I've seen some APIs that have a def foo and then a slightly different def foo1, but I can't figure out what this means other than "foo1 is kind of like foo but slightly different." It reminds me of "Ex" ( What does "Ex" stand for in Windows API function names? )
I'm guessing this is a reference to a Haskell/FP or mathematical convention, is it just foo-prime?
Is there any meaning implied (does foo1 have to relate to foo in some specific way) or is it more "I needed two similar functions and overloading was ambiguous, screw it, let's put a 1 on the end"? Should I assume anything beyond "these two functions are somehow related"?
The example I can think of is rep and rep1 in scala.util.parsing.combinator.Parsers, where rep is any number of repeats and rep1 is for 1 or more. There's also a repN method for N repeats, so here the meaning of the 1 suffix is pretty obvious.
I think that context is probably everything here. The only example I can think of this is the foldl1 method in scalaz. As the library authors would no doubt point out, what the method actually does it completely transparent from looking at the types themselves:
trait MA[M[_], A] {
def foobar(f: (A, A) => A)(implicit FoldableM: Foldable[M]): Option[A]
}
However, in this case, it's clearly a special form of foldl, that is, fold left but use the 1st element as a seed rather than an explicitly provided value. Hence foldl1 is sensible and intuitive in context.
You say you have seen this a few times: can you point out other occurrences?
Scala's contains types like Function and Tuple, which have a family of types, each member being named for the number of arguments it takes.
Function1[T1] => R
Function2[T1, T2] => R
Tuple1[T1]
Tuple2[T1, T2]
In both cases the name of the type must be distinct and using the number of arguments is a great way to describe the type (Function4 is the type of a function that takes 4 arguments, Tuple3 is the type of a Tuple containing 3 items).
As the other answers have said, it helps to have some context but it could be that your examples are inspired (rightly or wrongly) by these choices in the standard library.
Related
I'm a beginner of Scala who is struggling with Scala syntax.
I got the line of code from https://www.tutorialspoint.com/scala/higher_order_functions.htm.
I know (x: A) is an argument of layout function
( which means argument x of Type A)
But what is [A] between layout and (x: A)?
I've been googling scala function syntax, couldn't find it.
def layout[A](x: A) = "[" + x.toString() + "]"
It's a type parameter, meaning that the method is parameterised (some also say "generic"). Without it, compiler would think that x: A denotes a variable of some concrete type A, and when it wouldn't find any such type it would report a compile error.
This is a fairly common thing in statically typed languages; for example, Java has the same thing, only syntax is <A>.
Parameterized methods have rules where the types can occur which involve concepts of covariance and contravariance, denoted as [+A] and [-A]. Variance is definitely not in the scope of this question and is probably too much for you too handle right now, but it's an important concept so I figured I'd just mention it, at least to let you know what those plus and minus signs mean when you see them (and you will).
Also, type parameters can be upper or lower bounded, denoted as [A <: SomeType] and [A >: SomeType]. This means that generic parameter needs to be a subtype/supertype of another type, in this case a made-up type SomeType.
There are even more constructs that contribute extra information about the type (e.g. context bounds, denoted as [A : Foo], used for typeclass mechanism), but you'll learn about those later.
This means that the method is using a generic type as its parameter. Every type you pass that has the definition for .toString could be passed through layout.
For example, you could pass both int and string arguments to layout, since you could call .toString on both of them.
val i = 1
val s = "hi"
layout(i) // would give "[1]"
layout(s) // would give "[hi]"
Without the gereric parameter, for this example you would have to write two definitions for layout: one that accepts integers as param, and one that accepts string as param. Even worse: every time you need another type you'd have to write another definition that accepts it.
Take a look at this example here and you'll understand it better.
I also recomend you to take a look at generic classes here.
A is a type parameter. Rather than being a data type itself (Ex. case class A), it is generic to allow any data type to be accepted by the function. So both of these will work:
layout(123f) [Float datatype] will output: "[123]"
layout("hello world") [String datatype] will output: "[hello world]"
Hence, whichever datatype is passed, the function will allow. These type parameters can also specify rules. These are called contravariance and covariance. Read more about them here!
