Is there a "better" way to do this?
import java.awt.event.MouseEvent
trait MouseListener extends java.awt.event.MouseListener{
def mouseClicked(e:MouseEvent){}
def mousePressed(e:MouseEvent){}
def mouseReleased(e:MouseEvent){}
def mouseEntered(e:MouseEvent){}
def mouseExited(e:MouseEvent){}
}
I made this trait so I could do this without overriding the methods I am not using.
This works fine I am just wondering if there is an alternative method?
new ActionListener{
override def mouseClicked(e:MouseEvent){
//Blah Blah
}
}
This is built-in already: MouseAdapter. There are similar classes for other Swing listener interfaces with more than one method.
By "better" way, I suppose you mean "Scala way."
On a scala.swing.UIElement:
listenTo(mouse.clicks, mouse.moves, mouse.wheel, keys)
reactions += {
case e: MousePressed => startDrag(e)
case e: MouseDragged => doDrag(e)
case e: MouseReleased => endDrag(e)
case e: MouseWheelMoved => wheeling(e)
case _ => null // println ("Unreacted event")
}
Related
I had written a Reads converter in play-json for Option[Option[A]] that had the following behavior:
//given this case class
case class MyModel(field: Option[Option[String]])
//this JSON -- maps to --> this MyModel:
//"{ \"field\": \"value\" }" --> MyModel(field = Some(Some("value")))
//"{ \"field\": null, ... }" --> MyModel(field = Some(None))
//"{ }" --> MyModel(field = None)
So, providing the value mapped to Some[Some[A]], providing null mapped to Some[None] (i.e. Some[Option.empty[A]]), and not providing the value mapped to just None (i.e. Option.empty[Option[A]]). Here's the play-json converter:
def readOptOpt[A](implicit r: Reads[A]): Reads[Option[Option[A]]] = {
Reads[Option[Option[A]]] { json =>
path.applyTillLast(json).fold(
identity,
_.fold(_ => JsSuccess(None), {
case JsNull => JsSuccess(Some(None))
case js => r.reads(js).repath(path).map(a => Some(Some(a)))
})
)
}
}
Now I am converting my play-json code to Circe, but I can't figure out how to write a Decoder[Option[Option[A]] that has the same behavior. That is, I need
def optOptDecoder[A](implicit d: Decoder[A]): Decoder[Option[Option[A]] = ??? //help!
Any ideas on how I can make this work? Thanks
I figured this out:
There were two problems:
1) How to deal with the case where the field was completely missing from the JSON. Turns out you have to use Decoder.reattempt in your custom decoder, following Circe's decodeOption code, which works.
2) How to have the compiler recognize cases of Option[Option[A]] when your decoder code is sitting in a helper object (or wherever). Turns out if you're using semi-auto derivation, you can create an implicit in the companion object and that will override the defaults:
//companion object
object MyModel {
implicit def myModelOptOptDecoder[A](implicit d: Decoder[A]): Decoder[Option[Option[A]]] =
MyHelperObject.optOptDecoder
implicit val myModelDecoder: Decoder[MyModel] = deriveDecoder
}
Anyway, I don't think this will be much help to anybody in the future, so unless I get any upvotes in the next few hours I think I'll just delete this.
Edit2: Okay it was answered so I won't delete it. Stay strong, esoteric circe question, stay strong...
An Option[Option[A]] is a bit odd. I understand and mostly agree with the reasoning, but I think it's weird enough that it may warrant just replacing it with your own class (and writing a decoder for that). Something like:
sealed trait OptionalNull[+A] {
def toOption: Option[Option[A]]
}
object NotPresent extends OptionalNull[Nothing] {
override def toOption = None
}
object PresentButNull extends OptionalNull[Nothing] {
override def toOption = Some(None)
}
case class PresentNotNull[A](value: A) extends OptionalNull[A] {
override def toOption = Some(Some(value))
}
This has the additional benefit of not having to worry about implicit precedence and stuff like that. Might simplify your decoder.
