John Carmack on Functional Programming in C++ (2018)
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I think this probably the most important concept for programmers to keep at the front of their minds when working day to day. So many of the problems I see come from developers (myself included) adding new business logic to a process, or fixing a bug and not thinking through the side effects of the change
Good code design, regardless of paradigm used, encapsulates complexity in order to minimize the amount of context needed to understand any one bit of code. The better the encapsulation, the better we can scale our limited mental capacity to larger and more complex systems.
If the complexity is better encapsulated, it's also faster and easier for the reviewer to acquire the necessary context.
When I was first learning OOP, I wish someone had mentioned why classes are useful: they leverage our natural mental abstraction machinery for understanding the world. Walking down the road, we generally think of each car as a whole and ignore thinking about the thousands of parts that make it up. It would be overwhelming to simply cross a busy street if we were considering all of the millions of car components flowing down the streets or considering the various differences in cars all the time, rather than abstractly thinking of various differing collections of parts as just "car". All of this happens at a subconscious level, and we can consciously tap into some of this mental machinery to help us reason about code. Of course, we don't need OOP to leverage this mental machinery, but it's one technique for doing so.
It was related to deserialized data being transposed from one complex digraph to another. The reason I did not catch it as being problematic sooner was precisely because the state management was loosely distributed within the transposing functions' call hierarchy. The code which was trying to manage the states had emerged from fixes for a series of different problems related to edge cases, and once the new problems started relating to fixes for the others, I had that "Ahhh, well sh*t.." moment you get when finding you were missing something important.
> I think this probably the most important concept for programmers to keep at the front of their minds when working day to day.
The lesson I had learned was to be more mindful when synthesizing a solution and there might be more state interdependence than we expect. Centralizing state management has helped me solve it in this case.
Swift enums are really nice[0].
[0] https://littlegreenviper.com/miscellany/swiftwater/enums-wit...
I'm a fairly decent chap, and have nothing against you. Not sure why it's so important to you to begin our relationship with an attack. I suspect we have far more in common, than we do, differences.
I know that I sound like a demented old man, when I say that I miss the old days, when we all shared passions for the tech, and, even when we were in competition with each other, we tended to have respect for one another.
But those were also the days of the UseNet flamewars, so it wasn't all flowers and bunnies (and I was definitely a flame warrior).
Here’s an interesting little exploration: https://littlegreenviper.com/miscellany/swiftwater/writing-a...
In algebraic data types we have a sum (union) of a labeled cartesian products, and each product may have arbitrary number of values:
data MaybeInt = NoInt | AnInt Int
data MaybeItsPair = NoIntsPair | AnIntPair Int Int
The first constructor in both data types is a labeled (No...) product with empty number of value to apply cartesian product to.
The second constructor in both data types is a cartesian product: in first case an Int and in case a pair of Ints.
Of course you can have more involved data type:
data Expr = Void | Const Int | Var String | Bin BinOp Expr Expr | Un UnOp Expr
A label, two single value constructors and much more involved operations as well.
I haven't studied much theoretical computer science so I'd love to hear some good beginner resources on this stuff.
The number of variants of the AnIntPair construction is the number of distinct elements in the Int type, squared.
I used definition of algebraic data type in Haskell: http://wiki.haskell.org/Algebraic_data_type
An example in the wild that demonstrates this `fp-ts`'s explanation of how to do ADTs in TypeScript where you can see the comparison in PureScript is a one-liner (both data and polymorphic data): https://github.com/gcanti/fp-ts/blob/master/docs/guides/pure...
class Bar { tag="bar" as const; constructor(public value:string){} } class Baz { tag="baz" as const; constructor(public value:boolean){} } type Foo = Bar|Baz;
Also while pattern matching isn't an explicit thing in TS you get most benefits from compiler checked accesses that can recognize type-tests in your code and derive types and do property access checks.
Adding logic is the smallest part of the problem: adding types, that aren't constrained to represent the smallest set of values needed for the problem at hand, that's the biggest problem. This 'looseness' doesn't show up as a compilation error and looks on the surface to be just fine. Then the real-world comes along and creates a value that should never exist and the code falls over.
Constraining the data by properly defining types is the (primary) way to reduce the possible states an application can get in. This can be via sum-types (discriminated unions), or even making sure you don't use `int`, `string`, etc. and other types that are just one step away from being dynamic.
When doing pure functional programming, along with properly constrained types, you tend to find the code writes itself. What I mean by this is that it becomes very much about composing functions via 'type lego'. Once the types fit, your code works [1]. This is a very profound feeling once you grok it, because it gives oneself a feeling of confidence in ones own code that doesn't happen in imperative land. I will sometimes go days or weeks without even running the code I'm writing because I just have confidence in it.
[1] It's still possible to write logic errors, but they're the only major class of bugs you get really.
In the micro scale in terms of variables this is not the biggest problem that exists with programs. Enums don't somehow become garbage because it's technically possible to overwrite them with garage. The macro state of the entire system adds much more complexity and bugs. There is too many things that you have to keep in mind and systems are too big for one single person to understand it all. Pure functional still has this macro state. The state is stored in the program counter and by carrying along the current state with it.
If you break any programming task into fine enough steps it will feel like lego. It's nothing special. If I need to print out the numbers 1 through 10 there is a single canonical way my whole team could come up with independent from one another.
>I will sometimes go days or weeks without even running the code I'm writing because I just have confidence in it.
On a small scare this is possible but once you start working on a larger team or if you have to integrate your code with constantly changing systems you will want to be testing your code.
>[1] It's still possible to write logic errors, but they're the only major class of bugs you get really.
You can still make security bugs and performance bugs. Logic bugs is a giant category of bugs and are what most bugs belong to.
Types are composable. Sum-types and product-types allow the composition of smaller types into larger ones. They come in the form of discriminated-unions and records in FP languages. So when it comes to the question of how do I know when I've constricted my data enough, it's when I know all the components of all types have been constricted enough.
You don't have to design some massive schema for your entire application. You can absolutely do it as you go. Just create a type that represents the smallest set of values that are needed for whatever concept it reprsents. Then compose those.
> Logic bugs is a giant category of bugs and are what most bugs belong to.
I think logic bugs are relatively rare once you constrain the possible states your code can be in - and they usually come about because of some cyclomatic complexity issue (i.e. putting the if/else branches the wrong way around). Logic just happens to be the bit of your codebase that can't be captured in types (well, not with trivial types anyway). It's the bit of what we write that we interpret from the human world. That's our job, to translate the requirements into code, and the logic is that bit and therefore more prone to human error. I still find I have fewer of them when I have good constraining types that limit the scope for fuckups.
