Type class derivation with ZIO Schema

An overview of different ways to implement type class derivation in Scala, including the new Deriver feature of ZIO Schema

Tech Trends
02 December 2023

# SCALA

# FUNCTIONAL PROGRAMMING

# DERIVATION

# ZIO-SCHEMA

An overview of different ways to implement type class derivation in Scala, including the new Deriver feature of ZIO Schema

Introduction

Making the compiler automatically derive implementations of a type class for your custom algebraic data types is a common technique in programming languages. Haskell, for example, has built-in syntax for it:

data Literal = StringLit String
             | BoolLit Bool
               deriving (Show)


and Rust is using macros instantiated by annotations to do the same:

#[deriving(Debug)]
enum Literal {
  StringLit(String),
  BoolLit(bool)
}


Scala 3 has its own syntax for deriving type classes:

enum Literal deriving Show:
  case StringLit(value: String)
  case BoolLit(value: Boolean)


but the more traditional way that works with Scala 2 as well is to define an implicit in the type's companion object by an explicit macro invocation:

sealed trait Literal
object Literal {
  final case class StringLit(value: String) extends Literal
  final case class BoolLit(value: String) extends Literal

  implicit val show: Show[Literal] = DeriveShow[Literal]
}


All these examples from different languages are common in a way in that to automatically generate an implementation for an arbitrary type we need to be able to gather information about these types as (compilation-) runtime values, and to generate new code fragments (or an actual abstract syntax tree) which is then part of the compilation, producing the same result as writing the implementation by hand.

This means using some kind of macro, depending on which programming language we use. But writing these macros is never easy, and in some cases can be very different from the usual way of writing code - so in each programming language people are writing libraries helping type class derivation in one way or the other.

In this post I will show a library like that for Scala, the Deriver feature of ZIO Schema that I added at the end of last year (2022). But before that let's see a real world example and what alternatives we had.

Example

Desert is a Scala serialization library I wrote in 2020. Not surprisingly in the core of Desert is a trait that describes serialization and deserailization of a type:

trait BinaryCodec[T] extends BinarySerializer[T] with BinaryDeserializer[T]

trait BinarySerializer[T] {
  def serialize(value: T)(implicit context: SerializationContext): Unit
  // ...
}

trait BinaryDeserializer[T] {
  def deserialize()(implicit ctx: DeserializationContext): T
  // ...
}


Although we can implement these traits manually, in order to take advantage of Desert's type evolution capabilities, for complex types like case classes or enums we want the user to be able to write something like this:

final case class Point(x: Int, y: Int, z: Int)
object Point {
  implicit val codec: BinaryCodec[Point] = DerivedBinaryCodec.derive
}

Alternatives

Scala 3 mirrors

First of all, Scala 3 has some built-in support for implementing derivation macros using its Mirror type, explained in the official documentation. We can see a simple example of this technique in the ZIO codebase where I have implemented a deriving mechanism for the Gen[R, A] trait which is Scala 3 specific. (The Scala 2 version is using the Magnolia library, introduced below, which did not have a Scala 3 version back then). The Mirror values are summoned by the compiler and they provide the type information:

inline def gen[T](using m: Mirror.Of[T]): DeriveGen[T] =
  new DeriveGen[T] {
    def derive: Gen[Any, T] = {
      val elemInstances = summonAll[m.MirroredElemTypes]
      inline m match {
        case s: Mirror.SumOf[T]     => genSum(s, elemInstances)
        case p: Mirror.ProductOf[T] => genProduct(p, elemInstances)
      }
    }
  }


As this function is an inline function, it gets evaluated at compile time, using this summoned Mirror value to produce an implementation of Gen[Any, T].

This is a little low level and requires knowledge of inline functions and things like summonAll etc., but is otherwise a relatively easy way to solve the type class derivation problem. But it is Scala 3 only.

Back in 2020 when I wrote the first version of Desert, there was no Scala 3 at all, and the three main way to do this were

writing a (Scala 2) macro by hand

using Shapeless

using Magnolia

Scala 2 macros

Writing custom derivation logic with Scala 2 macros is not easy, but it is completely possible. It starts by defining a whitebox macro:

object Derive {
  def derive[A]: BinaryCodec[A] = macro deriveImpl[A]

  def deriveImpl[A: c.WeakTypeTag](
    c: whitebox.Context
  ): c.Tree = {
    import c.universe._
    // ...
  }
}


