JarCastingcoop
Based on http://www.haskellforall.com/2013/06/from-zero-to-cooperative-threads-in-33.html. All credit to Gabriel Gonzales. The minor API and implementation differences from his Haskell version are due to Scala being Scala, and not any sign of original thought on my part.
...okay I take that back. The implementation of monitors is original, but it's so obvious that I'm not sure it counts as anything to take credit for.
Usage
libraryDependencies += "org.typelevel" %% "coop" % "<version>"
Published for Scala 2.13, 2.12, and Dotty (0.26.0 and 0.27.0-RC1), with cross-publication for ScalaJS 1.x. Depends on Cats Free 2.2.0 and Cats MTL 1.0.0.
import coop.ThreadT
// with cats-effect
import cats.effect.IO
import cats.implicits._
val thread1 = (ThreadT.liftF(IO(println("yo"))) >> ThreadT.cede).foreverM
val thread2 = (ThreadT.liftF(IO(println("dawg"))) >> ThreadT.cede).foreverM
val main = ThreadT.start(thread1) >> ThreadT.start(thread2)
val ioa = ThreadT.roundRobin(main) // => IO[Unit]
ioa.unsafeRunSync()
This will print the following endlessly:
yo
dawg
yo
dawg
yo
dawg
...
Or if you think that some witchcraft is happening due to the use of IO in the above, here's an example using State instead:
import coop.ThreadT
import cats.data.State
import cats.implicits._
import scala.collection.immutable.Vector
val thread1 = {
val mod = ThreadT.liftF(State.modify[Vector[Int]](_ :+ 0)) >> ThreadT.cede
mod.untilM_(ThreadT.liftF(State.get[Vector[Int]]).map(_.length >= 10))
}
val thread2 = {
val mod = ThreadT.liftF(State.modify[Vector[Int]](_ :+ 1)) >> ThreadT.cede
mod.untilM_(ThreadT.liftF(State.get[Vector[Int]]).map(_.length >= 10))
}
val main = ThreadT.start(thread1) >> ThreadT.start(thread2)
ThreadT.roundRobin(main).runS(Vector()).value // => Vector(0, 1, 0, 1, 0, 1, 0, 1, 0, 1)
Of course, it's quite easy to see what happens if we're not cooperative and a single thread hogs all of the resources:
import coop.ThreadT
import cats.data.State
import cats.implicits._
import scala.collection.immutable.Vector
val thread1 = {
val mod = ThreadT.liftF(State.modify[Vector[Int]](_ :+ 0)) // >> ThreadT.cede
mod.untilM_(ThreadT.liftF(State.get[Vector[Int]]).map(_.length >= 10))
}
val thread2 = {
val mod = ThreadT.liftF(State.modify[Vector[Int]](_ :+ 1)) // >> ThreadT.cede
mod.untilM_(ThreadT.liftF(State.get[Vector[Int]]).map(_.length >= 10))
}
val main = ThreadT.start(thread1) >> ThreadT.start(thread2)
ThreadT.roundRobin(main).runS(Vector()).value // => Vector(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1)
The first thread runs until it has filled the entire vector with 0s, after which it finally yields and the second thread only has a chance to insert a single value (which immediately overflows the length).
MTL Style
A ApplicativeThread typeclass, in the style of Cats MTL, is also provided to make it easier to use ThreadT as part of more complex stacks:
def thread1[F[_]: Monad: ApplicativeThread](implicit F: MonadState[F, Vector[Int]]): F[Unit] = {
val mod = F.modify(_ :+ 0) >> ApplicativeThread[F].cede
mod.untilM(F.get.map(_.length >= 10))
}
def main[F[_]: Apply: ApplicativeThread](thread1: F[Unit], thread2: F[Unit]): F[Unit] =
ApplicativeThread[F].start(thread1) *> ApplicativeThread[F].start(thread2)
ApplicativeThread will inductively derive over Kleisli and EitherT, and defines a base instance for FreeT[S[_], F[_], *] given an InjectK[ThreadF, S] (so, strictly more general than just ThreadT itself). It notably will not auto-derive over StateT or WriterT, due to the value loss which occurs in fork/start. If you need StateT-like functionality, it is recommended you either include some sort of StateF in your FreeT suspension, or nest the State functionality within the FreeT.
You will probably want to import FreeTInstances._ to ensure that the appropriate Cats MTL machinery for FreeT itself is made available in scope, otherwise instances will not propagate correctly across FreeT in the transformer stack. This machinery is probably going to get contributed upstream into a Cats MTL submodule.
MVar
An experimental implementation of MVar is made available, mostly because it makes it theoretically possible to define all the things. The API mirrors Haskell's. The implementation assumes an ApplicativeAsk (from Cats MTL) of an UnsafeRef which contains the uniquely indexed state for every MVar. This could have just been done using a private var instead, but I wanted to be explicit about the state management.
UnsafeRef is used rather than StateT or InjectK[StateF[S, *], F] to avoid issues with MonadError instances in the stack. Additionally, as mentioned earlier, ApplicativeThread will not auto-derive over StateT due to issues with value loss, so all in all it's a better approach if we aren't going to use a var.
Monitors and Locks
ThreadT implements a relatively basic form of await/notify locking as part of the core system. This takes the form of the following three constructors:
ThreadT.monitor: ThreadT[M, MonitorId]ThreadT.await(id: MonitorId): ThreadT[M, Unit]ThreadT.notify(id: MonitorId): ThreadT[M, Unit]
Notification has "notify all" semantics. MonitorId is simply an opaque identifier which can be used for awaiting/notifying. Sequencing await places the current fiber into a locked state. Any number of fibers may be awaiting a given monitor at any time. Sequencing notify with a given monitor will reactivate all fibers that are locked on that monitor, in the order in which they awaited.
This system exists in order to allow roundRobin to implement a form of deadlock detection. Rather than producing an M[Unit] from a ThreadT[M, A], it actually produces an M[Boolean], where the Boolean indicates whether or not all work was fully completed. If the produced value is false, then one or more monitors had awaiting fibers when the worker queue emptied, meaning that those fibers are deadlocked.