The current state of the world can absolutely be captured in types, then the logic is simply a fold over a sequence of events that take the old state of the world and transforms it into a new one. This is the best way of modeling time also, which is a major source of bugs in imperative code. Mutating data structures kills any effective modeling of time, so cheats like locks are needed. Even with locks there's no effective history and no understanding of now, which makes code difficult to reason about.
That is the idealised end goal of pure functional programming. The reality is most people will still use an `int` when they should use a constrained value (say [1..10] rather than [int.min..int.max]); and so their program can still get into a bad-state™. The more you constrain though, the fewer bugs you will have.
This is the lot of most devs, working out how much risk you're willing to take.
* Those using dynamic languages take the most risk. They do it because they believe they can write code quicker. Their trade off is lots of runtime errors, or runtime checks, or building lots of unit tests.
* Those using languages like Java or C# take fewer risks, as the compiler can help them out, but they don't tend to create types like `Month` that can only hold the constrained set of ints [1..12]. So they run the risk of accepting 13. They also have extensible type-hierarchies (inheritance) that means its impossible to know the full scope of any 'abstracted' interface reference passed around. They must cross their fingers and hope nobody screwed up the implementation. They're also not composable, which leads to additional logic problems as they're glued together manually.
* Those using languages like Haskell don't have the inheritance problem. Everything is sum-types, product-types, or exponential-types (functions) and is therefore composable. This creates an opportunity to create the idealised program (as I mention above). Logic bugs still exist and usually people don't constrain their types completely, but the situation is often much less risky and turns into more robust code.
> On a small scare this is possible but once you start working on a larger team or if you have to integrate your code with constantly changing systems you will want to be testing your code.
I run two development teams. But I also write a lot of research prototypes, this tends to be where I will just have some open ended project that I am playing around with - often programming with types to see what 'level ups' I can get from doing so. But even in a day-to-day project I could easily spend a few days writing code before I need to check it. For example, I recently wrote a whole language virtual-machine: AST, type-system, type-inference, type-checking, and evaluation without needing to run it - all the types lined-up and each subsystem was clear from a logic point-of-view. Currently, I'm building a C# monad-transformer system for language-ext [1] based around transducers, this is pure abstract type-level programming - I haven't run it yet. If I can run it in my head and it type-checks, that's usually good enough.
Of course at some point I do actually run the thing ;)
Do you not see that this is a tautology?
>That's our job, to translate the requirements into code, and the logic is that bit and therefore more prone to human error.
The requirements given are typically ambiguous and it is our job to come up with the complete specification ourself. Our specification may have bugs ignoring the implementation. I am not denying that with enough diligence you can mostly avoid errors in implementing the spec, but I am saying finding the correct specification for what your code should do is too hard to do compared to implementing a wrong spec and then fixing it as bugs come up.
>Those using languages like Haskell don't have the inheritance problem. Everything is sum-types, product-types, or exponential-types (functions) and is therefore composable.
Inheritance is just a different way to create sum types.
It might be if you hadn't butchered what I wrote. If I have a type called Month, and it can only accept values 1-12. Then I know I have constrained that type enough. If I then create types called Day and Year and constrain those I know they're constrained enough.
If I then compose Day, Month, Year into a type called Date and check the rules of the number of Days in a month so that an invalid date can't be instantiated then I have a more complex type leveraging the simpler ones. I could then put Date into a record type called Appointment, etc. etc. For each type I create I know what data I am trying to represent, so I constrain the type at that point. There's no tautology here, it's just composition of types. Making larger types from smaller ones and making sure they can't ever hold bad state.
> Inheritance is just a different way to create sum types.
Not really, they're open. Sum-types are closed. i.e. they represent a concrete set of states. The openness of an inheritance hierarchy is the problem. We nearly never need completely open type hierarchies, it is pretty rare that something needs to be extensible in that way outside of library development.
That doesn't mean inheritance is always bad, but the 'always inheritance' approach that is often championed in the OO world certainly is.
The goal is to write total functions [1] where every value in the domain can be mapped to a value in the co-domain. If that can't happen then your code is less predictable (throws exceptions or has undeclared side-effects). Having complete control of the states of any data-type is critical to making the approach easy to implement.
This breaks down in the etc. What if an appointment is not valid on them weekend, or a holiday, or when someone's child has soccer practice, or when that appointment conflicts with another one. There are all sorts of restriction that can be added and yes they technically cover some potential bug that someone technically could introduce. It may be hard to predict all of these restrictions ahead of time.
>Not really, they're open
At compile time 99% of the time they are closed. Languages also add features to let you make it closed.
>If that can't happen then your code is less predictable (throws exceptions or has undeclared side-effects).
This is impossible in the real world where hardware issues are real. Exceptions and crashes are okay and is just a fact of life when working with large software syshems. We know these systems are not going to be perfect and embrace it rather than trying to perfectly handle every single thing perfectly every time. It's accepting that these bugs exist and that processes should be put in place in identifying them and monitoring them so they can be fixed.
Total functions also don't allow for infinite loops which is common place in real systems that mare expected to potentially run forever instead of only being able to serve 1000 requests before exiting.
Either the rules are an instrinsic part of an appointment or they're not. If you need something that represents exceptions to the state of a basic appointment then that's the job of the containing type (say a type called WeeklySchedule or something like that). Just like `Day` can be [1..31], but when used in a a `Date` then the constructor of `Date` wouldn't allow a `Day` of 31 and a `Month` of 2.
> At compile time 99% of the time they are closed. Languages also add features to let you make it closed.
You can't close an interface.
> This is impossible in the real world where hardware issues are real.
Exceptions in imperative-land tend to be used for all errors, not just exceptional ones, but expected ones. The problem is that no one knows what these side-effects are from the surface (i.e. a function prototype doesn't expose it).
Truly exceptional events should probably shut down the application (things like out-of-memory), or, as with Erlang, escalate to a supervision node that would restart the service.
Exceptions exist in functional languages too. However, expected errors, like 'file not found' and other application level 'could happen' errors should be caught at the point they are raised and represented in the type-system.
For example, a common pure pattern would be `Either l r = Left l | Right r` where the result is either a success (Right) or an alternative value (Left - usually used to mean 'failed').
When your function has this in its declaration, you are then aware of possible alternative paths your code could go down. This isn't known with exceptions.
For example the `parseInt` from C# and Haskell (below), one declares its side-effect upfront, then other doesn't. It may be semi-obvious that the C# one must throw an exception, but that isn't the only outcome - it could return `0`.
int parseInt(string x);
parseInt :: String -> Either Error Int
> Total functions also don't allow for infinite loops which is common place in real systems that mare expected to potentially run forever instead of only being able to serve 1000 requests before exiting.