The job of deriveImpl is to examine the type of A and generate a Tree that represents the implementation of the BinaryCodec trait for A. We can start by getting a Type value for A:

val tpe: Type = weakTypeOf[A]


and then use that to get all kind of information about this type. For example to check if it is a case class, we could write

def isCaseClass(tpe: Type): Boolean = tpe.typeSymbol.asClass.isCaseClass

and then try to collect all the fields of that case class:

val fields = tpe.decls.sorted.collect {
  case p: TermSymbol if p.isCaseAccessor && !p.isMethod => p
}

As we can see this is a very direct and low level way to work with the types, much harder then the Mirror type we used for Scala 3. Once we gathered all the necessary information for generating the derived type class, we can use quotes to construct fragments of Scala AST:

val fieldSerializationStatements = // ...

val codec = q"new BinaryCodec[$tpe] {
  def serialize(value: T)(implicit context: SerializationContext): Unit = {
    ..$fieldSerializationStatements
  }
}

In the end, this quoted  value is a Tree which we can return from the macro.

Shapeless

Shapeless is a library for type level programming in Scala 2 (and there is a new version for Scala 3 too). It provides things like type-level heterogeneous lists and all of operations on them, and it also defines macros that can convert an arbitrary case class into a generic representation, which is essentially a type level list containing all the fields. Similarly it can convert an arbitrary sum type (sealed trait in Scala 2) to a generic representation of coproducts. For example the Point case class we used in an earlier example would be represented like this:

final case class Point(x: Int, y: Int, z: Int)

val point: Point = Point(1, 2, 3)
val genericPoint: Int :: Int :: Int :: HNil = // type
  1 :: 2 :: 3 :: HNil // value
val labelledGenericPoint = // type too complex to show here
  ("x" ->> 1) :: ("y" ->> 2) :: ("z" ->> 3) :: HNil // value

In connection with type class derivation the idea is that by using Shapeless we no longer have to write macros to extract type information for our types - we can work with these generic representations instead using advanced type level programming techniques. So the complexity of writing macros is replaced with the complexity of doing type level computation.

Let's see what it would look like. First we start by creating a derive method that gets the type we are deriving the codec for as a type parameter:

def derive[T] = // ...

This  is an arbitrary type, for example our Point structure. In order to get its generic representation provided by Shapeless we have to start using type level techniques, by introducing new type parameters for the things we want to calculate (as types) and implicits to drive these computations. The following version, when compiles, will "calculate" the generic representation of  as the type parameter :

def derive[T, H](implicit gen: LabelledGeneric.Aux[T, H]) = {
  new BinaryCodec[T] {
    def serialize(value: T)(implicit context: SerializationContext): Unit = {
      val h: H = gen.to(value) // generic representation of (value: T)
      // ...
    }
    // ...
  }
}

This is not that hard yet but we need to recursively summon implicit codecs for our fields, so we can't just use this  value to go through all the fields in a traditional way - we need to traverse it on the type level.

To do that we need to write our own type level computations implemented as implicit instances for HNil and :: etc. The serialization part of the codec would look something like this:

implicit val hnilSerializer: BinarySerializer[HNil] =
  new BinarySerializer[HNil] {
    def serialize(value: HNil)(implicit context: SerializationContext) => {
      // no (more) fields
    }
  }

implicit def hlistSerializer[K <: Symbol, H, T <: HList](implicit
  witness: Witness.Aux[K] // type level extraction of the field's name
  headSerializer: BinarySerializer[H] // type class summoning for the field
  tailSerializer: BinarySerializer[T] // hlist recursion
): BinarySerializer[FieldType[K, H] :: T] = // ...

Similar methods have to be implemented for coproducts too, and also in the codec example we would have to simultaneously derive the serializer and the deserializer. A real implementation would also require access to the annotations of various fields to drive the serialization logic, which requires more and more type level calculations and complicates these type signatures.

I did choose to use Shapeless in the first version of Desert, and the real derive method has the following signature:

def derive[T, H, Ks <: HList, Trs <: HList, Trcs <: HList, KsTrs <: HList, TH](implicit
      gen: LabelledGeneric.Aux[T, H],
      keys: Lazy[Symbols.Aux[H, Ks]],
      transientAnnotations: Annotations.Aux[transientField, T, Trs],
      transientConstructorAnnotations: Annotations.Aux[transientConstructor, T, Trcs],
      taggedTransients: TagTransients.Aux[H, Trs, Trcs, TH],
      zip: Zip.Aux[Ks :: Trs :: HNil, KsTrs],
      toList: ToTraversable.Aux[KsTrs, List, (Symbol, Option[transientField])],
      serializationPlan: Lazy[SerializationPlan[TH]],
      deserializationPlan: Lazy[DeserializationPlan[TH]],
      toConstructorMap: Lazy[ToConstructorMap[TH]],
      classTag: ClassTag[T]
  ): BinaryCodec[T]

Although this works, there are many problems with this approach. All these type and implicit resolutions can make the compilation quite slow, the code is very complex and hard to understand or modify, and most importantly error messages will be a nightmare. A user trying to derive a type class for our serialization library should not get an error that complains about not being able to find an implicit value of Zip.Aux for a weird type that does not even fit on one screen!

