LEARN D PROGRAMMING LANGUAGE

Learn the D Programming Language: A Project-Based Guide

Goal: To learn the D programming language by building projects that leverage its unique strengths in metaprogramming, safety, and performance, and to understand how it differs from languages like C++, Python, and C#.

Why Learn D?

D is a general-purpose systems and applications programming language created by Walter Bright, co-creator of the first C++ compiler. It is the result of decades of experience in compiler design, aiming to be the spiritual successor to C++. It seeks to provide the low-level power of C++ while incorporating the productivity, safety, and expressiveness of modern languages.

Learning D isn’t just about learning another syntax; it’s about exploring a different philosophy of language design that combines performance, pragmatism, and incredible compile-time power.

After completing these projects, you will:

Be proficient in D’s syntax and its powerful standard library (Phobos).
Understand and use D’s unique features: Compile-Time Function Execution (CTFE), static if, ranges, and built-in unittest blocks.
Know how to write memory-safe code using D’s @safe attribute system.
Appreciate the difference between D’s approach and that of C++, C#, Java, and Python.
Be able to build high-performance, modern applications with D.

Core Concept Analysis: D’s Strengths and Differentiators

D’s philosophy is “pragmatic power.” It provides high-level abstractions for productivity but always allows you to go low-level for performance.

1. Memory Safety & Control (The “Safe C++”)

D offers a unique, graduated approach to memory safety.

Garbage Collection (GC): By default, D is garbage-collected, making most memory management automatic and preventing many common bugs, similar to C# or Java.
Optional GC: The GC is a library. You can disable it (--betterC mode) or manage memory manually with malloc/free for real-time or performance-critical code.
@safe, @trusted, @system: This is D’s safety system.
- @safe: The default. The compiler guarantees this code cannot corrupt memory (no pointer arithmetic, no out-of-bounds writes).
- @system: Unsafe code. You have the full power of C/C++ with raw pointers.
- @trusted: A bridge. A function marked @trusted can be called from @safe code, but its internals can contain @system code. The programmer makes a promise to the compiler that this function is a safe abstraction over its unsafe implementation.

2. Metaprogramming (D’s Superpower)

This is where D truly shines and differs from almost every other mainstream language. D’s metaprogramming is not about macros; it’s about using the D language itself to generate code and run logic at compile time.

Compile-Time Function Execution (CTFE): You can mark almost any function as pure and the compiler can execute it at compile time if given compile-time known inputs. You can parse files, perform complex calculations, or generate lookup tables before your program even runs.
static if: A revolution compared to C++’s SFINAE or if constexpr. It’s a simple if statement that is evaluated at compile time. If the condition is false, the code block is not just ignored; it is never parsed. This allows you to write templates that work for many types with incredible clarity.
```
// Compiles for any type that has a 'length' property
auto getLength(T)(T val) {
    static if (is(typeof(val.length))) {
        return val.length;
    } else {
        return 1; // e.g., for a single number
    }
}
```
String Mixins: The ability to generate code as a string at compile time and have the compiler mix it into your program. mixin("int y = " ~ to!string(compileTimeValue) ~ ";");. This is unbelievably powerful.

3. Productivity & Expressiveness (“Python with Static Types”)

D includes many features that make coding fast and enjoyable.

Ranges: D’s answer to iterators, but far more powerful and composable. They are lazy by default, leading to highly efficient, functional-style code.

import std.algorithm, std.range;
// Reads a file, splits it by line, filters for lines with "Error", and takes the first 5.
// No memory is allocated for intermediate steps.
auto errors = File("log.txt").byLine.filter!(a => a.canFind("Error")).take(5);

Uniform Function Call Syntax (UFCS): A game-changer. myObject.foo() can also be written as foo(myObject). This means you can add new “member functions” to any type from outside the class, and it allows for the beautiful chaining seen with ranges.
Built-in unittest Blocks: You can write unit tests directly alongside your code in unittest { ... } blocks. They are automatically compiled out of release builds and can be run with dub test.
auto and Type Inference: Clean, powerful type inference that works as you’d expect.

4. The Tools

dmd: The reference D compiler.
dub: D’s official package manager and build tool. It makes managing dependencies and building projects trivial, similar to cargo or npm.