In pure maths, sure, in reality yeah they do. Haskell has ⊥ [1] in its type-system for exactly this reason.
Anyway, you seem hellbent on shooting down this approach rather than engaging in inquisative discussion about something you don't know or understand. So, this will be my last reply in this thread. Feel free to have the last word as I'm not wasting more time explaining something that is literally about soundness in code and why it's valuable. If you don't get it now, you never will.
...in theory. The problem is usually that you had already spent a lot more time designing all your type constraints before "the code can write itself", then it would have been to just sit down and write some code to get the damn thing working first.
And at least IME, spending so much time upfront without any visible process results in too rigid "over-architected" systems which are hard to extend when the requirements ineviatably change next month (or even just an external system your code needs to talk to).
The best solution is usually to not give in to either extremes, but find a well balanced approach somewhere in the middle where you have some sane restrictions designed into the types to prevent the most obvious bugs, but not enough that the whole house needs to be rebuild if a little requirement detail changes.
> I will sometimes go days or weeks without even running the code I'm writing because I just have confidence in it.
...that's *exactly* what I mean, just that I don't see this as a good thing ;)
It's really not that hard to do (well in FP languages anyway) and ends up improving throughput for any coding task due to the reduced need to constatly check if something does what you think it should do.
> The best solution is usually to not give in to either extremes, but find a well balanced approach somewhere in the middle where you have some sane restrictions designed into the types to prevent the most obvious bugs.
That isn't the best solution, it's a solution. As I mentioned in another comment [1], it's about risk, and how much you're willing to take on as a developer and as a team. I work in healthcare software, I want it to be right, because I don't want my fuck-ups to kill a patient. But even if I didn't, I'd still prefer to write code this way because it is sooo much easier to manage the mental model of the code-base, especially when the code-base is super-large.
> but not enough that the whole house needs to be rebuild if a little requirement detail changes
That isn't my experience at all. If the whole house needs a rebuild when some little requirement detail changes then you're doing it wrong, sorry. One thing that can happen when using sum-types instead of inheritance, is that adding a new case to the sum-type requires you to go and consider all pattern-matching usages of it. This can be painful, but it forces you to consider the impact of your change. I believe this to be a good thing and compilers usually help you here anyway. This trade-off is captured in the Expression Problem [2]
I realise sometimes there's aspects of a game that needs to write to the metal, and compromises need to be made, but that isn't necessary for an entire game. I know people are using my language-ext library with Unity projects too, so I assume there are others in games that don't agree (although I don't know exactly what kind of titles they're working on).
Lets say you want to test your bullets having HP and can be shot down. So you add that in an hour, now bullets have HP, you test it, it doesn't work, so you remove it. Things like that needs to be seamless, because you need to iterate on ideas and test if things are fun. Big upfront design as you suggest where you very carefully constrain everything doesn't do well with that. It works well for 3d engines for example sure, but 3d engines are much more constrained than games.
I also did lots of the creative bit ;). I just assumed this was going to go down a performance route question, sorry!
> Big upfront design as you suggest
No, I am not suggesting that at all - I'm saying the opposite of that. You're the third person to think that's what I am saying now, so I assume I didn't explain myself very well. Rather than repeat myself again, my other replies cover this [1] [2]:
So, you say it works for you, but basically every example of the things I talked about are done in a very different way than you describe, so unless you have programmed some very impressive games I would say that the data strongly supports another style of programming than you suggest here. For compilers or parsers or stuff like medical systems? Sure, go ahead, it seems to work for those domains, but it doesn't seem to be a panacea. If games were so much easier to write that way we would see tons of them, especially among the better games, but we don't.
Games is one of the easiest sectors to be in, the complexity level is low compared to other sectors, especially now all the difficult bits are done for you (off the shelf engines). I assume you're not working on titles that have 15 million lines of code in, like I do every day.
That is possibly why there hasn't been a big push for FP in games yet, because they haven't yet hit the complexity limit of OO like others have. Your argument is "Well we've always done it like this, why change?". Probably for the same reason people don't write games in assembly languages any more: at some point the current tools aren't good enough. Games might not have hit that ceiling yet, but you'll almost certainly be affected by the artefacts of getting close to it.
I see you're even questioning my history in games. Below [1][2][3][4][5][6][7][8] are some of the titles I worked on (or my code is in). But to be clear, we didn't use these techniques back then either, because it was too soon to be doing it (or we weren't enlightened then). You should consider being a little more open minded about how you could, possibly, write software more effectively. Especially when you have someone who's been on both sides of the fence advocating it. Just writing it off because you think people who do FP just write "compilers or similar processing throughput systems" is shooting yourself in the foot more than anything.
[1] Battalion Wars: https://en.wikipedia.org/wiki/Battalion_Wars
[2] Fire Blade: https://en.wikipedia.org/wiki/Fire_Blade_(video_game)
[3] Warmhammer 40,000 Fire Warrior: https://en.wikipedia.org/wiki/Warhammer_40%2C000%3A_Fire_War...
[4] Call of Duty - Finest Hour: https://en.wikipedia.org/wiki/Call_of_Duty:_Finest_Hour
[5] The Regiment: https://en.wikipedia.org/wiki/The_Regiment_(video_game)
[6] Reign of Fire: https://en.wikipedia.org/wiki/Reign_of_Fire_(video_game)
[7] Attack of the Saucerman: https://en.wikipedia.org/wiki/Attack_of_the_Saucerman
[8] Lunatik [canned]: https://www.unseen64.net/2020/11/16/lunatik-pure-entertainme...
Are you saying the biggest and probably most complex software (OS-es, browsers, etc.) out there is written in fp languages?
>> Games is one of the easiest sectors to be in, the complexity level is low compared to other sectors, especially now all the difficult bits are done for you (off the shelf engines).
As far as I know those "off the shelf engines" are not written in fp languages. And I would say they are complex.
You know I didn't say that.
The imperative coding approach is cognitively more difficult. So at scale (size of code-base) you either have more bugs and therefore more overhead/cost of development; or you look for coping strategies to minimise the bugs and the cost of development.
Declarative, pure functional programming reduces the cognitive load, because you get to compose things reliably. And because it's declarative and pure you don't have to look under the hood to see what is going on, you know from the surface what large bodies of code do.
A simple example, two functions prototypes:
One in Java
int foo(string x);
foo :: String -> Int
You are of course free to take whichever approach you like. The imperative one is however just going to get progressively more difficult as applications get more complex. I spent 27 years writing various combinations of assembly, procedural, and OO. I've spent the past 10 using the functional paradigm. If I ever had to go back to the imperative paradigm it would fill me with dread. I still need to use it occasionally for performance optimisations, but I would always wrap it with a declarative interface (where the contract always holds).