Here is another solution I found (This is not my gist):
sealed trait UpdateOrDelete[+A]
case object Delete extends UpdateOrDelete[Nothing]
final case class UpdateOptionalFieldWith[A](value: A) extends UpdateOrDelete[A]
object UpdateOrDelete {
implicit def optionalDecoder[A](implicit decodeA: Decoder[A]): Decoder[UpdateOptionalField[A]] =
Decoder.withReattempt {
// We're trying to decode a field but it's missing.
case c: FailedCursor if !c.incorrectFocus => Right(None)
case c =>
Decoder.decodeOption[A].tryDecode(c).map {
case Some(a) => Some(UpdateOptionalFieldWith(a))
case None => Some(Delete)
}
}
// Random UUID to _definitely_ avoid collisions
private[this] val marker: String = s"$$marker-${UUID.randomUUID()}-marker$$"
private[this] val markerJson: Json = Json.fromString(marker)
implicit def optionalEncoder[A](implicit encodeA: Encoder[A]): Encoder[UpdateOptionalField[A]] =
Encoder.instance {
case Some(Delete) => Json.Null
case Some(UpdateOptionalFieldWith(a)) => encodeA(a)
case None => markerJson
}
def filterMarkers[A](encoder: Encoder.AsObject[A]): Encoder.AsObject[A] =
encoder.mapJsonObject { obj =>
obj.filter {
case (_, value) => value =!= markerJson
}
}
}
Option class has a good method foreach, which calls passed code if value is specified. Is there any similar techinque for None value? I know about .orElse method, but, using it, I am required to return Option from code block:
x orElse {
// do something
None // <-- I want to avoid this line
}
If you want to do something in the None case I assume you are side-effecting. So what's wrong with:
if(o.isEmpty){
// do things
}
I don't think that it exists in standard Option library, but you can add it with implicit class
class OptionFunctions[T](val opt: Option[T]) extends AnyVal {
def ifEmpty[A](f: => A): Unit = {
if (opt.isEmpty) f
}
}
and use it like this:
val o = Some(1)
o.ifEmpty { println("empty") }
A pattern match perhaps?
option match {
case Some(foo) => println("Have " + foo)
case None => println("Have nothing.")
}
Are there any conversions from Either to Try and vice versa in the Scala standard library ? Maybe I am missing something but I did not find them.
To the best of my knowledge this does not exist in the standard library. Although an Either is typically used with the Left being a failure and the Right being a success, it was really designed to support the concept of two possible return types with one not necessarily being a failure case. I'm guessing these conversions that one would expect to exist do not exist because Either was not really designed to be a Success/Fail monad like Try is. Having said that it would be pretty easy to enrich Either yourself and add these conversions. That could look something like this:
object MyExtensions {
implicit class RichEither[L <: Throwable,R](e:Either[L,R]){
def toTry:Try[R] = e.fold(Failure(_), Success(_))
}
implicit class RichTry[T](t:Try[T]){
def toEither:Either[Throwable,T] = t.transform(s => Success(Right(s)), f => Success(Left(f))).get
}
}
object ExtensionsExample extends App{
import MyExtensions._
val t:Try[String] = Success("foo")
println(t.toEither)
val t2:Try[String] = Failure(new RuntimeException("bar"))
println(t2.toEither)
val e:Either[Throwable,String] = Right("foo")
println(e.toTry)
val e2:Either[Throwable,String] = Left(new RuntimeException("bar"))
println(e2.toTry)
}
In Scala 2.12.x Try has a toEither method: http://www.scala-lang.org/api/2.12.x/scala/util/Try.html#toEither:scala.util.Either[Throwable,T]
import scala.util.{ Either, Failure, Left, Right, Success, Try }
implicit def eitherToTry[A <: Exception, B](either: Either[A, B]): Try[B] = {
either match {
case Right(obj) => Success(obj)
case Left(err) => Failure(err)
}
}
implicit def tryToEither[A](obj: Try[A]): Either[Throwable, A] = {
obj match {
case Success(something) => Right(something)
case Failure(err) => Left(err)
}
}
The answer depends on how to convert the Failure to Left (and vice versa). If you don't need to use the details of the exception, then Try can be converted to Either by going the intermediate route of an Option:
val tried = Try(1 / 0)
val either = tried.toOption.toRight("arithmetic error")
The conversion the other way requires you to construct some Throwable. It could be done like this:
either.fold(left => Failure(new Exception(left)), right => Success(right))
I am in the process of converting Akka UntypedActors in Java code to their Scala equivalent.
