Two Vexing Problems in Functional Programming

Two Vexing Problems in Functional Programming(matthewbutterick.com)

183 points by tomilola39 4 years ago | 210 comments

js8 4 years ago |

Not sure if the author will read this, but there is a simple answer to both problems.

In functional programming, you typically do not manipulate state directly, instead you compose functions that manipulate that state, into more complex functions. (In Haskell, for example, this composition is often done through monads.) Eventually, you get a function that represents the whole program.

This is somewhat dual to imperative programming, and is kind of a shift of the frame of reference. We could make an analogy with an assembly line in a factory. Imperative programming is like looking standing at the factory floor, every machine (procedure) receives a thing (input), does something with it, and outputs it. Functional programming is like being together with the thing as it is manipulated through the factory, in its own frame of reference. So instead of passing the thing (data) around, you see passing of the machinery that modifies that thing (functions), in the opposite direction.

jayd16 4 years ago | |

That's fine from a philosophical sense, but doesn't it make consecutive reads extremely heavy? You're throwing away the efficiency of in place updates at the language level, no?

throwaway81523 4 years ago | | |

> That's fine from a philosophical sense, but doesn't it make consecutive reads extremely heavy? You're throwing away the efficiency of in place updates at the language level, no?

You use special data structures to deal with that. There's a great book about them, "Purely Functional Data Structures", by Jeff Okasaki. You generally don't get O(1) update like for traditional arrays, but you can usually get O(log n), like you would get for a database table with B-tree indexes.

closeparen 4 years ago | | |

This is why Clojure’s big thing is to also, at the language level, emphasize copy-on-write data structures.

lupire 4 years ago | | |

"in-place updates" are handled by the compiler and garbage collector, using liveness analysis, not by the programmer managing memory explicitly.

saeranv 4 years ago | | |

Yes, although I suppose even in languages where this isn't mitigated (i.e. Clojure example below), you can offset that inefficiency with the parallelism enabled by functional programming.

zodiac 4 years ago | |

Using the interpreter example in the article, would it be accurate to say that the author's inefficient "each instruction copies the existing bytes into a new array, change the byte of interest, return this new byte array, and abandon the old array (which would eventually be garbage-collected)" is "manipulating the state directly"?

vnorilo 4 years ago | | |

Yes, and also not a data structure that is suitable for the task. If you want to keep every version of the buffer as a value (which has nice properties like versioning) you would need a data structure that shares most of its structure with the previous versions. Like a persistent radix-n tree. Slower than mutable buffer for sure, but not pathological.

lupire 4 years ago | | |

No, its not manipulating the state, which is why it is inefficient.

An example of what parent was talking about is instead of looking over a list 10 times calling different functions that build a new result list each time, you compose those inti one big function that visits each element once.

ChadNauseam 4 years ago | |

> In functional programming, you typically do not manipulate state directly, instead you compose functions that manipulate that state, into more complex functions. (In Haskell, for example, this composition is often done through monads.) Eventually, you get a function that represents the whole program.

A bit more detail. In Haskell this is implemented very elegantly with list fusion. If you write

    map (\x -> x+1) mylist

You'll map over a list and add one to every element. this allocates a whole new list where all the elements are increased by one [0]. Now let's say we take the output of that map and map over it again, but this time we multiply every element by two:

    map (\x -> x*2) (map (+1) mylist)

Naively implemented that would copy allocate two lists, one where all the numbers are incremented and another where all the incremented numbers are multiplied by two. Any good dev will know that's performance poison. So the Haskell compiler implements "list fusion" – it sees that you don't use the intermediate list for anything, and rewrites your code so it's equivalent (in semantics and performance) to:

    map (\x -> (x+1) * 2) mylist)

(For the compiler devs in here, this optimization is commonly known as deforestation.) This leads to huge speedups in many cases. But there's a problem. If elsewhere you use the result of `map (\x -> x+1) mylist` for anything besides mapping over it exactly once, this optimization is impossible! So list fusion has a reputation for being fragile – the compiler has to know with 100% certainty that you aren't going to use the list for anything else, and sometimes innocent-seeming changes (like moving the intermediate map into another module) can break it.

The solution the Haskell community finds promising is to be able to give a tag to value, like a type only not quite, that says "I promise to use this value exactly once". If you use it twice or zero times, the compiler will yell at you. The compiler is still on the hook for doing the analysis of what's used once and what's used multiple times, but now the programmer and the compiler can be guaranteed to be on the same page.

