I am still new to Scala, and one of the things I read about is that for-comprehension is equivalent to a flatMap to a certain extent. However, in my code (below), flatMap is taking at least as twice as long to compute. What could be the reason for this?
This is the slow one:
facts.flatMap(f => factActionsMap(f)).filter(_.isValid(facts))
This is the fast equivalent one:
for {
f <- facts
a <- factActionsMap(f)
if a.isValid(facts)
} yield a
factActionsMap is a map between Fact and a Set[Action].
facts is just a Set[Fact].
Let's check the translation:
scala> trait Fact
defined trait Fact
scala> trait Action { def isValid(s: Set[Fact]): Boolean }
defined trait Action
scala> def facts: Set[Fact] = ???
facts: Set[Fact]
scala> def factActionsMap: Map[Fact, Set[Action]] = ???
factActionsMap: Map[Fact,Set[Action]]
scala> import scala.reflect.runtime.{universe => u}
import scala.reflect.runtime.{universe=>u}
scala> val expr = u reify {
| for {
| f <- facts
| a <- factActionsMap(f)
| if a.isValid(facts)
| } yield a
| }
expr: reflect.runtime.universe.Expr[scala.collection.immutable.Set[Action]] = Expr[scala.collection.immutable.Set[Action]]($read.f
acts.flatMap(((f) => $read.factActionsMap.apply(f).withFilter(((a) => a.isValid($read.facts))).map(((a) => a))(Set.canBuildFrom)))
(Set.canBuildFrom))
scala> u show expr.tree
res0: String = $read.facts.flatMap(((f) => $read.factActionsMap.apply(f).withFilter(((a) => a.isValid($read.facts))).map(((a) => a
))(Set.canBuildFrom)))(Set.canBuildFrom)
So, removing REPL stuff (everything starting with $) and the implicit parameters, plus reformatting, we get:
facts.flatMap(f => factActionsMap(f).withFilter(a => a.isValid(facts)).map(a => a))
There are two main differences between this and what you came up with. First, withFilter is applied to fastActionsMap(f) result, whereas you apply to facts.flatMap result. This means flatMap will work over all results, instead of just the ones accepted.
Second, it uses withFilter instead of filter, which avoids creating an extra collection.
Unless I'm mistaken, a more precise equivalent to the second expression would be
facts.flatMap(f => factActionsMap(f).withFilter(_.isValid(facts)))
withFilter is like filter, except that is is lazy, so if you're not doing anything with the contents of the Set, you haven't actually applied the filter. You could check this hypothesis by, for example, printing all elements of the set before calling System.currentTimeMillis for the second time.
Related
How to conveniently convert Seq[Try[Option[String, Any]]] into Try[Option[Map[String, Any]]].
If any Try before convert throws an exception, the converted Try should throw as well.
Assuming that the input type has a tuple inside the Option then this should give you the result you want:
val in: Seq[Try[Option[(String, Any)]]] = ???
val out: Try[Option[Map[String,Any]]] = Try(Some(in.flatMap(_.get).toMap))
If any of the Trys is Failure then the outer Try will catch the exception raised by the get and return Failure
The Some is there to give the correct return type
The get extracts the Option from the Try (or raises an exception)
Using flatMap rather than map removes the Option wrapper, keeping all Some values and discaring None values, giving Seq[(String, Any)]
The toMap call converts the Seq to a Map
Here is something that's not very clean but may help get you started. It assumes Option[(String,Any)], returns the first Failure if there are any in the input Seq and just drops None elements.
foo.scala
package foo
import scala.util.{Try,Success,Failure}
object foo {
val x0 = Seq[Try[Option[(String, Any)]]]()
val x1 = Seq[Try[Option[(String, Any)]]](Success(Some(("A",1))), Success(None))
val x2 = Seq[Try[Option[(String, Any)]]](Success(Some(("A",1))), Success(Some(("B","two"))))
val x3 = Seq[Try[Option[(String, Any)]]](Success(Some(("A",1))), Success(Some(("B","two"))), Failure(new Exception("bad")))
def f(x: Seq[Try[Option[(String, Any)]]]) =
x.find( _.isFailure ).getOrElse( Success(Some(x.map( _.get ).filterNot( _.isEmpty ).map( _.get ).toMap)) )
}
Example session
bash-3.2$ scalac foo.scala
bash-3.2$ scala -classpath .
Welcome to Scala 2.13.1 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_66).
