MiniBook: Idiomatic Zig Programming

12/16/2025

Idiomatic Zig Programming: A Guide for C Developers and Zig Beginners

Zig is a modern systems programming language created as a "successor to C," aiming to improve upon C's weaknesses while retaining its efficiency and control[1]. It was designed by Andrew Kelley with a philosophy of questioning assumptions and seeking global maximums in language design rather than settling for local optima[2]. The result is a language with no preprocessor, no macros, and no hidden control flow, yet one that can outperform C in speed[3]. Zig provides low-level control, manual memory management, and direct C interoperability, but couples these with modern safety features and compile-time execution for a robust development experience.

This mini-book provides a fundamental knowledge base of idiomatic Zig programming. It is aimed at experienced C developers (familiar with low-level code and manual memory management) who are beginning their journey with Zig. We will cover Zig's core concepts, language philosophy, and native techniques & patterns – from memory management and error handling to compile-time programming and the new async I/O model. Each chapter includes examples illustrating how to write clear, idiomatic Zig code. The goal is to help you "think in Zig," leveraging the language's features to write code that is safe, optimal, and reusable in the Zig way.

Zig's Philosophy and Design

Zig's design philosophy is heavily influenced by the "Zen of Zig" – a set of principles that emphasize simplicity, correctness, and developer clarity[4][5]:

Communicate intent precisely. (Explicit code is preferred; APIs make requirements obvious.)
Edge cases matter. (Correctness in all cases is valued; no undefined behaviors glossed over.)
Favor reading code over writing code. (Code is optimized for clarity and maintainability for future readers.)
Only one obvious way to do things. (The language avoids multiple complex features for the same task, reducing mental overhead.)
Runtime crashes are better than bugs. (Fail fast and loudly rather than corrupt state silently.)
Compile errors are better than runtime crashes. (Catch issues early at compile-time whenever possible.)
Incremental improvements. (You can gradually improve a codebase with Zig, interop with C, etc.)
Avoid local maximums. (Don't settle for a suboptimal design just because it's familiar – Zig is willing to break and rebuild abstractions.)
Reduce the amount one must remember. (Keep the language simple; avoid needing to remember many corner cases or multiple ways to do something.)
Resource allocation may fail; resource deallocation must succeed. (Design APIs with allocation failure in mind, and ensure cleanup always runs.)
Memory is a resource. (Emphasize that managing memory is as important as managing any other resource.)

These guiding tenets shape every aspect of Zig. In practice, Zig favors a minimalist and consistent feature set. For example, Zig has no language-level preprocessor or macros, unlike C[3]. Instead, it uses compile-time execution and conditional compilation to achieve the same results in a safer way. Andrew Kelley questioned why C needs a separate preprocessor and found many legacy reasons unconvincing[6]. In Zig, if an if condition is known at compile time, the compiler simply omits dead branches entirely (even if they contain invalid code)[7]. This means you can replace C macros with const values, comptime conditionals, and inline code execution – gaining clarity since there's no separate "meta-language" to learn[8][7].

Another core principle is no hidden control flow[3]. There are no exceptions unwinding the stack behind your back, no secret garbage collector pauses, and no implicit memory allocations happening without the programmer's consent. Zig code is meant to be straightforward to reason about: if something happens, it's because you explicitly wrote it. This is one reason Zig opts for explicit error handling (using error return values) instead of exceptions, and why it requires the programmer to manage memory manually (or explicitly use library allocators). The philosophy "favor reading code over writing code" shines here – Zig code may be a bit more verbose than code in languages with many implicit behaviors, but it is transparent. A Zig programmer reading another's code should be able to clearly see the resource management and control flow without surprises.

Finally, Zig's motto of being a robust, optimal, and reusable language is reflected in how it balances performance and safety. Zig does not chase complete memory safety through an onerous borrow checker (as Rust does), but it does include many optional safety checks (bounds checking, use-after-free detection in debug mode, etc.) and encourages safe practices by default. The designers describe Zig as taking an "incremental approach to safety" that avoids sweeping mandates in favor of practical trade-offs[9]. For instance, Zig allows potentially unsafe features like manual memory management or even untagged unions for performance, but it offsets these with testing, compile-time checks, and guard rails (like optional runtime checks) to catch mistakes. A striking example: Zig's compiler uses an untagged union design for its AST nodes to reduce memory footprint and improve cache usage, yielding a 35% speed boost – something that would be difficult to achieve in Rust safely, as Rust doesn't allow untagged unions without unsafe code[10][9]. Zig can get this performance while still performing safety checks on union field accesses, demonstrating the "no compromise" approach to speed and safety[11][9].

In summary, Zig's idioms stem from its philosophy: clarity, explicitness, and control. Writing idiomatic Zig means writing code that clearly states its intent, handles errors and resources explicitly, and uses compile-time capabilities to simplify runtime logic. In the following chapters, we'll explore how these principles manifest in Zig's specific features and patterns.

Basics of Zig Syntax and Types

Zig's syntax will feel familiar to C/C++ developers in many ways: it uses braces for blocks, semicolons to end statements, and similar control flow keywords (if, while, for, etc.). But there are notable differences and simplifications aimed at clarity and safety. Below are some core basics of Zig syntax and type system that a beginner should grasp:

Variables and Constants

Zig distinguishes between mutable and immutable bindings using var and const keywords. Use const whenever a value should not change. Zig encourages immutability for clarity. Types are specified after a colon. For example:

const greeting: []const u8 = "Hello, Zig!"; // immutable string slice (UTF-8 bytes)
var count: u32 = 42;                         // mutable 32-bit unsigned int

If the type can be inferred from context, you can omit it. Zig has local type inference for assignments, so you could also write const greeting = "Hello, Zig!"; and Zig will infer the type as []const u8 (a slice of constant bytes).

Arrays and Slices

Zig has both fixed-size arrays and slices (dynamic views into arrays). A type like [10]u8 is an array of 10 bytes. Slices are written as []T (or []const T for read-only slices) and package a pointer with a length. For example, []const u8 often represents a string (pointer to bytes + length). Unlike C, Zig slices carry their length, which helps prevent overflow errors. Slices are not null-terminated by default (they are not C-style strings), but Zig supports sentinel-terminated slices when needed (for C interop or certain patterns, you can specify a sentinel value).