As for the other issue mentioned in the original post, of modifying a subelement of a tree: this is a well-known problem and there are many solutions. If the tree is only used once, the same optimization as list fusion can be applied to mutate the list in place (although the "you must use the value only once" restriction doesn't help quite as much as you'd think it would). The more common solution, that doesn't depend on compiler optimization at all, is to use a data structure that supports this efficiently. For example, if you have a tree and you want to change one leaf on the tree, the only thing you really need to copy is the part of the tree that's actually different now - for the unchanged branches, the new tree just has a pointer to branches of the old tree, so they can be (and are) reused without copying. That's why it's very common to see maps implemented in functional languages using trees, instead of hashmaps. With a hashmap, it's much harder to get around the need to copy the whole thing when you just want to change one part.

[0]: Well, it might do that that once it gets around to it, laziness etc., but let's ignore that for now.

munificent 4 years ago | | |

> The solution the Haskell community finds promising is to be able to give a tag to value, like a type only not quite, that says "I promise to use this value exactly once". If you use it twice or zero times, the compiler will yell at you.

I suspect the contortions required to fit your code into that affine type style will likely lead to code that is just as hard to understand and maintain as the equivalent imperative code (though with better static checking, which might be nice).

dahfizz 4 years ago | | |

> For example, if you have a tree and you want to change one leaf on the tree, the only thing you really need to copy is the part of the tree that's actually different now - for the unchanged branches, the new tree just has a pointer to branches of the old tree, so they can be (and are) reused without copying.

This still only works if a child has one / a known-ahead-of-time number of parents. If you need to update an object that N objects point to, you need to update all N references.

voxl 4 years ago | |

Ah yes, monadic programming, liked by no one but championed by those who probably have never really had to do it.

OCaml does state without monads the way it does for a reason, because working with monads is a massive headache.

pyrale 4 years ago | | |

You work with monads all day long, you just don't think about them as such.

There is no such thing as "monadic programing", even in Haskell. The thing making haskellers harp about monads is that the std lib went a bit far in making sure that every fitting data structure implements the monad interface, which includes IO.

dgreensp 4 years ago |

These problems have lots of other well-known solutions.

For large objects: Persistent data structures with path-copying. Batching small mutations (and using mutation internally in a way that doesn’t leak out). Using the type system somehow to distinguish cases where copying is not necessary (experimental in certain new languages). Structuring your program around a “data store” that is mutable.

For the “references” problem: Explicit references, like by ID. Or mutable “atoms” as in Clojure, which are maybe sort of like the “mediators” in the article.

nightski 4 years ago |

You should not approach problems in functional languages with imperative techniques. It's fitting a square peg in a round hole. The approaches & data structures aren't always the same.

In my experience with FP you start with more of a top down approach in contrast to the bottom up approach discussed in the blog post.

There are many good resources on this. I've referenced a few below. Of course there are many more.

[1] https://www.amazon.com/Pearls-Functional-Algorithm-Design-Ri...

[2] https://www.amazon.com/Purely-Functional-Data-Structures-Oka...

[3] https://www.amazon.com/Algorithm-Design-Haskell-Richard-Bird...

[4] https://www.amazon.com/Structure-Interpretation-Computer-Pro...

Barrin92 4 years ago |

Personally I never really understood the benefit of thinking in functional terms about things that are conceptually more naturally thought of as persistent objects.

For example when the author picks "parents" or "children" or in general maybe any entity of any kind, I don't understand what functional thinking gets me. Sure instead of manipulating mutable objects I can think of every entity being 'one thing' at 'one moment in time' and I do a gazillion state transitions that all return new things and I have never mutated anything, but not only is that bad from a performance standpoint, I never understood how that's clearer or easier, it just seems like a tortured metaphor.

Monads and other functional tools are being brought up in this thread as well but I always had the same problem. Sure, I can stuff my operations into a box, say it's the "state box" and then everything is neat but for what purpose? I still remember when I was in university I rewrote a tetris clone I had made into Haskell. It took a lot of time and weird error messages, but at the end of the day translating all my imperative or object like procedures into functional paradigms didn't make the program any easier to understand.

The places where I think functional programming is natural is for something like accounting, data processing or version control systems. Where I really do want to think of every state as its own, immutable little world, where keeping track of changes pays off, and so on. In a lot of domains it to me seems ill-suited.

colanderman 4 years ago |

Relational systems (e.g. SQL) solve the parent-child problem by making references explicit -- instead, keys are used to reference children in a well-known collection.

That is, if you're trying to emulate sharing of mutable structures (i.e. pointers) in a functional language -- that is, reflecting changes to children across multiple parents -- model the pointers explicitly (as integer keys) and put the shared structures in a map, which is necessary to interpret a parent in full.

