I have the following Slick code that given an id returns a customer (if exists). If there's a problem (such as connectivity lost) a Failure clause will throw an exception:
def read (id: Int): Future[Option[Customer]] = {
val db = // ....
val customers = TableQuery[CustomerDB]
val action = customers.filter(_.id === id).result
val future = db.run(action.asTry)
future.map{
case Success(s) =>
if (s.length>0)
Some(s(0))
else
None
case Failure(f) => throw new Exception (f.getMessage)
}
}
Now, my understanding is that instead of using try/catch/finally of exceptions, in Scala one should use Try. In addition, no exceptions should be thrown. But if the exception is not thrown, how to notify the upper layer that a problem occurred?
Future itself does already have Try inside. So, I would say that you need to just flatten (also you code a bit complicated, I simplified):
future.flatMap {
case Success(s) => Future.successful(s.headOption)
case Failure(f) => Future.failed(f)
}
Result Future when in failed state notifies caller that execution failed (with wrapped original exception). Otherwise, successful.
The right way to do report errors is by using Either.
trait Error
case class NotFound(id: Int) extends Error
case class QueryFailed(msg: String) extends Error
def read (id: Int): Future[Either[Error, Customer]] = {
val db = // ....
val customers = TableQuery[CustomerDB]
val action = customers.filter(_.id === id).result
val future = db.run(action.asTry)
future.map{
case Success(s) =>
if (s.length>0)
Right(s(0))
else
Left(NotFound(id))
case Failure(f) => Left(QueryFailed(f.getMessage))
}
}
Ok so, in general you can use Future.successful or Future.failed(msg: String) to "signal" the upper level (aka calling method) you got the value or not.
Better approach
A good approach is however to use .recoverWith{} on a Future in case of failure.
For example:
def getUserFromCloud (userId: String): Future[String] = Future{
cloudProviderApi.getUsername(userId)
}.recoverWith{
Future.failed(s"$userId does not exist.")
}
What about the calling method?
Well you just map the success with and underscode and deal with the error by using recover:
getUserFromCloud("test").map(_ => {
//In case of success
}).recover{
//In case of failure, like return BadRequest.
}
More on recover and recoverWith in case you are interested: Scala recover or recoverWith
Related
Considering a sequence of futures each returning Either[Status, Resp].
How would you propagate error status codes through a for comprehension which is using Future and not Either?
The code bellow does not work, since the parsing exception is not caught by .recover of the last future
The use case is Scala Play ActionRefiners which returns Future[Either[Status, TRequest[A]]].
def parseId(id: String):Future[Int] = {
Future.successful(Integer.parseInt(id))
}
def getItem(id: Int)(implicit ec: ExecutionContext): Future[Either[Status, String]] =
Future(Some("dummy res from db " + id)).transformWith {
case Success(opt) => opt match {
case Some(item) => Future.successful(Right(item))
case _ => Future.successful(Left(NotFound))
}
case Failure(_) => Future.successful(Left(InternalServerError))
}
(for {
id <- parseId("bad request")
resp <- getItem(id)
} yield resp).recover {
case _:NumberFormatException => Left(BadRequest)
}
I could move the .recover to parseId, but this makes the for comprehension very ugly - having to treat the Either[Status, id] in the middle
def parseId(id: String):Future[Either[Status, Int]] = {
Future.successful(Right(Integer.parseInt(id))).recover {
case _:NumberFormatException => Left(BadRequest)
}
}
Your exception is not caught because you are not throwing it inside the Future: Future.successful is immediately satisfied with the result of the expression you give it, if it throws an exception, it is executed on the current thread.
Try removing the .successful: Future(id.toInt) will do what you want.
Also, I would recommend to get rid of all the Eithers: these are highly overrated/overused, especially in the context of Future (that already wrap their result into Try anyhow), and just make the code more complicated and less readable without offering much benefit.
case class FailureReason(status: Status)
extends Exception(status.toString)
def notFound() = throw FailureReason(NotFound)
def internalError() = throw FailureReason(InternalError)
def badRequest() = throw FailureReason(BadRequest)
def parseId(id: String):Future[Int] = Future(id.toInt)
def getItem(id: Int): Future[String] = Future(Some("dummy"))
.map { _.getOrElse(notFound) }
.recover { _ => internalError }
// this is the same as your for-comprehension, just looking less ugly imo :)
parseId("foo").flatMap(getItem).recover {
case _: NumberFormatException => badRequest()
}
// if you still want `Either` in the end for some reason:
.map(Right.apply[Status, String])
.recover {
case _: NumberFormatException => Left(BadRequest) // no need for the first recover above if you do this
case FailureReason(status) => Left(status)
}
How can I output mongoDB errors in a Result with ReactiveMongo (16.6)? I've spent virtually the whole day looking through samples but have not been able to achieve this as of yet. The error section of the documentation returns a Future[Unit] rather than a Future[Result]. And every other example/sample that I can find either is outdated or does not do this; example_1, example2
Here is what I would like to do:
def updateById(collName: String, id: BSONObjectID) = authAction.async(parse.json) { implicit request: Request[JsValue] =>
val oWriteJso = request.body.asOpt[JsObject]
lazy val qJso = Json.obj("_id" -> id)
val res = oWriteJso.map(
wJso => mongoRepo.update(collName)(qJso, wJso)().recoverWith {
case WriteResult.Code(11000) => Future.successful(BadRequest("it went bad"))
case _ => Future.successful(BadRequest("also bad"))
}
)
res
}
Of course with the function signature as recoverWith[U >: T](pf: PartialFunction[Throwable, Future[U]])(implicit executor: ExecutionContext): Future[U] this code above will return an error as it needs to return a Future[WriteResult]. But how then would I be able to put any error messages, codes, etc (from mongoDB) into a Result?
