yet significantly more difficult, challenge:

Write a class template that mocks an interface.

For example:

interface JsonSerializable
{
string asJson() const;
}

void main()
{
auto mock = new Mock!JsonSerializable();
}

Extrapolating the techniques acquired during the previous challenge, a beginner would probably try this first:

class Mock(alias Interface) : Interface
{
import std.format : format;
import std.traits: Parameters;
static foreach (member; __traits(allMembers, Interface)) {
static foreach (fun; __traits(getOverloads, Interface, member)) {
mixin(q{
@(__traits(getAttributes, fun))
auto ref %s(Parameters!fun args)
{
// record call
static if (!is(ReturnType!fun == void)) {
return ReturnType!fun.init;
}
}
}.format(member));
}
}
}

Alas, this fails to compile, throwing errors like:

Error: function challenge.Mock!(JsonSerializable).Mock.asJson return type
inference is not supported if may override base class function

In other words, auto cannot be used here. We have to fall back to explicitly specifying the return type:

class Mock(alias Interface) : Interface
{
import std.format : format;
import std.traits: Parameters, ReturnType;
static foreach (member; __traits(allMembers, Interface)) {
static foreach (fun; __traits(getOverloads, Interface, member)) {
mixin(q{
@(__traits(getAttributes, fun))
ReturnType!fun %s(Parameters!fun args)
{
// record call
static if (!is(ReturnType!fun == void)) {
return ReturnType!fun.init;
}
}
}.format(member));
}
}
}

This will not handle ref functions though. What about adding a ref in front of the return type, like we did in the first challenge?

// as before
ref ReturnType!fun %s(Parameters!fun args) ...

This will fail with all the functions in the interface that do not return a reference.

The reason why everything worked almost magically in the first challenge is that we called the wrapped function inside the template. It enabled the compiler to deduce almost all of the characteristics of the original function and copy them to the forwarder function. But we have no model to copy from here. The compiler will copy some of the aspects of the function (pure, @safe, etc.) to match those of the overriden function, but not some others (ref, const, and the other modifiers).

Then, there is the issue of the function modifiers: const, immutable, shared, and static. These are yet another category of function “aspects”.

At this point, there is no other option than to analyze some of the function attributes by means of traits, and convert them to a string to be injected in the string mixin:

      mixin(q{
@(__traits(getAttributes, fun))
%sReturnType!fun %s(Parameters!fun args)
{
// record call
static if (!is(ReturnType!fun == void)) {
return ReturnType!fun.init;
}
}
}.format(
(functionAttributes!fun & FunctionAttribute.const_ ? "const " : "")
~ (functionAttributes!fun & FunctionAttribute.ref_ ? "ref " : "")
~ ...,
member));
}

If you look at the implementation of std.typecons.wrap, you will see that part of the code deals with synthesizing bits of a string mixin for the storage classes and modifiers.

The Ugly

So far, we have looked at the function storage classes, modifiers, and UDAs, but we have merely passed the parameter list as a single, monolithic block. However, sometimes we need to perform adjustments to the parameter list of the generated function. This may seem far-fetched, but it does happen. I encountered this problem in my openmethods library. During the “Perfect forwarding” discussion, it appeared that I was not the only one who wanted to do this.

I won’t delve into the details of openmethods here (see an older blog post for an overview of the module); for the purpose of this article, it suffices to say that, given a function declaration like this one:

Matrix times(virtual!Matrix a, double b);

openmethods generates this function:

Matrix dispatcher(Matrix a, double b)
{
return resolve(a)(a, b);
}

The virtual template is a marker; it indicates which parameters should be taken into account (i.e., passed to resolve) when picking the appropriate specialization of times. Note that only a is passed to the resolve function—that is because the first parameter uses the virtual! marker and the second does not.

Bear in mind, though, that dispatcher is not allowed to use the type of the parameters directly. Inside the openmethods module, there is no Matrix type. Thus, when openmethods is handed a function declaration, it needs to synthesize a dispatcher function that refers to the declaration’s parameter types exclusively via the declaration. In other words, it needs to use the ReturnType and Parameters templates from std.traits to extract the types involved in the declaration – just like we did in the examples above.

