This article describes the design and implementation of the Schemy interpreter. Note that the design and implementation of schemy is heavily inspired by Peter Norvig's article.

tl;dr - here's a flowchart summarizing an implementation of a scheme interpreter:

S-Expression

S-expression is the central construct of a Scheme program. An S-expression can be in the form of any of the following:

a value, e.g., 3.14 , "some text" , or any other values that your runtime supports (in the case of schemy, this could be any .NET object we exposed).

a symbol, e.g., count (a variable), sum (a function).

a list of s-expressions, e.g., (sum (+ 1 x) y (get-value "total"))

Formally:

Expression := Symbol | (Expression ...) | Value

S-Expression representation

In Schemy, we simply represent an expression as an object , which could be either:

an instance of a Symbol a list of objects any .NET object

In a language that supports discriminated union, it could be more elegantly modeled. But that's not in the scope of this discussion.

One may also note that in above representation, #2 and #3 could overlap - a .NET object (#3) could be a list of objects that could be treated by the interpreter as an expression (#2). This is as expected, and a powerful feature - in Scheme, a program (s-expression) can be treated as data - and be processed, transformed! This is called Homoiconicity.

Evaluation

Now, S-expression alone is not very useful alone. For example, for a symbol s-expression count , it doesn't make much sense without knowing what information count holds. This leads to the concept of evaluating an s-expression.

S-expression is evaluated in a context, or "environment", which is nothing but a mapping from symbols to values. Therefore we could define a EvaluateExpression function:

eval(expr: Expression, env: Environment) -> object

If expr is already a value, simply return it

If expr is a symbol, we just look up that symbol in env and return the value

If expr is a list of s-expressions - this could be a syntax form evaluation or function invocation. In the simplest idea, we first evaluate each element expression of the list recursively, then depending on the meaning of the first value (a function, or a syntax form indicator, e.g, if ), we handle them differently. Below gives some examples on how to handle them naively, just for illustration, we'll cover optimizations later. for (if test conseq alt) , we evaluate test , if true, we evaluate and return conseq , otherwise, alt . for (define id expr) , we evaluate expr , and update the environment to associate symbol id to value of expr . for (func expr1 expr2 expr3) , this is function invocation. We drill into the detail in the following section.



Function

What is a function and how to invoke a function? A function is made of the following parts:

a list of parameters - this is a list of symbols which should be bound to some value at invocation time. an environment under which the body expression should be evaluated an s-expression representing the body (or implementation) of the function. This s-expression usually references some symbols whose definitions reside either in the parameters (defined at invocation time) or in the environment (defined at definition time - lexical scoping (see below))

Now for a function defined as:

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( define f ( lambda (x y) ( + x y)))



And when we evaluate (f 1 2) , we first make an environment containing the mapping x=1, y=2 , and evaluate the body (+ x y) by using the parameters environment.

But that's not really what happens. What if the body of f references symbols which are not as the parameters, e.g.:

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( define x 2 )

( define f ( lambda (y) ( + x y)))



When invoking (f 3) , we would bind y=3 . But where to get value for x ? When we define f , its environment contains the definition for x . So when we construct the parameter environment for the invocation, we link it to an outer environment that contains x=2 . And the lookup logic for a key in a environment is this:

Try to look up the key in current environment's symbol table. If found, return it. If not found in current environment, go to the parent (outer) environment and attemp lookup there.

There can be many layers of environemnts. If none of the environment contains the mapping for key, an error is thrown.

This is a core concept and language feature called lexical scoping, or closure. Many more advanced language features can be implemented based up on this, including classes, but we'll not go into the detail.

Wrapping up, we now know how to evaluate an s-expression or a function. An interesting observation we should make now is that:

Evaluating an S-expression and a function is quite similar - both requires an expression and an environment. And we evaluate the expression using the symbol definition in the environment.

Tail call optimization

With the above description, the function evaluation looks like the following in the eval function:

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define eval(expr, env):

...

if (is_invocation(expr)):

(func, args) = (expr[ 0 ], expr[ 1 ,:])

func_env = make_env(func.params, args).link(func.env)

func_body = func.body

return eval(func_body, func_env)



However, this implementation involves a recursive call (more specifically, a tail call) into the eval function. And for implementation language like C# or Python which doesn't support tail call optimization, that means if we evaluate a recursive function, the evaluation itself is a recursion in the implementation language, and is subject to stack overflow:

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( define ( sum-to n acc)

( if ( = n 0 ) acc

( sum-to ( - n 1 ) ( + acc n))))



This would cause eval to be called each time we encounter (sum-to ...) and the stack size is O(n).