Pointers

Zig pointers are explicit and non-nullable by default. For instance, *T is a pointer to T that is assumed valid; a nullable pointer must be typed as ?*T (an optional pointer). This means Zig forces you to consider nullability as part of the type system, preventing accidental null dereferences unless you explicitly allow them. Pointer types also carry information like alignment and constness. Zig does not implicitly decay arrays to pointers as C does – you must explicitly take a pointer or slice to an array when needed.

Control Flow

Zig's control structures (if, while, for, switch) are similar to C's but with some enhancements. For example, if can be used as an expression (it can return a value), and Zig has a switch expression for exhaustive handling of enums. Zig also offers labeled break and continue, and a return which can be used to early-exit from functions. One unique construct is defer, which we will cover in the next chapter – it allows scheduling a block of code to run at function exit (similar to defer in Go or finally blocks, and analogous to RAII scope guards in C++). There's also errdefer for deferring cleanup only in error cases.

Functions and Types as First-class

Functions are declared with fn and can be nested inside other functions or at global scope. Zig does not have overloading – each function name is unique within its scope. Instead of function overloading, Zig relies on compile-time generics or different names. Also, types are first-class values in Zig; you can pass types to functions or variables (marked with comptime as needed). This is key to Zig's approach to generics and compile-time programming, which we will explore later.

Structs and Enums

Zig's structs are similar to C structs and can have functions (methods) defined inside them. However, Zig has no classes or inheritance – composition is preferred for building complex types. Zig enums are like in C but safer: they are proper types (you can iterate over them, switch exhaustively, etc.), and Zig also has error enums and union types, which are integral to error handling and variant types.

Example Program

As a quick example, here is a simple Zig program that prints a greeting and demonstrates some basic syntax:

const std = @import("std");

pub fn main() !void {
    const name = "Zig";
    std.debug.print("Hello, {s}!\n", .{name}); // prints "Hello, Zig!"

    var numbers: [3]u32 = [3]u32{ 10, 20, 30 };
    var slice = numbers[0..]; // a slice over the array

    // Iterate over the slice:
    for (slice) |num, idx| {
        std.debug.print("numbers[{d}] = {d}\n", .{idx, num});
    }
}

In this snippet, std.debug.print is analogous to C's printf (it's Zig's standard way to print formatted output). We define an array and then a slice covering the whole array (numbers[0..] produces a slice of all elements). The for loop syntax for (slice) |item, index| { ... } iterates over the slice, giving us each item and its index. This example highlights Zig's explicit style (we explicitly request a slice, we explicitly handle the loop index) and its safety (the for loop knows the slice length and won't overflow).

Overall, Zig's syntax aims to be familiar yet more strict than C's, eliminating common sources of bugs (null references, unchecked array accesses, uninitialized variables, etc.). The idiomatic Zig code often looks a bit more verbose than idiomatic C (due to explicit error checks and no implicit conversions), but it is self-documenting – it communicates exactly what it's doing. In the following sections, we will dive into Zig's approach to memory management, error handling, and other key areas, and how to use them in an idiomatic way.

Memory Management and Allocators in Zig

Memory management in Zig is manual (like C), but with language and standard library support that encourages safe and flexible patterns. A core tenet is that Zig has no global allocator baked into the language – you are expected to pass an allocator to any code that needs to allocate memory[12][13]. This design avoids hidden allocations and gives the programmer control over when and how memory is allocated and freed, which is crucial in systems programming. It also allows Zig code to be used in freestanding environments (OS kernels, bare metal) by providing a custom allocator.

Allocator Basics

An allocator in Zig is an object (typically implementing the std.mem.Allocator interface) that can provide and free memory. The standard library offers general-purpose allocators (for example, std.heap.GeneralPurposeAllocator which is a malloc/free wrapper, or std.heap.ArenaAllocator for bump allocation, etc.), but you can write your own. By convention, many functions that need to allocate will take an Allocator parameter. For example, to create a dynamic array (an ArrayList in Zig):

const std = @import("std");

pub fn exampleAllocation() !void {
    var gpa = std.heap.GeneralPurposeAllocator(.{}){}; // a general-purpose allocator
    const alloc = &gpa.allocator;

    // allocate an array list of integers
    var list = std.ArrayList(u32).init(alloc);
    defer list.deinit(); // ensure memory is freed when we're done

    try list.append(100);
    try list.append(200);
    std.debug.print("List length: {d}\n", .{list.len});
    // We can still access C-like: e.g., list.items[0] == 100
}

In this snippet, we initialize a general-purpose allocator and then pass it to an ArrayList. The ArrayList.init function requires an allocator because it will allocate a backing buffer. We use defer list.deinit() to ensure that regardless of how the function returns (even if an error occurs), the allocated memory is freed. This pattern of RAII via defer is very common in Zig – defer behaves like a scope guard to run cleanup code when leaving the scope. Here it replaces what would be a manual free in C, but with the guarantee it won't be forgotten even if a premature return happens (for example due to an error). Zig's philosophy "resource deallocation must succeed" and "runtime crashes are better than bugs" is evident: if list.deinit() failed (it shouldn't in this case), Zig would print a stack trace and exit, rather than silently leaking.

Resource Management Pattern

Zig encourages tying allocations and deallocations closely together in the code structure. Often you'll see patterns like:

Allocate a resource (memory, file handle, etc.)
Immediately follow with a defer to free/close that resource
Use the resource in between, propagating errors as needed.

This makes the lifetime of resources obvious and prevents leaks even in error cases. For instance, if you allocate multiple things that depend on each other, Zig provides errdefer which only runs on error paths, allowing conditional cleanup. But in many cases, simple defer works if you allocate and then might error out later.

Why Pass Allocators?

Because it gives control to the caller. A library function that takes an Allocator can be used in a kernel (with a custom allocator) or in a game (with a frame allocator) or in a script (with a GC-like arena), all without changing the function's code. It also makes dependencies explicit – if a function takes an allocator, you know it might allocate memory, and you won't be caught by surprise by hidden heap usage[14]. This is part of Zig's goal to avoid hidden interference in code reuse.

No Hidden Memory Allocations

Zig's standard library follows this rule stringently. For example, functions that format strings, manipulate data structures, or perform I/O will either use a provided allocator or operate on caller-provided buffers. There's no global malloc happening under the hood. This explicitness can be contrasted with languages like Python or even C++ where simply constructing a std::string might allocate memory unbeknownst to the caller. In Zig, if memory allocation happens, it's because you (or someone up the call stack) asked for it.

