ValidatedSection
Related Doc:
package catslib
When no errors are present in the configuration, we get a ConnectionParams wrapped in a Valid instance.
Our parallelValidate function looks awfully like the Apply#map2 function.
Our parallelValidate function looks awfully like the Apply#map2 function.
def map2[F[_], A, B, C](fa: F[A], fb: F[B])(f: (A, B) => C): F[C]
Which can be defined in terms of Apply#ap and Apply#map, the very functions needed to create an Apply instance.
Can we perhaps define an Apply instance for Validated? Better yet, can we define an Applicative instance?
import cats.Applicative implicit def validatedApplicative[E : Semigroup]: Applicative[Validated[E, ?]] = new Applicative[Validated[E, ?]] { def ap[A, B](f: Validated[E, A => B])(fa: Validated[E, A]): Validated[E, B] = (fa, f) match { case (Valid(a), Valid(fab)) => Valid(fab(a)) case (i@Invalid(_), Valid(_)) => i case (Valid(_), i@Invalid(_)) => i case (Invalid(e1), Invalid(e2)) => Invalid(Semigroup[E].combine(e1, e2)) } def pure[A](x: A): Validated[E, A] = Validated.valid(x) def map[A, B](fa: Validated[E, A])(f: A => B): Validated[E, B] = fa.map(f) def product[A, B](fa: Validated[E, A], fb: Validated[E, B]): Validated[E, (A, B)] = ap(fa.map(a => (b: B) => (a, b)))(fb) }
Awesome! And now we also get access to all the goodness of Applicative, which includes map{2-22}, as well as the
Cartesian syntax |@|.
We can now easily ask for several bits of configuration and get any and all errors returned back.
import cats.Apply import cats.data.ValidatedNel implicit val nelSemigroup: Semigroup[NonEmptyList[ConfigError]] = SemigroupK[NonEmptyList].algebra[ConfigError] val config = Config(Map(("name", "cat"), ("age", "not a number"), ("houseNumber", "1234"), ("lane", "feline street"))) case class Address(houseNumber: Int, street: String) case class Person(name: String, age: Int, address: Address)
Thus.
val personFromConfig: ValidatedNel[ConfigError, Person] = Apply[ValidatedNel[ConfigError, ?]].map4(config.parse[String]("name").toValidatedNel, config.parse[Int]("age").toValidatedNel, config.parse[Int]("house_number").toValidatedNel, config.parse[String]("street").toValidatedNel) { case (name, age, houseNumber, street) => Person(name, age, Address(houseNumber, street))
We can now rewrite validations in terms of Apply.
flatMaps and Xors Option has flatMap, Xor has flatMap, where's Validated's? Let's try to implement it - better yet,
let's implement the Monad type class.
import cats.Monad implicit def validatedMonad[E]: Monad[Validated[E, ?]] = new Monad[Validated[E, ?]] { def flatMap[A, B](fa: Validated[E, A])(f: A => Validated[E, B]): Validated[E, B] = fa match { case Valid(a) => f(a) case i@Invalid(_) => i } def pure[A](x: A): Validated[E, A] = Valid(x) }
Note that all Monad instances are also Applicative instances, where ap is defined as
trait Monad[F[_]] { def flatMap[A, B](fa: F[A])(f: A => F[B]): F[B] def pure[A](x: A): F[A] def map[A, B](fa: F[A])(f: A => B): F[B] = flatMap(fa)(f.andThen(pure)) def ap[A, B](fa: F[A])(f: F[A => B]): F[B] = flatMap(fa)(a => map(f)(fab => fab(a))) }
However, the ap behavior defined in terms of flatMap does not behave the same as that of
our ap defined above. Observe:
val v = validatedMonad.tuple2(Validated.invalidNel[String, Int]("oops"), Validated.invalidNel[String, Double]("uh oh"))
This one short circuits! Therefore, if we were to define a Monad (or FlatMap) instance for Validated we would
have to override ap to get the behavior we want. But then the behavior of flatMap would be inconsistent with
that of ap, not good. Therefore, Validated has only an Applicative instance.
If you do want error accumulation but occasionally run into places where you sequential validation is needed, then Validated provides a couple methods that may be helpful.
andThen The andThen method is similar to flatMap (such as Xor.flatMap). In the cause of success, it passes the valid value into a function that returns a new Validated instance.
val houseNumber = config.parse[Int]("house_number").andThen{ n => if (n >= 0) Validated.valid(n) else Validated.invalid(ParseError("house_number")) }
But what happens when having one or more errors? They are accumulated in a NonEmptyList
wrapped in a Invalid instance.
The withXor method allows you to temporarily turn a Validated instance into an Xor instance and apply it to a function.
withXor The withXor method allows you to temporarily turn a Validated instance into an Xor instance and apply it to a function.
import cats.data.Xor def positive(field: String, i: Int): ConfigError Xor Int = { if (i >= 0) Xor.right(i) else Xor.left(ParseError(field)) }
So we can get Xor's short-circuiting behaviour when using the Validated type.
This method has been deprecated in favor of macro assertion and will be removed in a future version of ScalaTest. If you need this, please copy the source code into your own trait instead.
This method has been deprecated in favor of macro assertion and will be removed in a future version of ScalaTest. If you need this, please copy the source code into your own trait instead.
This method has been deprecated in favor of macro assumption and will be removed in a future version of ScalaTest. If you need this, please copy the source code into your own trait instead.
This method has been deprecated in favor of macro assumption and will be removed in a future version of ScalaTest. If you need this, please copy the source code into your own trait instead.
Please use 'an [Exception] should be thrownBy { ... }' syntax instead
This expect method has been deprecated. Please replace all invocations of expect with an identical invocation of assertResult instead.
This expect method has been deprecated. Please replace all invocations of expect with an identical invocation of assertResult instead.
This expectResult method has been deprecated. Please replace all invocations of expectResult with an identical invocation of assertResult instead.
This expectResult method has been deprecated. Please replace all invocations of expectResult with an identical invocation of assertResult instead.
Imagine you are filling out a web form to signup 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 ascala.util.Either(orcats.data.Xor). Your parsing may look something like: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 be 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 aReadtype class - we provide instances just forStringandIntfor brevity.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.
We need a data type that can represent either a successful value (a parsed configuration), or an error. It'd look like in the following example, which cats provides in
cats.data.Validated.Now we are ready to write our parser.
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.
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
Semigrouptype class captures this idea.Perfect! But.. going back to our example, we don't have a way to combine
ConfigErrors. But as clients, we can change ourValidatedvalues where the error can be combined, say, aList[ConfigError]. It is more common however to use aNonEmptyList[ConfigError]- theNonEmptyListstatically guarantees we have at least one value, which aligns with the fact that if we have anInvalid, then we most certainly have at least one error. This technique is so common there is a convenient method onValidatedcalledtoValidatedNelthat turns anyValidated[E, A]value to aValidated[NonEmptyList[E], A]. Additionally, the type aliasValidatedNel[E, A]is provided.Time to parse.