At this moment there are at least 6 json libraries for scala, not counting the java json libraries. All these libraries have a very similar AST. This project aims to provide a single AST to be used by other scala json libraries.
At this m F438 oment the approach taken to working with the AST has been taken from lift-json and the native package is in fact lift-json but outside of the lift project.
This project also attempts to set lift-json free from the release schedule imposed by the lift framework. The Lift framework carries many dependencies and as such it's typically a blocker for many other scala projects when a new version of scala is released.
So the native package in this library is in fact verbatim lift-json in a different package name, this means that your import statements will change if you use this library.
import org.json4s._
import org.json4s.native.JsonMethods._After that everything works exactly the same as it would with lift-json
In addition to the native parser there is also an implementation that uses jackson for parsing to the AST. The jackson module includes most of the jackson-module-scala functionality and the ability to use it with the lift-json AST.
To use jackson instead of the native parser:
import org.json4s._
import org.json4s.jackson.JsonMethods._Be aware that the default behavior of the jackson integration is to close the stream when it's done. If you want to change that:
import com.fasterxml.jackson.databind.SerializationFeature
org.json4s.jackson.JsonMethods.mapper.configure(SerializationFeature.CLOSE_CLOSEABLE, false)Parsing and formatting utilities for JSON.
A central concept in lift-json library is Json AST which models the structure of a JSON document as a syntax tree.
sealed abstract class JValue
case object JNothing extends JValue // 'zero' for JValue
case object JNull extends JValue
case class JString(s: String) extends JValue
case class JDouble(num: Double) extends JValue
case class JDecimal(num: BigDecimal) extends JValue
case class JInt(num: BigInt) extends JValue
case class JLong(num: Long) extends JValue
case class JBool(value: Boolean) extends JValue
case class JObject(obj: List[JField]) extends JValue
case class JArray(arr: List[JValue]) extends JValue
type JField = (String, JValue)All features are implemented in terms of above AST. Functions are used to transform the AST itself, or to transform the AST between different formats. Common transformations are summarized in a following picture.
Summary of the features:
- Fast JSON parser
- LINQ style queries
- Case classes can be used to extract values from parsed JSON
- Diff & merge
- DSL to produce valid JSON
- XPath like expressions and HOFs to manipulate JSON
- Pretty and compact printing
- XML conversions
- Serialization
- Low level pull parser API
You can add the json4s as a dependency in following ways. Note, replace {latestVersion} with correct Json4s version.
You can find available versions here:
http://search.maven.org/#search%7Cga%7C1%7Cg%3A%22org.json4s%22
For the native support add the following dependency to your project description:
val json4sNative = "org.json4s" %% "json4s-native" % "{latestVersion}"For the Jackson support add the following dependency to your project description:
val json4sJackson = "org.json4s" %% "json4s-jackson" % "{latestVersion}"For the native support add the following dependency to your pom:
<dependency>
<groupId>org.json4s</groupId>
<artifactId>json4s-native_${scala.version}</artifactId>
<version>{latestVersion}</version>
</dependency>For the jackson support add the following dependency to your pom:
<dependency>
<groupId>org.json4s</groupId>
<artifactId>json4s-jackson_${scala.version}</artifactId>
<version>{latestVersion}</version>
</dependency>Support for Enum, Joda-Time, ...
Applicative style parsing with Scalaz
Support for Box
json4s 3.3 basically should be source code compatible with 3.2.x. Since json4s 3.3.0, We've started using MiMa for binary compatibility verification not to repeat the bin compatibility issue described here.
The behavior of .toOption on JValue has changed. Now both JNothing and JNull return None.
For the old behavior you can use toSome which will only turn a JNothing into a None.
All the merged pull requests: https://github.com/json4s/json4s/pulls?q=is%3Apr+is%3Aclosed+milestone%3A3.3
JField is no longer a JValue. This means more type safety since it is no longer possible to create invalid JSON where JFields are added directly into JArrays for instance. Most noticeable consequence of this change is that map, transform, find and filter come in two versions:
def map(f: JValue => JValue): JValue
def mapField(f: JField => JField): JValue
def transform(f: PartialFunction[JValue, JValue]): JValue
def transformField(f: PartialFunction[JField, JField]): JValue
def find(p: JValue => Boolean): Option[JValue]
def findField(p: JField => Boolean): Option[JField]
//...Use *Field functions to traverse fields in the JSON, and use the functions without 'Field' in the name to traverse values in the JSON.
Path expressions were changed after 2.2 version. Previous versions returned JField which unnecessarily complicated the use of the expressions. If you have used path expressions with pattern matching like:
val JField("bar", JInt(x)) = json \ "foo" \ "bar"It is now required to change that to:
val JInt(x) = json \ "foo" \ "bar"Any valid json can be parsed into internal AST format. For native support:
scala> import org.json4s._
scala> import org.json4s.native.JsonMethods._
scala> parse(""" { "numbers" : [1, 2, 3, 4] } """)
res0: org.json4s.JsonAST.JValue =
JObject(List((numbers,JArray(List(JInt(1), JInt(2), JInt(3), JInt(4))))))
scala> parse("""{"name":"Toy","price":35.35}""", useBigDecimalForDouble = true)
res1: org.json4s.package.JValue =
JObject(List((name,JString(Toy)), (price,JDecimal(35.35))))For jackson support:
scala> import org.json4s._
scala> import org.json4s.jackson.JsonMethods._
scala> parse(""" { "numbers" : [1, 2, 3, 4] } """)
res0: org.json4s.JsonAST.JValue =
JObject(List((numbers,JArray(List(JInt(1), JInt(2), JInt(3), JInt(4))))))
scala> parse("""{"name":"Toy","price":35.35}""", useBigDecimalForDouble = true)
res1: org.json4s.package.JValue =
JObject(List((name,JString(Toy)), (price,JDecimal(35.35))))You can generate json in 2 modes either in DoubleMode or in BigDecimalMode; the former will map all decimal values
into a JDouble the latter into a JDecimal.