Stack vs Heap

Zig encourages using the stack for fixed-size or short-lived data when possible (like local arrays), and only using the heap (via allocators) for dynamic or large data. This again is about clarity and performance – stack allocation is trivial and verified at compile time for size, whereas heap allocation has to be managed. Zig does not have a built-in garbage collector; memory lifetime is a concern the programmer must handle, similar to C. However, tools like Zig's test allocator can help catch leaks. Zig's testing mode provides a special allocator that will track allocations and automatically fail a test if something was never freed[15] – a helpful feature to enforce discipline during development.

In idiomatic Zig, you'll often see function signatures like fn foo(alloc: std.mem.Allocator, ...) !ReturnType. The exclamation mark (!) indicates the function can fail with an error (which we'll cover in the next chapter), and the first parameter being an Allocator indicates this function may allocate. This makes it very clear to the caller what the function does and what it needs, following the principle "communicate intent precisely" (the intent here includes "I need memory allocation to do my job").

Example – Reading a File

To illustrate memory management, let's consider a simple example of reading a file into memory:

const std = @import("std");

pub fn readFileContent(alloc: std.mem.Allocator, path: [:0]const u8) ![]u8 {
    // Open the file
    var file = try std.fs.cwd().openFile(path, .{});
    defer file.close(); // ensure file is closed

    // Get file size and allocate buffer
    const file_size = try file.getEndPos();
    var buffer = try alloc.alloc(u8, file_size);
    defer alloc.free(buffer); // free buffer when function exits

    // Read file into buffer
    try file.readAll(buffer);

    return buffer; // return the allocated buffer (caller will own it)
}

A few things to note in this example:

path: [:0]const u8 is a null-terminated byte slice (Zig's way to accept a C-style string). We use it because openFile expects a null-terminated string for the path.
We open the file and defer its close immediately. openFile returns a std.fs.File handle.
We determine file size, then allocate a buffer of that size using the provided alloc. We defer freeing that buffer.
We read all content into the buffer and return it.

This function cleanly handles resources: if any step fails (open file, get size, allocate, read), the defer statements ensure that any opened file is closed and any allocated memory is freed before the error propagates upward. The caller of readFileContent gets either an error or the buffer with file data. Importantly, ownership of that buffer transfers to the caller – the caller must eventually free it (using the same allocator). Zig makes no apologies about this manual aspect; it's by design, to avoid hidden complexity.

To sum up, idiomatic Zig code treats memory as a resource that must be managed consciously. You pass around allocators (or other context objects like we'll see with I/O), you use defer to tie resource lifetimes to scopes, and you avoid global state or hidden allocations. This explicit model brings confidence that when you review a Zig function, you can easily see where memory is obtained and released, which is key in systems programming. As the Zig documentation puts it: "Memory is a resource." If you manage it well, Zig will give you both performance and safety benefits in return.

Error Handling in Zig

Error handling is one of Zig's most distinctive features. Instead of exceptions or heavy-handed result types, Zig uses a mechanism of error unions and error propagation that combines the rigor of checked errors with zero runtime overhead in safe scenarios. In Zig, errors are values, not control flow exceptions[16]. This means functions that can fail will explicitly return an error value (tagged in the type), and the caller must handle or propagate that error. This design yields no hidden control flow on errors and forces error cases to be considered at compile time[17].

Error Unions and Error Sets

Zig's type system has a built-in concept of an error set: a special error{...} type that enumerates possible error tags. For example, you can define:

const FileError = error{ NotFound, PermissionDenied, InvalidPath };

This defines an error set FileError with three possible error values[18]. It's similar to an enum specifically for errors. Functions in Zig can be annotated to return an error union type, which is written as !T (meaning "either an error or a T value"). For instance, a function returning FileError!u32 would either return a u32 result or one of the FileError errors.

In practice, you often don't name a custom error set for every function; Zig can infer an error set from what errors you actually return. But using named error sets (especially for public APIs) is encouraged to precisely document what errors can occur[19]. One idiomatic pattern is to define error sets per subsystem or module (e.g., a DatabaseError or NetworkError set).

Propagating Errors with `try`

The most common way to handle errors in Zig is to propagate them to the caller if you can't or don't want to handle them locally. Zig provides the try keyword as a convenient shorthand. Using try before a call that returns !T will either produce the T value (if the call succeeded) or, if an error was returned, it will bail out (return the error from the current function)[20][21]. You can think of try as "propagate the error upward if any, otherwise give me the result." This is analogous to the ? operator in Rust or the throw propagation in languages with exceptions, but here it's explicit in the code and checked by the compiler.

Example:

fn readNumber(str: []const u8) !u32 {
    if (str.len == 0) return error.EmptyString;
    return std.fmt.parseInt(u32, str, 10);
}

pub fn example() !void {
    const num = try readNumber("123");
    std.debug.print("The number is: {}\n", .{num});

    // Handling an error case explicitly:
    const bad = readNumber("abc") catch |err| {
        std.debug.print("Invalid input: {} error\n", .{err});
        return err; // propagate the error after logging
    };
}

In readNumber, we return an error (error.EmptyString) if the input string is empty, otherwise we delegate to Zig's std.fmt.parseInt which itself can fail if the string isn't a valid integer. The !u32 return type indicates this function can return an error or a u32. In example, we use try to get the number or propagate the error. Then we show another way to handle errors: using catch |err| { ... }. The expression expr catch |err| handle will catch an error from expr (if it errors) and run the handler block with err. In our example, we catch the error from readNumber("abc"), print a message, and then return err; to propagate it up (perhaps to main, which might print a stack trace or handle it further).

This leads to another Zig idiom: error union values can be caught and transformed. The catch expression can either handle the error or even translate it to a different error. For instance, you could do:

const data = readData() catch |err| switch (err) {
    error.OutOfMemory => return error.CannotProcess,
    error.InvalidData => return error.BadFormat,
    else => return err,
};

This catches specific errors and maps them to higher-level errors, otherwise propagating anything else[22]. Zig's compiler will ensure you cover all possible errors (the else covers any not explicitly listed). This is how you convert low-level errors to more abstract ones when needed.

