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关于大数据:开发工具scala处理json格式利器json4s

jiezi

1. 为什么是 json4s

从 json4s 的官网形容

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，至多有 6 个 json 库，并且不包含 java 的 json 库，这些库都有着相似的形象语法树 AST，json4s 的目标就是为了应用简略的一种语法反对这些 json 库，因而说 json4s 能够说是一种 json 的标准解决，配合 scala 开发过程中极其简介的语法个性，能够轻松地实现比方 json 合并，json 的 diff 操作，能够不便地解决 jsonArray 的字符串，所以如果应用 scala，那么 json4s 肯定不能错过，在理论场景下应用 json 解决数据很常见，比方 spark 开发中解决原始 json 数据等等，开始上手可能看起来比较复杂，然而用起来你会很爽。

2.json4s 的数据结构

json4s 包含 10 个类型和一个 type 类型的对象，别离如下 

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)

能够看到，他们都继承自 JValue,JValue 是 json4s 外面相似于 java 的 object 位置，而 JField 是用来一次性匹配 json 的 key,value 对而筹备的。

3.json4s 的实际

上面来看，咱们如何来应用 json4s

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

看上面的代码即可，正文写的比拟清晰，一般来说 json 的应用无外乎是字符串到对象或者对象到字符串，而字符串到对象能够用 case class 也能够用原始的比方下面提到的类 

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 办法示意从字符串到 json-object
    val person = parse("""|{"name":"Toy","price":35.35}
        |""".stripMargin, useBigDecimalForDouble = true)
    // 1. 模式匹配提取，\ 示意提取
    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 逐层拜访
    val value = (parseJson \ "name" \ "tome").extract[String]
    //3.2 循环拜访
    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，并增加过滤
    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，然而每个级别要明确]
    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. 应用 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. 字符串寻找差别
    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，应用 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. 留神

4.1 compact 和 render 的应用

罕用写法 compact(render(json)), 用来把一个 json 对象转成字符串，并压缩显示，当然也能够用 prety(render(json))

4.2 序列化时候须要一个隐式对象

例如上面的 

implicit val formats = Serialization.formats(NoTypeHints)

参考

更多参考官网或者非官方博客等

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
吴邪，小三爷，混迹于后盾，大数据，人工智能畛域的小菜鸟。
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