Representing case classes as JSON arrays
The first thing to note is that the circe-shapes module provides instances for Shapeless's HLists that use an array representation like the one we want for our case classes. For example:
scala> import io.circe.shapes._
import io.circe.shapes._
scala> import shapeless._
import shapeless._
scala> ("foo" :: 1 :: List(true, false) :: HNil).asJson.noSpaces
res4: String = ["foo",1,[true,false]]
…and Shapeless itself provides a generic mapping between case classes and HLists. We can combine these two to get the generic instances we want for case classes:
import io.circe.{ Decoder, Encoder }
import io.circe.shapes.HListInstances
import shapeless.{ Generic, HList }
trait FlatCaseClassCodecs extends HListInstances {
implicit def encodeCaseClassFlat[A, Repr <: HList](implicit
gen: Generic.Aux[A, Repr],
encodeRepr: Encoder[Repr]
): Encoder[A] = encodeRepr.contramap(gen.to)
implicit def decodeCaseClassFlat[A, Repr <: HList](implicit
gen: Generic.Aux[A, Repr],
decodeRepr: Decoder[Repr]
): Decoder[A] = decodeRepr.map(gen.from)
}
object FlatCaseClassCodecs extends FlatCaseClassCodecs
And then:
scala> import FlatCaseClassCodecs._
import FlatCaseClassCodecs._
scala> Cake("cherry", 100).asJson.noSpaces
res5: String = ["cherry",100]
scala> Hat("cowboy", "felt", "brown").asJson.noSpaces
res6: String = ["cowboy","felt","brown"]
Note that I'm using io.circe.shapes.HListInstances to bundle up just the instances we need from circe-shapes together with our custom case class instances, in order to minimize the number of things our users have to import (both as a matter of ergonomics and for the sake of keeping down compile times).
Encoding the generic representation of our ADTs
That's a good first step, but it doesn't get us the representation we want for Item itself. To do that we need some more complex machinery:
import io.circe.{ JsonObject, ObjectEncoder }
import shapeless.{ :+:, CNil, Coproduct, Inl, Inr, Witness }
import shapeless.labelled.FieldType
trait ReprEncoder[C <: Coproduct] extends ObjectEncoder[C]
object ReprEncoder {
def wrap[A <: Coproduct](encodeA: ObjectEncoder[A]): ReprEncoder[A] =
new ReprEncoder[A] {
def encodeObject(a: A): JsonObject = encodeA.encodeObject(a)
}
implicit val encodeCNil: ReprEncoder[CNil] = wrap(
ObjectEncoder.instance[CNil](_ => sys.error("Cannot encode CNil"))
)
implicit def encodeCCons[K <: Symbol, L, R <: Coproduct](implicit
witK: Witness.Aux[K],
encodeL: Encoder[L],
encodeR: ReprEncoder[R]
): ReprEncoder[FieldType[K, L] :+: R] = wrap[FieldType[K, L] :+: R](
ObjectEncoder.instance {
case Inl(l) => JsonObject("tag" := witK.value.name, "contents" := (l: L))
case Inr(r) => encodeR.encodeObject(r)
}
)
}
This tells us how to encode instances of Coproduct, which Shapeless uses as a generic representation of sealed trait hierarchies in Scala. The code may be intimidating at first, but it's a very common pattern, and if you spend much time working with Shapeless you'll recognize that 90% of this code is essentially boilerplate that you see any time you build up instances inductively like this.
Decoding these coproducts
The decoding implementation is a little worse, even, but follows the same pattern:
import io.circe.{ DecodingFailure, HCursor }
import shapeless.labelled.field
trait ReprDecoder[C <: Coproduct] extends Decoder[C]
object ReprDecoder {
def wrap[A <: Coproduct](decodeA: Decoder[A]): ReprDecoder[A] =
new ReprDecoder[A] {
def apply(c: HCursor): Decoder.Result[A] = decodeA(c)
}
implicit val decodeCNil: ReprDecoder[CNil] = wrap(
Decoder.failed(DecodingFailure("CNil", Nil))
)
implicit def decodeCCons[K <: Symbol, L, R <: Coproduct](implicit
witK: Witness.Aux[K],
decodeL: Decoder[L],
decodeR: ReprDecoder[R]
): ReprDecoder[FieldType[K, L] :+: R] = wrap(
decodeL.prepare(_.downField("contents")).validate(
_.downField("tag").focus
.flatMap(_.as[String].right.toOption)
.contains(witK.value.name),
witK.value.name
)
.map(l => Inl[FieldType[K, L], R](field[K](l)))
.or(decodeR.map[FieldType[K, L] :+: R](Inr(_)))
)
}
In general there will be a little more logic involved in our Decoder implementations, since each decoding step can fail.
Our ADT representation
Now we can wrap it all together:
import shapeless.{ LabelledGeneric, Lazy }
object Derivation extends FlatCaseClassCodecs {
implicit def encodeAdt[A, Repr <: Coproduct](implicit
gen: LabelledGeneric.Aux[A, Repr],
encodeRepr: Lazy[ReprEncoder[Repr]]
): ObjectEncoder[A] = encodeRepr.value.contramapObject(gen.to)
implicit def decodeAdt[A, Repr <: Coproduct](implicit
gen: LabelledGeneric.Aux[A, Repr],
decodeRepr: Lazy[ReprDecoder[Repr]]
): Decoder[A] = decodeRepr.value.map(gen.from)
}
This looks very similar to the definitions in our FlatCaseClassCodecs above, and the idea is the same: we're defining instances for our data type (either case classes or ADTs) by building on the instances for the generic representations of those data types. Note that I'm extending FlatCaseClassCodecs, again to minimize imports for the user.
In action
Now we can use these instances like this:
scala> import Derivation._
import Derivation._
scala> item1.asJson.noSpaces
res7: String = {"tag":"Cake","contents":["cherry",100]}
scala> item2.asJson.noSpaces
res8: String = {"tag":"Hat","contents":["cowboy","felt","brown"]}
…which is exactly what we wanted. And the best part is that this will work for any sealed trait hierarchy in Scala, no matter how many case classes it has or how many members those case classes have (although compile times will start to hurt once you're into the dozens of either), assuming all of the member types have JSON representations.
