Typeclasses in Swift, Haskell and Scala

What is a typeclass?

Typeclass is a Haskell way of creating the type composition in the world without inheritance. It allows to define a desired behavior in a form of function signatures. The concrete implementation is provided separately for all the required types. In other words, it splits the familiar object-oriented encapsulation (data and functionality together) in two separate parts: data and functionality. At the same time typeclass defines a contract that we can build upon. It’s like defining the same function multiple times – each time the only thing that differs is the type in the signature – and making it possible to use this function in some other place without specifying which one of its variants should be used. The compiler guesses it for us.

Doesn’t it sound like Swift or Objective-C protocols? Well, it does. It’s no surprise, because they’re all fueled by same basic idea. This is the first and arguably the most important thing to know about typeclasses – although they do have a word class in their name, they are more similar to protocols. Actually, they are far enough from classes so they can independently coexist with them, orthogonal to each other.

Typeclasses, being protocol cousins, are used in similar fashion: to express a feature that spreads across multiple types. In Haskell there are typeclasses like Eq, expressing that things are equatable, or Ord, expressing that they are sortable, or Functor, expressing that they can be mapped over.

If you’ve seen WWDC 2015 session “Protocol-Oriented Programming in Swift”, you’re gonna feel at home (or run away screaming, depending on how you liked it). One thing to notice: while in a more strict functional language, namely Haskell, typeclass is a part of its type semantics, in Swift or Scala typeclass is more of a design pattern. We’re using their native type semantics to achieve similar effects.

Enough with introduction. Let’s define a typeclass so we can more easily grasp what’s going on.

Things that can be encoded in Ceasar cipher

Have you heard of Caesar cipher? It is a very basic cryptography method: we express anything as a string and than we shift each letter by a fixed number of places in the alphabet. So, for 3-letter Ceasar cipher we write D instead of A, E instead of B, F instead of C and so on.

Our typeclass is gonna describe the ability to be expressed in a Ceasar cipher form. It’s gonna be based on position of particular character in the ASCII table. For the sake of simplicity I’ll ignore the fact that in the ASCII table there are some special characters after the last letters of the alphabet. No one is actually sending messages to Roman legions anymore, so no one is gonna get surprised by some %$#.

Here is the Ceasarable typeclass defined in Haskell:

class Ceasarable c where 
    toCeasar :: c -> String

Just to mess with object-oriented minds, it uses keyword class to kick off its definition. Then it declares one function signature: toCeasar. The function takes one argument of any type and returns a string, presumably with the cipher shift applied. This is our desired behavior. It must be implemented (with the actual type instead of c) by the typeclass instances.

How does it gonna look like in Scala? We’re gonna use Scala’s type semantics. The most obvious way is to use trait:

trait Ceasarable[T] {
    def toCeasar(input: T): String
}

The translation is straightforward. Any type in Haskell becomes a generic parameter in Scala. The signature is the same.

In Swift the closest thing to traits/interfaces are protocols, so let’s search no further:

protocol Ceasarable { 
    typealias T static func toCeasar(input: T) -> String 
}

Apart from minor syntax differences, like associated type instead of generic parameter, it’s the same as in Scala. Not surprising, as those two languages share a lot of similarities (and I mean like, a lot). One thing to notice is the use of static method. Why is it static? Because we want to emulate the split between the data and behavior. If the method is not static, than it can use the instance data and there is (in our simple case) no need to pass input at all. An instance method declaration would make the typeclass a little more object-oriented, and there’s nothing wrong with that, but for now let’s stick to the original idea. We’re providing the implementation for a type, not for an instance.

When I grow up I wanna be a typeclass!

Once we’ve defined what we expect, it’d be nice to provide some actual implementations for chosen types. This way we’d be able to use the behavior. Let’s choose two simple types to work with: strings and integers. In Haskell, the implementation is provided by defining the concrete typeclass instance:

{-# LANGUAGE TypeSynonymInstances, FlexibleInstances #-}
    
instance Ceasarable String where
    toCeasar x = [toEnum $ fromEnum c + 3 | c <- x]
    
instance Ceasarable Integer where
    toCeasar x = [toEnum $ fromEnum c + 3 | c <- show x]

Keyword instance means that the implementation is coming. For String, we map over characters, get their ASCII numbers with fromEnum, add three and than encode again with toEnum. For Integer we just express it as String using show and we do exactly the same.

