Scala Future[A] and Future[Option[B]] composition - scala

I have an app that manages Items. When the client queries an item by some info, the app first tries to find an existing item in the db with the info. If there isn't one, the app would
Check if info is valid. This is an expensive operation (much more so than a db lookup), so the app only performs this when there isn't an existing item in the db.
If info is valid, insert a new Item into the db with info.
There are two more classes, ItemDao and ItemService:
object ItemDao {
def findByInfo(info: Info): Future[Option[Item]] = ...
// This DOES NOT validate info; it assumes info is valid
def insertIfNotExists(info: Info): Future[Item] = ...
}
object ItemService {
// Very expensive
def isValidInfo(info: Info): Future[Boolean] = ...
// Ugly
def findByInfo(info: Info): Future[Option[Item]] = {
ItemDao.findByInfo(info) flatMap { maybeItem =>
if (maybeItem.isDefined)
Future.successful(maybeItem)
else
isValidInfo(info) flatMap {
if (_) ItemDao.insertIfNotExists(info) map (Some(_))
else Future.successful(None)
}
}
}
}
The ItemService.findByInfo(info: Info) method is pretty ugly. I've been trying to clean it up for a while, but it's difficult since there are three types involved (Future[Boolean], Future[Item], and Future[Option[Item]]). I've tried to use scalaz's OptionT to clean it up but the non-optional Futures make it not very easy either.
Any ideas on a more elegant implementation?

To expand on my comment.
Since you've already indicated a willingness to go down the route of monad transformers, this should do what you want. There is unfortunately quite a bit of line noise due to Scala's less than stellar typechecking here, but hopefully you find it elegant enough.
import scalaz._
import Scalaz._
object ItemDao {
def findByInfo(info: Info): Future[Option[Item]] = ???
// This DOES NOT validate info; it assumes info is valid
def insertIfNotExists(info: Info): Future[Item] = ???
}
object ItemService {
// Very expensive
def isValidInfo(info: Info): Future[Boolean] = ???
def findByInfo(info: Info): Future[Option[Item]] = {
lazy val nullFuture = OptionT(Future.successful(none[Item]))
lazy val insert = ItemDao.insertIfNotExists(info).liftM[OptionT]
lazy val validation =
isValidInfo(info)
.liftM[OptionT]
.ifM(insert, nullFuture)
val maybeItem = OptionT(ItemDao.findByInfo(info))
val result = maybeItem <+> validation
result.run
}
}
Two comments about the code:
We are using the OptionT monad transformer here to capture the Future[Option[_]] stuff and anything that just lives inside Future[_] we're liftMing up to our OptionT[Future, _] monad.
<+> is an operation provided by MonadPlus. In a nutshell, as the name suggests, MonadPlus captures the intuition that often times monads have an intuitive way of being combined (e.g. List(1, 2, 3) <+> List(4, 5, 6) = List(1, 2, 3, 4, 5, 6)). Here we're using it to short-circuit when findByInfo returns Some(item) rather than the usual behavior to short-circuit on None (this is roughly analogous to List(item) <+> List() = List(item)).
Other small note, if you actually wanted to go down the monad transformers route, often times you end up building everything in your monad transformer (e.g. ItemDao.findByInfo would return an OptionT[Future, Item]) so that you don't have extraneous OptionT.apply calls and then .run everything at the end.

You don't need scalaz for this. Just break your flatMap into two steps:
first, find and validate, then insert if necessary. Something like this:
ItemDao.findByInfo(info).flatMap {
case None => isValidInfo(info).map(None -> _)
case x => Future.successful(x -> true)
}.flatMap {
case (_, true) => ItemDao.insertIfNotExists(info).map(Some(_))
case (x, _) => Future.successful(x)
}
Doesn't look too bad, does it? If you don't mind running validation in parallel with retrieval (marginally more expensive resource-vise, but likely faster on average), you could further simplify it like this:
ItemDao
.findByInfo(info)
.zip(isValidInfo(info))
.flatMap {
case (None, true) => ItemDao.insertIfNotExists(info).map(Some(_))
case (x, _) => x
}
Also, what does insertIfNotExists return if the item does exist? If it returned the existing item, things could be even simpler:
isValidInfo(info)
.filter(identity)
.flatMap { _ => ItemDao.insertIfNotExists(info) }
.map { item => Some(item) }
.recover { case _: NoSuchElementException => None }