However, I am having trouble understanding how to correctly implement the receive() abstract method. The ScalaDoc is a little confusing and most of the examples I see just involve String messages!
My Actor can support multiple message types and this is my solution so far:
override def receive = {
case message if message.isInstanceOf[ClassOne] => {
// do something after message.asInstanceOf[ClassOne]
}
case message if message.isInstanceOf[ClassTwo] => {
// do something after message.asInstanceOf[ClassTwo]
}
case message => unhandled(message)
}
Is there a better way to achieve the above?
override def receive = {
case c: ClassOne =>
// do something after message.asInstanceOf[ClassOne]
case c: ClassTwo =>
// do something after message.asInstanceOf[ClassTwo]
case message => unhandled(message)
}
If you're using case classes, you can get more sophisticated.
case class ClassOne(x: Int, y: String)
case class ClassTwo(a: Int, b: Option[ClassOne])
override def receive = {
case ClassOne(x, y) =>
println(s"Received $x and $y")
case ClassTwo(a, Some(ClassOne(x, y)) if a == 42 =>
// do something
case ClassTwo(a, None) =>
case c # ClassOne(_, "foo") => // only match if y == "foo", now c is your instance of ClassOne
}
All sorts of fun stuff.
receive's type is really just a PartialFunction[Any,Unit], which means you can use Scala's pattern match expressions - in fact, you're already doing it, just not entirely succinctly. A terser equivalent that would also let you handle the type of the match for each case:
def receive = {
case classOneMessage : ClassOne => {
// do something
}
case classTwoMessage : ClassTwo => {
// do something
}
case _ => someCustomLogicHereOtherWiseThereWillBeASilentFailure
//you can, but you don't really need to define this case - in Akka
//the standard way to go if you want to process unknown messages
//within *this* actor, is to override the Actor#unhandled(Any)
//method instead
}
Read the tour article, and the already-linked tutorial for more info on pattern matching, especially in the context of using the feature together with case classes - this pattern is applied regularly when working with Akka, for example here in the Akka manual when handling the ActorIdentity case class.
receive is a regular partial function in Scala. You can write something like this in your case:
case class Example(number: Int, text: String)
override def receive = {
case message: ClassOne =>
// do something with ClassOne instance
case message: ClassTwo =>
// do something with ClassTwo instance
case Example(n, t) =>
println(t * n)
case Example(n, t) if n > 10 =>
println("special case")
}
You don't have to include a special case for unhandled messages unless your application logic requires you to handle all possible messages.
First two cases just match by type of a message and subtypes will be matched as well. Last one not only matches the type Example but also "deconstructs" it using pattern matching.
Very often i end up with lots of nested .map and .getOrElse when validating several consecutives conditions
for example:
def save() = CORSAction { request =>
request.body.asJson.map { json =>
json.asOpt[Feature].map { feature =>
MaxEntitiyValidator.checkMaxEntitiesFeature(feature).map { rs =>
feature.save.map { feature =>
Ok(toJson(feature.update).toString)
}.getOrElse {
BadRequest(toJson(
Error(status = BAD_REQUEST, message = "Error creating feature entity")
))
}
}.getOrElse {
BadRequest(toJson(
Error(status = BAD_REQUEST, message = "You have already reached the limit of feature.")