In Scala things get a little weird, so feel free to skip over the details. The Ceasarable behavior is enclosed in the object and marked as implicit. This way it can be implicitly passed to the place we want to use it:

object Ceasarable {
    implicit object CeasarableInt extends Ceasarable[Int] {
        override def toCeasar(input: Int): String = {
            s"$input".map(_ + 3).map(_.toChar).mkString
        }
    }
   
    implicit object CeasarableString extends Ceasarable[String] {
        override def toCeasar(input: String): String = {
            input.map(_ + 3).map(_.toChar).mkString
        }
    }
}

The object scope and implicit passing are part of Scala peculiarities, no need to dive deeper in them. If you really want to, look here. What matters is that we’ve created separated objects with Ceasarable implementations for String and Int types and we enclosed them in static-like objects (object is as close as you can get to static in Scala). Those types know nothing about their ability to be expressed in Ceasar cipher.

Let’s try the same approach in Swift:

struct CeasarableInt : Ceasarable {
    typealias T = Int
    static func toCeasar(input: Int) -> String {
        return "(input)".unicodeScalars.reduce("") { 
            (acc, char) in
            return acc + String(UnicodeScalar(char.value + 3))
        }
    }
}

struct CeasarableString : Ceasarable {
    typealias T = String
    static func toCeasar(input: String) -> String {
        return input.unicodeScalars.reduce("") { 
            (acc, char) in
            return acc + String(UnicodeScalar(char.value + 3))
        }
    }
}

Looks valid. This way we’ve defined the ability to be encoded in Ceasar cipher for strings and integers in each language we consider.

Now we can work with Ceasarable objects just as with any other group of objects sharing common characteristics, i.e. type. We can declare that we expect it as function parameter, we can return it in a function result and so on. Let’s see the example usages. In Haskell:

encodeInCeasar :: (Ceasarable c) => c -> String
encodeInCeasar = toCeasar
    
encodeInCeasar 1234 -- "4567"
    
encodeInCeasar "ABCabc" -- "DEFdef"

We are using the typeclass just like we’d use the protocol – to define a contract without explicitly defining what object are gonna conform to this contract.

How about Scala?

def encodeInCeasar[T: Ceasarable](c: T) = {
    val encoded = implicitly[Ceasarable[T]].toCeasar(c)
    println(encoded)
}

encodeInCeasar(1234) // "4567"

    encodeInCeasar("ABCabc") // "DEFdef"

The sky, once again, gets a little bit cloudy. Instead of requiring the protocol confirmation, we’re explicitly asking for the proper implementation using implicitly. Implicitly needs to have the implementations passed inside the function, and the enclosing scope is passed via a mechanism called context bound. T: Ceasarable is a syntax for context bound. It might sound confusing, but it’s fine, actually. This way we can easily see that we’re using a typeclass. In Swift, however, we encounter a problem:

func encodeInCeasar<C : Ceasarable>(c: C.T) -> String {
    return C.toCeasar(c)
}

encodeInCeasar(c: 1234) // Compiler error: Cannot invoke 'encodeInCeasar' with an argument list of type '(Int)'

Swift compiler cannot infer the generic parameter. There is a struct that does exactly what we want: conforms to Ceasarable and defines Int as its associated type. However, it cannot be found automatically. Swift doesn’t have semantics for Scala-like context bound. However, we’ve got the second best thing… Wait! It’s actually the first best thing, only Swift is 2.0. Protocol extensions.

Swift typeclasses defined with protocol extensions

In Swift we can use the extension keyword to provide implementations for already existing types. The beauty of extension lays in its two properties: universality and ability to be constraint. By universality I mean that you can extend all the Swift types: protocols, classes, structs and enums. The ability to be constraint let us express what we want to extend in a great detail – greater than allowed by protocol confirmation or class inheritance alone.