If you are comfortable with path-dependent type and higher-kinded type, something like the following can be an elegant solution:
type Const[A] = A
sealed trait Request {
type F[_]
type A
type FA = F[A]
def query(client: Client): Future[FA]
}
case class FindByInfo(info: Info) extends Request {
type F[x] = Option[x]
type A = Item
def query(client: Client): Future[Option[Item]] = ???
}
case class CheckIfValidInfo(info: Info) extends Request {
type F[x] = Const[x]
type A = Boolean
def query(client: Client): Future[Boolean] = ???
}
class DB {
private val dbClient: Client = ???
def exec(request: Request): request.FA = request.query(dbClient)
}
What this does is basically to abstract over both the wrapper type (eg. Option[_]) as well as inner type. For types without a wrapper type, we use Const[_] type which is basically an identity type.
In scala, many problems alike this can be solved elegantly using Algebraic Data Type and its advanced type system (i.e path-dependent type & higher-kinded type). Note that now we have single point of entry exec(request: Request) for executing db requests instead of something like DAO.

Related

Multiple futures that may fail - returning both successes and failures?

I have a situation where I need to run a bunch of operations in parallel.
All operations have the same return value (say a Seq[String]).
Its possible that some of the operations may fail, and others successfully return results.
I want to return both the successful results, and any exceptions that happened, so I can log them for debugging.
Is there a built-in way, or easy way through any library (cats/scalaz) to do this, before I go and write my own class for doing this?
I was thinking of doing each operation in its own future, then checking each future, and returning a tuple of Seq[String] -> Seq[Throwable] where left value is the successful results (flattened / combined) and right is a list of any exceptions that occurred.
Is there a better way?
Using Await.ready, which you mention in a comment, generally loses most benefits from using futures. Instead you can do this just using the normal Future combinators. And let's do the more generic version, which works for any return type; flattening the Seq[String]s can be added easily.
def successesAndFailures[T](futures: Seq[Future[T]]): Future[(Seq[T], Seq[Throwable])] = {
// first, promote all futures to Either without failures
val eitherFutures: Seq[Future[Either[Throwable, T]]] =
futures.map(_.transform(x => Success(x.toEither)))
// then sequence to flip Future and Seq
val futureEithers: Future[Seq[Either[Throwable, T]]] =
Future.sequence(eitherFutures)
// finally, Seq of Eithers can be separated into Seqs of Lefts and Rights
futureEithers.map { seqOfEithers =>
val (lefts, rights) = seqOfEithers.partition(_.isLeft)
val failures = lefts.map(_.left.get)
val successes = rights.map(_.right.get)
(successes, failures)
}
}
Scalaz and Cats have separate to simplify the last step.
The types can be inferred by the compiler, they are shown just to help you see the logic.
Calling value on your Future returns an Option[Try[T]]. If the Future has not completed then the Option is None. If it has completed then it's easy to unwrap and process.
if (myFutr.isCompleted)
myFutr.value.map(_.fold( err: Throwable => //log the error
, ss: Seq[String] => //process results
))
else
// do something else, come back later
Sounds like a good use-case for the Try idiom (it's basically similar to the Either monad).
Example of usage from the doc:
import scala.util.{Success, Failure}
val f: Future[List[String]] = Future {
session.getRecentPosts
}
f onComplete {
case Success(posts) => for (post <- posts) println(post)
case Failure(t) => println("An error has occurred: " + t.getMessage)
}
It actually does a little bit more than what you asked because it is fully asynchronous. Does it fit your use-case?
I'd do it this way:
import scala.concurrent.{Future, ExecutionContext}
import scala.util.Success
def eitherify[A](f: Future[A])(implicit ec: ExecutionContext): Future[Either[Throwable, A]] = f.transform(tryResult => Success(tryResult.toEither))
def eitherifyF[A, B](f: A => Future[B])(implicit ec: ExecutionContext): A => Future[Either[Throwable, B]] = { a => eitherify(f(a)) }
// here we need some "cats" magic for `traverse` and `separate`
// instead of `traverse` you can use standard `Future.sequence`
// there is no analogue for `separate` in the standard library
import cats.implicits._
def myProgram[A, B](values: List[A], asyncF: A => Future[B])(implicit ec: ExecutionContext): Future[(List[Throwable], List[B])] = {
val appliedTransformations: Future[List[Either[Throwable, B]]] = values.traverse(eitherifyF(asyncF))
appliedTransformations.map(_.separate)
}

