Joy part I

Posted on by Chris Warburton

No programmer can claim to be any good if they are only proficient in one language. In fact most languages can be grouped with similar ones, so that knowing several languages still gives a very limited outlook.

Some examples of the most prevalent groups are:

There are several others, which I haven’t had too much exerience with (eg. those based on process calculi, pattern calculi, etc.). However, here I am concerned with the last of the above categories; concatenative languages.

Concatenative Languages

In very general terms, a language is ‘concatenative’ if, given any program F which implements some function f, and any program G which implements some function g, we can always concatenate the two programs FG to implement the function h(f, g), where h is some way of combining functions.

In less general and more concrete terms, we can take the language Joy as an example. Every Joy program implements a function from stacks to stacks; the input is on the incoming stack and the output will be on the resulting stack.

We can write a program dup which implements the duplicate function, duplicating the top item of the stack. If we have a stack containing the number 3 (which I’ll write as {3}) then duplicate({3}) = {3 3}.

We can write another program * which implements the multiply function, replacing the top 2 items of a stack with their product: multiply({3 3}) = {9}.

In Joy, concatenating programs combines their functions by composition which we write as “.”. “f . g” is the function which runs f then runs g, so (f . g)(x) = g(f(g)). If we apply this to dup and * we can concatenate them to get the program dup *, and its function is (duplicate . multiply).

(duplicate . multiply)({3}) = multiply(duplicate({3}))
                            = multiply({3 3})
                            = {9}

So what might we call this (duplicate . multiply) function? It’s none other than the squaring function! From now on I’ll not make the distinction between programs (dup, *, dup *, etc.) and the functions they implement (duplicate, multiply, square, etc.). I’ll also define square so I can refer to it later:

DEFINE sq = dup *

Types and Stack Effects

Of course, composing functions only works when their types are compatible. I’ll use pseudo-Haskell notation for types, where single lowercase letters are type variables, single uppercase letters are ‘stack variables’ which represent the (potentially empty) contents of a stack, Capitalised names are literal types and square brackets denote stacks (left-to-right is bottom-to-top). Here’s sq:

The nice thing about Joy, when compared to a language like FORTH, is that it’s not only concatenative, it’s also purely functional. The functions we define and use do everything with stacks. All of their input must be on the argument stack (no Input) and their effects are limited to effecting their returned stack (no Output). The stacks, of course, are immutable. In FORTH we have procedures which modify a few global stacks, but in Joy we consume input stacks and construct return stacks, just like tail-recursive lists which are so prevalent in functional programming.

Concatenative Combinators

Rather than using the Lambda Calculus as a semantic model, like LISP, Haskell, ML, etc. do, Joy uses a form of Combinator Calculus. It doesn’t use Schoenfinkel’s famous combinators S and K, which are ‘applicative’ (combinators are applied to combinators to make new combinators); instead Joy can be characterised by ‘concatenative’ or ‘compositional’ combinators: combinators are composed with combinators to make new combinators. Even though we talk about ‘stacks’ in Joy, when we’re programming we never actually see a single stack. What we really deal with are the ‘stack effects’ of our functions; the effects they would have on a stack, if we ever applied it to one. This makes Joy an inherently point-free (or ‘pointless’) language, and a ‘function-level’ or ‘tacit’ language, since it never refers to any data.

Implementation strategies

A surprising amount can be accomplished by just looking at stack effects. For example, if we use the 3 function, which pushes the number 3 on to a stack (3 :: [A] -> [A {3}]), we can then get 9 on the stack by using sq:

3 sq

-- This expands to:
3 dup *

-- Since Joy is concatenative, we can ignore the "*" for now and focus
-- on "3 dup", which has the following stack effect:
-- 3 dup :: [A] -> [A {3} {3}]

-- This is the same stack effect as "3 3", so we can swap it out to
-- get:
3 3 *

-- We know that "3 3 *" has this stack effect:
-- 3 3 * :: [A] -> [A {9}]
-- This is the same as the stack effect of the "9" function, so we can
-- write:

Here we’ve taken the program 3 sq, which takes a stack and performs 4 pushes, 3 pops, a duplication and a multiplication and we’ve optimised it to a function with 1 push. The interesting thing is that we’ve lifted operations on stacks to operations on stack functions; we took a function which duplicates elements on a stack, and we optimised it away by duplicating functions-of-stacks. Likewise we optimised away multiplication of stack elements by multiplying functions-of-stacks.

This can get us quite far, especially when we use functions like zap:

-- Given these functions:
-- zap :: [A a] -> [A]
-- fermats_last_theorem :: [A] -> [A {fermats_last_theorem}]
-- prove :: [A statement] -> [A proof]
-- 5 :: [A] -> [A {5}]

-- We can write this program:

5 fermats_last_theorem prove zap
-- 5 fermats_last_theorem prove zap :: [A] -> [A {5}]

-- Since we're zapping the top item, proving Fermat's last theorem
-- isn't necessary. "zap" is polymorphic in its argument, and constant
-- in its effect, so zapping anything off a stack will have the same
-- final result. Thus we can optimise away the proof function and zap
-- the statement of the theorem rather than its result. We can only do
-- this because of Joy's purity.