This seems to be what the article is getting at with its "mediator" concept, albeit in very abstract terms. The author seems to find this distasteful, wanting to "hide" these relationships, but I disagree -- the difference between value and reference semantics is very important; making the difference explicit is a good thing.

gavinray 4 years ago |

The Koka research language has something it calls "FBIP" (Functional, but in-place)

Where you write functional/immutable methods and the compiler deterministically can convert it to in-place mutating operations for performance:

https://koka-lang.github.io/koka/doc/book.html#sec-fbip

  "With Perceus reuse analysis we can write algorithms that dynamically adapt to use in-place mutation when possible (and use copying when used persistently). Importantly, you can rely on this optimization happening, e.g. see the match patterns and pair them to same-sized constructors in each branch."

  "This style of programming leads to a new paradigm that we call FBIP: “functional but in place”. Just like tail-call optimization lets us describe loops in terms of regular function calls, reuse analysis lets us describe in-place mutating imperative algorithms in a purely functional way (and get persistence as well)."

pbiggar 4 years ago |

I've been coding in functional languages for 10 years, and they have problems, but these are not them.

The first problem is solved in every functional language I've used (clojure, F#, OCaml) by immutable data structures (aka purely functional data structures, which is also the name of the de facto book on the topic, which is excellent but also you don't need to know any of this to use them. [1]). I'm surprised to see this critique without a mention of this.

The second problem seems to be due to trying to do OO in FP. The thing is you don't have a glyph, you have a whole system which contains glyphs. So you can't do an update on a glyph because it won't update the system.

This is a common frustration when you're used to OO and can be really annoying in FP but it's also part of the design modelling that FP forces you to do: the problem was that he was modeling in the small and not the large.

The solution is not the 1, 2, or 3 from the article (1 and 2 are bad, 3 is not as bad but not FP), but is straightforward. When you want to update a glyph, you use a function like `updateGlyph(pathToGlyph, functionToExecuteOnGlyph)`. That's at the top-level, and it allows you compose your lower level `updateGlyph` functions.

[1] https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.64...

jonnycat 4 years ago |

The a-ha moment for me was that functional programming - in most real world applications - is less about "no state" and "no side effects", and more about "properly managed state" and "properly managed side effects". This is one of the things I really like about Elixir/Erlang/OTP - of course your application needs to manage state, but it's well contained and well managed, avoiding a lot of common pitfalls.

z5h 4 years ago |

Purely Functional Datastructures (for example) introduces efficient operations on “large” objects. Next, as Out of the Tarpit suggested in 2006, use a relational model (developed in 1969). Hardly seems vexing. Yes, maybe not as efficient as other ways to compute.

dboreham 4 years ago |

Interesting thread. Although most of the comments are "you're holding it wrong", I think the author brings up good points, which are roughly along the lines of : the FP language tutorials don't tell you how to write real programs. Now, granted the problems cited will show up at some level of scale in non-FP programming -- you can't just rummage around in some huge nested data structure inside a C++ server application for example because you'll end up holding dangling pointers among other things. However, perhaps you can get much further with non-FP languages by following the basic tutorials.

Also interesting to note that modern UI application frameworks such as React/Redux don't mutate state in-place. They tend to generate some kind of mutation object that is "played" against the in-memory stored application state. Probably similar to the FP-kosher solutions suggested here.

isaacimagine 4 years ago |

There are a number of patterns that have arisen over the years to deal with these problems. For the large-object problem, look into persistent data structures or the 'Functional but in Place' paradigm (compiler optimizes modification on a copied unshared array to a mutation).

For the parent-child problem, typed handles, continuation-based state propagation, and reifying state updates for a declarative interpreter may be used.

Monads and effects are common in strongly typed functional languages, but have less bearing on lisp, which usually embraces dynamism to some extent.

PaulHoule 4 years ago |

The "big object" problem is solved by the persistent data structures in Clojure

https://clojure.org/reference/data_structures

These are a little slower than conventional data structures (e.g. Java Collections) but are much more efficient than copying the whole structure every time you want to "change" it. The garbage collector picks up behind you so you're not wasting unbounded space on unreachable versions of the data structures.

CornCobs 4 years ago |

I think that copying an entire array just to modify one bit doesn't really demonstrate the flaw of FP as much as it is just bad programming. The problem isn't really with the paradigm, per se, rather it is the understanding of each problem domain.

I hate to be pedantic, and I know this sounds like a no true scotsman argument, but I really feel FP discussions always devolve into this because people have so many different levels of understanding of what FP is supposed to mean.

I think that many people who espouse FP like the "aesthetic" of FP. They like that map/filter/reduce look "cleaner" than nesting ifs, breaks and continues in loops. They love how pattern matching on Discriminated Unions is so much clearer than imperative switch statements or OOP-style polymorphism, where to see the behavior of a single function you need to look through 5 different files. This is the kind of evangelising that stops at "cute little values" and toy examples.