))
}
}.getOrElse {
BadRequest(toJson(
Error(status = BAD_REQUEST, message = "Invalid feature entity")
))
}
}.getOrElse {
BadRequest(toJson(
Error(status = BAD_REQUEST, message = "Expecting JSON data")
))
}
}
You get the idea
I just wanted to know if there's some idiomatic way to keep it more clear
If you hadn't had to return a different message for the None case this would be an ideal use-case for for comprehension. In your case , you probably want to use the Validation monad, as the one you can find in Scalaz. Example ( http://scalaz.github.com/scalaz/scalaz-2.9.0-1-6.0/doc.sxr/scalaz/Validation.scala.html ).
In functional programming, you should not throw exceptions but let functions which can fail return an Either[A,B], where by convention A is the type of result in case of failure and B is the type of result in case of success. You can then match against Left(a) or Right(b) to handle, reespectively, the two cases.
You can think of the Validation monad as an extended Either[A,B] where applying subsequent functions to a Validation will either yield a result, or the first failure in the execution chain.
sealed trait Validation[+E, +A] {
import Scalaz._
def map[B](f: A => B): Validation[E, B] = this match {
case Success(a) => Success(f(a))
case Failure(e) => Failure(e)
}
def foreach[U](f: A => U): Unit = this match {
case Success(a) => f(a)
case Failure(e) =>
}
def flatMap[EE >: E, B](f: A => Validation[EE, B]): Validation[EE, B] = this match {
case Success(a) => f(a)
case Failure(e) => Failure(e)
}
def either : Either[E, A] = this match {
case Success(a) => Right(a)
case Failure(e) => Left(e)
}
def isSuccess : Boolean = this match {
case Success(_) => true
case Failure(_) => false
}
def isFailure : Boolean = !isSuccess
def toOption : Option[A] = this match {
case Success(a) => Some(a)
case Failure(_) => None
}
}
final case class Success[E, A](a: A) extends Validation[E, A]
final case class Failure[E, A](e: E) extends Validation[E, A]
Your code now can be refactored by using the Validation monad into three validation layers. You should basically replace your map with a validation like the following:
def jsonValidation(request:Request):Validation[BadRequest,String] = request.asJson match {
case None => Failure(BadRequest(toJson(
Error(status = BAD_REQUEST, message = "Expecting JSON data")
)
case Some(data) => Success(data)
}
def featureValidation(validatedJson:Validation[BadRequest,String]): Validation[BadRequest,Feature] = {
validatedJson.flatMap {
json=> json.asOpt[Feature] match {
case Some(feature)=> Success(feature)
case None => Failure( BadRequest(toJson(
Error(status = BAD_REQUEST, message = "Invalid feature entity")
)))
}
}
}
And then you chain them like the following featureValidation(jsonValidation(request))
This is a classic example of where using a monad can clean up your code. For example you could use Lift's Box, which is not tied to Lift in any way. Then your code would look something like this:
requestBox.flatMap(asJSON).flatMap(asFeature).flatMap(doSomethingWithFeature)
where asJson is a Function from a request to a Box[JSON] and asFeature is a function from a Feature to some other Box. The box can contain either a value, in which case flatMap calls the function with that value, or it can be an instance of Failure and in that case flatMap does not call the function passed to it.
If you had posted some example code that compiles, I could have posted an answer that compiles.
I tried this to see if pattern matching offered someway to adapt the submitted code sample (in style, if not literally) to something more coherent.
object MyClass {
case class Result(val datum: String)
case class Ok(val _datum: String) extends Result(_datum)
case class BadRequest(_datum: String) extends Result(_datum)
case class A {}
case class B(val a: Option[A])
case class C(val b: Option[B])
case class D(val c: Option[C])
def matcher(op: Option[D]) = {
(op,
op.getOrElse(D(None)).c,
op.getOrElse(D(None)).c.getOrElse(C(None)).b,
op.getOrElse(D(None)).c.getOrElse(C(None)).b.getOrElse(B(None)).a
) match {
case (Some(d), Some(c), Some(b), Some(a)) => Ok("Woo Hoo!")
case (Some(d), Some(c), Some(b), None) => BadRequest("Missing A")
case (Some(d), Some(c), None, None) => BadRequest("Missing B")
case (Some(d), None, None, None) => BadRequest("Missing C")
case (None, None, None, None) => BadRequest("Missing D")
case _ => BadRequest("Egads")
}
}
}
Clearly there are ways to write this more optimally; this is left as an exercise for the reader.