Handling loads of different message-types at runtime in an elegant way

In order to be able to handle large amounts of different request types I created a .proto file like this:
message Message
{
string typeId = 1;
bytes message = 2;
}
I added the typeId so that one knows what actual protobuf bytes represents. (Self-describing)
Now my problem is handling that different "concrete types" in an elegant way. (Note: All works fine if I simple use a switch-case-like approach!)
I thought about a solution like this:
1) Have a trait the different handlers have to implement, e.g.:
trait Handler[T]
{
def handle(req: T): Any
}
object TestHandler extends Handler[Test]
{
override def handle(req: Test): String =
{
s"A success, $req has been handled by TestHandler
}
}
object OtherHandler extends Handler[Other]
{
override def handle(req: Other): String =
{
s"A success, $req has been handled by OtherHandler
}
}
2) provide some kind of registry to query the right handler for a given message:
val handlers = Map(
Test -> TestHandler,
Other -> OtherHandler
)
3) If a request comes in it identifies itself, so we need another Mapper:
val reqMapper = Map(
"Test" -> Test
"Other" -> Other
)
4) If a request comes in, handle it:
val request ...
// Determine the requestType
val requestType = reqMapper(request.type)
// Find the correct handler for the requestType
val handler = handlers(requestType)
// Parse the actual request
val actualRequest = requestType.parse(...) // type of actualRequest can only be Test or Other in our little example
Now, until here everything looks fine and dandy, but then this line breaks my whole world:
handler.handle(actualRequest)
It leads to:
type mismatch; found : com.trueaccord.scalapb.GeneratedMessage with Product with com.trueaccord.scalapb.Message[_ >: tld.test.proto.Message.Test with tld.test.proto.Message.Other <: com.trueaccord.scalapb.GeneratedMessage with Product] with com.trueaccord.lenses.Updatable[_ >: tld.test.proto.Message.Other with tld.test.proto.Message.Test <: com.trueaccord.scalapb.GeneratedMessage with Product]{def companion: Serializable} required: _1
As far as I understand - PLEASE CORRECT ME HERE IF AM WRONG - the compiler cannot be sure here, that actualRequest is "handable" by a handler. That means it lacks the knowledge that the actualRequest is definitely somewhere in that mapper AND ALSO that there is a handler for it.
It's basically implicit knowledge a human would get, but the compiler cannot infer.
So, that being said, how can I overcome that situation elegantly?
your types are lost when you use a normal Map. for eg
object Test{}
object Other{}
val reqMapper = Map("Test" -> Test,"Other" -> Other)
reqMapper("Test")
res0: Object = Test$#5bf0fe62 // the type is lost here and is set to java.lang.Object
the most idomatic way to approach this is to use pattern matching
request match {
case x: Test => TestHandler(x)
case x: Other => OtherHandler(x)
case _ => throw new IllegalArgumentException("not supported")
}
if you still want to use Maps to store your type to handler relation consider HMap provided by Shapeless here
Heterogenous maps
Shapeless provides a heterogenous map which supports an arbitrary
relation between the key type and the corresponding value type,
I settled for this solution for now (basically thesamet's, a bit adapted for my particular use-case)
trait Handler[T <: GeneratedMessage with Message[T], R]
{
implicit val cmp: GeneratedMessageCompanion[T]
def handle(bytes: ByteString): R = {
val msg: T = cmp.parseFrom(bytes.newInput())
handler(msg)
}
def apply(t: T): R
}
object Test extends Handler[Test, String]
{
override def apply(t: Test): String = s"$t received and handled"
override implicit val cmp: GeneratedMessageCompanion[Test] = Test.messageCompanion
}
One trick you can use is to capture the companion object as an implicit, and combine the parsing and handling in a single function where the type is available to the compiler:
case class Handler[T <: GeneratedMessage with Message[T]](handler: T => Unit)(implicit cmp: GeneratedMessageCompanion[T]) {
def handle(bytes: ByteString): Unit = {
val msg: T = cmp.parseFrom(bytes.newInputStream)
handler(t)
}
}
val handlers: Map[String, Handler[_]] = Map(
"X" -> Handler((x: X) => Unit),
"Y" -> Handler((x: Y) => Unit)
)
// To handle the request:
handlers(request.typeId).handle(request.message)
Also, take a look at any.proto which defines a structure very similar to your Message. It wouldn't solve your problem, but you can take advantage of it's pack and unpack methods.

