D is not
source-compatible with C and C++ source code in general. However, any code that is legal in both C/C++ and D should behave in the same way. Like C++, D has
closures,
anonymous functions,
compile-time function execution,
design by contract, ranges, built-in container iteration concepts, and
type inference. D's declaration, statement and expression
syntaxes also closely match those of C++. Unlike C++, D also implements
garbage collection,
first-class arrays (std::array in C++ are technically not first-class),
array slicing,
nested functions and
lazy evaluation. D uses Java-style single inheritance with
interfaces and
mixins rather than C++-style
multiple inheritance. D is a systems programming language. Like C++, and unlike application languages such as
Java and
C#, D supports
low-level programming, including
inline assembler. Inline assembler allows programmers to enter machine-specific
assembly code within standard D code. System programmers use this method to access the low-level features of the
processor that are needed to run programs that interface directly with the underlying
hardware, such as
operating systems and
device drivers. Low-level programming is also used to write higher
performance code than would be produced by a
compiler. D supports
function overloading and
operator overloading. Symbols (
functions,
variables,
classes) can be declared in any order;
forward declarations are not needed. In D, text character strings are arrays of characters, and arrays in D are bounds-checked. D has
first-class types for complex and imaginary numbers.
Programming paradigms D supports five main
programming paradigms: •
Concurrent (
actor model) •
Object-oriented •
Imperative •
Functional •
Metaprogramming Imperative Imperative programming in D is almost identical to that in C. Functions, data, statements, declarations and expressions work just as they do in C, and the C
runtime library may be accessed directly. However, unlike C, D's
foreach loop construct allows looping over a collection. D also allows
nested functions, which are functions that are declared inside another function, and which may access the enclosing function's
local variables. import std.stdio; void main() { int multiplier = 10; int scaled(int x) { return x * multiplier; } foreach (i; 0 .. 10) { writefln("Hello, world %d! scaled = %d", i, scaled(i)); } }
Object-oriented Object-oriented programming in D is based on a single
inheritance hierarchy, with all classes derived from class Object. D does not support multiple inheritance; instead, it uses Java-style
interfaces, which are comparable to C++'s pure abstract classes, and
mixins, which separate common functionality from the inheritance hierarchy. D also allows the defining of static and final (non-virtual) methods in interfaces. Interfaces and inheritance in D support
covariant types for return types of overridden methods. D supports type forwarding and optional custom
dynamic dispatch. Classes (and interfaces) in D can contain
invariants which are automatically checked before and after entry to public methods, in accord with the
design by contract methodology. Many aspects of classes (and structs) can be
introspected automatically at
compile time (a form of
reflective programming (reflection) using type traits) and at
runtime (RTTI / TypeInfo), to facilitate generic code or automatic code generation (usually using compile-time techniques).
Functional D supports
functional programming features such as
function literals,
closures, recursively-immutable objects and the use of
higher-order functions. There are two syntaxes for anonymous functions, including a multiple-statement form and a "shorthand" single-expression notation: int function(int) g; g = (x) { return x * x; }; // longhand g = (x) => x * x; // shorthand There are two built-in types for function literals, function, which is simply a pointer to a stack-allocated function, and delegate, which also includes a pointer to the relevant
stack frame, the surrounding ‘environment’, which contains the current local variables. Type inference may be used with an anonymous function, in which case the compiler creates a delegate unless it can prove that an environment pointer is not necessary. Likewise, to implement a closure, the compiler places enclosed local variables on the
heap only if necessary (for example, if a closure is returned by another function, and exits that function's scope). When using type inference, the compiler will also add attributes such as pure and nothrow to a function's type, if it can prove that they apply. Other functional features such as
currying and common higher-order functions such as
map,
filter, and
reduce are available through the standard library modules std.functional and std.algorithm. import std.stdio, std.algorithm, std.range; void main() { int[] a1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]; int[] a2 = [6, 7, 8, 9]; // must be immutable to allow access from inside a pure function immutable pivot = 5; int mySum(int a, int b) pure nothrow /* pure function */ { if (b (b Alternatively, the above function compositions can be expressed using Uniform function call syntax (UFCS) for more natural left-to-right reading: auto result = a1.chain(a2).reduce!mySum(); writeln("Result: ", result); result = a1.chain(a2).reduce!((a, b) => (b
Parallelism Parallel programming concepts are implemented in the library, and do not require extra support from the compiler. However the D type system and compiler ensure that data sharing can be detected and managed transparently. import std.stdio : writeln; import std.range : iota; import std.parallelism : parallel; void main() { foreach (i; iota(11).parallel) { // The body of the foreach loop is executed in parallel for each i writeln("processing ", i); } } iota(11).parallel is equivalent to std.parallelism.parallel(iota(11)) by using UFCS. The same module also supports taskPool which can be used for dynamic creation of parallel tasks, and for map-filter-reduce and fold style operations on ranges (and arrays), which is useful when combined with functional operations. std.algorithm.map returns a lazily evaluated range rather than an array. This way, the elements are computed by each worker task in parallel automatically. import std.stdio : writeln; import std.algorithm : map; import std.range : iota; import std.parallelism : taskPool; /* On Intel i7-3930X and gdc 9.3.0: * 5140ms using std.algorithm.reduce * 888ms using std.parallelism.taskPool.reduce * * On AMD Threadripper 2950X, and gdc 9.3.0: * 2864ms using std.algorithm.reduce * 95ms using std.parallelism.taskPool.reduce */ void main() { auto nums = iota(1.0, 1_000_000_000.0); auto x = taskPool.reduce!"a + b"( 0.0, map!"1.0 / (a * a)"(nums) ); writeln("Sum: ", x); }
