I am trying to model (in my Scala application) a list of options presented in my web page and am scratching my head coming up with a solution for mapping a String value posted from the client to it's corresponding object in the backend.
eg. Let's say it is a list of Animals and the user can choose 1 which gets posted to the backend.
Animals
Polar Bear
Rabbit
Great White Shark
When a request comes in, I want to convert the Great White Shark String to an Animal but not sure on how best to match the
String to the appropriate type in the backend.
So far I have this.
sealed abstract class Animal(val name: String)
case object GreatWhite extends Animal("Great White Shark")
case object PolarBear extends Animal("Polar Bear")
Which allows me to do this to match the String from the UI to it's corresponding case object in my Scala application.
def matcher(animal: String) = animal match {
case GreatWhite.name => GreatWhite
case PolarBear.name => PolarBear
}
Problem
If the List of Animal's grows long however, this matcher is going to be very cumbersome since I need to have a case expression for every Animal.
I would much appreciate any experienced Scala guys giving me a pointer on a more elegant solution.
It's looks like what you need is simply have a hash table of String to Animal.
Such approach gives you ability to get result in constant time O(1) even with extensivly growing list.
val mapping = Map[String, Animal]("Rabbit" -> Rabbit, "Polar Bear" -> PolarBear /* ... */ )
// matcher
mapping.get(animal)
UPD.
Some useful comments below.
sealed abstract class Animal(val name: String)
case object GreatWhite extends Animal("Great White Shark")
case object PolarBear extends Animal("Polar Bear")
val mapping: Map[String, Animal] = Seq(GreatWhite, PolarBear).map(x => x.name -> x).toMap
mapping
Have you looked at Enums? If they are usable for you, Enums have a .withName method http://yefremov.net/blog/scala-enum-by-name/
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
I've got a case class Game which I have no trouble serializing/deserializing using json4s.
case class Game(name: String,publisher: String,website: String, gameType: GameType.Value)
In my app I use mapperdao as my ORM. Because Game uses a Surrogate Id I do not have id has part of its constructor.
However, when mapperdao returns an entity from the DB it supplies the id of the persisted object using a trait.
Game with SurrogateIntId
The code for the trait is
trait SurrogateIntId extends DeclaredIds[Int]
{
def id: Int
}
trait DeclaredIds[ID] extends Persisted
trait Persisted
{
#transient
private var mapperDaoVM: ValuesMap = null
#transient
private var mapperDaoDetails: PersistedDetails = null
private[mapperdao] def mapperDaoPersistedDetails = mapperDaoDetails
private[mapperdao] def mapperDaoValuesMap = mapperDaoVM
private[mapperdao] def mapperDaoInit(vm: ValuesMap, details: PersistedDetails) {
mapperDaoVM = vm
mapperDaoDetails = details
}
.....
}
When I try to serialize Game with SurrogateIntId I get empty parenthesis returned, I assume this is because json4s doesn't know how to deal with the attached trait.
I need a way to serialize game with only id added to its properties , and almost as importantly a way to do this for any T with SurrogateIntId as I use these for all of my domain objects.
Can anyone help me out?
So this is an extremely specific solution since the origin of my problem comes from the way mapperDao returns DOs, however it may be helpful for general use since I'm delving into custom serializers in json4s.
The full discussion on this problem can be found on the mapperDao google group.
First, I found that calling copy() on any persisted Entity(returned from mapperDao) returned the clean copy(just case class) of my DO -- which is then serializable by json4s. However I did not want to have to remember to call copy() any time I wanted to serialize a DO or deal with mapping lists, etc. as this would be unwieldy and prone to errors.
So, I created a CustomSerializer that wraps around the returned Entity(case class DO + traits as an object) and gleans the class from generic type with an implicit manifest. Using this approach I then pattern match my domain objects to determine what was passed in and then use Extraction.decompose(myDO.copy()) to serialize and return the clean DO.