No Hidden Exceptions (and Error Traces)

Because Zig doesn't use stack unwinding exceptions, you won't find try-catch-finally blocks for exceptions. Instead, errors bubble up through ! types. This has zero runtime cost when not hitting an error (just like returning a nullable or error code in C), but Zig adds improvements: error return traces. In debug mode (or when enabled), Zig can provide a trace of the error's origin, akin to an exception stack trace, without the runtime overhead when not needed[23][24]. This is extremely useful for debugging. For example:

fn deep() !void {
    return error.SomethingWentWrong;
}

fn middle() !void {
    try deep();
}

pub fn main() void {
    middle() catch |err| {
        std.debug.print("Error: {}\n", .{err});
        // This will print "Error: SomethingWentWrong" along with a stack trace
        // of where the error came from, even in an optimized build (when enabled).
    };
}

In this code, if deep returns an error, middle will propagate it, and main catches it and prints it. The {err} formatting in Zig automatically includes the error trace (if available) when printing an error. Notably, this does not incur overhead in release builds unless you request the trace, due to Zig's design.

Best Practices for Zig Errors

Idiomatic Zig code treats errors similarly to how one would treat return codes in C, but with stronger type checking and nicer syntax. Some best practices include:

Use Specific Error Sets: If your function can fail in specific ways, define an error set for it. This makes your API self-documenting (e.g., io.read() !void might return any I/O error, but Database.query() DatabaseError!Result specifically returns only DatabaseError variants)[25]. Zig ensures at compile time that you only return errors that are in your function's error set, preventing accidental leakage of unexpected error types.

Propagate Unless You Can Handle or Enrich: Generally, you should propagate errors (try) up the call stack until a place where you have enough context to handle them or report to the user. At top-level (like main or in tests), if an error isn't caught, Zig will print it and a stack trace. Many Zig programs liberally use try in most functions and only catch at boundary points (similar to how you'd bubble errors up in a Go program until you can handle or log them).

Avoid Control Flow for Non-Errors: Zig's errors are for exceptional conditions, not routine control flow. Use them for things that truly are errors (cannot open file, invalid input, etc.), not just to break out of loops or other logic – Zig has loops and break/continue for that.

Prefer Error Unions to Out-Parameters for Errors: In C, a common idiom is to return an error code and have an out-parameter for the result. In Zig, the error union accomplishes this in one return type. The idiomatic way is to return !T. Only if you need to return both a primary result and some error details do you consider something like an out-parameter or a struct. For example, a parsing function might on error want to tell the position of error – one might design it to return an error and fill a provided struct with details (this pattern is sometimes called the "error payload pattern"). But if you just need a message, you could include it in the error itself since errors can be printed with details or even be structs in Zig (advanced usage). Simpler is better: most Zig code just uses the basic error.Foo values.

Zig's approach to errors brings a blend of safety (the compiler enforces that you deal with errors) and performance (no heap allocations or table unwinding for errors). It also aligns with Zig's philosophy: there's no hidden magic, you see error handling at the call site (via try or catch), making the control flow explicit. As the Zig documentation emphasizes, this approach yields clear error propagation, no need for try-catch blocks everywhere, compile-time checking of error cases, and zero-cost abstraction for release builds[17]. Idiomatic Zig code embraces these error handling constructs, resulting in robust and maintainable error management.

Compile-Time Programming with `comptime`

One of Zig's superpowers is its ability to execute code at compile time, which unlocks patterns that would require templates or macros in other languages. In Zig, types, functions, and even arbitrary computations can be evaluated during compilation (using the comptime keyword or by virtue of being in a const context). This feature allows Zig to achieve generics, reflection, and optimized code paths without adding separate complex mechanisms. Idiomatic Zig code often leverages comptime to reduce runtime overhead and avoid repetition.

Compile-Time Execution

Any function in Zig can potentially be executed at compile time if all of its inputs are known at compile time. You can force a function to run at compile time by marking the call with comptime or by computing the result into a const. For example:

const std = @import("std");

// A function that computes factorial, which we can run at compile-time
fn factorial(n: comptime_int) usize {
    return if (n <= 1) 1 else n * factorial(n - 1);
}

const FIVE_FACTORIAL = factorial(5); // This is computed at compile time
const ARR: [FIVE_FACTORIAL]u8 = [_]u8{0} ** FIVE_FACTORIAL; // array of length 120

pub fn main() void {
    std.debug.print("5! is {d}\n", .{FIVE_FACTORIAL});
}

In this snippet, factorial(5) is computed during compilation, and the result (120) is known at compile time and used to declare an array size. Zig's ability to use a runtime-looking function for a compile-time constant is a huge improvement over C where you might resort to macros or constant-folding tricks. Zig simply "just fixed that so it works" – recalling Andrew Kelley's point about using constants in places C would require a #define[8]. In Zig, any const or comptime var must be evaluable at compile time, and Zig's compile-time interpreter will do the job.

The keyword comptime can be used in various ways:

To mark variables that must be known at compile time: comptime var x = ...;
To restrict function parameters to compile-time known values: fn f(comptime T: type) { ... } or fn f(comptime n: usize) { ... }. If you call f with a runtime value for n in the latter case, it's a compile error.
To execute a block of code at compile time, e.g. comptime { /* code */ } within a function (less common, but occasionally used for compile-time loops or debugging).

One common idiom is compile-time reflection. Zig has built-in compile-time reflection functions (in the @ builtins) such as @typeInfo(T) which gives you a description of a type T at compile time, or @field to access struct fields by name at compile time, etc. This enables writing code that adapts based on type information or generates boilerplate automatically.

Generics via Compile-Time Parameters

Zig does not have traditional generics (like C++ templates or Rust's parameterized types). Instead, Zig achieves generic programming by using comptime parameters in functions and types. For example, to make a function that works for any type that supports a certain operation, you might write:

fn max(comptime T: type, a: T, b: T) T {
    return if (a > b) a else b;
}

Here, T is a compile-time type parameter. You call this as const m = max(i32, x, y); or even let Zig infer the type by calling in a comptime context. The Zig compiler will monomorphize max for each unique T it's called with (just like C++ templates or Rust generics) – but the syntax and model is simpler. You could also write pub fn max(T: type, a: T, b: T) T comptime { ... } to indicate it must be evaluated at compile time, but usually it's not needed; Zig figures it out.

The standard library uses this pattern pervasively. For example, std.ArrayList(T) is actually a generic type defined as a struct inside a fn ArrayList(comptime T: type) type { return struct { ... }; }. Calling std.ArrayList(u8) actually invokes that function at compile time to produce a specialized struct type for u8 elements. This is a bit mind-bending at first: Zig can generate new types on the fly at compile time by executing code. But it means you can do things like:

const IntArrayList = std.ArrayList(i32);
var list = IntArrayList.init(allocator);

And similarly for any type. The creation of IntArrayList happens during compilation.