Concurrency Concurrency is fully implemented in the library, and it does not require support from the compiler. Alternative implementations and methodologies of writing concurrent code are possible. The use of D typing system does help ensure memory safety. import std.stdio, std.concurrency, std.variant; void foo() { bool cont = true; while (cont) { receive( // Delegates are used to match the message type. (int msg) => writeln("int received: ", msg), (Tid sender) { cont = false; sender.send(-1); }, (Variant v) => writeln("huh?") // Variant matches any type ); } } void main() { auto tid = spawn(&foo); // spawn a new thread running foo() foreach (i; 0 .. 10) tid.send(i); // send some integers tid.send(1.0f); // send a float tid.send("hello"); // send a string tid.send(thisTid); // send a struct (Tid) receive((int x) => writeln("Main thread received message: ", x)); }
Metaprogramming Metaprogramming is supported through templates, compile-time function execution,
tuples, and string mixins. The following examples demonstrate some of D's compile-time features. Templates in D can be written in a more imperative style compared to the C++ functional style for templates. This is a regular function that calculates the
factorial of a number: ulong factorial(ulong n) { if (n Here, the use of static if, D's compile-time conditional construct, is demonstrated to construct a template that performs the same calculation using code that is similar to that of the function above: template Factorial(ulong n) { static if (n In the following two examples, the template and function defined above are used to compute factorials. The types of constants need not be specified explicitly as the compiler
infers their types from the right-hand sides of assignments: enum fact_7 = Factorial!(7); This is an example of
compile-time function execution (CTFE). Ordinary functions may be used in constant, compile-time expressions provided they meet certain criteria: enum fact_9 = factorial(9); The std.string.format function performs
printf-like data formatting (also at compile-time, through CTFE), and the "msg"
pragma displays the result at compile time: import std.string : format; pragma(msg, format("7! = %s", fact_7)); pragma(msg, format("9! = %s", fact_9)); String mixins, combined with compile-time function execution, allow for the generation of D code using string operations at compile time. This can be used to parse
domain-specific languages, which will be compiled as part of the program: import FooToD; // hypothetical module which contains a function that parses Foo source code // and returns equivalent D code void main() { mixin(fooToD(import("example.foo"))); }
Memory management Memory is usually managed with
garbage collection, but specific objects may be finalized immediately when they go out of scope. This is what the majority of programs and libraries written in D use. In case more control over memory layout and better performance is needed, explicit memory management is possible using the
overloaded operator new, by calling
C's
malloc and free directly, or implementing custom allocator schemes (i.e. on stack with fallback, RAII style allocation, reference counting, shared reference counting). Garbage collection can be controlled: programmers may add and exclude memory ranges from being observed by the collector, can disable and enable the collector and force either a generational or full collection cycle. The manual gives many examples of how to implement different highly optimized memory management schemes for when garbage collection is inadequate in a program. In functions, struct instances are by default allocated on the stack, while class instances by default allocated on the heap (with only reference to the class instance being on the stack). However this can be changed for classes, for example using standard library template std.typecons.scoped, or by using new for structs and assigning to a pointer instead of a value-based variable. In functions, static arrays (of known size) are allocated on the stack. For dynamic arrays, one can use the core.stdc.stdlib.alloca function (similar to alloca in C), to allocate memory on the stack. The returned pointer can be used (recast) into a (typed) dynamic array, by means of a slice (however resizing array, including appending must be avoided; and for obvious reasons they must not be returned from the function). std.experimental.allocator contains a modular and composable allocator templates, to create custom high performance allocators for special use cases.
SafeD SafeD is the name given to the subset of D that can be guaranteed to be
memory safe. Functions marked @safe are checked at compile time to ensure that they do not use any features that could result in corruption of memory, such as pointer arithmetic and unchecked casts. Any other functions called must also be marked as @safe or @trusted. Functions can be marked @trusted for the cases where the compiler cannot distinguish between safe use of a feature that is disabled in SafeD and a potential case of memory corruption.
Scope lifetime safety Initially under the banners of DIP1000 and DIP25 (now part of the language specification), D provides protections against certain ill-formed constructions involving the lifetimes of data. The current mechanisms in place primarily deal with function parameters and stack memory however it is a stated ambition of the leadership of the programming language to provide a more thorough treatment of lifetimes within the D programming language (influenced by ideas from
Rust programming language).