I am nearly completely new to Scala, a few months on. I noticed some wild signatures. I have worked through generics with contrapositive/copositive/extensions/invariance, and most of the basics. However, I continue to find some of the method signatures a bit confusing. While I find examples and know what the signatures produce, I am still a bit at a loss as to some of the functionality. Googling my questions has left me with no answers. I do have the general idea that people like to beat the basic CS 1 stuff to death. I have even tried to find answers on the scala website. Perhaps I am phrasing things like "expanded method signature" and "defining function use in scala signature" wrong. Can anyone explain this signature?
futureUsing[I <: Closeable, R](resource: I)(f: I => Future[R])(implicit ec: ExecutionContext):Future[R]
My guess is that after the initial generics and parameter declaration with a parameter of type I, the body is defined and the final portion is any objects specific to the function or that must be looked up in an implicit scope (are they destroyed afterwards?). Can anyone layout an expanded method signature so I know what code I am using? Is there a particular order the last two parts must be in?
Note
After a bunch more searching, I found a few valid responses I can throw together:
-Scala - Currying and default arguments
-why in Scala a function type needs to be passed in separate group of arguments into a function
There is no set ordering just that implicits must be last. Placement is about dependency which flows left to right as someone down the list in one of the above answers pointed out. Why I cannot have implicits first and everything depending on them afterwards is odd since having nothing available causes an error and things will likely depend on a given implicit.
However, I am still a bit confused. When specifying f: I => Future[R], and needing to supply the last argument, lets pretend it would be any implicit, would I need to do something more like:
futureUsing(resourceOfI)({stuff => doStuff(stuff)})(myImplicit)
Is this even correct?
Could I do:
futureUsing(resourceOfI)(myImplicit)({stuff => doStuff(stuff)})
Why? I am really trying to get at the underlying reasons rather than just a binary yes or no.
Final Note
I just found this answer. It appears the order cannot be changed. Please correct me if I am wrong.
Scala: Preference among overloaded methods with implicits, currying and defaults
Can anyone explain this signature?
futureUsing[I <: Closeable, R]
futureUsing works with two separate types (two type parameters). We don't know exactly what types they are, but we'll call one I (input), which is a (or derived from) Closable, and the other R (result).
(resourse: I)
The 1st curried argument to futureUsing is of type I. We'll call it resourse.
(f: I => Future[R])
The 2nd curried argument, f, is a function that takes an argument of type I and returns a Future that will (eventually) contain something of type R.
(implicit ec: ExecutionContext)
The 3rd curried argument, ec, is of type ExecutionContext. This argument is implicit, meaning if it isn't supplied when futureUsing is invoked, the compiler will look for an ExecutionContext in scope that has been declared implicit and it will pull that in as the 3rd argument.
:Future[R]
futureUsing returns a Future that contains the result of type R.
Is there a specific ordering to this?
Implicit parameters are required to be the last (right most) parameters. Other than that, no, resourse and f could have been declared in either order. When invoked, of course, the order of arguments must match the order as declared in the definition.
Do I need ... implicits to drag in?
In the case of ExecutionContext let the compiler use what's available from import scala.concurrent.ExecutionContext. Only on rare occasions would you need something different.
...how would Scala use the 2nd curried argument...
In the body of futureUsing I would expect to see f(resourse). f takes an argument of type I. resourse is of type I. f returns Future[R] and so does futureUsing so the line f(resourse) might be the last statement in the body of futureUsing.
In the book Functional Programming in Scala MEAP v10, the author mentions
Polymorphic functions are often so constrained by their type that they only have one implementation!
and gives the example
def partial1[A,B,C](a: A, f: (A,B) => C): B => C = (b: B) => f(a, b)
What does he mean by this statement? Are polymorphic functions restrictive?
Here's a simpler example:
def mysteryMethod[A, B](somePair: (A, B)): B = ???
What does this method do? It turns out, that there is only one thing this method can do! You don't need the name of the method, you don't need the implementation of the method, you don't need any documentation. The type tells you everything it could possibly do, and it turns out that "everything" in this case is exactly one thing.
So, what does it do? It takes a pair (A, B) and returns some value of type B. What value does it return? Can it construct a value of type B? No, it can't, because it doesn't know what B is! Can it return a random value of type B? No, because randomness is a side-effect and thus would have to appear in the type signature. Can it go out in the universe and fetch some B? No, because that would be a side-effect and would have to appear in the type signature!
