在scala中利用org.json4s 操作json

https://github.com/json4s/json4s/tree/v.3.2.0_scala2.10

SON4S 

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 moment 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.

Lift JSON

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

Jackson

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.SerializationFeatureorg.json4s.jackson.JsonMethods.mapper.configure(SerializationFeature.CLOSE_CLOSEABLE, false)

Guide

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 JValuecase object JNothing extends JValue // 'zero' for JValuecase object JNull extends JValuecase class JString(s: String) extends JValuecase class JDouble(num: Double) extends JValuecase class JDecimal(num: BigDecimal) extends JValuecase class JInt(num: BigInt) extends JValuecase class JLong(num: Long) extends JValuecase class JBool(value: Boolean) extends JValuecase class JObject(obj: List[JField]) extends JValuecase class JArray(arr: List[JValue]) extends JValuetype JField = (String, JValue)

All features are implemented in terms of the 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

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

SBT users

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}"

Maven users

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>

Extras

• ext

Support for Enum, Joda-Time, ...

• scalaz

Applicative style parsing with Scalaz

Migration from older versions

3.3.0 ->

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

3.0.0 ->

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. The most noticeable consequence of this change are that map, transform, find and filter come in two versions:

def map(f: JValue => JValue): JValuedef mapField(f: JField => JField): JValuedef transform(f: PartialFunction[JValue, JValue]): JValuedef transformField(f: PartialFunction[JField, JField]): JValuedef 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.

2.2 ->

Path expressions were changed after version 2.2. 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. 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))))

Producing JSON

You can generate json in 2 modes: either in DoubleMode or in BigDecimalMode; the former will map all decimal values into JDoubles, and the latter into JDecimals.

For the double mode dsl use:

import org.json4s.JsonDSL._// orimport org.json4s.JsonDSL.WithDouble._

For the big decimal mode dsl use:

import org.json4s.JsonDSL.WithBigDecimal._

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. The field and value are 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 => JValue

Example

object 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]}]}}

The above example produces the 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 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 lotto2scala> 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 lotto1changed: org.json4s.JsonAST.JValue = JNothingadded: org.json4s.JsonAST.JValue = JNothingdeleted: 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))))))))))))))

Querying JSON

"LINQ" style

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 ageres0: 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

The json AST can be queried using XPath-like functions. The 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 org.json4s._scala> import org.json4s.native.JsonMethods._

or

scala> import org.json4s.jackson.JsonMethods._scala> import org.json4s.JsonDSL._scala> val json: JObject =  ("person" ->    ("name" -> "Joe") ~    ("age" -> 35) ~    ("spouse" ->      ("person" ->        ("name" -> "Marilyn") ~        ("age" -> 33)      )    )  )scala> json \\ "spouse"res0: org.json4s.JsonAST.JValue = JObject(List(      (person,JObject(List((name,JString(Marilyn)), (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 removeField { _ == JField("name", JString("Marilyn")) }) \\ "name"))res3: String = "Joe"scala> compact(render(json \ "person" \ "name"))res4: String = "Joe"scala> compact(render(json \ "person" \ "spouse" \ "person" \ "name"))res5: String = "Marilyn"scala> json findField {         case JField("name", _) => true         case _ => false       }res6: Option[org.json4s.JsonAST.JValue] = Some((name,JString(Joe)))scala> json filterField {         case JField("name", _) => true         case _ => false       }res7: List[org.json4s.JsonAST.JField] = List(JField(name,JString(Joe)), JField(name,JString(Marilyn)))scala> json transformField {         case JField("name", JString(s)) => ("NAME", JString(s.toUpperCase))       }res8: org.json4s.JsonAST.JValue = JObject(List((person,JObject(List((NAME,JString(JOE)), (age,JInt(35)), (spouse,JObject(List((person,JObject(List((NAME,JString(MARILYN)), (age,JInt(33)))))))))))))scala> json.valuesres8: 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: org.json4s.JsonAST.JValue = JObject(List((name,JString(Mary)), (age,JInt(5))))scala> (json \ "children")(1) \ "name"res1: org.json4s.JsonAST.JValue = JString(Mazy)scala> json \\ classOf[JInt]res2: List[org.json4s.JsonAST.JInt#Values] = List(5, 3)scala> json \ "children" \\ classOf[JString]res3: List[org.json4s.JsonAST.JString#Values] = List(Mary, Mazy)

Extracting values

Case classes can be used to extract values from parsed JSON. Non-existent values can be extracted into scala.Option and strings can be automatically converted into java.util.Dates.