Project List

These projects are designed to force you to use and understand D’s most impactful features.

Project 1: A Smarter `grep` with Ranges

File: LEARN_D_PROGRAMMING_LANGUAGE.md
Main Programming Language: D
Coolness Level: Level 2: Practical but Forgettable
Business Potential: 1. The “Resume Gold”
Difficulty: Level 1: Beginner
Knowledge Area: Standard Library / Functional Programming
Software or Tool: dub, Phobos (D Standard Library)
Main Book: “Programming in D” by Ali Çehreli

What you’ll build: A command-line tool that searches for a given text pattern in files. It will be more powerful than a simple grep because you’ll use D’s ranges to chain operations, like searching only in certain line numbers or filtering by length.

Why it teaches D: This project is a fantastic introduction to D’s syntax, its powerful standard library, the dub build tool, and the elegance of ranges. You’ll immediately see how D allows you to write expressive, high-level code without sacrificing performance.

Core challenges you’ll face:

Setting up a dub project → maps to using dub init and understanding dub.json or dub.sdl
Reading command-line arguments → maps to working with std.stdio and std.getopt
Reading a file lazily → maps to using std.file.File("path").byLine
Chaining range algorithms → maps to using filter, map, and enumerate to build a processing pipeline

Key Concepts:

D’s Standard Library (Phobos): “Programming in D” - Part III.
Ranges: “Programming in D” - Chapter 12.
dub: The official dub documentation.

Difficulty: Beginner Time estimate: Weekend Prerequisites: Basic programming knowledge in any C-style language.

Real world outcome: A fast, flexible command-line search tool.

# Your tool in action
> ./dgrep "Error" log.txt | head -n 5
[2023-10-27 10:15:00] Error: Connection timed out.
[2023-10-27 10:15:05] Error: Failed to resolve host.

# Chain operations, thanks to ranges and UFCS
> ./dgrep "Error" log.txt | grep "resolve"
[2023-10-27 10:15:05] Error: Failed to resolve host.

Implementation Hints:

Use dub init my_grep to create your project.
In your main function, process the command-line arguments.
The core of your program will be a single, beautiful line: foreach (i, line; File(filename).byLine.enumerate) { ... }
Inside the loop, use std.string.canFind(line, pattern) to check for a match.
Try adding more features by chaining range functions, e.g., .take(10) to only find the first 10 matches.

Learning milestones:

You can build and run a D program with dub → You understand the basic D workflow.
Your tool can read a file and print lines that match a pattern → You understand ranges and basic string manipulation.
You can chain at least two range algorithms together (e.g., filter and take) → You have grasped the power and elegance of D’s ranges and UFCS.

Project 2: Unit Testing Your `grep`

File: LEARN_D_PROGRAMMING_LANGUAGE.md
Main Programming Language: D
Coolness Level: Level 2: Practical but Forgettable
Business Potential: 1. The “Resume Gold”
Difficulty: Level 1: Beginner
Knowledge Area: Software Engineering / Testing
Software or Tool: dub
Main Book: “Programming in D” by Ali Çehreli

What you’ll build: You will take the code from Project 1 and add unittest blocks to your functions, creating a self-testing codebase.

Why it teaches D: This highlights a core part of D’s philosophy: testing is a first-class citizen, not an afterthought. You’ll see how easy it is to maintain high-quality, tested code compared to the heavyweight frameworks required in other systems languages.

Core challenges you’ll face:

Writing a unittest block → maps to learning the simple syntax and how to use assert
Testing pure functions → maps to refactoring your code to make it more testable
Running tests with dub → maps to simply running dub test

Key Concepts:

Unit Testing: “Programming in D” - Chapter 8.

Difficulty: Beginner Time estimate: A few hours. Prerequisites: Project 1.

Real world outcome: When you run dub test, your tests will automatically compile and run. If you run dub build, the test code will be completely removed from the final executable.

Implementation Hints:

Refactor your core search logic into a separate function, e.g., SearchResult[] findMatches(string content, string pattern).
Directly below this function, add a unittest block.