In fact, the only thing it can do is return the value of type B that was passed into it, the second element of the pair. So, this mysteryMethod is really the second method, and its only sensible implementation is:
def second[A, B](somePair: (A, B)): B = somePair._2
Note that in reality, since Scala is neither pure nor total, there are in fact a couple of other things the method could do: throw an exception (i.e. return abnormally), go into an infinite loop (i.e. not return at all), use reflection to figure out the actual type of B and reflectively invoke the constructor to fabricate a new value, etc.
However, assuming purity (the return value may only depend on the arguments), totality (the method must return a value normally) and parametricity (it really doesn't know anything about A and B), then there is in fact an awful lot you can tell about a method by only looking at its type.
Here's another example:
def mysteryMethod(someBoolean: Boolean): Boolean = ???
What could this do? It could always return false and ignore its argument. But then it would be overly constrained: if it always ignores its argument, then it doesn't care that it is a Boolean and its type would rather be
def alwaysFalse[A](something: A): Boolean = false // same for true, obviously
It could always just return its argument, but again, then it wouldn't actually care about booleans, and its type would rather be
def identity[A](something: A): A = something
So, really, the only thing it can do is return a different boolean than the one that was passed in, and since there are only two booleans, we know that our mysteryMethod is, in fact, not:
def not(someBoolean: Boolean): Boolean = if (someBoolean) false else true
So, here, we have an example, where the types don't give us the implementation, but at least, they give as a (small) set of 4 possible implementations, only one of which makes sense.
(By the way: it turns out that there is only one possible implementation of a method which takes an A and returns an A, and it is the identity method shown above.)
So, to recap:
purity means that you can only use the building blocks that were handed to you (the arguments)
a strong, strict, static type system means that you can only use those building blocks in such a way that their types line up
totality means that you can't do stupid things (like infinite loops or throwing exceptions)
parametricity means that you cannot make any assumptions at all about your type variables
Think about your arguments as parts of a machine and your types as connectors on those machine parts. There will only be a limited number of ways that you can connect those machine parts together in a way that you only plug together compatible connectors and you don't have any leftover parts. Often enough, there will be only one way, or if there are multiple ways, then often one will be obviously the right one.
What this means is that, once you have designed the types of your objects and methods, you won't even have to think about how to implement those methods, because the types will already dictate the only possible way to implement them! Considering how many questions on StackOverflow are basically "how do I implement this?", can you imagine how freeing it must be not having to think about that at all, because the types already dictate the one (or one of a few) possible implementation?
Now, look at the signature of the method in your question and try playing around with different ways to combine a and f in such a way that the types line up and you use both a and f and you will indeed see that there is only one way to do that. (As Chris and Paul have shown.)
def partial1[A,B,C](a: A, f: (A,B) => C): B => C = (b: B) => f(a, b)
Here, partial1 takes as parameters value of type A, and a function that takes a parameter of type A and a parameter of type B, returning a value of type C.
partial1 must return a function taking a value of type B and returning a C. Given A, B, and C are arbitary, we cannot apply any functions to their values. So the only possibility is to apply the function f to the value a passed to partial, and the value of type B that is a parameter to the function we return.
So you end up with the single possibility that's in the definition f(a,b)
To take a simpler example, consider the type Option[A] => Boolean. There's only a couple ways to implement this:
def foo1(x: Option[A]): Boolean = x match { case Some(_) => true
case None => false }
def foo2(x: Option[A]): Boolean = !foo1(x)
def foo3(x: Option[A]): Boolean = true
def foo4(x: Option[A]): Boolean = false
The first two choices are pretty much the same, and the last two are trivial, so essentially there's only one useful thing this function could do, which is tell you whether the Option is Some or None.
The space of possible implementation is "restricted" by the abstractness of the function type. Since A is unconstrained, the option's value could be anything, so the function can't depend on that value in any way because you know nothing about what it. The only "understanding" the function may have about its parameter is the structure of Option[_].