Please see more examples in ExtractionExampleSpec.scala.

scala> import org.json4s._scala> import org.json4s.jackson.JsonMethods._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)))scala> val addressJson = json  \ "address"  // Extract address objectscala> addressJson.extract[Address]res1: Address = Address(Bulevard,Helsinki)scala> (json \ "children").extract[List[Child]]  // Extract list of objectsres2: List[Child] = List(Child(Mary,5,Some(Sat Sep 04 23:36:22 IST 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 are two solutions for this. (See LottoExample.scala for a 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 transformField {         case ("first-name", x) => ("firstname", x)       }

If the json field names are snake case (i.e., separated_by_underscores), but the case class uses camel case (i.e., firstLetterLowercaseAndNextWordsCapitalized), you can convert the keys during the extraction using camelizeKeys:

scala> import org.json4s._scala> import org.json4s.native.JsonMethods._scala> implicit val formats = DefaultFormatsscala> val json = parse("""{"first_name":"Mary"}""")scala> case class Person(firstName: String)scala> json.camelizeKeys.extract[Person]res0: Person = Person(Mazy)

See the "Serialization" section below for details on converting a class with camel-case fields into json with snake case keys.

The extraction function tries to find the best-matching constructor when the 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 implementing trait 'Formats'):

scala> implicit val formats = new DefaultFormats {         override def dateFormatter = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSS'Z'")       }

A 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")))

Note that when the extraction of an Option[_] fails, the default behavior of extract is to return None. You can make it fail with a [MappingException] instead; you can override this behavior by using a custom Formats object:

val formats: Formats = new DefaultFormats {  override val strictOptionParsing: Boolean = true}

Serialization

Case classes can be serialized and deserialized. Please see other examples in SerializationExamples.scala.

scala> import org.json4s._scala> import org.json4s.native.Serializationscala> import org.json4s.native.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)

If you're using jackson instead of the native one:

scala> import org.json4s._scala> import org.json4s.jackson.Serializationscala> import org.json4s.jackson.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 that are not supported (see below)

If the class contains camel-case fields (i.e: firstLetterLowercaseAndNextWordsCapitalized) but you want to produce a json string with snake casing (i.e., separated_by_underscores), you can use the snakizeKeys method:

scala> val ser = write(Person("Mary"))ser: String = {"firstName":"Mary"}scala> compact(render(parse(ser).snakizeKeys))res0: String = {"first_name":"Mary"}

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 Animalscala> case class Dog(name: String) extends Animalscala> case class Fish(weight: Double) extends Animalscala> 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 the short classname for all instances of configured objects. FullTypeHints outputs the full classname. Other strategies can be implemented by extending the 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 classes defined in traits or classes

We've added support for case classes defined in a trait. But they do need custom formats. I'll explain why and then how.

Why?

For classes defined in a trait it's a bit difficult to get to their companion object, which is needed to provide default values. We could punt on those but that brings us to the next problem, that the compiler generates an extra field in the constructor of such case classes. The first field in the constructor of those case classes is called $outer and is of type of the defining trait. So somehow we need to get an instance of that object, naively we could scan all classes and collect the ones that are implementing the trait, but when there are more than one: which one to take?

How?

