Introduction
The previous tutorial covered basic XML processing in Scala, but as I noted, XML is not the primary choice for data serialization these days. Instead, JSON (JavaScript Object Notation) is more widely used for data interchange, in part because it is less verbose and better captures the core data structures (such as lists and maps) that are used in defining many objects. It was originally designed for working with JavaScript, but turned out to be quite effective as a language neutral format. A very nice feature of it is that it is straightforward to translate objects as defined in languages like Java and Scala into JSON and back again, as I’ll show in this tutorial. If the class definitions and the JSON structures are appropriately aligned, this transformation turns out to be entirely trivial to do — given a suitable JSON processing library.
In this tutorial, I cover basic JSON processing in Scala using the Jerkson library, which itself is essentially a Scala wrapper around the Jackson library (written in Java). Note that other libraries like lift-json are perfectly good alternatives, but Jerkson seems to have some efficiency advantages for streaming JSON due to Jackson’s performance. Of course, since Scala plays nicely with Java, you can directly use whichever JVM-based JSON library you like, including Jackson.
This post also shows how to do a quick start with SBT that will allow you to easily access third-party libraries as dependencies and start writing code that uses them and can be compiled with SBT.
Note: As a “Jason” I insist that JSON should be pronounced Jay-SAHN (with stress on the second syllable) to distinguish it from the name. :)
An easy way to use the Jerkson library in the context of a tutorial like this is for the reader to set up a new SBT project, declare Jerkson as a dependency, and then fire up the Scala REPL using SBT’sconsole action. This sorts out the process of obtaining external libraries and setting up the classpath so that they are available in an SBT-initiated Scala REPL. Follow the instructions in this section to do so.
Note: if you have already been working with Scalabha version 0.2.5 (or later), skip to the bottom of this section to see how to run the REPL using Scalabha’s build. Alternatively, if you have an existing project of your own, you can of course just add Jerkson as a dependency, import its classes as necessary and use it in your normal programming setup. The examples below will then help as some straightforward recipes for using it in your project.
First, create a directory to work in and download the SBT launch jar.
$ mkdir ~/json-tutorial $ cd ~/json-tutorial/ $ wget http://typesafe.artifactoryonline.com/typesafe/ivy-releases/org.scala-sbt/sbt-launch/0.11.3/sbt-launch.jar
Note: If you don’t have wget installed on your machine, you can download the above sbt-launch.jar file in your browser and move it to the ~/json-tutorial directory.
Now, save the following as the file ~/json-tutorial/build.sbt. Be aware that it is important to keep the empty lines between each of the declarations.
name := 'json-tutorial' version := '0.1.0 ' scalaVersion := '2.9.2' resolvers += 'repo.codahale.com' at 'http://repo.codahale.com' libraryDependencies += 'com.codahale' % 'jerkson_2.9.1' % '0.5.0'
Then save the following in the file ~/json-tutorial/runSbt.
java -Xms512M -Xmx1536M -Xss1M -XX:+CMSClassUnloadingEnabled -XX:MaxPermSize=384M -jar `dirname $0`/sbt-launch.jar '$@'
Make that file executable and run it, which will show SBT doing a bunch of work and then leave you with the SBT prompt.
$ cd ~/json-tutorial $ chmod a+x runSbt $ ./runSbt update Getting org.scala-sbt sbt_2.9.1 0.11.3 ... downloading http://repo.typesafe.com/typesafe/ivy-releases/org.scala-sbt/sbt_2.9.1/0.11.3/jars/sbt_2.9.1.jar ... [SUCCESSFUL ] org.scala-sbt#sbt_2.9.1;0.11.3!sbt_2.9.1.jar (307ms) ... ... more stuff including getting the the Jerkson library ... ... [success] Total time: 25 s, completed May 11, 2012 10:22:42 AM $
You should be back in the Unix shell at this point, and now we are ready to run the Scala REPL using SBT. The important thing is that this instance of the REPL will have the Jerkson library and its dependencies in the classpath so that we can import the classes we need.