Magnolia

The Magnolia library provides a much more friendly solution for deriving type classes for algebraic data types - it moves the whole problem into the value space by hiding the necessary macros. The derivation implementation for a given type class then only requires defining two functions (one for working with products, one for working with coproducts) that are regular Scala functions getting a "context" value and producing an instance of the derived type class. The context value contains type information - for example the name and type of all the fields of a case class - and also contains an instance of the derived type class for each of these inner elements.

To write a Magnolia based deriver you have to create an object with a join and a split method and a Typeclass type:

object BinaryCodecDerivation {
  type Typeclass[T] = BinaryCodec[T]

  def join[T](ctx: CaseClass[BinaryCodec, T]): BinaryCodec[T] =
    new BinaryCodec[T] {
      def serialize(value: T)(implicit context: SerializationContext) => {
        for (parameter <- ctx.parameters) {
          // recursively serialize the fields
          parameter.typeclass.serialize(parameter.dereference(value))
        }
        // ...
      }
    }

  def split[T](ctx: SealedTrait[BinaryCodec, T]): BinaryCodec[T] =
    // ...

  def gen[T]: BinaryCodec[T] = macro Magnolia.gen[T]
}

There is a Magnolia version for Scala 3 too, which although quite similar, is not source compatible with the Scala 2 version, leading to the need to define these derivations twice in cross-compiled projects.

Why not Magnolia?

Magnolia already existed when I wrote the first version of Desert, but I could not use it because of two reasons. In that early version of the library the derivation had to take a user defined list of evolution steps, so the actual codec definitions looked something like this:

object Point {
  implicit val codec: BinaryCodec[Point] = BinaryCodec.derive(FieldAdded[Int]("z", 1))
}

It was not clear how could I pass these parameters to Magnolia context - with Shapeless it was not a problem because it is possible to simply pass them as a parameter to the derive function that "starts" the type level computation.

This requirement no longer exists though, as in recent versions the evolution steps are defined by attributes, which are fully supported by Magnolia as well:

@evolutionSteps(FieldAdded[Int]("z", 1))
final case class Point(x: Int, y: Int, z: Int)

The second reason was a much more important limitation in Magnolia that still exists - it is not possible to shortcut the derivation tree. Desert has transient field and transient constructor support. For those fields and constructors which are marked as transient we don't want to, and cannot define codec instances. They can be things like open files, streams, actor references, sockets etc. Even though Magnolia only instantiates the type class instances when they are accessed, the derivation fails if there are types in the tree that does not have an instance. This issue is tracked here.

There was one more decision I did not like regarding Magnolia - the decision to have an incompatible Scala 3 version. I believe it was a big missed opportunity to seamlessly support cross-compiled type class derivation code.

ZIO Schema based derivation

All these issues lead to writing a new derivation library - as part of the ZIO Schema project. It was first released in version v0.3.0 in November of 2022.

From the previously demonstrated type class derivation techniques the closest to ZIO Schema's deriver is Magnolia. On the other hand it does supports the transient field use case, and it is fully cross-compilation compatible between Scala 2 and Scala 3.

To implement type class derivation based on ZIO Schema you need to implement a trait called deriver:

trait Deriver[F[_]] {
  def deriveRecord[A](
    record: Schema.Record[A],
    fields: => Chunk[WrappedF[F, _]],
    summoned: => Option[F[A]]
  ): F[A]

  // more deriveXXX methods to impelment
}

This looks similar to Magnolia's join method but has some significant differences. The first thing to notice is that we get a Schema.Record value describing our case class. This is one of the cases of the core data type Schema[T] which describes Scala data types and provides a lot of features to work with them. So having a Schema[A] is a requirement to derive an F[A] with Deriver - but luckily ZIO schema has derivation support for Schema itself.

The second thing to notice is that Schema[A] itself does not know anything about type class derivation and especially about the actual F type class that is being derived, so the second parameter of deriveRecord is a collection of potentially derived instances of our derived type class for each field. WrappedF is just making this lazy so if we decide we don't need instances for (some of) the fields they won't be traversed (they still need to have a Schema though - but it can even be a Schema.fail for things not representable by ZIO Schema - it will be fine if we never touch them by unwrapping the WrappedF value).

