*This is the second of a series of articles on “Monadic EDSLs in Scala.”*

Perhaps the most direct way to start writing an EDSL is to start writing functions. Let’s say we want a language for talking about sets of integers.

```
trait SetLang {
def add(i: Int, set: Set[Int]): Set[Int]
def remove(i: Int, set: Set[Int]): Set[Int]
def exists(i: Int, set: Set[Int]): Boolean
}
```

This works… to the extent that we want only to work with
`scala.collection.Set`

s. As it stands we cannot talk about
other sets such as bloom filters or sets controlled by other threads.
Our language isn’t *abstract* enough, so let’s remove
all traces of `Set`

.

```
trait SetLang[F[_]] {
def add(i: Int, set: F[Int]): F[Int]
def remove(i: Int, set: F[Int]): F[Int]
def exists(i: Int, set: F[Int]): Boolean
// Given unknown F we no longer know how to create an empty set
// so we add the capability to our language
def empty: F[Int]
}
```

We’ve parameterized our language with a higher-kinded type which
represents the context of our set. A similar parameterization could be
done with a *-kinded type (e.g. `SetLang[A]`

) but since this series
focuses on **monadic** EDSLs, the choice is made for us.

Now we can write mini-programs which talk about some abstract set yet to be determined.

```
def program[F[_]](lang: SetLang[F]): Boolean = {
import lang._
exists(10, remove(5, add(10, add(5, empty))))
}
```

Interpretation of our program is done by implementing `SetLang`

and
passing an instance into `program`

.

However, our language is still not abstract enough. Replacing `Set`

with `F`

allows us to swap in implementations of sets, but doesn’t
allow us to talk about the context. Consider the behavior of `exists`

if `F`

represents some remote set. Since `exists`

returns a `Boolean`

,
checking membership must be a synchronous operation despite the set living
on another node.

It’s also tedious to thread the set through each method manually.

We can solve both problems by generalizing the use of `F`

to some
context that is able to read and write to some set
(think `Set[Int] => (Set[Int], A)`

).

```
trait SetLang[F[_]] {
def add(i: Int): F[Unit]
def remove(i: Int): F[Unit]
def exists(i: Int): F[Boolean]
// No longer need `empty` since the "context" has it already
}
```

`SetLang`

can now talk about the **effects** around interpretation, such as
asynchronity.

```
import scala.concurrent.Future
type AsyncSet[A] = Set[Int] => Future[(Set[Int], A)]
object AsyncSet extends SetLang[AsyncSet] {
def add(i: Int): Set[Int] => Future[(Set[Int], Unit)] = ???
def remove(i: Int): Set[Int] => Future[(Set[Int], Unit)] = ???
def exists(i: Int): Set[Int] => Future[(Set[Int], Boolean)] = ???
}
```

This new encoding introduces a new but important problem: how do we
combine the results of multiple calls to `SetLang`

methods? In the previous
encoding we could add and remove by threading the set from one call to
the next. With this change to represent a context, it’s not clear how to do
that.

Fortunately we are now in a position to leverage a powerful tool: monads. By extending our set language to be monadic we recover composition in an elegant way. The Cats library is used for demonstration purposes, but the discussion applies equally to Scalaz.

```
import cats.Monad
import cats.implicits._
trait SetLang[F[_]] {
// See [this post][typeClassSubType] for why the `Monad` instance is defined
// as a member as opposed to through inheritance.
def monad: Monad[F]
def add(i: Int): F[Unit]
def remove(i: Int): F[Unit]
def exists(i: Int): F[Boolean]
}
def program[F[_]](lang: SetLang[F]): F[Boolean] = {
import lang._
implicit val monadInstance = monad
for {
_ <- add(5)
_ <- add(10)
_ <- remove(5)
b <- exists(10)
} yield b
}
```

Defining an interpreter starts by identifying a target context. Since the context computes values while updating state, this suggests the state monad.

```
import cats.data.State
object ScalaSet extends SetLang[State[Set[Int], ?]] {
val monad = Monad[State[Set[Int], ?]]
def add(i: Int): State[Set[Int], Unit] =
State.modify(_ + i)
def remove(i: Int): State[Set[Int], Unit] =
State.modify(_ - i)
def exists(i: Int): State[Set[Int], Boolean] =
State.inspect(_(i))
}
```

```
val state = program[State[Set[Int], ?]](ScalaSet)
// state: cats.data.StateT[cats.Eval,scala.collection.immutable.Set[Int],Boolean] = cats.data.StateT@ce9f626
state.run(Set.empty).value
// res5: (scala.collection.immutable.Set[Int], Boolean) = (Set(10),true)
```

