Scala's Try is very useful.
I'd like to use that pattern, but log all exceptions.
How can I do this?
Define the following helper:
import scala.util.{Try, Failure}
def LogTry[A](computation: => A): Try[A] = {
Try(computation) recoverWith {
case e: Throwable =>
log(e)
Failure(e)
}
}
Then you can use it as you would use Try, but any exception will be logged through log(e).
Starting Scala 2.13, the chaining operation tap can be used to apply a side effect (in this case some logging) on any value while returning the original value:
import util.chaining._
val x = Try("aa".toInt).tap(_.failed.foreach(println))
// java.lang.NumberFormatException: For input string: "aa"
// x: Try[Int] = Failure(java.lang.NumberFormatException: For input string: "aa")
Or an equivalent pattern matching version:
val x = Try("aa".toInt).tap { case Failure(e) => println(e) case _ => }
// java.lang.NumberFormatException: For input string: "aa"
// x: Try[Int] = Failure(java.lang.NumberFormatException: For input string: "aa")
The tap chaining operation applies a side effect (in this case println or some logging) on a value (in this case a Try) while returning the original unmodified value on which tap is applied (the Try):
def tap[U](f: (A) => U): A
You can tweak it even further using implicit class
def someMethod[A](f: => A): Try[A] = Try(f)
implicit class LogTry[A](res: Try[A]) {
def log() = res match {
case Success(s) => println("Success :) " + s); res
case Failure(f) => println("Failure :( " + f); res
}
}
Now you can call someMethod and on its result call log like this:
scala> someMethod(1/0).log
Failure :( java.lang.ArithmeticException: / by zero
and
scala> someMethod(1).log
Success :) 1
Of course println method inside implicit class can be substituted with any logging you want.
You used the term "exceptions" which is ambiguous. (java.lang.)Throwable is the root of anything that can be placed behind the throw term. java.lang.Exception is one of the two descendants of Throwable (the other being java.lang.Error). Further making this ambiguous is java.lang.RuntimeException, a descendant of Exception, which is probably where you mostly want to spend your logging time (unless you are doing lower level application framework or hardware driver implementations).
Assuming you are wanting to log literally ALL instances of Throwable, then you would need something like this (NOT RECOMMENDED):
def logAtThrowable(f: => A): Try[A] =
try
Try(f) match {
case failure # Failure(throwable) =>
log(s"Failure: {throwable.getMessage}")
failure
case success # _ =>
//uncomment out the next line if you want to also log Success-es
//log(s"Success: {throwable.getMessage}")
success
}
catch throwable: Throwable => {
//!NonFatal pathway
log(s"Failure: {throwable.getMessage}")
throw throwable
}
The external try/catch is required to capture all the Throwable instances which are filtered away by scala.util.control.NonFatal within the Try's try/catch block.
That said...there is a Java/JVM rule: you should never define a catch clause at the resolution of Throwable (again, unless you are doing lower level application framework or hardware driver implementations).
Following the intention of this rule, you would need to narrow the Throwable to you only emitted logging at the finer grained level, say something more refined, like java.lang.RuntimeException. If so, the code would look like this (recommended):
def logAtRuntimeException(f: => A): Try[A] =
Try(f) match {
case failure # Failure(throwable) =>
throwable match {
case runtimeException: RuntimeException =>
log(s"Failure: {runtimeException.getMessage}")
}
failure
case success # _ =>
success
}
In both code snippets above, you will notice that I used match as opposed to .recoverWith. This is to facilitate easily adding a rethrow that works. It turns out that all the methods on Try are themselves also wrapped with try/catch blocks. This means that if you want to log the Throwable and then rethrow it, if you are using one of the Try methods like recoverWith, the rethrow is immediately recaught and placed into a Failure thereby completely undermining the value of the intentional rethrow. By using match, the rethrow is guaranteed to succeed as it remains outside any of the Try methods.
If you would like to see more of the rabbit holes around this particular area, I created a blog post of my own exploration.
Related
The Scala Try construct together with its flatMap does not work as I would expect or want it to. The TL;DR is that I want to do a series of operations that can fail in two ways: either by raising an exception, which should be promoted and caught higher up in the call stack, or by returning Failure, as the failure must logically be handled in different parts of the program.
