Lift Framework example source code file (README.md)
The Lift Framework README.md source codeParsing 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 JInt(num: BigInt) extends JValue
case class JBool(value: Boolean) extends JValue
case class JField(name: String, value: JValue) extends JValue
case class JObject(obj: List[JField]) extends JValue
case class JArray(arr: List[JValue]) extends 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
Installation
============
It comes with Lift, but non-Lift users can add lift-json as a dependency in following ways.
Note, replace XXX with correct Lift version.
### SBT users
Add dependency to your project description:
val lift_json = "net.liftweb" %% "lift-json" % "XXX"
### Maven users
Add dependency to your pom:
<dependency>
<groupId>net.liftweb
<artifactId>lift-json
<version>XXX
</dependency>
### Others
Download following jars:
* http://scala-tools.org/repo-releases/net/liftweb/lift-json/XXX/lift-json-XXX.jar
* http://mirrors.ibiblio.org/pub/mirrors/maven2/com/thoughtworks/paranamer/paranamer/2.1/paranamer-2.1.jar
* scalap (Only for Scala-2.9 compatible versions)
Extras
------
* [lift-json-ext](https://github.com/lift/framework/tree/master/core/json-ext)
Support for Box, Enum, Joda-Time, ...
* [lift-json-scalaz](https://github.com/lift/framework/tree/master/core/json-scalaz)
Applicative style parsing with Scalaz
Migration from older versions
=============================
2.2 ->
------
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"
Parsing JSON
============
Any valid json can be parsed into internal AST format.
scala> import net.liftweb.json._
scala> parse(""" { "numbers" : [1, 2, 3, 4] } """)
res0: net.liftweb.json.JsonAST.JValue =
JObject(List(JField(numbers,JArray(List(JInt(1), JInt(2), JInt(3), JInt(4))))))
Producing JSON
==============
DSL rules
---------
* 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"}
Example
-------
object JsonExample extends Application {
import net.liftweb.json._
import net.liftweb.json.JsonDSL._
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]
}]
}
}
Merging & Diffing
-----------------
Two JSONs can be merged and diffed with each other.
Please see more examples in src/test/scala/net/liftweb/json/MergeExamples.scala and src/test/scala/net/liftweb/json/DiffExamples.scala
scala> import net.liftweb.json._
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: net.liftweb.json.JsonAST.JValue = JNothing
added: net.liftweb.json.JsonAST.JValue = JNothing
deleted: net.liftweb.json.JsonAST.JValue = JObject(List(JField(lotto,JObject(List(JField(winners,
JArray(List(JObject(List(JField(winner-id,JInt(54)), JField(numbers,JArray(
List(JInt(52), JInt(3), JInt(12), JInt(11), JInt(18), JInt(22))))))))))))))
Querying JSON
=============
"LINQ" style
------------
JSON values can be extracted using for-comprehensions.
Please see more examples in src/test/scala/net/liftweb/json/JsonQueryExamples.scala
scala> import net.liftweb.json._
scala> val json = parse("""
{ "name": "joe",
"children": [
{
"name": "Mary",
"age": 5
},
{
"name": "Mazy",
"age": 3
}
]
}
""")
scala> for { JField("age", JInt(age)) <- json } 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))
XPath + HOFs
------------
Json AST can be queried using XPath like functions. Following REPL session shows the usage of
'\\', '\\\\', 'find', 'filter', 'transform', 'remove' and 'values' functions.
