I want to define a function that could compare two class types. I have defined two different classes:
abstract class Task
case class DefinedTask(id: Long) extends Task
case class EmptyTask() extends Task
Then I have a function that returns an object of type Task, that could be either DefinedTask or EmptyTask.
Now, what I'd like to do is to have a function that identifies if this object is a DefinedTask or just an EmptyTask. The problem would be simple, I'd use pattern matching. But I want to generalize it because multiple classes go with this Defined/Empty pattern.
What I'd tried so far is:
def makeReturned[T: Manifest, U: Manifest](obj: T)(u: U)(f: T => Value) =
if(manifest[U] equals manifest[T]) f(obj) else
throw new IllegalArgumentException()
}
//call to it
makeReturned(task)(DefinedTask)(makeTask)
U is always DefinedTask, T could be either DefinedTask or EmptyTask, but it is returned as Task.
manifest[U].erasure.toString //"class DefinedTask$"
manifest[T].erasure.toString //"class Task"
Which is right from the compiler's stand point but it's not for me. So, my question is how could I compare them in a way that would give me what I want?
It looks like you want run-time, not compile-time checking. So I think you mean
def makeReturned[U <: Task, T <: Task](obj: T)(u: U)(f: T => Value) = {
if (obj.getClass.isInstance(u.getClass)) f(obj)
else throw new IllegalArgumentException
}
or something like that. Look at the methods on java.lang.Class and pick the one that does what you want.
There are some mistakes in your code:
You should make the abstract class Task sealed for exhaustive pattern matching
Empty case classes are deprecated. The compiler should have warned you about it. case object EmptyTask extends Task would be the correct alternative
Both case classes and case objects extend the trait Product. You can check the product on being empty with either of the following ways:
task.productArity == 0
task.productIterator.isEmpty
I think you'd be much better off reapproaching your problem. Why not just use the standard Option and have the instances of a simple case class Task(id: Int) wrapped in it? This could be the general approach for all the other similar entities of yours.
Related
Why does this produce an error?
import scala.io.StdIn
enum Flow[+A] {
case Say (text: String) extends Flow[Unit]
case Ask extends Flow[String]
}
def eval [T](flow: Flow[T]): T = flow match {
case Flow.Say(text) => println(text)
case Flow.Ask => StdIn.readLine // error here
}
18 | case Flow.Ask => StdIn.readLine
| ^^^^^^^^^^^^^^
| Found: String
| Required: T
(no error if the enum is left invariant on T)
But when the enum is replaced with a (seemingly equivalent?) ADT implemented with a sealed trait, no such error is reported:
sealed trait Flow[+A]
object Flow {
case class Say(text: String) extends Flow[Unit]
case object Ask extends Flow[String]
}
// match statement in eval works fine
We can look at the initial proposal which added enums to Scala 3 for some insight.
By the wording of this proposal, enum cases fall into three categories.
Class cases are those cases that are parameterized, either with a type parameter section [...] or with one or more (possibly empty) parameter sections (...).
Simple cases are cases of a non-generic enum class that have neither parameters nor an extends clause or body. That is, they consist of a name only.
Value cases are all cases that do not have a parameter section but that do have a (possibly generated) extends clause and/or a body.
Let's take a look at your enum declaration.
enum Flow[+A] {
case Say (text: String) extends Flow[Unit]
case Ask extends Flow[String]
}
Now, simple cases are right out, because your enum is generic. Your Say is pretty clearly a class case, since it's parameterized. Ask, on the other hand, is non-generic and non-enumerated, so it's a value case. If we scroll down a bit, we see what value cases do
(6) A value case
case C extends <parents> <body>
expands to a value definition
val C = new <parents> { <body>; def enumTag = n; $values.register(this) }
where n is the ordinal number of the case in the companion object, starting from 0. The statement $values.register(this) registers the value as one of the enumValues of the enumeration (see below). $values is a compiler-defined private value in the companion object.
The bottom line here is that Ask is not defined as
object Ask extends Flow[String]
but more like
val Ask = new Flow[String]() { ... }
So your pattern match
case Flow.Ask => StdIn.readLine
is actually an equality check against the value Flow.Ask, rather than a type check against a singleton object. The former, unfortunately, proves nothing to the compiler about the generic type of your value, so it remains T, as opposed to specializing to String.
The rationale for this is provided on the same page
Objectives
...
Enumerations should be efficient, even if they define many values. In particular, we should avoid defining a new class for every value.