I would expect something like this to do the trick:
def firstStepSucceeds(): Try[Int] = Try {
1
}
def secondStepThrows(input: Int) = {
throw new Exception("Exception thrown in second step")
}
// I expect this to propagate the exception thrown in secondStepThrows
firstStepSucceeds() flatMap (secondStepThrows _)
(Full Scastie with example)
However, in this case, the flatMap() call actually implicitly catches the uncaught exception thrown by secondStepThrows, which is not what I want (which is why I left out the Try block). Is there a way to get the same behaviour without the implicit exception-catching?
What happens in a Try should stay in a Try. Most Scala programmers would be very surprised if a function returning a Try also sometimes threw an exception.
The typical pattern if you want to handle exceptions in different places is to differentiate by the type of the exception. So
val partiallyRecoveredTry = originalTry.recover{
case _: SecondStepException => "second step had an exception"
}
// Further up the call stack
partiallyRecoveredTry.getOrElse("first step had an exception")
Try.flatMap() did not caught exceptions implicitely, it is the essence of Try.
When you use it, it is very explicit, and that's the goal.
I don't really understand what you want, but is something like that is possible for you ?
try {
val first = firstStepSucceeds()
val second = first.map(secondStepThrows).get
val third = secondStepFails(second)
// ...
}
catch {
case e: Exception => ???
}
I did some further experimentation, and what I ended up with was this reimplementation of Try as (the now right-biased and hence monadic) Either:
object CatchAll {
def apply[SomeType](block: => SomeType) = try { Right(block) }
catch { case e: Throwable => Left(e) }
}
def firstStepSucceeds() = CatchAll {
1
}
def firstStepFails() = CatchAll {
throw new Exception("First step failed")
}
def secondStepSucceeds(input: Int) = CatchAll {
input + 1
}
def secondStepFails(input: Int) = CatchAll {
throw new Exception("Second step failed in try block!")
}
def secondStepThrows(input: Int) = {
throw new Exception("Second step failed unexpectedly!")
}
firstStepSucceeds() flatMap (secondStepSucceeds _)
firstStepFails() flatMap (secondStepSucceeds _)
firstStepSucceeds() flatMap (secondStepFails _)
// This now throws an exception as expected
//firstStepSucceeds() flatMap (secondStepThrows _)
I have handled exception as follows:
def calculate(input: Option[Double]): Try[String] =
Try {
input match {
case (Some(value)) => value.toString
case (None) => throw new IllegalArgumentException("No value found")
}
}
And in client code:
val result = calculate(....)
result match {
case Success(i) => println(i)
case Failure(s) => throw s // or log(s) to log the issue and continue
}
Is it good enough practice or much better can be done for clean and elegant code base?
Try usually used to cover parts which might throw an error, like in cases if you are using some Java libs, which can throw an exception. But, if you would like to return possible error and force client to handle it, Either[A, B] is much better option, at least because you can specify more precise error type for Left[A] and safely to pattern match over your's A type, instead of do possibly incorrect pattern matching against some Throwable, like you would do for Failure(t).
So, in your case possible solution would look like:
sealed trait CalculationError
case class Error1(cause: String) extends CalculationError
def calculate(input: Option[Double]): Either[CalculationError, String] =
input match {
case (Some(value)) => Right(value.toString)
case (None) => Left(Error1("No value found"))
}
}
val result = calculate(....)
result match {
case Right(i) => println(i)
case Left(Error1(s)) => println(s)
}
This is safer approach, because you can later add another type of error , say case class Error2(cause: String) extends CalculationError and on client pattern matching code part, compile will show a warn message that you missed handling of new error: Match is not exhaustive. In case of Failure(t) compile won't be able suggest such warning, so it's easier to make mistake on error handling side.
Hope this helps!
We have some legacy code in our codebase that is eventually going to be refactored to use Validated and Either from Cats library. This is because Validated does not use fail-fast mechanics. The unrefactored code uses fail-fast mechanics of Try monad.
