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.



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 = { p =>
    }.mkString("{", ",", "}")

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

  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.


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.

Current Status

Magnolia is currently experimental. It has been shown to work for a variety of test cases, though it has not had the same exposure to real-world datatypes that, for example, Shapeless has had.

The API for defining derivations has been shown to be adequate for the test cases, and is not expected to change significantly, but some convenience methods may be provided in the future.

In terms of production-readiness, the macro will not produce code which fails to typecheck. But it can still refuse to generate a derivation, so users should be cautious of becoming reliant upon it until it has received more thorough testing.

However, should Magnolia fail, in all cases it should be possible to write typeclass instances manually, and have these take precedence.

Macro-based generic derivation is known to be quite heavy on compile times, and significant effort has been invested in trying to minimize the work Magnolia does in deriving a typeclass. Preliminary testing comparing Magnolia with Shapeless suggests that Magnolia offers between a 4x and 15x performance improvement over Shapeless, depending on the structure of the ADT being derived.

The runtime performance of Magnolia-generated code has not been tested, and while some work has gone into minimizing the amount of runtime heap allocations from generate typeclasses, it is known that it will generate some ephemeral garbage, and this is an area which is expected to see some improvement in subsequent versions.