Type in expressions for evaluation. Or try :help.
scala> import foo.foo._
import foo.foo._
scala> f(x0)
res0: scala.util.Try[Option[Equals]] = Success(Some(Map()))
scala> f(x1)
res1: scala.util.Try[Option[Equals]] = Success(Some(Map(A -> 1)))
scala> f(x2)
res2: scala.util.Try[Option[Equals]] = Success(Some(Map(A -> 1, B -> two)))
scala> f(x3)
res3: scala.util.Try[Option[Equals]] = Failure(java.lang.Exception: bad)
scala> :quit
If you're willing to use a functional support library like Cats then there are two tricks that can help this along:
Many things like List and Try are traversable, which means that (if Cats's implicits are in scope) they have a sequence method that can swap two types, for example converting List[Try[T]] to Try[List[T]] (failing if any of the items in the list are failure).
Almost all of the container types support a map method that can operate on the contents of a container, so if you have a function from A to B then map can convert a Try[A] to a Try[B]. (In Cats language they are functors but the container-like types in the standard library generally have map already.)
Cats doesn't directly support Seq, so this answer is mostly in terms of List instead.
Given that type signature, you can iteratively sequence the item you have to in effect push the list type down one level in the type chain, then map over that container to work on its contents. That can look like:
import cats.implicits._
import scala.util._
def convert(listTryOptionPair: List[Try[Option[(String, Any)]]]): Try[
Option[Map[String, Any]]
] = {
val tryListOptionPair = listTryOptionPair.sequence
tryListOptionPair.map { listOptionPair =>
val optionListPair = listOptionPair.sequence
optionListPair.map { listPair =>
Map.from(listPair)
}
}
}
https://scastie.scala-lang.org/xbQ8ZbkoRSCXGDJX0PgJAQ has a slightly more complete example.
One way to approach this is by using a foldLeft:
// Let's say this is the object you're trying to convert
val seq: Seq[Try[Option[(String, Any)]]] = ???
seq.foldLeft(Try(Option(Map.empty[String, Any]))) {
case (acc, e) =>
for {
accOption <- acc
elemOption <- e
} yield elemOption match {
case Some(value) => accOption.map(_ + value)
case None => accOption
}
}
You start off with en empty Map. You then use a for comprehension to go through the current map and element and finally you add a new tuple in the map if present.
The following solutions is based on this answer to the point that almost makes the question a duplicate.
Method 1: Using recursion
def trySeqToMap1[X,Y](trySeq : Seq[Try[Option[(X, Y)]]]) : Try[Option[Map[X,Y]]] = {
def helper(it : Iterator[Try[Option[(X,Y)]]], m : Map[X,Y] = Map()) : Try[Option[Map[X,Y]]] = {
if(it.hasNext) {
val x = it.next()
if(x.isFailure)
Failure(x.failed.get)
else if(x.get.isDefined)
helper(it, m + (x.get.get._1-> x.get.get._2))
else
helper(it, m)
} else Success(Some(m))
}
helper(trySeq.iterator)
}
Method 2: directly pattern matching in case you are able to get a stream or a List instead:
def trySeqToMap2[X,Y](trySeq : LazyList[Try[Option[(X, Y)]]], m : Map[X,Y]= Map.empty[X,Y]) : Try[Option[Map[X,Y]]] =
trySeq match {
case Success(Some(h)) #:: tail => trySeqToMap2(tail, m + (h._1 -> h._2))
case Success(None) #:: tail => tail => trySeqToMap2(tail, m)
case Failure(f) #:: _ => Failure(f)
case _ => Success(Some(m))
}
note: this answer was previously using different method signatures. It has been updated to conform to the signature given in the question.
I am trying to use Cats datatype Ior to accumulate both errors and successes of using a service (which can return an error).
def find(key: String): F[Ior[NonEmptyList[Error], A]] = {
(for {
b <- service.findByKey(key)
} yield b.rightIor[NonEmptyList[Error]])
.recover {
case e: Error => Ior.leftNel(AnotherError)
}
}
def findMultiple(keys: List[String]): F[Ior[NonEmptyList[Error], List[A]]] = {
keys map find reduce (_ |+| _)
}
My confusion lies in how to combine the errors/successes. I am trying to use the Semigroup combine (infix syntax) to combine with no success. Is there a better way to do this? Any help would be great.