But (subjective opinion here) I think what the more experienced FP proponents are trying to promote are in fact just sane, good programming practices made explicit, but they call it FP too (thus "no true scotsman").

For example, for the OP's bf problem, is copying a 30k byte array for every operation a good idea? Of course not! Is isolating the "stateful" operations (operations that modify this byte array) to small sections of code, possibly even gathering all the modifications that need to be made and only applying them after the function returns, rather than littering stateful operations throughout the code, so that it becomes much easier to figure out when and why some state changes, a good, "FP" idea? Hell yeah! Is changing your data structure to a persistent data structure so you can retain your "op: Buffer -> Buffer" mental model while preserving more performance? Possibly so! (Though I think for such a problem the mutable buffer is the obviously correct data structure; a persistent DS doesn't give much benefit since each operation takes the whole array and returns the whole array before the next operation is executed)

In conclusion I think focusing on the principles of good programming are much more important than focusing on the external appearances of each paradigm. You can architect clean, loosely coupled components with little shared state in OOP as well as in FP. We ought to focus on teaching that.

mirceal 4 years ago | |

strongly agree with you. it should be about the principle and not about a poor understanding and application of the said principle. if the principle is: data should be immutable, data should be immutable. that does not mean copy things over until we throw our hands in the air and say "performance". you also don't mix the semantics of a function call with the underlying representation of the data you are using (if you are using ADT)

parksy 4 years ago |

Tangential but what is the most successful and most complex example of a fully FP-based application where I can view the source-code today? I feel like I still don't quite grasp the essence of the conflict between FP and whatever it seems to be opposing. I also feel like I use what a lot of FP people claim as FP in my day-to-day.

I think if I can see some code beyond the old map/reduce toy examples it might click. Not wanting to be confrontational, and I know that this forum isn't here specifically to educate me personally, I just genuinely have had problems understanding what FP is "supposed" to be that any good programmer doesn't already do when possible, regardless of language or whatever.

I'm sure it will click and I'll look dumb but yeah. Isn't loose coupling and strong coherence kind of covering this already? Is it just down to doing map/reduce/filter to avoid mutating some variable and instead get a new set of results? Is mutation the charm?

I genuinely really feel I'm missing out on something important here because I have older views on programming, I've struggled to get a good explanation. Do I have to go back to school again? I'm not against that but yeah. I'm sorry if this sounds ignorant, but I kind of am.

mbrodersen 4 years ago | |

I used to be in your shoes. What helped me was learning Haskell and watching all YouTube videos by the Clojure creator (what’s his name again?) It was hard for me to get it (old dog learning new tricks). But I persisted and suddenly the light bulb went off and it all became super simple and easy. It was a hard journey but worth it. I am a much better programmer today, in any language, because of it.

rramadass 4 years ago | |

https://news.ycombinator.com/item?id=30878534#30888405

skybrian 4 years ago |

This article explains the problems pretty well, but they are old problems and functional programmers have various solutions that the author seems unaware of.

I'm not a Haskell programmer but I do know about monads and have heard of lenses.

charcircuit 4 years ago | |

Lenses don't help. They just make copying and updating large data structures cleaner to write. They don't solve the performance problem of copying everything.

WkndTriathlete 4 years ago | | |

Incorrect. Optics implementations can ensure updating a node in a large balanced tree is O(log n) instead of O(n), trivially, and I'm sure there are sufficiently clever collections with optics that would enable much better performance than the trivial optimizations I can think of.

The same holds true for large structure updates.

bts 4 years ago |

In the Haskell world, folks have solutions to both of these problems:

The "large-object problem" can be tackled in a principled fashion using strict state threads (aka the ST monad: https://wiki.haskell.org/Monad/ST) or using vanilla mutable (IORef) references.

The "parent-child problem" is well-addressed by lenses, also known as functional references. They are basically composable getters and setters that allow you to read or update deep into nested structures.

SinParadise 4 years ago | |

I don't fully understand how parent-child problem is addressed by lenses. How does lens helps to address the problem that a reference update is akin to changes an external state?

My understanding is that lens helps to address the large-object problem.

marcosdumay 4 years ago | |

You mean, Haskell developers ditch the functional idiom and use a more imperative one when they need it for performance? And yes, they do, and it works nicely. But it's not solving any problem with the functional paradigm.

Anyway, none of them is a problem with the semantics of FP. They are a problem of insufficient optimization. So they will probably get solved at some point.

travisathougies 4 years ago | | |

ST is not 'imperative'. The monadic interface can look imperative sure, but then you can also use Applicative and friends and that's back to being functional. Of course, I prefer to think of what you call 'imperative' programming as function composition where >>= is the reverse of the 'normal' composition operator. Indeed, sequential imperative commands and function composition are isomorphic.

danidiaz 4 years ago |

> Consistent with my loyalty to the functional-programming idiom, I designed my initial bf implementation so that each instruction would copy the existing bytes into a new array, change the byte of interest, return this new byte array, and abandon the old array (which would eventually be garbage-collected).

