Imagine you are filling out a web form to sign up for an account. You input your username and password and submit. Response comes back saying your username can't have dashes in it, so you make some changes and resubmit. Can't have special characters either. Change, resubmit. Passwords need to have at least one capital letter. Change, resubmit. Password needs to have at least one number.

Or perhaps you're reading from a configuration file. One could imagine the configuration library you're using returns a scala.util.Try, or maybe a scala.util.Either. Your parsing may look something like:

case class ConnectionParams(url: String, port: Int)

for {
url  <- config[String]("url")
port <- config[Int]("port")
} yield ConnectionParams(url, port)

You run your program and it says key "url" not found, turns out the key was "endpoint". So you change your code and re-run. Now it says the "port" key was not a well-formed integer.

It would be nice to have all of these errors reported simultaneously. That the username can't have dashes can be validated separately from it not having special characters, as well as from the password needing to have certain requirements. A misspelled (or missing) field in a config can be validated separately from another field not being well-formed.

Enter Validated.

Parallel validation

Our goal is to report any and all errors across independent bits of data. For instance, when we ask for several pieces of configuration, each configuration field can be validated separately from one another. How then do we enforce that the data we are working with is independent? We ask for both of them up front.

As our running example, we will look at config parsing. Our config will be represented by a Map[String, String]. Parsing will be handled by a Read type class - we provide instances only for String and Int for brevity.

trait Read[A] {
def read(s: String): Option[A]
}

object Read {
def apply[A](implicit A: Read[A]): Read[A] = A

implicit val stringRead: Read[String] =
 new Read[String] { def read(s: String): Option[String] = Some(s) }

implicit val intRead: Read[Int] =
 new Read[Int] {
   def read(s: String): Option[Int] =
     if (s.matches("-?[0-9]+")) Some(s.toInt)
     else None
 }
}

Then we enumerate our errors—when asking for a config value, one of two things can go wrong: the field is missing, or it is not well-formed with regards to the expected type.

sealed abstract class ConfigError
final case class MissingConfig(field: String) extends ConfigError
final case class ParseError(field: String) extends ConfigError

We need a data type that can represent either a successful value (a parsed configuration), or an error. It would look like the following, which cats provides in cats.data.Validated:

sealed abstract class Validated[+E, +A]

object Validated {
final case class Valid[+A](a: A) extends Validated[Nothing, A]
final case class Invalid[+E](e: E) extends Validated[E, Nothing]
}

Now we are ready to write our parser.

import cats.data.Validated
import cats.data.Validated.{Invalid, Valid}

case class Config(map: Map[String, String]) {
def parse[A : Read](key: String): Validated[ConfigError, A] =
 map.get(key) match {
   case None        => Invalid(MissingConfig(key))
   case Some(value) =>
     Read[A].read(value) match {
       case None    => Invalid(ParseError(key))
       case Some(a) => Valid(a)
     }
 }
}

Everything is in place to write the parallel validator. Recall that we can only do parallel validation if each piece is independent. How do we enforce the data is independent? By asking for all of it up front. Let's start with two pieces of data.

def parallelValidate[E, A, B, C](v1: Validated[E, A], v2: Validated[E, B])(f: (A, B) => C): Validated[E, C] =
(v1, v2) match {
 case (Valid(a), Valid(b))       => Valid(f(a, b))
 case (Valid(_), i@Invalid(_))   => i
 case (i@Invalid(_), Valid(_))   => i
 case (Invalid(e1), Invalid(e2)) => ???
}

We've run into a problem. In the case where both have errors, we want to report both. But we have no way of combining the two errors into one error! Perhaps we can put both errors into a List, but that seems needlessly specific—clients may want to define their own way of combining errors.

How then do we abstract over a binary operation? The Semigroup type class captures this idea.

import cats.Semigroup

def parallelValidate[E : Semigroup, A, B, C](v1: Validated[E, A], v2: Validated[E, B])(f: (A, B) => C): Validated[E, C] =
(v1, v2) match {
 case (Valid(a), Valid(b))       => Valid(f(a, b))
 case (Valid(_), i@Invalid(_))   => i
 case (i@Invalid(_), Valid(_))   => i
 case (Invalid(e1), Invalid(e2)) => Invalid(Semigroup[E].combine(e1, e2))
}

Perfect! But, going back to our example, we don't have a way to combine ConfigErrors. But as clients, we can change our Validated values where the error can be combined, say, a List[ConfigError]. It is more common however to use a NonEmptyList[ConfigError]—the NonEmptyList statically guarantees we have at least one value, which aligns with the fact that if we have an Invalid, then we most certainly have at least one error. This technique is so common there is a convenient method on Validated called toValidatedNel that turns any Validated[E, A] value to a Validated[NonEmptyList[E], A]. Additionally, the type alias ValidatedNel[E, A] is provided.

Time to parse.

import cats.SemigroupK
import cats.data.NonEmptyList
import cats.implicits._

implicit val nelSemigroup: Semigroup[NonEmptyList[ConfigError]] =
SemigroupK[NonEmptyList].algebra[ConfigError]

implicit val readString: Read[String] = Read.stringRead
implicit val readInt: Read[Int] = Read.intRead