./runSbt console [info] Set current project to json-tutorial (in build file:/Users/jbaldrid/json-tutorial/) [info] Starting scala interpreter... [info] Welcome to Scala version 2.9.2 (Java HotSpot(TM) 64-Bit Server VM, Java 1.6.0_31). Type in expressions to have them evaluated. Type :help for more information. scala> import com.codahale.jerkson.Json._ import com.codahale.jerkson.Json._
If nothing further is output, then you are all set. If things are amiss (or if you are running in the default Scala REPL), you’ll instead see something like the following.
scala> import com.codahale.jerkson.Json._ <console>:7: error: object codahale is not a member of package com import com.codahale.jerkson.Json._
If this is what you got, try to follow the instructions above again to make sure that your setup is exactly as above. However, if you continue to experience problems, an alternative is to get version 0.2.5 of Scalabha (which already has Jerkson as a dependency), follow the instructions for setting it up and then run the following commands.
$ cd $SCALABHA_DIR $ scalabha build console
If you just want to see some examples of using Jerkson as an API and not use it interactively, then it is entirely unnecessary to do the SBT setup — just read on and adapt the examples as necessary.
As usual, let’s begin with a very simple example that shows some of the basic properties of JSON.
{‘foo’: 42 ‘bar’: [‘a’,’b’,’c’], ‘baz’: { ‘x’: 1, ‘y’: 2 }}
This describes a data structure with three fields, foo, bar and baz. The field foo‘s value is the integer 42, bar‘s value is a list of strings, and baz‘s value is a map from strings to integers. These are language neutral (but universal) types.
Let’s first consider deserializing each of these values individually as Scala objects, using Jerkson’s parse method. Keep in mind that JSON in a file is a string, so the inputs in all of these cases are strings (at times I’ll use triple-quoted strings when there are quotes themselves in the JSON). In each case, we tell the parse method what type we expect by providing a type specification before the argument.
scala> parse[Int]('42')
res0: Int = 42
scala> parse[List[String]]('''['a','b','c']''')
res1: List[String] = List(a, b, c)
scala> parse[Map[String,Int]]('''{ 'x': 1, 'y': 2 }''')
res2: Map[String,Int] = Map(x -> 1, y -> 2)
So, in each case, the string representation is turned into a Scala object of the appropriate type. If we aren’t sure what the type is or if we know for example that a List is heterogeneous, we can use Any as the expected type.
scala> parse[Any]('42')
res3: Any = 42
scala> parse[List[Any]]('''['a',1]''')
res4: List[Any] = List(a, 1)
If you give an expect type that can’t be parsed as such, you’ll get an error.
scala> parse[List[Int]]('''['a',1]''')
com.codahale.jerkson.ParsingException: Can not construct instance of int from String value 'a': not a valid Integer value
at [Source: java.io.StringReader@2bc5aea; line: 1, column: 2]
<...many more lines of stack trace...>
How about parsing all of the attributes and values together? Save the whole thing in a variable simpleJson as follows.
scala> :paste
// Entering paste mode (ctrl-D to finish)
val simpleJson = '''{'foo': 42,
'bar': ['a','b','c'],
'baz': { 'x': 1, 'y': 2 }}'''
// Exiting paste mode, now interpreting.
simpleJson: java.lang.String =
{'foo': 42,
'bar': ['a','b','c'],
'baz': { 'x': 1, 'y': 2 }}
Since it is a Map from Strings to different types of values, the best we can do is deserialize it as a Map[String, Any].
scala> val simple = parse[Map[String,Any]](simpleJson)
simple: Map[String,Any] = Map(bar -> [a, b, c], baz -> {x=1, y=2}, foo -> 42)
To get these out as more specific types than Any, you need to cast them to the appropriate types.
scala> val fooValue = simple('foo').asInstanceOf[Int]
fooValue: Int = 42
scala> val barValue = simple('bar').asInstanceOf[java.util.ArrayList[String]]
barValue: java.util.ArrayList[String] = [a, b, c]
scala> val bazValue = simple('baz').asInstanceOf[java.util.LinkedHashMap[String,Int]]
bazValue: java.util.LinkedHashMap[String,Int] = {x=1, y=2}Of course, you might want to be working with Scala types, which is easy if you import the implicit conversions from Java types to Scala types.
scala> import scala.collection.JavaConversions._
import scala.collection.JavaConversions._
scala> val barValue = simple('bar').asInstanceOf[java.util.ArrayList[String]].toList
barValue: List[String] = List(a, b, c)
scala> val bazValue = simple('baz').asInstanceOf[java.util.LinkedHashMap[String,Int]].toMap
bazValue: scala.collection.immutable.Map[String,Int] = Map(x -> 1, y -> 2)
Voila! When you are working with Java libraries in Scala, the JavaConversions usually prove to be extremely handy.
Though we were able to parse the simple JSON expression above and even cast values into appropriate types, things were still a bit clunky. