I was surprised that the author didn’t translate the piped representation of the program to a procedural program with explicit calls to subprocedures. It’s bigger, but it’s dead-simple and easy to slip instructions in the middle of.
Strong resonance with the famous essay "The Rise of Worse is Better" [1], which contrasted the (better) "MIT/Stanford style of design" with the (worse) "New Jersey approach".
MIT/Stanford:
> Simplicity -- the design must be simple, both in implementation and interface. It is more important for the interface to be simple than the implementation.
New Jersey:
> Simplicity -- the design must be simple, both in implementation and interface. It is more important for the implementation to be simple than the interface. Simplicity is the most important consideration in a design.
TFA maps "simplicity" to "MIT/Stanford simplicity" (simplicity for the user) and "smallness" to "New Jersey simplicity" (simplicity for the developer).
I wonder if the root of the tension between the two schools comes down to the ambiguity of the user/developer distinction. Developers are also users. Simplicity of implementation is helpful to developers when they are working directly on implementation, while simplicity of interface is helpful to developers when they are using other developers' work.
I think you're right on that point of contention, it's too far to assert a universal and clear good/bad dynamic here because that line between developer/user is contextual and fuzzy.
Another interesting irony I'll note, Lisp is the "New Jersey approach" towards symbolic AI. Americans clinging to their Lisp systems were deeply entrenched in a "worse-is-better" mindset. Your interface, the computational model, didn't need to be designed for logic programming, that was wholly secondary. Do everything as much as possible in Lisp, and then offload the relational description to a small (not simple) library. American knowledge engineers were looked at as overpaid procedural hackers with zero mathematical elegance and very little credibility. More or less the same perception these self-same Lisp-machine users had for Unix and C programmers.
It's all about perspective, at the end of the day. Where we draw the line in the sand on these categories is free-to-choose, yet it also determines everything. We're always someone else's villain under different semantics.
> The reason for this is that in Rust, a struct couples type-checking to a fixed data representation. You can't get one without the other.
> Clojure decouples data representations from type checking.
This is funny to me because seen from the other side, (this) Clojure couples runtime type information to data structures: you're no longer allowed to define a data structure that doesn't have some runtime type information attached. A fixed static structure is just the consequence of not adding dynamic type information.
Meanwhile in Rust you can get type-checking ‘without’ a fixed structure by using trait objects.
Great piece, very straightforward examples, although I did have to squint for quite a while to grok the Closure portion.
I am currently building a piece of very modular software and it has been the hardest-to-design project of my entire career. I would never be allotted this amount of time-effort at any job I have held to make something this robust and clearly defined. Many aspects of this project have taken 3-5 rounds trying-and-trashing to get an abstraction that is uncomplicated.
This is precisely why vibe coding is so successful for building tiny isolated scripts, and so disastrous for anything else. It's just really dang hard to build something large and simple.
Personally, I'm big fan of those pipes mentioned. And the tooling that can be created around those _simple_ "primitives". Comparison to Google drive is obscure, in a way that it compares one gigantic piece of software into these small and simple.
Bottom line is probably true, but if you are an open-source maintainer mentioned, and you have only so few hours to spend, you just cannot create those gigantic softwares either. You need to choose from the cards on your hand.
> There's no native Unix equivalent to frequencies, this sort | uniq -c is the closest we can get. Not only is it less performant (it has to collect the full input into memory before continuing), but it ties aggregation to ordering.
One of the core features of the Unix command-line is that it is user-extensible. If there's no "native" command equivalent to frequencies, you can write your own, and it will be given the same first-class treatment as any other binary in your PATH. This is entirely in keeping with the Unix philosophy of simple implementations.
Important to note as well, is that "simple" isn't "lesser" or "dumber", it can be "more" and "wider", yet still "simpler".
Expectedly, Rich Hickey explains it best, watch the "Simple Made Easy" talk if you haven't before, one of the few talks I probably watch bi-yearly: https://www.youtube.com/watch?v=SxdOUGdseq4
Few things, concepts and ideas have changed as much of my programming mind as Hickey's talk and ultimately Clojure have done over the years.
Wish we had new amazing Hickey talks to link to, maybe it seems he's about the hang up the hammock perhaps?