Now, back to your example. You have no idea what C is, so there's no way you can construct one yourself. Therefore the function you create is going to have to call f to get a C. And in order to call f, you need to provide an arguments of types A and B. Again, since there's no way to create an A or a B yourself, the only thing you can do is use the arguments that are given to you. So there's no other possible function you could write.
I was exchanging emails with an acquaintance that is a big Kotlin, Clojure and Java8 fan and asked him why not Scala. He provided many reasons (Scala is too academic, too many features, not the first time I hear this and I think this is very subjective)
but his biggest pain point was as an example, that he doesn't like a language where he can't understand the implementation of basic data structures, and he gave LinkedList as an example.
I took a look at scala.collection.LinkedList and counted the things I either understand or somewhat understand.
CanBuildFrom - after some effort, I get it, type classes, not the longest suicide note
in history [1]
LinkedListLike - I can't remember where I read it, but I got convinced this is there for a good reason
But then I started to stare at these
GenericTraversableTemplate - now I'm scratching my head as well...
SeqFactory, GenericCompanion - OK, now you lost me, I start to understand his point
Can someone who understand this well please explain GenericTraversableTemplate SeqFactory and GenericCompanion in the context of LinkedList? What they are for, what impact on the end user they have (e.g. I'm sure they are there for a good reason, what is that reason?)
Are they there for a practical reason? or is it a level of abstraction that could have been simplified?
I like Scala collections because I don't have to understand the internals to be able to effectively use them. I don't mind a complex implementation if it helps me to keep my usage simpler. e.g. I don't mind paying the price of a complex library if I get the ability to write cleaner more elegant code using it in return. but it will sure be nice to better understand it.
[1] - Is the Scala 2.8 collections library a case of "the longest suicide note in history"?
I will try to describe the concepts from the point of view of a random pedestrian (I've never contributed a single line to the Scala collection library, so don't hit me too hard if I'm wrong).
Since LinkedList is now deprecated, and because Maps provide a better example, I will use TreeMap as example.
CanBuildFrom
The motivation is this: If we take a TreeMap[Int, Int] and map it with
case (x, y) => (2 * x, y * y * 0.3d)
we get TreeMap[Int, Double]. This type safety alone would already explain the necessity for
simple genericBuilder[X] constructs.
However, if we map it with
case (x, y) => x
we obtain an Iterable[Int] (more precisely: a List[Int]), this is no longer a Map, the type of the container has changed. This is where CBF's come into play:
CanBuildFrom[This, X, That]
can be seen as a kind of "type-level function" that tells us: if we map a collection of type
This with a function that returns values of type X, we can build a That. The most specific
CBF is provided at compile time, in the first case it will be something like
CanBuildFrom[TreeMap[_,_], (X,Y), TreeMap[X,Y]]
in the second case it will be something like
CanBuildFrom[TreeMap[_,_], X, Iterable[X]]
and so we always get the right type of the container. The pattern is pretty general.
Every time you have a generic function
foo[X1, ..., Xn](x1: X1, ..., xn: Xn): Y
where the result type Y depends on X1, ..., Xn, you can introduce an implicit parameter as
follows:
foo[X1, ...., Xn, Y](x1: X1, ..., xn: Xn)(implicit CanFooFrom[X1, ..., Xn, Y]): Y
and then define the type-level function X1, ..., Xn -> Y piecewise by providing multiple
implicit CanFooFrom's.
LinkedListLike
In the class definition, we see something like this:
TreeMap[A, B] extends SortedMap[A, B] with SortedMapLike[A, B, TreeMap[A, B]]
This is Scala's way to express the so-called F-bounded polymorphism.
The motivation is as follows: Suppose we have a dozen (or at least two...) implementations of the trait SortedMap[A, B]. Now we want to implement a method withoutHead, it could look
somewhat like this:
def withoutHead = this.remove(this.head)
If we move the implementation into SortedMap[A, B] itself, the best we can do is this:
def withoutHead: SortedMap[A, B] = this.remove(this.head)
But is this the most specific result type we can get? No, that's too vague.
We would like to return TreeMap[A, B] if the original map is a TreeMap, and
CrazySortedLinkedHashMap (or whatever...) if the original was a CrazySortedLinkedHashMap.