This would be a fun task to attempt with the new linear features of Haskell https://hackage.haskell.org/package/linear-base-0.1.0/docs/D...

galaxyLogic 4 years ago |

I believe mutable data-structures per se are not a bad thing. What is bad is Unobservable State Mutation.

When you assign a field into a data-structure it is bad because later on you will see the changed data-structure and try to use it but it has a wrong value which causes an error in your program - maybe not a crash just an error in the output. And then it's really hard to say what caused that error because you have no visibility into who or where did that erroneous assignment happen.

If instead you could forbid direct assignments and could write a "watcher" (a.k.a "setter") for all data-modifications, you could observe how your program-state evolves during its execution, and you could write assertions that prevent wrong type of data-manipulation from happening.

One such language is Smalltalk. You can only modify data by sending a "message" to an object to be modified, and the object can only respond by executing one of its methods. You can then write assertions in those methods to halt in debugger or write to console if you see the program write a specific value or type of value into your data-structure.

Even on the rather primitive level of Arrays you could easily modify their #at:put: -method in Smalltalk and halt or log the stack-trace if somebody tries to store the number 42 into the array. :-)

In the end the problem is understanding what your program does in terms of its state, how the state evolves. Why? Because: Modifying state in essence modifies your program. And to understand your program you must understand all evolving versions of it. Like, if you want to understand a 'caterpillar' you must also understand its next mutated state 'butterfly'.

FP makes it easier to understand the state-evolution because new state is just the result of some calculation which is easy (?) to understand by understanding each "pure" function in your program and reasoning what their combined result is.

But if you can understand the state-evolution of your program by imperative means that is fine too. That is why many suggest using a database to keep the state. A database is mutable by definition. But it makes it easy to observe how your state evolves by observing the UPDATE and DELETE and INSERT calls made into your database.

throwaway81523 4 years ago |

Article is overall unpersuasive. The first part is silly: copying a whole array instead of using a functional data structure such as a red-black tree is just asking for a huge slowdown. The second part doesn't give enough detail to really make its case. Author's solution was a big tree with mutable nodes, but there's a cautionary tale in an old article[0] by Ramsey and Dias about using a zipper[1] as a functional representation for the control node graph of a C-- compiler.

[0] https://doi.org/10.1016/j.entcs.2005.11.042

[1] https://en.wikipedia.org/wiki/Zipper_(data_structure)

dkarl 4 years ago |

> Indeed, Perlis’s quip gestures toward the ultimate tension within all programming languages: how to connect the gossamer realm of human ideas to the grubby reality of a computing machine. Arguably the whole point of programming languages is to insulate us from those details, and allow us to express ourselves naturally. But the farther we drift from reality of the machine, the less efficiently we can use its resources.

I would call it challenge, rather than tension, and I don't think expressing ourselves naturally is the only goal. We also want to express a solution that is tractable, that we can understand and have confidence in. "Natural" imperative programming can become intractable when mutating state; the goal of functional programming is to find a different way that is more tractable for humans, without worrying about if it feels "natural." If there's one thing programming (and mathematics!) has shown us, it's that anything we can do will eventually feel natural if we do it enough.

(We produced multiple generations of programmers for whom C-style for loops were the most natural thing in the world, almost the benchmark for what felt natural in a programming language. The "map" function was weird-ass shit to them. I know because the experienced programmers I worked with called it weird-ass shit when I discovered it in Python as a wee junior in 2000 and wondered why Java and C++ didn't have it.)

I don't think functional programmers consistently write code that is more tractable than imperative code, not yet. (I think some of them aren't trying, honestly.) But I think functional programming languages will evolve to better support that goal. It has only been in the last 10-20 years that the majority of working software engineers have fully recognized the difficulty of managing shared mutable state, generating widespread demand for functional programming languages for practical applications like web services, data processing, user interfaces, and so on. The trade-offs for those applications are different from the trade-offs for academic research, and the trade-offs for most industry programmers are different from the trade-offs for researchers who are immersed in the mathematical foundations. The academics have shaped functional languages for decades, but working software engineers will have more and more influence in the future.

mrkeen 4 years ago | |

Agree with most, especially that C-style for-loops used to be the most natural thing.