Using "length of the correctness statement + length of its proof" works quite well as proxy for complexity of a component (the longer, the more complex).
Copy pasted functions with subtle changes mean you cannot reuse the proof (DRY). Giant functions with lots of if/else statements however might cause a branch explosion in the proof. The right abstraction removes lots of assumptions that a proof could depend on, limiting the search space and often forcing elegance (this also applies to math, eg. when reasoning with abstract groups instead of integers).
The wrong abstraction might force case distinctions on consumers of the abstraction.
I've been thinking about a new AI based dimension to this. If your program is split into smaller decoupled "modules" then all the code for each "module" can fit into an AI context window. In this way the AI can have all the context to edit a "module" by just loading all the code for that "module". You would not need things like vector search as much. If we assume 20 tokens per line of code and the AI context window is 100k to 1M tokens, then that would argue for having "modules" between 5,000 and 50,000 loc, depending on which AI model you are using.
> then that would argue for having "modules" between 5,000 and 50,000 loc, depending on which AI model you are using.
FWIW, I set hard limits to 200 LOC for every single source code file in any AI-related projects, also with restrictions on "formatting hacks" and other golf-like stuff.
I think beyond 5000 LOC in a single file and all available models already get lost frequently, even if the "context limit" theoretically is way above that. Maybe aim for like 1K LOC at max unless you want to have lots of misunderstandings.
The author uses the term “coupling” to describe a simple program from the code’s perspective - “you need to have a good mental model of your program”. I agree, but I’d argue that more importantly you need to have a good domain model. Good design communicates accurately and a high Gulf of Evaluation/Execution are actually what makes programs feel “complex” to a user.
These concepts simplify for the user these questions: “I know what I want - how do I make the program do it?” (Execution) and “The program did something — what state is it actually in?” (Evaluation)
I believe the author was getting at these concepts, especially in their Google Drive example - how the large program has a “small” UX. Understanding the domain model provides a much stronger basis for designing user interfaces, and understanding the Gulf of Evaluation/Execution allows you to build incredibly complex-looking, large UX’s without confusing or overwhelming the user.
Complexity (the opposite of simplicity) has nothing to do with the size of a program, but usually there is a high chance that a large program is more complex than smaller one, purely because the complexity multiplies, not just adds up.
A single regex line could be far more complex than a 100-line java program.
That is an understatement! And not only that, "simple" is relative. The common saying that gets push back is "Do the simplest thing possible." People always seem to ignore the "possible" part of that phrase. Forgive me for being a little cute here, but a complex solution is simpler than a really complex solution.
Honestly, having watched people argue about what simple is for about the last 10 years, I've pretty much settled on it not being a well-defined term. We know complex when we see it for sure, at least when it is present in quantity, but simplicity is not just the absense of complexity. There's at least three concepts we're all trying to stuff into the same word, and they are not only not "orthogonal" they are often in conflict with each other. I don't even think it can be rehabilitated, it can only really be abandoned, to clear the way to trying to characterize the multiple concepts we're trying to stuff into this one word.
It is especially dangerous when something is "good" and people try to appropriate the term to appropriate the goodness of the term, as if goodness flows from a term to the thing it is attached to rather than the other way around. "Simple" is good so my good thing must be "simple" to be "good". But it doesn't. Simple can even be bad, in the wrong place or in the wrong sort of "simple" for a given job.
Another solution to the pipeline example, this time making use of a subprogram for the frequency/accumulation:
< README.md \
tr -c '[:alpha:]' '\n' \
| tr '[:upper:]' '[:lower:]' \
| awk '
NF {
if (!($0 in count)) order[++n] = $0
count[$0]++
}
END {
for (i = 1; i <= n; i++) {
print count[order[i]], order[i]
}
}
'
If you don't allow `awk` in your "pure bash" then ofc this is not satisfactory. But it has the upside that the associative arrays are pretty explicit data structures (for the ordering and counts, respectively).
My reaction to the Unix pipeline was that, the reason it exploded in complexity is because the pieces were too simple. They were insufficiently expressive.
But the word is used in a different way here, and I'll have to watch the talk to understand what exactly is meant. (Something like orthogonality?)