The documentation indicates how to recover a Future[WriteResult]:
.recover {
case WriteResult.Code(11000) =>
// if the result is defined with the error code 11000 (duplicate error)
println("Match the code 11000")
case WriteResult.Message("Must match this exact message") =>
println("Match the error message")
// ...
}
Thanks to Future combinators (not specific to ReactiveMongo), it can be used whatever is the type of the successful value to be lifted inside the Future.
def foo[T](future: Future[WriteResult], recoveredValue: => T)(success: WriteResult => T): Future[T] = future.map(success).recover {
case WriteResult.Code(11000) =>
// if the result is defined with the error code 11000 (duplicate error)
recoveredValue
case WriteResult.Message("Must match this exact message") =>
recoveredValue
}
I have a method that returns Future[Try[Option[Int]]]. I want to extract value of Int for further computation. Any idea how to process it??
future.map(_.map(_.map(i => doSomethingWith(i))))
If you want use cats you can do fun (for certain definitions of fun) things like:
import scala.concurrent._
import scala.util._
import scala.concurrent.ExecutionContext.Implicits.global
import cats.Functor
import cats.instances.option._
import cats.implicits._
val x = Future { Try { Some(1) } } // your type
Functor[Future].compose[Try].compose[Option].map(x)(_ + 2)
This is suggested ONLY if you're already familiar with cats or scalaz.
Otherwise, you're great to go with any of the other valid answers here (I especially like the map-map-map one).
Just map the future and use match case to handle the different cases:
val result: Future[Try[Option[Int]]] = ???
result.map {
case Success(Some(r)) =>
println(s"Success. Result: $r")
//Further computation here
case Success(None) => //Success with None
case Failure(ex) => //Failed Try
}
Converting Future[Try[Option[Int]]] to Future[Int]
One hacky way is to convert the unfavourable results into failed future and flatMapping over.
Convert try failures to Future failures preserving the information that exception originated from Try and convert None to NoneFound exception.
val f: Future[Try[Option[Int]]] = ???
case class TryException(ex: Throwable) extends Exception(ex.getMessage)
case object NoneFound extends Exception("None found")
val result: Future[Int] = f.flatMap {
case Success(Some(value)) => Future.successful(value)
case Success(None) => Future.failed(NoneFound)
case Failure(th) => Future.failed(TryException(th))
}
result.map { extractedValue =>
processTheExtractedValue(extractedValue)
}.recover {
case NoneFound => "None case"
case TryException(th) => "try failures"
case th => "future failures"
}
Now in every case you know from where the exception has originated. In case of NoneFound exception you know Future and Try are successful but option is none. This way information is not lost and nested structure is flattened to Future[Int].
Now result type would be Future[Int]. Just use map, flatMap, recover and recoverWith to compose further actions.
If you really concerned about extraction see this, else go through the answer by #pamu to see how you actually use your Future.
Suppose your Future value is result.
Await.ready(result, 10.seconds).value.get.map { i => i.get}.get
Obviously this wont get through your failure and None cases and would throw exceptions and Await is not recommended.
So if you want to handle Failure and None case ->
val extractedValue = Await.ready(f, 10.seconds).value.get match {
case Success(i) => i match {
case Some(value) => value
case None => println("Handling None here")
}
case Failure(i) => println("Handling Failure here")
}
I am trying to create a neat construction with for-comprehension for business logic built on futures. Here is a sample which contains a working example based on Exception handling:
(for {
// find the user by id, findUser(id) returns Future[Option[User]]
userOpt <- userDao.findUser(userId)
_ = if (!userOpt.isDefined) throw new EntityNotFoundException(classOf[User], userId)
user = userOpt.get
// authenticate it, authenticate(user) returns Future[AuthResult]
authResult <- userDao.authenticate(user)
_ = if (!authResult.ok) throw new AuthFailedException(userId)
// find the good owned by the user, findGood(id) returns Future[Option[Good]]
goodOpt <- goodDao.findGood(goodId)
_ = if (!good.isDefined) throw new EntityNotFoundException(classOf[Good], goodId)
good = goodOpt.get
// check ownership for the user, checkOwnership(user, good) returns Future[Boolean]
ownership <- goodDao.checkOwnership(user, good)
if (!ownership) throw new OwnershipException(user, good)
_ <- goodDao.remove(good)
} yield {
renderJson(Map(
"success" -> true
))
})
.recover {
case ex: EntityNotFoundException =>
/// ... handle error cases ...
