And yet slices.Sorted was added which does exactly this already, but only for single-valued iterators.
It does not. slices.Sorted accepts an iterator, but returns a slice.
Like the earlier comments point out, Go tries its best to give a reasonable idea of what kind of complexity is involved at the API level. slices.Sorted returning an iterator would mask that. By returning a slice, it makes clear that the entire collection of data needs to be first iterated over as the parent described.
I think returning an iterator is acceptable, the docs could explain the expense of the operation, and the implementation could change in the future as needed. But that does hide some complexity.
If it ought to return a slice of entries, that opens up new problems. What is an entry? A two-member generic struct? Ok, fine, but then how do I ergonomically iterate over them, pulling out both members? There's no clear solution to that problem yet.
Sure, there may be some edge case situations, like where you are dealing with someone else's code where you don't have control over the structures you've been given, but:
1. The standard library doesn't appeal to edge cases.
2. The "noiser" solutions to deal with the edge case serve as a reminder that you aren't working in the optimal space.
But I'd also suggest that if you think you need sorted keys, double-check. I program an awful lot of things without sorted keys, and I am quite aware of the issues around sorting, and I suspect without proof that a lot of people swearing by sorted maps are imposing false ordering requirements on their code more often than they realize. The ideal solution is not need order at all.
(I am especially suspicious of extensive use of maps where the keys are sorted by insertion order. That smells... antipatternish to me.)
Whether in particular cases, a properly ordered data structure (like a tree) should be used instead, is a valid question to ask, and thanks to the custom iterators, it'll now be more ergonomic to use. But if I usually use a particular map for its O(1) operations and only occasionally iterate over the whole thing, yet need consistent iteration order, then the built-in map still seems like the right choice, and having a standard way to iterate it is a reasonable request.
That is literally the edge case example I gave. Perhaps there is a better way to describe it than "edge case", but semantics is a silly game.
> then the built-in map still seems like the right choice, and having a standard way to iterate it is a reasonable request.
And, indeed, the standard library provides slices.Sorted(maps.Keys(m)) for exactly that. Ergonomic enough, while making the compromise being made reasonably explicit to help with readability – which is far more important than saving a few keystrokes. If typing is your bottleneck, practice will quickly solve that problem.
However, as examined in a sibling thread, there doesn't seem to actually be any missed optimization which could potentially be applied here.
Even ignoring that in the most common case the map isn't the right structure to begin with, what even is the general case for the situations that remain? You mentioned the marshalling of arbitrary data case, but in that case you also have reflection details to worry about, and which you can optimize for with a custom implementation, and thus wouldn't likely use the built-in anyway. A sibling thread discussed the cache benefits of colocating the values with the keys if a map is exceedingly small, but as soon as the map is of any reasonable size the added overhead of the values is almost certainly going to blow the cache even where the keys alone might still fit.
All of which is to say that the best approach is highly context dependent. How do you even begin to choose which is the general case if you were to include such a function?
if a function returns an iterator, it should be iterating the input. thats impossible in this situation. you'd need to loop the entire map, then return an iterator that tricks the user into thinking they are getting better performance when they are getting the worst possible performance.