The Third Hard Problem(mmapped.blog) |
The Third Hard Problem(mmapped.blog) |
Any hierarchical taxonomy classifies on one dimension at each taxonomic level. Invariably someone wants to classify on one criteria when someone else wants to classify on another. Taxonomies that humans use aren’t multi-dimensional. So if there is a disagreement, someone wins and someone(s) has to lose.
No one is wrong; they just have different priorities or preferences or goals.
So now as an architect I never argue (and seldom discuss) taxonomies. I make two points and then bow out:
1. Whatever your taxonomy is, you need a rubric for each level. You need a procedure or set of questions that unambiguously map any $THING you encounter into exactly one bucket. Validate that competent people with no specific domain knowledge can properly classify things with your rubric; it must be repeatable by amateurs, not just experts (software is dumb).
2. Existence trumps theory. If there exists a taxonomy and rubric for what you’re classifying, you need to provide a $DARN_GOOD_REASON why this wheel needs reinventing. Personal preference and your 1% edge case probably don’t justify all the work to reinvent everything.
Then, I go back to the implementers and tell them to design in a tagging system, which is a DIY taxonomy, and except in ridiculous use cases, I can make indexes make it fast enough to let everyone overlay their own classification system.
This 100x! I wish this were more common.
The key property of a tree is that there a unique path (address) for each element, which is a useful property in the implementation layer. But forcing that on users is a horribly leaky abstraction.
Ideally separate the low-level implementation from the interface, and allow users their own way to address content. I imagine object storage (with UUIDs or whatever) is often good enough for the lower layer. For the interface layer, tags are an improvement on categories (tree structure), but I think there's also room for more innovation (fuzzy matching, AI-driven interfaces, etc) that start by allowing trading-off precision for recall but then allow regaining precision by adding more approximate qualifiers to the filtering.
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PS: Pushing this approach to 11/10...
An intriguing (crazy?) application of this idea would be: what if we did this to the concept of a codebase? Make it a database (with all the corresponding improvements over a filesystem) -- it's no longer a tree of files, and allow users to query code like "that foo which accepts a bar, frobnicates its internal state, and emits a mutated baz". Note that this might also solve the "naming things" problem.
This setup seems like a powerful abstraction for AI coding agents. All that back-end power (database >> filesystem) is something they can easily leverage, and they can also be built to robustly resolve your fuzzy queries into precise addresses, and then update the code based on your desired outcome.
[Unison](https://www.unison-lang.org/docs/the-big-idea/) content addresses every definition. Kinda interesting.
A [Code Property Graph](https://en.wikipedia.org/wiki/Code_property_graph) takes a codebase and turns it into three graph representations: it's AST, a Control Flow Graph, and Program Dependence Graphs. These graphs are overlaid and shoved into a single property graph. It's a structure mainly used by some static analysis tools like [Joern](https://joern.io/)
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This has been a topic of a lot of interest and research for me. I've been experimenting with figuring out a system inspired by these ideas, among others, to apply the same idea (shoving multiple graph representations together) to a broader set of information
Tangential, but that reminds of the Haskell "hoogle" tool which allows searching for functions _by type_ across a large database of libraries, even by abstract types. So you might wonder "hmm what's that function that has a type structure like `t a -> (a -> t b) -> t b`?" and it'll happily tell you that it's monad `bind`
I've got a legacy tag cloud curation tool for random collections (each collection gets its own id) of URLs. It IFRAMEs each URL to present it; no whining. I've used it for classifying technical docs, photo libraries (then I used the tags to train an image classifier), and to present an analysis of a customer's web site.
It's written in Perl, and (still) runs on modern Perl. Make friends and maybe I'll toss the code your way and help you with your project.
Younger me wondered: "Don't they have more important things to do? Why do they never delegate such a menial and boring task, especially when the structure is kind of obvious"
Today it makes total sense to me. Even if it looks obvious no one has the exact same hierarchy in mind. It was fast for them to materialize the hierarchy themselves than to convince anyone about it in every detail. Some things just can't be delegated.
And that's a problem because Aggregability is NP-Hard: https://dl.acm.org/doi/abs/10.1145/1165555.1165556
So a tree is a way to take a high dimensionality graph and make it usefully lower dimensionality, but, given the aforementioned proof, that reduction is going to go from being a lossless compression to a heuristic. So any interesting problem (at least, any problem interesting to me) is only going to be aided (read: not solved exhaustively) by that hierarchy.
I'm okay with this. Being okay with this has been one of the most freeing things over the last 20 years of my career. Accept inaccuracy, and find usefulness in your data structures.
Big insight in that article is also from https://matklad.github.io/2021/08/22/large-rust-workspaces.h... about structuring large rust workspaces as a flat list.
1. Naming things 2. Cache invalidation 3. off-by-one errors
There are only three hard problems in computer science:
1. Naming things
2. Cache invalidation
3. Off-by4. Multi-threading
-one errors.Are these two things actually the same thing, or they separate?
It's a great question, much deeper and more interesting than it seems. The essay suggests thinking in terms of isomorphisms (relative to the structure you care about) rather than equality in some absolute sense, and I've found a fuzzy version of that to be a really useful perspective even in areas that can't be fully formalized.
Years ago, I stumbled upon the "idea" was already debated in other fields long before programming. Lumpers and Splitters.
https://interactivity.ucsd.edu/articles/In_Process/MultiTree...