I agree with Edmondo suggestion of using for comprehension but not with the part about using a validation library (At least not anymore given the new features added to scala standard lib since 2012). From my experience with scala, dev that struggle to come up with nice statement with the standard lib will also end up doing the same of even worst when using libs like cats or scalaz. Maybe not at the same place, but ideally we would solve the issue rather than just moving it.
Here is your code rewritten with for comprehension and either that is part of scala standard lib :
def save() = CORSAction { request =>
// Helper to generate the error
def badRequest(message: String) = Error(status = BAD_REQUEST, message)
//Actual validation
val updateEither = for {
json <- request.body.asJson.toRight(badRequest("Expecting JSON data"))
feature <- json.asOpt[Feature].toRight(badRequest("Invalid feature entity"))
rs <- MaxEntitiyValidator
.checkMaxEntitiesFeature(feature)
.toRight(badRequest("You have already reached the limit"))
} yield toJson(feature.update).toString
// Turn the either into an OK/BadRequest
featureEither match {
case Right(update) => Ok(update)
case Left(error) => BadRequest(toJson(error))
}
}
Explanations
Error handling
I'm not sure how much you know about either but they are pretty similar in behaviour as Validation presented by Edmondo or Try object from the scala library. Main difference between those object regard their capability and behaviour with errors, but beside that they all can be mapped and flat mapped the same way.
You can also see that I use toRight to immediately convert the option into Either instead of doing it at the end. I see that java dev have the reflex to throw exception as far as they physically can, but they mostly do so because the try catch mechanism is unwieldy: in case of success, to get data out of a try block you either need to return them or put them in a variable initialized to null out of the block. But this is not the case is scala: you can map a try or an either, so in general, you get a more legible code if you turn results into error representation as soon as have identified it as they are identified as incorrect.
For comprehension
I also know that dev discovering scala are often quite puzzled by for comprehension. This is quite understandable as in most other language, for is only used for iteration over collections while is scala, it seem to use usable on a lot of unrelated types. In scala for is actually more nicer way to call the function flatMap. The compiler may decide to optimize it with map or foreach but it remain correct assume that you will get a flatMap behavior when you use for.
Calling flatMap on a collection will behave like the for each would in other language, so scala for may be used like a standard for when dealing with collection. But you can also use it on any other type of object that provide an implementation for flatMap with the correct signature. If your OK/BadRequest also implement the flatMap, you may be able to use in directly in the for comprehension instead of usong an intermediate Either representation.
For the people are not at ease with using for on anything that do not look like a collection, here is is how the function would look like if explicitly using flatMap instead of for :
def save() = CORSAction { request =>
def badRequest(message: String) = Error(status = BAD_REQUEST, message)
val updateEither = request.body.asJson.toRight(badRequest("Expecting JSON data"))
.flatMap { json =>
json
.asOpt[Feature]
.toRight(badRequest("Invalid feature entity"))
}
.flatMap { feature =>
MaxEntitiyValidator
.checkMaxEntitiesFeature(feature)
.map(_ => feature)
.toRight(badRequest("You have already reached the limit"))
}
.map { rs =>
toJson(feature.update).toString
}
featureEither match {
case Right(update) => Ok(update)
case Left(error) => BadRequest(toJson(error))
}
}
Note that in term of parameter scope, for behave live if the function where nested, not chained.
Conclusion
I think that more than not using the right framework or the right language feature, the main issue with the code your provided is how errors are dealt with. In general, you should not write error paths as after thought that you pile up at the end of the method. If you can deal with the error immediately as they occur, that allow you to move to something else. On the contrary, the more you push them back, the more you will have code with inextricable nesting. They are actually a materialization of all the pending error cases that scala expect you to deal with at some point.