I'm going to assume that you want both all errors and all successful results. Here's a possible implementation:
class Foo[F[_]: Applicative, A](find: String => F[IorNel[Error, A]]) {
def findMultiple(keys: List[String]): F[IorNel[Error, List[A]]] = {
keys.map(find).sequence.map { nelsList =>
nelsList.map(nel => nel.map(List(_)))
.reduceOption(_ |+| _).getOrElse(Nil.rightIor)
}
}
}
Let's break it down:
We will be trying to "flip" a List[IorNel[Error, A]] into IorNel[Error, List[A]]. However, from doing keys.map(find) we get List[F[IorNel[...]]], so we need to also "flip" it in a similar fashion first. That can be done by using .sequence on the result, and is what forces F[_]: Applicative constraint.
N.B. Applicative[Future] is available whenever there's an implicit ExecutionContext in scope. You can also get rid of F and use Future.sequence directly.
Now, we have F[List[IorNel[Error, A]]], so we want to map the inner part to transform the nelsList we got. You might think that sequence could be used there too, but it can not - it has the "short-circuit on first error" behavior, so we'd lose all successful values. Let's try to use |+| instead.
Ior[X, Y] has a Semigroup instance when both X and Y have one. Since we're using IorNel, X = NonEmptyList[Z], and that is satisfied. For Y = A - your domain type - it might not be available.
But we don't want to combine all results into a single A, we want Y = List[A] (which also always has a semigroup). So, we take every IorNel[Error, A] we have and map A to a singleton List[A]:
nelsList.map(nel => nel.map(List(_)))
This gives us List[IorNel[Error, List[A]], which we can reduce. Unfortunately, since Ior does not have a Monoid, we can't quite use convenient syntax. So, with stdlib collections, one way is to do .reduceOption(_ |+| _).getOrElse(Nil.rightIor).
This can be improved by doing few things:
x.map(f).sequence is equivalent to doing x.traverse(f)
We can demand that keys are non-empty upfront, and give nonempty result back too.
The latter step gives us Reducible instance for a collection, letting us shorten everything by doing reduceMap
class Foo2[F[_]: Applicative, A](find: String => F[IorNel[Error, A]]) {
def findMultiple(keys: NonEmptyList[String]): F[IorNel[Error, NonEmptyList[A]]] = {
keys.traverse(find).map { nelsList =>
nelsList.reduceMap(nel => nel.map(NonEmptyList.one))
}
}
}
Of course, you can make a one-liner out of this:
keys.traverse(find).map(_.reduceMap(_.map(NonEmptyList.one)))
Or, you can do the non-emptiness check inside:
class Foo3[F[_]: Applicative, A](find: String => F[IorNel[Error, A]]) {
def findMultiple(keys: List[String]): F[IorNel[Error, List[A]]] = {
NonEmptyList.fromList(keys)
.map(_.traverse(find).map { _.reduceMap(_.map(List(_))) })
.getOrElse(List.empty[A].rightIor.pure[F])
}
}
Ior is a good choice for warning accumulation, that is errors and a successful value. But, as mentioned by Oleg Pyzhcov, Ior.Left case is short-circuiting. This example illustrates it:
scala> val shortCircuitingErrors = List(
Ior.leftNec("error1"),
Ior.bothNec("warning2", 2),
Ior.bothNec("warning3", 3)
).sequence
shortCircuitingErrors: Ior[Nec[String], List[Int]]] = Left(Chain(error1))
One way to accumulate both errors and successes is to convert all your Left cases into Both. One approach is using Option as right type and converting Left(errs) values into Both(errs, None). After calling .traverse, you end up with optList: List[Option] on the right side and you can flatten it with optList.flatMap(_.toList) to filter out None values.
class Error
class KeyValue
def find(key: String): Ior[Nel[Error], KeyValue] = ???
def findMultiple(keys: List[String]): Ior[Nel[Error], List[KeyValue]] =
keys
.traverse { k =>
val ior = find(k)
ior.putRight(ior.right)
}
.map(_.flatMap(_.toList))
Or more succinctly:
def findMultiple(keys: List[String]): Ior[Nel[Error], List[KeyValue]] =
keys.flatTraverse { k =>
val ior = find(k)
ior.putRight(ior.toList) // Ior[A,B].toList: List[B]
}
Suppose I have:
val res:Future[Option[Boolean]] = Future(Some(true))
and I can do:
res.map(opt => opt.map(r => print(!r)))
I guess the for comprehension for this would be:
for {
opt <- res
r <- opt
} yield (print(!r))
but this does not work! I get an error ie:
error: type mismatch;
found : Option[Unit]
required: scala.concurrent.Future[?]
r <- opt
How do I use a Future[Option[Boolean]] in a for comprehension to extract or convert the Boolean?