I'm more pessimistic about:

> It has only been in the last 10-20 years that the majority of working software engineers have fully recognized the difficulty of managing shared mutable state

I suspect that the number of practicing programmers who don't worry about shared mutable state is growing much faster than the number who do worry. To quote Uncle Bob: "the industry as a whole will remain dominated by novices, and exist in a state of perpetual immaturity"

evilotto 4 years ago |

I rather like tcl's solution: copy on write, with reference counting. Values are immutable, so if you to alter a value in place, it gets copied; but iff nothing else is pointing to the same value it can get modified in place because the end result is indistinguishable. This does lead to the occasional need to explictly unshare things for performance[1], at least until the compiler gets smart enough to prove that the value is unshared.

[1] https://wiki.tcl-lang.org/page/K#c2a6014c2d129837889d8a8000d...

prophesi 4 years ago |

Glad to see Cunningham’s Law working its magic. The article was a good read, and the HN comments section has a lot of good alternative solutions to the proposed problems.

TuringTest 4 years ago |

For anyone interested in programming paradigms, I strongly recommend the online 'Paradigms'[1] course by Peter Van Roy, which you can take for free if you don't want to get certified. It uses the multi-paradigm language Oz to explain the essence of each paradigm and the differences between them. (Learning that new language makes sense in this case, as it prevents you from being distracted by the idioms of languages you already know).

The course distills the primitives of different programming languages in terms of how they see state (see Van Roy's diagram in [2]), and how deciding to incorporate and combine some of those primitives and not others gives rise to the different programming styles. I found it enlightening, in the way that programmers' koans refer to.

[1] https://www.edx.org/es/course/paradigms-of-computer-programm...

[2] https://en.wikipedia.org/wiki/Programming_paradigm#Overview

(This is a repost of a comment I made below, so that readers who don't read the whole thread can still see it).

nunez 4 years ago |

> Did this implementation work? Yes. Did it run fast? No. It was glacial. After noodling with the code for a while, I had my galaxy-brain moment: maybe the incessant duplication and deletion of 30K data arrays had something to do with it?

I thought OCaml handled this problem with copy-on-write and/or fancy pointer math. Is there more to it?

hurril 4 years ago |

Making a huge array of values to in-place edit by copy is just... drinking water with the glass upside down. Why would you do that?

Either do a nice imperative for-loop as that is an excellent and imperative way of solving the problem (which I don't know what it is.)

Or solve it in any of the normal functional ways. Pick the style that you and your team likes.

twic 4 years ago |

Another fun one is the multiple index problem:

http://pepijndevos.nl/2012/08/12/the-multiple-index-problem....

ljloisnflwef 4 years ago |

Other options:

* Use linear / affine / session types / move semantics

* Describe an interface, then weave side effects through it, such as the State Monad.

* Use lenses

mirceal 4 years ago |

> To be clear, there’s nothing inherently superior about functional programming. (Despite the fact that these self-contained functions are also known as pure functions.) It is not the way computers work at a hardware level.

Hah. Weird way of approaching it but okay. Here's the thing: it is exactly how computers work at hardware level. If you look at an electronic circuit you and a program written in a functional way, the equivalence is almost 1:1. The circuits are self contained and you compose them to generate a new output from the inputs that are fed in.

dahfizz 4 years ago | |

That works for simple circuits. Actual computers have oodles of state - we usually call it "main memory". Assembly is a language that can only do one thing: mutate this state.

mrkeen 4 years ago | | |

But it's a pain to write out assembly, so we go via LLVM and SSA.

In SSA, rather than modelling your program using 16 mutable registers (imperative folk love talking about "the real world" or "real computer"), we use an infinite number of immutable registers.

Infinite registers won't fit in a computer (we only have 16), and not being able to overwrite registers must kill performance right?

mirceal 4 years ago | | |

that’s the point. more complex circuits are made from simple circuits. principle still applies. if you think about it, most problems with writing code stem from state management.

sfvisser 4 years ago |

> Functional programming, however, suggests we should create and destroy these structures willy-nilly.

It really saddens me that some people completely miss the point of what FP is about.

How did this happen?

mrkeen 4 years ago | |

Two reasons:

1) Uncle Bob's "perpetual state of immaturity" [1]

2) Non-FP languages and "hybrid" languages do a bad enough job of FP to stop people from seeking out more. E.g.

FP lang: Doesn't have null. Uses Maybe in the rare cases its useful. The benefit is not having null. Imp lang: Keeps null. Adds Optional so now there's two ways not to have a value.

FP lang: Functions are lambdas are first class. Imp lang: Separate Function object. Lambdas can't throw checked exceptions or update variables in the method's scope.

FP lang: Understands when mutation can and cannot happen. Can share data and fuse code efficiently. Imp lang: Cannot assume underlying data doesn't change. Programmer needs to make defensive copies.

FP lang: map (+1) [1,2,3] Imp lang: Arrays.asList(1,2,3).stream().map(x -> x + 1).collect(Collectors.toList())

If I grew up with the kind of FP that made it into mainstream languages I'd probably be against it too.

