It makes obvious sense to consider an array as a function with the index as its input argument and the element its output, i.e. f(x) = A[x]... but this isn't the first time I've encountered this and I still don't see the practical benefit of considering things from this perspective.

When I'm writing code and need to reach for an array-like data structure, the conceptual correspondence to a function is not even remotely on my radar. I'm considering algorithmic complexity of reads vs writes, managed vs unmanaged collections, allocation, etc.

I guess this is one of those things that's of primary interest to language designers?

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tialaramex 4 hours ago | parent | next [-]

Well for example this insight explains Memoization

https://en.wikipedia.org/wiki/Memoization

If you know that Arrays are Functions or equivalently Functions are Arrays, in some sense, then Memoization is obvious. "Oh, yeah, of course" we should just store the answers not recompute them.

This goes both ways, as modern CPUs get faster at arithmetic and yet storage speed doesn't keep up, sometimes rather than use a pre-computed table and eat precious clock cycles waiting for the memory fetch we should just recompute the answer each time we need it.

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vacuity 4 hours ago | parent | next [-]

I think this is an after-the-fact connection, rather than an intuitive discovery. I wouldn't explain memoization this way. Memoization doesn't need to specifically use an array, and depending on the argument types, indexing into the array could be very unusual.

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stackghost 4 hours ago | parent | prev [-]

>Well for example this insight explains Memoization

I don't think it does.

In fact I don't see (edit: the logcial progression from one idea to the other) at all. Memorization is the natural conclusion of the thought process that begins with the disk/CPU trade off and the idea that "some things are expensive to compute but cheap to store", aka caching.

	▲	KK7NIL 3 hours ago \| parent [-]
		Both arrays and (pure) functions are just mappings of inputs to outputs, this is why memoization is possible without any loss of functionality. Whether storing (arrays) or computing (functions) is faster is a quirk of your hardware and use case.

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morshu9001 3 hours ago | parent | prev [-]

There are some appealing-sounding arguments for designing languages around functions. Having given this a very fair shot with things like Erlang... turns out this optimizes for rare use cases at the expense of common ones. There's no 100% general-purpose language, they all have use cases in mind, and I guess some people are still trying to find a way around that.

Similar conclusion for using a graph DB for something you'd typically do in a relational DB. Just because you can doesn't mean you should.