I am using latest version of Play Framework and it's JSON lib like this Json.toJson(obj). But toJson is not capable of converting any Scala object to JSON, because the structure of data is unknown. Someone suggested using case convert, but here my Scala knowledge falls short. The data comes from database, but the structure of table is not known.
Where should I look further to create convert such unknown data structure to JSON?
Given that there is only a limited number of types you want to serialize to JSON, this should work:
object MyWriter {
implicit val anyValWriter = Writes[Any] (a => a match {
case v:String => Json.toJson(v)
case v:Int => Json.toJson(v)
case v:Any => Json.toJson(v.toString)
// or, if you don't care about the value
case _ => throw new RuntimeException("unserializeable type")
})
}
You can use it by then by importing the implicit value at the point where you want to serialize your Any:
import MyWriter.anyValWriter
val a: Any = "Foo"
Json.toJson(a)
Using json4s, you can import the package:
import org.json4s.DefaultFormats
import org.json4s.native.Serialization.write
Then create an implicit variable inside your trait:
implicit val formats: DefaultFormats = DefaultFormats
And finally, in your method, use it:
write(myObject)
Related
The JSON output that I am looking for is
{[[1, 1.5, "String1"], [-2, 2.3, "String2"]]}
So I want to have an Array of Arrays and the inner array is storing different types.
How should I store my variables so I can create such JSON in Scala?
I thought of List of Tuples. However, all the available JSON libraries try to convert a Tuple to a map instead of an Array. I am using json4s library.
Here is a custom serializer for those inner arrays using json4s:
import org.json4s._
class MyTupleSerializer extends CustomSerializer[(Int, Double, String)](format => ({
case obj: JArray =>
implicit val formats: Formats = format
(obj(0).extract[Int], obj(1).extract[Double], obj(2).extract[String])
}, {
case (i: Int, d: Double, s: String) =>
JArray(List(JInt(i), JDouble(d), JString(s)))
}))
The custom serialiser converts JArray into a tuple and back again. This will be used wherever the Scala object being read or written has a value of the appropriate tuple type.
To test this against the sample input I have modified it to make it valid JSON by adding a field name:
{"data": [[1, 1.5, "String1"], [-2, 2.3, "String2"]]}
I have defined a container class to match this:
case class MyTupleData(data: Vector[(Int, Double, String)])
The name of the class is not relevant but the field name data must match the JSON field name. This uses Vector rather than Array because Array is really a Java type rather than a Scala type. You can use List if preferred.
import org.json4s.jackson.Serialization.{read, write}
case class MyTupleData(data: Vector[(Int, Double, String)])
object JsonTest extends App {
val data = """{"data": [[1, 1.5, "String1"], [-2, 2.3, "String2"]]}"""
implicit val formats: Formats = DefaultFormats + new MyTupleSerializer
val td: MyTupleData = read[MyTupleData](data)
println(td) // MyTupleData(Vector((1,1.5,String1), (-2,2.3,String2)))
println(write(td)) // {"data":[[1,1.5,"String1"],[-2,2.3,"String2"]]}
}
If you prefer to use a custom class for the data rather than a tuple, the code looks like this:
case class MyClass(i: Int, d: Double, s: String)
class MyClassSerializer extends CustomSerializer[MyClass](format => ({
case obj: JArray =>
implicit val formats: Formats = format
MyClass(obj(0).extract[Int], obj(1).extract[Double], obj(2).extract[String])
}, {
case MyClass(i, d, s) =>
JArray(List(JInt(i), JDouble(d), JString(s)))
}))
Use a List of List rather than List of Tuples.
an easy way to convert list of tuples to list of list is:
val listOfList: List[List[Any]] = listOfTuples.map(_.productIterator.toList)
I would use jackson, which is a java library and can deal with arbitrary datatypes inside collections of type Any/AnyRef, rather than trying to come up with a custom serializer in one of scala json libraries.
To convert scala List to java List use
import collection.JavaConverters._
So, in summary the end list would be:
val javaListOfList: java.util.List[java.util.List[Any]] = listOfTuples.map(_.productIterator.toList.asJava).asJava
Using this solution, you could have arbitrary length tuples in your list and it would work.
import com.fasterxml.jackson.databind.ObjectMapper
import collection.JavaConverters._
object TuplesCollectionToJson extends App {
val tuplesList = List(
(10, false, 43.6, "Text1"),
(84, true, 92.1, "Text2", 'X')
)
val javaList = tuplesList.map(_.productIterator.toList.asJava).asJava
val mapper = new ObjectMapper()
val json = mapper.writeValueAsString(javaList)
println(json)
}
Would produce:
[[10,false,43.6,"Text1"],[84,true,92.1,"Text2","X"]]
PS: Use this solution only when you absolutely have to work with variable types. If your tuple datatype is fixed, its better to create a json4s specific serializer/deserializer.