The webs are so much more malleable, but they're also not free. All the 'good enough's you were that a hierarchy that was taking care of implicitly are now your responsibility to model precisely and make sure they're performant as well. Look at ReBAC, for example. It gives you expressive power, but it also forces you to reason precisely about relationships, graph traversal, consistency, and cost. Strikingly similar is GraphQL.
Interestingly, source code is hierarchical, but compiles almost immediately to a graph IR and most analysis and optimisations happen there. But almost nobody looks at a CFG/SSA graph directly. You author in a hierarchical manner, yet the operational substrate is a malleable graph.
The most fundamental problem in computer science is problem decomposition: how to take a complex problem and divide it up into pieces that can be solved independently.
> The next time you sit down to an empty design doc and don’t know where to start, be kind to yourself. You’re solving a hard problem.
This supposed hard problem in computing has always been with us, in real life. Which he admits multiple times, e.g.:
> Yet Victorian-era gentlemen might have pondered the same questions while sorting letters as we do while sorting virtual paper.
He appears to claim that the sole organizing principle in real life is the hierarchy, but, of course, that computers and ideas are different:
> Hierarchies are so natural to us that they ... [work] for physical objects that can be in only one place at a time. Ideas and information, however, resist taxonomies. They form intricate webs that penetrate rigid boundaries.
This distinction of physical vs. virtual requirements doesn't hold up under any sort of rigorous analysis. As he admits, hierarchies are not always ideal in physical space -- do we organize parts and supplies separate from tools, or place them next to their probable job sites?
And of course, the "in only one place at a time" is certainly true for any given group of atoms, but we have become adept at making fungible copies of atoms for many things. I might have drywall screws or 33 ohm resistors in multiple cached locations, and I have soldering irons and screwdrivers and pliers on more than one workbench.
One thing that is true is that we can usually add non-hierarchical groupings to information more easily than we can to groupings of atoms.
Another thing that is true is that we already often do so whenever the convenience outweighs the various costs.
And the third thing that is true is that this, also, is not much different than the physical world, where we routinely both break our hierarchies and create copies of things when needed.
I have found that the best answer I have is:
1) Gossip and use the existing ontology and add new items only if genuinely needed
2) Combine synonyms, sometimes using vector embedding and cosine similarity during search
The insight is that trees are not a storage constraint but a traversal consequence — any walk through a graph starting from a given node produces a tree as a byproduct, and different starting points or orderings produce different trees from the same underlying structure. Rather than forcing a choice of canonical hierarchy at write time (the dentist-bill-goes-in-which-folder problem), you store the graph and let the tree emerge lazily from context: whoever is querying, from wherever they enter, gets the tree that is relevant to them, and no connections are sacrificed to produce it.
In general, federated systems have this quality emerge. For example, in OpenStreetMap, one group can label Nagorno Karabakh, the other Artsakh, and no one has to fight Google etc.
But we could. Early filesystems didn't have heirachy. They'd sometimes support a single level of organization by treating a prefix of the filename as directory, but nothing below that. We looked at that and considered heirarchy better.
What you lament is that we stopped with heirarchy. You wish for something more general, and that you have received. With hardlinks and ACL the filesystem is freed from its shackles of heirarchy. Most people don't need that most of the the time.
The strict heirarchy sits as the third step of a complexity ladder. The reason we stopped there, in most cases, is that it's enough complexity.
Once a system commits to one hierarchy, it becomes very hard to introduce another authoritative model. Not only you cannot have alternative models with equal authority, you do not even get a clean parallel hierarchy later; you get the first hierarchy, now ossified, plus workarounds around it. On the other hand, webs are harder to reason about and more expensive to operate, but they can produce hierarchies 'on-demand' and you can get rid of them as easily as you've established them. Ergo, a ton of different programming languages just compile to LLVM IR.
All physical things have to be arranged in a linear fashion, this is pauli exclusion principle writ large.
A type of mereological nihilism is true, so hierarchy is real but in a weak emergence sense.
When I am standing on a street corner in a city, I can walk into a building and then navigate into a room inside that building and pick up a piece of paper inside.
Or I can walk down the street and as I pass landmarks, I will pass things in order, this is also a perceived hierarchy, because my starting position and movement are bound by time, I will always have a fixed order of experiences from any x,y coordinate.
So everything bounded by time and space acts as an emergent hierarchy via movement for a specific loci.
The paper can be moved however, and that same piece of paper sitting in the drawer at an attorneys office means something very different than when its sitting in the approved licenses cabinet in an offical government building.
Semantic meaning is first class but its an entirely different dimension than other types of meaning and due to pointers it isn't bound by pauli exclusion.
When people start thinking about the semantics of objects in computer programs they often become confused, because the semantic representation of the object follows semantic ontology laws not physical object ontology.
People can fuck this up in either direction and think physical laws apply to semantic meaning or physical meaning applies to semantics.
Not in a position to argue about physics, but I could think of quite a few things that are better described as web-like phenomena rather than hierarchical ones, electricity in a complex circuit comes to mind.
This is one of the ugliest parts of POSIX design, making idioms like -o /dev/null and file attributes unpredictable (I've had a server run out of disk space because a root-owned process used rename-style writing on /dev/null)
/ # stat /dev/null
File: /dev/null
Size: 0 Blocks: 0 IO Block: 4096 character special file
...
/ # sed -i 's/foo/bar/' /dev/null
/ # stat /dev/null
File: /dev/null
Size: 0 Blocks: 0 IO Block: 4096 regular empty file
...