Hackle's blog
between the abstractions we want and the abstractions we get.

On the geometric beauty of programming

With simple shapes to represent a program: circles for values and arrows for computations, we can reveal simple yet powerful ideas that can make big differences in how we see, design and implement software.

More importantly, there are geometric beauty to be found, awareness and observation of which can be rewarding.

Statements vs Expressions, forward or backward?

Let's start with statements and expressions. Take this small example,

fun goodMorning(weather, time) {
    var isGoodMorning
    var isAm = time.AmPm == 'Am'
    if not isAm
        isGoodMorning = false

    val isSunny = weather.code == 'Sunny'
    if not isSunny
        isGoodMorning = false

    isGoodMorning = true

    return isGoodMorning


The above code uses statements, as most of us were taught to do when learning programming. A statement does not return a value, instead it assigns to or mutates a variable, or has side effects such as writing to a file.

In our case, when an assignment is executed (isGoodMorning = false), it creates no new value; instead it mutates an existing variable isGoodMorning that is declared previously, hence the dotted lines that go backwards. Depending on the choice of mutations the outcome of the program is different.

Local variables require the arrows to go back locally, which is not THAT bad; class fields, global variables can be nastier, as they require the arrows go outside of the current code block; moreover, such variables are fair game, they can be mutated elsewhere, at arbitrary locations. We poor programmers must stay alert all the time: "Has this.foo changed since this code was last run?! Or would it when it is running?"

Alternatively, the same function can be implemented purely with expressions, and for complete absence of mutation.

fun goodMorning(weather, time) {
    val isAm = time.AmPm == 'Am'
    val isSunny = weather.code == 'Sunny'

    return (isAm and isSunny)

This can be represented as below.


We can see the expression-based implementation translates to a nice, one-directional diagram. Why one-directional? It's because,

These are incredibly powerful assurances to have, and it puts an end to the anxiety of variables being changed unexpectedly. This is what straight-FORWARD code really looks like - people may contend that statement-based programs can also be "simple" or "clear", that maybe, but remember, they will always be BACKWARDS in the literal sense.

Loop vs Recursion

Let's look at this function that sums up numbers in a list.

fun sum(numbers) {
    var state = 0
    for (var p = 0; p < numbers.length; p++) {
        state = state + numbers[p]

    return state

Simple enough, and it's illustrated as below.


Changes of p (the index), combined with the changes to state, lead to changes to state itself. Loops are knee-deep in statements.

It looks harmless enough at first sight, until we ask the question, what was the value of state in the nth iteration?

Sure we can add code such as if (p == 1) then log(state), or introduce more code to keep the audit trail; the point is, without special care, past state gets overridden and disappears (like tears in the rain - sorry I couldn't help it).

For coding with immutability, loops are usually the first serious road block. The expression-based answer is none other than recursion, best implemented with pattern matching and induction. for our example,

fun sum(numbers, state) {
    match numbers {
        [] -> state,
        [n,...rest] -> sum(rest, state + n)

sum([1,2,3], 0)

// this is the long form of 
[1,2,3].reduce((state, n) -> state + n, 0)

// or
[1,2,3].fold((+), 0)

And it is illustrated as,


Here P stands for the parameters to the sum function, in this case, numbers and state. What's happening here with this shape that looks like a snail shell or a stair-well?

Again, a simple idea that is not to be underestimated! This is behind some of the most powerful architecture styles or frameworks out there. Some reader would have recognised the similarity to fancier diagrams in the wild, buzzwords and phrases such as Reactive, Redux, Event-driven, Event-sourcing, One-directional data-flow, Time-traveling, etc.

One-directional - does that remind you of expressions? And hello, Reducers!

Exceptions vs Unions

Previously we compared error handling with values and expressions, also with diagrams, which can be brought into the mix. About time!

Exception-based error handling allows us to present very neat code - seemingly.

fun calcDiscount(memberId) -> DiscountRate {
    member = readMember(memberId)
    purchases = findPurchases(memberId)
    discountRate = calcDiscountRate(member, purchases)

    return discountRate

expression naive

But this is an illusion at best, because as we all know - each of these innocent looking function can throw exceptions and blow up calcDiscount, as it doesn't take special care to catch!

expression facts

To recap, these below questions are never really answered satisfyingly (despite noble efforts from Java)

Something worse but less talked about. As exceptions could carry arbitrary payload, in a way, they actually account for arbitrary return types for any code. It's dynamic typing in disguise. A loop-hole in many so called "strongly-typed" languages.

On the contrary, value based error handling, especially with the support of union types, brings sanity.

// Rust style binding, and_then applies to happy path and short-circuits errors
fun calcDiscount(memberId) -> DiscountRate | Error {
    return readMember(memberId).and_then(
        member -> findPurchases(memberId).and_then(
            purchases -> calcDiscountRate(member, purchases)

// or F# style binding, let! will return early when an Error is encountered
fun calcDiscount(memberId) -> DiscountRate | Error {
    let! member = readMember(memberId)
    let! purchases = findPurchases(memberId)
    let! discountRate = calcDiscountRate(member, purchases)
    return discountRate

expression naive

What's the difference?

This is not nearly as smart as exception-based error handling, and requires much less magic. It can be more verbose in simpler languages (as is the case with Go), but when supported with more advanced facilities (as in Rust, F#, Elm and Haskell), it can look very neat, as in the example above.

Syntax aside, this style of error handling can assure us that,

Such assurances scale incredibly well with lines of code and aggregation of complexity.

Inheritance vs Union

Previously we discussed one big difference between designing with inheritance and Unions. To recap,

An inheritance-based design can be illustrated as below,

expression naive

Consider the popular "Strategy" design pattern. The idea is to branch as early as possible to choose the applicable "Strategy", after that, following code path should not care which implementation has been chosen; it's an anti-pattern to peek the type of the chosen strategy to introduce ad-hoc logic, such as

// don't do this - it's an anti-pattern!
if (chosen_strategy.getType() == typeof(PremiumCustomerStrategy)) {
    discountRate = MAX_DISCOUNT
} else {
    discountRate = NORMAL_DISCOUNT

When a new implementation is added, a new branch appears, which shouldn't interfere with existing branches, nor should it require changes to existing implementations; this is the beauty of well thought-out object-oriented design.

The story and the diagram are both quite different with Unions. With the assurance of a union type being closed, and exhaustive checking that's usually provided by languages, we are empowered to embrace branching.

expression naive

This will look more verbose than the inheritance-based diagram at first sight, but it affords extra benefits,

You would found the similarity in the diagrams - union type is what enables value-based error handling. It's quite a handful to design and implement with inheritance - despite many noble attempts!

In closing

I hope the diagrams in this post help shed lights on some of the choices made in day-to-day programming, and how such choices can result in different levels of conceptual simplicity/complexity.

It's a misconception that we only need to call for simplicity only required when things get complex, this is not the case! Simplicity scales truly if it's respected from the get-go; as complexity aggregates, so should the assurances of correctness, true scalability and geometric beauty.