Note that calling `program`

did not require any context-specific knowledge -
we could define another interpreter, perhaps one that talks to a set
concurrently.

```
import cats.data.StateT
import scala.concurrent.{ExecutionContext, Future}
// Asynchronous state
def AsyncSet(implicit ec: ExecutionContext): SetLang[StateT[Future, Set[Int], ?]] =
new SetLang[StateT[Future, Set[Int], ?]] {
val monad = Monad[StateT[Future, Set[Int], ?]]
def add(i: Int): StateT[Future, Set[Int], Unit] =
StateT.modify(_ + i)
def remove(i: Int): StateT[Future, Set[Int], Unit] =
StateT.modify(_ - i)
def exists(i: Int): StateT[Future, Set[Int], Boolean] =
StateT.inspect(_(i))
}
```

```
// No changes to `program` required
val result = program(AsyncSet(ExecutionContext.global))
// result: cats.data.StateT[scala.concurrent.Future,scala.collection.immutable.Set[Int],Boolean] = cats.data.StateT@1c029382
```

`SetLang`

captures the *structure* of a computation, but leaves open
its *interpretation*.

As it turns out, `SetLang`

is an example of an encoding often referred to as
MTL-style.

Among the motivations for monad classes is to remove the need to specify monad transformer stacks. The following example is adapted from Functional Programming with Overloading and Higher-Order Polymorphism by Professor Mark P. Jones.

Consider a program that is open to failure and computes with some state. This
suggests a combinator of `Either`

and `State`

, both of which have
monad transformers. All that is left is to decide which transformer to use.

```
type App1[A] = EitherT[State[S, ?], Error, A]
// State[S, Either[Error, A]]
// S => (S, Either[Error, A])
type App2[A] = StateT[Either[Error, ?], S, A]
// S => Either[Error, (S, A)]
```

While `App1`

and `App2`

are both valid compositions, the
semantics of the compositions differ. `App1`

describes a program where
the computation of a *value* at each transition may fail - but any changes
are preserved - whereas `App2`

describes a program where the *entire*
transition may fail.

We can abstract away the difference by creating a type class which provides the relevant operations we need.

```
trait MonadError[F[_], E] {
def monad: Monad[F]
def raiseError[A](e: E): F[A]
def handleErrorWith[A](fa: F[A])(f: E => F[A]): F[A]
}
trait MonadState[F[_], S] {
def monad: Monad[F]
def get: F[S]
def set(s: S): F[Unit]
}
```

Similar type classes exist for the `Reader`

and `Writer`

data types.
These type classes are provided in both Cats and Scalaz,
with some caveats.

With these type classes in place we can write functions against these as opposed to specific transformer stacks. Furthermore our functions can specify exactly what operations they need which helps correctness and parametricity.

```
import cats.{MonadError, MonadState}
import cats.data.{EitherT, State, StateT}
def program[F[_]](implicit F0: MonadError[F, String],
F1: MonadState[F, Int]): F[Int] =
F0.flatMap(F1.get) { i =>
F0.raiseError[Int]("fail")
}
```

Our program can then be instantiated with either transformer stack.

```
import cats.implicits._
// At the time of this writing Cats does not have these instances
// so they are defined here.
//
// Additionally, both Cats and Scalaz 7 have encoding issues
// with these MTL type classes which requires us to redefine Monad when
// defining MonadState instances, despite there already being one.
implicit def eitherTMonadState[F[_], E, S](implicit F: MonadState[F, S]): MonadState[EitherT[F, E, ?], S] =
new MonadState[EitherT[F, E, ?], S] {
def get: EitherT[F, E, S] =
EitherT(F.get.map(Right(_)))
def set(s: S): EitherT[F, E, Unit] =
EitherT(F.set(s).map(Right(_)))
def flatMap[A, B](fa: EitherT[F, E, A])
(f: A => EitherT[F, E, B]): EitherT[F, E, B] =
fa.flatMap(f)
def pure[A](x: A): EitherT[F, E, A] =
EitherT.pure(x)
def tailRecM[A, B](a: A)(f: A => EitherT[F, E, Either[A, B]]): EitherT[F, E, B] =
EitherT.catsDataMonadErrorForEitherT[F, E].tailRecM(a)(f)
}
implicit def stateTMonadError[F[_], E, S](implicit F: MonadError[F, E]): MonadError[StateT[F, S, ?], E] =
new MonadError[StateT[F, S, ?], E] {
def handleErrorWith[A](fa: StateT[F, S, A])(f: E => StateT[F, S, A]): StateT[F, S, A] =
StateT[F, S, A] { (s: S) =>
val state: F[(S, A)] = fa.run(s)
F.handleErrorWith(state)(e => f(e).run(s))
}
def raiseError[A](e: E): StateT[F, S, A] =
StateT.lift(F.raiseError(e))
def flatMap[A, B](fa: StateT[F, S, A])(f: A => StateT[F, S, B]): StateT[F, S, B] =
fa.flatMap(f)
def pure[A](x: A): StateT[F, S, A] = StateT.pure(x)
def tailRecM[A, B](a: A)(f: A => StateT[F, S, Either[A, B]]): StateT[F, S, B] =
StateT.catsDataMonadStateForStateT[F, S].tailRecM(a)(f)
}
type App1[A] = EitherT[State[Int, ?], String, A]
type App2[A] = StateT[Either[String, ?], Int, A]
```

```
val app1 = program[App1]
// app1: App1[Int] = EitherT(cats.data.StateT@5fdc056d)
val app2 = program[App2]
// app2: App2[Int] = cats.data.StateT@72493a33
```