I'm using websockets in Play framework 2.3.
Referencing this snippet from the official how-to page.
import play.api.mvc._
import play.api.Play.current
def socket = WebSocket.acceptWithActor[InEvent, OutEvent] { request => out =>
MyWebSocketActor.props(out)
}
How would I catch a JSON parse error (RuntimeException: Error parsing JSON)?
This question is very similar to the one linked below, however, I'm using custom types (InEvent, OutEvent) and not the JsValue type. I don't want to transform to a JsValue or string. I need it to transform to the InEvent type if successful, or throw a more descriptive error.
How do I catch json parse error when using acceptWithActor?
Somewhere within your scope there are implicit definitions of FrameFormatter[InEvent] and FrameFormatter[OutEvent]:
implicit val inEventFrameFormatter = FrameFormatter.jsonFrame[InEvent]
implicit val outEventFrameFormatter = FrameFormatter.jsonFrame[OutEvent]
You just have to rewrite both of them, instead of using the predefined jsonFrame a custom method like customJsonFrame:
def customJsonFrame[A: Format]: FrameFormatter[A] =
FrameFormatter.jsonFrame.transform[A](
out => Json.toJson(out),
in => Json.fromJson[A](in).fold(
error => throw new CustomException(),
a => a
)
)
and replace the aforementioned rows with:
implicit val inEventFrameFormatter = customJsonFrame[InEvent]
implicit val outEventFrameFormatter = customJsonFrame[OutEvent]
I am trying to implement a generic pattern with which to generate marshallers and unmarshallers for an Akka HTTP REST service using Argonaut, handling both entity and collection level requests and responses. I have no issues in implementing the entity level as such:
case class Foo(foo: String)
object Foo {
implicit val FooJsonCodec = CodecJson.derive[Foo]
implicit val EntityEncodeJson = FooJson.Encoder
implicit val EntityDecodeJson = FooJson.Decoder
}
I am running into issues attempting to provide encoders and decoders for the following:
[
{ "foo": "1" },
{ "foo": "2" }
]
I have attempted adding the following to my companion:
object Foo {
implicit val FooCollectionJsonCodec = CodecJson.derive[HashSet[Foo]]
}
However, I am receiving the following error:
Error:(33, 90) value jencode0L is not a member of object argonaut.EncodeJson
I see this method truly does not exist but is there any other generic method to generate my expected result. I'm strongly avoiding using an additional case class to describe the collection since I am using reflection heavily in my use case.
At this point, I'd even be fine with a manually constructed Encoder and Decoder, however, I've found no documentation on how to construct it with the expected structure.
Argonaut have predefined encoders and decoders for Scala's immutable lists, sets, streams and vectors. If your type is not supported explicitly, as in the case of java.util.HashSet, you can easily add EncodeJson and DecodeJson for the type:
import argonaut._, Argonaut._
import scala.collection.JavaConverters._
implicit def hashSetEncode[A](
implicit element: EncodeJson[A]
): EncodeJson[java.util.HashSet[A]] =
EncodeJson(set => EncodeJson.SetEncodeJson[A].apply(set.asScala.toSet))
implicit def hashSetDecode[A](
implicit element: DecodeJson[A]
): DecodeJson[java.util.HashSet[A]] =
DecodeJson(cursor => DecodeJson.SetDecodeJson[A]
.apply(cursor)
.map(set => new java.util.HashSet(set.asJava)))
// Usage:
val set = new java.util.HashSet[Int]
set.add(1)
set.add(3)
val jsonSet = set.asJson // [1, 3]
jsonSet.jdecode[java.util.HashSet[Int]] // DecodeResult(Right([1, 3]))
case class A(set: java.util.HashSet[Int])
implicit val codec = CodecJson.derive[A]
val a = A(set)
val jsonA = a.asJson // { "set": [1, 3] }
jsonA.jdecode[A] // DecodeResult(Right(A([1, 3])))
Sample is checked on Scala 2.12.1 and Argonaut 6.2-RC2, but as far as I know it shouldn't depend on some latest changes.
Approach like this works with any linear or unordered homogenous data structure that you want to represent as JSON array. Also, this is preferable to creating a CodecJson: latter can be inferred automatically from JsonEncode and JsonDecode, but not vice versa. This way, your set will serialize and deserialize both when used independently or within other data type, as shown in example.
I don't use Argonaut but use spray-json and suspect solution can be similar.
