There are only three things Zig/Jai could do in face of this problem
1. Don't do any memoization at all and just see if the result has been computed before (would be extremely slow and probably dumb)
2. Memoize arguments which can become ingrained with the returned struct (which is what they already do)
3. The only other possible thing would be:
If the function that generates the struct is
fn generate_struct(p) type {
struct {
field_with_default = f(p)
}
}
the compiler would have to detect if f(x) is a one-to-one function or not. If it is then strategy 2 can be used safely, if it isn't then you'd have to use strategy 1.
the only issue with this third option is that from trying to read mathematics far beyond my pay-grade it seems like generally determining if any given f(x) is one-to-one is an undecidable problem for functions with unbounded domains and ranges. For functions that have bounded domains and ranges (say, functions that can only operate on integers within 0 and 4 billion) it's easily decidable... if you plug in every possible input and see what comes out.
This would take so much more time than even strategy 1 and I'm not even sure how useful it would really be. Maybe in a much much much more complex codebase that was doing a lot of weird things something like this might become a bug? Idek
So unfortunate as it is, I think strategy 2 is the best option.
You create a parameterised type and then use it, it works as expected.
kingstnap•1d ago
No, it's not. The way structural equivalence is keyed in Zig was changed (mid 2024) to be keyed on two things:
Source location + Captured constants
The issue for that was raised here.
https://github.com/ziglang/zig/issues/18816
I don't actually think that that's sufficient. It makes more sense, imo, to capture some implicit context. Specifically inline context (so an inlined function or inline unrolled for loop will create a unique struct) and opened an issue here.
https://github.com/ziglang/zig/issues/24931
But it got closed "to make incremental compilation simpler to implement."
I consider it a kinda janky dismissal. I assume some actual reasoning about this is on Zulip / Meeting notes / git commit somewhere, but Idk im not a lawyer doing discovery. I get not designing yourself into a corner, but I don't think you need to be so afraid of sometimes needing to do a full recompile because some changes have viral behaviour.
Anyway, something interesting in your post is that as a side effect, you get to view compilation order in the name.
jamiejquinn•1d ago
I have no doubt incremental compilation will make many large projects easier to compile, and perhaps it really is the future of all compiled languages, but you're right, it's being wielded as a janky dismissal of many good ideas.
kingstnap•1d ago
Working with / testing systems that have huge input spaces is difficult. You have to stamp out lots of edge cases because the boundary of the space is huge.
Working with systems that both have huge input spaces but also have internal state is exponentially worse. You basically have edge cases to the power of edge cases to deal with.
Simultaneously, it's not always faster / easier to manage deltas in programming. As an example, consider the case of sorting a 1 million element list. How much difference is there between starting from completely scrambled vs. I instead told you it's mostly sorted, but only 1% of elements were out of place.
Its definitely not 99% easier let me tell you that.
tialaramex•15h ago
https://github.com/Voultapher/sort-research-rs/blob/main/wri...
The left chart there is the proposed (now current) Rust unstable sort, named IPN Sort and the right chart is the old (at the time current) unstable sort
That nice black triangle line going straight up and to the right is random input, a typical input for testing sort performance. Compare those essentially flat lines at the bottom in orange and green, for the easy ascending and descending inputs (sorting them is no work, or trivial as appropriate) and the distinct brown circle s95 which is markedly worse than the black line.
s95 is 95% sorted data, plus 5% random data, which is what you'd see if you:
1. sort container of N Things 2. do work which overall removes about 0.05 x N Things, maybe they're completed 3. add 0.05 x N Things, maybe they're new 4. repeat from step 1
Compared to the usual test workload this is actually more work because indeed it's mostly sorted but the non-sorted parts are far out of position.
The vertical on these charts is mean comparisons per item, that is if we can sort 100 items with total 1000 comparisons, that scores 10 on the vertical axis, the reason to do this is that it's independent of physical realisation. Because the horizontal axis is log size a horizontal line means the sort is O(n) for that class of input, a diagonal means O(n log n) which is what we're hoping for from a sort algorithm.