slamdata / matryoshka

Generalized recursion schemes and traversals for Scala.

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Matryoshka

Generalized folds, unfolds, and traversals for fixed point data structures in Scala.

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Usage

  1. Add a dependency
libraryDependencies += "com.slamdata" %% "matryoshka-core" % "0.18.3"

Optionally, you can also depend on matryoshka-scalacheck to get Arbitrary/Cogen/Shrink instances for a bunch of pattern functors and fixed points.

  1. Apply some fix for SI-2712. Prior to 2.12, use @milessabin’s compiler plugin. As of 2.12, you can simply add scalacOptions += "-Ypartial-unification" to your build.sbt.

  2. Add imports as needed. Usually the following should suffice

import matryoshka._
import matryoshka.implicits._

but if you need some of our pattern functors, then matryoshka.patterns._ should be added. Also, there will be cases where you need to specify explicit types (although we generally recommend abstracting over {Bir|Cor|R}ecursive type classes), so you may need matryoshka.data._ (for Fix, Mu, and Nu) and/or matryoshka.instances.fixedpoint._ for things like Nat, List, Cofree, etc. defined in terms of Mu/Nu.

Introduction

This library is predicated on the idea of rewriting your recursive data structures, replacing the recursive type reference with a fresh type parameter.

sealed abstract class Expr
final case class Num(value: Long)      extends Expr
final case class Mul(l: Expr, r: Expr) extends Expr

could be rewritten as

sealed abstract class Expr[A]
final case class Num[A](value: Long) extends Expr[A]
final case class Mul[A](l: A, r: A)  extends Expr[A]

This abstract class generally allows a Traverse instance (or at least a Functor instance). Then you use one of the fixed point type constructors below to regain your recursive type.

You may also want instances for Delay[Equal, ?], Delay[Order, ?], and Delay[Show, ?] (which are very similar to their non-Delay equivalents) to get instances for fixed points of your functor.

Fixpoint Types

These types take a one-arg type constructor and provide a recursive form of it.

All of these types have instances for Recursive, Corecursive, FunctorT, TraverseT, Equal, Show, and Arbitrary type classes unless otherwise noted.

  • Fix – This is the simplest fixpoint type, implemented with general recursion.
  • Mu – This is for inductive (finite) recursive structures, models the concept of “data”, aka, the “least fixed point”.
  • Nu – This is for coinductive (potentially infinite) recursive structures, models the concept of “codata”, aka, the “greatest fixed point”.
  • Cofree[?[_], A] – Only has a Corecursive instance if there’s a Monoid for A. This represents a structure with some metadata attached to each node. In addition to the usual operations, it can also be folded using an Elgot algebra.
  • Free[?[_], A] – Does not have a Recursive instance. In addition to the usual operations, it can also be created by unfolding with an Elgot coalgebra.

So a type like Mu[Expr] is now isomorphic to the original recursive type. However, the point is to avoid operating on recursive types directly …

Algebras

A structure like this makes it possible to separate recursion from your operations. You can now write transformations that operate on only a single node of your structure at a time.

algebras and coalgebras

This diagram covers the major classes of transformations. The most basic ones are in the center and the arrows show how they can be generalized in various ways.

Here is a very simple example of an algebra (eval) and how to apply it to a recursive structure.

// we will need a Functor[Expr] in order to call embed bellow
implicit val exprFunctor = new scalaz.Functor[Expr] {
  override def map[A, B](fa: Expr[A])(f: (A) => B) = fa match{
    case Num(value) => Num[B](value)
    case Mul(l, r) => Mul(f(l), f(r))
  }
}

val eval: Algebra[Expr, Long] = { // i.e. Expr[Long] => Long
  case Num(x)    => x
  case Mul(x, y) => x * y
}
 
def someExpr[T](implicit T: Corecursive.Aux[T, Expr]): T =
  Mul(Num[T](2).embed, Mul(Num[T](3).embed,
      Num[T](4).embed).embed).embed

import matryoshka.data.Mu 

someExpr[Mu[Expr]].cata(eval) // ⇒ 24

The .embed calls in someExpr wrap the nodes in the fixed point type. embed is generic, and we abstract someExpr over the fixed point type (only requiring that it has an instance of Corecursive), so we can postpone the choice of the fixed point as long as possible.

