endpoints 0.14.0

JSON schemas

JsonSchemas

This algebra provides vocabulary to define JSON schemas of data types.

"org.julienrf" %% "endpoints-algebra-json-schema" % "0.14.0"

API documentation

This module is dependency-free, it can be used independently of endpoints to define JSON schemas and interpret them as actual encoder, decoders or documentation.

The algebra introduces the concept of JsonSchema[A]: a JSON schema for a type A.

Basic types and record types

The trait provides some predefined JSON schemas (for String, Int, Boolean, Seq, etc.) and ways to combine them together to build more complex schemas.

For instance, given the following Rectangle data type:

case class Rectangle(width: Double, height: Double)
JsonSchemasDocs.scala

We can represent instances of Rectangle in JSON with a JSON object having properties corresponding to the case class fields. A JSON schema for such objects would be defined as follows:

implicit val rectangleSchema: JsonSchema[Rectangle] = (
  field[Double]("width", Some("Rectangle width")) zip
  field[Double]("height")
).xmap((Rectangle.apply _).tupled)(rect => (rect.width, rect.height))
JsonSchemasDocs.scala

The field constructor defines a JSON object schema with one field of the given type and name (and an optional text documentation). A similar constructor, optField, defines an optional field in a JSON object.

The return type of rectangleSchema is declared to be JsonSchema[Rectangle], but we could have used a more specific type: Record[Rectangle]. This subtype of JsonSchema[Rectangle] provides additional operations such as zip or tagged (see the next section).

In the above example, we actually define two JSON object schemas (one for the width field, of type Record[Double], and one for the height field, of type Record[Double]), and then we combine them into a single JSON object schema by using the zip operation. Finally, we call the xmap operation to turn the Record[(Double, Double)] value returned by the zip operation into a Record[Rectangle].

Sum types (sealed traits)

It is also possible to define schemas for sum types. Consider the following type definition, defining a Shape, which can be either a Circle or a Rectangle:

sealed trait Shape
case class Circle(radius: Double) extends Shape
case class Rectangle(width: Double, height: Double) extends Shape
JsonSchemasDocs.scala

A possible JSON schema for this data type consists in using a JSON object with a discriminator field indicating whether the Shape is a Rectangle or a Circle. Such a schema can be defined as follows:

// Given a `circleSchema: Record[Circle]` and a `rectangleSchema: Record[Rectangle]`
(
  circleSchema.tagged("Circle") orElse
  rectangleSchema.tagged("Rectangle")
).xmap[Shape] {
  case Left(circle) => circle
  case Right(rect)  => rect
} {
  case c: Circle    => Left(c)
  case r: Rectangle => Right(r)
}
JsonSchemasDocs.scala

(We have omitted the definition of circleSchema for the sake of conciseness)

First, all the alternative record schemas (in this example, circeSchema and rectangleSchema) must be tagged with a unique name. Then, the orElse operation combines the alternative schemas into a single schema that accepts one of them.

The result of the tagged operation is a Tagged[A] schema. This subtype of JsonSchema[A] models a schema that accepts one of several alternative schemas. It provides the orElse operation.

The orElse operation turns the Tagged[Circle] and Tagged[Rectangle] values into a Record[Either[Circle, Rectangle]], which is then, in this example, transformed into a Record[Shape] by using xmap.

By default, the discriminator field is named type, but you can use another field name either by overriding the defaultDiscriminatorName method of the algebra, or by wrapping the Tagged schema in a withDiscriminator call specifying the field name to use.

Refining schemas

The examples above show how to use xmap to transform a JsonSchema[A] into a JsonSchema[B]. In case the transformation function from A to B can fail (for example, if it applies additional validation), you can use xmapPartial instead of xmap:

val evenNumberSchema: JsonSchema[Int] =
  intJsonSchema.xmapPartial { n =>
    if (n % 2 == 0) Valid(n)
    else Invalid(s"Invalid even integer '$n'")
  }(n => n)
JsonSchemasTest.scala

In this example, we check that the decoded integer is even. If it is not, we return an error message.

Enumerations

There are different ways to represent enumerations in Scala:

  • scala.util.Enumeration
  • Sealed trait with case objects
  • Third-party libraries, e.g. Enumeratum

For example, an enumeration with three possible values can be defined as a sealed trait with three case objects:

sealed trait Status
case object Active extends Status
case object Inactive extends Status
case object Obsolete extends Status
JsonSchemasDocs.scala

The method stringEnumeration in the JsonSchemas algebra supports mapping the enum values to JSON strings. It has two parameters: the possible values, and a function to encode an enum value as a string.

implicit lazy val statusSchema: JsonSchema[Status] =
  stringEnumeration[Status](Seq(Active, Inactive, Obsolete))(_.toString)
JsonSchemasDocs.scala

The resulting JsonSchema[Status] allows defining JSON members with string values that are mapped to our case objects.

It will work similarly for other representations of enumerated values. Most of them provide values which can conveniently be passed into stringEnumeration. However, it is still possible to explicitly pass a certain subset of allowed values.