...
So it seems the Scala developers wanted to avoid the bloat that would result from an enum declaring tons of unparameterized values (i.e. a type that looks like an ordinary Java-style enum).
The simple solution, then, is to provide a parameter list
case Ask() extends Flow[String]
It's ever so slightly more cumbersome to work with, but it does define a new type, and then your pattern match
case Flow.Ask() => StdIn.readLine
will succeed
Is there a better explanation than "this is how it works". I mean I tried this one:
class TestShortMatch[T <: AnyRef] {
def foo(t: T): Unit = {
val f = (_: Any) match {
case Val(t) => println(t)
case Sup(l) => println(l)
}
}
class Val(t: T)
class Sup(l: Number)
}
and compiler complaints:
Cannot resolve symbol 'Val'
Cannot resolve symbol 'Sup'
Of course if I add case before each of the classes it will work fine. But what is the reason? Does compiler make some optimization and generate a specific byte-code?
The reason is twofold. Pattern matching is just syntactic sugar for using extractors and case classes happen to give you a couple methods for free, one of which is an extractor method that corresponds to the main constructor.
If you want your example above to work, you need to define an unapply method inside objects Val and Sup. To do that you'd need extractor methods (which are only defined on val fields, so you'll have to make your fields vals):
class Val[T](val t: T)
class Sup(val l: Number)
object Val {
def unapply[T](v: Val[T]): Option[T] = Some(v.t)
}
object Sup {
def unapply(s: Sup): Option[Number] = Some(s.l)
}
And which point you can do something like val Val(v) = new Val("hi"). More often than not, though, it is better to make your class a case class. Then, the only times you should be defining extra extractors.
The usual example (to which I can't seem to find a reference) is coordinates:
case class Coordinate(x: Double, val: Double)
And then you can define a custom extractors like
object Polar {
def unapply(c: Coordinate): Option[(Double,Double)] = {...}
}
object Cartesian {
def unapply(c: Coordinate): Option[(Double,Double)] = Some((c.x,c.y))
}
to convert to the two different representations, all when you pattern match.
You can use pattern matching on arbitrary classes, but you need to implement an unapply method, used to "de-construct" the object.
With a case class, the unapply method is automatically generated by the compiler, so you don't need to implement it yourself.
When you write match exp { case Val(pattern) => ... case ... }, that is equivalent to something like this:
match Val.unapply(exp) {
case Some(pattern) =>
...
case _ =>
// code to match the other cases goes here
}
That is, it uses the result of the companion object's unapply method to see whether the match succeeded.
If you define a case class, it automatically defines a companion object with a suitable unapply method. For a normal class it doesn't. The motivation for that is the same as for the other things that gets automatically defined for case classes (like equals and hashCode for example): By declaring a class as a case class, you're making a statement about how you want the class to behave. Given that, there's a good chance that the auto generated will do what you want. For a general class, it's up to you to define these methods like you want them to behave.
Note that parameters for case classes are vals by default, which isn't true for normal classes. So your class class Val(t: T) doesn't even have any way to access t from the outside. So it isn't even possible to define an unapply method that gets at the value of t. That's another reason why you don't get an automatically generated unapply for normal classes: It isn't even possible to generate one unless all parameters are vals.
Part of a current project involves converting from types coupled to a database and a generic type used when serializing the results out to clients via Json, the current implementation in Scala uses type inference to correctly perform the transformation, using Scala's TypeTag:
def Transform[A: TypeTag](objects:Seq[A]):Seq[Children] = typeOf[A] match {
case pc if pc =:= typeOf[ProductCategory] =>
TransformProductCategory(objects.asInstanceOf[Seq[ProductCategory]])
case pa if pa =:= typeOf[ProductArea] =>
TransformProductArea(objects.asInstanceOf[Seq[ProductArea]])
case pg if pg =:= typeOf[ProductGroup] =>
TransformProductGroup(objects.asInstanceOf[Seq[ProductGroup]])
case psg if psg =:= typeOf[ProductSubGroup] =>
TransformProductSubGroup(objects.asInstanceOf[Seq[ProductSubGroup]])
case _ =>
throw new IllegalArgumentException("Invalid transformation")
}
The types used as input are all case classes and are defined internally within the application, for example:
case class ProductCategory(id: Long, name: String,
thumbnail: Option[String],
image:Option[String],
sequence:Int)
This approach, although suitable at the moment, doesn't feel functional or scalable when potentially more DB types are added. I also feel using asInstanceOf should be redundant as the type has already been asserted. My limited knowledge of implicits suggests they could be used instead to perform the transformation, and remove the need for the above Transform[A: TypeTag](objects:Seq[A]):Seq[Children] method altogether. Or maybe there is a different approach I should have used instead?