I don’t know. This reads more like a “Clojure is great” post, and Clojure is great. But the author takes a swing and a miss on the Rich Hickey magic. The UNIX example is contrived (the number of occurrences in the order they occur?), and trying to redefine simple in a way that excludes UNIX pipelines doesn’t work.
Agreed. There are multiple senses for "simple" and on of those senses is "small". It is actually a very useful sense too, as Solomonov/Levin/Kolmogorov/Chaitin-style inductive reasoning has shown.
And it is important not to just make snappy quips by equivocating.
The first example feels like too much of a straw man, and I'm not sure how I feel about the definition of simple (and yes I've seen Hickey's talk which I very much do agree with). Obviously a cohesive general purpose programming language like clojure is going to do better on a problem with abitrary sub-structure, especially when you want to rethink that substructure. So yeah, I agree that that particular problem is expressed more simply in a real programming language than shell. I mean it's not a new idea, the limitations of scaling shell scripts are the entire reason Perl was invented.
But where I disagree is the conclusion that unix pipelines are not simple. IMHO unix pipelines as a platform are incredibly simple and powerful, allowing for solving a massive range of small problems much more elegantly than any general purpose programming language. Obviously the constraints that enable this simplicity at the low-end, are real tradeoffs that prevent simplicity at the high-end. But one of the core principles of effective engineering is do the minimum to solve the problem at hand, no more, no less.
The general point is true, but the shell pipeline gets a lot more elegant if you use the sort-and-accumulate paradigm that the classic shell utilities were written for (which uses O(1) memory, by sorting on disk). Using mostly the author's own code, and adding --count to uniq:
(Where the final awk papers over the fact that we're mixing tabs and spaces here; obviously, awk is also good at doing the accumulation step, but uniq --count suffices here.)
(I originally posted the above as a comment on lobste.rs, on this same article.)
Very interesting solution, and in the spirit of the original article. If I understood the snippet right, you are sorting the input sequence on the first column (the words) and then on the second one (the frequencies)
It is nevertheless "complecting": the uniq assumes the data is sorted and the columns of your data structure move together. Maybe this algorithm is already complex regardless of the implementation.
the point is to do a stable sort on (word, line number) lexicographically, then when we do "uniq" we can take the first line number.
In contrast to the "we need a frequency table" idea in the article, this solution trades off memory by transferring all the line numbers in the stream. This is very much in the spirit of the infamous McIlroy/Knuth "bakeoff"[1] -- tradeoff some efficiency (via extra book-keeping or sorts) in return for composability.
I mean I am not part of the same company, so I am just talking out of my butt, but I cannot imagine a dev worth their money taking more than two weeks, to fix a bug, especially today with AI assistance, most bugs are found the same day, the gnarly ones maybe take two days and in my lifetime as an engineer (10 years), I have not yet seen a bug that took me more than a week.
Taking 9 months to fix a bug sounds alarming to me.
UNIX Pipes do not mandate having to use tons of different programs
with stupid commandline options. I simulate them in ruby itself;
method chaining works in a very similar way, but I built a pseudo
pipe around it. The idea was more to have an object oriented shell,
e. g. combine good ideas from UNIX pipes and the MS powershell.
They are simple if you design them well and have them be flexible
too. The reason UNIX pipes were awkward is because they delegated
onto many different programs such as awk or sed with their own
strange rules. Nowhere does it say you HAVE to use such awkward
tools. Use better tools and the idea of piping becomes simple,
similar to (a more flexible variant of) method chaining. Just
without being tied down to a specific object per se (I do use
the pipe-handler master object to handle the pipe instructions;
each pipe instruct I call cmdlet, e. g. shorter for commandlet,
as this is how I like to think about this in terms. This also
combines e. g. virtualdub + avisynth ideas. I loved them when I
used windows. The idea behind avisynth is great - not necessarily
all of the syntax, but the idea that all multimedia audio can be
operated on at all times in flexible ways.)
Unix pipes are easy, and they are a very good abstraction, and their choice of abstraction boundaries is superlative, but they are not simple. (I have more than once seen a colleague implementing a ring buffer for feeding data into a Unix pipe!)
This argument is just based on "I wish the things I need to do were baked into the language." It's nice when that happens, but once programs get sufficiently large and complex, it stops mattering--you're dealing with domain-specific concepts that have zero built-in helpers and you're just building everything yourself regardless.
