Comment by embedding-shape
12 hours ago
"Epigrams in Programming" by Alan J. Perlis has a lot more, if you like short snippets of wisdom :) https://www.cs.yale.edu/homes/perlis-alan/quotes.html
> Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
Always preferred Perlis' version, that might be slightly over-used in functional programming to justify all kinds of hijinks, but with some nuance works out really well in practice:
> 9. It is better to have 100 functions operate on one data structure than 10 functions on 10 data structures.
There's also:
>I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships.
-- Linus Torvalds
> >I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships.
> -- Linus Torvalds
What about programmers
- for whom the code is a data structure?
- who formulate their data structures in a way (e.g. in a very powerful type system) such that all the data structures are code?
- who invent a completely novel way of thinking about computer programs such that in this paradigm both code and data structures are just trivial special cases of some mind-blowing concept ζ of which there exist other special cases that are useful to write powerful programs, but these special cases are completely alien from anything that could be called "code" or "data (structures)", i.e. these programmers don't think/worry about code or data structures, but about ζ?
From what I understand from the vibe coders, they tell a machine what the code should do, but not how it should do it. They leave the important decisions (the shape of data) to an LLM and thus run afoul of this, which is gonna bite them in the ass eventually.
I think this is sometimes a barrier to getting started for me. I know that I need to explore the data structure design in the context of the code that will interact with it and some of that code will be thrown out as the data structure becomes more clear, but still it can be hard to get off the ground when me gut instinct is that the data design isn't right.
This kind of exploration can be a really positive use case for AI I think, like show me a sketch of this design vs that design and let's compare them together.
AI is terrible for this.
My recommendation is to truly learn a functional language and apply it to a real world product. Then you’ll learn how to think about data, in its pure state, and how it is transformed to get from point A to point B. These lessons will make for much cleaner design that will be applicable to imperative languages as well.
Or learn C where you do not have the luxury of using high-level crutches.
> This kind of exploration can be a really positive use case for AI I think
Not sure if SoTA codegen models are capable of navigating design space and coming up with optimal solutions. Like for cybersecurity, may be specialized models (like DeepMind's Sec-Gemini), if there are any, might?
I reckon, a programmer who already has learnt about / explored the design space, will be able to prompt more pointedly and evaluate the output qualitatively.
> sometimes a barrier to getting started for me
Plenty great books on the topic (:
Algorithms + Data Structures = Programs (1976), https://en.wikipedia.org/wiki/Algorithms_%2B_Data_Structures...
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That is excellent. I'm putting that in my notes.
I believe the actual quote is:
"Show me your flowchart and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won't usually need your flowchart; it'll be obvious." -- Fred Brooks, The Mythical Man Month (1975)
This is the biggest issue I see with AI driven development. The data structures are incredibly naive. Yes it's easy to steer them in a different direction but that comes at a long term cost. The further you move from naive the more often you will need to resteer downstream and no amount of context management will help you, it is fighting against the literal mean.
The rule may not hold with AI driven development. The rule exists because it's expensive to rewrite code that depends on a given data structure arrangement, and so programmers usually resort to hacks (eg. writing translation layers or views & traversals of the data) so they can work with a more convenient data structure with functionality that's written later. If writing code becomes free, the AI will just rewrite the whole program to fit the new requirements.
This is what I've observed with using AI on relatively small (~1000 line) programs. When I add a requirement that requires a different data structure, Claude will happily move to the new optimal data structure, and rewrite literally everything accordingly.
I've heard that it gets dicier when you have source files that are 30K-40K lines and programs that are in the million+ line range. My reports have reported that Gemini falls down badly in this case, because the source file blows the context window. But even then, they've also reported that you can make progress by asking Gemini to come up with the new design, and then asking it to come up with a list of modules that depend upon the old structure, and then asking it to write a shim layer module-by-module to have the old code use the new data structure, and then have it replace the old data structure with the new one, and then have it remove the shim layer and rewrite the code of each module to natively use the new data structure. Basically, babysit it through the same refactoring that an experienced programmer would use to do a large-scale refactoring in a million+ line codebase, but have the AI rewrite modules in 5 minutes that would take a programmer 5 weeks.
Naive doesn't mean bad. 99% of software can be written with understood, well documented data structures. One of the problems with ai is that it allows people to create software without understanding the trade offs of certain data structures, algorithms and more fundamental hardware management strategies.
You don't need to be able to pass a leet code interview, but you should know about big O complexity, you should be able to work out if a linked list is better than an array, you should be able to program a trie, and you should be at least aware of concepts like cache coherence / locality. You don't need to be an expert, but these are realities of the way software and hardware work. They're also not super complex to gain a working knowledge of, and various LLMs are probably a really good way to gain that knowledge.