The third parameter is also interesting as it provides full control to the developer to choose between the summoned implicit and the derivation logic. If your deriveRecord is called for a record type A and there is already an implicit F[A] that the compiler can find (for example defined in A's companion object), it will be passed in the summoned parameter to deriveRecord. The usual logic is to choose the summoned value when it is available and only derive an instance when there isn't any. By calling .autoAcceptSummoned on our Deriver class we can automatically enable this behavior - in this case deriveRecord will only be called for the cases where summoned was None.

Another method we have on Deriver is .cached which stores the generated type class instances in a concurrent hash map shared between the macro invocations.

Our ZIO Schema based Desert codec derivation is defined using these modifiers:

object DerivedBinaryCodec {
  lazy val deriver = BinaryCodecDeriver().cached.autoAcceptSummoned

  private final case class BinaryCodecDeriver() extends Deriver[BinaryCodec] {
    // ...
  }
}

As ZIO Schema not only describes records and enums but also primitive types, tuples, and special cases like Option and Either and collection types, the deriver has to support all these.

The minimum set of methods to implement is deriveRecord, deriveEnum, derivePrimitive, deriveOption, deriveSequence, deriveMap and deriveTransformedRecord. In addition to that we can also override deriveEither, deriveSet and deriveTupleN (1-22) to handle these cases specially.

In the case of Desert the deriveRecord and deriveEnum are calling to the implementation of the same data-evolution aware binary format that was previously implemented using Shapeless, but this time it is automatically supporting Scala 2 and Scala 3 the same time. The derivePrimitive is just choosing from predefined BinaryCodec instances based on the primitive's type:

override def derivePrimitive[A](
  st: StandardType[A],
  summoned: => Option[BinaryCodec[A]]
): BinaryCodec[A] =
  st match {
    case StandardType.UnitType           => unitCodec
    case StandardType.StringType         => stringCodec
    case StandardType.BoolType           => booleanCodec
    case StandardType.ByteType           => byteCodec
    // ...
  }

The same applies for option, either, sequence etc - it is just a mapping to the library's own definition of these binary codecs.

Under the hood Deriver is a macro (implemented separately both for Scala 2 and Scala 3) that traverses the types simultaneously with the provided Schema (so it does not need to regenerate those) and maps these informations into calls through the Deriver interface. The whole process is initiated by calling the derive method on our Deriver, which is the entry point of these macros, so it has a different looking (but source-code compatible) definition for Scala 2 and Scala 3:

// Scala 3
inline def derive[A](implicit schema: Schema[A]): F[A]

// Scala 2
def derive[F[_], A](deriver: Deriver[F])(
  implicit schema: Schema[A]
): F[A] = macro deriveImpl[F, A]

These are compatible if you are directly calling them: so you can write

val binaryCodecDeriver: Deriver[BinaryCodec] = // ...
val pointCodec: BinaryCodec[Point] = binaryCodecDeriver.derive[Point]

Or even:

object BinaryCodecDeriver extends Deriver[BinaryCodec] {
  // ...
}

val pointCodec: BinaryCodec[Point] = BinaryCodecDeriver.derive[Point]

But if you want to wrap this derive call you have to be aware that they are macro calls, and they have to be wrapped by (version-specific) macros. This is what Desert is doing - as shown before, it uses the cached and autoAcceptSummoned modifiers to create a deriver, but still exposes a simple derive method through an object. To do so it needs to wrap the inner deriver macro with its own macro like this:

// Scala 2
trait DerivedBinaryCodecVersionSpecific {
  def deriver: Deriver[BinaryCodec]

  def derive[T](implicit schema: Schema[T]): BinaryCodec[T] =
    macro DerivedBinaryCodecVersionSpecific.deriveImpl[T]
}

object DerivedBinaryCodecVersionSpecific {
    def deriveImpl[T: c.WeakTypeTag](
      c: whitebox.Context)(
      schema: c.Expr[Schema[T]]
    ): c.Tree = {
      import c.universe._
      val tpe = weakTypeOf[T]
      q"_root_.zio.schema.Derive.derive[BinaryCodec, $tpe]  (_root_.io.github.vigoo.desert.zioschema.DerivedBinaryCodec.deriver)($schema)"
    }
}

// Scala 3
trait DerivedBinaryCodecVersionSpecific {
  lazy val deriver: Deriver[BinaryCodec]

  inline def derive[T](implicit schema: Schema[T]): BinaryCodec[T] =
    Derive.derive[BinaryCodec, T](DerivedBinaryCodec.deriver)
}

Conclusion

We have a new alternative for deriving type class instances from type information, based on ZIO Schema. You may want to use it if you want to have a single deriver source code for both Scala 2 and Scala 3, if you need more flexibility than what Magnolia provides, or if you are already using ZIO Schema in your project.



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