The example json is:
{
"person": {
"name": "Joe",
"age": 35,
"spouse": {
"person": {
"name": "Marilyn"
"age": 33
}
}
}
}
Translated to DSL syntax:
scala> import net.liftweb.json._
scala> import net.liftweb.json.JsonDSL._
scala> val json =
("person" ->
("name" -> "Joe") ~
("age" -> 35) ~
("spouse" ->
("person" ->
("name" -> "Marilyn") ~
("age" -> 33)
)
)
)
scala> json \\ "spouse"
res0: net.liftweb.json.JsonAST.JValue = JObject(List(
JField(person,JObject(List(JField(name,JString(Marilyn)), JField(age,JInt(33)))))))
scala> compact(render(res0))
res1: String = {"person":{"name":"Marilyn","age":33}}
scala> compact(render(json \\ "name"))
res2: String = {"name":"Joe","name":"Marilyn"}
scala> compact(render((json remove { _ == JField("name", JString("Marilyn")) }) \\ "name"))
res3: String = {"name":"Joe"}
scala> compact(render(json \ "person" \ "name"))
res4: String = "Joe"
scala> compact(render(json \ "person" \ "spouse" \ "person" \ "name"))
res5: String = "Marilyn"
scala> json find {
case JField("name", _) => true
case _ => false
}
res6: Option[net.liftweb.json.JsonAST.JValue] = Some(JField(name,JString(Joe)))
scala> json filter {
case JField("name", _) => true
case _ => false
}
res7: List[net.liftweb.json.JsonAST.JValue] = List(JField(name,JString(Joe)), JField(name,JString(Marilyn)))
scala> json transform {
case JField("name", JString(s)) => JField("NAME", JString(s.toUpperCase))
}
res8: net.liftweb.json.JsonAST.JValue = JObject(List(JField(person,JObject(List(
JField(NAME,JString(JOE)), JField(age,JInt(35)), JField(spouse,JObject(List(
JField(person,JObject(List(JField(NAME,JString(MARILYN)), JField(age,JInt(33)))))))))))))
scala> json.values
res8: scala.collection.immutable.Map[String,Any] = Map(person -> Map(name -> Joe, age -> 35, spouse -> Map(person -> Map(name -> Marilyn, age -> 33))))
Indexed path expressions work too and values can be unboxed using type expressions.
scala> val json = parse("""
{ "name": "joe",
"children": [
{
"name": "Mary",
"age": 5
},
{
"name": "Mazy",
"age": 3
}
]
}
""")
scala> (json \ "children")(0)
res0: net.liftweb.json.JsonAST.JValue = JObject(List(JField(name,JString(Mary)), JField(age,JInt(5))))
scala> (json \ "children")(1) \ "name"
res1: net.liftweb.json.JsonAST.JValue = JString(Mazy)
scala> json \\ classOf[JInt]
res2: List[net.liftweb.json.JsonAST.JInt#Values] = List(5, 3)
scala> json \ "children" \\ classOf[JString]
res3: List[net.liftweb.json.JsonAST.JString#Values] = List(Mary, Mazy)
scala> json \ "children" \ classOf[JField]
res4: List[net.liftweb.json.JsonAST.JField#Values] = List((name,Mary), (age,5), (name,Mazy), (age,3))
Extracting values
=================
Case classes can be used to extract values from parsed JSON. Non-existing values
can be extracted into scala.Option and strings can be automatically converted into
java.util.Dates.
Please see more examples in src/test/scala/net/liftweb/json/ExtractionExamplesSpec.scala
scala> import net.liftweb.json._
scala> implicit val formats = DefaultFormats // Brings in default date formats etc.
scala> case class Child(name: String, age: Int, birthdate: Option[java.util.Date])
scala> case class Address(street: String, city: String)
scala> case class Person(name: String, address: Address, children: List[Child])
scala> val json = parse("""
{ "name": "joe",
"address": {
"street": "Bulevard",
"city": "Helsinki"
},
"children": [
{
"name": "Mary",
"age": 5
"birthdate": "2004-09-04T18:06:22Z"
},
{
"name": "Mazy",
"age": 3
}
]
}
""")
scala> json.extract[Person]
res0: Person = Person(joe,Address(Bulevard,Helsinki),List(Child(Mary,5,Some(Sat Sep 04 18:06:22 EEST 2004)), Child(Mazy,3,None)))
By default the constructor parameter names must match json field names. However, sometimes json
field names contain characters which are not allowed characters in Scala identifiers. There's two
solutions for this (see src/test/scala/net/liftweb/json/LottoExample.scala for bigger example).
