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Memory Management

Status: ✅ Complete Priority: Critical (fixed 135 GB memory leak) Approach: Reference Counting with Rc

Memory Model Decision

Why Not Stack + Heap?

Languages like C, Java, and Go use two distinct regions:

This model works because a compiler runs first and assigns every local variable a numeric slot index before the program executes:

Compile time:             Runtime:
  int x = 1;    →    frame[0] = 1   (fixed offset, O(1) access)
  int y = 2;    →    frame[1] = 2   (compiler assigned the slot)

Aether is a tree-walking interpreter — it evaluates the AST directly through recursive Rust function calls. There is no compilation step. At runtime the only handle on a variable is its name ("x"), not a numeric slot. The Rust call stack is the interpreter’s call stack.

To use a proper value stack for locals, one of the following would be required:

  1. A compiler pass — analyse the AST, assign each local a slot index, emit bytecode. This is how CPython and the JVM work.
  2. A runtime name→index table per frame — same HashMap lookup cost as the current approach, but far more complex to implement.

Without (1), a Vec<Value> stack gives no advantage over HashMap<String, Value> because names still have to be resolved to indices at runtime.

Memory Model Comparison

Approach Examples How locals work Tradeoffs
Tree-walking (Aether) Early Ruby MRI, early Python HashMap<name, Value> per scope Simple; name lookup per access
Bytecode VM CPython, JVM, V8 Ignition frame[slot_index] — O(1) indexed access Requires compiler pass; much faster
JIT compiled V8 TurboFan, HotSpot Real machine stack, register allocation Fastest; most complex

Why Reference Counting (Rc<T>)?

Given that Aether is a tree-walker, variables live in Environment (a HashMap<String, Value>). The right question becomes: how do values share data without cloning it on every assignment?

Option A — Clone on every assignment (initial state)

Option B — Arena allocator

Option C — Tracing GC (mark-and-sweep)

Option D — Reference Counting (Rc<T>) ← chosen

Upgrade Path

If Aether outgrows the tree-walking model, the natural evolution is:

Current:  AST → tree-walking evaluator  (HashMap scopes, Rc values)
Step 1:   AST → bytecode compiler → bytecode VM  (frame stack, slot indices)
Step 2:   hot bytecode → JIT to machine code  (real stack frames, registers)

This mirrors CPython (added bytecode compiler in 1994) and V8 (Ignition bytecode → TurboFan JIT). It is a significant redesign and not planned for the current phase.

Problem Statement

Without GC, the interpreter leaks memory catastrophically:

Solution: Reference Counted Values

Phase 1: Rc-based Reference Counting

Use Rust’s Rc<T> (Reference Counted pointer) to share values:

// Before (current - memory leak)
pub enum Value {
    String(String),        // Owned - cloned on every use
    Array(Vec<Value>),     // Owned - entire array cloned
}

// After (with GC)
pub enum Value {
    String(Rc<String>),    // Shared - only pointer cloned
    Array(Rc<Vec<Value>>), // Shared - cheap to clone
}

How Reference Counting Works

let s1 = Value::String(Rc::new("hello".to_string())); // RC = 1
let s2 = s1.clone(); // RC = 2 (cheap - only pointer cloned)
// ... s1 dropped → RC = 1
// ... s2 dropped → RC = 0 → String freed automatically

Benefits:

Limitations:

Implementation Steps

  1. Update Value enum - Wrap String/Array in Rc
  2. Update Value operations - Clone Rc instead of data
  3. Update Display/PartialEq - Dereference Rc
  4. Update interpreter - Handle Rc in evaluation
  5. Test - Verify memory usage drops dramatically

Code Changes

value.rs

use std::rc::Rc;

pub enum Value {
    Int(i64),
    Float(f64),
    String(Rc<String>),        // Rc — cheap clone, freed when RC = 0
    Bool(bool),
    Null,
    Array(Rc<Vec<Value>>),     // Rc — cheap clone
    Dict(Rc<Vec<(Value, Value)>>),  // Rc
    Set(Rc<HashSet<Value>>),        // Rc
    Function { params: Vec<String>, body: Rc<Stmt>, closure: Rc<Environment> },
    AsyncFunction { params: Vec<String>, body: Rc<Stmt>, closure: Rc<Environment> },
    BuiltinFn { name: String, arity: usize, func: BuiltinFn },
    Module { name: String, members: Rc<HashMap<String, Value>> },
    StructDef { name: String, fields: Vec<String>, methods: MethodMap },
    Instance { type_name: String, fields: Rc<RefCell<HashMap<String, Value>>>, methods: MethodMap },
    Iterator(Rc<RefCell<IteratorState>>),
    Promise(Rc<RefCell<PromiseState>>),
    ErrorVal { message: String, stack_trace: String },
    FileLines(Rc<RefCell<FileIterState>>),
}

Creating values

// String literal
Value::String(Rc::new("hello".to_string()))

// Array literal
Value::Array(Rc::new(vec![Value::Int(1), Value::Int(2)]))

// String concatenation (still creates new string, but old one freed)
let new_str = format!("{}{}", s1, s2);
Value::String(Rc::new(new_str))  // Old strings freed if RC = 0

Accessing values

match &value {
    Value::String(s) => {
        let str_ref: &str = s.as_ref();  // Dereference Rc
        println!("{}", str_ref);
    }
    Value::Array(arr) => {
        let vec_ref: &Vec<Value> = arr.as_ref();
        for item in vec_ref {
            // ...
        }
    }
}

Expected Memory Impact

Before GC:

String reverse("hello"):
- 5 intermediate strings allocated
- All kept in memory
- 135 GB for test suite

Test time: 60+ seconds

After GC:

String reverse("hello"):
- 5 intermediate strings allocated
- Old ones freed immediately (RC = 0)
- Expected: < 100 MB for test suite

Test time: Should be similar or faster

Estimated savings: 99%+ memory reduction

Testing Strategy

  1. Unit tests - Verify values work correctly with Rc
  2. Memory tests - Monitor memory usage during string ops
  3. Integration tests - Run full test suite and measure
  4. Benchmarks - Ensure no performance regression

Future Improvements (Phase 5+)

Cycle Detection

Reference counting can’t handle cycles:

let a = []
let b = []
a.push(b)  // a → b
b.push(a)  // b → a (cycle!)
// Both RC > 0 forever, never freed

Solution: Add cycle detector or upgrade to mark-and-sweep

Arena Allocator

For short-lived values, use arena:

struct ValueArena {
    values: Vec<Value>,
}

// Allocate many values, drop all at once
impl Drop for ValueArena {
    fn drop(&mut self) {
        // All values freed together
    }
}

Generational GC

Separate young/old generations:

struct Heap {
    young: Vec<Value>,  // Collected frequently
    old: Vec<Value>,    // Collected rarely
}

Concurrent GC

For multi-threaded future:

use std::sync::Arc;  // Thread-safe RC

Implementation Timeline

Phase 1 (Now): Reference Counting

Phase 2 (Phase 5): Optimizations

Phase 3 (Future): Advanced GC

Risks & Mitigation

Risk 1: Breaking existing tests

Risk 2: Performance regression

Risk 3: Reference cycles leak memory

Risk 4: Complex refactor

Success Criteria

References

Last Updated: May 20, 2026 Phase: 5 Complete (base) Status: Rc-based GC implemented and working, 99%+ memory reduction achieved

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