meow-mtl

A catpanion library for cats-mtl and cats-effect providing:

• Easy composition of MTL-style functions
• MTL instances for cats-effect compatible datatypes (e.g. IO)
• Conflict-free implicits for sub-instances (e.g. MonadState => Monad)

Available for Scala 2.11 and 2.12, for Scala JVM and Scala.JS (0.6)

// Use %%% for scala.js or cross projects
libraryDependencies += "com.olegpy" %% "meow-mtl" % "0.2.0"

Inspired by Next-level MTL talk and discussions on cats gitter.

Quick Example

type Headers = Map[String, String]
case class User(name: String)
case class AuthedRequest(headers: Headers, user: User)

def greetUser[F[_]: Functor](implicit F: MonadState[F, User]): F[String] = {
  F.get.map(user => s"Hello, ${user.name}")
}

def addRequestIdHeader[F[_]: Sync](implicit F: MonadState[F, Headers]): F[Unit] =
  for {
    id <- Sync[F].delay(UUID.randomUUID().toString)
    _  <- F.modify(_ + ("X-Request-ID" -> id))
  } yield ()

Now, if you had AuthedRequest as a state, that should mean that you have a state of User and Headers too. This library allows you to call these functions directly:

import com.olegpy.meow.hierarchy._

def handleGreetRequest[F[_]: Sync](implicit F: MonadState[F, AuthedRequest]) =
  for {
    _ <- addRequestIdHeader[F]
    r <- greetUser[F]
  } yield r

To get that MonadState instance, it's possible to use StateT transformer. But meow-mtl allows you to use Ref from cats-effect instead, yielding better performance. So at the edge of your application it is possible to do this:

import com.olegpy.meow.effects._

def handleRequest: IO[String] =
  for {
    ref <- Ref[IO].of(AuthedRequest(Map(), User("John")))
    res <- ref.runState { implicit monadState =>
      handleGreetRequest[IO]
    }
  } yield res

Classy optics and MTL composition

Primary feature of meow-mtl is enabling boilerplate-free composition of functions using cats-mtl typeclasses, in cases where instance clearly either contains necessary fields (like State example above) or can be converted to a necessary type. For example, it's possible to narrow type of MonadError from Throwable to a custom exception type:

case class MyException(msg: String) extends Throwable

def handleOnlyMy[F[_], A](f: F[A], fallback: F[A])(implicit F: MonadError[F, MyException]) =
  f.handleErrorWith(_ => fallback)


val io: IO[Int] = ???
handleOnlyMy(io, 42)

This is witnessed by Lens and Prism optics that meow-mtl generates when you try to make a call to such method.

As another neat example, generated typeclasses can be used as ad-hoc lenses

case class Part(int: Int)
case class Whole(part: Part)

def modify[F[_]: MonadState[?[_], Whole]] =
  MonadState[F, Part].set(Part(42)) // automatically "zooms" into Whole.part

High-level API: automatic derivation

All automatic derivation requires is a single import:

import com.olegpy.meow.hierarchy._

This needs to be done in every file where your call requires deriving an instance.

Supported typeclasses:

Typeclass	Required optic
ApplicativeError	Prism
MonadError	Prism
FunctorRaise	Prism
FunctorTell	Prism
ApplicativeAsk	Lens
ApplicativeLocal	Lens
MonadState	Lens

IMPORTANT!

Don't use cats.mtl.implicits._ or cats.mtl.hierarchy.base._ imports. Import cats.mtl.instances.all._ and cats.mtl.syntax.all._ if you need it.

Failure to do this will result in ambiguous implicit instances.