Did I mention that if you’ve watched “Protocol-Oriented Programming in Swift” you’ll feel at home? Our better implementation of typeclasses starts with a slight change to the Ceasarable definition:

protocol Ceasarable {
        static func toCeasar(input: Self) -> String
}

Instead of requiring the associated type in protocol, we can add a Self requirement. This way we’re expressing that for whatever type we’re providing the typeclass implementation, it requires the value of that type as the parameter. It stays closer to the original Haskell definition, because the typeclass doesn’t need to be generic. It is just like a template for multiple function definitions that differ only by the type in signature. Self expresses exactly that. There is also another way of expressing the same idea: see this article on how to do it using Swift 1.2 (spoiler alert: <C: Ceasarable where C.T == C>), but for Swift 2.0 the most straightforward way is with Self. The actual implementations become easier to write and more readable:

extension Int : Ceasarable {
    static func toCeasar(input: Int) -> String {
        return "(input)".unicodeScalars.reduce("") { 
            (acc, char) in
            return acc + String(UnicodeScalar(char.value + 3))
        }
    }
}

extension String : Ceasarable {
    static func toCeasar(input: String) -> String {
        return input.unicodeScalars.reduce("") { 
            (acc, char) in
            return acc + String(UnicodeScalar(char.value + 3))
        }
    }
}

It looks like a straightforward protocol confirmation and it’s just what we need. Having that, the usage get simpler as well:

func encodeInCeasar<T : Ceasarable>(c: T) -> String {
    return T.toCeasar(c)
}

encodeInCeasar(1234) // "4567"

encodeInCeasar("ABCabc") // "DEFdef"

This is what we tried to achieve. At the same time we’re providing behavior separate from data (since it’s static method of T) and expressing the common functionality (since T must be Ceasarable). By using protocol extensions, we’ve enabled the second dimension, somewhat orthogonal to inheritance, in which we can compose our functionalities.

What are Swift typeclasses, then?

A typeclass in Swift is a pattern build using the protocols and extensions. It’s simple and there’s nothing new, really, as we’ve been already using those concepts extensively. As a side note, the process of learning functional programming is very often like that: concepts we used for a long time, but differently named, generalized and ready to build upon.

Typeclasses are a way of providing a behavior for the type separately from the type and at the same time defining a contract that the type conforms to. It might be used to add functionalities and build composition without inheritance.

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33rd Degree day 2 review

Second day of 33rd had no keynotes, and thus was even more intense. A good conference is a conference, where every hour you have a hard dilemma, because there are just too many interesting presentations to see. 33rd was definitely such a conference, and the seconds day really shined.

There were two workshops going on through the day, one about JEE6 and another about parallel programming in Java. I was considering both, but decided to go for presentations instead. Being on the Spring side of the force, I know just as much JEE as I need, and with fantastic GPars (which has Fork/Join, actors, STM , and much more), I won't need to go back to Java concurrency for a while.

GEB - Very Groovy browser automation

Luke Daley works for Gradleware, and apart from being cheerful Australian, he's a commiter to Grails, Spock and a guy behind Geb, a  browser automation lib using WebDriver, similar to Selenium a bit (though without IDE and other features).

I have to admit, there was a time where I really hated Selenium. It just felt so wrong to be writing tests that way, slow, unproductive and against the beauty of TDD. For years I've been treating frontend as a completely different animal. Uncle Bob once said at a Ruby conference: "I'll tell you what my solution to frontend tests is: I just don't". But then, you can only go so far with complex GUIs without tests, and once I've started working with Wicket and its test framework, my perspective changed. If Wicked has one thing done right, it's the frontend testing framework. Sure tests are slow, on par with integration tests, but it is way better than anything where the browser has to start up front, and I could finally do TDD with it.

Working with Grails lately, I was more than eager to learn a proper way to do these kind of tests with Groovy.

GEB looks great. You build your own API for every page you have, using CSS selectors, very similar to jQuery, and then write your tests using your own DSL. Sounds a bit complicated, but assuming you are not doing simple HTML pages, this is probably the way to go fast. I'd have to verify that on a project though, since with frontend, too many things look good on paper and than fall out in code.

