Validation¶
Tapir supports validation for primitive/base types. Validation of composite values, whole data structures, business
rules enforcement etc. should be done as part of the server logic of the endpoint, using the
dedicated error output (the E
in Endpoint[I, E, O, S]
) to report errors.
Single type validation¶
Validation rules are part of the codec for a given type. They can be specified when creating the codec
(using the Codec.validate()
method):
case class MyId(id: String)
implicit val myIdCodec: Codec[MyId, TextPlain, _] = Codec.stringPlainCodecUtf8
.mapDecode(decode)(encode)
.validate(Validator.pattern("^[A-Z].*").contramap(_.id))
Or added to individual inputs/outputs:
val e = endpoint.in(
query[Int]("amount")
.validate(Validator.min(0))
.validate(Validator.max(100)))
Validation rules added using the built-in validators are translated to OpenAPI documentation.
Validation rules and automatic codec derivation¶
When a codec is automatically derived for a type (see custom types), validators for all types
(for json this is a recursive process) are looked up through implicit Validator[T]
values.
Note that to validate a nested member of a case class, it needs to have a unique type (that is, not an Int
, as
providing an implicit Validator[Int]
would validate all ints in the hierarchy), as validator lookup is type-driven.
To introduce unique types for primitive values, you can use value classes or type tagging.
For example, to support an integer wrapped in a value type in a json body, we need to provide Circe encoders and decoders (if that’s the json library that we are using), schema information and a validator:
case class Amount(v: Int) extends AnyVal
case class FruitAmount(fruit: String, amount: Amount)
val e: Endpoint[FruitAmount, Unit, Unit, Nothing] = {
implicit val schemaForAmount: SchemaFor[Amount] = SchemaFor(Schema.SInteger)
implicit val encoder: Encoder[Amount] = Encoder.encodeInt.contramap(_.v)
implicit val decode: Decoder[Amount] = Decoder.decodeInt.map(Amount.apply)
implicit val v: Validator[Amount] = Validator.min(1).contramap(_.v)
endpoint.in(jsonBody[FruitAmount])
}
Decode failures¶
Codecs support reporting decoding failures, by returning a DecodeResult
from the Codec.decode
method. However, this
is meant for input/output values which are in an incorrect low-level format, when parsing a “raw value” fails. In other
words, decoding failures should be reported for format failures, not business validation errors.
Decoding failures should be reported when the input is in an incorrect low-level format, when parsing a “raw value” fails. In other words, decoding failures should be reported for format failures, not business validation errors.
To customise error messages that are returned upon validation/decode failures by the server, see error handling.
Enum validators¶
Validators for enumerations can be created using the Validator.enum
method, which either:
- takes a type parameter, which should be an abstract, sealed base type, and using a macro determines the possible implementations
- takes the list of possible values
To properly represent possible values in documentation, the enum validator additionally needs an encode
method, which
converts the enum value to a raw type (typically a string). This method is inferred only if the validator is directly
added to a codec (without any mapping etc.), for example:
sealed trait Color
case object Blue extends Color
case object Red extends Color
implicit def plainCodecForColor: PlainCodec[Color] = {
Codec.stringPlainCodecUtf8
.map[Color]({
case "red" => Red
case "blue" => Blue
})(_.toString.toLowerCase)
.validate(Validator.enum)
}
If the enum is nested within an object, regardless of whether the codec for that object is defined by hand or derived, we need to specify the encode function by hand:
implicit def colorValidator: Validator[Color] = Validator.enum.encode(_.toString.toLowerCase)
Like other validators, enum validators need to be added to a codec, or through an implicit value, if the codec and validator is automatically derived.
Next¶
Read on about json support.