Let’s put aside function attributes and UDAs – we already discussed those in the previous section. The obvious solution then seems to be:

ReturnType!times dispatcher(
RemoveVirtual!(Parameters!times[0]) a, Parameters!times[1] b)
{
return resolve(a)(a, b);
}

pragma(msg, typeof(&dispatcher)); // Matrix function(Matrix, double)

where RemoveVirtual is a simple template that peels off the virtual! marker from the type.

Does this preserve parameter storage classes and UDAs? Unfortunately, it does not:

@nogc void scale(ref virtual!Matrix m, lazy double by);

@nogc ReturnType!scale dispatcher(RemoveVirtual!(Parameters!scale[0]) a, Parameters!scale[1] b)
{
return resolve(a)(a, b);
}

pragma(msg, typeof(&dispatcher)); // void function(Matrix a, double b)

We lost the ref on the first parameter and the lazy on the second. What happened to them?

The culprit is Parameters. This template is a wrapper around an obscure feature of the is operator used in conjunction with the __parameters type specialization. And it is quite a strange beast. We used it above to copy the parameter list of a function, as a whole, to another function, and it worked perfectly. The problem is what happens when you try to process the parameters separately. Let’s look at a few examples:

pragma(msg, Parameters!scale.stringof); // (ref virtual!(Matrix), lazy double)
pragma(msg, Parameters!scale[0].stringof); // virtual!(Matrix)
pragma(msg, Parameters!scale[1].stringof); // double

We see that accessing a parameter individually returns the type… and discards everything else!

There is actually a way to extract everything about a single parameter: use a slice instead of an element of the paramneter pack (yes, this is getting strange):

pragma(msg, Parameters!scale[0..1].stringof); // (ref virtual!(Matrix))
pragma(msg, Parameters!scale[1..2].stringof); // (lazy double)

So this gives us a solution for handling the second parameter of scale:

ReturnType!scale dispatcher(???, Parameters!scale[1..2]) { ... }

But what can we put in place of ???. RemoveVirtual!(Parameters!scale[0..1]) would not work. RemoveVirtual expects a type, and Parameters!scale[1..2] is not a type—it is a sort of conglomerate that contains a type, and perhaps storage classes, type constructors, and UDAs.

At this point, we have no other choice but to construct a string mixin once again. Something like this:

mixin(q{
%s ReturnType!(scale) dispatcher(
%s RemoveVirtual!(Parameters!(scale)[1]) a,
Parameters!(scale)[1..2] b)
{
resolve(a)(a, b);
}
}.format(
functionAttributes!scale & FunctionAttribute.nogc ? "@nogc " : ""
/* also handle other function attributes */,
__traits(getParameterStorageClasses, scale, 0)));

pragma(msg, typeof(dispatcher)); // @nogc void(ref double a, lazy double)

This is not quite sufficient though, because it still doesn’t take care of parameter UDAs.

To Boltly Refract…

openmethods once contained kilometers of mixin code like the above. Such heavy use of string mixins was too ugly and messy, so much so that the project began to feel less like fun and more like work. So I decided to sweep all the ugliness under a neat interface, once and for all. The result was a “refraction” module, which I later carved out of openmethods and donated to Ali Akhtarzada’s excellent bolts library. bolts attempts to fill in the gaps and bring some regularity to D’s motley set of meta-programming features.

refraction’s entry point is the refract function template. It takes a function and an “anchor” string, and returns an immutable Function object that captures all the aspects of a function. Function objects can be used at compile-time. It is, actually, their raison d’être.

Function has a mixture property that returns a declaration for the original function. For example:

Matrix times(virtual!Matrix a, double b);
pragma(msg, refract!(times, "times").mixture);
// @system ReturnType!(times) times(Parameters!(times) _0);

Why does refract need the anchor string? Can’t the string "times" be inferred from the function by means of __traits(identifier...)? Yes, it can, but in real applications we don’t want to use this. The whole point of the library is to be used in templates, where the function is typically passed to refract via an alias. In general, the function’s name has no meaning in the template’s scope—or if, by chance, the name exists, it does not name the function. All the meta-expressions used to dissect the function must work in terms of the local symbol that identifies the alias.