From one angle we can view our set language, or more generally any EDSL
in MTL-style, as an effect like `MonadError`

and `MonadState`

. From another
angle we can view `MonadError`

and `MonadState`

as EDSLs that talk about errors
and stateful computations. We can eliminate the distinctions by renaming
`SetLang`

to `MonadSet`

and treating it as a type class.

```
import cats.Monad
import cats.implicits._
trait MonadSet[F[_]] {
def monad: Monad[F]
def add(i: Int): F[Unit]
def remove(i: Int): F[Unit]
def exists(i: Int): F[Boolean]
}
```

Composing multiple languages then becomes adding constraints to functions, and interpretation becomes instantiating type parameters that satisfy the constraints.

```
trait MonadCalc[F[_]] {
def monad: Monad[F]
def lit(i: Int): F[Int]
def plus(l: F[Int], r: F[Int]): F[Int]
}
def setProgram[F[_]: MonadSet](i: Int): F[Boolean] =
implicitly[MonadSet[F]].exists(i)
def calcProgram[F[_]: MonadCalc]: F[Int] = {
val calc = implicitly[MonadCalc[F]]
calc.plus(calc.lit(1), calc.lit(2))
}
def composedProgram[F[_]: MonadCalc: MonadSet]: F[Boolean] = {
implicit val monad: Monad[F] = implicitly[MonadCalc[F]].monad
for {
i <- calcProgram[F]
b <- setProgram(i)
} yield b
}
// Instance
// Instances are defined together but nothing is stopping us from defining
// these separately, perhaps one in the MonadSet object and another in the
// SetState object.
implicit val stateInstance: MonadSet[State[Set[Int], ?]] with MonadCalc[State[Set[Int], ?]] =
new MonadSet[State[Set[Int], ?]] with MonadCalc[State[Set[Int], ?]] {
val monad = Monad[State[Set[Int], ?]]
def add(i: Int): State[Set[Int], Unit] = State.modify(_ + i)
def remove(i: Int): State[Set[Int], Unit] = State.modify(_ - i)
def exists(i: Int): State[Set[Int], Boolean] = State.inspect(_(i))
def lit(i: Int): State[Set[Int], Int] = State.pure(i)
def plus(l: State[Set[Int], Int], r: State[Set[Int], Int]): State[Set[Int], Int] =
(l |@| r).map(_ + _)
}
```

```
val result = composedProgram[State[Set[Int], ?]].run(Set.empty[Int]).value
// result: (scala.collection.immutable.Set[Int], Boolean) = (Set(),false)
```

As before, `composedProgram`

, `calcProgram`

, and `setProgram`

are defined
independent of interpretation, so alternative interpretations simply require
defining appropriate instances.

Type classes should come with laws - this lets us give meaning to their use.
The `Monoid`

type class requires data types to have an **associative** binary
operation and a corresponding identity element. These laws allow us to
parallelize batch operations, such as partitioning a `List[A]`

into
multiple chunks to be scattered across threads or machines and gathered
back.

Since our EDSLs are type classes, we should think about what laws we expect to hold. Below are some possible candidates for laws:

```
// MonadSet
set *> add(i) *> remove(i) = set
set *> remove(i) *> exists(i) = false
set *> add(i) *> exists(i) = true
// MonadCalc - these are just the Monoid laws
plus(lit(0), x) = plus(x, lit(0)) = x
plus(x, plus(y, z)) = plus(plus(x, y), z)
```

Next up we’ll take a look at some pitfalls of this approach, and a modified encoding that solves some of them.

*This article was tested with Scala 2.11.8, Cats 0.7.2, kind-projector 0.9.0,
and si2712fix-plugin 1.2.0 using tut.*