Notes on "Erik Meijer: Functional Programming"
Simon Horlick
Posted on October 15, 2020
My notes on an interview of Erik Meijer talking about functional programming: https://www.youtube.com/watch?v=z0N1aZ6SnBk.
It's a super interesting interview. Erik is very articulate and gets straight to the heart of the problem without being overly theoretical. Here are some of my notes on the video.
12:50 - Java is dishonest about it's type signatures. For example int f()
doesn't tell you whether f
returns the same int each time it is called, or whether it does something in the background. Take for example the following functions:
public class Example {
int f(int x) {
return x + 5;
}
int g(int x) {
return x + new Random().nextInt();
}
}
Without looking at the implementation there's no way to know that g
changes each time it's called.
Here's another example that says it returns an int, but actually never returns an int.
int h(int x) {
throw new RuntimeException("not an int!");
}
Why is this problematic? Imagine you're working on the code in the following example. You might notice that there's some duplication going on and want to refactor it.
int example() {
return g(5) + g(5);
}
So you go ahead and refactor out the repeated code and get this:
int example() {
int g5 = g(5);
return g5 + g5;
}
Great! We no longer compute g(5)
twice so the function probably runs twice as fast! Unfortunately it's no longer correct though, because behind the scenes g
is accessing global state. Inside the constructor of Random
is a call to the OS clock in order to initialise the random number generator, which changes every time it's called.
This is a pretty contrived example, but it illustrates a real problem - it's not safe to refactor without first checking whether the implementation has side effects.
16:50 - FP is programming with mathematical functions. Every time a function is executed with the same inputs you always get the same result.
27:34 Some more examples. Assume we have the current time stored in a variable called Ticks
:
static long Ticks // the current time
If we write the following functional-style code, this illustrates the problem before, that Ticks
is side-effectful so returning a pair (x,x)
is not the same as returning (Ticks, Ticks)
:
let x = Ticks // not a valid refactoring
in (x, x)
Suppose we do the following instead where we turn x
into a lambda and call it twice:
let x = () => Ticks
in (x(), x())
That seems to have solved our problem, except that we could also make the following refactoring and be right back where we started:
let x = () => Ticks
let y = x
in (y, y)
Actually what we need is a way to model the sequential nature of the problem. We need the value of Ticks
and then we need the value of Ticks
again.
Let's say we have w
to represent "the state of the world" and we make x
a lambda that takes the state of the world and returns a pair containing Ticks
and the state of the world.
let x = w => (Ticks, w)
We can call it like this, feeding in the current state of the world and getting back an updated state:
f(w) {
(t, w') = x(w)
(t', w'') = x(w')
(t, t')
}
What we're trying to acomplish here is a sort-of "threading" through the state of the world w
goes into x
and comes out as a new state of the world w'
. We thread it through again to get w''
. Now we've made this sequential dependency explicit so that t
and t'
really must be different values of Ticks
.
You can think about haskell in the following way. Imagine you have the following function:
A, W ------------> R, W'
some state of function result new state
value the world of the world
We can rewrite this as:
A -> W -> R, W'
Bracketing this slightly differently give us a function that takes an argument and returns a function that takes the world and returns a result and the state of the world.
A -> (W -> R, W')
Now we just hide the details of this "state of the world" thing behind a type called IO.
A -> IO R
Conclusion
I've skimmed many details here, and some of it was quite hand-wavy in the video too, but I think it really helps to build and intuition for why Monads exist (sequencing) and why it's a good idea to make computations with side-effects explicit.
Posted on October 15, 2020
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