Even if you explicitly deny a guarantee of a certain behavior in your contract,
if you usually deliver that behavior,
most of your customers will depend on it.
If you make a queueing system, it's impossible to guarantee anything other than delivery "at most once" (some loss occurs), or "at least once" (some duplication occurs), but if you usually provide "exactly once" in practice, most of your customers will depend on this.
If you provide a data bucket service, and guarantee availability, but not performance, and you usually provide 100MB/s throughput, your customers will have major problems if you only provide 10MB/s throughput in some cases.
If you make a self-driving car, and it requires human monitoring, but it's really good, say one intervention per year of driving . . . your customers will die because they aren't paying attention.
It recognizes that legitimacy often emerges organically from social acceptance rather than top-down imposition. In technology we often see that evolving reference implementations work better than elaborate specifications.
That's only a condition at termination. For ongoing communication, you can guarantee exactly once delivery. When communication ceases, the final state between the ends is indeterminate. If you can keep talking, or resume after breaks, it's a solvable problem.
In a large system, terminations (both planned and unplanned) happen all the time. For the unplanned ones, it is very difficult to ensure exactly once, at least from the perspective of the queueing service, who can't check that the processing of the message did or did not occur outside of the original connection and it's "acks".
Sure it does. It's essential to making banking work. See ISO 20022 reconcilation.[1] This is the process by which errors in money transactions between banks are resolved. ISO 20022 is used by SWIFT, FedNow, FedWire, and systems in about 70 countries.
Unique IDs make transactions idempotent, but that's just the start. Banking is an eventually consistent system. Messages go back and forth until both ends agree. If there's a loss of communication, some transactions will be resubmitted, but they won't be applied twice.
Banks have been doing reconciliation for centuries. It's been sped up recently, but the concepts have not changed much.
[1] https://www.finextra.com/blogposting/19808/how-exactly-will-...
In the self-driving car scenario, you'd probably go with cold statistics: is it killing fewer people than ones that need more interventions? Just like queueing though, experiments in production could be problematic.
So what the x-ray interface does, is randomly insert guns and bombs into the scan at a relatively frequent rate. The operator must click on these problem areas. If it is a synthetic object, it then disappears and the operator can continue screening the bag. If it isn't synthetic, the bag gets shunted for manual inspection.
So for a self-driving car, if it must be monitored (it's not L5 within the driving mission), then you would perhaps need the car to randomly ask the driver to take over, even though it's unnecessary. Or randomly appear to be making a mistake, to see if the user reacts.
If the user doesn't react appropriately or in time, then self-driving is disabled for a time the next time the car starts.
For the queuing system, it perhaps makes sense to inject a certain number of duplicates by default. Say 0.1%. Enough that it simply can't be ignored during development of the clients. Then, when duplicates arise as a consequence of system failures, the code is already expecting this and there's no harm to the workload.
No. There is a big difference in an accident caused by human error and an accident caused by machine failure.
We tolerate much more of the former than the latter.
This feels like a cognitive failure, but I do not think it is
I thought Netflix was nuts for Chaos Monkey, but having read several more treatises on human cognition and particularly cognitive biases, I now see they are crazy like a fox. Guaranteeing something breaks every week keeps things at the mental forefront.
Users assume a system is always correct if it has been mostly correct before.
This isn't a new phenomenon, although AI certainly brings it to the fore.
It's almost always unintentional. Someone wrote some code, it works, they ship it, not realizing it only works if the list comes back in a specific order, or with a specific timing. Then a year or two later they do some updates, the list comes back in a different order, or something is faster or slower, and suddenly what worked before doesn't work.
This is why in Golang, for instance, when you iterate over map keys, it purposely does it in a random order -- to make sure that your program doesn't accidentally begin to rely on the internal implementation of the hash function.
ETA: But of course, that's not truly random, just pseudorandom. It's not impossible that someone's code only works because of the particular pseudorandom order they're generating, and that if Golang even changes the pseudorandom number generator they're using to evade Hyrum's Law that someone's code will break.
Prior that, yeah, that's just a bug.
> This is why in Golang, for instance, when you iterate over map keys, it purposely does it in a random order
It could be that Go's intentions are different here, but IIRC languages will mix randomization into hashtables as it is otherwise a security issue. (You typically know the hash function, usually, so without randomization you can force hash collisions & turn O(1) lookups into O(n).)
I believe you don't understand.
In go, they literally randomly permute the iteration order of the map each time you iterate over it.
e.g
for x in map {
}
for x in map {
// different order
}
var randomUser User
for userId, user in usersMap {
randomUser = user
break
}
That's … pretty surprising, since that would seem to imply that iteration would require a O(n) sized chunk of memory somewhere to reify the order into. (And probably O(n) time to do the shuffle, or at least, a copy of the ordering; we should shuffle as we go, I suppose, but we'd need to track what we've emitted & what we've not, hence O(n) time & space to populate that scratch…) That seems undesirable.
1. choose starting bucket randomly
2. From there iterate through buckets in usual order (wrapping around)
3. Within each bucket, generate permutation of 0..7, and stream it in that order.
See func mapiterinit() in runtime/map.go
This is just me musing, but you can probably precompute all permutations of 0..7 and store it for ~20KB once for the whole runtime, and just index that each time. Can avoid the fischer yates each time.
For example, they write code that unintentionally depends on some distantly-invoked async tasks resolving in a certain order, and then the library implementation changes performance characteristics and the other order happens instead, and it creates a new bug in the application.