The examples in the essay are perhaps not specific enough - they do gesture in the direction of the author’s point, but they also admit other valid interpretations like your own. I think the Rich Hickey talk “Simple Made Easy”, which this post is based on, makes the point more clearly and precisely.
(For what it’s worth, the point of both this essay and the aforementioned talk is that programs do not have to get complex, even when they get large, even when it gets hard because there are no more easy / close-at-hand / familiar helpers in the language to tackle the domain specifics. In support of this point I will note that Rich Hickey is the creator of Clojure, a language in which “building domain-specific helpers yourself” is very nearly idiomatic.)
1. "You need to have taste (so: experience) to do DRY right". Same chunk of code more than once, so natural/expected behavior is to export to a helper... Aaaand the now introduced coupling is (too) often ignored, as in "no thought given whatsoever".
2. "Sometimes it's better to just leave it as is". 95% to 98% of "same chunk of code" in a number of places. The temptation to DRY is strong, yet the "numerically mere 2 to 5 pp" make the extracted helper an exercise in all manners of gymnastics. The only correct answer is: do not start.
(Own experience; your mileage may vary - if it does, feel free to comment back!)
I don't think it's possible to have a universal "colloquial" definition of "simple" that is also precise. It's all relative.
This is why I think the formalists studying complexity back in the 50s had the right approach. You can only give "simple" precise meaning within some kind of formal system with a shared set of initial axioms or assumptions. From that point you can define it quantitatively over some set of objects (relations, programs).
Funnily enough, this approach also touches on Hickey's etymological derivation. The root words also fundamentally have to do with the quantity of relationships.
I was surprised that the author didn’t translate the piped representation of the program to a procedural program with explicit calls to subprocedures. It’s bigger, but it’s dead-simple and easy to slip instructions in the middle of.
Strong resonance with the famous essay "The Rise of Worse is Better" [1], which contrasted the (better) "MIT/Stanford style of design" with the (worse) "New Jersey approach".
MIT/Stanford:
> Simplicity -- the design must be simple, both in implementation and interface. It is more important for the interface to be simple than the implementation.
New Jersey:
> Simplicity -- the design must be simple, both in implementation and interface. It is more important for the implementation to be simple than the interface. Simplicity is the most important consideration in a design.
TFA maps "simplicity" to "MIT/Stanford simplicity" (simplicity for the user) and "smallness" to "New Jersey simplicity" (simplicity for the developer).
I wonder if the root of the tension between the two schools comes down to the ambiguity of the user/developer distinction. Developers are also users. Simplicity of implementation is helpful to developers when they are working directly on implementation, while simplicity of interface is helpful to developers when they are using other developers' work.
[1] https://dreamsongs.com/RiseOfWorseIsBetter.html
I think you're right on that point of contention, it's too far to assert a universal and clear good/bad dynamic here because that line between developer/user is contextual and fuzzy.
Another interesting irony I'll note, Lisp is the "New Jersey approach" towards symbolic AI. Americans clinging to their Lisp systems were deeply entrenched in a "worse-is-better" mindset. Your interface, the computational model, didn't need to be designed for logic programming, that was wholly secondary. Do everything as much as possible in Lisp, and then offload the relational description to a small (not simple) library. American knowledge engineers were looked at as overpaid procedural hackers with zero mathematical elegance and very little credibility. More or less the same perception these self-same Lisp-machine users had for Unix and C programmers.
It's all about perspective, at the end of the day. Where we draw the line in the sand on these categories is free-to-choose, yet it also determines everything. We're always someone else's villain under different semantics.
> The reason for this is that in Rust, a struct couples type-checking to a fixed data representation. You can't get one without the other.
> Clojure decouples data representations from type checking.
This is funny to me because seen from the other side, (this) Clojure couples runtime type information to data structures: you're no longer allowed to define a data structure that doesn't have some runtime type information attached. A fixed static structure is just the consequence of not adding dynamic type information.
Meanwhile in Rust you can get type-checking ‘without’ a fixed structure by using trait objects.
Great piece, very straightforward examples, although I did have to squint for quite a while to grok the Closure portion.