For the double mode dsl use:
import org.json4s.JsonDSL._
// or
import org.json4s.JsonDSL.WithDouble._For the big decimal mode dsl use:
import org.json4s.JsonDSL.WithBigDecimal._- Primitive types map to JSON primitives.
- Any seq produces JSON array.
scala> val json = List(1, 2, 3)
scala> compact(render(json))
res0: String = [1,2,3]- Tuple2[String, A] produces field.
scala> val json = ("name" -> "joe")
scala> compact(render(json))
res1: String = {"name":"joe"}- ~ operator produces object by combining fields.
scala> val json = ("name" -> "joe") ~ ("age" -> 35)
scala> compact(render(json))
res2: String = {"name":"joe","age":35}- Any value can be optional. Field and value is completely removed when it doesn't have a value.
scala> val json = ("name" -> "joe") ~ ("age" -> Some(35))
scala> compact(render(json))
res3: String = {"name":"joe","age":35}
scala> val json = ("name" -> "joe") ~ ("age" -> (None: Option[Int]))
scala> compact(render(json))
res4: String = {"name":"joe"}- Extending the dsl
To extend the dsl with your own classes you must have an implicit conversion in scope of signature:
type DslConversion = T => JValueobject JsonExample extends App {
import org.json4s._
import org.json4s.JsonDSL._
import org.json4s.jackson.JsonMethods._
case class Winner(id: Long, numbers: List[Int])
case class Lotto(id: Long, winningNumbers: List[Int], winners: List[Winner], drawDate: Option[java.util.Date])
val winners = List(Winner(23, List(2, 45, 34, 23, 3, 5)), Winner(54, List(52, 3, 12, 11, 18, 22)))
val lotto = Lotto(5, List(2, 45, 34, 23, 7, 5, 3), winners, None)
val json =
("lotto" ->
("lotto-id" -> lotto.id) ~
("winning-numbers" -> lotto.winningNumbers) ~
("draw-date" -> lotto.drawDate.map(_.toString)) ~
("winners" ->
lotto.winners.map { w =>
(("winner-id" -> w.id) ~
("numbers" -> w.numbers))}))
println(compact(render(json)))
}scala> JsonExample
{"lotto":{"lotto-id":5,"winning-numbers":[2,45,34,23,7,5,3],"winners":
[{"winner-id":23,"numbers":[2,45,34,23,3,5]},{"winner-id":54,"numbers":[52,3,12,11,18,22]}]}}Example produces following pretty printed JSON. Notice that draw-date field is not rendered since its value is None:
scala> pretty(render(JsonExample.json))
{
"lotto":{
"lotto-id":5,
"winning-numbers":[2,45,34,23,7,5,3],
"winners":[{
"winner-id":23,
"numbers":[2,45,34,23,3,5]
},{
"winner-id":54,
"numbers":[52,3,12,11,18,22]
}]
}
}Two JSONs can be merged and diffed with each other. Please see more examples in MergeExamples.scala and DiffExamples.scala.
scala> import org.json4s._
scala> import org.json4s.jackson.JsonMethods._
scala> val lotto1 = parse("""{
"lotto":{
"lotto-id":5,
"winning-numbers":[2,45,34,23,7,5,3],
"winners":[{
"winner-id":23,
"numbers":[2,45,34,23,3,5]
}]
}
}""")
scala> val lotto2 = parse("""{
"lotto":{
"winners":[{
"winner-id":54,
"numbers":[52,3,12,11,18,22]
}]
}
}""")
scala> val mergedLotto = lotto1 merge lotto2
scala> pretty(render(mergedLotto))
res0: String =
{
"lotto":{
"lotto-id":5,
"winning-numbers":[2,45,34,23,7,5,3],
"winners":[{
"winner-id":23,
"numbers":[2,45,34,23,3,5]
},{
"winner-id":54,
"numbers":[52,3,12,11,18,22]
}]
}
}
scala> val Diff(changed, added, deleted) = mergedLotto diff lotto1
changed: org.json4s.JsonAST.JValue = JNothing
added: org.json4s.JsonAST.JValue = JNothing
deleted: org.json4s.JsonAST.JValue = JObject(List((lotto,JObject(List(JField(winners,
JArray(List(JObject(List((winner-id,JInt(54)), (numbers,JArray(
List(JInt(52), JInt(3), JInt(12), JInt(11), JInt(18), JInt(22))))))))))))))JSON values can be extracted using for-comprehensions. Please see more examples in JsonQueryExamples.scala.
scala> import org.json4s._
scala> import org.json4s.native.JsonMethods._
scala> val json = parse("""
{ "name": "joe",
"children": [
{
"name": "Mary",
"age": 5
},
{
"name": "Mazy",
"age": 3
}
]
}
""")
scala> for {
JObject(child) <- json
JField("age", JInt(age)) <- child
} yield age
res0: List[BigInt] = List(5, 3)
scala> for {
JObject(child) <- json
JField("name", JString(name)) <- child
JField("age", JInt(age)) <- child
if age > 4
} yield (name, age)
res1: List[(String, BigInt)] = List((Mary,5))