Consider:

module matrix;

Matrix times(virtual!Matrix a, double b);

Method!times timesMethod; // openmethods creates a Method object for each
// declared method

module openmethods;

struct Method(alias fun)
{
enum returnTypeMixture = refract!(fun, "fun").returnType;
pragma(msg, returnTypeMixture);              // ReturnType!(fun)
mixin("alias R = ", returnTypeMixture, ";"); // ok
pragma(msg, R.stringof);                     // Matrix
}

There is no times and no Matrix in module openmethods. Even if they existed, they could not be the times function and the Matrix class from module matrix, as this would require a circular dependency between the two modules, something that D forbids by default. However, there is a fun symbol, and it aliases to the function; thus, the return type can be expressed as ReturnType!(fun).

All aspects of the function are available piecemeal. For example:

@nogc void scale(ref virtual!Matrix m, lazy double by);
pragma(msg, refract!(scale, "scale").parameters[0].storageClasses); // ["ref"]

Function also has methods that return a new Function object, with an alteration to one of the aspects. They can be used to create a variation of a function. For example:

pragma(msg,
refract!(scale, "scale")
.withName("dispatcher")
.withBody(q{{ resolve(_0[0])(_0); }})
.mixture
);

@nogc @system ReturnType!(scale) dispatcher(ref Parameters!(scale)[0] _0, lazy Parameters!(scale)[1] _1)
{
resolve(_0[0])(_0);
}

This is the reason behind the name “refraction”: the module creates a blueprint of a function, performs some alterations on it, and returns a string—called a mixture—which, when passed to mixin, will create a new function.

openmethods needs to change the type of the first parameter while preserving storage classes. With bolts.experimental.refraction, this becomes easy:

original = refract!(scale, "scale");

pragma(msg,
original
.withName("dispatcher")
.withParameters(
[original.parameters[0].withType(
"RemoveVirtual!(%s)".format(original.parameters[0].type)),
original.parameters[1],
])
.withBody(q{{
return resolve(_0)(%s);
}}.format(original.argumentMixture))
);

This time, the generated code splits the parameter pack into individual components:

@nogc @system ReturnType!(scale) dispatcher(
ref RemoveVirtual!(Parameters!(scale)[0]) _0, Parameters!(scale)[1..2] _1)
{
return resolve(_0)(_0);
}

Note how the first and second parameters are handled differently. The first parameter is cracked open because we need to replace the type. That forces us to access the first Parameters value via indexing, and that loses the storage classes, UDAs, etc. So they need to be re-applied explicitly.

On the other hand, the second parameter does not have this problem. It is not edited; thus, the Parameters slice trick can be used. The lazy is indeed there, but it is inside the parameter conglomerate.

Conclusion

Initially, D looked almost as good as Lisp for generating functions. As we tried to gain finer control of the generated function, our code started to look a lot more like C macros; in fact, in some respects, it was even worse: we had to put an entire function definition in a string mixin just to set its name.

This is due to the fact that D is not as “regular” a language as Lisp. Some of the people helming the evolution of D are working on changing this, and it is my hope that an improved D will emerge in the not-too-distant future.

In the meantime, the experimental refraction module from the bolts meta-programming library offers a saner, easier way of generating functions without compromising on the idiosyncrasies that come with them. It allows you to pretend that functions can be disassembled and reassembled at will, while hiding all the gory details of the string mixins that are necessarily involved in that task.

Jean-Louis Leroy is not French, but Belgian. He got his first taste of programming from a HP-25 calculator. His first real programming language was Forth, where CTFE is pervasive. Later he programmed (a little) in Lisp and Smalltalk, and (a lot) in C, C++, and Perl. He now works for Bloomberg LP in New York. His interests include object-relational mapping, open multi-methods, DSLs, and language extensions in general.