Project 7: `comptime`-Reflecting JSON Parser

A high-performance JSON parsing library that can deserialize JSON strings directly into native Zig structs without requiring boilerplate code from the user.

Quick Reference

Attribute	Value
Primary Language	Zig
Alternative Languages	Go, Rust
Difficulty	Level 3: Advanced
Time Estimate	2-3 weeks
Knowledge Area	Parsing / Metaprogramming
Tooling	A data serialization library
Prerequisites	Project 2. Strong grasp of `comptime`.

What You Will Build

A high-performance JSON parsing library that can deserialize JSON strings directly into native Zig structs without requiring boilerplate code from the user.

Why It Matters

This project builds core skills that appear repeatedly in real-world systems and tooling.

Core Challenges

Parsing the JSON format → maps to building a recursive descent parser or state machine
Generic deserialization function fromJson(T, json_string) → maps to using @TypeOf and comptime
Iterating over struct fields at compile time → maps to using @typeInfo and comptime loops
Generating field-specific parsing logic → maps to inline for loops and conditional comptime logic to handle strings, numbers, bools, etc.

Key Concepts

Type Reflection: std.meta.fields and @typeInfo.
Parsing Techniques: “Crafting Interpreters”, Part II - A good mental model for parsing.
comptime Code Generation: Zig SHOWTIME - Ep. 2 - Comptime JSON

Real-World Outcome

const User = struct {
    id: u64,
    name: []const u8,
    is_active: bool,
};

const json_data =
    \\{
    \\  "id": 123,
    \\  "name": "Alice",
    \\  "is_active": true
    \\}
;

// Your library does the magic here!
var user = try my_json_lib.fromJson(User, json_data, allocator);
std.debug.print("{any}\n", .{user});

// Output:
// main.User{ .id = 123, .name = "Alice", .is_active = true }

Implementation Guide

Reproduce the simplest happy-path scenario.
Build the smallest working version of the core feature.
Add input validation and error handling.
Add instrumentation/logging to confirm behavior.
Refactor into clean modules with tests.

Milestones

Milestone 1: Minimal working program that runs end-to-end.
Milestone 2: Correct outputs for typical inputs.
Milestone 3: Robust handling of edge cases.
Milestone 4: Clean structure and documented usage.

Validation Checklist

Output matches the real-world outcome example
Handles invalid inputs safely
Provides clear errors and exit codes
Repeatable results across runs

References

Main guide: LEARN_ZIG_DEEP_DIVE.md
“Crafting Interpreters” by Robert Nystrom (for parsing fundamentals)

Project 7: comptime-Reflecting JSON Parser