This is why we move the implementation into SortedMapLike, and give the following signature to the withoutHead method:
trait SortedMapLike[A, B, Repr <: SortedMap[A, B]] {
...
def withoutHead: Repr = this.remove(this.head)
}
now because TreeMap[A, B] extends SortedMapLike[A, B, TreeMap[A, B]], the result type of
withoutHead is TreeMap[A,B]. The same holds for CrazySortedLinkedHashMap: we get the exact type back. In Java, you would either have to return SortedMap[A, B] or override the method in each subclass (which turned out to be a maintenance nightmare for the feature-rich traits in Scala)
GenericTraversableTemplate
The type is: GenericTraversableTemplate[+A, +CC[X] <: GenTraversable[X]]
As far as i can tell, this is just a trait that provides implementations of
methods that somehow return regular collections with same container type but
possibly different content type (stuff like flatten, transpose, unzip).
Stuff like foldLeft, reduce, exists are not here because these methods care only about content type, not container type.
Stuff like flatMap is not here, because the container type can change (again, CBF's).
Why is it a separate trait, is there a fundamental reason why it exists?
I don't think so... It probably would be possible to group the godzillion of methods somewhat differently. But this is just what happens naturally: you start to implement a trait, and it turns out that it has very many methods. So instead you group loosely related methods, and put them into 10 different traits with awkward names like "GenTraversableTemplate", and them mix them all into traits/classes where you need them...
GenericCompanion
This is just an abstract class that implements some basic functionality which is common
for companion objects of most collection classes (essentially, it just implements very
simple factory methods apply(varargs) and empty).
For example there is method apply that takes varargs of some type A and returns a collection of type CC[A]:
Array(1, 2, 3, 4) // calls Array.apply[A](elems: A*) on the companion object
List(1, 2, 3, 4) // same for List
The implementation is very simple, it's something like this:
def apply[A](varargs: A*): CC[A] = {
val builder = newBuilder[A]
for (arg <- varargs) builder += arg
builder.result()
}
This is obviously the same for Arrays and Lists and TreeMaps and almost everything else, except 'constrained irregular Collections' like Bitset. So this is just common functionality in a common ancestor class of most companion objects. Nothing special about that.
SeqFactory
Similar to GenericCompanion, but this time more specifically for Sequences.
Adds some common factory methods like fill() and iterate() and tabulate() etc.
Again, nothing particularly rocket-scientific here...
Few general remarks
In general: I don't think that one should attempt to understand every single trait in this library. Rather, one should try to look at the library as a whole. As a whole, it has a very interesting architecture. And in my personal opinion, it's actually a very aesthetic piece of software, but one has to stare at it for quite a while (and try to re-implement the whole architectural pattern several times) to grasp it. On the other hand: for example CBF's are kind of "design pattern" that clearly should be eliminated in successors of this language. The whole story with the scope of implicit CBF's still seems like a total nightmare to me. But many things seemed completely inscrutable at first, and almost always, it ended with an epiphany (which is very specific for Scala: for the majority of other languages, such struggles usually end with the thought "Author of this is a complete idiot").
A function of mine is to take from zero to five integer arguments and from zero to five string arguments. So I consider it to define as a function of 2 lists: f(numbers: List[Int], strings: List[String]). But I think it is good to constraint the lengths if it is possible, for an IDE and/or a compiler can enforce it. Is this possible?
I think you're really asking a lot of the type system for this one... This is a classic task for dependently typed programming, in which category Scala unfortunately does not belong.
You could look at Mark Harrah's type-level Naturals:
type _0 = Nat0
type _1 = Succ[_0]
type _2 = Succ[_1]
// ...
But if you go down this route, you'll have to build all your lists in such a way that the length-type is evident to the compiler. That means no recursion, no unbounded looping, etc. Also you'll have to come up with a way to encode "<" in the type system... since you can't do recursion I'm not sure how you'd do that. So, probably not worth it.
Maybe another way to approach the problem is to figure out where '0..5' comes from and constrain some other type based on that information?
As a last resort you could define special cases for the allowable sizes, separated so that you don't have 25 cases:
case class Small[+X](l: List[X])
def small(): Small[Nothing] = Small(List())
def small[A](a: A): Small[A] = Small(List(a))
def small[A](a1: A, a2: A): Small[A] = Small(List(a1,a2))