The following block of code fails to build with error :
value flatMap is not a member of Product with Serializable
[error] if (matchingUser.isDefined) {
Here's the code:
for {
matchingUser <- userDao.findOneByEmail(email)
user <- {
if (matchingUser.isDefined) {
matchingUser.map(u => {
// update u with new values...
userDao.save(u)
u
})
} else {
val newUser = new User(email)
userDao.create(newUser)
newUser
}
}
} yield user
Method userDao.findOneByEmail(email) returns anFuture[Option[User]]object.
My Google searches are only aboutEitherwithRightandLeft` types.
Maybe I'm not doing this the proper way, please teach me how to properly do this.
The first branch of the if statement returns Option[User], the other one returns User. So, the result of the entire statement is inferred to have type Product with Serializable because it is the only common supertype of the two.
You could wrap the last statement inside the if into an Option (just do Option(newUser) instead of newUser) or, better yet, use fold instead of the whole if(matchingUser.isDefined) {...} thingy:
matchingUser.fold {
val u = new User(email)
userDao.create(u)
u
} { u =>
userDao.save(u)
u
}
This will make the result of that statement to be Option[User] as you probably intended ... but it still will not compile.
The problem is that you cannot mix different types of monads in the for-comprehension: since the first one was Future, all the others have to be as well. You can't have an Option there.
How to get around that? Well, one possibility is to make userDao.create and userDao.save return a future of the object they just saved. That is, probably a better thing to do in general, then what you have, because now you are returning the user before it was actually stored ... What if the create operation fails afterwards? Then you can just rewrite your for-comprehension like this:
for {
matchingUser <- userDao.findOneByEmail(email)
user <- matchingUser.fold(userDao.create(new User(email)))(userDao.save)
} yield user
Or just get rid of it entirely (for-comprehension is an overkill for simple cases like this):
userDao
.findOneByEmail(email)
.flatMap(_.fold(usrDao.create(new User(email)))(userDao.save))
Or, it may look a little nicer with pattern matching instead of fold in this case:
userDao
.findOneByEmail(email)
.flatMap {
case Some(u) => userDao.save(u)
case None => userDao.create(new User(email))
}
Here is my test example to reproduce your issue with solution.
Basically your user returns wrapper around Future (Option of Future), but it expected to be Future, as the first statement in for-comprehension.
That is why I've applied some addition unwrapping. See sample below.
Note: it does not looks so nice, I'd prefer to rewrite it with flatMap map.
object T {
import scala.concurrent.Future
import scala.concurrent.ExecutionContext.Implicits.global
def future1: Future[Option[Int]] = ???
def future2(i: Int): Future[Double] = ???
for {
matchingUser <- future1
user <- {
if (matchingUser.isDefined) {
matchingUser.map { i =>
future2(i)
}
} else {
Some(future2(42))
}
} match {
case None => Future.successful(-42.0)
case Some(x) => x
}
} yield user
}
And the same implemented with flatMap:
val userFuture = future1.flatMap {
case Some(i) => future2(i)
case None => future2(42)
}
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.")
}
I want to take a function that returns a Future[Option[String]] and use that in conjunction with spray routing's onComplete directive. But no matter what I do, I can't seem to get it work.
Let's say that I have the following function:
def expensiveOperation: Future[Option[String]] = { ... do stuff ... }
And then I want to define a portion of my Route as such:
onComplete(expensiveOperation) {
case Success(string) => complete(string)
case Failure(_) => complete("failure")
}
Is there a way to do this without writing a separate function to transform the Future[Option[String]] into a basic Future[String]?
onComplete(expensiveOperation) {
case Success(Some(string)) => complete(string)
case _ => complete("failure")
}
or:
onComplete(expensiveOperation.map(_.get)) {
case Success(string) => complete(string)
case Failure(_) => complete("failure")
}
A Late Answer. Found this to be working.
post{
entity(as[Project]) { project =>
complete {
(projectActor ? Update(project)).mapTo[Project]
}
}
}
Hope this Fixes the issue
https://groups.google.com/forum/#!topic/spray-user/FM6mF6JXuNM
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)
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.