In my scala code, I have some nested Try() match {}, which look ugly:
import scala.util._
Try(convertJsonToObject[User]) match {
case Success(userJsonObj) =>
Try(saveToDb(userJsonObj.id)) match {
case Success(user) => Created("User saved")
case _ => InternalServerError("database error")
}
case _ => BadRequest("bad input")
}
Is there any better way of writing such code?
There's a bunch of ways to solve this problem. I'll give you one possibility. Consider this cleaned up version of your code:
trait Result
case class BadRequest(message:String) extends Result
case class InternalServerError(message:String) extends Result
case class Created(message:String) extends Result
def processRequest(json:String):Result = {
val result =
for{
user <- Try(parseJson(json))
savedUser <- Try(saveToDb(user))
} yield Created("saved")
result.recover{
case jp:JsonParsingException => BadRequest(jp.getMessage)
case other => InternalServerError(other.getMessage)
}.get
}
def parseJson(json:String):User = ...
def saveToDb(user:User):User = ...
The caveat to this code is that it assumes that you can differentiate the json parsing failure from the db failure by the exception each might yield. Not a bad assumption to make though. This code is very similar to a java try/catch block that catches different exception types and returns different results based on catching those different types.
One other nice thing about this approach is that you could just define a standard recovery Partial Function for all kinds of possible exceptions and use it throughout your controllers (which I'm assuming this code is) to eliminate duplicate code. Something like this:
object ExceptionHandling{
val StandardRecovery:PartialFunction[Throwable,Result] = {
case jp:JsonParsingException => BadRequest(jp.getMessage)
case sql:SQLException => InternalServerError(sql.getMessage)
case other => InternalServerError(other.getMessage)
}
}
And then in your controller:
import ExceptionHandling._
result.recover(StandardRecovery).get
Another approach is to define implicit reads for User (if using Play Framework) and then doing something like
someData.validate[User].map { user =>
saveToDb(user.id) match { // you can return Try from saveToDb
case Success(savedUser) => Created("User saved")
case Failure(exception) => InternalServerError("Database Error")
}
}.recoverTotal {
e => BadRequest(JsError.toFlatJson(e))
}
Try(convertJsonToObject[User]).map([your code]).toOption.getOrElse(fallback)
Related
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!
I have a case class QueryParamsas follows:
case class QueryParams(
limit: Option[Integer] = None,
refresh: Option[Boolean] = None,
organisationalUnit: Option[String] = None)
These values limit,refresh,organisationalUnit are actually passed as query parameters in request url for play application.
I need to write a code to check if request URL contains any value for organisationalUnit and if yes I need to throw error .If no, I need to proceed with further operations.
Can anyone help me here
Options are quite good for this kind of thing:
val params: QueryParams = ???
params.organizationalUnit.foreach(_ => throw new Exception("your error message"))
In this way you'll throw only if organizationalUnit is defined. You can also express it as follows:
for (_ <- params.organizationalUnit) {
throw new Exception("your error message")
}
Or alternatively:
if (params.organizationalUnit.isDefined) {
throw new Exception("your error message")
}
The latter is probably the most readable, even though it may not be recognized as very idiomatic according to certain coding styles.
The answer from stefanobaghino is good but I prefer pattern matching for such cases:
params.organisationalUnit match {
case Some(_) => // processing
case _ => //logging
}
If you need other values you can match the whole instance
params match {
case QueryParams(Some(limit), Some(refresh), Some(organisationalUnit)) =>
case QueryParams(mayBeLimit, mayBeRefresh, Some(organisationalUnit)) =>
case _ =>
}
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 need to use some Java library, which might throw some exceptions in one method and return error codes in another set of methods. So far it leads to the ugly code like
val txn = mgr.prepareTransaction()
val accessRecord = txn.readByQuery(...)
var state : Either[MyError, Result] = null //
try {
// do something here
val result = txn.runCodeWithin(new Callable[Result]() {...})
if (result == -1) {
state = Left(CanNotReadRecord)
} else {
state = Right(txn.getCachedRecord())
}
} catch {
case e: Exception => state = Left(GeneralError(e))
} finally {
state match {
case Right(_) => txn.commit();
case _ => txn.rollback();
}
}
I mostly interested in getting rid of state as var and ability to check the state in finally block. Please advice.
Scala 2.10 introduced the Try class, which is a more functional replacement to the use case of Either[Throwable, Result]. It's got all of the usual monad ops (the things that make for-comprehensions work), and some other helpful methods. (check out the docs for Try here)
Here's a possible re-implementation of your code, using Try, and replacing CanNotReadRecord with a CanNotReadRecordException. It should be functionally equivalent to your example, with the exception of that replacement.
def txResults(txn: Transaction): Try[Record] = for {
result <- Try{ txn.runCodeWithin(...) }
checked <- result match {
case -1 => Failure( new CanNotReadRecordException )
case _ => Success( txn.getCachedRecord )
}
} yield checked
txResults(txn) match {
case Success(record) => txn.commit()
case Failure(e) => txn.rollback() //and maybe handle `e`
}
The Scala ARM (Automatic Resource Management) library handles all this sort of thing elegantly and in a completely air-tight manner.
Check it out.
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.