Recursion Schemes

Here is a cheat-sheet (also available in PDF) for some of them.

folds and unfolds

Folds

Those algebras can be applied recursively to your structures using many different folds. cata in the example above is the simplest fold. It traverses the structure bottom-up, applying the algebra to each node. That is the general behavior of a fold, but more complex ones allow for various comonads and monads to affect the result.

Unfolds

These are the dual of folds – using coalgebras to deconstruct values into parts, top-down. They are defined in the Corecursive type class.

Refolds

Refolds compose an unfold with a fold, never actually constructing the intermediate fixed-point structure. Therefore, they are available on any value, and are not part of a type class.

Transformations

The structure of these type classes is similar to Recursive and Corecursive, but rather than separating them between bottom-up and top-down traversals, FunctorT has both bottom-up and top-down traversals (and refold), while TraverseT has all the Kleisli variants (paralleling how Traverse extends Functor). A fixed-point type that has both Recursive and Corecursive instances has an implied TraverseT instance.

The benefits of these classes is that it is possible to define the required map and traverse operations on fixed-point types that lack either a project or an embed (e.g., Cofree[?[_], A] lacks embed unless A has a Monoid instance, but can easily be mapped over).

The tradeoff is that these operations can only transform between one fixed-point functor and another (or, in some cases, need to maintain the same functor).

The names of these operations are the same as those in Recursive and Corecursive, but prefixed with trans.

There is an additional (restricted) set of operations that also have a T suffix (e.g., transCataT). These only generalize in “the Elgot position” and require you to maintain the same functor. However, it can be the most natural way to write certain transformations, like matryoshka.algebras.substitute.

Generalization

There are generalized forms of most recursion schemes. From the basic cata (and its dual, ana), we can generalize in a few ways. We name them using either a prefix or suffix, depending on how they’re generalized.

G…

Most well known (in fact, even referred to as “generalized recursion schemes”) is generalizing over a Comonad (or Monad), converting an algebra like F[A] => A to F[W[A]] => A. Many of the other named folds are instances of this –

  • when W[A] = (T[F], A), it’s para,
  • when W[A] = (B, A), it’s zygo, and
  • when W[A] = Cofree[F, A], it’s histo.

These specializations can give rise to other generalizations. zygoT uses EnvT[B, ?[_], A] and ghisto uses Cofree[?[_], A].

…M

Less unique to recursion schemes, there are Kleisli variants that return the result in any monad.

Elgot…

This generalization, stolen from the “Elgot algebra”, is similar to standard generalization, except it uses W[F[A]] => A rather than F[W[A]] => A, with the Comonad outside the functor. Not all of the forms seem to be as useful as the G variants, but in some cases, like elgotZygo, it offers benefits of its own.

GElgot…M

Any of these generalizations can be combined, so you can have an algebra that is generalized along two or three dimensions. A fold like cofPara takes an algebra that’s generalized like zygo ((B, ?)) in the “Elgot” dimension and like para ((T[F], ?)) in the “G” dimension, which looks like (B, F[(T[F], A)]) => A. It’s honestly useful. I swear.

Implementation

Since we can actually derive almost everything from a fairly small number of operations, why don’t we? Well, there are a few reasons, enumerated here in descending order of how valid I think they are:

  1. Reducing constraints. In the case of para, using gcata(distPara, …) would introduce a Corecursive constraint, and all of the Kleisli variants require Traverse for the functor, not just Functor.
  2. Improving performance. cata implemented directly (presumably) performs better than gcata[Id, …]. We should have some benchmarks added eventually to actually determine when this is worth doing.
  3. Helping inference. While we are (planning to) use kinda-curried type parameters to help with this, it’s still the case that gcata generally requires all the type parameters to be specified, while, say, zygo doesn’t. You can notice these instances because their definition actually is just to call the generalized version, rather than being implemented directly.

Contributing

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

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