The presentation was great, Luke managed to answer all the questions and get people interested. On a side note, WebDriver may become a W3C standard soon, which would really easy browser manipulation for us. Apart from thing I expected Geb to have, there are some nice surprises like working with remote browsers (e.g. IE on remote machine), dumping HTML at the end of the test and even making screenshots (assuming you are not working with headless browser).

Micro services - Java, the Unix Way

James Lewis works for ThoughtWorks and gave a presentation, for which alone it was worth to go to Krakow. No, seriously, that was a gem I really didn't see coming. Let me explain what it was about and then why it was such a mind-opener.
ThoughtWorks had a client, a big investment bank, lots of cash, lots of requirements. They spent five weeks getting the analysis done on the highest possible level, without getting into details yet (JEDI: just enough design initially). The numbers were clear: it was enormous, it will take them forever to finish, and what's worse, requirements were contradictory. The system had to have all three guarantees of the CAP theorem, a thing which is PROVED to be impossible.
So how do you deal with such a request? Being ThoughtWorks you probably never say "we can't", and having an investment bank for a client, you already smell the mountains of freshly printed money. This isn't something you don't want to try, it's just scary and challenging as much as it gets.
And then, looking at the requirements and drawing initial architecture, they've reflected, that there is a way to see the light in this darkness, and not to end up with one, monstrous application, which would be hard to finish and impossible to maintain. They have analyzed flows of data, and came up with an idea.
What if we create several applications, each so small, that you can literally "fit it in your head", each communicating with a simple web protocol (Atom), each doing one thing and one thing only, each with it's own simple embedded web server, each working on it's own port, and finding out other services through some location mechanism. What if we don't treat the web as an external environment for our application, but instead build the system as if it was inside the web, with the advantages of all the web solutions, like proxies, caches, just adding a small queue before each service, to be able to turn it off and on, without loosing anything. And we could even use a different technology, with different pair of CAP guarantees, for each of those services/applications.
Now let me tell you why it's so important for me.
If you read this blog, you may have noticed the subtitle "fighting chaos in the Dark Age of Technology". It's there, because for my whole IT life I've been pursuing one goal: to be able to build things, that would be easy to maintain. Programming is a pure pleasure, and as long as you stay near the "hello world" kind of complexity, you have nothing but fun. If we ever feel burned out, demotivated or puzzled, it's when our systems grow so much, that we can no longer understand what's going on. We lose control. And from that point, it's usually just a way downward, towards complete chaos and pain.
All the architecture, all the ideas, practices and patterns, are there for just this reason - to move the border of complexity further, to make the size of "possible to fit in your head" larger. To postpone going into chaos. To bring order and understanding into our systems.
And that really works. With TDD, DDD, CQRS I can build things which are larger in terms of features, and simpler in terms of complexity. After discovering and understanding the methods (XP, Scrum/Kanbad) my next mental shift came with Domain Driven Design. I've learned the building block, the ideas and the main concept of Bounded Contexts. And that you can and should use a different architecture/tools for each of them, simplifying the code with the usage patterns of that specific context in your ming.
That has changed a lot in my life. No longer I have to choose one database, one language and one architecture for the whole application. I can divide and conquer, choose what I want to sacrifice and what advantages I want here, in this specific place of my app, not worrying about other places where it won't fit.
But there is one problem in here: the limit of technologies I'm using, to keep the system simple, and not require omnipotence to be able to maintain, to fix bugs or implement Change Requests.
And here is the accidental solution, ThoughtWorks' micro services bring: if you system is build of the web, of small services that do one thing only, and communicate through simple protocol (like Atom), there is little code to understand, and in case of bugs or Change Requests, you can just tear down one of the services. and build it anew.
James called that "Small enough to throw them away. Rewrite over maintain". Now, isn't that a brilliant idea? Say you have a system like that, build over seven years ago, and you've got a big bag of new requests from your client. Instead of re-learning old technologies, or paying extra effort to try to bring them up-to-date (which is often simply impossible), you decide which services you are going to rewrite using the best tools of your times, and you do it, never having to dig into the original code, except for specification tests.
Too good to be true? Well, there are caveats. First, you need DevOps in your teams, to get the benefits of the web inside your system, and to build in the we as opposite to against it. Second, integration can be tricky. Third, there is not enough of experience with this architecture, to make it safe. Unless... unless you realize, that UNIX was build this way, with small tools and pipes.
That, perhaps. is the best recommendation possible.

