Software for Days

Using JavaScript to Implement the Same Stateful Program in both Object-Oriented and Functional Styles

Excerpted from Functional Programming and the Semantics of Change, State & Time.

Some real programs are designed to produce output based solely on input. Ideally, compilers output the same binaries provided the same input files, for example. More frequently, however, programs require state, and user input together with the current state of the program determine the next state or output of the program. Such is the case with our ATM example, where the current balance is crucial to calculating any subsequent balance post withdrawal. To be generally useful, either paradigm must include a model for state, and perhaps time, even if composed entirely of well-behaved stateless mathematical functions.

Stateful Object-Oriented Programs #

Modeling with objects is powerful and intuitive, largely because this matches the perception of interacting with a world of which we are part. — SICP Section 3.5.5

Creating stateful object-oriented programs is straightforward. An ATM program, for example, that allows the user to set a “withdrawal amount” and effect a withdraw,

decomposes naturally into withdrawalAmount and and bankAccount objects, representing the chosen amount to be withdrawn and the user account underlying the session, respectively. The withdrawal amounts are incorporated by and read from withdrawalAmount. Withdrawals are incorporated by, and balance confirmations are read from, bankAccount. The current balance and the amount to potentially withdraw — the state of the program — are reflected directly by the state of bankAccount and withdrawalAmount — the state of its composite objects.

Time #

If we wish to write programs that model this kind of natural decomposition in our world (as we see it from our viewpoint as a part of that world) with structures in our computer, we make computational objects that… must change with time. — SICP Section 3.5.5

When we model objects, we also model time. Objects change — as we discussed, the notion of an “object,” having parts that change without changing the identity of the whole, articulates this ability. The flip-side to changing objects is time. Since objects change, when an object is examined is vital to the examination, it goes without saying.

Look no further than the object representations of our computer programs. The “having parts that can change without changing the identity of the whole” quality of bankAccount in our ATM program, for example, is implemented by withdraw.

class BankAccount {
public withdraw(amount) {
this.balance = this.balance - amount;

Underlying withdraw lies a mutable balance binding that may be assigned new values. Calls to withdraw change the associated balance of bankAccount as a side-effect, without altering the identity of bankAccount, by design.

const bankAccount = new BankAccount(100);

bankAccount.checkBalance(); // 100
bankAccount.checkBalance(); // 80

The flip-side to changing objects is time. In addition to changing the associated balance, any call to withdraw also “delineates moments in time” when balance may change. Whether bankAccount.checkBalance() resolves to 100 or 80 “depends not only on the expression itself, but also on whether the evaluation occurs before or after these moments.” As a result, by modeling objects, “we are forced to admit time into our computational models.” (SICP Section 3.4)

Stateful #

Object-oriented programming reifies objects. Further, objects can be composed in other objects, received by object methods, and generally manipulated like numbers, strings and other first-class citizens of the program. No such affordances are made for state, on the other hand. State is a quality (i.e. “stateful” or “statefulness”) adjacent to objects. Unlike the objects to which they belong, state can only be observed at runtime, rather than expressed through language.

bankAccount and withdrawalAmount identify objects, for example. They can be composed in other objects, received by object methods and generally manipulated like other first-class citizens of the program. By reifying these objects, however, balance and amount state are relegated to object qualities, observable at runtime only through calls to bankAccount.checkBalance and withdrawalAmount.get.

That objects indeed lack any express notion of state is highlighted by object signatures with more than one getter, where any such notion may only exist through definition, signature by signature:

How can you perceive a mutable object that has more than one getter? … How do you it? … Who could say right now how to do this? No one can, right? You cannot do this, because you need this other thing. You need the recipe for doing this, and the recipe is something that everybody has to make up over and over and over again. … We cannot actually perceive the whole. Cannot do it without help. Without these recipes. — Rich Hickey, The Value of Values

An object with a single read method (like bankAccount) in a sense defines the method for accessing the whole of an object’s state. However, each additional read method dilutes this claim, underscoring the need for a method specific to the task (e.g. toJSON, serialize, inspect, etc.). In stark contrast to numbers, string and objects themselves, state may be expressed only through programmer-defined constructs evaluated at runtime.

Stateful Functional Programs #

Is there another approach? Can we avoid identifying time in the computer with time in the modeled world? Must we make the model change with time in order to model phenomena in a changing world? — SICP Section 3.5

Building stateful functional programs is less straightforward. To start, notice that imperative iteration can be restructured into functional iteration by recursively calling an iterative function with the results of the previous call. An imperative implementation of factorial, for example,

const factorial = (n) => {
let total = 1;
while (n > 0) {
total = total * n;
n = n - 1;
return total;

factorial(0); // => 1
factorial(1); // => 1
factorial(2); // => 2
factorial(3); // => 6

maintains iteration and running total state through assignments to n and total, respectively, with every iteration. An alternative implementation avoids mutation by returning the iteration and running total state from an iteration function,

const factorial = (n) => {
const iterate = (total, i) => {
if (i === 0) return total;
return iterate(total * i, i - 1);
return iterate(1, n);

factorial(0); // => 1
factorial(1); // => 1
factorial(2); // => 2
factorial(3); // => 6

which may be used by the same function to calculate the next values in the next iteration.