> As far as I know those "off the shelf engines" are not written in fp languages. And I would say they are complex.
When I talked about 'easy', what I meant was that games development has much less of the real world to deal with:
* You tend to know your user
* The user doesn't get to have a say
* You often know exactly what hardware you're going to be running on
* You don't have to integrate with 20 other systems by 3rd party providers
* To a certain extent you can put it in a box and move on (I know that's not strictly true, but you're unlikely to be maintaining a game for more than a decade)
* The feature-set of the game won't double every few years as the product competes with others
* Most games have a relatively similar management system
* There aren't massively complex compliance issues or laws that get in the way (yes there are some, but less than other industries)
* Although networking and game-state synchronisation is a hard problem, the scope of distributed computing problems are small
A game engine really isn't that complex. Of course it's hard to make it as optimal as possible, but the problem isn't at the real difficult end of the spectrum. Probably the hardest thing in games now is writing the tooling. I spent a number of years writing core tech and tools for a 3 studio games company and that was just at the beginning of the tooling journey. I assume that's quite a sizeable coding challenge now; one I would definitely us FP for if I was still in the industry.
Functional programming only started going mainstream a few years ago. Give it time, it will make an impact. You can already see the influence of functional reactive programming in UI frameworks like React, where the screen state is a pure function of the program state. That's exactly what games have been doing for years, ie. rendering every from based on program state, only now we have a purely functional model of how this works.
Some of the "purely functional" aspects will be optimized/compiled away at various layers, but the programmer would still be able to work with the high-level model without attending to those details.
Cool library. I've had a few of these patterns in my Sasa library for years, but you've taken it to the Haskell extreme! Probably further than most C# developers could stomach. ;-)
You might be interested in checking out the hash array mapped trie from Sasa [1]. It cleverly exploits the CLR's reified generics to unbox the trie at various levels which ends up saving quite a bit of space and indirections, so it performs almost on par with the mutable dictionary.
I had an earlier version that used an outer struct to ensure it's never null [2], similar to how your collections seem to work, but switched to classes to make it more idiomatic in C#.
I recently started sketching out a Haskell-like generic "Deriving" source generator, contrasted with your domain-specific piecemeal approach, ie. [Record], [Reader], etc. Did you ever try that approach?
[1] https://sourceforge.net/p/sasa/code/ci/default/tree/Sasa.Col...
[2] https://sourceforge.net/p/sasa/code/ci/57417faec5ed442224a0f...
I notice you have a comment that says "This trie is now the fastest immutable dictionary I'm aware of". Unfortunately I have to make you aware that my implementation is faster, sorry! ;)
Here's the add-items benchmark:
BenchmarkDotNet=v0.13.5, OS=Windows 11 (10.0.22621.1265/22H2/2022Update/SunValley2)
AMD Ryzen Threadripper PRO 3995WX 64-Cores, 1 CPU, 128 logical and 64 physical cores
.NET SDK=6.0.300
[Host] : .NET 6.0.5 (6.0.522.21309), X64 RyuJIT AVX2
DefaultJob : .NET 6.0.5 (6.0.522.21309), X64 RyuJIT AVX2
| Method | N | Mean |
|-------------------------- |------- |---------------:|
| SysColImmutableDictionary | 100 | 32.315 μs |
| SasaTrie | 100 | 8.007 μs |
| SysColDictionary | 100 | 1.440 μs |
| LangExtHashMap | 100 | 7.512 μs |
|-------------------------- |------- |---------------:|
| SysColImmutableDictionary | 1000 | 524.368 μs |
| SasaTrie | 1000 | 140.537 μs |
| SysColDictionary | 1000 | 17.395 μs |
| LangExtHashMap | 1000 | 125.663 μs |
|-------------------------- |------- |---------------:|
| SysColImmutableDictionary | 10000 | 7,598.532 μs |
| SasaTrie | 10000 | 2,055.220 μs |
| SysColDictionary | 10000 | 301.683 μs |
| LangExtHashMap | 10000 | 1,842.514 μs |
|-------------------------- |------- |---------------:|
| SysColImmutableDictionary | 100000 | 129,811.705 μs |
| SasaTrie | 100000 | 46,752.702 μs |
| SysColDictionary | 100000 | 6,103.999 μs |
| LangExtHashMap | 100000 | 37,869.597 μs |
* SysColDictionary = System.Collections.Generic.Dictionary
My other benchmarks are still running, so I won't share them all, but we actually trade blows, your ContainsKey appears to be faster, my collection iteration is twice as fast as yours. I suspect if I removed my struct wrapper it would be a bit quicker, but wouldn't be a real-world fair comparison.
We're both much faster than Microsoft's ImmutableCollections though!
I am certainly interested in what that technique is with the nested Node<Node<Node<Node<... and why it works. Do you have any more detail on that? You might also want to look into the data-structure I use: CHAMP [1]. I notice you use HAMT, CHAMP is a more efficient version of HAMT. It has better data-locality, which is much more efficient especially when working with value-types.
With regards to the 'deriving' question: Yeah, I'm working on a more general solution now, as well as a way to do monad-transformers properly. I've also figured out a way to do infinite tail-recursion for my monadic types, which I'll be deploying in the next major update.
[1] https://michael.steindorfer.name/publications/phd-thesis-eff...
* EDIT *
Here's the full suite of benchmarks, but I've trimmed them down to just the 10,000 item tests and removed the error and deviations for clarity:
+-----------------------+----------------+
| Add 10000 items (value type) |
+-----------------------+----------------+
| SasaTrie | 2,055.220 μs |
| LangExtHashMap | 1,842.514 μs |
+-----------------------+----------------+
| Add 10000 items (reference type) |
+-----------------------+----------------+
| SasaTrie | 2,988.663 μs |
| LangExtHashMap | 2,905.552 μs |
+-----------------------+----------------+
| ContainsKey (10000 value items map) |
+-----------------------+----------------+
| SasaTrie | 173,215.0 ns |
| LangExtHashMap | 235,577.2 ns |
+-----------------------+----------------+
| ContainsKey (10000 reference map) |
+-----------------------+----------------+
| SasaTrie | 518.789 μs |
| LangExtHashMap | 549.952 μs |
+-----------------------+----------------+
| Iterate (10000 value items map) |
+-----------------------+----------------+
| SasaTrie | 457,819.0 ns |
| LangExtHashMap | 227,632.7 ns |
+-----------------------+----------------+
| Iterate (10000 references map) |
+-----------------------+----------------+
| SasaTrie | 712,623.1 ns |
| LangExtHashMap | 519,250.7 ns |
+-----------------------+----------------+
| Random remove (10000 value items map) |
+-----------------------+----------------+
| SasaTrie | 2,276,772.4 ns |
| LangExtHashMap | 1,765,042.9 ns |
+-----------------------+----------------+
| Random remove (10000 references map |
+-----------------------+----------------+
| SasaTrie | 2,946.775 μs |
| LangExtHashMap | 2,576.813 μs |
+-----------------------+----------------+Also wondering whether you checked the Trie from the latest repo or the older struct version I also linked. I think the struct version was a little faster but I switched for the aforementioned idiomatic reasons.
