for {
url <- Try(new URL(s))
connection <- Try(url.openConnection())
is <- Try(connection.getInputStream)
} yield Source.fromInputStream(is).getLines()
Each try properly depends on the success of its predecessor. That's to say, this is a sequential conjunction.
But suppose we want to compose tries disjunctively? In other words, success is not defined (as above) when all components succeed but when one succeeds. And again, we want our composition to be sequential (the first try is tried first). How should we go about that?
The obvious solution is to use orElse:
val x1 = "10.0"
val x: Try[Any] = Try(x1.toInt) orElse Try(x1.toDouble)
This works just as expected: the value of x is Success(10.0).
Indeed, orElse has the signature:
Ideally, we'd like another signature something like this:
def orElseTry[U >: T](alt: => U): Try[U] = orElse(Try(f))
def toNumber(x: String) = Try(x.toInt) orElseTry(x.toDouble)
Unlike with the conjunctive set of Try operations that we started with, the disjunctive set is much more likely to be based on a single input, as in the case of the toNumber example above. I therefore set about creating a class to support multiple successive Try operations:
case class Trial[V, T](f: V=>Try[T]) extends TrialBase[V,T](f)
abstract class TrialBase[V, T](f: V=>Try[T]) extends (V=>Try[T]) {
def apply(v: V): Try[T] = f(v)
private def orElse(f: V=>Try[T], g: V=>Try[T]): V=>Try[T] = {v => f(v) orElse g(v) }
def |: (g: V => Try[T]): Trial[V,T] = Trial(orElse(g,f))
def :| (g: V => Try[T]): Trial[V,T] = Trial(orElse(f,g))
def ^: (g: V => T): Trial[V,T] = Trial(orElse(Lift(g),f))
def :^ (g: V => T): Trial[V,T] = Trial(orElse(f,Lift(g)))
}
This case class called Trial takes a function f of type V=>Try[T] such that its instances are composable with other functions of type V=>Try[T] (using |: or :|) or even V=>T functions (using ^: or :^). These latter functions are "lifted" to V=>Try[T]. Such Trial objects can be composed with other functions both leftwards and rightwards using the |: and ^: operators with the colon on the proper side. And, of course we can even use partial functions to allow us to fork the path of control. Let me provide an example taken from a generic CSV parser that I created:
val defaultParser = Trial.none[String,Any] :|
{case s @ (date0(_) | date4(_) | date1(_)) => dateParser(s)} :^
{case quoted(w) => w} :^
{case whole(s) => s.toInt} :^
{case truth(w) => true} :^
{case untruth(w) => false} :^
{case s: String => s match { case floating(_) | floating(_,_) | floating(_,_,_) => s.toDouble}} :^
{case s: String => s}
val dateFormatStrings = Seq("yyyy-MM-dd","yyyy-MM-dd-hh:mm:ss.s") // etc.
val dateParser = Trial[String,Any]((parseDate _)(dateFormatStrings))
val date0 = """(\d\d\d\d-\d\d-\d\d)""".r
val date1 = ??? // omitting for brevity
def parseDate(dfs: Seq[String])(s: String): Try[DateTime] = ??? // omitting for brevity
val quoted = """"([^"]*)"""".r
val whole = """(\d+)""".r
val floating = """-?(\d+(\.\d*)?|\d*\.\d+)([eE][+-]?\d+)?[fFdD]?""".r
val truth = """(?i)^([ty]|true|yes)$""".r
val untruth = """(?i)^([fn]|false|no)$""".r
In this fragment, we define a default element parser for our CsvParser object. Trial.none simply starts the chain of functions (we could also put the date parsing function as the argument to Trial). If the input String is recognized as a valid date format (in reality there are many more possible date formats), then we invoke dateParser with the String as its parameter. Otherwise, we try to interpret the String as a quoted string, an integer, a boolean, etc.
If you're interested in looking at the code you can see it here: Trial.scala, while the specification is here: TrialSpec.scala.