5 fermats_last_theorem zap
-- 5 fermats_last_theorem zap :: [A] -> [A {5}]

-- Now we have a function which pushes a value, composed with a
-- function which pops a value. Their stack effects cancel out, so we
-- can optimise them away to get:

-- 5 :: [A] -> [A {5}]

Since the zap function pops an item off the stack and discards it, we can optimise it away by discarding the function which pushed that value in the first place; lifting the function from stacks to functions-of-stacks, just like before.

We can generalise this notion to get a form of lazy evaluation. Stack functions can do two things: manipulate a stack and construct values to push. These are actually orthogonal things:

The core principle of concatenative languages like Joy is to get arguments to functions in a more flexible way than the rigid name- binding used in many other languages. We could recover some of the rigid semantics by, for example, using de Bruijn indexing to get values from the stack. This would give us a stack language with no stack manipulation. Likewise we could only include functions to rearrange stacks, but nothing which ever pushed a new value. The elegance comes when we couple the two, but we can still treat them separately.

Since calculaions only use stacks to get their arguments and return their results, once we have our arguments we can do our calculations without the stack. Importantly, we can defer it until an arbitrary time later. Consider these alternative implementations of *, written in Javascript:

// Multiply 2 numbers
var multiply = function(x, y) { return x * y; };

// Take a stack, send the top 2 values to multiply and join the result
// with the rest of the stack's values.
var strict_multiply = function(stack) {
    return [
        multiply(stack[0], stack[1])

// Take a stack and return a closure joined with the stack, but
// without the top two elements. When called, the closure gets the top
// two elements from the stack and calls them (they'll also be
// closures), passes their results to multiply and returns that.
var lazy_multiply = function(stack) {
    return [function() {
        return multiply(stack[0](), stack[1]());

// Same as lazy_multiply, but less naive. Less concise, but prevents
// entire stacks hanging around in memory.
var lazier_multiply = (function() {
    // Define multiplication out here, where there's no stack
    var make_multiply = function(x, y) {
        return function() {
            return multiply(x(), y());

    // Now make the stack-handler; the stack is only in scope long
    // enough to split it into two heads and a tail
    return function(stack) {
        return [
            make_multiply(stack[0], stack[1])

// Same as lazier_multiply, but less naive. Don't bother running every
// closure every time; memoise the results.
var laziest_multiply = (function() {
    var make_multiply = function(x, y) {
        var result;
        // "go" calculates the result, then replaces itself with an
        // empty function so that it won't be recalculated
        var go = function() {
            result = multiply(x(), y());
            go = function() {};
        // This is our closure. It uses go to set the result then just
        // returns the constant.
        return function() {
            return result;

    return function(stack) {
        return [
            make_multiply(stack[0], stack[1])

strict_multiply, lazy_multiply, lazier_multiply and laziest_multiply all do the same thing; they take a stack topped by two numbers and return a stack topped by their product. The difference is in how they represent numbers. (Side note: compare the verbosity of Javascript to Joy!)

In the strict version we represent numbers on the stack by Javascript numbers, which forces us to perform the multiplication immediately, so everything must wait until it’s finished.

In the other versions we represent numbers on the stack by Javascript functions, which take no arguments and return a Javascript number. This is much more flexible. We initially pay a high price in memory usage and time complexity, since lazy_multiply stores the whole stack every time, and recalculates its arguments every time (which may recalculate their arguments, and so on exponentially). However, we can get the memory usage back down if we pay attention to scope, as lazier_multiply does, and we can turn that exponential complexity back into an (amortised) constant by memoising the result, as laziest_multiply does.

We can recover a value-based stack by simply calling each closure:

v_stack = { return element(); });

The reason the closure-based approach is better is that we can clearly see the separation of value calculation and stack manipulation (they’ve become separate functions). The stack manipulation is strict, since it’s become relativeley cheap. We could make it lazy, and write an optimiser which replaces ineffiencient combinations of functions, but most analysis performed by such an optimiser would be about as efficient as just performing the manipulations.

Let’s apply this to the two examples above. I’ll write (x) to represent closures. First the value-based stack:

Program Stack Value
3 sq 3 dup * dup * * {} {} {3} {3 3} {3*3} {9} {} 9

Now with closures:

[code=“Joy”] Program Stack Value 3 sq {} () 3 dup * {} () dup * {(3)} () * {(3) (3)} () {((3) * (3))} () – Done, evaluate the result {} ((3) * (3)) {} (3 * 3) – (3) is memoised; both eval at once {} (9) {} 9 [/code]

They’re both about the same, although the closure-based one goes through more ‘unwrapping’ steps.

