Continuations in Java

CSP or Continuation-Passing Style is a style of programming in which functions return results via callbacks. For example + operator is a function that takes two numbers and returns their sum. In CSP + operator becomes a function that takes three arguments, two terms and a callback, usually called a continuation in the context of CSP. In Java we can express this as:

void add(int a, int b, Cont<Integer> cont) {
    cont.apply(a + b);

private interface Cont<R> {
  void apply(R result);

Because functions’ results are always returned via callback calls, CSP is forcing us to name the returned values by naming callback parameters. In addition CSP makes the order of evaluation of an expression explicit. For example, a simple Java program in imperative style:

System.out.println(1 + 2 + 3);

Can be expressed in CSP as follows:

add(1, 2, partialSum ->
  add(partialSum, 3, sum ->
    print(sum, unit ->

static void print(int n, Cont<Void> cont) {

While transforming imperative programs into CSP form we may encounter problems with handling procedures (methods returning void in Java). A lot of functional programming languages do not support procedures, instead they define a special type called Unit, that has only a single value and use that type to signify that function does not return any meaningful data. So defined Unit type is often identified with the empty tuple (). In Java we do not have Unit, but we may use Void type with its only allowed value null to simulate it.

While looking at our last example we may notice that in CSP form, function arguments can be in one of three forms: a constant, a variable or a lambda expression. There is no rule preventing us from passing two or more callbacks to a single function. Indeed this is necessary to translate if statement to CSP counterpart:

void iff(boolean expr,
         Cont<Boolean> trueBranch,
         Cont<Boolean> falseBranch) {
    if (expr) trueBranch.apply(true);
    else falseBranch.apply(false);

Instead of Cont<Boolean> we could use here Cont<Void> as well.

To get a better feel for CSP we will look at three more examples. We will start with a simple (naive) program for computing sum of all numbers between given two numbers:

static long sum(int from, int to) {
  long sum = 0;
  for (int i = from; i <= to; i++) {
    sum += i;
  return sum;

The transformation to CSP will become easier if we first replace for loop with recursion:

static long sum_rec(int from, int to) {
  return (from > to)
    ? 0
    : from + sum_rec(from+1, to);

This version can be easily translated into CSP:

static void sumCC(int from, int to, Cont<Long> cont) {
  gt(from, to, fromGreaterThanTo ->
      x -> cont.apply(0L),
      x -> add(from, 1, from1 ->
        sumCC(from1, to, sumCC1 ->
          addLong(from, sumCC1, cont)))));

Where gt is the CSP counterpart of > operator.

Next we will transform factorial computing function. This time we will start with a recursive definition that is easier to translate:

static int factorial(int n) {
  if (n == 0) return 1;
  return factorial(n-1)*n;

CSP version of factorial looks like this:

private static void factorial(int n, Cont<Integer> cont) {
  eq(n, 0, isNZero ->
      x -> cont.apply(1),
      x -> add(n, -1, nm1 ->
        factorial(nm1, fnm1 ->
          multiply(n, fnm1, cont)))));

As the last example we will transform a function that computes Fibonacci sequence:

static int fib1(int n) {
    if (n < 2) return 1;
    return fib1(n-1) + fib1(n-2);

In CSP it looks like this:

static void fib(int n, Cont<Integer> cont) {
  lt(n, 2, nlt2 ->
      x -> cont.apply(1),
      x -> add(n, -1, nm1 ->
        fib(nm1, fnm1 ->
          add(n, -2, nm2 ->
            fib(nm2, fnm2 ->
              add(fnm1, fnm2, cont)))))));

Now we should have, at least intuitive feel, how the transformation to CSP works. In fact any program can be transformed to CSP. The last point is quite interesting, especially if we pass () -> exit(0) or some other not-returning function as the last continuation. Why? Because in that case we will never return from any of the called functions. Let’s see how this works on a simple example:

static void main(String[] args) {
    factorial(6, fac6 ->
      print(fac6, x ->

    System.out.println("Will never be printed");

The entire idea of having a call stack is about providing a way for the called functions to return the control to the callers. But if we are never returning, then we don’t need a call stack, right? Not so fast, some of you may say - what about passing arguments to the called functions, call stack is used for that too. Yes, the arguments are also stored on the call stack but with CSP we capture the values of arguments using closures. Of course JVM does not know that our programs are in CSP form or that they would do fine without having a call stack at all. Instead we get a new call stack frame every time we call something, this results in StackOverflowError quickly when we call e.g. factorial(3000, r -> ...).

Too avoid StackOverflowErrors we may use a technique called trampolining. Trampolining in connection with CSP could reduce the required call stack space to a constant number of slots. The idea of trampolining is very simple, we split computation into parts and then we compute only the first part and return a continuation (called thunk) that is responsible for computing the rest. The returned continuation captures the result of the first computation in its closure so we don’t have to recompute it. Let’s see how a trampolined + operator would looks like:

static Thunk add(int a, int b, Cont<Integer> cont) {
    int sum = a + b;
    return () -> cont.apply(sum);

static Thunk add3(int a, int b, int c, Cont<Integer> cont) {
    return add(a, b, sum ->
            add(sum, c, cont));

private interface Cont<R> {
    Thunk apply(R result);

private interface Thunk {
    Thunk run();

Notice that trampolined + operator splits its computation into two parts: computing the sum and calling the continuation. The called continuation will again split it’s work and so on and on.

add3 function illustrates two key points. One is that the logical flow of the program stays the same, we just call the passed continuations like in a pure CSP program. The other is, that to introduce trampolining we only need to modify primitives provided by our programming language (operators and statements). The program code stays the same. Of course because Java is a statically-typed language we need to change functions return type from void into Thunk, but this is a simple mechanical change that would not be necessary in a dynamically-typed language.

Next example illustrates how trampolined if statement and factorial looks like. Notice that factorial code did not change, not counting the return type:

static Thunk iff(boolean expr,
                 Cont<Boolean> trueBranch,
                 Cont<Boolean> falseBranch) {
  return (expr)
    ? () -> trueBranch.apply(true)
    : () -> falseBranch.apply(false);

static Thunk factorial(int n, Cont<Integer> cont) {
  return eq(n, 0, isNZero ->
      x -> cont.apply(1),
      x -> add(n, -1, nm1 ->
        factorial(nm1, fnm1 ->
          multiply(n, fnm1, cont)))));

Because we are now performing computation “in parts”, we need a procedure that will be continually invoking returned thunks, thus ensuring that out computation is making progress. A procedure like this is called a trampoline:

static void trampoline(Thunk thunk) {
  while (thunk != null) {
      thunk =;

static <T> Cont<T> endCall(Consumer<T> call) {
  return r -> {
      return null;

We are also providing a new primitive operator endCall that can be used to mark the last part of the computation. Using trampoline we may now compute factorial(3000) without any troubles:

AtomicInteger res = new AtomicInteger(-1);
trampoline(factorial(400000, endCall(res::set)));

As a side effect, we may now use trampoline to mix CSP and imperative code in the same program.

CSP and trampolining are not mere theoretical concepts, there where and are still used to implement e.g. LISP interpreters. Continuations can also be used to simplify backtracking algorithms. Source code for this blog post can be found here.