renderJson(Map(
"success" -> false,
"error" -> "Your blahblahblah was not found in our database"
))
case ex: AuthFailedException =>
/// ... handle error cases ...
case ex: OwnershipException =>
/// ... handle error cases ...
}
However this might be seen as a non-functional or non-Scala way to handle the things. Is there a better way to do this?
Note that these errors come from different sources - some are at the business level ('checking ownership') and some are at controller level ('authorization') and some are at db level ('entity not found'). So approaches when you derive them from a single common error type might not work.
Don't use exceptions for expected behaviour.
It's not nice in Java, and it's really not nice in Scala. Please see this question for more information about why you should avoid using exceptions for regular control flow. Scala is very well equipped to avoid using exceptions: you can use Eithers.
The trick is to define some failures you might encounter, and convert your Options into Eithers that wrap these failures.
// Failures.scala
object Failures {
sealed trait Failure
// Four types of possible failures here
case object UserNotFound extends Failure
case object NotAuthenticated extends Failure
case object GoodNotFound extends Failure
case object NoOwnership extends Failure
// Put other errors here...
// Converts options into Eithers for you
implicit class opt2either[A](opt: Option[A]) {
def withFailure(f: Failure) = opt.fold(Left(f))(a => Right(a))
}
}
Using these helpers, you can make your for comprehension readable and exception free:
import Failures._
// Helper function to make ownership checking more readable in the for comprehension
def checkGood(user: User, good: Good) = {
if(checkOwnership(user, good))
Right(good)
else
Left(NoOwnership)
}
// First create the JSON
val resultFuture: Future[Either[Failure, JsResult]] = for {
userRes <- userDao.findUser(userId)
user <- userRes.withFailure(UserNotFound).right
authRes <- userDao.authenticate(user)
auth <- authRes.withFailure(NotAuthenticated).right
goodRes <- goodDao.findGood(goodId)
good <- goodRes.withFailure(GoodNotFound).right
checkedGood <- checkGood(user, good).right
} yield renderJson(Map("success" -> true)))
// Check result and handle any failures
resultFuture.map { result =>
result match {
case Right(json) => json // serve json
case Left(failure) => failure match {
case UserNotFound => // Handle errors
case NotAuthenticated =>
case GoodNotFound =>
case NoOwnership =>
case _ =>
}
}
}
You could clean up the for comprehension a little to look like this:
for {
user <- findUser(userId)
authResult <- authUser(user)
good <- findGood(goodId)
_ <- checkOwnership(user, good)
_ <- goodDao.remove(good)
} yield {
renderJson(Map(
"success" -> true
))
}
Assuming these methods:
def findUser(id:Long) = find(id, userDao.findUser)
def findGood(id:Long) = find(id, goodDao.findGood)
def find[T:ClassTag](id:Long, f:Long => Future[Option[T]]) = {
f(id).flatMap{
case None => Future.failed(new EntityNotFoundException(implicitly[ClassTag[T]].runtimeClass, id))
case Some(entity) => Future.successful(entity)
}
}
def authUser(user:User) = {
userDao.authenticate(user).flatMap{
case result if result.ok => Future.failed(new AuthFailedException(userId))
case result => Future.successful(result)
}
}
def checkOwnership(user:User, good:Good):Future[Boolean] = {
val someCondition = true //real logic for ownership check goes here
if (someCondition) Future.successful(true)
else Future.failed(new OwnershipException(user, good))
}
The idea here is to use flatMap to turn things like Options that are returned wrapped in Futures into failed Futures when they are None. There are going to be a lot of ways to do clean up that for comp and this is one possible way to do it.
The central challenge is that for-comprehensions can only work on one monad at a time, in this case it being the Future monad and the only way to short-circuit a sequence of future calls is for the future to fail. This works because the subsequent calls in the for-comprehension are just map and flatmap calls, and the behavior of a map/flatmap on a failed Future is to return that future and not execute the provided body (i.e. the function being called).