[1] https://blog.cleancoder.com/uncle-bob/2014/06/20/MyLawn.html

zodiac 4 years ago | |

I think the author somewhat explained why FP "suggests" that. In his explanation, it's because when you read tutorials that operate on "numbers and strings and simple lists" (as the author puts it), you do create and destroy those structures willy-nilly.

So when you operate on a large array (as in his example), a natural approach is to also create and destroy it.

Of course after you gain experience you learn not to do that, and techniques that let you not do that, but I don't think he is missing the point.

j-pb 4 years ago |

I found that a fast immutable in memory database like Datascript solves both problems quite nicely.

ARandomerDude 4 years ago | |

Came here to say this. The author wasn't actually describing problems with functional programming, he was describing his troubles due to naive implementations.

Naive implementations will always, by definition, not be great. But instead of spending the time, energy, and money on crafting a custom solution each time, use off-the-shelf libraries that do the job. DataScript, Storm, etc. are great for this.

rini17 4 years ago |

Common Lisp solves the big structure problem using "setf" form. It also generally has both destructive/inplace and nondestructive structure manipulation versions of practically all functions in standard library.

It solves parent-child problem by having symbols as built-in data type. They can be used exactly as the "mutable mediator" describes. And beyond, such as you can generate a code that uses these symbols and have it natively compiled - at runtime.

pmarreck 4 years ago |

I'm confused; has this person actually never heard of "purely functional data structures"?

zelphirkalt 4 years ago |

I am surprised, that there is no mention of concurrency or running things in parallel in the article. That is one of the areas, where you profit the most from avoiding mutating state. Was there any parallelization effort for the example project "Archetype"? If any, how did that go? What were the problems with parallelizing to make things run faster?

I think there might also be another idea to avoid too heavy updating in case of the many many functional updates to the 30K data, but it depends on how Archetype works and what it does. It might be possible to not apply some updates immediately, but group them together with other updates or simplify multiple subsequent updates into one update in clever ways. This might be difficult to implement though and as I said, might not be appropriate for all situations, as you might want to give immediate feedback in the GUI for any change.

Lichtso 4 years ago |

Assuming we define functional as referential transparent and imperative in turn as referential opaque.

Then, I think there are two different aspects of the problem:

- There is the identity of each object in your program (e.g. a key, reference or pointer), regardless of its value.

- And there are the versions of values each object has / had.

Imperative tracks identities but no versions while functional tracks versions but no identities. Ideally the program would run in a VCS so that one could track both versions and identities.

kkfx 4 years ago |

The real issue, IMVHO, about functional programming is that's more suited for a development model modern society do not follow.

That's not much an FP issue but an actual IT issue, since modern development for modern business reasons produce monsters and solutions waiting for problems. That's also hard to teach and probably the reason functional programming is seen as "superior but not for the real world by many.

otikik 4 years ago |

Naming things, and cache invalidation :).

Now seriously: while reading this, and especially the first problem, my head was shouting "immutable data structures! immutable data structures!".

The author surely must have encountered IDSs during their 10-year tenure with Lisp. I'm curious about why they thought they would not fit the bill for the problems they are writing about.

NyxWulf 4 years ago | |

Did you read the article? The entire discussion was on the tension between immutable data structures and cognitive complexity on one side, and machine performance and mutability on the other side.

otikik 4 years ago | | |

First, if I sounded snarky I apologize. I assure you it was not my intention.

Secondly, I did read the article. If they are allocating a big chunk of memory and copying it over every single time they are making a change, they are not using immutable data structures. Ok, technically speaking, they might be. But they are using the worst implementation possible as a base to make conclusions from. That'd be like looking at the C language and concluding that strong types are not good, because of all the limitations C has.

sly010 4 years ago |

Particularly the "parent-child" example feels like a non issue to me. You probably want to keep multiple versions of a glyph anyway to support things like undo, in which case explicit parent update is a requirement, not an annoyance. No reason you can't have both:

  g2 = updateGlyph(g1)
  f2 = updateFontInGlyph(f1, g2)

TuringTest 4 years ago | |

I think the crux is that any complex data structure f1 using glyphs would not need to be updated explicitly in an imperative program through a function like updateFontInGlyph; the runtime would do it as part of its semantics. Of course that's a double-edged sword, as you may get undesired side effects as well as the desired ones like this.

There are ways to achieve such "automatic updating" of items in a functional data structure, but you need to build them explicitly instead of relying on the runtime, which gives you more responsibility but also more control. The most direct approach as compared to imperative code would be to build the data structure on top of mutable state-based streams, though there are other possibilities like monotonic or immutable structures.

https://blog.waleedkhan.name/monotonicity/

Sinidir 4 years ago |

"When a functional update on a child structure occurs, the mediator is updated with the reference to the new child. But all the parents still point at the same mediator".