Use back ticks.
scala> case class Person(`first-name`: String)
Use transform function to postprocess AST.
scala> case class Person(firstname: String)
scala> json transform {
case JField("first-name", x) => JField("firstname", x)
}
Extraction function tries to find the best matching constructor when case class has auxiliary
constructors. For instance extracting from JSON {"price":350} into the following case class
will use the auxiliary constructor instead of the primary constructor.
scala> case class Bike(make: String, price: Int) {
def this(price: Int) = this("Trek", price)
}
scala> parse(""" {"price":350} """).extract[Bike]
res0: Bike = Bike(Trek,350)
Primitive values can be extracted from JSON primitives or fields.
scala> (json \ "name").extract[String]
res0: String = "joe"
scala> ((json \ "children")(0) \ "birthdate").extract[Date]
res1: java.util.Date = Sat Sep 04 21:06:22 EEST 2004
DateFormat can be changed by overriding 'DefaultFormats' (or by implmenting trait 'Formats').
scala> implicit val formats = new DefaultFormats {
override def dateFormatter = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSS'Z'")
}
JSON object can be extracted to Map[String, _] too. Each field becomes a key value pair
in result Map.
scala> val json = parse("""
{
"name": "joe",
"addresses": {
"address1": {
"street": "Bulevard",
"city": "Helsinki"
},
"address2": {
"street": "Soho",
"city": "London"
}
}
}""")
scala> case class PersonWithAddresses(name: String, addresses: Map[String, Address])
scala> json.extract[PersonWithAddresses]
res0: PersonWithAddresses("joe", Map("address1" -> Address("Bulevard", "Helsinki"),
"address2" -> Address("Soho", "London")))
Serialization
=============
Case classes can be serialized and deserialized.
Please see other examples in src/test/scala/net/liftweb/json/SerializationExamples.scala
scala> import net.liftweb.json._
scala> import net.liftweb.json.Serialization.{read, write}
scala> implicit val formats = Serialization.formats(NoTypeHints)
scala> val ser = write(Child("Mary", 5, None))
scala> read[Child](ser)
res1: Child = Child(Mary,5,None)
Serialization supports:
* Arbitrarily deep case class graphs
* All primitive types, including BigInt and Symbol
* List, Seq, Array, Set and Map (note, keys of the Map must be strings: Map[String, _])
* scala.Option
* java.util.Date
* Polymorphic Lists (see below)
* Recursive types
* Serialization of fields of a class (see below)
* Custom serializer functions for types which are not supported (see below)
Serializing polymorphic Lists
-----------------------------
Type hints are required when serializing polymorphic (or heterogeneous) Lists. Serialized JSON objects
will get an extra field named 'jsonClass' (the name can be changed by overriding 'typeHintFieldName' from Formats).
scala> trait Animal
scala> case class Dog(name: String) extends Animal
scala> case class Fish(weight: Double) extends Animal
scala> case class Animals(animals: List[Animal])
scala> implicit val formats = Serialization.formats(ShortTypeHints(List(classOf[Dog], classOf[Fish])))
scala> val ser = write(Animals(Dog("pluto") :: Fish(1.2) :: Nil))
ser: String = {"animals":[{"jsonClass":"Dog","name":"pluto"},{"jsonClass":"Fish","weight":1.2}]}
scala> read[Animals](ser)
res0: Animals = Animals(List(Dog(pluto), Fish(1.2)))
ShortTypeHints outputs short classname for all instances of configured objects. FullTypeHints outputs full
classname. Other strategies can be implemented by extending TypeHints trait.
