1.為什么是json4s

從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庫中，至少有6個json庫，并且不包括 java的json庫，這些庫都有著類似的抽象語法樹AST，json4s的目的就是為了使用簡單的一種語法支持這些json庫，因此說json4s可以說是一種json的規范處理，配合scala開發程序中極其簡介的語法特性，可以輕松地實作比如json合并，json的diff操作，可以方便地處理jsonArray的字串，所以如果使用scala，那么json4s一定不能錯過，在實際場景下使用json處理資料很常見，比如spark開發中處理原始json資料等等，開始上手可能看起來比較復雜，但是用起來你會很爽，

2.json4s的資料結構

json4s包括10個型別和一個type型別的物件，分別如下

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，他們都繼承自JValue,JValue是json4s里面類似于java的object地位，而JField是用來一次性匹配json的key,value對而準備的，

3.json4s的實踐

下面來看，我們如何來使用json4s

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

看下面的代碼即可，注釋寫的比較清晰，一般來說json的使用無外乎是字串到物件或者物件到字串，而字串到物件可以用case class 也可以用原始的比如上面提到的類

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方法表示從字串到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.多層嵌套取值
    val parseJson: JValue = https://www.cnblogs.com/hulichao/p/parse("""
        |{"name":{"tome":"new"},"price":35.35}
        |""".stripMargin, useBigDecimalForDouble = true)

    //3.1 逐層訪問
    val value = https://www.cnblogs.com/hulichao/p/(parseJson /"name" \ "tome").extract[String]
    //3.2 回圈訪問
    val value2 = (parseJson \\ "tome")
    println(value2)


    //4.嵌套json串決議
    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 某欄位的值
    val ages = for {
      JObject(child) <- json
      JField("age", JInt(age)) <- child
    } yield age

    println(ages)

    // 嵌套取陣列中某個欄位值，并添加過濾
    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和物件的轉換,[就是json陣列]
    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轉物件，[json 非json陣列，但是每個級別要明確]
    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產生json字串
//    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.字串尋找差異
    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.遍歷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

常用寫法compact(render(json)),用來把一個json物件轉成字串，并壓縮顯示，當然也可以用prety(render(json))

4.2 序列化時候需要一個隱式物件

例如下面的

implicit val formats = Serialization.formats(NoTypeHints)

參考

更多參考官網或者非官方博客等

https://json4s.org/

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

https://www.cnblogs.com/yyy-blog/p/11819302.html

https://www.shuzhiduo.com/A/Vx5MBVOYdN/

https://segmentfault.com/a/1190000007302496

https://www.coder.work/article/6786418

https://www.wolai.com/sTVar6XXjpuM9ANFn2sx9n#xcy85CRuHfRepDBAvtvdq9

https://www.wolai.com/sTVar6XXjpuM9ANFn2sx9n#7kKK1H1h2GPZnzhiXvWG38
吳邪，小三爺，混跡于后臺，大資料，人工智能領域的小菜鳥，
更多請關注

標籤：其他

上一篇：kubernetes和docker----2.學習Pod資源

下一篇：Hive-常見調優方式 && 兩個面試sql