Tuples

JSON schemas for tuples from 2 to 22 elements are provided out of the box. For instance, if there are implicit JsonSchema instances for types A, B, and C, then you can summon a JsonSchema[(A, B, C)]. Tuples are modeled in JSON with arrays, as recommended in the JSON Schema documentation.

Here is an example of JSON schema for a GeoJSON Point, where GPS coordinates are modeled with a pair (longitude, latitude):

type Coordinates = (Double, Double) // (Longitude, Latitude)
case class Point(coordinates: Coordinates)

implicit val pointSchema: JsonSchema[Point] =
  field[Coordinates]("coordinates")
    .tagged("Point")
    .xmap(Point(_))(_.coordinates)
JsonSchemasDocs.scala

Recursive types

You can reference a currently being defined schema without causing a StackOverflow error by wrapping it in the lazyRecord or lazyTagged constructor:

case class Recursive(next: Option[Recursive])

val recursiveSchema: Record[Recursive] = (
  optField("next")(lazyRecord(recursiveSchema, "Rec"))
).xmap(Recursive)(_.next)
JsonSchemasDocs.scala

Alternatives between schemas

You can define a schema as an alternative between other schemas with the operation orFallbackTo:

val intOrBoolean: JsonSchema[Either[Int, Boolean]] =
  intJsonSchema.orFallbackTo(booleanJsonSchema)
JsonSchemasTest.scala

Because decoders derived from schemas defined with the operation orFallbackTo literally “fallback” from one alternative to another, it makes it impossible to report good decoding failure messages. You should generally prefer using orElse on “tagged” schemas.

Schemas documentation

Schema descriptions can include documentation information which is used by documentation interpreters such as the OpenAPI interpreter. We have already seen in the first section that object fields could be documented with a description. This section shows two other features related to schemas documentation.

You can give names to schemas. These names are used by the OpenAPI interpreter to group the schema definitions at one place, and then reference each schema by its name (see the Swagger “Components Section” documentation).

Use the named method to give a name to a Record, a Tagged, or an Enum schema.

You can also include examples of values for a schema (see the Swagger “Adding Examples” documentation). This is done by using the withExample operation:

implicit val rectangleSchema: JsonSchema[Rectangle] = (
  field[Double]("width", Some("Rectangle width")) zip
  field[Double]("height")
)
  .xmap(Rectangle.tupled)(rect => (rect.width, rect.height))
  .withExample(Rectangle(10, 20))
JsonSchemasDocs.scala

Applying the OpenAPI interpreter to this schema definition produces the following JSON document:

{
  "type": "object",
  "properties": {
    "width": {
      "type": "number",
      "format":"double",
      "description": "Rectangle width"
    },
    "height":{
      "type": "number",
      "format": "double"
    }
  },
  "required": ["width","height"],
  "example": { "width": 10, "height": 20 }
}

Generic derivation of JSON schemas (based on Shapeless)

The module presented in this section uses Shapeless to generically derive JSON schemas for algebraic data type definitions (sealed traits and case classes).

"org.julienrf" %% "endpoints-json-schema-generic" % "0.14.0"

API documentation

JSON schemas derivation

With this module, defining the JSON schema of the Shape data type is reduced to the following:

implicit val shapeSchema: JsonSchema[Shape] = genericJsonSchema
JsonSchemasDocs.scala

The genericJsonSchema operation builds a JSON schema for the given type. The rules for deriving the schema are the following:

  • the schema of a case class is a JSON object,
  • the schema of a sealed trait is the alternative of its leaf case class schemas, discriminated by the case class names,
  • each case class field has a corresponding required JSON object property of the same name and type (for instance, the generic schema for the Rectangle type has a width required property of type integer),
  • each case class field of type Option[A] for some type A has a corresponding optional JSON object property of the same name and type,
  • documentation specific to case class fields can be defined by annotating the fields with the @docs annotation,
  • for sealed traits, the discriminator field name can be defined by the @discriminator annotation, otherwise the defaultDiscriminatorName value is used,
  • the schema is named by the @name annotation, if present, or by invoking the classTagToSchemaName operation with the ClassTag of the type for which the schema is derived.

Here is an example that illustrates how to configure the generic schema derivation process:

@discriminator("kind")
@name("ShapeSchema")
sealed trait Shape

@name("CircleSchema")
case class Circle(radius: Double) extends Shape

@name("RectangleSchema")
case class Rectangle(
  @docs("Rectangle width") width: Double,
  height: Double
)
JsonSchemasDocs.scala

In case you need to transform further a generically derived schema, you might want to use the genericRecord or genericTagged operations instead of genericJsonSchema. These operations have a more specific return type than genericJsonSchema: genericRecord returns a Record, and genericTagged returns a Tagged.

JSON schemas transformation

The module also takes advantage shapeless to provide a more convenient as operation for transforming JSON schema definitions, instead of xmap:

implicit val rectangleSchema: JsonSchema[Rectangle] = (
  field[Double]("width") zip
  field[Double]("height")
).as[Rectangle]
JsonSchemasDocs.scala

Generic derivation of JSON schemas (based on macros)

An alternative to the module presented in the preceding section is provided as a third-party module: endpoints-json-schemas-macros.

Please see the README of that project for more information on how to use it and its differences with the module provided by endpoints.