Concurrency without Pain in Pure Java

Throughout the whole conference, Grzegorz Duda had a publicly accessible wall, with sticky notes and two sides: what's bad and what's good. One of the note on the "bad" side was saying: "Sławek Sobótka and Paweł Lipiński at the same time? WTF?". 
I had the same thought. I wanted to see both. I was luckier though, since I'm pretty sure I'll yet be able too see their presentations this year, as 33rd is the first conference in a long run of conferences planned for 2012. Not being able to decide which one to see, I've decided to go for Venkat Subramaniam and his talk about concurrency. Unless we are lucky at 4Developers, we probably won't see Venkat again this year.
Unfortunately for me, the talk ("show" seems like a more proper word), was very basic, and while very entertaining, not deep enough for me. Venkat used Closure STM to show how bad concurrency is in pure Java, and how easy it is with STM. What can I say, it's been repeated so often, it's kind of obvious by now.
Venkat didn't have enough time to show the Actor model in Java. That's sad, as the further his talk, the more interesting it was. Perhaps there should be a few 90min sessions next year?

Smarter Testing with Spock

After the lunch, I had a chance to go for Sławek Sobótka again, but this time I've decided to listen to one of the commiters of Spock, the best thing in testing world since Mockito. 
Not really convinced? Gradle is using Spock (not surprisingly), Spring is starting to use Spock. I've had some experience with Spock, and it was fabulous. We even had a Spock workshop at TouK, lately. I wanted to see what Luke Daley can teach me in an hour. 
That was a time well spent. Apart from things I knew already, Luke explained how to share state between tests (@Shared), how to verify exceptions (thrown()), keep old values of variables (old()), how to parametrize description with @Unroll and #parameterName, how to set up data from db or whatever with <<, and a bit more advanced trick with mocking mechanism. Stubbing with closures was especially interesting.

What's new in Groovy 2.0?

Guillaume Laforge is the project lead of Groovy and his presentation was the opposite to what we could see earlier about next versions of Java. Most visible changes were already done in 1.8, with all the AST transformations, and Guillaume spent some time re-introducing them, but then he moved to 2.0, and here apart from multicatch in "throw", the major thing is static compilation and type checking.
We are in the days, were the performance difference between Java and Groovy falls to a mere 20%.  That's really little compared to where it all started from (orders of magnitude). That's cool. Also, after reading some posts and successful stories about Groovy++ use, I'd really like to try static compilation with this language
Someone from the audience asked a good question. Why not use Groovy++ as the base for static compilation instead. It turned out that Groovy++ author was also there. The main reason Guillaume gave, were small differences in how they want to handle internal things. If static compilation works fine with 2.0, Groovy++ may soon die, I guess.

Scala for the Intrigued


For the last talk this day, I've chosen a bit of Scala, by Venkat Subramaniam. That was unfortunately a completely basic introduction, and after spending 15 minutes listening about differences between var and val, I've left to get prepared to the BOF session, which I had with Maciek Próchniak.

BOF: Beautiful failures


I'm not in the position to review my own talk, and conclude whether it's failure was beautiful or not, but there is one things I've learned from it.
Never, under none circumstances, never drink five coffees the day you give a talk. To keep my mind active without being overwhelmed by all the interesting knowledge, I drank those five coffees, and to my surprise, when the talk started, the adrenaline shot brought me over the level, where you loose your breath, your pulse, and you start to loose control over your own voice. Not a really nice experience. I've had the effects of caffeine intoxication for the next two days. Lesson learned, I'm staying away from black beans for some time.
If you want the slides, you can find them here.
And that was the end of the day. We went to the party, to the afterparty, we got drunk, we got the soft-reset of our caches, and there came another day of the conference.

You can find my review from the last day in here.