This compile-time approach extends to more than just types. You can compute lookup tables at compile time, unroll loops for small bounds, or even implement domain-specific languages. It replaces many uses of C macros. For instance, if you needed a constant lookup table of sin/cos values for an embedded system, you could compute it with a comptime for loop and store it in a const array – no runtime cost.

Conditional Compilation and Inline For

As mentioned, Zig doesn't have a preprocessor. Instead, you use normal if statements, but if the condition is known at compile time, Zig will include or exclude code accordingly[7]. For example:

if (std.builtin.os.tag == .windows) {
    // windows-specific code (compiled only on Windows)
} else {
    // non-Windows code
}

std.builtin.os.tag is a compile-time constant representing the target OS; the compiler will only compile the relevant branch for the target. This is how Zig achieves what C/C++ would do with #ifdef – but in a way that's integrated into the language's semantics (the unused branch is just not compiled). This makes configuration and platform-specific code clearer and type-checked in context.

Another feature is inline for, which can be used to unroll loops at compile time if the number of iterations is known and small. E.g.:

inline for (std.meta.fields(MyStruct)) |field| {
    // do something with each field at compile-time (reflection)
}

This loop would iterate over the fields of MyStruct at compile time (using std.meta.fields which returns a list of field info).

When to Use Compile-Time Code

Idiomatic Zig uses compile-time programming to eliminate boilerplate and runtime overhead. Some common use cases:

Generic data structures and algorithms: As seen, creating reusable code that works for any type (like ArrayList, sorting functions, etc.).
Building lookup tables or constant data: Precomputing expensive results at compile time so that at runtime you have a ready array or value.
Interface-like patterns: Zig can use comptime techniques to implement trait-like behavior. For instance, you might have a function that accepts a comptime T: type and then uses @hasDecl or @compileError to require that T has certain functions or fields (thus achieving a static duck-typing requirement).
Avoiding branches in critical code: You can write code that at compile time specializes for certain constants. For example, if you had a math function that for size 4 does something unrolled, you can detect comptime if (N == 4) { ... } else { ... }.

A quick example combining some of these ideas is a function that serializes any struct into a byte buffer (a simplistic example):

fn serializeStruct(comptime T: type, bytes: []u8, value: T) !usize {
    // ensure the buffer is big enough
    if (bytes.len < @sizeOf(T)) return error.BufferTooSmall;
    // Copy memory directly (only valid if T has no pointers needing special handling)
    std.mem.write(bytes[0..@sizeOf(T)], @ptrCast([*]u8, &value)[0..@sizeOf(T)]);
    return @sizeOf(T);
}

This uses @sizeOf(T) (comptime known size of type) and @ptrCast to treat the struct as bytes. It's not a complete robust solution (since endianness and padding matters), but it illustrates how T can be used in a function to generate code for any struct. The check if (bytes.len < @sizeOf(T)) is a runtime check here (since bytes.len is runtime), but @sizeOf(T) is compile-time known constant.

To summarize, Zig's comptime provides macro-like metaprogramming and template-like generics in a unified, type-safe way. Idiomatic Zig code will use compile-time programming to avoid repetition and to push computations out of runtime. This often results in very efficient code (since unnecessary abstraction cost is optimized away) and concise code (since you can write a generic once instead of multiple overloads). However, it's also idiomatic to not overuse comptime for trivial things – use it where it meaningfully improves the code. Remember, Zig favors readability: if compile-time magic makes the code harder to understand, consider a simpler approach. The power is there when you need it, and it truly shines in implementing low-level patterns and domain-specific optimizations that would be clumsy in C.

Data-Oriented Design and Performance Patterns

One phrase you'll often hear in the Zig community (and from its creator) is data-oriented design (DOD) – which is part of the "other Zig native techniques and patterns" the user of this guide is curious about. Data-oriented design is not unique to Zig, but Zig's philosophy and features make it particularly amenable to writing code in a data-oriented way. In essence, DOD means structuring your program around the data layout and access patterns for optimal performance, rather than around objects or classes. It often entails thinking in terms of arrays, memory layout, and cache behavior, as opposed to deep pointer-chasing or classical object hierarchies.

Andrew Kelley applied data-oriented design principles when developing the Zig compiler, resulting in major performance wins[11]. For example, he rearranged data structures to be smaller and more cache-friendly, such as using untagged unions with separate arrays of type tags to reduce per-element size[10]. By doing so, the Zig compiler's speed improved significantly (35% faster) because the CPU cache was utilized more efficiently[10]. This kind of refactoring is a textbook DOD move: split or pack data to minimize memory footprint and maximize sequential memory access.

Zig's Support for DOD

Zig as a language doesn't force any particular paradigm (it's multi-paradigm in that sense), but it gives you the low-level control to implement DOD techniques:

You can easily replace array-of-structs with struct-of-arrays layouts.
You can use value types and slices instead of heap allocating every little object.
You can pack data into bits or bytes manually, and Zig will help ensure you don't overstep bounds.
There's no built-in OOP overhead; Zig's abstractions are zero-cost when you don't need them, so an array of plain structs is as efficient as it gets.

Contrast with OOP

A question on the Zig forum once showed a pattern that was more object-oriented – a Field struct tracking a value and its previous value and a dirty flag for a UI system. The advice given was to lean more toward a data-oriented approach: instead of bundling current value, previous value, and dirty flag in one struct (which means every access brings in all that data, even if you only need one piece), consider separating them into parallel arrays or structures[26][27]. That way, if you are updating values en masse, you can do so in one array; if you are checking dirty flags, you can scan a compact array of booleans; this improves memory locality and avoids pulling irrelevant data into cache.

For example, say you have an array of game entities with positions and health. An OOP approach might make an Entity struct with many fields and then an array of Entity. A DOD approach might keep a separate array for positions (x,y coordinates), a separate array for health values, etc., especially if not all systems need all data. Iterating over just health to update regeneration would only touch the health array memory, which is densely packed.

Manual Memory Layout

Zig empowers you to manage memory directly, which is crucial for DOD:

You can allocate a single block of memory and carve it into pieces for different arrays, ensuring they are contiguous.
You can align structures in specific ways. Zig's @sizeOf and @alignOf can help introspect how things are laid out.
Zig's lack of hidden pointers or object headers means a struct is just its data; you can copy it, reinterpret it as bytes, etc., with confidence about what's in it.

Zig doesn't automatically do structure field reordering or anything (that's up to you), but it gives you the tools to implement any memory layout you need.

