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readme.md

GitHub Workflow

Magnolia

Magnolia is a generic macro for automatic materialization of typeclasses for datatypes composed from case classes (products) and sealed traits (coproducts). It supports recursively-defined datatypes out-of-the-box, and incurs no significant time-penalty during compilation. If derivation fails, error messages are detailed and informative.

Features

  • derives typeclasses for case classes, case objects and sealed traits
  • offers a lightweight, non-macro syntax for writing derivations
  • works with recursive and mutually-recursive definitions
  • supports parameterized ADTs (GADTs), including in recursive types
  • supports typeclasses whose generic type parameter is used in either covariant and contravariant positions
  • caches implicit searches for compile-time efficiency
  • prints an error stack to help debugging when derivation fails
  • provides access to case class default parameter values
  • offers predictable resolution of prioritized implicits
  • does not require additional type annotations, like Lazy[T]

Getting Started

Given an ADT such as,

sealed trait Tree[+T]
case class Branch[+T](left: Tree[T], right: Tree[T]) extends Tree[T]
case class Leaf[+T](value: T) extends Tree[T]

and provided an implicit instance of Show[Int] is in scope, and a Magnolia derivation for the Show typeclass has been provided, we can automatically derive an implicit typeclass instance of Show[Tree[Int]] on-demand, like so,

Branch(Branch(Leaf(1), Leaf(2)), Leaf(3)).show

Typeclass authors may provide Magnolia derivations in the Typeclass's companion object, but it is easy to create your own.

The derivation typeclass for a Show typeclass might look like this:

import language.experimental.macros, magnolia._

object ShowDerivation {
  type Typeclass[T] = Show[T]
  
  def combine[T](ctx: CaseClass[Show, T]): Show[T] = new Show[T] {
    def show(value: T): String = ctx.parameters.map { p =>
      s"${p.label}=${p.typeclass.show(p.dereference(value))}"
    }.mkString("{", ",", "}")
  }

  def dispatch[T](ctx: SealedTrait[Show, T]): Show[T] =
    new Show[T] {
      def show(value: T): String = ctx.dispatch(value) { sub =>
        sub.typeclass.show(sub.cast(value))
      }
    }

  implicit def gen[T]: Show[T] = macro Magnolia.gen[T]
}

The gen method will attempt to construct a typeclass for the type passed to it. Importing ShowDerivation.gen from the example above will make generic derivation for Show typeclasses available in the scope of the import. The macro Magnolia.gen[T] binding must be made in a static object, and the type constructor, Typeclass, and the methods combine and dispatch must be defined in the same object.

If you control the typeclass you are deriving for, the companion object of the typeclass makes a good choice for providing the implicit derivation methods described above.

Debugging

Deriving typeclasses is not always guaranteed to succeed, though. Many datatypes are complex and deeply-nested, and failure to derive a typeclass for a single parameter in one of the leaf nodes will cause the entire tree to fail.

Magnolia tries to be informative about why failures occur, by providing a "stack trace" showing the path to the type which could not be derived.

For example, when attempting to derive a Show instance for Entity, given the following hypothetical datatypes,

sealed trait Entity
case class Person(name: String, address: Address) extends Entity
case class Organization(name: String, contacts: Set[Person]) extends Entity
case class Address(lines: List[String], country: Country)
case class Country(name: String, code: String, salesTax: Boolean)

the absence, for example, of a Show[Boolean] typeclass instance would cause derivation to fail, but the reason might not be obvious, so instead, Magnolia will report the following compile error:

could not derive Show instance for type Boolean
    in parameter 'salesTax' of product type Country
    in parameter 'country' of product type Address
    in parameter 'address' of product type Person
    in chained implicit of type Set[Person]
    in parameter 'contacts' of product type Organization
    in coproduct type Entity

This "derivation stack trace" will only be displayed when invoking a derivation method, e.g. Show.gen[Entity], directly. When the method is invoked through implicit search, to reduce spurious error messages (when Magnolia's derivation fails, but implicit search still finds a valid implicit) the errors are not shown.

Status

Magnolia is classified as maturescent. Propensive defines the following five stability levels for open-source projects:

  • embryonic: for experimental or demonstrative purposes only, without guarantee of longevity
  • fledgling: of proven utility, seeking contributions, but liable to significant redesigns
  • maturescent: major design decisions broady settled, seeking probatory adoption and refinement of designs
  • dependable: production-ready, subject to controlled ongoing maintenance and enhancement; tagged as version 1.0 or later
  • adamantine: proven, reliable and production-ready, with no further breaking changes ever anticipated

Availability

Magnolia’s source is available on GitHub, and may be built with Fury by cloning the layer propensive/magnolia.

fury layer clone -i propensive/magnolia

or imported into an existing layer with,

fury layer import -i propensive/magnolia

A binary is available on Maven Central as com.propensive:magnolia_<scala-version>:0.17.0. This may be added to an sbt build with:

libraryDependencies += "com.propensive" %% "magnolia" % "0.17.0"

Contributing

Contributors to Magnolia are welcome and encouraged. New contributors may like to look for issues marked label: good first issue.

We suggest that all contributors read the Contributing Guide to make the process of contributing to Magnolia easier.

Please do not contact project maintainers privately with questions, as other users cannot then benefit from the answers.

Author

Magnolia was designed and developed by Jon Pretty, and commercial support and training is available from Propensive OÜ.

License

Magnolia is copyright © 2018-20 Jon Pretty & Propensive OÜ, and is made available under the Apache 2.0 License.

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