Note: this is a simplification of the problem I have at the moment with many Future[Option[Boolean]] variables that I would like to use together in a for comprehension.
A for-comprehension really makes this look like it should all work, doesn't it? But let's think about what you're asking for.
First, note that for un-nests:
for {xs <- List(List(5)); x <- xs} yield x
produces
List(5)
Now, without even getting into the type signatures or desugaring, we can think about replacing List with some arbitrary type T, and we'll call the contained type A:
for { xs <- T(T(a: A)); x <- xs } yield x
and we should get a
T[A]
back (presumably the one we put in, but the types don't actually promise us that).
Okay, but what about
for { xs <- T(U(a: A)); x <- xs } yield x
? This is strictly more general than the case where the two things have the same nesting. Well, if T and U both have a common supertype S, then we can just view the whole thing as S(S(a: A)), so we'll at least get an S back. But what about in the general case?
The bottom line is that it depends. For example, let's consider the case where T=Future, U=Option. We have the following possibilities:
Success(Some(a))
Success(None)
Failure(t: Throwable)
Now, can we come up with any coherent policy for unwrapping? If we unwrap into a Future, then what A do you use for the Success(None) case? You don't have one available to return. Likewise, if you try to vanquish the outer Future, how do you know, without explicitly stating it somehow to the compiler, that Failure should be mapped to None (if indeed it should--maybe it should go to a default!).
So the bottom line is that you just can't do this correctly in general without specifying what is supposed to happen for every pair T[U[_]]. (I encourage the interested reader to peruse tutorials on monads and monad transformers.)
There is a way out, though: if you can explicitly turn your U into a T, or your T into your U, you can take advantage of the unwrapping capability. It's pretty easy to turn an Option into a Future , so the easiest solution is
for { opt <- res; r <- Future(opt.get) } yield r
(where just let the exception get thrown on none.get). Alternatively, you can turn the Future into an Option with the slightly ugly
for { opt <- res.value.flatMap(_.toOption); r <- opt } yield r
Equivalent code for
for {
opt <- res
r <- opt
} yield (print(!r))
is not
res.map(opt => opt.map(r => print(!r)))
but
res.flatMap(opt => opt.map(r => print(!r)))
And it makes no sense in this case.
For the chain of maps you could use nested for-comprehensions
for { opt <- res }
for { r <- opt }
print(!r)
But map looks better.
Well, for comprehensions are deceiving in the way they look. Your for-comprehension expands to:
res.flatMap(opt => opt.map(r => print(!r))
Which is obviously wrong as flatMap expects a return type of Future[T] and you are providing Option[Unit]
Though sometimes, for code tidiness you would want to have a single for loop with many such expressions. In those cases you could:
scala> implicit def toFuture[T](t: => T):Future[T] = {
| val p = Promise[T]()
| p.tryComplete(Try(t))
| p.future
| }
scala> for {
| opt <- res
| r <- opt
| } yield {print(!r)}
false
The above produces:
res.flatMap[Option[Unit]](((opt: Option[Boolean]) =>
toFuture[Option[Unit]](opt.map[Unit](((r: Boolean) => print(!r))))))
Edit: Though you need to take all the pain if you are using yield. If you do not wish to use for comprehension as an expression, then you can do as you desired:
scala> val i = Future(Some(true))
i: scala.concurrent.Future[Some[Boolean]] = scala.concurrent.impl.Promise$DefaultPromise#6b24a494
scala> val j = Option(1)
j: Option[Int] = Some(1)
scala> val k = Right(1).right
k: scala.util.Either.RightProjection[Nothing,Int] = RightProjection(Right(1))
scala>
| for{
| x <- i
| y <- j
| z <- k
| }{
| println(i,j,k)
| }
(scala.concurrent.impl.Promise$DefaultPromise#6b24a494,Some(1),RightProjection(Right(1)))
This way no implicit is required. As compiler uses foreach at every junction. -Xprint:typer gives:
i.foreach[Unit](((x: Option[Boolean]) =>
j.foreach[Any](((y: Int) =>
k.foreach[Unit](((z: Int) => println(scala.this.Tuple3.apply[scala.concurrent.Future[Option[Boolean]], Option[Int], Either.RightProjection[Nothing,Int]](i, j, k))))))))
}
I wish to find a match within a List and return values dependant on the match. The CollectFirst works well for matching on the elements of the collection but in this case I want to match on the member swEl of the element rather than on the element itself.
