Java开发工具-scala处理json格式利器-json4s详解

绝代码农

1.为什么是json4s
从json4s的官方描述

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.

在scala库中，至少有6个json库，并且不包括 java的json库，这些库都有着类似的抽象语法树AST，json4s的目的就是为了使用简单的一种语法支持这些json库，因此说json4s可以说是一种json的规范处理，配合scala开发过程中极其简介的语法特性，可以轻松地实现比如json合并，json的diff操作，可以方便地处理jsonArray的字符串，所以如果使用scala，那么json4s一定不能错过，在实际场景下使用json处理数据很常见，比如spark开发中处理原始json数据等等，开始上手可能看起来比较复杂，但是用起来你会很爽。
2.json4s的数据结构
json4s包括10个类型和一个type类型的对象，分别如下

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)

可以看到，他们都继承自JValue,JValue是json4s里面类似于java的object地位，而JField是用来一次性匹配json的key,value对而准备的。
3.json4s的实践
下面来看，我们如何来使用json4s

<dependency>
 <groupId>org.json4s</groupId>
 <artifactId>json4s-native_2.11</artifactId>
 <version>3.7.0-M6</version>
</dependency>

看下面的代码即可，注释写的比较清晰，一般来说json的使用无外乎是字符串到对象或者对象到字符串，而字符串到对象可以用case class 也可以用原始的比如上面提到的类

package com.hoult.scala.json4s
import org.json4s._
import org.json4s.JsonDSL._
import org.json4s.native.JsonMethods._
 
object Demo1 {
 def main(args: Array[String]): Unit = {
 
 //parse方法表示从字符串到json-object
 val person = parse(
  """
  |{"name":"Toy","price":35.35}
  |""".stripMargin, useBigDecimalForDouble = true)
 // 1.模式匹配提取， \表示提取
 val JString(name) = (person \ "name")
 println(name)
 
 // 2.extract[String]取值
// implicit val formats = org.json4s.Formats
 implicit val formats = DefaultFormats
 val name2 = (person \ "name").extract[String]
 val name3 = (person \ "name").extractOpt[String]
 val name4 = (person \ "name").extractOrElse("")
 
 // 3.多层嵌套取值
 val parseJson: JValue = parse(
  """
  |{"name":{"tome":"new"},"price":35.35}
  |""".stripMargin, useBigDecimalForDouble = true)
 
 //3.1 逐层访问
 val value = (parseJson \ "name" \ "tome").extract[String]
 //3.2 循环访问
 val value2 = (parseJson \\ "tome")
 println(value2)
 
 
 //4.嵌套json串解析
 val json = parse(
  """
   { "name": "joe",
   "children": [
  {
  "name": "Mary",
  "age": 20
  },
  {
  "name": "Mazy",
  "age": 10
  }
   ]
   }
  """)
 
// println(json \ "children")
 
 //模式匹配
 for (JArray(child) <- json) println(child)
 
 //提取object 下 某字段的值
 val ages = for {
  JObject(child) <- json
  JField("age", JInt(age)) <- child
 } yield age
 
 println(ages)
 
 // 嵌套取数组中某个字段值，并添加过滤
 val nameAges = for {
  JObject(child) <- json
  JField("name", JString(name)) <- child
  JField("age", JInt(age)) <- child
  if age > 10
 } yield (name, age)
 
 println(nameAges)
 
 // 5.json和对象的转换,[就是json数组]
 case class ClassA(a: Int, b: Int)
 
 val json2: String = """[{"a":1,"b":2},{"a":1,"b":2}]"""
 
 val bb: List[ClassA] = parse(json2).extract[List[ClassA]]
 println(bb)
 
 // 6.json转对象，[json 非json数组，但是每个级别要明确]
 case class ClassC(a: Int, b: Int)
 
 case class ClassB(c: List[ClassC])
 
 val json3: String = """{"c":[{"a":1,"b":2},{"a":1,"b":2}]}"""
 
 val cc: ClassB = parse(json3).extract[ClassB]
 println(cc)
 
 // 7.使用org.json4s产生json字符串
// import org.json4s.JsonDSL._
 val json1 = List(1, 2, 3)
 val jsonMap = ("name" -> "joe")
 val jsonUnion = ("name" -> "joe") ~ ("age" -> 10)
 val jsonOpt = ("name" -> "joe") ~ ("age" -> Some(1))
 val jsonOpt2 = ("name" -> "joe") ~ ("age" -> (None: Option[Int]))
 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(10, List(1, 2, 5)), Winner(11, List(1, 2, 0)))
 val lotto = Lotto(11, List(1, 2, 5), winners, None)
 
 val jsonCase =
  ("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(json1)))
 println(compact(render(jsonMap)))
 println(compact(render(jsonUnion)))
 println(compact(render(jsonOpt)))
 println(compact(render(jsonOpt2)))
 println(compact(render(jsonCase)))
 
 // 8.json格式化
 println(pretty(render(jsonCase)))
 
 // 9.合并字符串
 val lotto1 = parse("""{
   "lotto":{
   "lotto-id": 1,
   "winning-numbers":[7,8,9],
   "winners":[{
  "winner-id": 1,
  "numbers":[7,8,9]
   }]
   }
  }""")
 
 val lotto2 = parse("""{
   "lotto":{
   "winners":[{
  "winner-id": 2,
  "numbers":[1,23,5]
   }]
   }
  }""")
 
 val mergedLotto = lotto1 merge lotto2
// println(pretty(render(mergedLotto)))
 
 // 10.字符串寻找差异
 val Diff(changed, added, deleted) = mergedLotto diff lotto1
 println(changed)
 println(added)
 println(deleted)
 
 val json10 = parse(
  """
 
  """)
 
 println("********8")
 println(json10)
 for (JObject(j) <- json10) println(j)
 
 println("********11")
 
 // 11.遍历json，使用for
 // key1 values key1_vk1:v1 ....
 val str = "{"tag_name":"t_transaction_again_day","tag_distribute_json":"{\\"1\\":\\"0.0011231395\\",\\"0\\":\\"0.9988768605\\"}"}"
 
 val valueJson = parse(str) \ "tag_distribute_json"
 println(valueJson)
 for {
  JString(obj) <- valueJson
  JObject(dlist) <- parse(obj)
  (key, JString(value))<- dlist
 } {
  println(key + "::" + value)
//  val kvList = for (JObject(key, value) <- parse(obj)) yield (key, value)
//  println("obj : " + kvList.mkString(","))
 }
 }
}

4.注意
4.1 compact 和 render的使用
常用写法compact(render(json)),用来把一个json对象转成字符串，并压缩显示，当然也可以用prety(render(json))
4.2 序列化时候需要一个隐式对象
例如下面的

implicit val formats = Serialization.formats(NoTypeHints)

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原文链接：https://www.cnblogs.com/hulichao/p/14435161.html