I'll have to check out the CHAMP design for sure, I'm curious how it differs.
Edit: I also recall that I didn't optimize iteration at all and that it was fairly inefficient, but can't really confirm now. I recall optimizing merge carefully though so that should perform well.
Edit 2: forgot to mention that Node<...> generic is nested 6 times because that's the max depth of the trie for 32 bit keys with 32 element nodes.
I included Sasa.Collections 1.0.0-RC4 from Nu-Get. So I assume it's your latest version. I've added Sasa.Collections permanently to my benchmarks seeing as you're the first library to get close to the performance of the language-ext collections - I'd like to keep an eye on you, haha! Always good to have a bit of friendly competition :)
> forgot to mention that Node<...> generic is nested 6 times because that's the max depth of the trie for 32 bit keys with 32 element nodes.
Yep, I figured that one. Still I look at it and go 'huh?'. I've been looking at code all day though, so I'll have a proper look tomorrow when less tired.
struct Node<TKey, TValue>
{
uint bitmap;
KeyValuePair<TKey, TValue>[] entries;
Node<TKey, TValue>[] children;
// Node = Leaf of bitmap * children | Internal of bitmap * children
// We inline that sum into a struct where
// Leaf -> entries != null
// Internal -> children != null
}
Also, because these are all structs, the JIT should specialize the code generated for each level so it should be pretty fast. IIRC, the speed difference between the original and the inlined representation was noticeable but not crazy, but the memory use difference was considerable. This was all years ago though, first implementation was 2013.
This is an unboxed representation of a sum type. Rather than relying on the CLR's inheritance to model sums I lay it out explicitly at the cost of a little space. It may seem wasteful since it looks like I'm adding 8 unused bytes to each node, but the CLR object header is 16 bytes so you're actually saving 8 bytes per node by avoiding classes and using structs that have no header! You can really see the difference in the working set if you do a full GC.Collect and check the allocated bytes after constructing some large tries.
Also, because I'm removing a heap object per node, this representation incurs one less indirection per node accessed, so I'm doing half as many loads as an equivalent class-based representation for every operation. This is a large constant factor difference, and probably partly explains why Sasa's ContainsKey is faster than yours.
Finally, no trie operatioms incur any virtual dispatch, which are instead replaced by a simple if-checks which are generally more predictable. This probably has less impact these days since branch predictors are pretty good now.
I think if you replaced your class-based representation that uses the superior CHAMP model with this unboxed representation your hash map would be the uncontested champion. You might even challenge the mutable dictionary for performance!
Don't tell me that, I'm an optimisation nut, I might actually do it! ;)
I woke up this morning with a clear understanding of what your optimisation is, so I guess I just needed to load up my brain with the details! It's definitely a super interesting approach and one that I'm surprised I've not come across before.
I'm definitely a bit wary of changing my current implementation, mostly because it's been battle hardened, but I'm sure I won't be able to resist having a go at some point. Thanks for the tip!
Unboxing sums is a nice optimization but then you can't naively use switch-patern matching to deconstruct them.
I have a bunch of other things in Sasa and other libraries you might find useful. I'm not actively working on most of them anymore except for bug fixes. I learned a lot but didn't end up using a lot of these features as much as I'd hoped.
For instance, being able to create open instance delegates in a way that automatically works around the CLR limits against such delegates to virtual methods. Some of the concurrency primitives are also interesting, as they implement an efficient atomic read/write protocols for arbitrary sized types using only volatile reads/writes (ie. avoid torn reads), and a sort of LLSC using only volatile read/write and a single interlocked inc/dec. Also, I added a kind system to CLR reflection to make working with it much easier [2].
It seems we're thinking along the same lines for numeric types. I reproduced the Haskell numeric hierarchy [1], but I put that on hold because I was thinking a [Deriving] attribute would eliminate a lot of redundancy.
Just FYI, clicking Num<A> on the main GitHub markdown page doesn't jump to the link on the markup.
Lots more to see if you're interested! I've played with parser combinators but never liked how they turned out, and settled on a simpler approach that was pretty interesting.
good luck trying to fix a bug in a complex project without doing some sort of test to make sure the change does not break one of the thousands of requirements implements so far.
from my experience fixing a bug without validating the system has a very high chance to spawn multiple bugs found later on...
That's a great take, but I'd turn it upside down. Problems come from developers adding new features while not being mindful of the importance of helping out whoever is reading that code in the future to understand what are all the possible states.
Anyone can hack together code that's all over the place, but forcing people in the future to do shotgun surgery is as bad as adding the bugs ourselves.
Every time you have a function or clause that takes an object or data with many possible versions or states, but only will work correctly with particular assumptions, ASSERT that your assumptions are satisfied. Every time.
This helps in several ways. At the time of writing the code, it makes you think about possible states that could be represented and the generality of what your new code actually handles, and so encourages you to write general solutions and avoid unhandled or mishandled cases.
At the time of reading the code, it helps the reader see what cases the writer considered. It helps document the intended scope and use cases the code is designed for.
At the time of extending surrounding code, it helps to catch cases where the extension introduced state combinations that had not been considered before. Its nice to make an extension, run a test case, and get an assert that tells you exactly what other function needs an extension. Without the assert you would make the extension and run your test case and get wrong behavior or a crash and have a big debugging mystery or even worse: apparently working but subtly wrong code.
Others in the thread are talking about making invalid states unrepresentable, which is nice, but not always doable. In a big code base you don't have the luxury of redesigning things; usually you have to stick to more local changes. But you can always add ASSERTs -- that is a completely local way to mark invalid states as invalid.
It is very easy to just say people should know everything, but in practice people don‘t review every single library, nor defend against every single possible case, and surely don’t keep everything in memory while working on any single problem.
I really can’t imagine someone “fully understanding” how the linux kernel works for instance, or even a full graphic card driver, or the software on a POS works as working knowledge.
