OptionT

advanced

OptionT also known as the Option monad transformer allows to compute inside the context when Option is nested in a different monad.

One issue we face with monads is that they don’t compose. This can cause your code to get really hairy when trying to combine structures like ObservableK and Option. But there’s a simple solution, and we’re going to explain how you can use Monad Transformers to alleviate this problem.

For our purposes here, we’re going to utilize a monad that serves as a container that may hold a value and where a computation can be performed.

Given that both ObservableK<A> and Option<A> would be examples of datatypes that provide instances for the Monad typeclasses.

Because monads don’t compose, we may end up with nested structures such as ObservableK<Option<ObservableK<Option<A>>> when using ObservableK and Option together. Using Monad Transformers can help us to reduce this boilerplate.

In the most basic of scenarios, we’ll only be dealing with one monad at a time making our lives nice and easy. However, it’s not uncommon to get into scenarios where some function calls will return ObservableK<A>, and others will return Option<A>.

So let’s test this out with an example:

import arrow.*
import arrow.core.*

data class Country(val code: Option<String>)
data class Address(val id: Int, val country: Option<Country>)
data class Person(val name: String, val address: Option<Address>)

fun getCountryCode(maybePerson : Option<Person>): Option<String> =
  maybePerson.flatMap { person ->
    person.address.flatMap { address ->
      address.country.flatMap { country ->
        country.code
      }
    }
  }

Nested flatMap calls flatten the Option but the resulting function starts looking like a pyramid and can easily lead to callback hell.

We can further simplify this case by using Arrow fx facilities that enables monad comprehensions for all datatypes for which a monad instance is available.

import arrow.typeclasses.*
import arrow.core.extensions.*
import arrow.core.extensions.option.fx.fx

fun getCountryCode(maybePerson : Option<Person>): Option<String> =
  fx {
    val (person) = maybePerson
    val (address) = person.address
    val (country) = address.country
    val (code) = country.code
    code
  }

Alright, a piece of cake right? That’s because we were dealing with a simple type Option. But here’s where things can get more complicated. Let’s introduce another monad in the middle of the computation. For example what happens when we need to load a person by id, then their address and country to obtain the country code from a remote service?

Consider this simple database representation:

val personDB: Map<Int, Person> = mapOf(
  1 to Person(
        name = "Alfredo Lambda",
        address = Some(
          Address(
            id = 1,
            country = Some(
              Country(
                code = Some("ES")
              )
            )
          )
        )
      )
)

val adressDB: Map<Int, Address> = mapOf(
  1 to Address(
    id = 1,
    country = Some(
      Country(
        code = Some("ES")
      )
    )
  )
)

Now we’ve got two new functions in the mix that are going to call a remote service, and they return a ObservableK. This is common in most APIs that handle loading asynchronously.

import arrow.effects.rx2.*
import arrow.effects.rx2.extensions.*

fun findPerson(personId : Int) : ObservableK<Option<Person>> =
  ObservableK.just(Option.fromNullable(personDB.get(personId))) //mock impl for simplicity

fun findCountry(addressId : Int) : ObservableK<Option<Country>> =
  ObservableK.just(
    Option.fromNullable(adressDB.get(addressId)).flatMap { it.country }
  ) //mock impl for simplicity

A naive implementation attempt to get to a country.code from a person.id might look something like this.

fun getCountryCode(personId: Int) =
  findPerson(personId).map { maybePerson ->
    maybePerson.map { person ->
      person.address.map { address ->
        findCountry(address.id).map { maybeCountry ->
          maybeCountry.map { country ->
            country.code
          }
        }
      }  
    }
  }

val lifted = { personId: Int -> getCountryCode(personId) }
lifted
// (kotlin.Int) -> arrow.effects.rx2.ObservableK<arrow.core.Option<arrow.core.Option<arrow.effects.rx2.ObservableK<arrow.core.Option<arrow.core.Option<kotlin.String>>>>>>

This isn’t actually what we want since the inferred return type is ObservableK<Option<Option<ObservableK<Option<Option<String>>>>>>. We can’t use flatMap in this case because the nested expression does not match the return type of the expression they’re contained within. This is because we’re not flatMapping properly over the nested types.