Inside the unittest block, create a sample string, call your function, and assert that the results are what you expect.

unittest {
    string testContent = "line1\nError on line2\nline3";
    auto results = findMatches(testContent, "Error");
    assert(results.length == 1);
    assert(results[0].lineText == "Error on line2");
}

Learning milestones:

You can write a unittest block that compiles and runs → You understand the syntax.
dub test passes when your logic is correct and fails when you introduce a bug → You have a working test suite.
You appreciate not needing a separate test framework, makefiles, or project setup → You have embraced the D philosophy of integrated testing.

Project 3: A Compile-Time CSV Parser

File: LEARN_D_PROGRAMMING_LANGUAGE.md
Main Programming Language: D
Coolness Level: Level 5: Pure Magic (Super Cool)
Business Potential: 1. The “Resume Gold”
Difficulty: Level 4: Expert
Knowledge Area: Metaprogramming / CTFE
Software or Tool: D’s CTFE engine
Main Book: “D programming Language” by Andrei Alexandrescu

What you’ll build: A function that takes a CSV file’s path as a compile-time string. This function will, during compilation, open the file, read its header, and generate a struct with fields matching the header names. The function’s return type will be an array of this compile-time-generated struct, and it will be populated with the CSV data.

Why it teaches D: This project is impossible or monstrously complex in most other languages. In D, it’s clean and elegant. It will force you to understand CTFE and string mixins, which are D’s most powerful and unique features. This is the ultimate metaprogramming project.

Core challenges you’ll face:

Executing code at compile time → maps to using static variables or template parameters to run functions during compilation
Reading a file during compilation → maps to using import(filename) which the compiler executes
Generating code as a string → maps to building a string like "struct CsvRow { int columnA; string columnB; }"
Using string mixins → maps to using mixin() to turn your generated string into actual, compiled code

Key Concepts:

CTFE: “Programming in D” - Chapter 20.
String Mixins: Dlang.org documentation.
Templates: “D programming Language” by Andrei Alexandrescu - Chapter 5.

Difficulty: Expert Time estimate: 1-2 weeks Prerequisites: A solid grasp of D syntax from the first two projects.

Real world outcome: You will write code that looks like magic. The compiler will give you a type-safe, custom-built struct for any CSV file without you ever having to write the struct definition yourself.

Code you write:

// data.csv:
// id,name
// 1,Alice
// 2,Bob

// Your D code:
auto rows = loadCsv!("data.csv");
// The compiler generates `struct CsvRow { int id; string name; }`
// and `rows` is an array of `CsvRow`.

assert(rows[0].id == 1);
assert(rows[0].name == "Alice"); // This is fully type-checked!

Implementation Hints:

Create a template function: auto loadCsv(string filename)() { ... }
Inside, use enum csvContent = import(filename); to get the file’s content at compile time.
Write a simple CTFE-able function to parse the header from csvContent.
Build up a string for the struct definition. Use __traits(allMembers, MyStruct) to get member names and iterate over them.
Use mixin to declare the struct: mixin("struct CsvRow { " ~ generatedFields ~ " }");.
Finally, write the runtime part of the function that parses the data into an array of your newly created CsvRow struct.

Learning milestones:

You can read a file’s contents into a variable at compile time → You understand import().
You can generate a string of D code based on the file content → You understand basic string manipulation for metaprogramming.
You can use mixin to successfully compile the generated string into your program → You have grasped string mixins.
You have a working, type-safe CSV loader that adapts to any CSV file at compile time → You are thinking in D.

Project 4: A `@safe` String Class with `unittest`s

File: LEARN_D_PROGRAMMING_LANGUAGE.md
Main Programming Language: D
Coolness Level: Level 3: Genuinely Clever
Business Potential: 1. The “Resume Gold”
Difficulty: Level 3: Advanced
Knowledge Area: Memory Management / Language Safety
Software or Tool: D’s safety system
Main Book: “D programming Language” by Andrei Alexandrescu

What you’ll build: Your own version of a dynamic string/array class. The internal implementation will use low-level, unsafe pointer arithmetic and manual memory management (@system code). However, the public API you expose will be marked @safe, providing a guaranteed memory-safe interface.