This is literally atoms in clojure.

gorgoiler 4 years ago |

For the latter problem, I believe lenses are a known solution. I’ve never really used them mind you, but it might be worth reading up on them?

jerf 4 years ago |

So, as the trend of forcing functional programming into imperative languages continues on its inexorable path, I just can't help but bring the old joke to mind more and more often:

    "Doctor, it hurts when I do this."

    "Well, then, don't do that."

You mean, jamming a foreign paradigm into a language, where the advantages of the foreign paradigm are reduced and the disadvantages greatly magnified, hurts sometimes?

Have you considered... not doing that?

I simply can not apprehend the mindset that goes:

    1. Wow, map/filter/reduce sure is fun in this language over here
       designed to work with it.
    2. Now I'm going to use everywhere I go, no matter how much it hurts
       and no matter what compromises I have to make.
    3. When it hurts, I will blame anything and everything except myself
       for forcing a paradigm in where it doesn't belong.

The paradigm has to be good. It just has to! Even if it hurts! Even if I'm really not enjoying using it! Even if it's murdering my runtime and killing my performance and I'm in a language where functions take quite a lot of text rather than \x -> x + 1! Even if my code is hard to read and hard to abstract and hard to refactor. Even if the foreign paradigm is critically based on pervasive laziness my language doesn't have! Or some other foundation my current language totally lacks! Gosh darn it, I'm going to use this paradigm in this code if it kills me! Because it was so much fun... over there.

I'm not saying that it isn't sometimes useful to borrow paradigms in certain places even in languages that don't support it. When it doesn't hurt like this, by all means use it as you like. What I don't understand is this constant, if not growing, stream of posts about "my gosh, my forced FP programming in this not-FP language is kinda painful here... grimaces yup, it's pretty painful all right... grits teeth yup, this is really hurting me and making me really angry and isn't fun at all... but I'm so glad I'm using this style that is making things hard and hurting me and making me angry, wouldn't dream of doing anything else!"

I know Haskell. There are many lessons I've taken from Haskell and applied to other languages, like the virtue of very strongly separating IO code from logic, the virtues of taking extra steps to provide and use composability in your APIs, the virtues of certain things being immutable data. But taking these lessons from functional programming languages shouldn't mean you ape the surface solutions functional programming uses; you should be thinking about how to idiomatically bring the target virtues into the other languages you use. There will be compromises, but sometimes there will also be things the current language does better than Haskell. (Really quite a lot of things. Haskell's great but there's many reasons it hasn't conquered the world.) You shouldn't be so focused on importing the foreign paradigm that you completely throw away the virtues of the environment you're in.

I do not understand why so many of you are so vigorously adamant that you're going to program in the intersection of functional programming languages and imperative programming languages. It's an incredibly aggravating place to try to be. The (near) union, applied with the wisdom gleaned from both camps, is much more fun.

colordrops 4 years ago |

Author's solution to problems in FP: break the FP model.

nesarkvechnep 4 years ago |

Again, functional programming does not forbid side-effects!

ncmncm 4 years ago |

What, just two?

AtNightWeCode 4 years ago |

Have not got the time to read this but. Let me guess. Another programmer in a functional programming language realizing that the state is all over the place and gets copied around but still is so importantly immutable.

TuringTest 4 years ago | |

That, and the comments in this thread reminding us that you have powerful tools in pure functional languages to represent state and handle those pesky side effects causing the bugs that plague imperative programmers' nightmares.

AtNightWeCode 4 years ago | | |

Nothing prevents you from doing pure functions in other langs. Fact is that it is standard today. The imperative programming model and the core problem with functional programming is much the same. The only difference is that you have a copy of the state in functional programming. The “real” state is still all over the place. That is why any sane business avoids functional langs except for areas were functional langs shine.

GuB-42 4 years ago |

Is there a reason for using pure functional programming except as an exercise?

Functional programming is great, and most languages nowadays give you the necessary tools like lambdas, closures, list processing, first class functions, etc... but it isn't the right tool for every job. The same languages also typically support object oriented programming with classes, methods, inheritance, encapsulation, etc... Declarative programming is usually not supported by the "main" language but there is often some way to use a secondary language for that, like SQL.

Multiparadigm languages are here for a reason, and trying to use a paradigm that doesn't fit only makes your life harder. It is not a bad thing if you are learning or doing research on the subject, but it is a bad thing if you are trying to be productive.

mirceal 4 years ago | |

yes. there are reasons.

and no, you don't need to go all in. you can make a judgement call and pick and choose what you want to leverage.

for anything but a trivial service immutability of data as you are processing requests should at a minimum be considered.