Data-Driven Programming

Data-driven programming (as mentioned in the prompt) can also refer to the idea of writing logic that is driven by data tables or configuration rather than hard-coded logic. Zig's compile-time features can help here: you could, for instance, define a compile-time list of entities or levels, and Zig can generate code or data from it. Or more commonly, use arrays of function pointers or pointers to data to avoid large switch statements. Zig's functional pointers are just values (and with anyframe/callable features, you can even store and call async functions, etc., but that's advanced).

A specific Zig twist on DOD is how allocators and I/O contexts are passed in: this is somewhat analogous to dependency injection and makes code more data-driven in the sense that the behavior can change based on the provided allocator or I/O object. It inverts control – the caller provides the policy (allocator strategy or I/O model) as data, and the callee just uses it. This keeps functions generic and decoupled from global state or specific implementations, which is a form of data-oriented thinking at the design level.

When to Favor DOD

Not every piece of code needs extreme optimization or data packing. Zig's mantra "Optimize for reading code" still applies. However, in inner loops or performance-critical sections (like game loops, processing large datasets, compiler analysis, etc.), thinking about how data is laid out in memory often pays off. Zig programmers are encouraged to measure and understand their program's memory access patterns. Tools like std.benchmark can measure performance, and then you might refactor a struct-of-arrays to an array-of-structs if needed (or vice versa).

Safety vs Performance Trade-offs

Zig allows some things that higher-level languages disallow for the sake of performance. For example, using an enum combined with another field could leave unused padding – Zig might let you exploit that or explicitly pack data tighter with bit fields in a struct (by using integers of specific bit sizes, e.g. u1 for a boolean packed with other fields). You can even cast pointers to treat data differently if you're careful (though you must ensure alignment and no aliasing rule violations). Zig will let you do lower-level tricks, but it's on you to ensure correctness. The benefit is, you can squeeze out performance when you need to. As noted in the Sourcegraph interview, Zig's approach let Andrew achieve optimizations that would be "not possible in languages like Rust" without using unsafe code[28].

Example – Data Packing

Suppose we have a game with creatures that can be either Elf or Orc and can be alive or dead. An OOP approach might have:

const CreatureType = enum { Elf, Orc };

const Creature = struct {
    kind: CreatureType,
    isAlive: bool,
    // ... other fields
};

But that means isAlive might add padding (say CreatureType is an u32 and a bool could be 1 byte, likely padded to 4 bytes alignment, making the struct 8 bytes with 3 bytes wasted). A DOD approach could encode both the kind and alive/dead status in one enum:

const CreatureStatus = enum { AliveElf, DeadElf, AliveOrc, DeadOrc };

Now one field encodes what was two fields[29]. Or even better, keep separate arrays: one array for all alive creatures, one for all dead creatures (so you don't even need an isAlive flag at all, context is the container)[30]. These techniques harken back to old-school C tricks (as an article noted, many DOD techniques are how we used to program in constrained systems decades ago)[31][32], and Zig's low-level nature makes them natural to implement.

Summary

The takeaway is: Zig doesn't mandate data-oriented design, but it enables it, and the community often reaches for it in pursuit of performance. Idiomatic Zig in a high-performance context will often look like plain arrays of data being crunched in tight loops, with as little abstraction or indirection as possible. You might see manual memory pools, custom allocators tuned for specific usage, and a preference for composition over hierarchy (for example, an ECS – Entity Component System – in Zig might use integers or struct indexes to refer to components instead of pointers to objects).

When writing Zig, always consider the shape of your data. If you find yourself following an OOP pattern out of habit (embedding a lot of state in one object and passing it around), ask if splitting it or reorganizing it might yield better cache use or simpler code. Many times, the answer is yes, and Zig gives you the freedom to do it. Data-driven techniques (like using data tables or config files) also fit well with Zig because you can generate or embed data easily, and you can parse or interpret it with full control.

In conclusion, idiomatic Zig code tends to be data-centric. The language's features (manual memory, pointers, flexible unions, comptime) and philosophy (reduce hidden costs, maximize performance) encourage you to structure programs around the data layout that works best for your problem. This often leads to code that is both faster and, once understood, more straightforward because it's clear what data is flowing where. With Zig, you are "closer to the metal" in a good way – you think about memory and data as much as about abstract interfaces.

Concurrency and Async in Zig

Concurrency in Zig has evolved significantly, especially with recent updates to the language (the addition of async/await in the latest branch). Historically, Zig did not have built-in async/await like some languages; instead, it offered low-level threading (via std.Thread) and encouraged an explicit event-loop or state-machine style for asynchronous I/O. However, as of 2025, Zig has introduced a new async I/O model that revives async/await in a unique, "colorblind" way, aligning with Zig's philosophy of code reuse and explicit control[33][34].

Threads and Basics

At the basic level, Zig can spawn OS threads using the standard library. For example, std.Thread.spawn can start a new thread running a given function. This is similar to threads in C or C++, and synchronization primitives like mutexes, atomics, and channels exist in Zig's standard library (std.Sync module). That said, idiomatic Zig often avoids threads unless needed, as thread scheduling overhead can be significant. Instead, there's a focus on asynchronous I/O and event-driven concurrency for high scalability (similar to how e.g. Node.js or Go handle many tasks without spawning OS threads for each).

The New Async/Await Model

The newest Zig concurrency features revolve around an Io interface and the keywords async and await. Zig's approach here is quite different from other languages. Rather than making async a function modifier that transforms its type (as in C# or JavaScript), Zig has introduced an I/O abstraction (std.Io) that you pass around, and methods on it to create and await asynchronous tasks[35][36]. This is somewhat analogous to passing an allocator for memory, now you pass an Io for any operation that might block or involve concurrency.

Key points of Zig's async model:

The Io interface encapsulates whatever event loop or threading mechanism you want to use for running tasks concurrently. The application (or higher-level code) provides a concrete Io implementation (for example, an event loop that uses epoll or IOCP, or an implementation that simply delegates to blocking calls on separate threads).
Functions that perform I/O or blocking work now typically accept an Io parameter. For instance, file operations in std.fs now require an Io argument so that if the underlying Io is asynchronous (say, using non-blocking system calls), the function can cooperate with it[37].
To launch asynchronous work, you call io.async(function, args_tuple). This returns a Future (a handle for the in-flight operation)[38]. You later call future.await(io) to wait for its result[39]. Both async and await are now keywords/methods in this context, but notably, an async call is not the same as an async function in other languages – here it's an expression that uses the Io to run a function possibly concurrently.
Because Zig requires explicit context, there's no function coloring issue of needing separate "sync" and "async" versions of every function. You can write a function like saveFile(io: Io, data: []u8) that just writes data; whether io does it blocking or non-blocking is up to the Io implementation. This means the same Zig code can work in synchronous or asynchronous mode, achieving Zig's goal of code reuse[33][34]. Libraries don't have to provide two versions of everything; one function can handle both cases.
Zig's design thus defeats the "function color" problem by making nearly every function potentially async or sync depending on context[34]. In other languages, you'd have separate call stacks or different keywords – Zig erases that by shifting the asynchrony into a passed-in object rather than the function's static type.