Why it teaches D: This project directly addresses D’s philosophy of graduated safety. You will learn how to write high-performance, low-level code when you need to, and then wrap it in a safe abstraction. This demonstrates the power of @safe, @system, and @trusted.

Core challenges you’ll face:

Writing @system code → maps to using raw pointers, malloc/free, and disabling the GC for this module
Writing a @safe public API → maps to designing functions that can’t be used to corrupt memory
Using @trusted correctly → maps to creating functions that bridge the safe/system gap, promising the compiler that your unsafe implementation is actually safe
Integrating unittests → maps to proving that your safe API works as intended and that your unsafe implementation is correct

Key Concepts:

Memory Safety in D: Dlang.org documentation on @safe.
Interfacing with C: The techniques for manual memory management are similar to C (core.stdc.stdlib.malloc).
Structs vs Classes: D has both. A struct is a value type, a class is a reference type. A dynamic array is best implemented as a struct.

Difficulty: Advanced Time estimate: 1-2 weeks Prerequisites: Familiarity with pointers from C/C++.

Real world outcome: A custom MyString type that is as safe to use as a built-in D string but whose internals you wrote yourself. You will have a clear, practical understanding of D’s safety features.

Implementation Hints:

Define a struct MyString. It will contain a char* ptr and a size_t length.
The append function will be @trusted. Inside, you’ll have @system logic to calculate the new required size, allocate a new buffer with malloc, copy the old data, copy the new data, and free the old buffer.
The public append function itself will just call this trusted internal function. It can be @safe because it doesn’t do any pointer magic itself.
An index operator opIndex should perform bounds checking before accessing ptr[index]. Because of this check, it can be marked @safe.
Write extensive unittest blocks to test appending, indexing, out-of-bounds access, etc.

Learning milestones:

You can write a function that fails to compile in @safe code (e.g., takes a pointer) → You understand what @safe prevents.
You can write low-level memory management code inside a @system block → You know how to bypass the safety.
You can successfully call your @system logic from @safe code via a @trusted function → You understand how to build safe abstractions.
All your unit tests pass, proving your safe API is correct → You have successfully built a safe, performant data structure.

Project Comparison Table

Project	Difficulty	Time	Core Concept Highlighted	Fun Factor
1. A Smarter `grep`	Beginner	Weekend	Ranges & `dub`	★★★☆☆
2. Unit Testing `grep`	Beginner	Hours	`unittest` blocks	★★★☆☆
3. Compile-Time CSV Parser	Expert	1-2 weeks	CTFE & Metaprogramming	★★★★★
4. A `@safe` String Class	Advanced	1-2 weeks	Safety System & Memory	★★★★☆

Recommendation

Your first step should be Project 1: A Smarter grep with Ranges. This project is the perfect introduction. It gets you comfortable with D’s syntax, its excellent standard library, and the dub build tool. Most importantly, it introduces you to ranges and UFCS, which are fundamental to idiomatic D code. It’s a practical tool that immediately shows off D’s expressive power.

After that, move to Project 2: Unit Testing Your grep. This is a quick but vital project for understanding D’s philosophy.

With that foundation, you’ll be ready to have your mind blown by Project 3: A Compile-Time CSV Parser. This project, more than any other, will show you what makes D truly special and different from any other language you’ve used. It’s challenging, but it’s the real “aha!” moment for learning D.

Summary

Project 1: A Smarter grep with Ranges: D
Project 2: Unit Testing Your grep: D
Project 3: A Compile-Time CSV Parser: D
Project 4: A @safe String Class with unittests: D

Learn the D Programming Language: A Project-Based Guide

Why Learn D?

Core Concept Analysis: D’s Strengths and Differentiators

1. Memory Safety & Control (The “Safe C++”)

2. Metaprogramming (D’s Superpower)

3. Productivity & Expressiveness (“Python with Static Types”)

4. The Tools

Project List

Project 1: A Smarter grep with Ranges

Project 2: Unit Testing Your grep

Project 3: A Compile-Time CSV Parser

Project 4: A @safe String Class with unittests

Project Comparison Table

Recommendation

Summary

Project 1: A Smarter `grep` with Ranges

Project 2: Unit Testing Your `grep`

Project 4: A `@safe` String Class with `unittest`s