Choosing & Using a JSON Library

The Scala SDK supports multiple options for working with JSON.

Philosophy

The Couchbase Server is a key-value store that’s agnostic to what’s stored, but it’s very common to store JSON.

The Scala SDK has these main objectives for JSON:

Be usable 'out-of-the-box'. A simple JSON library is included, so you can get started right away. Bonus: it’s simple to use and very fast!
Be agnostic. Your application may already be using its own JSON representation, and it shouldn’t be forced to use the built-in JSON library.
Be inclusive. There’s a wide range of great, popular JSON libaries for the JVM, and we’ve supported many of them directly. That is, you can use types from the Circe library, and many others, when doing any operation. And if we’re missing support for your favourite, then please let us know on the forums.

Optional Dependencies

Dependencies on third-party JSON libraries are performed with Maven’s optional scope. This means that if you have the Circe dependency in your own project (be it SBT, Gradle or Maven based), then you will be able to use the Circe types with the Scala SDK. But the SDK will not pull in the dependency itself, to stay lean and mean.

Getting Started

The examples below assume you’re familiar with connecting to a Couchbase cluster using the Scala SDK, and opening resources. Please check out the Getting Started guide for help with this.

In the examples below we’ll use the following case classes:

case class Address(address: String)
case class User(name: String, age: Int, addresses: Seq[Address])

val user = User("John Smith", 29, List(Address("123 Fake Street")))

So without further ado, let’s dive into some practical examples of the many ways you can use JSON with the Couchbase Scala SDK.

Circe

Circe has the very useful property of being able to encode case classes directly to and from Circe data types, without having to write the 'codec' logic usually required for this.

Here’s an example of how to use Circe with Couchbase:

import io.circe.generic.auto._
import io.circe.syntax._

// Circe can encode case classes directly to its `Json` type, with no codec code required
val json: io.circe.Json = user.asJson

// Can provide Circe types for all mutation operations
val result1: Try[MutationResult] = collection.insert("id", json)

result1 match {
  case Success(_) =>
    val result2: Try[GetResult] = collection.get("id")

    result2 match {
      case Success(doc) =>
        // Can retrieve document content as Circe types
        val content: Try[io.circe.Json] = doc.contentAs[io.circe.Json]

      case Failure(err) => println(s"Error: ${err}")
    }

  case Failure(err) => println(s"Error: ${err}")
}

That works, but it’s very verbose to handle all those Try one-by-one. Here’s an example of how to do the same thing, but chaining all the Try together using flatMap:

import io.circe.generic.auto._
import io.circe.syntax._

val json: io.circe.Json = user.asJson

val result: Try[io.circe.Json] = collection.insert("id", json)
    .flatMap(_ => collection.get("id"))
    .flatMap(doc => doc.contentAs[io.circe.Json])

And finally the same thing again, but this time using a for-comprehension:

import io.circe.generic.auto._
import io.circe.syntax._

val json: io.circe.Json = user.asJson

val result: Try[io.circe.Json] = for {
  _       <- collection.insert("id", json)
  doc     <- collection.get("id")
  content <- doc.contentAs[io.circe.Json]
} yield content

If for-comprehensions are unfamiliar then they’re essentially syntactic sugar, with each of the ← being a flatMap call.

The following examples will use the for-comprehension style, for brevity.

µPickle / µJson

µPickle is a serialization library with its own JSON library, µJson.

Unlike the majority of JSON Scala libraries µJson is mutable, with the author’s rationale an interesting read.

Here’s an example of how to use µJson with Couchbase:

val content = ujson.Obj("name" -> "John Smith",
  "age" -> 29,
  "addresses" -> ujson.Arr(
    ujson.Obj("address" -> "123 Fake Street")
  ))

val result: Try[ujson.Obj] = for {
  // Can provide upickle types for all mutation operations
  _       <- collection.insert("id", content)
  doc     <- collection.get("id")

  // Can retrieve document content as upickle types
  content <- doc.contentAs[ujson.Obj]
} yield content

JsonObject and JsonArray

The SDK includes a built-in JSON library, JsonObject. Its main goals are:

Convenience. Not everyone wants to evaluate multiple JSON libraries before getting started. JsonObject is a decent default choice.
Speed. Our internal benchmarking indicates JsonObject is up to 20 times faster than the nearest Scala JSON library on some important operations. It achieves this mostly by being built around simple, but very fast, mutable JVM data structures. Unlike the rest of the SDK it also throws exceptions rather than incur the small cost on the good path of functional-style error handling (e.g. Try) - though there is an optional alternative JsonObjectSafe interface that does provide Try.
Ease-of-use and mutability. We find ourselves in agreement with the author of µJson that though immutability is usually desirable, it’s actually not always the best choice for JSON. Dealing with deeply nested JSON requires functional tools such as lenses which are rarely easy to read, not to mention incurring a performance penalty. And JSON is most often dealt with briefly and in a limited scope (e.g. getting and modifying a document), so rarely benefits from the safety of immutability. So JsonObject presents a simple mutable API.