How can we optimize this case? If, when evaluating eval(expr, env) , we know expr is a function call: (f x y ...) , then instead of calling eval recursively, we could swap out expr with f.body (which is also an expression), and swap out env with make_env(func.params, args).link(env) :

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define eval(expr, env):

while (true):

...

if (is_invocation(expr)):

(func, args) = expr

env = make_env(func.params, args).link(env)

expr = func.body







Same optimization can be applied to other language constructs, like if :

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define eval(expr, env):

while (true):

...

if (expr[ 0 ] is SYMBOL_IF):

if (eval(test, env)): expr = conseq

else : expr = alt



Also for begin :

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define eval(expr, env):

while (true):

...

if (expr[ 0 ] is SYMBOL_BEGIN):

for (e in expr[ 1 : -2 ]): eval(e, env)

expr = expr[ -1 ]



With these optimization, recursive calls are converted to loops and stack usage is elinminated. Note that this optimization only applies to the specific cases like above where we handled them specially. For example, the form:

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( define ( f x) ( f ...))



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( define ( f x) ( if test ( f ...) ...))



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( define ( f x) ( begin ... ... ( f ...)))



Basically, tail call optimization applies to recursive call at the location of:

the top level of the function body the last expression of the function body

We can see that 1 is a special case of 2 above. So we can say in general,

tail call optimization applies to cases when the recursive call is invoked as the last expression of the function body.

Note the definition of last expression is not rigid. Here last could be the last of if , begin , or any forms derived from them (e.g., cond , multi-expression function body, etc.).

Evaluating a Scheme program

We talked about how to evaluate a single Scheme expression above, but what about a scheme program? Well, first we could argue that a Scheme program is a single expression - using the form (begin expr1 expr2 ...) , which evaluates each expression, and returns the result of the last expression.

Alternatively, we could treat or convert a multi-expression program as/into a begin form:

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expr1

expr2

...



can be converted into:

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( begin

expr1

expr2

...)



This idea could be applied to multi-expression function definition/evaluation:

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( define ( f )

expr1

expr2

...)



Alternatively, for multi-expression program, at the interpreter level, we could simply evaluate expressions as we iterate them, until the interpreter finds the end of the program. This is what the Schemy interpreter does.

Parsing

In the above sections, we talked about how to evaluate expressions, given that they have been properly parsed from texts and constructed. But how exactly are they parsed from texts and constructed?

Let's take a look at the modeling of expression again, but from the form of a text representation. An expression can be:

a symbol ( foo , bar ) a literal value ( 1.23 , "some string" ) a list of the above ( (foo (bar "some string") 1.23) )

Here we can see that, from the form of its text representation, an s-expression can be composed of:

atoms, i.e., symbols or literal values, or

list of atoms

Therefore, from some text, we can implement a tokenizer that parses the text into a stream of tokens. The tokens could be:

atoms ( foo , "string" , 1.23 ) open/close parenthesis ( , ) quote and unquote: ' ` , ,@

Now from a stream of tokens, we can device a read function that reads expressions one-by-one from the stream of tokens.

read(stream of tokens) -> expression

The read function works roughly like so:

if the token is an atom, we parse the atom into a value (number, string, ...) and return it. if we encounter an open parenthesis, we construct a list, recursively call read and add the expressions to the list until we find the corresponding closing parenthesis.

Now that we have read , we can call it repeatedly, for each expressions yielded from read , we feed it to eval for evaluation.

Expanding

For a flexible Scheme interpreter, one usually supports many syntax features and even custom syntax transformations (macros). Do we need to implement and handle all such feature in eval ? No we don't need to.

The beauty of Scheme is that its core language is rather small. Most of the features are a transformation from the small core.

For example, define has a variation to define function:

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( define ( double x) ( * x 2 ))



This is merely a syntax sugar for:

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( define double ( lambda (x) ( * x 2 )))



We don't need to support this form of define in eval . Instead, we can add a stage after we call read and before we call eval to transform the expression:

Whenever we see (define (id rest...) expr) , we transform it into:

(define id (lambda (rest...) expr))

This way, eval is kept simple and only need to handle (define id expr) and (lambda (id...) expr) .

Similarly, multi-expression bodied function:

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( lambda (id...) expr1 expr2 ...)



is converted to single-expression bodied function by using begin form:

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( lambda (id...) ( begin expr1 expr2 ...))



The expansion stage is critical for supporting custom program transformation (aka macro). A macro is a function that the user defines that gets invoked to transform the program, before the program is evaluated.

For example, if we have the if form: (if test conseq alt) . But we want to support cond form:

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( cond

( test1 expr1)

( test2 expr2)

...

( else expr_default))



We could just write a macro function to convert an expression starting with cond into a functionally equivalent expression in the if form:

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( define-macro cond

( lambda args

(if (= 0 (length args)) ''()

( begin

( define first ( car args))

( define rest ( cdr args))

( define test1 ( if ( equal? ( car first) 'else ) ' #t ( car first)))

( define expr1 ( car ( cdr first)))

`(if ,test1 ,expr1 (cond ,@rest))))))



define-macro merely defines a function that will be invoked when an expression begins with cond . It will be invoked on the rest of the expression whose first element is cond . It returns the transformed expression.

The definition of a macro is evaluated at eval() on an earlier expression. This macro gets registered to a macro table, just like the Environment s. Then when a later expression is expanded (not evaluated), if an expression matches the name of a macro, the macro is invoked on that expression to transform it. The transformed expression is then evaluated by eval() .

The below flow chart illustrate the entire interpreter workflow: