Java开发工具之scala处理json格式利器的示例-创新互联
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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
org.json4s json4s-native_2.11 3.7.0-M6
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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