Then don't let the AI write the data structures. I don't. I usually don't even let the AI write the class or method names. I give it a skeleton application and let it fill in the code. Works great, and I retain knowledge of how the application works.
> This is the biggest issue I see with AI driven development. The data structures are incredibly naive.
Bill Gates, for example, always advocated for thinking through the entire program design and data structures before writing any code, emphasizing that structure is crucial to success.
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I’m really going to need to see both. There’s a lot of business logic that simply is not encoded in a data storage model.
As I'm sure more and more people are using AI to document old systems, even just to get a foothold in them personally if they don't intend to share it, here's a hint related to that: By default, if you fire an AI at a programming base, at least in my experience you get the usual documentation you expect from a system: This is the list of "key modules", this module does this, this module does that, this module does the other thing.
This is the worst sort of documentation; technically true but quite unenlightening. It is, in the parlance of the Fred Brooks quote mentioned in a sibling comment, neither the "flowchart" nor the "tables"; it is simply a brute enumeration of code.
To which the fix is, ask for the right thing. Ask for it to analyze the key data structures (tables) and provide you the flow through the program (the flowchart). It'll do it no problem. Might be inaccurate, as is a hazard with all documentation, but it makes as good a try at this style of documentation as "conventional" documentation.
Honestly one of the biggest problems I have with AI coding and documentation is just that the training set is filled to the brim with mediocrity and the defaults are inferior like this on numerous fronts. Also relevant to this conversation is that AI tends to code the same way it documents and it won't have either clear flow charts or tables unless you carefully prompt for them. It's pretty good at doing it when you ask, but if you don't ask you're gonna get a mess.
(And I find, at least in my contexts, using opus, you can't seem to prompt it to "use good data structures" in advance, it just writes scripting code like it always does and like that part of the prompt wasn't there. You pretty much have to come back in after its first cut and tell it what data structures to create. Then it's really good at the rest. YMMV, as is the way of AI.)
Reminded me of this thread between Alan Kay and Rich Hickey where Alan Kay thinks "data" is a bad idea.
My interpretation of his point of view is that what you need is a process/interpreter/live object that 'explains' the data.
https://qr.ae/pCVB9m
Thanks for the pointer to this 2016 dialog!
One part of it has interesting new resonance in the era of agentic LLMs:
alankay on June 21, 2016 | root | parent | next [–]
This is why "the objects of the future" have to be ambassadors that can negotiate with other objects they've never seen. Think about this as one of the consequences of massive scaling ...
Nowdays rather than the methods associated with data objects, we are dealing with "context" and "prompts".
Quite a nice insight there!
I should probably be thinking more in this direction.
Hm, not sure. Data on its own (say, a string of numbers) might be meaningless - but structured data? Sure, there may be ambiguity but well-structured data generally ought to have a clear/obvious interpretation. This is the whole idea of nailing your data structures.
Yeah, structured data implies some processing on raw data to improve its meaning. Alan Kay seems to want to push this idea to encapsulate data with rich behaviour.
I’m with Rich Hickey on this one, though I generally prefer my data be statically typed.
Sure, static typing adds some sort of process that provides a coarse interpretation of the data.
I find languages like Haskell, ReScript/OCaml to work really well for CRUD applications because they push you to think about your data and types first. Then you think about the transformations you want to make on the data via functions. When looking at new code I usually look for the types first, specifically what is getting stored and read.
Similarly, that approach works really well in Clojure too, albeit with a lot less concern for types, but the "data and data structures first" principle is widespread in the ecosystem.
I've heard good things about Clojure, and it'ss different from what I am used to (bonus points because I like an intellectual challenge), so trying it out is definitely on my todo list.
Aren't they basically saying opposite things? Perlis is saying "don't choose the right data structure, shoehorn your data into the most popular one". This advice might have made sense before generic programming was widespread; I think it's obsolete.
Pike: strongly typed logic is great!
Perlin: stringly typed logic is great!
> Perlis is saying "don't choose the right data structure, shoehorn your data into the most popular one"
I don't take it like that. A map could be the right data structure for something people typically reach for classes to do, and then you get a whole bunch of functions that can already operate on a map-like thing for free.
If you take a look at the standard library and the data structures of Clojure you'd see this approach taken to a somewhat extreme amount.
This quote from “Dive into Python” when I was a fresh graduate was one of the most impacting lines I ever read in a programming book.
> Busywork code is not important. Data is important. And data is not difficult. It's only data. If you have too much, filter it. If it's not what you want, map it. Focus on the data; leave the busywork behind.
> Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
If I have learned one thing in my 30-40 years spent writing code, it is this.