I am currently building a piece of very modular software and it has been the hardest-to-design project of my entire career. I would never be allotted this amount of time-effort at any job I have held to make something this robust and clearly defined. Many aspects of this project have taken 3-5 rounds trying-and-trashing to get an abstraction that is uncomplicated.
This is precisely why vibe coding is so successful for building tiny isolated scripts, and so disastrous for anything else. It's just really dang hard to build something large and simple.
Personally, I'm big fan of those pipes mentioned. And the tooling that can be created around those _simple_ "primitives". Comparison to Google drive is obscure, in a way that it compares one gigantic piece of software into these small and simple.
Bottom line is probably true, but if you are an open-source maintainer mentioned, and you have only so few hours to spend, you just cannot create those gigantic softwares either. You need to choose from the cards on your hand.
> There's no native Unix equivalent to frequencies, this sort | uniq -c is the closest we can get. Not only is it less performant (it has to collect the full input into memory before continuing), but it ties aggregation to ordering.
One of the core features of the Unix command-line is that it is user-extensible. If there's no "native" command equivalent to frequencies, you can write your own, and it will be given the same first-class treatment as any other binary in your PATH. This is entirely in keeping with the Unix philosophy of simple implementations.
Important to note as well, is that "simple" isn't "lesser" or "dumber", it can be "more" and "wider", yet still "simpler".
Expectedly, Rich Hickey explains it best, watch the "Simple Made Easy" talk if you haven't before, one of the few talks I probably watch bi-yearly: https://www.youtube.com/watch?v=SxdOUGdseq4
Few things, concepts and ideas have changed as much of my programming mind as Hickey's talk and ultimately Clojure have done over the years.
Wish we had new amazing Hickey talks to link to, maybe it seems he's about the hang up the hammock perhaps?
He's speaking at the coming Clojure Conj, so there will be a new talk soon.
Using "length of the correctness statement + length of its proof" works quite well as proxy for complexity of a component (the longer, the more complex).
Copy pasted functions with subtle changes mean you cannot reuse the proof (DRY). Giant functions with lots of if/else statements however might cause a branch explosion in the proof. The right abstraction removes lots of assumptions that a proof could depend on, limiting the search space and often forcing elegance (this also applies to math, eg. when reasoning with abstract groups instead of integers). The wrong abstraction might force case distinctions on consumers of the abstraction.
I've been thinking about a new AI based dimension to this. If your program is split into smaller decoupled "modules" then all the code for each "module" can fit into an AI context window. In this way the AI can have all the context to edit a "module" by just loading all the code for that "module". You would not need things like vector search as much. If we assume 20 tokens per line of code and the AI context window is 100k to 1M tokens, then that would argue for having "modules" between 5,000 and 50,000 loc, depending on which AI model you are using.
> then that would argue for having "modules" between 5,000 and 50,000 loc, depending on which AI model you are using.
FWIW, I set hard limits to 200 LOC for every single source code file in any AI-related projects, also with restrictions on "formatting hacks" and other golf-like stuff.
I think beyond 5000 LOC in a single file and all available models already get lost frequently, even if the "context limit" theoretically is way above that. Maybe aim for like 1K LOC at max unless you want to have lots of misunderstandings.
The author uses the term “coupling” to describe a simple program from the code’s perspective - “you need to have a good mental model of your program”. I agree, but I’d argue that more importantly you need to have a good domain model. Good design communicates accurately and a high Gulf of Evaluation/Execution are actually what makes programs feel “complex” to a user.
These concepts simplify for the user these questions: “I know what I want - how do I make the program do it?” (Execution) and “The program did something — what state is it actually in?” (Evaluation)
I believe the author was getting at these concepts, especially in their Google Drive example - how the large program has a “small” UX. Understanding the domain model provides a much stronger basis for designing user interfaces, and understanding the Gulf of Evaluation/Execution allows you to build incredibly complex-looking, large UX’s without confusing or overwhelming the user.
Complexity (the opposite of simplicity) has nothing to do with the size of a program, but usually there is a high chance that a large program is more complex than smaller one, purely because the complexity multiplies, not just adds up.
A single regex line could be far more complex than a 100-line java program.
Simple never means it is easy and it is always the biggest misconception in software.