Have you tried something like this ?
implicit def HashSetJsonCodec[T : CodecJson] = CodecJson.derive[Set[T]]
if it doesn't work I'd probably try creating more verbose implicit function like
implicit def SetJsonCodec[T: CodecJson](implicit codec: CodecJson[T]): CodecJson[Set[T]] = {
CodecJson(
{
case value => JArray(value.map(codec.encode).toList)
},
c => c.as[JsonArray].flatMap {
case arr: Json.JsonArray =>
val items = arr.map(codec.Decoder.decodeJson)
items.find(_.isError) match {
case Some(error) => DecodeResult.fail[Set[T]](error.toString(), c.history)
case None => DecodeResult.ok[Set[T]](items.flatMap(_.value).toSet[T])
}
}
)
}
PS. I didn't test this but hopefully it leads you to the right direction :)
I have a simple single key-valued Map(K,V) myDictionary that is populated by my program and at the end I want to write it as JSON format string in a text file - as I would need parse them later.
I was using this code earlier,
Some(new PrintWriter(outputDir+"/myDictionary.json")).foreach{p => p.write(compact(render(decompose(myDictionary)))); p.close}
I found it to be slower as the input size increased. Later, I used this var out = new
var out = new PrintWriter(outputDir+"/myDictionary.json");
out.println(scala.util.parsing.json.JSONObject(myDictionary.toMap).toString())
This is proving to be bit faster.
I have run this for sample input and found that this is faster than my earlier approach. I assuming my input map size would reach at least a million values( >1GB text file) (K,V) hence I want to make sure that I follow the faster and memory efficient approach for Map serialization process.What are other approaches that you would recommend,that I can look into to optimize this.
The JSON support in the standard Scala library is probably not the best choice. Unfortunately the situation with JSON libraries for Scala is a bit confusing, there are many alternatives (Lift JSON, Play JSON, Spray JSON, Twitter JSON, Argonaut, ...), basically one library for each day of the week... I suggest you have a look at these at least to see if any of them is easier to use and more performative.
Here is an example using Play JSON which I have chosen for particular reasons (being able to generate formats with macros):
object JsonTest extends App {
import play.api.libs.json._
type MyDict = Map[String, Int]
implicit object MyDictFormat extends Format[MyDict] {
def reads(json: JsValue): JsResult[MyDict] = json match {
case JsObject(fields) =>
val b = Map.newBuilder[String, Int]
fields.foreach {
case (k, JsNumber(v)) => b += k -> v.toInt
case other => return JsError(s"Not a (string, number) pair: $other")
}
JsSuccess(b.result())
case _ => JsError(s"Not an object: $json")
}
def writes(m: MyDict): JsValue = {
val fields: Seq[(String, JsValue)] = m.map {
case (k, v) => k -> JsNumber(v)
} (collection.breakOut)
JsObject(fields)
}
}
val m = Map("hallo" -> 12, "gallo" -> 34)
val serial = Json.toJson(m)
val text = Json.stringify(serial)
println(text)
val back = Json.fromJson[MyDict](serial)
assert(back == JsSuccess(m), s"Failed: $back")
}
While you can construct and deconstruct JsValues directly, the main idea is to use a Format[A] where A is the type of your data structure. This puts more emphasis on type safety than the standard Scala-Library JSON. It looks more verbose, but in end I think it's the better approach.
There are utility methods Json.toJson and Json.fromJson which look for an implicit format of the type you want.
On the other hand, it does construct everything in-memory and it does duplicate your data structure (because for each entry in your map you will have another tuple (String, JsValue)), so this isn't necessarily the most memory efficient solution, given that you are operating in the GB magnitude...
Jerkson is a Scala wrapper for the Java JSON library Jackson. The latter apparently has the feature to stream data. I found this project which says it adds streaming support. Play JSON in turn is based on Jerkson, so perhaps you can even figure out how to stream your object with that. See also this question.
I'm trying to html-escape all strings in my objects when serializing them with Lift's json library. I thought I could do this by passing an escape (partial) function to the objects' FieldSerializer, but this seems to add fields to the json-object - not replace them. You can see what I mean by running the following simple example and note that it outputs
{"y":"test","x":"test"} while I wanted {"y":"test"}:
import net.liftweb.json.Serialization
import net.liftweb.json.FieldSerializer
import net.liftweb.json.FieldSerializer._
import net.liftweb.json.DefaultFormats
case class Simple(x: String)
implicit val formats = DefaultFormats +
FieldSerializer[Simple](renameTo("x","y"),renameFrom("y", "x"))
Serialization.write(Simple("test"))
Is there a way I can tell FieldSerializer to replace the field matched by my partial function?
Try CoustomeSerializer:
implicit val formats = DefaultFormats +
new CustomSerializer[Simple](ser => ( {
case JObject(JField("y", JString(x)) :: Nil) => Simple(x)
}, {
case simple: Simple => JObject(JField("y", JString(simple.x)) :: Nil)
}))
ps.I asked this on lift's google group,and got this answer,checkout https://groups.google.com/d/msg/liftweb/ShRrGNrsu6Y/sNw4JGdSU6sJ