I agree. The biggest lesson I try to drive home to newer programmers that join my projects is that its always best to transform the data into the structure you need at the very end of the chain, not at the beginning or middle. Keep the data in it's purest form and then transform it right before displaying it to the user, or right before providing it in the final api for others to consume.
You never know how requirements are going to change over the next 5 years, and pure structures are always the most flexible to work with.
Related: your business logic should work on metric units. It is a UI concern if the user wants to see some other measurement system. Convert to feet, chains, cubits... or whatever obscure measurement system the user wants at display time. (if you do get an embedded device that reports non-metric units convert when it comes in - you will get a different device in the future that reports different units anyway)
You still have to worry about someone using kg when you use g, but you avoid a large class of problems and make your logic easier.
But doesn't No. 2 directly conflict with Pike's 5th rule? It seems to me these are all aphorisms that have to be taken with a grain of salt.
> 2. Functions delay binding; data structures induce binding. Moral: Structure data late in the programming process.
Nice to see Perlis mentioned once in a while. Reading SICP again, still learning new things.
There is a matching video series for SICP:
https://ocw.mit.edu/courses/6-001-structure-and-interpretati...
which I found very helpful in (finally) managing to get through that entire text (and do all the exercises).
I feel like these are far more vague and less actionable than the 5 Pike rules.
With 100 functions and one datastructure it is almost as programming with a global variables where new instance is equivalent to a new process. Doesn’t seem like a good rule to follow.
The scope of where that data structure or functions are available is a different concern though, "100 functions + 1 data structure" doesn't require globals or private, it's a separate thing.
One can always look as global variables equivalent to a context object that’s is passed in every function. It’s just a syntactic difference whether one constructs such data structure or uses it implicitly via globals.
What I am getting at is that when one has such gigantic data structure there is no separation of concerns.
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As much as relational DBs have held back enterprise software for a very long time by being so conservative in their development, the fact that they force you to put this relationship absolutely front-of-mind is excellent.
I'd personally consider "persistence" AKA "how to store shit" to be a very different concern compared to the data structures that you use in the program. Ideally, your design shouldn't care about how things are stores, unless there is a particular concern for how fast things read/writes.
Often significant improvements to every aspect of a system that interacts with a database can be made by proper design of the primary keys, instead of the generic id way too many people jump to.
The key difficulty is identifying what these are is far from obvious upfront, and so often an index appears adjacent to a table that represents what the table should have been in the first place.
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I meant to reply to a different comment originally, specifically the one including this quote from Torvalds:
> Good programmers worry about data structures and their relationships.
> -- Linus Torvalds
I was specifically thinking about the "relationship" issues. The worst messes to fix are the ones where the programmer didn't consider how to relate the objects together - which relationships need to be direct PK bindings, which can be indirect, which things have to be cached vs calculated live, which things are the cache (vs the master copy), what the cardinality of each relationship is, which relationships are semantically ownerships vs peers, which data is part of the system itself vs configuration data vs live, how you handle changes to the data, (event sourcing vs changelogging vs vs append-only vs yolo update), etc.
Not quite "data structures" I admit but absolutely thinking hard about the relationship between all the data you have.
SQL doesn't frame all of these questions out for you but it's good getting you to start thinking about them in a way you might not otherwise.
" 9. It is better to have 100 functions operate on one data structure than 10 functions on 10 data structures."
That's great
Was the "J" short for "Cassandra"?
Also basically everything DHH ever said (I stopped using Rails 15 years ago but just defining data relationships in YAML and typing a single command to get a functioning website and database was in fact pretty cool in the oughts).
Perlis is just wrong in that way academics so often are.
Pike is right.
Hang on, they mostly agree with each other. I've spoken to Rob Pike a few times and I never heard him call out Perlis as being wrong. On this particular point, Perlis and Pike are both extending an existing idea put forward by Fred Brooks.
Perlis absolutely is not saying the same thing, and as the commenter notes the functional community interpret it in a particularly extreme way.
I would guess Pike is simply wise enough not to get involved in such arguments.
Perlis is right in the way that academics so often are and Pike is right in the way that practitioners often are. They also happen to be in rough agreement on this, unsurprisingly so.
Treating either as gospel is lazy, Perlis was pushing back on dogma and Pike on theory, while legacy code makes both look cleaner on paper.
Could you be more specific?
Promoting the idea of one data structure with many functions contradicts:
“If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident.”
And:
“Use simple algorithms as well as simple data structures.”
A data structure general enough to solve enough problems to be meaningful will either be poorly suited to some problems or have complex algorithms for those problems, or both.
There are reasons we don’t all use graph databases or triple stores, and rely on abstractions over our byte arrays.
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