That is an understatement! And not only that, "simple" is relative. The common saying that gets push back is "Do the simplest thing possible." People always seem to ignore the "possible" part of that phrase. Forgive me for being a little cute here, but a complex solution is simpler than a really complex solution.
Honestly, having watched people argue about what simple is for about the last 10 years, I've pretty much settled on it not being a well-defined term. We know complex when we see it for sure, at least when it is present in quantity, but simplicity is not just the absense of complexity. There's at least three concepts we're all trying to stuff into the same word, and they are not only not "orthogonal" they are often in conflict with each other. I don't even think it can be rehabilitated, it can only really be abandoned, to clear the way to trying to characterize the multiple concepts we're trying to stuff into this one word.
It is especially dangerous when something is "good" and people try to appropriate the term to appropriate the goodness of the term, as if goodness flows from a term to the thing it is attached to rather than the other way around. "Simple" is good so my good thing must be "simple" to be "good". But it doesn't. Simple can even be bad, in the wrong place or in the wrong sort of "simple" for a given job.
> We know complex when we see it for sure, at least when it is present in quantity
Because familiarity is a confound for intuition about complexity, even this is not always true.
Maxwell's equations will look complex to the uninitiated, and can represent the pinnacle of simplicity to those who already understand them.
Another solution to the pipeline example, this time making use of a subprogram for the frequency/accumulation:
If you don't allow `awk` in your "pure bash" then ofc this is not satisfactory. But it has the upside that the associative arrays are pretty explicit data structures (for the ordering and counts, respectively).The word simple is used here a way I'm having trouble wrapping my head around.
This specific usage appears to come from this linked talk, Simple Made Easy:
https://www.youtube.com/watch?v=SxdOUGdseq4
My reaction to the Unix pipeline was that, the reason it exploded in complexity is because the pieces were too simple. They were insufficiently expressive.
But the word is used in a different way here, and I'll have to watch the talk to understand what exactly is meant. (Something like orthogonality?)
I don’t know. This reads more like a “Clojure is great” post, and Clojure is great. But the author takes a swing and a miss on the Rich Hickey magic. The UNIX example is contrived (the number of occurrences in the order they occur?), and trying to redefine simple in a way that excludes UNIX pipelines doesn’t work.
Agreed. There are multiple senses for "simple" and on of those senses is "small". It is actually a very useful sense too, as Solomonov/Levin/Kolmogorov/Chaitin-style inductive reasoning has shown.
And it is important not to just make snappy quips by equivocating.
"In Clojure this is fairly straightforward"
Somehow when things get complex, I could never find fully functional style to be more understandable than imperative
The first example feels like too much of a straw man, and I'm not sure how I feel about the definition of simple (and yes I've seen Hickey's talk which I very much do agree with). Obviously a cohesive general purpose programming language like clojure is going to do better on a problem with abitrary sub-structure, especially when you want to rethink that substructure. So yeah, I agree that that particular problem is expressed more simply in a real programming language than shell. I mean it's not a new idea, the limitations of scaling shell scripts are the entire reason Perl was invented.
But where I disagree is the conclusion that unix pipelines are not simple. IMHO unix pipelines as a platform are incredibly simple and powerful, allowing for solving a massive range of small problems much more elegantly than any general purpose programming language. Obviously the constraints that enable this simplicity at the low-end, are real tradeoffs that prevent simplicity at the high-end. But one of the core principles of effective engineering is do the minimum to solve the problem at hand, no more, no less.
“When is it useful to be small?”
I like this question. Some projects will sacrifice usefulness in the name of simplicity.
The general point is true, but the shell pipeline gets a lot more elegant if you use the sort-and-accumulate paradigm that the classic shell utilities were written for (which uses O(1) memory, by sorting on disk). Using mostly the author's own code, and adding --count to uniq:
(Where the final awk papers over the fact that we're mixing tabs and spaces here; obviously, awk is also good at doing the accumulation step, but uniq --count suffices here.)(I originally posted the above as a comment on lobste.rs, on this same article.)
Very interesting solution, and in the spirit of the original article. If I understood the snippet right, you are sorting the input sequence on the first column (the words) and then on the second one (the frequencies)
It is nevertheless "complecting": the uniq assumes the data is sorted and the columns of your data structure move together. Maybe this algorithm is already complex regardless of the implementation.
btw, this paradigm reminds me of APL
the point is to do a stable sort on (word, line number) lexicographically, then when we do "uniq" we can take the first line number.