Example: Old vs New Approach

Old approach (sequential):

fn saveData(data: []const u8) !void {
    const file1 = try std.fs.cwd().createFile("A.txt", .{});
    defer file1.close();
    try file1.writeAll(data);

    const file2 = try std.fs.cwd().createFile("B.txt", .{});
    defer file2.close();
    try file2.writeAll(data);
}

This writes two files one after the other. If we wanted concurrency earlier, Zig had some coroutine support but it was limited and removed during compiler refactoring.

New approach with Io (concurrent):

const std = @import("std");
const Io = std.Io;

fn saveFile(io: Io, data: []const u8, name: []const u8) !void {
    const file = try Io.Dir.cwd().createFile(io, name, .{});
    defer file.close(io);
    try file.writeAll(io, data);
}

fn saveData(io: Io, data: []const u8) !void {
    var a_future = io.async(saveFile, .{io, data, "A.txt"});
    var b_future = io.async(saveFile, .{io, data, "B.txt"});

    // Wait for both to complete:
    try a_future.await(io);
    try b_future.await(io);

    const out: Io.File = .stdout();
    try out.writeAll(io, "save complete\n");
}

In this code, io.async launches two file writes "at the same time" (the actual concurrency depends on io – if io is a blocking implementation, it might just call them sequentially; if it's an event loop, they'll run in parallel). We then await both futures. Note how saveFile is written in a blocking style (open, write, close), but because it receives an io, if io is non-blocking it will integrate with the event loop properly. This code will work correctly whether io is synchronous or asynchronous – if synchronous, io.async might execute immediately and await is basically a no-op[40]; if asynchronous, it truly overlaps operations[40]. Thus the same code adapts to both scenarios, fulfilling the idea of one library for both sync and async.

Colorblind Async

Another noteworthy aspect: Zig's async/await are not built on a single mechanism like user-space threads or promises; they are more like syntax for interacting with whatever Io is given. Internally, if the Io uses an event loop, it might use stackless coroutines (which Zig supports under the hood) or native threads to achieve concurrency. But Zig decoupled the language async from the execution model[41][42]. In earlier attempts, Zig's async was tied to a specific coroutine model which turned out limiting; now io.async can use a variety of execution models (blocking, threads, or event loops) without changing the code that uses it[42].

This decoupling also means no viral calling convention: previously, if Zig async was implemented as a stackless coroutine, any function calling an async function had to itself be async (function coloring). Now, because io.async is just a normal call taking an Io, your function need not be marked specially – you just need access to an Io. This effectively colors every function as both, or colorless (hence "colorblind async")[33]. Indeed, Loris Cro writes: "Thanks to Zig's clever (and unorthodox) usage of async/await, a single library can work optimally in both synchronous and asynchronous mode... fully freeing you from any form of virality. With this last improvement Zig has completely defeated function coloring."[33][34]

Usage Considerations

From a user perspective:

If you want to write a high-level network server, you might use std.net with an evented Io (perhaps the default provided is an event loop that uses io_uring or similar on Linux). You write your server logic with io.async for each connection, and it looks somewhat like writing synchronous code, but under the hood, it's non-blocking.
If you write a library (say an HTTP parser), you likely just write it in a synchronous fashion (e.g., a function that reads from a reader, writes to a writer). Zig ensures that if someone uses your library in an async context, it still works without modification, because your reader/writer will be tied to the Io from above. If they use you in a simple script (blocking file I/O), it also works.

Important: Not every Zig programmer needs to sprinkle async everywhere now. The Zig FAQ on this feature clarifies: writing normal sequential code is fine; it will compose with the chosen I/O strategy of the program[43]. Library authors are not forced to use async everywhere to be "good citizens"[43]. In other words, if your library just reads files sequentially, it can still operate under an async runtime because the Io abstraction will handle the integration. You might only need to be aware of specific features like supporting Io.Writer.sendFile or other advanced I/O, but regular code composes naturally[43][44].

Cancellation and Timeouts

The new Future type that comes from io.async also supports cancellation. As shown in Loris Cro's article, you can do defer future.cancel(io) catch {}; to ensure that if the function returns early (say, error or manual cancellation), the spawned async task is canceled and cleaned up[45][46]. This pattern ensures no background tasks linger if you abort an operation. The Zig design makes cancellation explicit as well – you decide when to cancel futures.

Example – Networking

Although a full networking example is beyond scope, a pseudo-snippet might be:

const server = try io.async(startServer, .{io, listener_socket});
defer server.cancel(io) catch {};

const client = try io.async(startClient, .{io});
// ... do other work ...

try server.await(io);
try client.await(io);

This could represent starting a server accept loop and a client concurrently. In a blocking Io context, this would deadlock if truly simultaneous tasks are needed (because one will block)[47], but in an evented context it would work. Zig's philosophy is that if you misuse async (like expecting concurrency where the Io is actually single-threaded blocking), that's a programmer error, not something the language will magically resolve[47]. In other words, asynchrony is not magic concurrency. Zig distinguishes asynchrony (tasks can happen out of order) from parallelism (tasks actually run at the same time). The io.async expresses the former; if you need true parallel threads, you might either rely on an Io that uses threads or explicitly spawn threads.

Performance Considerations

The new Io interface uses a vtable (dynamic dispatch) internally for its operations[48]. This might introduce a slight overhead (virtual function calls). However, Zig's optimizer can devirtualize calls if there's only one Io implementation used in the program[49]. That means if your whole program uses, say, the default event loop Io, the calls can be resolved at compile time to direct calls (especially in release mode). If you use multiple Io implementations in one binary, you pay the cost of virtual calls, but Zig deems that acceptable given those are rare scenarios (and even then, they optimized buffering to ensure minimal overhead in those cases[50][51]).

