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
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case 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 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
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<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
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package com.hoult.scala.json4simport 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
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implicit val formats = Serialization.formats(NoTypeHints) |
参考
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
https://www.wolai.com/sTVar6XXjpuM9ANFn2sx9n
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原文链接：https://www.cnblogs.com/hulichao/p/14435161.html