Either to Try and vice versa in Scala

Are there any conversions from Either to Try and vice versa in the Scala standard library ? Maybe I am missing something but I did not find them.
To the best of my knowledge this does not exist in the standard library. Although an Either is typically used with the Left being a failure and the Right being a success, it was really designed to support the concept of two possible return types with one not necessarily being a failure case. I'm guessing these conversions that one would expect to exist do not exist because Either was not really designed to be a Success/Fail monad like Try is. Having said that it would be pretty easy to enrich Either yourself and add these conversions. That could look something like this:
object MyExtensions {
implicit class RichEither[L <: Throwable,R](e:Either[L,R]){
def toTry:Try[R] = e.fold(Failure(_), Success(_))
}
implicit class RichTry[T](t:Try[T]){
def toEither:Either[Throwable,T] = t.transform(s => Success(Right(s)), f => Success(Left(f))).get
}
}
object ExtensionsExample extends App{
import MyExtensions._
val t:Try[String] = Success("foo")
println(t.toEither)
val t2:Try[String] = Failure(new RuntimeException("bar"))
println(t2.toEither)
val e:Either[Throwable,String] = Right("foo")
println(e.toTry)
val e2:Either[Throwable,String] = Left(new RuntimeException("bar"))
println(e2.toTry)
}
In Scala 2.12.x Try has a toEither method: http://www.scala-lang.org/api/2.12.x/scala/util/Try.html#toEither:scala.util.Either[Throwable,T]
import scala.util.{ Either, Failure, Left, Right, Success, Try }
implicit def eitherToTry[A <: Exception, B](either: Either[A, B]): Try[B] = {
either match {
case Right(obj) => Success(obj)
case Left(err) => Failure(err)
}
}
implicit def tryToEither[A](obj: Try[A]): Either[Throwable, A] = {
obj match {
case Success(something) => Right(something)
case Failure(err) => Left(err)
}
}
The answer depends on how to convert the Failure to Left (and vice versa). If you don't need to use the details of the exception, then Try can be converted to Either by going the intermediate route of an Option:
val tried = Try(1 / 0)
val either = tried.toOption.toRight("arithmetic error")
The conversion the other way requires you to construct some Throwable. It could be done like this:
either.fold(left => Failure(new Exception(left)), right => Success(right))

Some help needed to help the type inferring engine

I've problems understanding where to put type informations in scala, and how to put it. Here I create several sequences of Actors and I don't type them. Even if I had to, I wouldn't know which type of sequence map produces to give them the proper type.
Then later when the compiler yells at me because I'm trying to sum Anys, I've no idea where to begin filling in the gaps.
Here is my code, I tried to minimize it while still letting the necessary info available.
object Actors {
def main(args: Array[String]) {
val array = randomArray(5)
val master = new Master(array, 5)
master.start
}
def randomArray(length: Int): Array[Int] = {
val generator = new Random
new Array[Int](length) map((_:Int) => generator nextInt)
}
}
class Master(array: Array[Int], slavesNumber: Int) extends Actor {
def act () {
val slaves = (1 to slavesNumber).map(_ => new Slave)
slaves.foreach(s => s.start)
val futures = slaves.map(s => s !! Work(array))
val results = awaitAll(3000, futures:_*)
val res2 = results.flatMap(x => x)
println((0 /: res2)(_+_))
}
}
class Slave() extends Actor {
def act () {
Actor.loop {
receive {
case Work(slice) =>
reply((slice :\ 0)(_+_))
}
}
}
}
I'd appreciate too some good pointers towards comprehensive doc on the matter.
The object that are passed between actors are not typed, actors have to filter the object themselves -- as you already do in the Slave actor. As you can see, !! is defined as
def !!(msg: Any): Future[Any]
so there is no type information in the returned Future. Probably the easiest solution is to replace the line var res2 .. with
val res2 = results collect {case Some(y:Int) => y}
this filters out just those Some results that are of type Int.

scala style - how to avoid having lots of nested map

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.