abstract class CanvNode (var swElI: Either[CSplit, VistaT])
{
private[this] var _swEl: Either[CSplit, VistaT] = swElI
def member = _swEl
def member_= (value: Either[CSplit, VistaT] ){ _swEl = value; attach}
def attach: Unit
attach
def findVista(origV: VistaIn): Option[Tuple2[CanvNode,VistaT]] = member match
{
case Right(v) if (v == origV) => Option(this, v)
case _ => None
}
}
def nodes(): List[CanvNode] = topNode :: splits.map(i => List(i.n1, i.n2)).flatten
//Is there a better way of implementing this?
val temp: Option[Tuple2[CanvNode, VistaT]] =
nodes.map(i => i.findVista(origV)).collectFirst{case Some (r) => r}
Do I need a View on that, or will the collectFirst method ensure the collection is only created as needed?
It strikes me that this must be a fairly general pattern. Another example could be if one had a List member of the main List's elements and wanted to return the fourth element if it had one. Is there a standard method I can call? Failing that I can create the following:
implicit class TraversableOnceRichClass[A](n: TraversableOnce[A])
{
def findSome[T](f: (A) => Option[T]) = n.map(f(_)).collectFirst{case Some (r) => r}
}
And then I can replace the above with:
val temp: Option[Tuple2[CanvNode, VistaT]] =
nodes.findSome(i => i.findVista(origV))
This uses implicit classes from 2.10, for pre 2.10 use:
class TraversableOnceRichClass[A](n: TraversableOnce[A])
{
def findSome[T](f: (A) => Option[T]) = n.map(f(_)).collectFirst{case Some (r) => r}
}
implicit final def TraversableOnceRichClass[A](n: List[A]):
TraversableOnceRichClass[A] = new TraversableOnceRichClass(n)
As an introductory side node: The operation you're describing (return the first Some if one exists, and None otherwise) is the sum of a collection of Options under the "first" monoid instance for Option. So for example, with Scalaz 6:
scala> Stream(None, None, Some("a"), None, Some("b")).map(_.fst).asMA.sum
res0: scalaz.FirstOption[java.lang.String] = Some(a)
Alternatively you could put something like this in scope:
implicit def optionFirstMonoid[A] = new Monoid[Option[A]] {
val zero = None
def append(a: Option[A], b: => Option[A]) = a orElse b
}
And skip the .map(_.fst) part. Unfortunately neither of these approaches is appropriately lazy in Scalaz, so the entire stream will be evaluated (unlike Haskell, where mconcat . map (First . Just) $ [1..] is just fine, for example).
Edit: As a side note to this side note: apparently Scalaz does provide a sumr that's appropriately lazy (for streams—none of these approaches will work on a view). So for example you can write this:
Stream.from(1).map(Some(_).fst).sumr
And not wait forever for your answer, just like in the Haskell version.
But assuming that we're sticking with the standard library, instead of this:
n.map(f(_)).collectFirst{ case Some(r) => r }
I'd write the following, which is more or less equivalent, and arguably more idiomatic:
n.flatMap(f(_)).headOption
For example, suppose we have a list of integers.
val xs = List(1, 2, 3, 4, 5)
We can make this lazy and map a function with a side effect over it to show us when its elements are accessed:
val ys = xs.view.map { i => println(i); i }
Now we can flatMap an Option-returning function over the resulting collection and use headOption to (safely) return the first element, if it exists:
scala> ys.flatMap(i => if (i > 2) Some(i.toString) else None).headOption
1
2
3
res0: Option[java.lang.String] = Some(3)
So clearly this stops when we hit a non-empty value, as desired. And yes, you'll definitely need a view if your original collection is strict, since otherwise headOption (or collectFirst) can't reach back and stop the flatMap (or map) that precedes it.
In your case you can skip findVista and get even more concise with something like this:
val temp = nodes.view.flatMap(
node => node.right.toOption.filter(_ == origV).map(node -> _)
).headOption
Whether you find this clearer or just a mess is a matter of taste, of course.
say, I have a bunch of "validation" functions that return None if there is no error, otherwise it return Some(String) specifying the error message. Something like the following ...
def validate1:Option[String]
def validate2:Option[String]
def validate3:Option[String]
I am going to call them in a sequence and as soon as one returns Some(String), I stop and return the same. If it returns None, I go to the next until the sequence is over. If all of them return None, I return None.
