so the behavior is going to change: sorting becomes unstable, i.e. it doesn't preserve the relative order of equal elements. I'm pretty sure this will affect users
https://github.com/golang/go/blob/master/src/sort/sort.go#L4...
[citation needed]
This is the wrong wording, IE had a decently enough installed/captive user base. It was never particularly stable.
Otherwise the only way to have a non-stable sort function that currently happens-to-be stable would have to include a randomized shuffle afterwards to ensure no one goes around depending on it…
It would be equivalent to assuming the golang ABI was stable because it survived unmodified for a couple versions, despite it being explicitly not. And then freezing the ABI to maintain a guarantee it explicitly didn’t want to maintain.
On a similar note, Im sure there was a suggestion to artificially make the previous golang sort function unstable so that users wouldn't come to rely on stable behaviour where it wasn't guaranteed. I quite like the idea, start the fight against hyrum's law!
In theory, yes. In practice, if a OS or library update uncovers bugs in many programs, that update won’t get favorable reviews.
That’s the reason behind Linux’s “we do not break userspace!” rule (https://linuxreviews.org/WE_DO_NOT_BREAK_USERSPACE), and also why Microsoft Windows has layer upon layer of APIs, a copy of the Windows 95 memory manager (https://mcpmag.com/articles/2010/07/27/trip-down-memory-lane...), why large databases support character sets that probably haven’t been used anywhere since before many HN readers were born (https://docs.oracle.com/goldengate/1212/gg-winux/GWUAD/wu_ch...), etc.
(Apple has a totally different viewpoint on this. That’s one reason it never really got into businesses for a long time)
Block real innovation like what?
> Granted, Desktop Linux doesn't need another Python 2 -> 3 or X.org -> Wayland deathmarch...
That's all userspace? Userspace doesn't care it breaks. That's why userspace sucks. You need to keep compatibility if you want to have a stable base to build something. That's why we have (sometimes silly) standards like SUS or POSIX.
"we do not break userspace" at least garantees that you need to fix only the userspace, and not the whole thing all the time.
Significant changes in the kernel already often need years to land because of the multitude of configurations and interfaces that have to be supported. And even longer to deprecate and remove. Not necessarily a bad thing, just the price of success.
Large-scale breakages like the Wayland transition affect the whole Desktop Linux ecosystem, and are just an example of what mayhem fundamental changes can cause.
There's a debate/lesson in here about when implementations should change to actually take up all the space claimed by the spec.
As a result most people will go "Oh, I guess I should actually test the outcome I care about" (good) or "Oh, testing is hard, I'll skip it" (bad, but at least not a problem for the team maintaining the standard library). Very few will say to themselves "I bet I can defeat the randomisation somehow with a different incorrect test design" because humans are lazy.
There aren't a lot of places where stability matters, where it does the programmer should hopefully know and not have relied on an undefined behaviour… The most likely impact, I think, will be in UIs where the difference won't really matter but could be quite noticeable.
Do note though that "unstable" can mean two different things. A single threaded sort will produce the same result for the same input, and many parallels sorts have this property too due to the way they coordinate the threads and collate their results, so the output order of equal items is arbitrary but not random. Some parallel sorts do order items with the same sort key more randomly though due to being sensitive to timing differences caused by uncontrolled factors (the OS's scheduler and other things it is dealing with, differing IO delays if the sort is not in-memory, different numbers of threads if comparing run results with those done on other processor architectures, …). IIRC pdqsort is the former if those two types of unstable.
I'm just noting that users are necessarily flawed, and moving from "stable but not guaranteed to be" to "actually unstable" will be a breaking change for some real world codebases.
Seems really odd that the same team would have used a stable sort and just documented as “not guaranteed to be stable”.
The reason is that a hash table with a fixed hash function may be used for denial of service attacks. If an attacker knows how a hash map maps keys and can control the keys being inserted, they can insert thousands of keys that all map to the same hash, effectively turning the hash map into a list or vector that has to be searched linearly (on both lookups and insertions)
And that’s a real problem. https://lwn.net/Articles/474912/ says:
“That's where web frameworks come into play. Those frameworks helpfully collect up all of the POST form data that gets sent to a particular web page into a dictionary that gets handed off to the application for processing. The amount of data that the frameworks will allow is typically rather large (e.g. 1MB), so an attacker can create tens of thousands of form entries in a single HTTP request. Because they all collide, those entries can take tens of seconds to a few minutes to process on an i7 core according to the advisory [PDF] released by Wälde and Klink.”
If you manage to insert 10,000 keys with identical hashes, that’s about 10,000²/2 key comparisons.
They absolutely are.
> The reason is that a hash table with a fixed hash function may be used for denial of service attacks. If an attacker knows how a hash map maps keys and can control the keys being inserted, they can insert thousands of keys that all map to the same hash, effectively turning the hash map into a list or vector that has to be searched linearly (on both lookups and insertions)
That has nothing to do with what I'm talking about.
Independent from hash randomisation, Go will randomise the iteration of hashmaps, such that iterating the same hashmap twice, within the same process, without having modified it in any way, will yield different results. Currently this is done by offsetting the starting point of the iteration by a random amount.
For some reason they dropped this explicit mention when https://go.dev/blog/go-maps-in-action was moved over to https://go.dev/blog/maps, but it used to be there:
> Since the release of Go 1.0, the runtime has randomized map iteration order. Programmers had begun to rely on the stable iteration order of early versions of Go, which varied between implementations, leading to portability bugs.
You can trivially observe this behaviour by just iterating the same map multiple times. With mere hash randomisation, even if hash randomisation was done on a per-map basis by iterating the same map you'd observe the same order, but in Go you don't: https://go.dev/play/p/3HZBv2WHKcT
As you can see the items are always in the same relative order, but the starting point of the iteration varies.
You can also see this in the initialisation of the map iterator, it's even labelled with a nice little comment: https://github.com/golang/go/blob/master/src/runtime/map.go#...
Then I had a larger input (like 100k elements) and wondered, why does it take a few minutes to print the output, when it calculated the output in a second? Well, it was the merge sort being called after the calculation completed to print it sorted. A double fail, because the calculation already produced sorted output in this case.
Now I sort a list of indices with quick sort
And Hyrum's law in general: https://www.hyrumslaw.com/