Still not ideal. The levels of nesting are pyramidal with flatMap and map and are as deep as the number of operations that you have to perform.

Let’s look at how a similar implementation would look like using monad comprehensions without transformers:

import arrow.effects.rx2.extensions.observablek.fx.fx

fun getCountryCode(personId: Int): ObservableK<Option<String>> =
       fx {
        val maybePerson = findPerson(personId).bind()
        val person = maybePerson.fold(
          { ObservableK.raiseError<Person>(NoSuchElementException("...")) },
          { ObservableK.just(it) }
        ).bind()
        val address = person.address.fold(
          { ObservableK.raiseError<Address>(NoSuchElementException("...")) },
          { ObservableK.just(it) }
        ).bind()
        val maybeCountry = findCountry(address.id).bind()
        val country = maybeCountry.fold(
          { ObservableK.raiseError<Country>(NoSuchElementException("...")) },
          { ObservableK.just(it) }
        ).bind()
        country.code
      }       

While we’ve got the logic working now, we’re in a situation where we’re forced to deal with the None cases. We also have a ton of boilerplate type conversion with fold. The type conversion is necessary because in a monad comprehension you can only use a type of Monad. If we start with ObservableK, we have to stay in it’s monadic context by lifting anything we compute sequentially to a ObservableK whether or not it’s async.

This is a commonly encountered problem, especially in the context of async services. So how can we reconcile the fact that we’re mixing Option and ObservableK?

Monad Transformers to the Rescue!

Monad Transformers enable you to combine two monads into a super monad. In this case, we’re going to use OptionT from Arrow to express the effect of potential absence inside our async computations.

OptionT has the form of OptionT<F, A>.

This means that for any monad F surrounding an Option<A> we can obtain an OptionT<F, A>. So our specialization OptionT<ForObservableK, A> is the OptionT transformer around values that are of ObservableK<Option<A>>.

We can now lift any value to a OptionT<F, A> which looks like this:

import arrow.data.*
import arrow.effects.rx2.extensions.observablek.applicative.*

val optTVal = OptionT.just<ForObservableK, Int>(ObservableK.applicative(), 1)
optTVal
// OptionT(value=ObservableK(observable=io.reactivex.internal.operators.observable.ObservableJust@156e08fd))

or

val optTVal = OptionT.fromOption<ForObservableK, Int>(ObservableK.applicative(), Some(1))
optTVal
// OptionT(value=ObservableK(observable=io.reactivex.internal.operators.observable.ObservableJust@6689e9ed))

And back to the ObservableK<Option<A>> running the transformer

optTVal.value()
// ObservableK(observable=io.reactivex.internal.operators.observable.ObservableJust@6689e9ed)

So how would our function look if we implemented it with the OptionT monad transformer?

import arrow.effects.rx2.extensions.*
import arrow.data.extensions.optiont.fx.fx
import arrow.effects.rx2.extensions.observablek.monad.monad

fun getCountryCode(personId: Int): ObservableK<Option<String>> =
   fx(ObservableK.monad()) {
    val (person) = OptionT(findPerson(personId))
    val (address) = OptionT(ObservableK.just(person.address))
    val (country) = OptionT(findCountry(address.id))
    val (code) = OptionT(ObservableK.just(country.code))
    code
  }.value().fix()

Here we no longer have to deal with the None cases, and the binding to the values on the left side are already the underlying values we want to focus on instead of the optional values. We have automatically flatMapped through the ObservableK and Option in a single expression reducing the boilerplate and encoding the effects concerns in the type signatures.

Supported type classes

Take a look at the EitherT docs for an alternative version of this content with the EitherT monad transformer

Credits

Contents partially adapted from FP for the avg Joe at the 47 Degrees blog