Building a Rust-Style Result Type in Zig

I've been exploring Zig recently and wanted to recreate one of my favorite patterns from Rust: the Result type. This experiment taught me a lot about Zig's type system and reinforced why functional error handling is so powerful.

Why Result Types Matter

Traditional error handling with exceptions or error codes forces you to remember to check for failures. Result types make errors explicit in the type system—you literally cannot access a successful value without acknowledging that an error might have occurred.

In Rust, this looks like:

rust
match divide(10, 2) {
    Ok(value) => println!("Result: {}", value),
    Err(e) => println!("Error: {}", e),
}

Zig's Approach

Zig already has excellent error handling with error unions (!T), but I wanted something closer to Rust's API for learning purposes. Here's what I built:

zig
fn divide(a: i32, b: i32) AppResult(i32) {
    if (b == 0) return err(i32, .invalid_input, "Division by zero");
    return okWith(i32, @divTrunc(a, b));
}

// Usage
const result = divide(20, 4);
if (isSuccess(result)) {
    const value = getValue(result).?; // 5
}

Key Design Decisions

I chose a flat structure with separate success and error code enums rather than Rust's enum approach. This makes it easier to expose to C APIs—something crucial in systems programming. The helper functions (ok, okWith, err) provide the functional interface while maintaining C compatibility.

The design supports:
- Type-safe generics for any payload type
- Custom success/error codes with meaningful messages
- Clean functional composition
- Zero-cost C interop

Functional Programming Benefits

What I love about this pattern is how it encourages functional thinking. Instead of scattered if (error) checks, you chain operations and handle errors at natural boundaries. It's explicit without being verbose, and the compiler ensures you don't accidentally ignore failures.

Learning Path Forward

As I continue learning Zig, I plan to use this result type in real projects to understand Zig's memory model better. The next step is building higher-level combinators like map and flatMap to make error handling even more ergonomic.

This experiment showed me that Zig's comptime and type system are powerful enough to build sophisticated abstractions while maintaining the systems programming focus that makes Zig special.

Try it out: github.com/qa3-tech/appresult