In contrast to the "we need a frequency table" idea in the article, this solution trades off memory by transferring all the line numbers in the stream. This is very much in the spirit of the infamous McIlroy/Knuth "bakeoff"[1] -- tradeoff some efficiency (via extra book-keeping or sorts) in return for composability.
Agreed, very neat.
[1] https://homepages.cwi.nl/~storm/teaching/reader/BentleyEtAl8...
I mean I am not part of the same company, so I am just talking out of my butt, but I cannot imagine a dev worth their money taking more than two weeks, to fix a bug, especially today with AI assistance, most bugs are found the same day, the gnarly ones maybe take two days and in my lifetime as an engineer (10 years), I have not yet seen a bug that took me more than a week.
Taking 9 months to fix a bug sounds alarming to me.
> Unix pipelines are not simple
But they are.
UNIX Pipes do not mandate having to use tons of different programs with stupid commandline options. I simulate them in ruby itself; method chaining works in a very similar way, but I built a pseudo pipe around it. The idea was more to have an object oriented shell, e. g. combine good ideas from UNIX pipes and the MS powershell.
They are simple if you design them well and have them be flexible too. The reason UNIX pipes were awkward is because they delegated onto many different programs such as awk or sed with their own strange rules. Nowhere does it say you HAVE to use such awkward tools. Use better tools and the idea of piping becomes simple, similar to (a more flexible variant of) method chaining. Just without being tied down to a specific object per se (I do use the pipe-handler master object to handle the pipe instructions; each pipe instruct I call cmdlet, e. g. shorter for commandlet, as this is how I like to think about this in terms. This also combines e. g. virtualdub + avisynth ideas. I loved them when I used windows. The idea behind avisynth is great - not necessarily all of the syntax, but the idea that all multimedia audio can be operated on at all times in flexible ways.)
Unix pipes are easy, and they are a very good abstraction, and their choice of abstraction boundaries is superlative, but they are not simple. (I have more than once seen a colleague implementing a ring buffer for feeding data into a Unix pipe!)
This argument is just based on "I wish the things I need to do were baked into the language." It's nice when that happens, but once programs get sufficiently large and complex, it stops mattering--you're dealing with domain-specific concepts that have zero built-in helpers and you're just building everything yourself regardless.
The examples in the essay are perhaps not specific enough - they do gesture in the direction of the author’s point, but they also admit other valid interpretations like your own. I think the Rich Hickey talk “Simple Made Easy”, which this post is based on, makes the point more clearly and precisely.
(For what it’s worth, the point of both this essay and the aforementioned talk is that programs do not have to get complex, even when they get large, even when it gets hard because there are no more easy / close-at-hand / familiar helpers in the language to tackle the domain specifics. In support of this point I will note that Rich Hickey is the creator of Clojure, a language in which “building domain-specific helpers yourself” is very nearly idiomatic.)
> In Bash you need a bunch of temp files and ugly opaque regexes, sorts, and joins:
> That's because our original program was small but not simple.
I would say no - because it was inflexible.
Strikes a practical chord or two:
1. "You need to have taste (so: experience) to do DRY right". Same chunk of code more than once, so natural/expected behavior is to export to a helper... Aaaand the now introduced coupling is (too) often ignored, as in "no thought given whatsoever".
2. "Sometimes it's better to just leave it as is". 95% to 98% of "same chunk of code" in a number of places. The temptation to DRY is strong, yet the "numerically mere 2 to 5 pp" make the extracted helper an exercise in all manners of gymnastics. The only correct answer is: do not start.
(Own experience; your mileage may vary - if it does, feel free to comment back!)
I don't think it's possible to have a universal "colloquial" definition of "simple" that is also precise. It's all relative.
This is why I think the formalists studying complexity back in the 50s had the right approach. You can only give "simple" precise meaning within some kind of formal system with a shared set of initial axioms or assumptions. From that point you can define it quantitatively over some set of objects (relations, programs).
Funnily enough, this approach also touches on Hickey's etymological derivation. The root words also fundamentally have to do with the quantity of relationships.
simple = maintainable
"In Clojure this is straightforward"