Of course, if you’d rather have a JSON library with immutable data, lenses, cursors and other functional goodies, then one of the other options on this page may be a better choice.

Creating

Using JsonObject here’s how to create some simple JSON:

val json = JsonObject("name" -> "Eric Wimp",
  "age" -> 9,
  "addresses" -> JsonArray(JsonObject("address" -> "29 Acacia Road")))

val str = json.toString()
// """{"name":"Eric Wimp","age":9,"addresses":[{"address","29 Acacia Road"}]}"""

As JsonObject and JsonArray are both mutable, they can also be created this way:

val obj = JsonObject.create.put("name", "Eric Wimp")
obj.put("age", 9)
val arr = JsonArray.create
arr.add(JsonObject("address" -> "29 Acacia Road"))
obj.put("addresses", arr)

Retrieving Data

It’s easy to retrieve data:

json.str("name") // "Eric Wimp"
json.arr("addresses").obj(0).str("address") // "29 Acacia Road"

Or, using a feature of Scala called Dynamic, you can use an alternative syntax like this:

json.dyn.name.str // "Eric Wimp"
json.dyn.addresses(0).address.str // "29 Acacia Road"

Using with Key-Value Operations

As with the other supported JSON libraries, JsonObject (and JsonArray) can easily be used directly with any SDK operation:

val json = JsonObject("name" -> "Eric Wimp",
  "age" -> 9,
  "addresses" -> JsonArray(JsonObject("address" -> "29 Acacia Road")))

val result: Try[JsonObject] = for {
  // Can provide JsonObject for all mutation operations
  _       <- collection.insert("id", json)
  doc     <- collection.get("id")

  // Can retrieve document content as JsonOject (and JsonObjectSafe)
  content <- doc.contentAs[JsonObject]
} yield content

Error Handling and JsonObjectSafe

The majority of the Scala SDK will not throw exceptions. Methods on JsonObject are, well, an exception to this general rule. If a requested field does not exist a NoSuchElementException will be thrown.

If you’d rather not deal with exceptions, JsonObject comes with a counterpart JsonObjectSafe that provides an alternative interface in which all methods return Scala Try rather than throwing:

val safe: JsonObjectSafe = json.safe

val r: Try[String] = safe.str("name")

r match {
  case Success(name) => println(s"Their name is $name")
  case Failure(err) => println(s"Could not find field 'name': $err")
}

A JsonArraySafe counterpart also exists.

If you’re walking through JSON it can be useful to use flatMap or for-comprehensions for readability:

val address: Try[String] = for {
  addresses <- safe.arr("addresses")
  address   <- addresses.obj(0)
  line      <- address.str("address")
} yield line

or use the support for Dynamic to combine brevity with safety:

val add: Try[String] = safe.dyn.addresses(0).address.str

Note that JsonObjectSafe, though presenting a more functional interface, is still mutable.

Case Classes

It can be very useful to deal directly with Scala case classes, that is to send and retrieve them directly rather than via some interim type, and the Scala SDK includes built-in support for this.

It’s necessary to write a small amount of boilerplate code first. If you try and insert a case class directly, you’ll get an error. E.g. this won’t work:

    val user = User("Eric Wimp", 9, Seq(Address("29 Acacia Road")))

    // Will fail to compile
    collection.insert("eric-wimp", user)

This is because the Scala SDK does not currently know how to convert a User into JSON it can send to the server.

More technically, methods like insert that take content of type T also take an implicit JsonSerializer[T], which defines how to turn T into JSON. If the Scala compiler cannot find a suitable JsonSerializer[T], then it will report an error.

So, let’s provide an JsonSerializer[User]. It’s possible to create one manually, but the Scala SDK includes a convenient shortcut:

object User {
  implicit val codec: Codec[User] = Codec.codec[User]
}

(As this is a companion object for User, it needs to go in the same file as the User case class you added earlier.)

