Custom types¶
To support a custom type, you’ll need to provide an implicit Codec
for that type.
This can be done by writing a codec from scratch, mapping over an existing codec, or automatically deriving one. Which of these approaches can be taken, depends on the context in which the codec will be used.
Providing an implicit codec¶
To create a custom codec, you can either directly implement the Codec
trait, which requires to provide the following
information:
encode
andrawDecode
methods- codec meta-data (
CodecMeta
) consisting of:- schema of the type (for documentation)
- validator for the type
- codec format (
text/plain
,application/json
etc.) - type of the raw value, to which data is serialised (
String
,Int
etc.)
This might be quite a lot of work, that’s why it’s usually easier to map over an existing codec. To do that, you’ll need to provide two mappings:
- an
encode
method which encodes the custom type into the base type - a
decode
method which decodes the base type into the custom type, optionally reporting decode errors (the return type is aDecodeResult
)
For example, to support a custom id type:
def decode(s: String): DecodeResult[MyId] = MyId.parse(s) match {
case Success(v) => DecodeResult.Value(v)
case Failure(f) => DecodeResult.Error(s, f)
}
def encode(id: MyId): String = id.toString
implicit val myIdCodec: Codec[MyId, TextPlain, String] = Codec.stringPlainCodecUtf8
.mapDecode(decode)(encode)
// or, using the type alias for codecs in the TextPlain format and String as the raw value:
implicit val myIdCodec: PlainCodec[MyId] = Codec.stringPlainCodecUtf8
.mapDecode(decode)(encode)
Note that inputs/outputs can also be mapped over. However, this kind of mapping is always an isomorphism, doesn’t allow any validation or reporting decode errors. Hence, it should be used only for grouping inputs or outputs from a tuple into a custom type.
Automatically deriving codecs¶
In some cases, codecs can be automatically derived:
Automatic codec derivation usually requires other implicits, such as:
- json encoders/decoders from the json library
- codecs for individual form fields
- schema of the custom type, through the
Schema[T]
implicit
Schema derivation¶
For case classes types, Schema[_]
values are derived automatically using Magnolia, given
that schemas are defined for all of the case class’s fields. It is possible to configure the automatic derivation to use
snake-case, kebab-case or a custom field naming policy, by providing an implicit tapir.generic.Configuration
value:
implicit val customConfiguration: Configuration =
Configuration.default.withSnakeCaseMemberNames
Alternatively, Schema[_]
values can be defined by hand, either for whole case classes, or only for some of its fields.
For example, here we state that the schema for MyCustomType
is a String
:
implicit val schemaForMyCustomType: Schema[MyCustomType] = Schema(SchemaType.SString)
If you have a case class which contains some non-standard types (other than strings, number, other case classes, collections), you only need to provide the schema for the non-standard types. Using these schemas, the rest will be derived automatically.
Sealed traits / coproducts¶
Tapir supports schema generation for coproduct types (sealed trait hierarchies) of the box, but they need to be defined by hand (as implicit values). To properly reflect the schema in OpenAPI documentation, a discriminator object can be specified.
For example, given following coproduct:
sealed trait Entity{
def kind: String
}
case class Person(firstName:String, lastName:String) extends Entity {
def kind: String = "person"
}
case class Organization(name: String) extends Entity {
def kind: String = "org"
}
The schema may look like this:
val sPerson = implicitly[Schema[Person]]
val sOrganization = implicitly[Schema[Organization]]
implicit val sEntity: Schema[Entity] =
Schema.oneOf[Entity, String](_.kind, _.toString)("person" -> sPerson, "org" -> sOrganization)
Customising derived schemas¶
In some cases, it might be desirable to customise the derived schemas, e.g. to add a description to a particular
field of a case class. This can be done by looking up an implicit instance of the Derived[Schema[T]]
type,
and assigning it to an implicit schema. When such an implicit Schmea[T]
is in scope will have higher priority
than the built-in low-priority conversion from Derived[Schema[T]]
to Schema[T]
.
Schemas for products/coproducts (case classes and case class families) can be traversed and modified using
.modify
method. To traverse collections, use .each
.
For example:
case class Basket(fruits: List[FruitAmount])
case class FruitAmount(fruit: String, amount: Int)
implicit val customBasketSchema: Schema[Basket] = implicitly[Derived[Schema[Basket]]].value
.modify(_.fruits.each.amount)(_.description("How many fruits?"))
There is also an unsafe variant of this method, but it should be avoided in most cases. The “unsafe” prefix comes from the fact that the method takes a list of strings, which represent fields, and the correctness of this specification is not checked.
Non-standard collections can be unwrapped in the modification path by providing an implicit value of ModifyFunctor
.
Schema for cats datatypes¶
The tapir-cats
module contains Schema[_]
instances for some cats datatypes. See the tapir.codec.cats.TapirCodecCats
trait or import sttp.tapir.codec.cats._
to bring the implicit values into scope.
Next¶
Read on about validation.