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scala - 使用 µPickle 序列化多态类型

转载 作者:行者123 更新时间:2023-12-05 04:00:51 29 4
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我正在阅读 µPickle 的文档并在互联网上搜索,但我没能找到任何关于一个非常基本的功能的提及,我记得为我以前使用的所有序列化库(Jackson、Prickle ...)记录了它:多态类型。我找到的唯一文档是关于密封特征/类的。考虑以下代码:

import upickle.default._

trait Base

object Base{
implicit val rw: ReadWriter[Base] = ReadWriter.merge(C1.rw, C2.rw)
}
object C1 {
implicit val rw: ReadWriter[C1] = macroRW
}
object C2 {
implicit val rw: ReadWriter[C2] = macroRW
}
case class C1(x: Int) extends Base
case class C2(s: String) extends Base

object Main extends App {
val c1: Base = new C1(0)
val c2: Base = new C2("X")

val c1String = write(c1)
val c2String = write(c2)
println("c1 " + c1String)
println("c2 " + c2String)

}

如果我将 trait Base 更改为 sealed trait Base,此代码将起作用。我同意在序列化程序中列出所有派生类的要求,这也是我提到的其他库所需要的,但是在一个源文件中包含多个大类以便基可以被密封。如果基数不密封,如何使用 uPickle 序列化多态类型?

最佳答案

µPickle 在编译时工作(宏在编译时工作)。为了为具有子类实例的特征派生类型类实例,您应该在编译时知道所有特征子类。这仅适用于密封特征(通过 knownDirectSubclasses https://github.com/lihaoyi/upickle/blob/master/implicits/src/upickle/implicits/internal/Macros.scala#L124 )。

http://www.lihaoyi.com/upickle/#SupportedTypes

Supported Types

Out of the box, uPickle supports writing and reading the following types:

  • Boolean, Byte, Char, Short, Int, Long, Float, Double
  • Tuples from 1 to 22
  • Immutable Seq, List, Vector, Set, SortedSet, Option, Array, Maps, and all other collections with a reasonable CanBuildFrom implementation
  • Duration, Either
  • Stand-alone case classes and case objects, and their generic equivalents,
  • Non-generic case classes and case objects that are part of a sealed trait or sealed class hierarchy
  • sealed trait and sealed classes themselves, assuming that all subclasses are picklable
  • UUIDs
  • null

如您所见,仅支持密封特征。


解决方法是在多个源文件中具有密封特征,并使用自定义 pickler 具有共同的父特征。

  trait Base

object Base {
implicit val rw: ReadWriter[Base] = readwriter[ujson.Value].bimap[Base]({
case c: Base1 => writeJs(c)
case c: Base2 => writeJs(c)
},
s => Try(read[Base1](s)).getOrElse(read[Base2](s))
)
}

sealed trait Base1 extends Base
object Base1 {
implicit val rw: ReadWriter[Base1] = ReadWriter.merge(C1.rw, C11.rw)
}

case class C1(x: Int) extends Base1
object C1 {
implicit val rw: ReadWriter[C1] = macroRW
}

case class C11(x: Int) extends Base1
object C11 {
implicit val rw: ReadWriter[C11] = macroRW
}

sealed trait Base2 extends Base
object Base2 {
implicit val rw: ReadWriter[Base2] = ReadWriter.merge(C2.rw, C22.rw)
}

case class C2(s: String) extends Base2
object C2 {
implicit val rw: ReadWriter[C2] = macroRW
}

case class C22(s: String) extends Base2
object C22 {
implicit val rw: ReadWriter[C22] = macroRW
}

val c1: Base = new C1(0)
val c2: Base = new C2("X")

val c1String = write(c1)
val c2String = write(c2)
println("c1 " + c1String) // c1 {"$type":"App.C1","x":0}
println("c2 " + c2String) // c2 {"$type":"App.C2","s":"X"}

println(read[Base](c1String)) // C1(0)
println(read[Base](c2String)) // C2(X)

关于scala - 使用 µPickle 序列化多态类型,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/55917244/

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