Creating a DSL in Rust

pub enum Expr {
    Literal(i32),
    Variable(String),
    Binary {
        op: BinaryOp,
        lhs: Box<Expr>,
        rhs: Box<Expr>,
    },
    FunctionCall {
        name: String,
        args: Vec<Expr>,
    },
}

pub enum BinaryOp {
    Add,
    Subtract,
    Multiply,
    Divide,
}

Created an AST structure for a simple language that supports variables, functions, and binary operations. Each node in the tree can contain other nodes.

To create an AST from source code, you need to develop a parser. In Rust, this can be done with nom or pest:

fn parse_literal(input: &str) -> IResult<&str, Expr> {
    map(digit1, |s: &str| {
        Expr::Literal(s.parse::<i32>().unwrap())
    })(input)
}

fn parse_variable(input: &str) -> IResult<&str, Expr> {
    map(alpha1, |s: &str| {
        Expr::Variable(s.to_string())
    })(input)
}

fn parse_binary_expr(input: &str) -> IResult<&str, Expr> {
    let (input, left) = parse_atom(input)?;
    let (input, op) = parse_operator(input)?;
    let (input, right) = parse_atom(input)?;
    Ok((input, Expr::Binary {
        op,
        lhs: Box::new(left),
        rhs: Box::new(right),
    }))
}

Using the library nom for parsing simple expressions consisting of literals and variables. More complex expressions, such as binary operations, are parsed recursively.

Once the AST has been created, the next step is interpretation, executing the commands encoded in the tree. The interpreter traverses the tree and performs the actions corresponding to each node.

impl Expr {
    pub fn evaluate(&self, context: &mut Context) -> i32 {
        match self {
            Expr::Literal(val) => *val,
            Expr::Variable(name) => context.get_variable(name),
            Expr::Binary { op, lhs, rhs } => {
                let lhs_val = lhs.evaluate(context);
                let rhs_val = rhs.evaluate(context);
                match op {
                    BinaryOp::Add => lhs_val + rhs_val,
                    BinaryOp::Subtract => lhs_val - rhs_val,
                    BinaryOp::Multiply => lhs_val * rhs_val,
                    BinaryOp::Divide => lhs_val / rhs_val,
                }
            },
            Expr::FunctionCall { name, args } => {
                let func = context.get_function(name);
                let arg_vals: Vec<i32> = args.iter().map(|arg| arg.evaluate(context)).collect();
                func(&arg_vals)
            },
        }
    }
}

struct Context {
    variables: HashMap<String, i32>,
    functions: HashMap<String, Box<dyn Fn(&[i32]) -> i32>>,
}

impl Context {
    fn get_variable(&self, name: &str) -> i32 {
        *self.variables.get(name).expect("Variable not found")
    }

    fn get_function(&self, name: &str) -> Box<dyn Fn(&[i32]) -> i32> {
        self.functions.get(name).expect("Function not found").clone()
    }
}

Implemented a simple interpreter that supports variables and function calls. The context stores variables and functions available during execution.

Compilation involves converting the AST into machine code or intermediate code that can then be executed by a virtual machine. The compilation process involves generating instructions based on the AST structure.

Compilation on the AST structure:

enum Instruction {
    LoadLiteral(i32),
    LoadVariable(String),
    Add,
    Subtract,
    Multiply,
    Divide,
    CallFunction(String),
}

fn compile_expr(expr: &Expr, bytecode: &mut Vec<Instruction>) {
    match expr {
        Expr::Literal(val) => bytecode.push(Instruction::LoadLiteral(*val)),
        Expr::Variable(name) => bytecode.push(Instruction::LoadVariable(name.clone())),
        Expr::Binary { op, lhs, rhs } => {
            compile_expr(lhs, bytecode);
            compile_expr(rhs, bytecode);
            bytecode.push(match op {
                BinaryOp::Add => Instruction::Add,
                BinaryOp::Subtract => Instruction::Subtract,
                BinaryOp::Multiply => Instruction::Multiply,
                BinaryOp::Divide => Instruction::Divide,
            });
        },
        Expr::FunctionCall { name, args } => {
            for arg in args {
                compile_expr(arg, bytecode);
            }
            bytecode.push(Instruction::CallFunction(name.clone()));
        },
    }
}

The compiler generates bytecode that can then be executed by the VM. Each expression in the AST is converted to one or more instructions, which are added to the final instruction list.

When creating an interpreter and compiler, it is important to consider performance.

Constant Convolution: Evaluate expressions that contain only constants at compile time to reduce runtime overhead.
Type inference: Using the type system to optimize function calls and variable access.
JIT compilation: using Just-In-Time compilation to generate machine code.

Example of constant folding:

fn optimize_expr(expr: &Expr) -> Expr {
    match expr {
        Expr::Binary { op, lhs, rhs } => {
            let lhs = optimize_expr(lhs);
            let rhs = optimize_expr(rhs);
            if let (Expr::Literal(lhs_val), Expr::Literal(rhs_val)) = (&lhs, &rhs) {
                return Expr::Literal(match op {
                    BinaryOp::Add => lhs_val + rhs_val,
                    BinaryOp::Subtract => lhs_val - rhs_val,
                    BinaryOp::Multiply => lhs_val * rhs_val,
                    BinaryOp::Divide => lhs_val / rhs_val,
                });
            }
            Expr::Binary { op: *op, lhs: Box::new(lhs), rhs: Box::new(rhs) }
        },
        _ => expr.clone(),
    }
}

Here the compiler collapses constant expressions into single values, reducing the amount of computation at run time.

INmore inspiration and fewer bugs!