I've chosen to extend the formats to include a list of companion mappings for those case classes. So you can have formats that belong to your modules and keep the mappings in there. That will then make default values work and provide the much needed $outer field.

trait SharedModule {  case class SharedObj(name: String, visible: Boolean = false)}object PingPongGame extends SharedModuleimplicit val formats: Formats =  DefaultFormats.withCompanions(classOf[PingPongGame.SharedObj] -> PingPongGame)val inst = PingPongGame.SharedObj("jeff", visible = true)val extr = Extraction.decompose(inst)extr must_== JObject("name" -> JString("jeff"), "visible" -> JBool(true))extr.extract[PingPongGame.SharedObj] must_== inst

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 CustomSerializer[Interval](format => (         {           case JObject(JField("start", JInt(s)) :: JField("end", JInt(e)) :: Nil) =>             new Interval(s.longValue, e.longValue)         },         {           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

A custom serializer is created by providing two partial functions. The first evaluates to a value if it can unpack the data from JSON. The second creates the desired JSON if the type matches.

Extensions

Module json4s-ext contains extensions to extraction and serialization. The following types are supported.

// Lift's boximplicit val formats = org.json4s.DefaultFormats + new org.json4s.native.ext.JsonBoxSerializer// Scala enumsimplicit val formats = org.json4s.DefaultFormats + new org.json4s.ext.EnumSerializer(MyEnum)// orimplicit val formats = org.json4s.DefaultFormats + new org.json4s.ext.EnumNameSerializer(MyEnum)// Joda Timeimplicit val formats = org.json4s.DefaultFormats ++ org.json4s.ext.JodaTimeSerializers.all

XML support

JSON structure can be converted to XML nodes and vice versa. Please see more examples in XmlExamples.scala.

scala> import org.json4s.Xml.{toJson, toXml}scala> val xml =         <users>           <user>             <id>1</id>             <name>Harry</name>           </user>           <user>             <id>2</id>             <name>David</name>           </user>         </users>scala> val json = toJson(xml)scala> pretty(render(json))res3: String ={  "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 a 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 a JSON array. This is rarely a desired outcome. These both problems can be fixed by the following transformation function.

scala> json transformField {         case ("id", JString(s)) => ("id", JInt(s.toInt))         case ("user", x: JObject) => ("user", JArray(x :: Nil))       }

Other direction is supported too. Converting JSON to XML:

scala> toXml(json)res5: scala.xml.NodeSeq = NodeSeq(<users><user><id>1</id><name>Harry</name></user><user><id>2</id><name>David</name></user></users>)

Low-level pull parser API

The pull parser API is provided for cases requiring extreme performance. It improves parsing performance in two ways. First, no intermediate AST is generated. Second, you can stop parsing at any time, skipping the rest of the stream. Note: This parsing style is recommended only as an optimization. The above-mentioned functional APIs are easier to use.

Consider the following example, which shows how to parse one field value from a big JSON:

scala> val json = """  {    ...    "firstName": "John",    "lastName": "Smith",    "address": {      "streetAddress": "21 2nd Street",      "city": "New York",      "state": "NY",      "postalCode": 10021    },    "phoneNumbers": [      { "type": "home", "number": "212 555-1234" },      { "type": "fax", "number": "646 555-4567" }    ],    ...  }"""scala> val parser = (p: Parser) => {         def parse: BigInt = p.nextToken match {           case FieldStart("postalCode") => p.nextToken match {             case IntVal(code) => code             case _ => p.fail("expected int")           }           case End => p.fail("no field named 'postalCode'")           case _ => parse         }         parse       }scala> val postalCode = parse(json, parser)postalCode: BigInt = 10021

The pull parser is a function Parser => A; in this example it is concretely Parser => BigInt. The constructed parser recursively reads tokens until it finds a FieldStart("postalCode") token. After that the next token must be IntVal; otherwise parsing fails. It returns the parsed integer value and stops parsing immediately.

FAQ

Q1: I have a JSON object and I want to extract it to a case class:

scala> case class Person(name: String, age: Int)scala> val json = """{"name":"joe","age":15}"""

But extraction fails:

scala> parse(json).extract[Person]org.json4s.MappingException: Parsed JSON values do not match with class constructor

A1:

Extraction does not work for classes defined in the REPL. Compile the case class definitions with scalac and import those to the REPL.

Kudos

• The original idea for the DSL syntax was taken from the Lift mailing list (by Marius).

• The idea for the AST and rendering was taken from Real World Haskell book.