Lifetime safety of assignments Within @safe code, the lifetime of an assignment involving a
reference type is checked to ensure that the lifetime of the assignee is longer than that of the assigned. For example: @safe void test() { int tmp = 0; // #1 int* rad; // #2 rad = &tmp; // If the order of the declarations of #1 and #2 is reversed, this fails. { int bad = 45; // The lifetime of "bad" only extends to the scope in which it is defined. *rad = bad; // This is valid. rad = &bad; // The lifetime of rad is longer than bad, hence this is not valid. } }
Function parameter lifetime annotations within @safe code When applied to function parameter which are either of pointer type or references, the keywords
return and
scope constrain the lifetime and use of that parameter. The language standard dictates the following behaviour: An annotated example is given below. @safe: int* gp; void thorin(scope int*); void gloin(int*); int* balin(return scope int* p, scope int* q, int* r) { gp = p; // Error, p escapes to global variable gp. gp = q; // Error, q escapes to global variable gp. gp = r; // OK. thorin(p); // OK, p does not escape thorin(). thorin(q); // OK. thorin(r); // OK. gloin(p); // Error, p escapes gloin(). gloin(q); // Error, q escapes gloin(). gloin(r); // OK that r escapes gloin(). return p; // OK. return q; // Error, cannot return 'scope' q. return r; // OK. }
Interaction with other systems C's
application binary interface (ABI) is supported, as well as all of C's fundamental and derived types, enabling direct access to existing C code and libraries. D
bindings are available for many popular C libraries. Additionally, C's standard
library is part of standard D. On Microsoft Windows, D can access
Component Object Model (COM) code. As long as memory management is properly performed, many other languages can be mixed with D in a single binary. For example, the GDC compiler allows to link and intermix C, C++, and other supported language codes such as Objective-C. D code (functions) can also be marked as using C, C++, Pascal ABIs, and thus be passed to the libraries written in these languages as
callbacks. Similarly data can be interchanged between the codes written in these languages in both ways. This usually restricts use to primitive types, pointers, some forms of arrays,
unions, structs, and only some types of function pointers. Because many other programming languages often provide the C application programming interface (
API) for writing extensions or running the interpreter of the languages, D can interface directly with these languages also, using standard C bindings (with a thin D interface file). For example, bi-directional bindings exist for the languages
Python,
Lua, and others, often using compile-time code generation and compile-time type reflection methods.
Interaction with C++ code For D code marked as extern(C++), the following features are specified: • The name mangling conventions shall match those of C++ on the target. • For function calls, the ABI shall be equivalent. • The vtable shall be matched up to single inheritance (the only level supported by the D language specification). C++ namespaces are used via the syntax extern(C++, namespace) where
namespace is the name of the C++ namespace.
An example of C++ interoperation The C++ side import std; class Base { public: virtual void print3i(int a, int b, int c) = 0; }; class Derived : public Base { public: int field; Derived(int field): field(field) {} void print3i(int a, int b, int c) { std::println("a = {}", a); std::println("b = {}", b); std::println("c = {}", c); } int mul(int factor); }; int Derived::mul(int factor) { return field * factor; } Derived* createInstance(int i) { return new Derived(i); } void deleteInstance(Derived*& d) { delete d; d = 0; }
The D side extern(C++) { abstract class Base { void print3i(int a, int b, int c); } class Derived : Base { int field; @disable this(); override void print3i(int a, int b, int c); final int mul(int factor); } Derived createInstance(int i); void deleteInstance(ref Derived d); } void main() { import std.stdio; auto d1 = createInstance(5); writeln(d1.field); writeln(d1.mul(4)); Base b1 = d1; b1.print3i(1, 2, 3); deleteInstance(d1); assert(d1 is null); auto d2 = createInstance(42); writeln(d2.field); deleteInstance(d2); assert(d2 is null); }
Better C The D programming language has an official subset known as "". This subset forbids access to D features requiring use of runtime libraries other than that of C. Enabled via the compiler flags "-betterC" on DMD and LDC, and "-fno-druntime" on GDC, may only call into D code compiled under the same flag (and linked code other than D) but code compiled without the option may call into code compiled with it: this will, however, lead to slightly different behaviours due to differences in how C and D handle asserts.
Features included in Better C • Unrestricted use of compile-time features (for example, D's dynamic allocation features can be used at compile time to pre-allocate D data) • Full metaprogramming facilities • Nested functions, nested structs, delegates and lambdas • Member functions, constructors, destructors, operating overloading, etc. • The full module system • Array slicing, and array bounds checking • RAII • '''''' • Memory safety protections • Interfacing with C++ • COM classes and C++ classes •
assert failures are directed to the C runtime library •
switch with strings •
final switch •
unittest blocks •
printf format validation
Features excluded from Better C • Garbage collection • TypeInfo and ModuleInfo • Built-in threading (e.g. core.thread) • Dynamic arrays (though slices of static arrays work) and associative arrays • Exceptions •
synchronized and core.sync • Static module constructors or destructors ==History==