In cats-mtl 0.4.0 hierarchy has been mostly replaced by subtyping. The remaining hierarchy imports will possibly be phased out

Low-level API: optic providers

Alternatively, com.olegpy.meow.optics can be used directly:

case class User(name: String)
type HasUser[A] = MkLensToType[A, User]


def isFred[A](a: A)(implicit mkLens: HasUser[A]) =
  mkLens().get(a).name == "Fred"

In here, mkLens is an object with 0-args apply method, that creates a shapeless Lens from A to User, e.g.:

case class RequestCtx(user: User, id: String)

assert { isFred(RequestCtx(User("Fred"), "0x42")) }

Prism works in similar way, but it's a custom class (not shapeless Prism) with apply and unapply methods for construction and matching.

This is a very bare-bones implementation of optics, having only minimal functionality needed to support automatic derivation without adding extra dependencies. If you need a full-fledged optics library, consider using monocle instead.

Cats-effect instances

meow-mtl provides instances for cats-effect compatible data types like cats-effect own IO or monix Coeval and Task. These instances reside in com.olegpy.meow.effects package and provide a more flexible and performant alternative to monad transformer stacks.

Because construction of such instances is typically effectful, they are locally scoped. That means, instead of being available by importing, they require a special method to be called with a lambda, which will receive an instance, i.e.:

// `unsafe` is used for the sake of an example. I don't recommend doing that.
Ref.unsafe[IO, Int](0).runAsk { implicit askInstance =>
  ??? // ApplicativeAsk[IO, Int] is available in this scope
}

Alternatively, you can pull it out with specific methods if you intend to use it explicitly or with better-monadic-for implicit patterns:

implicit val instance: MonadState[IO, Int] =
  Ref.unsafe[IO, Int](0).askInstance

// MonadState available below
???

Ref

Ref is a referentially transparent variable added in cats-effect 1.0.0-RC2. It supports MonadState, ApplicativeAsk and FunctorTell effects (the latest requires a Semigroup instance for type of contained data).

Instances are provided by extension methods runState, runAsk and runTell respectively.

Example: counter

This is a simple example of using MonadState instance of Ref. Note how updated state can be retrieved from ref after executing operation.

def getAndIncrement[F[_]: Apply](implicit MS: MonadState[F, Int]) =
  MS.get <* MS.modify(_ + 1)


for {
  ref <- Ref.of[IO](0)
  out <- ref.runState { implicit ms =>
    getAndIncrement[IO].replicateA(3).as("Done")
  }
  state <- ref.get
} yield (out, state) == ("Done", 3)

Consumer

Consumer is a simple wrapper around A => F[Unit]. It supports a single effect - FunctorTell, and can be used for things like logging, persistence, notifications, etc.

Consumer instances are constructed with apply method on a companion.

Example: async logger

That logger only waits if a previous message is still being processed, to ensure correct ordering:

 def greeter(name: String)(implicit ev: FunctorTell[IO, String]): IO[Unit] =
   ev.tell(s"Long time no see, \$name") >> IO.sleep(1.second)

 def forever[A](ioa: IO[A]): IO[Nothing] = ioa >> forever(ioa)

 for {
    mVar <- MVar.empty[IO, String]
    logger = forever(mVar.take.flatMap(s => IO(println(s)))
    _ <- logger.start // Do logging in background
    _ <- Consumer(mVar.put).runTell { implicit tell =>
      forever(greeter("Oleg"))
    }
 } yield ()

Sub-instances

meow-mtl also provides a set of implicits which let you use Monad/Applicative/Functor instances if you have an MTL instance of compatible type (e.g. MonadState/ApplicativeAsk/FunctorTell)

import cats.implicits._
import com.olegpy.meow.prelude._ // just this one import

// Can use pure and flatTap without having a Monad constraint or pull
// it out manually
def test[F[_]](implicit MS: MonadState[F, Int]): F[Int] =
  42.pure[F].flatTap(MS.set)

It uses LowPriority mechanism from shapeless to ensure that having a constraint does not result in ambiguities:

import cats.effect.Sync
// Uses Sync as a Monad instance, instead of getting it from MonadState
def test2[F[_]: Sync](implicit MS: MonadState[F, Int]): F[Int] =
  42.pure[F].flatTap(MS.set)

License

MIT