Now the second example on a value-based stack:

[code=“Joy”] Program Stack Value 5 fermats_last_theorem prove zap {} fermats_last_theorem prove zap {5} prove zap {5 fermats_last_theorem} zap {5 proof_of_flt} {5} {} 5 [/code]

Uh oh, we ended up proving Fermat’s last theorem. That probably took a while! How about with closures?

[code=“Joy”] Program Stack Value 5 fermats_last_theorem prove zap {} fermats_last_theorem prove zap {(5)} prove zap {(5) (fermats_last_theorem)} zap {(5) (prove (fermats_last_theorem))} {(5)} {} (5) {} 5 [/code]

Much better, we just throw away the closure without running it!


The final part of the calculus underlying Joy is that it features quotation, similar to LISP’s. Quotes are written [in square brackets].

We know that symbols like “a”, “b” and “c” are functions from stacks to stacks, and we know that a concatenation of symbols “a b c” is a function from stacks to stacks (the composition of a, b and c). A quotation “[a b c]” is a function from stacks to stacks, which pushes a function on to the stack; in this case the stack would be topped with the “a b c” function.

With quotation in our arsenal, Brent Kerby has shown that we only need two other functions in order to be Universal. There are a few (countably many) combinations to choose from, but a nice pair are what Kerby calls “k” (since it’s similar to Schoenfinkel’s applicative K combinator) and “cake” (pairs elements into a pair of head-recursive and tail-recursive quotations). Here’s what they do:

[code=“Joy”] – k :: [A b [a]] -> [A a] – k pops a quotation and another element, and pushes the contents of – the quotation (in other words, unquoting it). For example:

5 7 11 [dup * +] k

– Let’s look at the types, concatenating up to the whole thing:

– 5 pushes a 5 – 5 :: [A] -> [A {5}]

– 7 pushes a 7 – 7 :: [A] -> [A {7}]

– 5 7 pushes a 5 then a 7 – 5 7 :: [A] -> [A {5} {7}]

– 11 pushes an 11 – 11 :: [A] -> [A {11}]

– 5 7 11 pushes a 5 then a 7 then an 11 – 5 7 11 :: [A] -> [A {5} {7} {11}]

– dup pushes a copy of whatever’s on the top of the stack – dup :: [A a] -> [A a a]

– * pops two Nums and pushes their product – * :: [A Num Num] -> [A Num]

– dup * pops a number and pushes its square (copies then multiply) – dup * :: [A Num] -> [A Num]

– + pops two numbers and pushes their sum – + :: [A Num Num] -> [A Num]

– dup * + pops two numbers and pushes the second plus the square of – the first – dup * + :: [A Num Num] -> [A Num]

– [dup * +] pushes the “dup * +” function – [dup * +] :: [A] -> [A ([A Num Num] -> [A Num])]

– 5 7 11 [dup * +] :: [A] -> [A {5} {7} {11} ([A Num Num] -> [A Num])] – 5 7 11 [dup * +] pushes those three numbers and that function

– k :: [A b [a]] -> [A a] – k pops a quotation, pops another element, then applies the quoted – function to the rest of the stack

5 7 11 [dup * +] k :: [A] -> [A Num] – 5 7 11 [dup * +] k pushes some numbers and a function, then pops – the function and the number 11, and applies the function to the – stack containing 5 and 7. Let’s see how it unfolds, showing the – temporary values that are used by the functions too:

Program Stack Temporary 5 7 11 [dup * +] k {} 7 11 [dup * +] k {5} 11 [dup * +] k {5 7} [dup * +] k {5 7 11} k {5 7 11 [dup * +]} {5 7 11} [dup * +] {5 7} [dup * +] dup * + {5 7} * + {5 7} 7 * + {5 7 7} + {5 7} 7 + {5} 7 7 + {5} 49 + {5 49} {5} 49 {} 49 5 {} 54 {54} [/code]

What about cake?

[code=“Joy”] – cake :: [A b [a]] -> [A [b a] [a b]] – cakes pops a quotation and another item and pushes two quotations; – both pairings of the second item and the contents of the first. – For example:

Program Stack Temporary 5 7 11 [dup * +] cake {} 7 11 [dup * +] cake {5} 11 [dup * +] cake {5 7} [dup * +] cake {5 7 11} cake {5 7 11 [dup * +]} {5 7 11} [dup * +] {5 7} [dup * +] 11 {5 7} [dup * + 11] [11 dup * +] {5 7 [11 dup * +] [dup * + 11]} [/code]

With k, cake and quotation we get a Universal language. Similarly to Lambda Calculus, we get no datatypes other than functions, but we can encode data as functions (in a similar way to Church encoding and the like in Lambda Calculus) to get as much richness as we need.

What’s interesting is the way types and stack effects work in concatenative languages, but I’ll leave that for another post.

I’ve thrown together a quick interpreter for the not-quite-Joy I’ve been using in this blog post (Joy features hard-coded datatypes for numbers, characters, etc. which I don’t want to deal with), but due to gitorious troubles (damn you cookies!) I’ve not put it online yet.