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package org.atnos.eff
/**
* This trait provides a way to rewrite applicative effects
* when there is an operation allowing the batching of some effects based on the Batchable typeclass
*/
trait Batch {
def batch[R, T[_], A](eff: Eff[R, A])(implicit batchable: Batchable[T], m: T /= R): Eff[R, A] =
eff match {
case ImpureAp(unions, continuation, last) =>
// extract only the effects of type M
val collected = unions.extract
// we zip each effect with its indice in the original ImpureAp effect list
// this allows us to recreate a 'map' function for the rewritten ImpureAp
// where the result of each effect after interpretation will be at the right place as a argument to the
// 'map' function
collected.effects zip collected.indices match {
case v if v.isEmpty => eff
case e +: rest =>
// batch any effects which can be batched together
// by using the Batched datastructure keeping track
// of both unbatched and batch effects
val result: Batched[T] = rest.foldLeft(Batched.single(e)) { case (batched, (effect, i)) =>
batchable.batch[Any, Any](batched.batchedEffect.asInstanceOf[T[Any]], effect) match {
case Some(b) => batched.fuse(b, i)
case None => batched.append(effect, i)
}
}
result.effects match {
case v if v.isEmpty =>
eff
case (e1: T[?]) +: rest1 =>
ImpureAp(
Unions(m.inject(e1), rest1.map(r => m.inject(r.asInstanceOf[T[Any]])) ++ collected.otherEffects),
// the map operation has to reorder the results based on what could be batched or not
continuation.contramap(ls => reorder(ls, result.keys ++ collected.otherIndices)),
last
)
}
}
case _ => eff
}
// reorder an input list based on the expected indices for that list
private def reorder[T[_]](ls: Vector[Any], indices: Vector[Int])(implicit batchable: Batchable[T]): Vector[Any] =
indices.zip(flatten(ls)).sortBy(_._1).map(_._2)
// the result of batching
private def flatten[T[_]](ls: Vector[Any])(implicit batchable: Batchable[T]): Vector[Any] =
ls match {
case xs :+ z =>
xs ++ batchable.distribute(z.asInstanceOf[batchable.Z])
case v if v.isEmpty =>
Vector.empty
}
}
object Batch extends Batch
/**
* The Batched classes are used to store unbatched and batched effects
* depending on the result of the Batchable typeclass
*
* The assumption is that the order of the effects in 'effects'
* correspond to the order of the keys in 'keys'
*
*/
private sealed trait Batched[T[_]] {
def effects: Vector[T[?]]
def keys: Vector[Int]
def batchedEffect: T[?]
def append(ty: T[?], key: Int): Batched[T]
def fuse(ty: T[?], key: Int): Batched[T]
}
private object Batched {
def single[T[_], X](txi: (T[X], Int)): Batched[T] =
Single(txi._1, Vector(txi._2))
}
private case class Composed[T[_]](unbatched: Vector[Batched[T]], batched: Single[T]) extends Batched[T] {
def effects: Vector[T[?]] = unbatched.flatMap(_.effects)
def keys: Vector[Int] = unbatched.flatMap(_.keys) ++ batched.keys
def batchedEffect: T[?] = batched.batchedEffect
def append(ty: T[?], key: Int): Batched[T] =
copy(unbatched = unbatched :+ Batched.single[T, Any]((ty.asInstanceOf[T[Any]], key)))
def fuse(ty: T[?], key: Int): Batched[T] =
copy(batched = Single(ty, batched.keys :+ key))
}
private case class Single[T[_]](tx: T[?], keys: Vector[Int]) extends Batched[T] {
def effects: Vector[T[?]] = Vector(tx)
def batchedEffect = tx
def append(ty: T[?], key: Int): Batched[T] =
Composed(Vector(Batched.single((tx.asInstanceOf[T[Any]], key))), this)
def fuse(ty: T[?], key: Int): Batched[T] =
Single(ty, keys :+ key)
}