So yeah, people cause bugs because they don’t know it all, but I’m not sure that’s saying much.
PS: real progress is probably made starting from assuming people don’t understand, and having frameworks that somewhat keep the system manageable under that premise.
A. Understanding relevant computing fundamentals
B. Understanding the interfaces to code you use
C. Writing reliable implementations and documenting them well
D. Writing useful and appropriate interfaces for your implementations
In a perfect world, this creates a nice chain where every programmer only needs to look after these four points and then everything will work seamlessly.
Too bad we don't live there.
The thing that really strikes me from the article is the tone in which it’s couched. The pragmatism on display makes it so much easier to read than it could be. Coming from another few years writing Scala and thinking I was a functional programmer (writing hobby projects in Haskell, Elm etc.), I’ve realised I prefer programming in Java in a team (or at least my current workplace’s flavour of Java, and we’re on Java 17); a realisation that still surprises me now. In reality, there’s only so much purity needed to ship even a relatively complex microservice, and Java does that very well with some functional patterns thrown at it.
For microservices, where you deals with data, it's quite effective because it works like typed JSON.
The only real 'gotcha' there is that, while it prevents whatever it is applied to from being reassigned, if the value is a mutable object of some sort (e.g., an array, mutable collection, etc), it, of course, does not prevent you from modifying the contents of the array, etc. However, Guava provides nice immutable collections to mitigate that issue [1].
It's been a while since I've written modern Java, but I want to say JDK 14? also brought records, which I believe are basically the equivalent of having `final` and `public` on everything as well as overriding the equality and hashcode functions on everything by default so that comparisons are done based on the contents of the object rather than the reference.
And while I am sure many people are scared of the bytecode manipulation that it performs, Lombok [2] provides a lot of little utilities that make using immutable objects totally painless - e.g., applying `@Value` to the class generates all of the necessary boilerplate at compile time, or my favorite, `@With`, which generates methods for copying an immutable object with one of the fields changed (e.g., `val newUser = olduser.withName("Nayeon");`)
Lombok also offers Kotlin-style extension methods [3] which can be nice too for this kind of thing. Though, in my experience, it is the only feature in it that has bitten me at times.
[1]: <https://github.com/google/guava/wiki/ImmutableCollectionsExp...>
[2]: <https://projectlombok.org>
[3]: <https://projectlombok.org/features/experimental/ExtensionMet...>
Obviously process isn’t a good replacement for a proper syntax to prove these don’t occur, but in terms of shipping maintainable, readable software, these pragmatic approaches applied to a mutable, multi-threaded language have largely proved sufficient.
Also, as mentioned in other comments in this threads, Java 17 Records go some way to mitigating mutability concerns too - although mutable APIs are still everywhere in common frameworks and standard libraries. That said, most mutability in a microservice can be scoped to either boot time or locally to a function though, with the latter being invisible to the caller, i.e. constructing a map with the mutable `put` calls, all within the scope of a single method.
Java has come a long way since the "OOP is the bee's knees" days when people fully bought into OOP's out of sight, out of mind promise about mutable state. This started long before language features like records gave post-OOP the official blessing. Along the way have been a few odd intermediate stages like the bean craze that was all about exposing state and pretending that it's still OOP somehow, and the AOP adventures that explored ways to get along with all that mutability.
From what I can see in this article he mostly seemed to like the purity aspect of FP. It seems to me like a number of FP concepts aren’t really “FP things”, as Carmack points out you can use these patterns in imperative languages as well. Making your own DSL seems to also often be an FP thing that doesn’t need to be an FP thing.
I guess the other way is true as well. OCaml has OOP and support for imperative style (with mutation, early returns via exceptions, etc.)
That's a strange take by him and on pragmatism. There's also almost nothing pragmatic about C++. Many functional languages are actually quite pragmatic.
Huh? We’re talking about game dev. C or C++ is the primary language for just about every game engine ever. Even Unity is written C++.
I can’t say functional languages haven’t ever shipped a game. I’m sure there’s an example or three.
But yeah when it comes to shipping games C++ is the definition of pragmatic.
Just because C++ ships games doesn't make it pragmatic. Aren't many game codebases considered to be absolute hell? And none of that says anything about the pragmatism of functional-first languages.
Nice fantasy, but the real-life codebases that do this make even the most hellish C++ code look like future space-age magic technology.
You're not speaking from real-world pragmatic (heh) experience here, you're just complaining that C++ is hard and that you'd rather go shopping.
Assume source is src/foo.c and compiled to obj/foo.o, then introduce a third directory called gen. Change the makefile rule to copy .c from src to gen, and compile from gen to obj. Then add a makefile rule that says gen/foo.c can be built from src/foo.c.py by calling python on the source and redirecting stout.
That means a C source file can be turned into a python program that generates the same source by wrapping it in a string literal, calling print and renaming the source file. No build system change. Then change the python as you see fit to work around the limitations of C.
Works really well for a solo developer. Repo using this scheme is ~100 generated files (mostly lua, some python) vs ~600 C files. Plus ~20 C++ and one D, just getting started with that language.
[1] https://github.com/JonChesterfield/boring-makefile/blob/mast...
$(GEN_PY_SOURCE): $(GEN_ROOT_DIR)%.c: $(SRC_ROOT_DIR)%.c.py
$(PYTHON) $^ > $@C++ is indeed not C. C++ is a considerable improvement over C. You can stick with your C-style code and get the same result you'd get if you had used C, but you still have access to features that simplify a great deal your life and don't have a performance impact, like templates. With C++ you can have your C cake and eat the C++ cake too. The only drawback is compilation times, but any caching system takes care of that.
Unity is one of the reasons why Microsoft started to take Midori lessons into regular C#.
Ultimately if we want to be pedantic, all game engines are written in C and C++, as that is the only way current OSes expose their graphics APIs, no alternative implementation can change that unless they also write an OS in the process.
a programming language alone doesn’t practicality make or break. factor in programming tools, libraries, community, and of course one’s prior experience.
That's a heck of a weird take. C++'s main value proposition has always been pragmatism. In all possible and conceivable aspects of a programming language. The fact that is a (mostly) superset of C, support for OO, RAII, outstanding backwards compatibility, time-tested and future-proofed, "you pay for what you use", it targets everything and runs everywhere, it's the language of choice on most mainstream OSes and specially Windows, etc.
Why do you think people tolerate footguns and memory shenanigans? Why do you think that C++ is still one of the most popular languages ever devised and continued to hold the crown even after the C++98 cross through the desert?
C++ has been the optimal choice with regards to pragmatism at least for the past two decades. What does C++ offer other than pragmatic solutions?
