Introduction
As a programmer who has experienced the elegance of writing Elm, I’ve often wished for a language that extends Elm’s core philosophy beyond the browser. While many programming languages emphasize type safety, immutability, and purity, few address memory safety as a core language feature.
What if we designed a programming language where memory failures never crash a program? Where aggressive dead code elimination produces highly optimized output? And where every function is guaranteed to be pure and immutable?
This article outlines a conceptual framework for such a language—its principles, challenges, and potential optimizations.
Core Principles
1. Functional, Pure & Immutable
Everything in the language is a function. Functions are pure, meaning they always return the same output for the same input, and immutability is enforced throughout. Even variables are just functions with zero arguments.
This ensures strong guarantees for compiler optimization and program correctness.
2. Side-Effects Managed by the Runtime
Like Elm, side-effects cannot be executed directly by user code. Instead, side-effects must be passed to the runtime for execution. This delegates responsibility to the runtime designers and allows the compiler to assume that all side-effects are managed safely.
3. Memory Safety as a Core Language Feature
This language ensures programs never crash due to memory exhaustion. A special memory-safe module (Mem) allows functions to specify default return values in case of memory failure:
add : Int -> Int => Int
add x y =
x + y
|> Mem.withDefault 0
Mechanism
- The
=> syntax signals a memory-safe function.
Mem.withDefault 0 ensures a fallback return value in case of failure.- Default values are allocated at startup to prevent mid-execution failures.
By guaranteeing upfront memory allocation, runtime failures are prevented once the runtime passes the initial startup phase.
Handling Dynamic Data Structures
Since the language enforces immutability, dynamically sized data structures must be created at runtime. If memory limits are reached, functions must define fallback strategies:
- Return the original input if allocation fails.
- Return an default value specified by the developer.
Ideally, memory exhaustion can be explicitly handled with a dedicated return type:
type Answer = Number Int | OutOfMemory
fib : Int => Answer
fib n =
case n of
0 -> Number 1
1 -> Number 1
_ ->
case (fib (n - 1), fib (n - 2)) of
(Number a, Number b) -> Number (a + b)
_ -> OutOfMemory
|> Mem.withDefault OutOfMemory
Extreme Dead Code Elimination
The compiler aggressively removes unused computations, reducing program size. Consider:
type alias Message =
{ happy : String
, angry : String
, sad : String
, mood : Bool
}
toText : Message -> String
toText msg =
if msg.mood then msg.happy else msg.angry
main =
{ happy = "I am happy today."
, angry = "I am extremely mad!"
, sad = "I am kinda sad..."
, mood = True
}
|> toText
|> Mem.withDefault "Ran out of memory"
|> Console.print
Optimization Process
- Since
mood is always True, the else branch is never used.
- The function simplifies to
toText msg = msg.happy.
- The
.angry, .sad, and .mood fields are removed.
Message reduces to type alias Message = String.- The
toText function is removed as a redundant identity function.
Final optimized output:
main = Console.print "I am happy today!"
While this may require too many computations at compile-time, all of these optimizations seem fair assessments to make.
While immutability is enforced, the compiler introduces selective mutability when safe. If an old value is provably unused, it can be mutated in place to reduce memory allocations.
Example:
type alias Model = { name : String, age : Int }
capitalizeName : Model -> Model
capitalizeName model =
{ model | name = String.capitalize model.name }
Normally, this creates a new string and record. However, if the previous model.name isn't referenced anywhere else, the compiler mutates the name field in place, optimizing memory usage.
Compiler & Debugging Considerations
For effective optimizations, the compiler tracks:
- Global variable usage to detect always-true conditions.
- Usage patterns (e.g., optimizing predictable structures like
Message).
- External data sources, which are excluded from optimizations.
To aid debugging, the compiler could provide:
- Graph-based visualization of variable flow.
- Debugging toggles to disable optimizations selectively.
Conclusion: A New Paradigm for Functional Memory Safety?
Most languages handle memory safety through garbage collection (Java, Python), manual management (C, C++), or borrow-checking (Rust). This language proposes a fourth approach:
Memory-aware functional programming
By making memory failures a core language feature with predictable handling, functional programming can become more robust.
Would this approach be practical? The next step is to prototype a minimal interpreter to explore these ideas further.
If you're interested in language design, memory safety, and functional programming, I’d love to hear your thoughts!
#foss #functional #languagedesign
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