Version: 2.x

IdentityFlatten

IdentityFlatten[F] describes a way of combining two layers of a value of type F[F[A]] into a F[A] in a way that is associative and has an identity value any of type F[Any].

Its signature is:

trait AssociativeFlatten[F[+_]] {
  def flatten[A](f: F[F[A]]): F[A]
}

trait IdentityFlatten[F[+_]] extends AssociativeFlatten[F] {
  def any: F[Any]
}

The any value must be an identity with respect to the flatten operator. So if we add a new layer of nesting by mapping a value into the any value and then remove that layer with flatten we should get the original value back unchanged.

fa.map(a => any.map(_ => a)).flatten === fa
any.map(_ => fa).flatten === fa

This may look slightly different than the normal identity law because we have to map over the value but it is the same as the identity laws for the Identity, IdentityBoth and IdentityEither abstractions. Note how the two laws say whether we add the additional layer on the inside or the outside doesn't matter.

To be an identity element, the any value must not do anything when run other than return a value with no information content. This way we can always add a layer and remove it by using flatten to get back to the original value.

For example the any value for ZIO is unit. The unit workflow doesn't do anything and always succeeds.

So we can always flatten a ZIO workflow constructed by mapping over the unit value to the original workflow back.

import zio._

import java.io.IOException

val helloIdentity: ZIO[Console, IOException, Unit] =
  ZIO.unit.map { _ =>
    Console.printLine("Hello from an identity!")
  }.flatten
// helloIdentity: ZIO[Console, IOException, Unit] = FlatMap(
//   trace = "repl.MdocSession.MdocApp0.helloIdentity(identityflatten.md:33)",
//   first = FlatMap(
//     trace = "repl.MdocSession.MdocApp0.helloIdentity(identityflatten.md:31)",
//     first = Sync(
//       trace = "",
//       eval = zio.ZIO$$$Lambda$16992/0x0000000804676840@3d6521f3
//     ),
//     successK = zio.ZIO$$Lambda$17065/0x00000008046db040@3cc4bd47
//   ),
//   successK = zio.ZIO$$Lambda$17066/0x00000008046dc040@3d40b0ab
// )

Recall from our discussion of AssociativeFlatten that the flatten operator corresponds to running a workflow, which returns a new workflow, and then running the workflow. Here the unit workflow doesn't do anything and always succeeds so we can simply eliminate it.

Just like the any value that is an identity with respect to the both operator of a IdentityBoth, the any value represents a successful value that contains no information. So other examples of the any value would include:

val anyOption: Option[Any] =
  Some(())
// anyOption: Option[Any] = Some(value = ())

val anyEither: Either[Nothing, Any] =
  Right(())
// anyEither: Either[Nothing, Any] = Right(value = ())

val anyList: List[Any] =
  List(())
// anyList: List[Any] = List(())

By combining the structure described by the IdentityFlatten abstraction and the Covariant abstraction we can also define the succeed operator we saw from IdentityBoth.

import zio.prelude._

def succeed[F[+_]: IdentityFlatten : Covariant, A](a: => A): F[A] =
  IdentityFlatten[F].any.map(_ => a)

As discussed in the section on IdentityBoth, the succeed operator is helpful because it lets us "lift" any normal value into the context of the parameterized type.

For example, using the succeed operator on ZIO we can import an arbitrary block of Scala code into a ZIO workflow.

def helloScala: ZIO[Any, Nothing, Unit] =
  ZIO.succeed(println("Hello Scala!"))

For this operator to be well defined we have to be able to construct a value any that doesn't do anything and has no information content. If all values of the parameterized type required some additional information, for example, we would not be able to define the IdentityFlatten abstraction or succeed for it.

In addition to the succeed operator being generally useful, there are some other operators that require an IdentityFlatten instance so that they can use it in their own implementations.

In particular, the ForEach abstraction defines a set of operators that allow us to fold over a collection in the context of a parameterized type. These require that we have an IdentityFlatten instance so that if the collection is empty we can simply succeed with an empty collection in the context of the parameterized type.

The IdentityFlatten abstraction is quite powerful, combining the flexibility of the AssociativeFlatten abstraction to run one value and then use its result to run another value with the ability to lift arbitrary values into the context of the parameterized type.

If you are using data types from ZIO or the standard library there are some operators defined on the ForEach abstraction that can be helpful but otherwise the operators defined in terms of IdentityFlatten are likely already implemented directly on the data types you are using.

If you are implementing your own data type you should certainly consider whether your data type can implement flatMap, or whether you explicitly don't want to. Either way being able to lift a value into the parameterized type with succeed using either the IdentityBoth or IdentityFlatten abstraction is extremely useful so you should either implement one of those abstractions or have very specific reasons why that is not possible.

Finally, for writing generic code the combination of the IdentityFlatten and Covariant abstractions is also quite powerful, allowing modeling imperative code in a functional context.

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