I simply don't consider cans of worms pragmatic.
This personal assertion says nothing about C++ other than stating you have an irrational dislike for it. There are plenty of reasons that make C++ one of the most successful programming languages in mankind's history, if not the most successful, and inertia is not it.
I think this resonates with many. My take is: don’t be dogmatic. Regardless of the language, one should write as beautiful, composable and pure as possible - but no more than that. Where only performance matters, oop wins. When clarity is the only priority, Haskell wins. But in reality, we’re rarely dealing with either scenario. We’re somewhere in the noisy realm between, and ought to code accordingly. Fundamentalists rarely make policy with the nuance reality deserves.
If you tell that Mike Acton he will give you an unhappy face. [0]
In fact OOP can hurt performance a lot.
Perhaps "the other guys" aren't really fundamentalists and we just like a good old ad-hominem every now and then.
Reading this article, he's making a great argument for Rust. I assume the article predates rust by quite a bit. But basically Rust is not a pure functional language but still has a lot of elements in the language that add purity and rigidity. It allows you to do low level stuff when you need to but at the same time promotes safety and correctness.
I think his points are related to an approach which generally reduces complexity and errors. Parts of that philosophy are embodied by Rust but are not exclusive to it.
Until it does matter. Then it *really* matters at which point you're normally looking at a bottom up rewrite and likely retooling and rehiring for new skills.
Ive been bitten by "oh it's just a prototype" a few too many times now.
It might not be aesthetically pleasant but it works.
If we think about it, most of the time you do not need to do that because some library author has already done it.
If one needs to write the native code it is not overengineering. It is only overengineering if it was not needed in the first place.
It's a prototype because when you start a project :
- you don't understand what the client wants
- the client themselves don't really understand what they need (and sometimes, not even what they want, which is magnified as soon as you have to deal with multiple people)
- you might not know how to achieve that
- you might have an idea how to achieve that, but not have practical experience in it
- reality (including funding limitations) restricts what solutions can be implemented
- the usual code entropy...
Given all of the above, why would you NOT do a bottom up rewrite at least once ?!
P.S.: And in the cases the performance critical bits can be isolated, Python can outsource those to performant languages like FORTRAN or C.
I am guessing the answer usually is : the funding doesn't come directly from the end user, and so the incentives are not aligned for a successful project - which also explains why software projects have such poor success rates (~10%) ?
A pattern I’ve frequently seen is schedule pressure mounts and when you need to scale back on scope to meet a deadline, rewrites are the first to go.
I’d guess a good portion of the garbage people inherit from others fits into this category.
Referential transparency and immutability are fundamental to FP, therefore they are meaningfully described as FP things regardless of who is using them or where.
[1] https://www.gamedeveloper.com/programming/in-depth-functiona...
Looks left, looks right, quietly closes vscode and rm -rf's the dev folder
I'm curious : how do they manage to not make it (performance) disastrous ? Anyone could please forward me some functional-newbie-level examples ?
A great explanation of this is in “Practical Common Lisp”:
https://gigamonkeys.com/book/they-called-it-lisp-for-a-reaso...
Case in point: for small cardinality data in practice it’s often (or at least occasionally) faster to use a linear time search algorithm on a fixed length array than to put it, say, in a tree, map, etc. that is big-O “better”.
Got no real data for it but I felt the quality of the code I wrote was much better going forward.
Instantiating and then calling different methods helps me test a process at different points of the executing (testing and debugging). Inheritance helps me clean up stuff that's repeated in specific sub-problems. Mixins/modules help me in a similar way, but for stuff that's reusable in unrelated problems.
I'm writing this with Ruby in mind. Seeing how OOP constructs can help me manage code that's written in a functional style has given me a middle ground: I started as 'FP is superior; use everywhere; avoid OOP languages' and now see the OOP stuff just as tools that can make it easier for me to encapsulate and reuse logic. Obv this comes with footguns: taking outside params in methods, local variables changing behaviours, indirection from inheritance/extension - the list goes on and other languages have their own. There's problems with it but as the author says, these things aren't all-or-nothing - so you can nudge the codebase in the FP/'pure' direction.
"Objects are a poor man's closure.... Closures are a poor man's object."
Maybe what I'm trying to say is that a lot of functional programming takes place within the OO model when it's efficient to do so; but functional paradigms just generally ignore state altogether, so absolute purity doesn't leave a route for daily coders writing regular programs to do their jobs.
This also translates well to OOP in the form of things like hexagonal architecture (eg: business logic is pure, and state is modified by a database adapter), or to modern frontend programming (eg: the render tree is pure, visual representation state is stored/modified with by React), including flux architecture (eg: reducers are pure, state is stored/modified by Redux).
I really like this presentation about it: https://www.destroyallsoftware.com/screencasts/catalog/funct...
Maybe this all comes down to what I find easier to follow in 200 code files and we're talking about roughly the same thing. If the general rule is to make logic as independent and no-need-to-retype-it as possible in the hierarchy, I'm onboard. And letting functions manage state themselves makes no sense; even in OO paradigms, functions should be pure and not rely on the state per se. (Put in plain terms, I make heavy use of getters and setters on instances; but complex functions are almost always static, and take instances of the class they're in to modify, rather than reproducing those functions and having them work on their own instance).
Well, but who really follows that idea? What usually happens is, that the state is stored in the object (of a class) and the result of subsequent method calls depends on previous calls and arguments, since it changed the state of the object. Maybe there are even calls to other objects in the system changing their state, and in turn changing return values of their methods.
Also: If we have actual functions (in a mathematical sense), then they depend only on their inputs / arguments. Why then have them in a class, and not in say for example, a module?
This speaks to the muscle memory of daily coders way more than to the details of FP/OO.
You could easily make the same argument about “absolute encapsulation”.
In data types!
> But so many functions are more comprehensible when they take place bound to the scope of what it is they're acting upon
To me, the most understandable function (lowest context needed, lowest cognitive overhead) is a black box that takes type x as input and produces type y as output, without side-effects
> I don't think that thinking in terms of objects or even global states should be considered a code smell
Classes can mix state and behaviour. I prefer when state is encoded in types and behaviour in functions, instead of having implicit state changes in a class, because it is more difficult to track, and constraint.
Of course, in the real world, if a library or platforms uses OOP, so be it. I am not dogmatic. But ideally, I avoid OOP because of this implicit mixing of state and behaviour.
Yes. With guardrails. I mean, I do a lot of writing in JS/TS but I'd never write something that tacked a dynamic property onto some class and picked it up later. That's just gauche.