What you are trying to achieve is the short-cicuiting of a workflow based on some conditions and not do it by failing the future. This can be done by wrapping the result in another container, let's call it Result[A], which gives the comprehension a type of Future[Result[A]]. Result would either contain a result value, or be a terminating result. The challenge is how to:
provide subsequent function calls the value contained by a prior non-terminating Result
prevent the subsequent function call from being evaluated if the Result is terminating
map/flatmap seem like the candidates for doing these types of compositions, except we will have to call them manually, since the only map/flatmap that the for-comprehension can evaluate is one that results in a Future[Result[A]].
Result could be defined as:
trait Result[+A] {
// the intermediate Result
def value: A
// convert this result into a final result based on another result
def given[B](other: Result[B]): Result[A] = other match {
case x: Terminator => x
case v => this
}
// replace the value of this result with the provided one
def apply[B](v: B): Result[B]
// replace the current result with one based on function call
def flatMap[A2 >: A, B](f: A2 => Future[Result[B]]): Future[Result[B]]
// create a new result using the value of both
def combine[B](other: Result[B]): Result[(A, B)] = other match {
case x: Terminator => x
case b => Successful((value, b.value))
}
}
For each call, the action is really a potential action, as calling it on or with a terminating result, will simply maintain the terminating result. Note that Terminator is a Result[Nothing] since it will never contain a value and any Result[+A] can be a Result[Nothing].
The terminating result is defined as:
sealed trait Terminator extends Result[Nothing] {
val value = throw new IllegalStateException()
// The terminator will always short-circuit and return itself as
// the success rather than execute the provided block, thus
// propagating the terminating result
def flatMap[A2 >: Nothing, B](f: A2 => Future[Result[B]]): Future[Result[B]] =
Future.successful(this)
// if we apply just a value to a Terminator the result is always the Terminator
def apply[B](v: B): Result[B] = this
// this apply is a convenience function for returning this terminator
// or a successful value if the input has some value
def apply[A](opt: Option[A]) = opt match {
case None => this
case Some(v) => Successful[A](v)
}
// this apply is a convenience function for returning this terminator or
// a UnitResult
def apply(bool: Boolean): Result[Unit] = if (bool) UnitResult else this
}
The terminating result makes it possible to to short-circuit calls to functions that require a value [A] when we've already met our terminating condition.
The non-terminating result is defined as:
trait SuccessfulResult[+A] extends Result[A] {
def apply[B](v: B): Result[B] = Successful(v)
def flatMap[A2 >: A, B](f: A2 => Future[Result[B]]): Future[Result[B]] = f(value)
}
case class Successful[+A](value: A) extends SuccessfulResult[A]
case object UnitResult extends SuccessfulResult[Unit] {
val value = {}
}
The non-teminating result makes it possible to provide the contained value [A] to functions. For good measure, I've also predefined a UnitResult for functions that are purely side-effecting, like goodDao.removeGood.
Now let's define your good, but terminating conditions:
case object UserNotFound extends Terminator
case object NotAuthenticated extends Terminator
case object GoodNotFound extends Terminator
case object NoOwnership extends Terminator
Now we have the tools to create the the workflow you were looking for. Each for comprehention wants a function that returns a Future[Result[A]] on the right-hand side, producing a Result[A] on the left-hand side. The flatMap on Result[A] makes it possible to call (or short-circuit) a function that requires an [A] as input and we can then map its result to a new Result:
def renderJson(data: Map[Any, Any]): JsResult = ???
def renderError(message: String): JsResult = ???
val resultFuture = for {
// apply UserNotFound to the Option to conver it into Result[User] or UserNotFound
userResult <- userDao.findUser(userId).map(UserNotFound(_))
// apply NotAuthenticated to AuthResult.ok to create a UnitResult or NotAuthenticated
authResult <- userResult.flatMap(user => userDao.authenticate(user).map(x => NotAuthenticated(x.ok)))
goodResult <- authResult.flatMap(_ => goodDao.findGood(goodId).map(GoodNotFound(_)))
// combine user and good, so we can feed it into checkOwnership
comboResult = userResult.combine(goodResult)
ownershipResult <- goodResult.flatMap { case (user, good) => goodDao.checkOwnership(user, good).map(NoOwnership(_))}
// in order to call removeGood with a good value, we take the original
// good result and potentially convert it to a Terminator based on
// ownershipResult via .given
_ <- goodResult.given(ownershipResult).flatMap(good => goodDao.removeGood(good).map(x => UnitResult))
} yield {
// ownership was the last result we cared about, so we apply the output
// to it to create a Future[Result[JsResult]] or some Terminator
ownershipResult(renderJson(Map(
"success" -> true
)))
}
// now we can map Result into its value or some other value based on the Terminator
val jsFuture = resultFuture.map {
case UserNotFound => renderError("User not found")
case NotAuthenticated => renderError("User not authenticated")
case GoodNotFound => renderError("Good not found")
case NoOwnership => renderError("No ownership")
case x => x.value
}
I know that's a whole lot of setup, but at least the Result type can be used for any Future for-comprehension that has terminating conditions.
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.