Since the refactoring hasn't happened yet, I am doing a kludgy hack to get around the fact that the Try monad is fail-fast. I am having trouble implementing it however.
I basically have a list of type Try[T] that is guaranteed to all be Failures.
I am trying to aggregate all of the error messages of all the Failures into a single Failure.
Here is the function I am refactoring:
private def extractTry[T](xs: IndexedSeq[Try[T]]): Try[IndexedSeq[T]] = {
val failures = xs.collect { case Failure(ex) => Failure(ex) }
if (failures.size > 0) failures.head
else Success(xs.map(_.get))
}
Instead of failures.head in the second line of the method, I want to aggregate all the Failures.
So something like
if (failures.size > 0) failures.foldLeft(Failure(new IllegalArgumentException(""))){case (Failure(acc), Failure(e)) => Failure(new IllegalArgumentException(acc.getMessage + e.getMessage))}
The only thing I don't like about this implementation is that I would like each step of fold not to use IllegalArgumentException, but to use the new element's exception type. So the idea is to keep the exception type of the last element in failures, and not to use an arbitrary exception type.
We are planning to eventually use Either[Throwable, T] in place of Try and will probably run into the exact same problem there when we try to aggregate errors. We want to keep the exception type and not assign an arbitrary one like IllegalArgumentException. So this problem is going to have to be solved sooner or later, and I would prefer that it be sooner.
Does anyone have any suggestions? Any help would be appreciated.
Ideally, we would follow suggestion by #Luis. Until then consider perhaps something like so
sealed trait OverallResult[+T]
case class OverallError(accumulatedMessage: String, finalErrorCode: Int) extends OverallResult[Nothing]
case class OverallSuccess[T](xs: IndexedSeq[T]) extends OverallResult[T]
object OverallResult {
/**
* Aggregating over a chain of Failures, it will only keep the exception type of the last Failure.
* This is just a heuristic to decide on the error code. Depending on the exception type, we use
* a different error code. So NoSuchElementException is 404 and IllegalArgumentException is 400.
*/
def apply[T](xs: IndexedSeq[Try[T]]): OverallResult[T] = {
val failures = xs.collect { case Failure(ex) => ex }
if (failures.nonEmpty) {
val accMessage = failures.map(_.getMessage).mkString("[", ",", "]")
OverallError(accMessage, errorCode(failures.last))
}
else OverallSuccess(xs.map(_.get))
}
private def errorCode(ex: Throwable): Int = ex match {
case _: NoSuchElementException => 404
case _: IllegalArgumentException => 400
case e => throw new RuntimeException("Unexpected exception. Fix ASAP!", e)
}
}
OverallResult(Vector(Try(throw new NoSuchElementException("boom")), Try(throw new IllegalArgumentException("crash"))))
OverallResult(Vector(Try(42), Try(11)))
which outputs
res0: OverallResult[Nothing] = OverallError([boom,crash],400)
res1: OverallResult[Int] = OverallSuccess(Vector(42, 11))
Note explicit documentation of the heuristic mentioned in the comments:
/**
* Aggregating over a chain of Failures, it will only keep the exception type of the last Failure.
* This is just a heuristic to decide on the error code. Depending on the exception type, we use
* a different error code. So NoSuchElementException is 404 and IllegalArgumentException is 400.
*/
Error accumulation is simulated with
failures.map(_.getMessage).mkString("[", ",", "]")
and overall status code decided with
errorCode(failures.last)
Now clients of extractTry need to be refactored to pattern match on OverallResult ADT, and finalErrorCode instead of exceptions, but lower level codebase should remain unaffected.
Mario wrote up a response that I think deserves being the accepted answer because of its thoroughness. It was while reading his answer that I stumbled upon another solution that requires less code change, but still gets the job done.
The answer is to pattern match on the exception type, which seems very obvious in retrospect.
if (failures.size > 0) failures.foldLeft(Failure(new IllegalArgumentException(""))){case (Failure(acc), Failure(e)) =>
val message = acc.getMessage + e.getMessage
e match {
case ex: IllegalArgumentException => Failure(new IllegalArgumentException(message))
case ex: NoSuchElementException => Failure(new NoSuchElementException(message))
}
}
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.