Types and the Zig Programming Language

Notes on less-than-obvious aspects of Zigs type system and things that surprised me after diving deeper into the language.

Nominal Types

Zig has a nominal type system despite the fact that types lack names. A struct type is declared by struct { field: T }. Its anonymous; an explicit assignment is required to name the type:

const S = struct {
  field: T,

Still, the type system is nominal, not structural. The following does not compile:

fn f() struct { f: i32 } {
  return .{ .f = 92 };

fn g(s: struct { f: i32 }) void {
  _ = s;

pub fn main() void {
  g(f()); // <- type mismatch

The following does:

const S = struct { f: i32 };

fn f() S {
  return .{ .f = 92 };

fn g(s: S) void {
  _ = s;

pub fn main() void {

One place where Zig is structural are anonymous struct literals:

pub fn main() void {
  const x                      = .{ .foo = 1 };
  const y: struct { foo: i32 } = x;
  comptime assert(@TypeOf(x) != @TypeOf(y));

Types of x and y are different, but x can be coerced to y.

In other words, Zig structs are anonymous and nominal, but anonymous structs are structural!

No Unification

Simple type inference for an expression works by first recursively inferring the types of subexpressions, and then deriving the result type from that. So, to infer types in foo().bar(), we first derive the type of foo(), then lookup method bar on that type, and use the return type of the method.

More complex type inference works through so called unification algorithm. It starts with a similar recursive walk over the expression tree, but this walk doesnt infer types directly, but rather assigns a type variable to each subexpression, and generates equations relating type variables. So the result of this first phase look like this:

x = y
Int = y

Then, in the second phase the equations are solved, yielding, in this case, x = Int and y = Int.

Usually languages with powerful type systems have unification somewhere, though often unification is limited in scope (for example, Kotlin infers types statement-at-a-time).

It is curious that Zig doesnt do unification, type inference is a simple single-pass recursion (or at least it should be, I havent looked at how it is actually implemented). So, anytime theres a generic function like fn reverse(comptime T: type, xs: []T) void, the call site has to pass the type in explicitly:

pub fn main() void {
  var xs: [3]i32 = .{1, 2, 3};
  reverse(i32, &xs);

Does it mean that you have to pass the types all the time? Not really! In fact, the only place which feels like a burden are functions in std.mem module which operate on slices, but thats just because slices are builtin types (a kind of pointer really) without methods. The thing is, when you call a method on a generic type, its type parameters are implicitly in scope, and dont have to be specified. Study this example:

const std = @import("std");
const assert = std.debug.assert;

pub fn Slice(comptime T: type) type {
  return struct {
    ptr: [*]T,
    len: usize,

    fn init(ptr: [*]T, len: usize) @This() {
      return .{ .ptr = ptr, .len = len };

    fn reverse(slice: @This()) void{

pub fn main() void {
  var xs: [3]i32 = .{1, 2, 3};
  var slice = Slice(i32).init(&xs, xs.len);

  slice.reverse(); // <- look, no types!

Theres a runtime parallel here. At runtime, theres a single dynamic dispatch, which prioritizes dynamic type of the first argument, and multiple dynamic dispatch, which can look at dynamic types of all arguments. Here, at compile time, the type of the first argument gets a preferential treatment. And, similarly to runtime, this covers 80% of use cases! Though, Id love for things like std.mem.eql to be actual methods on slices

Mandatory Function Signatures

One of the best tricks a language server can pull off for as-you-type analysis is skipping bodies of the functions in dependencies. This works as long as the language requires complete signatures. In functional languages, its customary to make signatures optional, which precludes this crucial optimization. As per Modularity Of Lexical Analysis, this has repercussions for all of:

  • incremental compilation,
  • parallel compilation,
  • robustness to errors.

I always assumed that Zig with its crazy comptime requires autopsy. But thats not actually the case! Zig doesnt have decltype(auto), signatures are always explicit!

Lets look at, e.g., std.mem.bytesAsSlice:

fn bytesAsSlice(
  comptime T: type,
  bytes: anytype,
) BytesAsSliceReturnType(T, @TypeOf(bytes)) {

Note how the return type is not anytype, but the actual, real thing. You could write complex computations there, but you cant look inside the body. Of course, it also is possible to write fn foo() @TypeOf(bar()) {, but that feels like a fair game bar() will be evaluated at compile time. In other words, only bodies of functions invoked at comptime needs to be looked at by a language server. This potentially improves performance for this use-case quite a bit!

Its useful to contrast this with Rust. There, you could write

fn sneaky() -> impl Sized {

Although it feels like you are stating the interface, its not really the case. Auto traits like Send and Sync leak, and that can be detected by downstream code and lead to, e.g., different methods being called via Deref-based specialization depending on : Send being implemented:

struct X<T>(T);

impl<T: Send> X<T> {
  fn foo(&self) -> i32 { todo!() }

struct Y;
impl Y {
  fn foo(&self) -> String { todo!() }

impl<T> std::ops::Deref for X<T> {
  type Target = Y;
  fn deref(&self) -> &Y { todo!() }

fn f() -> impl Sized {
//  std::rc::Rc::new(())

fn main() {
  let x = X(f());
  let t = x.foo(); // <- which `foo`?
  // The answer is inside f's body!

Zig is much more strict here, you have to fully name the return type (the name doesnt have to be pretty, take a second look at bytesAsSlice). But its not perfect, a genuine leakage happens with inferred error types (!T syntax). A bad example would look like this:

fn f() !void {
   // Mystery!

pub fn main() !void {
  f() catch |err| {
    comptime assert(
      @typeInfo(@TypeOf(err)).ErrorSet.?.len == 1,

Here, to check main, we actually do need to dissect fs body, we cant treat the error union abstractly. When the compiler analyzes main, it needs to stop to process f signature (which is very fast, as it is very short) and then f’s body (this part could be quite slow, there might be a lot of code behind that Mystery! Its interesting to ponder alternative semantics, where, during type checking, inferred types are treated abstractly, and error exhastiveness is a separate late pass in the compiler. That way, complier only needs fs signature to check main. And that means that bodies of main and f could be checked in parallel.

Thats all for today! The type system surprising Ive found so far are:

  • Nominal type system despite notable absence of names of types.

  • Unification-less generics which dont incur unreasonable annotation burden due to methods closing over generic parameters.

  • Explicit signatures with no Voldemort types with a notable exception of error unions.

Discussion on ziggit.dev.