> a black box that takes type x as input and produces type y as output
I agree, but I'd rather keep that function in Class X. If it's specific and X is a final/protected class, I'd say `protected doSomething()=>Y` where the argument is the implicit `this`.
I'd never allow some other thing to run this function, definitely not some other object floating out there; if it were going to be run that way it would have to be a `static DoSomething(x:X)=>Y` ...but I would still keep it in the file for X.
True, however we must be careful not to go completely bananas either; it's really hard for a reader to piece back together a full mental picture if the functionality is spread into dozens even hundreds of general-purpose 3-line functions.
Simple, `y = f(x)` types of functions are indeed the easiest to understand, but you might be no closer to understanding what the code is actually doing.
Of course the functions can be grouped in modules. It is the same problem as having god objects, having lots of methods or many classes in OOP (it is orthogonal to OOP or functional programming).
Because, everything you are saying is either forbidden nor discouraged in (pure) functional programming. In fact, it is totally orthogonal.
The point of functional programming is solely to prevent the same code to behave differently just because it is used/called in different parts of the program. You can still use objects, you can still make them have methods that operate on only a limited part of the state and so on. No problem with that, I do it all them time in fully pure functional programs. In fact, I think this is best practice.
https://github.com/ESWAT/john-carmack-plan-archive/tree/mast...
(Tasks may appear in multiple days, and there's no guarantee of tasks being retrospectively marked as complete.)
Why is everyone teaching Python?
Of course a lot of the data science/machine learning is being done in Python, but that is likely due much of the frameworks/tooling around is Python in general in that world.
I think that devs are naturally prone to be obsessive compulsive about such things.
We can see 'defeating the borrow checker' as a 1st order objective.
I think devs fee that 'defeating the borrow checker' gives us much more satisfactiong than 'fulfilling the customers needs'.
There's something a big 'chinse finger trap' about FP that consumes our attention on an impusleve level that is not necessarily consistent with what it actually means to be a good engineer.
This, along with our will to abstract and to want to use 'shiny new things' etc..
I think all CS curricula should have some sort of class that explores languages like Haskell and Rust, the problems they attempt to solve and how they go about solving them, if only for the intellectual experience. What language you end up using, and what ideas you end up applying, is then up to you on a case-by-case basis.
OOP is about abstract the mesaging.
When your object is just a pure function, you achive the ultimate abstraction.
If they are not checked, they will be forgotten. When reading a function, if I do not have the guarantee that it is pure, it means the same as an old comment (not much).
If the only way to enforce it is convention and force of will it will not be enforced.
As an exaggerated illustration: we might add a "without-bugs" annotation to a function, to indicate it has no bugs. After all, nothing stops you from writing bug-free functions :)
In fact, most const class functions are by definition not pure at all, because they will read internal state, otherwise they wouldn’t even need to be members of the class. So I would be willing to argue that nearly all const class functions are impure.
In the context of a pure function, a local member variable is no different than a global variable because it’s outside the scope of the actual function. If you can’t take the function away from the class and have it still work perfectly, then it’s not pure.
length() is not pure, but a function like length(string) could be.
I didn’t mean to sound patronizing, I apologize for that. Your question suggested that you aren’t that familiar with functional programming, so I was just trying to steer you in the right direction. A function that takes no parameters is not a pure function in general, unless all it does is return a constant value, which wouldn’t make much sense. length() does not make sense in a functional programming style.
and passing a sequence of characters? hello, C strlen( const char * s); ! with all the problems that has. and if you don't want to pass some horrible pointer, you have to pass some object containing the characters which must have a length function itself. hello, std::string!
what you are ignoring here is how functional types either ignore functionality under the hood, or sacrifice it for performance.
which is why functional programming will never catch on (look at lisps), imho. programmers really like to know wtf their code is actually doing on a mutable machine.
No, I mean, a standalone function with no parameters can only be functional if it always returns the same value, which is effectively no different than declaring a constant. The function always returns the same value all time (which wouldn't be so useful).
I think you are trying to find C++ examples to match the broader implications of functional programming (which is what I'm talking about), which will be difficult.
As far as whether "functional programming will catch on", I don't think it will for C++ so much, but it certainly has been very successful for many other languages. I earn my living writing only functional code, but most of the time it is not in C++.
parameter0.membfun()
It doesn't really matter whether the parameter name is separated from the function name by a dot, or a bracket. In a different syntax, it might have been written as "membfun(parameter0)" without any practical difference.
This means that Foo::length() can be pure no problem, because Foo is its parameter, and, as such, in its scope.
> Pure functions have a lot of nice properties. Thread safety. A pure function with value parameters is completely thread safe. With reference or pointer parameters, even if they are const, you do need to be aware of the danger that another thread doing non-pure operations might mutate or free the data, but it is still one of the most powerful tools for writing safe multithreaded code.
Emphasis on 'value parameters'. The invisible 'this' argument is not passed by value, it is passed by reference. By Carmack's definition, C++ member functions are not pure.
1. For the given set of inputs ( including the attached object ) the output is always the same if called again with the same inputs ( including the attached object in the same state ).
2. No side effects. This means that nothing can be observed about the function apart from it's return value.
If you wish to quibble as to whether the object is a parameter or not then observe that in c++23 it will be allowed an explicit this parameter to methods This is not to make the methods more pure but to enable other types of optimizations and reduce duplication of code over const and non const methods.
> A pure function only looks at the parameters passed in to it, and all it does is return one or more computed values based on the parameters... It doesn’t maintain internal state... Ideally, it isn’t passed any extraneous data
If you send in an entire object state as an argument (or as an attached instance, as you argue), this is not a quintessence of functional purity and strays far from these concepts. Consider: your object can have all sorts of data in it that you are passing to every member function, and it is unlikely all that state needs to be used in every function -- as the article points out, this defeats the purpose as it's akin to passing in a big pointer to a bunch of state.
In a pure function, it accepts only the specific data it needs, nothing else or extraneous, and returns a value based on these specific inputs.
As I mentioned in another comment, you could say for a particular type that since it only has one data member, no harm done -- but in the general sense, most objects have more than one piece of data, and not every function needs every piece of data, so passing all that in is a sloppy approach to purity.
1. Does it always return the same result for the same input parameters 2. Does it modify anything other than return a value
These two properties define purity. Simple and not odd at all.
What IS really liberating is not being stuck to a single paradigm and let person decide which particular tool use to solve particular problem
It's not pure if you can modify the state of a global, the file system, the environment, the standard output, etc., and this is half of the reason why purity is desired.
It's a little bit limited, as it's only enforced for compile time evaluations.
I would also be reluctant to mark any function pure that takes pointers as arguments (directly or indirectly).