You can define a trait like this:
trait Transformer[A] {
def transformImpl(x: Seq[A]): Seq[Children]
}
Then you can define some instances:
object Transformer {
implicit val forProduct = new Transformer[ProductCategory] {
def transformImpl(x: Seq[ProductCategory]) = ...
}
...
}
And then finally:
def transform[A: Transformer](objects:Seq[A]): Seq[Children] =
implicitly[Transformer[A]].transformImpl(objects)
Preferably, you should define your implicit instances either in Transformer object or in objects corresponding to your category classes.
I'm not sure how your program is supposed to work exactly, nor do I know if any of your Transforms are self-made types or something you pulled from a library, however I may have a solution anyway
Something match is really good with, is case classes
So rather than manually checking the type of the input data, you could maybe wrap them all in case classes (if you need to bring data with you) or case objects (if you don't)
That way you can do something like this:
// this code assumes ProductCategory, ProductArea, etc.
// all extends the trait ProductType
def Transform(pType: ProductType): Seq[Children] = pType match {
case ProductCategory(objects) => TransformProductCategory(objects)
case ProductArea(objects) => TransformProductArea(objects)
case ProductGroup(objects) => TransformProductGroup(objects)
case ProductSubGroup(objects) => TransformProductSubGroup(objects)
}
By wrapping everything in a case class, you can specify exactly which type of data you want to bring along, and so long as they (the case classes, not the data) all inherit from the same class/trait, you should be fine
And since there's only a little handful of classes that extend ProductType you don't need the default case, because there is no default case!
Another benefit of this, is that it's infinitely expandable; just add more cases and case classes!
Note that this solution requires you to refactor your code a LOT, so bare that in mind before you throw yourself into it.
E.g.:
def updateAsinRecords(asins:Seq[String], recordType:String)
Above method takes a Seq of ASINs and a record type. Both have type of String. There are also other values that are passed around with type String in the application. Needless to say, this being Scala, I'd like to use the type system to my advantage. How to pass around string values in a type safe manner (like below)?
def updateAsinRecords(asins:Seq[ASIN], recordType:RecordType)
^ ^
I can imagine, having something like this:
trait ASIN { val value:String }
but I'm wondering if there's a better approach...
There is an excellent bit of new Scala functionality know as Value Classes and Universal Traits. They impose no runtime overhead but you can use them to work in a type safe manner:
class AnsiString(val inner: String) extends AnyVal
class Record(val inner: String) extends AnyVal
def updateAnsiRecords(ansi: Seq[AnsiString], record: Record)
They were created specifically for this purpose.
You could add thin wrappers with case classes:
case class ASIN(asin: String)
case class RecordType(recordType: String)
def updateAsinRecords(asins: Seq[ASIN], recordType: RecordType) = ???
updateAsinRecords(Vector(ASIN("a"), ASIN("b")), RecordType("c"))
This will not only make your code safer, but it will also make it much easier to read! The other big advantage of this approach is that refactoring later will be much easier. For example, if you decide later that an ASIN should have two fields instead of just one, then you just update the ASIN class definition instead of every place it's used. Likewise, you can do things like add methods to these types whenever you decide you need them.
In addition to the suggestions about using a Value Class / extends AnyVal, you should probably control the construction to allow only valid instances, since presumably not any old string is a valid ASIN. (And... is that an Amazon thing? It rings a bell somehow.)
The best way to do this is to make the constructor private and put a validating factory method in a companion object. The reason for this is that throwing exceptions in constructors (when an attempt is made to instantiate with an invalid argument) can lead to puzzling failure modes (I often see it manifest as a NoClassDefFoundError error when trying to load a different class).