I would like to glue them together in a "for expression". Something like ...
for( a <- validate1; b <- validate2; c <- validate3) yield None;
However, Option flows exactly the opposite what I want here. It stops at None and follows with Some(String).
How can I achieve something like that?
You could just chain the calls together with the orElse method on Option
validate1 orElse validate2 orElse validate3
or you could run a fold over a collection of validate methods converted to functions
val vlist= List(validate1 _, validate2 _, validate3 _)
vlist.foldLeft(None: Option[String]) {(a, b) => if (a == None) b() else a}
The scalaz library has a type called Validation which allows for some incredible gymnastics with building both errors and success. For example, suppose you have a few methods which can either return a failure message or some successful outcome (A/B/C):
import scalaz._; import Scalaz._
def fooA : ValidationNEL[String, A]
def fooB : ValidationNEL[String, B]
def fooC : ValidationNEL[String, C]
These can be used with the applicative functor to chain the calls together:
(foo1 <|**|> (foo2, foo3)) match {
case Success( (a, b, c) ) => //woot
case Failure(msgs) => //erk
}
Note that if any one of foo1/2/3 fails, then the whole composition fails with a non-empty list (NEL) of failure messages. If more than one fails, you get all failure messages.
It's a killer app. Examples of how tor return a success and failure are as follows
def foo1 : ValidationNEL[String, Int] = 1.success
def foo2 : ValidationNEL[String, Double] = "some error msg".failNel
Can't you just combine the iterators together and then take the first element? Something like:
scala> def validate1: Option[String] = {println("1"); None}
scala> def validate2: Option[String] = {println("2"); Some("error")}
scala> def validate3: Option[String] = {println("3"); None}
scala> (validate1.iterator ++ validate2.iterator ++ validate3.iterator).next
1
2
res5: String = error
I think you might benefit from using Lift's Box, which has Full (i.e. Some), Empty (i.e. None) and Failure (an Empty with a reason why it's empty and that can be chained). David Pollak has a good blog post introducing it. In short, you might do something like this (not tested):
def validate1: Box[String]
def validate2: Box[String]
def validate3: Box[String]
val validation = for (
validation1 <- validate1 ?~ "error message 1"
validation2 <- validate2 ?~ "error message 2"
validation3 <- validate3 ?~ "error message 3"
) yield "overall success message"
This isn't any shorter than the original example but it's, in my opinion, a bit more logical, with the result of a successful validation in a Full and a failed validation in Failure.
However, we can get smaller. First, since our validation function return Box[String], they can return Failures themselves and we don't need to transform Empty to Failure ourselves:
val validation = for (
validation1 <- validate1
validation2 <- validate2
validation3 <- validate3
) yield "overall success message"
But, Box also has an or method that returns the same Box if it is Full or the other Box if it is not. This would give us:
val validation = validate1 or validate2 or validate3
However, that line stops at the first validation success, not the first failure. It might make sense to make another method that does what you want (perhaps called unless?) though I can't say that it would really be much more useful than the for comprehension approach.
However, here's a little library pimping that does it:
scala> class Unless[T](a: Box[T]) {
| def unless(b: Box[T]) = {
| if (a.isEmpty) { a }
| else b
| }
| }
defined class Unless
scala> implicit def b2U[T](b: Box[T]): Unless[T] = new Unless(b)
b2U: [T](b: net.liftweb.common.Box[T])Unless[T]
scala> val a = Full("yes")
a: net.liftweb.common.Full[java.lang.String] = Full(yes)
scala> val b = Failure("no")
b: net.liftweb.common.Failure = Failure(no,Empty,Empty)
scala> val c = Full("yes2")
c: net.liftweb.common.Full[java.lang.String] = Full(yes2)
scala> a unless b
res1: net.liftweb.common.Box[java.lang.String] = Failure(no,Empty,Empty)
scala> a unless b unless c
res2: net.liftweb.common.Box[java.lang.String] = Failure(no,Empty,Empty)
scala> a unless c unless b
res3: net.liftweb.common.Box[java.lang.String] = Failure(no,Empty,Empty)
scala> a unless c
res4: net.liftweb.common.Box[java.lang.String] = Full(yes2)
This is a quick hack based upon my limited understanding of Scala's type system, as you can see in the following error:
scala> b unless a
<console>:13: error: type mismatch;
found : net.liftweb.common.Full[java.lang.String]
required: net.liftweb.common.Box[T]
b unless a
^
However, that should be enough to get you on the right track.
Of course the Lift ScalaDocs have more information on Box.