Summary

In summary, Zig's concurrency model as of the latest update is explicit, opt-in, and composable:

Explicit: you explicitly pass an Io and call async/await, nothing happens behind the scenes.
Opt-in: if you don't care about async, you can largely ignore it and your code will still work (just passing a default Io).
Composable: libraries written without specific knowledge of async can still be used in async contexts without modification, which is a big win for code reuse[33][34].

For a Zig beginner coming from C, it's useful to realize that you won't sprinkle pthread_create and mutexes manually everywhere; instead, you'll either write simpler single-threaded code or use this Io async framework for high concurrency needs. The design is different from what you might be used to in say Python or C++, but it serves Zig's goals of efficiency and reusability. It's a recent addition, showing that Zig continues to evolve, but does so in line with its core tenets – giving control to the programmer and avoiding hidden pitfalls (like colored function viral spread).

Conclusion and Next Steps

In this mini-book, we've covered the core of idiomatic Zig programming:

Zig's philosophy (simplicity, explicit intent, avoiding hidden behaviors) and how it influences language features.
The basics of Zig syntax and type system, which feel familiar to C developers but come with improvements that catch bugs early.
Manual memory management done right – with allocators and defer to ensure no leaks and no surprises, allowing maximum portability of code.
Zig's novel error handling that eliminates exceptions in favor of error unions, making error paths explicit and safe, with zero runtime cost in non-error cases.
The power of compile-time code execution (comptime) that lets you generate efficient code and avoid repetition, effectively giving you the abilities of C macros and C++ templates but in a unified and safer way.
Embracing data-oriented design for performance, structuring your programs around efficient data layout and processing, something Zig facilitates by letting you get close to the hardware details when needed.
The new async model in Zig, which shows Zig's commitment to robustness and code reuse by unifying synchronous and asynchronous paradigms under one roof, while still requiring the programmer to be explicit about I/O and concurrency.

As an experienced C developer learning Zig, you might find some things surprisingly familiar (pointers, manual memory, simple syntax) and others refreshingly different (no implicit conversions, no undefined behavior in safe code, compile-time tricks at your disposal). Zig is designed such that if you know C, you can map most concepts one-to-one[52] – but you'll gradually unlearn some C habits (like reaching for the preprocessor or not checking an error) because Zig offers better ways. Likewise, for high-level programmers, Zig gives a chance to understand what the machine is doing without being as footgun-prone as raw C.

The Journey Continues

The journey doesn't end here. To truly master Zig:

Read the official documentation and the Zig Standard Library: Zig's std lib is quite rich, and reading its source can teach idiomatic patterns (it's written in Zig itself). The "Zen of Zig" list we included is straight from Zig's docs[4] – keep those principles in mind as you code.
Practice by porting C code to Zig: Take a small C project and rewrite it in Zig, applying what you've learned about error handling and memory management. This hands-on approach will solidify the concepts.
Explore community resources: The Zig community is active on Zig's GitHub, forums, and Discord. There are plenty of projects (like the TigerBeetle database mentioned, written in Zig) that show real-world Zig code.
Keep an eye on Zig's evolution: Zig is reaching towards a 1.0 but isn't there yet (as of 2025). Features like the async model discussed are cutting-edge. Following the roadmap (e.g., the 2026 Roadmap talk[53]) or release notes will help you stay up to date with improvements.

Zig's ethos of "communication of intent" and "no compromise on performance or safety" can lead to very robust software. By understanding and using the idioms presented in this book – from Allocator passing to comptime metaprogramming to data-driven design – you'll be well-equipped to write Zig code that is clean, idiomatic, and efficient. Happy Zig coding!

Sources

Loris Cro, "Zig's New Async I/O", kristoff.it (Jul 2025) – Overview of Zig's revamped async/await and I/O model[35][33].
Sourcegraph Blog, "Revisiting the design approach to Zig" – Interview with Andrew Kelley on Zig's philosophy, memory allocation, and data-oriented optimizations[12][11].
Chris H., "Error Handling in Zig: A Fresh Approach to Reliability", DEV.to (Dec 2024) – Explanation of Zig's error sets, error unions, try/catch, and comparison to exceptions[54][17].
Ziglang Documentation, "The Zen of Zig" – Guiding principles for Zig design and idiomatic usage[4].
Zig git forum, "Architecture of a data-driven app" (2024) – Discussion on Zig idioms vs OOP, highlighting data-oriented design patterns for state management[26][27].

References

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [28] [52] Revisiting the design approach to the Zig programming language | Sourcegraph Blog https://sourcegraph.com/blog/zig-programming-language-revisiting-design-approach

[16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [54] Error Handling in Zig: A Fresh Approach to Reliability - DEV Community https://dev.to/chrischtel/error-handling-in-zig-a-fresh-approach-to-reliability-19o2

[26] [27] Architecture of a complex data-driven app - Explain - Ziggit https://ziggit.dev/t/architecture-of-a-complex-data-driven-app/3389

[29] [30] [31] [32] Data Oriented Design, a.k.a. Lower Level Programming? - hisham.hm https://hisham.hm/2022/02/19/data-oriented-design-aka-lower-level-programming/

[33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [53] Zig's New Async I/O | Loris Cro's Blog https://kristoff.it/blog/zig-new-async-io/

Idiomatic Zig Programming: A Guide for C Developers and Zig Beginners

Zig's Philosophy and Design

Basics of Zig Syntax and Types

Variables and Constants

Arrays and Slices

Pointers

Control Flow

Functions and Types as First-class

Structs and Enums

Example Program

Memory Management and Allocators in Zig

Allocator Basics

Resource Management Pattern

Why Pass Allocators?

No Hidden Memory Allocations

Stack vs Heap

Example – Reading a File

Error Handling in Zig

Error Unions and Error Sets

Propagating Errors with try

No Hidden Exceptions (and Error Traces)

Best Practices for Zig Errors

Compile-Time Programming with comptime

Compile-Time Execution

Generics via Compile-Time Parameters

Conditional Compilation and Inline For

When to Use Compile-Time Code

Data-Oriented Design and Performance Patterns

Zig's Support for DOD

Contrast with OOP

Manual Memory Layout

Data-Driven Programming

When to Favor DOD

Safety vs Performance Trade-offs

Example – Data Packing

Summary

Concurrency and Async in Zig

Threads and Basics

The New Async/Await Model

Example: Old vs New Approach

Colorblind Async

Usage Considerations

Cancellation and Timeouts

Example – Networking

Performance Considerations

Summary

Conclusion and Next Steps

The Journey Continues

Sources

References

Propagating Errors with `try`

Compile-Time Programming with `comptime`