So, in addition to:
case class ASIN private (asin: String) extends AnyVal { /* other stuff */ }
You should include something like this:
object A {
import scala.util.{Try, Success, Failure}
def fromString(str: String): Try[ASIN] =
if (validASIN(str))
Success(new ASIN(str))
else
Failure(new InvalidArgumentException(s"Invalid ASIN string: $str")
}
How about a type alias?
type ASIN = String
def update(asins: Seq[ASIN])
There are two ways of defining a method for two different classes inheriting the same trait in Scala.
sealed trait Z { def minus: String }
case class A() extends Z { def minus = "a" }
case class B() extends Z { def minus = "b" }
The alternative is the following:
sealed trait Z { def minus: String = this match {
case A() => "a"
case B() => "b"
}
case class A() extends Z
case class B() extends Z
The first method repeats the method name, whereas the second method repeats the class name.
I think that the first method is the best to use because the codes are separated. However, I found myself often using the second one for complicated methods, so that adding additional arguments can be done very easily for example like this:
sealed trait Z {
def minus(word: Boolean = false): String = this match {
case A() => if(word) "ant" else "a"
case B() => if(word) "boat" else "b"
}
case class A() extends Z
case class B() extends Z
What are other differences between those practices? Are there any bugs that are waiting for me if I choose the second approach?
EDIT:
I was quoted the open/closed principle, but sometimes, I need to modify not only the output of the functions depending on new case classes, but also the input because of code refactoring. Is there a better pattern than the first one? If I want to add the previous mentioned functionality in the first example, this would yield the ugly code where the input is repeated:
sealed trait Z { def minus(word: Boolean): String ; def minus = minus(false) }
case class A() extends Z { def minus(word: Boolean) = if(word) "ant" else "a" }
case class B() extends Z { def minus(word: Boolean) = if(word) "boat" else "b" }
I would choose the first one.
Why ? Merely to keep Open/Closed Principle.
Indeed, if you want to add another subclass, let's say case class C, you'll have to modify supertrait/superclass to insert the new condition... ugly
Your scenario has a similar in Java with template/strategy pattern against conditional.
UPDATE:
In your last scenario, you can't avoid the "duplication" of input. Indeed, parameter type in Scala isn't inferable.
It still better to have cohesive methods than blending the whole inside one method presenting as many parameters as the method union expects.
Just Imagine ten conditions in your supertrait method. What if you change inadvertently the behavior of one of each? Each change would be risked and supertrait unit tests should always run each time you modify it ...
Moreover changing inadvertently an input parameter (not a BEHAVIOR) is not "dangerous" at all. Why? because compiler would tell you that a parameter/parameter type isn't relevant any more.
And if you want to change it and do the same for every subclasses...ask to your IDE, it loves refactoring things like this in one click.
As this link explains:
Why open-closed principle matters:
No unit testing required.
No need to understand the sourcecode from an important and huge class.
Since the drawing code is moved to the concrete subclasses, it's a reduced risk to affect old functionallity when new functionality is added.
UPDATE 2:
Here a sample avoiding inputs duplication fitting your expectation:
sealed trait Z {
def minus(word: Boolean): String = if(word) whenWord else whenNotWord
def whenWord: String
def whenNotWord: String
}
case class A() extends Z { def whenWord = "ant"; def whenNotWord = "a"}
Thanks type inference :)
Personally, I'd stay away from the second approach. Each time you add a new sub class of Z you have to touch the shared minus method, potentially putting at risk the behavior tied to the existing implementations. With the first approach adding a new subclass has no potential side effect on the existing structures. There might be a little of the Open/Closed Principle in here and your second approach might violate it.
Open/Closed principle can be violated with both approaches. They are orthogonal to each other. The first one allows to easily add new type and implement required methods, it breaks Open/Closed principle if you need to add new method into hierarchy or refactor method signatures to the point that it breaks any client code. It is after all reason why default methods were added to Java8 interfaces so that old API can be extended without requiring client code to adapt.
This approach is typical for OOP.
The second approach is more typical for FP. In this case it is easy to add methods but it is hard to add new type (it breaks O/C here). It is good approach for closed hierarchies, typical example are Algebraic Data Types (ADT). Standardized protocol which is not meant to be extended by clients could be a candidate.
Languages struggle to allow to design API which would have both benefits - easy to add types as well as adding methods. This problem is called Expression Problem. Scala provides Typeclass pattern to solve this problem which allows to add functionality to existing types in ad-hoc and selective manner.
Which one is better depends on your use case.
Starting in Scala 3, you have the possibility to use trait parameters (just like classes have parameters), which simplifies things quite a lot in this case:
trait Z(x: String) { def minus: String = x }
case class A() extends Z("a")
case class B() extends Z("b")
A().minus // "a"
B().minus // "b"