Stateful functional programs can be constructed in a similar fashion — state that is returned from the previous turn of some larger, iterative “program” function becomes the starter state for the next — with one caveat. With synchronous iteration, results of the previous run can simply be passed to the next. The total result from factorial iteration, for example, is passed directly to the next. In a JavaScript web application, however, events are initiated asynchronously as the user interacts with the page, calling callbacks bound to such events, which run pieces of the program so encoded.

event → callback → javascript
event → callback → javascript

Communication between asynchronous scripts is performed through shared references. One script updates (mutates!) a place in memory from which another script may later read. Look no further than the object-oriented ATM program above for a concrete example. A user may begin the program by first selecting a withdrawal amount, then clicking withdraw:

select → onchange → "withdrawalAmount.set(value)"
click → onclick → "bankAccount.withdraw(withdrawalAmount.get())"

The “click withdraw” script occurs asynchronously sometime after the “select withdrawal amount” script has completed. Communication between the two scripts occurs through shared reference to the amount variable underlying the withdrawalAmount object. Calls to withdrawalAmount.get() will return updates by withdrawalAmount.set(value), notwithstanding the asynchrony of such reads and writes.

Synchronous functions can communicate by simply passing around results. The result from one function becomes the input of another. Asynchronous scripts, by contrast, share a mutable place in memory instead of the values themselves. Consequently, we must also share a mutable place in memory to communicate between asynchronous scripts in the functional program implementation. On the other hand, with a light amount of infrastructure, we can usher such imperative code to the application perimeter and carve out space for a functional core, creating a “functional core, imperative shell.”

The program now consists of functions parseInt and withdraw, called against specific events WITHDRAWAL_AMOUNT and WITHDRAW. The state of the program has not been reflected directly into distinct objects. Instead, a program function is called with the state resulting from the previous call, together with event data from any user interaction, in order to produce the starter state for the next. program resembles an iterative, recursive function. Yet, calls to program occur asynchronously. Just as with the object-oriented ATM program, a user may begin the functional ATM program by first selecting a withdrawal amount, then clicking withdraw:

select → onchange → "store.publish('WITHDRAWAL_AMOUNT', {amount:value})"
click → onclick → "store.publish('WITHDRAW')"

The imperative shell (i.e the store) maintains a reference to the state resulting from the previous call to program, in order to pass such state to the next, orchestrating communication between asynchronous calls to program.

Time as a Series of States #

Unlike object-oriented programming, functional programming provides no model for traditional time. Mathematical functions are timeless. Computation of a function f(x) a “second time” with the same argument will produce the same result whenever computed; a mathematical statement like f(20) = 80 will not be made any less true by insinuating time. Similarly, time is no matter against procedures that model mathematical function computation. Simple functions,

const decrement100 = (x) => 100 - x;

decrement100(20); // 80

compositions of functions,

const square = (x) => x * x;
const sum = (x, y) => x + y;

const sumOfSquares = (x, y, z) => sum(sum(square(x), square(y)), square(z));

sumOfSquares(1, 2, 3); // 14

as well as “program” functions like the ATM program introduced above,

const withdraw = (balance, amount) => balance - amount;

const ATM = (state = { balance: 100, amount: 10 }, event) => {
switch (event.type) {
case "WITHDRAW":
return {
balance: withdraw(state.balance, state.amount),
return {
amount: parseInt(event.amount),
return state;

will produce the same output provided the same input whenever evaluated, independent of time.

Where we once saw object state change as time elapsed, we now see the program jump from one state to the next at individual (i.e. discrete!) moments in time, as if producing entries in a list, log, “stream of information,” or other time-denominated series. Iterative, recursive functions model this same behavior. The functional factorial implementation shown above, for example, produces a value, say F, for each step, say i:

F₀: 1
F₁: 1
F₂: 2
factorial(i): iterate(Fᵢ₋₁ * i, i - 1)

Each run of iterate against the result of the previous run produces a new value just after the last — a discrete piece of information that can viewed together with the rest on the same list. Individual program events can be similarly listed,

E(i): Eᵢ

as can individual program states:

S₀: balance:100, amount:10
S₁: balance:100, amount:20
S₂: balance:80, amount:20
S(i): program(Sᵢ₋₁, Eᵢ)

Each run of program against the result of the previous run, together with event data, produces a new value after the last. Yet, program is a timeless function.

With the object-oriented approach, we decompose the state of the program into objects within the program. Each object houses mutable state, and each piece of state may underly a mutative expression that “delineates moments in time” when evaluated.

We modeled real-world objects with local state by computational objects with local variables. We identified time variation in the real world with time variation in the computer. We implemented the time variation of the states of the model objects in the computer with assignments to the local variables of the model objects. — SICP Section 3.5

A collection of objects, each delineating moments in time when state may change within the program, embeds a simulation of time within the program. By contrast, with the functional approach, state is kept at the application perimeter and functions are composed together in order to transition the program from one state to another. No simulation of time can be found within the program; no moments can be found within the program when state may change. Rather, the program provides change from one state to the next (i.e. it produces state). As a result, program states represent discrete moments in time when state has changed.

Think about the issue in terms of mathematical functions. We can describe the time-varying behavior of a quantity x as a function of time x(t). If we concentrate on x instant by instant, we think of it as a changing quantity. Yet if we concentrate on the entire time history of values, we do not emphasize change — the function itself does not change — SICP Section 3.5

Said another way, in object-oriented programs, we hold the identity of objects constant through change. Objects endure change as time elapses, and thus we model time. In functional programs, we forgo object identity. Change creates something new instead of altering something of old, and thus we model change directly. Change is described succinctly by functions as well as by an “entire time history,” list, log, stream, or other series of resulting states.

Reified State #

Unlike object-oriented programs, functional programs reify state. State can be passed to functions, returned by functions, and generally manipulated like numbers, strings, lists, and other citizens of the program. With a small addition to the ATM program, for example,

we can print (to the console) a representation of the each state of the program in sequence. This change is trivial precisely because state is a known quantity of the program and generally manipulable by program code. inspectReturn takes direct advantage of this quality, printing state to the console and returning state from the internal curried function.