Serializing fields of a class
-----------------------------
To enable serialization of fields, a FieldSerializer can be added for some type:
implicit val formats = DefaultFormats + FieldSerializer[WildDog]()
Now the type WildDog (and all subtypes) gets serialized with all its fields (+ constructor parameters).
FieldSerializer takes two optional parameters which can be used to intercept the field serialization:
case class FieldSerializer[A: Manifest](
serializer: PartialFunction[(String, Any), Option[(String, Any)]] = Map(),
deserializer: PartialFunction[JField, JField] = Map()
)
Those PartialFunctions are called just before a field is serialized or deserialized. Some useful PFs to
rename and ignore fields are provided:
val dogSerializer = FieldSerializer[WildDog](
renameTo("name", "animalname") orElse ignore("owner"),
renameFrom("animalname", "name"))
implicit val formats = DefaultFormats + dogSerializer
Serializing non-supported types
-------------------------------
It is possible to plug in custom serializer + deserializer functions for any type.
Now, if we have a non case class Interval (thus, not supported by default), we can still serialize it
by providing following serializer.
scala> class Interval(start: Long, end: Long) {
val startTime = start
val endTime = end
}
scala> class IntervalSerializer extends Serializer[Interval] {
private val IntervalClass = classOf[Interval]
def deserialize(implicit format: Formats): PartialFunction[(TypeInfo, JValue), Interval] = {
case (TypeInfo(IntervalClass, _), json) => json match {
case JObject(JField("start", JInt(s)) :: JField("end", JInt(e)) :: Nil) =>
new Interval(s.longValue, e.longValue)
case x => throw new MappingException("Can't convert " + x + " to Interval")
}
}
def serialize(implicit format: Formats): PartialFunction[Any, JValue] = {
case x: Interval =>
JObject(JField("start", JInt(BigInt(x.startTime))) ::
JField("end", JInt(BigInt(x.endTime))) :: Nil)
}
}
scala> implicit val formats = Serialization.formats(NoTypeHints) + new IntervalSerializer
Function 'serialize' creates a JSON object to hold serialized data. Function 'deserialize' knows how
to construct serialized object by pattern matching against type info and data.
Extensions
----------
Module lift-json-ext contains extensions to extraction and serialization. Following types are supported.
// Lift's box
implicit val formats = net.liftweb.json.DefaultFormats + new JsonBoxSerializer
// Scala enums
implicit val formats = net.liftweb.json.DefaultFormats + new EnumSerializer(MyEnum)
// or
implicit val formats = net.liftweb.json.DefaultFormats + new EnumNameSerializer(MyEnum)
// Joda Time
implicit val formats = net.liftweb.json.DefaultFormats ++ JodaTimeSerializers.all
XML support
===========
JSON structure can be converted to XML node and vice versa.
Please see more examples in src/test/scala/net/liftweb/json/XmlExamples.scala
scala> import net.liftweb.json.Xml.{toJson, toXml}
scala> val xml =
<users>
<user>
<id>1
<name>Harry
</user>
<user>
<id>2
<name>David
</user>
</users>
scala> val json = toJson(xml)
scala> pretty(render(json))
res3: {
"users":{
"user":[{
"id":"1",
"name":"Harry"
},{
"id":"2",
"name":"David"
}]
}
}
Now, the above example has two problems. First, the id is converted to String while we might want it as an Int. This
is easy to fix by mapping JString(s) to JInt(s.toInt). The second problem is more subtle. The conversion function
decides to use JSON array because there's more than one user-element in XML. Therefore a structurally equivalent
XML document which happens to have just one user-element will generate a JSON document without JSON array. This
is rarely a desired outcome. These both problems can be fixed by following transformation function.
scala> json transform {
case JField("id", JString(s)) => JField("id", JInt(s.toInt))
case JField("user", x: JObject) => JField("user", JArray(x :: Nil))
}
Other direction is supported too. Converting JSON to XML:
scala> toXml(json)
res5: scala.xml.NodeSeq = <users>
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