// Entity[Int, Persisted, Class[T]] is how my DOs are returned by mapperDao
class EntitySerializer[T: Manifest] extends CustomSerializer[Entity[Int, Persisted, Class[T]]](formats =>(
{PartialFunction.empty} //This PF is for extracting from JSON and not needed
,{
case g: Game => //Each type is one of my DOs
implicit val formats: Formats = DefaultFormats //include primitive formats for serialization
Extraction.decompose(g.copy()) //get plain DO and then serialize with json4s
case u : User =>
implicit val formats: Formats = DefaultFormats + new LinkObjectEntitySerializer //See below for explanation on LinkObject
Extraction.decompose(u.copy())
case t : Team =>
implicit val formats: Formats = DefaultFormats + new LinkObjectEntitySerializer
Extraction.decompose(t.copy())
...
}
The only need for a separate serializer is in the event that you have non-primitives as parameters of a case class being serialized because the serializer can't use itself to serialize. In this case you create a serializer for each basic class(IE one with only primitives) and then include it into the next serializer with objects that depend on those basic classes.
class LinkObjectEntitySerializer[T: Manifest] extends CustomSerializer[Entity[Int, Persisted, Class[T]]](formats =>(
{PartialFunction.empty},{
//Team and User have Set[TeamUser] parameters, need to define this "dependency"
//so it can be included in formats
case tu: TeamUser =>
implicit val formats: Formats = DefaultFormats
("Team" -> //Using custom-built representation of object
("name" -> tu.team.name) ~
("id" -> tu.team.id) ~
("resource" -> "/team/") ~
("isCaptain" -> tu.isCaptain)) ~
("User" ->
("name" -> tu.user.globalHandle) ~
("id" -> tu.user.id) ~
("resource" -> "/user/") ~
("isCaptain" -> tu.isCaptain))
}
))
This solution is hardly satisfying. Eventually I will need to replace mapperDao or json4s(or both) to find a simpler solution. However, for now, it seems to be the fix with the least amount of overhead.
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])
With a mutable object I can write something like
var user = DAO.getUser(id)
user.name = "John"
user.email ="john#doe.com"
// logic on user
If user is immutable then I need to clone\copy it on every change operation.
I know a few ways to perform this
case class copy
method (like changeName) that creates a new object with the new property
What is the best practice?
And one more question. Is there any existing technique to get "changes" relative to the original object(for example to generate update statement)?
Both ways you've mentioned belongs to functional and OO paradigms respectively. If you prefer functional decomposition with abstract data type, which, in Scala, is represented by case classes, then choose copy method. Using mutators is not a good practice in my option, cause that will pull you back to Java/C#/C++ way of life.
On the other hand making ADT case class like
case class Person(name: String, age: String)
is more consise then:
class Person(_name: String, _age: String) {
var name = _name
var age = _a
def changeName(newName: String): Unit = { name = newName }
// ... and so on
}
(not the best imperative code, can be shorter, but clear).
Of cause there is another way with mutators, just to return a new object on each call:
class Person(val name: String,
val age: String) {
def changeName(newName: String): Unit = new Person(newName, age)
// ... and so on
}
But still case class way is more consise.
And if you go futher, to concurrent/parallel programming, you'll see that functional consept with immutable value are much better, then tring to guess in what state your object currently are.
Update
Thanks to the senia, forgot to mention two things.
Lenses
At the most basic level, lenses are sort of getters and setters for immutable data and looks like this:
case class Lens[A,B](get: A => B, set: (A,B) => A) {
def apply(a: A) = get(a)
// ...
}
That is it. A lens is a an object that contains two functions: get and set. get takes an A and returns a B. set takes an A and B and returns a new A. It’s easy to see that the type B is a value contained in A. When we pass an instance to get we return that value. When we pass an A and a B to set we update the value B in A and return a new A reflecting the change. For convenience the get is aliased to apply. There is a good intro to Scalaz Lens case class
Records
This one, ofcause, comes from the shapeless library and called Records. An implementation of extensible records modelled as HLists of associations. Keys are encoded using singleton types and fully determine the types of their corresponding values (ex from github):
object author extends Field[String]
object title extends Field[String]
object price extends Field[Double]
object inPrint extends Field[Boolean]
val book =
(author -> "Benjamin Pierce") ::
(title -> "Types and Programming Languages") ::
(price -> 44.11) ::
HNil
// Read price field
val currentPrice = book.get(price) // Inferred type is Double
currentPrice == 44.11
// Update price field, relying on static type of currentPrice
val updated = book + (price -> (currentPrice+2.0))
// Add a new field
val extended = updated + (inPrint -> true)