I maintain a number of such poorly-documented systems (you could, loosely, call them "APIs") for internal customers. We've had a number of scenarios where we've found a bug, flagged it as a breaking change (which it is), said "there's _no way_ anybody's depending on that behavior", only to have one or two teams reach out and say yes, they are in fact depending on that behavior.
For that reason, we end up shipping many of those types of changes ship with a "bug flag". The default is to use the correct behavior; the flag changes the behavior to remain buggy, to keep the internal teams happy. It's then up to us to drive the users to change their ways, which.. doesn't always happen efficiently, let's say.
That was an era where lots of testing code barely worked, and what we found over and over again is that we had tests that were dependent on each other and the tests only passed because they ran in a particular order.
And now Java 5 changed the order in which the test functions were being enumerated in the test files. Oops.
How good-of-an-idea / best practice is API versioning?
/api/v1/foo
/api/v2/foo
- You have to decide whether to bump the entire API version or only the /foo endpoint. The former can be a big deal (and you don't want to do it often), the latter is messy. Especially if you end up with some endpoints on /v1 (you got it right first time) while others are on /v4 or /v5. Some clients like to hard-code the URL prefix of your API, including the version, as a constant.
- You still have to decide what your deprecation and removal policy will be. Does there come a time when you remove /api/v1/foo completely, breaking even the clients who are using it correctly, or will you support it forever?
It's not easy at all, especially if you have to comply with a backwards compatibility policy. I've had many debates about whether it's OK to introduce breaking changes if we consider them to be bug fixes. It depends on factors like whether either behaviour is documented and subjective calls on how "obviously unintended" the behaviour might be.
Minus, You will support v1 forever. It's almost impossible to make it go away.
Not true generally. One man's engineering malpractice is another man's clever hack.
Users of Windows 95 complained that Windows 95 broke SimCity.
What did Windows 95 break? It fixed an obscure allocator bug SimCity was relying on.
Users loved Windows 95, for ""fixing"" this. How was it fixed? By introducing an obscure switch to old allocator if it detected SimCity in the app name.
https://arstechnica.com/gadgets/2022/10/windows-95-went-the-...
Microsoft has a commitment to backwards compatibility that I think is going too far, but I understand why. Raymond Chen has explained that if a user buys the new version of Windows and their programs stop working, they will blame MS regardless because they don't have any way to know it's the program's fault. So MS is incentivized to go out of their way to enable these other programs' bad behavior, because it keeps their (Microsoft's) customers happy.
But same principle. Everyone loves for someone else to maintain backwards (and forwards compatibility).
> The users that GP was accusing of malpractice would be the Maxis devs in this case, not the end users who were trying to install SimCity on their Windows 95 machine
And Windows devs aren't to blame for backporting buggy allocators? If SimCity depended on buggy behavior was malpractice, what the hell is backporting bugs? Exporting malpractice?
Hyrum’s Law in Golang - https://news.ycombinator.com/item?id=42201892 - Nov 2024 (183 comments)
Hash Ordering and Hyrum's Law - https://news.ycombinator.com/item?id=41673295 - Sept 2024 (41 comments)
Hyrum's Law - https://news.ycombinator.com/item?id=39401973 - Feb 2024 (66 comments)
Git archive generation meets Hyrum's law - https://news.ycombinator.com/item?id=34631275 - Feb 2023 (76 comments)
Hyrum's Law - https://news.ycombinator.com/item?id=33283849 - Oct 2022 (52 comments)
Hyrum's Law - https://news.ycombinator.com/item?id=29848295 - Jan 2022 (36 comments)
Hyrum's Law - https://news.ycombinator.com/item?id=27386818 - June 2021 (5 comments)
Hyrum's Law: An Observation on Software Engineering - https://news.ycombinator.com/item?id=21515225 - Nov 2019 (6 comments)
Hyrum's Law - https://news.ycombinator.com/item?id=19249199 - Feb 2019 (1 comment)
Let's be clear that this is one interpretation of the phenomenon described here, which we might call "The Doomerist Interpretation of Hyrum's Law". For everyone else, the whole reason that we bother categorize interface details into "public" and "private" buckets is precisely so we have the moral high ground to to tell people to go kick rocks when they get they get uppity about their own failure to adhere to the publicly documented interface.
In terms of output of an LLM, there is no clear promise in the contract, only observable behaviour. Also the observable behaviour is subject to change with every update in LLM. So all the downstream systems have to have evals to counter this.
One good example is claude code where now people have started complaining them switching models effecting their downstream coding workflows.
This is the unfortunate thing about wrapping LLMs in API calls to provide services.
Unless you control the model absolutely (even then?) you can prompt the model with a well manicured prompt on Tuesday and get an answer - a block of text - and on Thursday, using the exact same prompt, get a different answer.
This is very hard to build good APIs around. If done expect rare corner case errors that cannot be fixed.
Or reproduced.
For instance, traversal order of maps in Go is always randomized, to prevent subtle bugs caused by depending on the order.
As AI generated code becomes cheaper, it may be worthwhile to change some subset of your internal behaviors from release to release, so that users don't become too complacent.
"assertions": true
Then the service running the API will do extra checking when the assertions option is true, basically making it less forgiving and following the specification closely.
Yep!
> [and that] constrains changes to the implementation, which must now conform to both the explicitly documented interface, as well as the implicit interface captured by usage
Nope!
Software authors define the rules for the software that they author. I understand it's a spectrum and the rules are different in different circumstances but at the end of the day my API is what I say it is and if you rely on something that I don't guarantee that's on you and not me. Hyrum's Law describes a common pathology, it doesn't define an expected rule or requirement.