This short line of boilerplate uses Scala macros to, at compile time, provide an JsonSerializer[User]. Note that we don’t need one for Address too - only the top-level case classes you’re dealing with need a Codec.

Now we can pass a User directly to insert, and it will work fine. The Scala compiler will look for an JsonSerializer[User] in a number of places, and find it in the User companion object.

Since the encoding logic is generated at compile time it can also be extremely fast - no reflection is used.

The generated JSON will be as you expect, with "name" and "age" fields, and an "addresses" array.

The same Codec also generates a JsonDeserializer[User] which can be used to pull data out as our case class, using contentAs:

    val r: Try[User] = for {
      doc <- collection.get("eric-wimp")
      user <- doc.contentAs[User]
    } yield user

    r match {
      case Success(user: User) => println(s"User: ${user}")
      case Failure(err)        => println("Error: " + err)
    }

There are other ways to handle case classes. Many of the supported JSON libraries have some method to encode and decode case classes into an interim type, as in the Circe example.

Json4s

Json4s aims to provide a single JSON representation that can be used by other JSON libraries.

Here’s an example of how to use Json4s with Couchbase:

import org.json4s.JsonAST._
import org.json4s.JsonDSL._

val json: JValue =
  ("name" -> "John Smith") ~
    ("age" -> 29) ~
    ("addresses" -> List(
      "address" -> "123 Fake Street")
      )

val result: Try[JValue] = for {
  // Can provide Json4s types for all mutation operations
  _ <- collection.insert("id", json)
  doc <- collection.get("id")

  // Can retrieve document content as Json4s types
  content <- doc.contentAs[JValue]
} yield content

Jawn

Here’s an example of how to use Jawn with Couchbase:

import org.typelevel.jawn.ast._

val json = JObject.fromSeq(Seq("name" -> JString("John Smith"),
  "age" -> JNum(29),
  "address" -> JArray.fromSeq(Seq(JObject.fromSeq(Seq("address" -> JString("123 Fake Street")))))))

val result: Try[JValue] = for {
  // Can provide Jawn types for all mutation operations
  _       <- collection.insert("id", json)
  doc     <- collection.get("id")

  // Can retrieve document content as Jawn types
  content <- doc.contentAs[JValue]
} yield content

Play JSON

Here’s an example of how to use Play Json with Couchbase:

import play.api.libs.json.Json._
import play.api.libs.json._

val json = obj("name" -> "John Smith",
  "age" -> 29,
  "address" -> arr(obj("address" -> "123 Fake Street")))

val result: Try[JsValue] = for {
  // Can provide Play JSON types for all mutation operations
  _       <- collection.insert("id", json)
  doc     <- collection.get("id")

  // Can retrieve document content as Play JSON types
  content <- doc.contentAs[JsValue]
} yield content

Strings

It’s possible to send and receive JVM String directly:

val json = """{"hello":"world"}"""

val result: Try[String] = for {
  // Can provide Play JSON types for all mutation operations
  _       <- collection.insert("id", json)
  doc     <- collection.get("id")

  // Can retrieve document content as String
  content <- doc.contentAs[String]
} yield content

Array[Byte]

And an Array[Byte] can be used directly:

val json: Array[Byte] = """{"hello":"world"}""".getBytes(StandardCharsets.UTF_8)

val result: Try[Array[Byte]] = for {
  // Can provide Play JSON types for all mutation operations
  _       <- collection.insert("id", json)
  doc     <- collection.get("id")

  // Can retrieve document content as String
  content <- doc.contentAs[Array[Byte]]
} yield content

With the support for String and Array[Byte], the Scala SDK can be used with any JSON library that can import and export those formats.

Adding Another Type

As alluded to above, when inserting a T, the Scala SDK looks for an JsonSerializer[T] in implicit scope. Similar for contentAs and JsonDeserializer[T].

For advanced users it’s possible to support any type desired by simply writing an JsonSerializer and/or JsonDeserializer for that type. Please see the code for those interfaces for details.

Choosing a Library

We’ve looked at multiple options for working with JSON - but which one should you choose?

In truth, they all fulfill different needs, provide different tradeoffs, and any of them can be a good option in certain situations. You may want to evaluate and benchmark them with representative data to see which fits your situation best.

If you’re not already using a JSON library, then the built-in JsonObject library is very fast, very simple to use, and makes a good default choice.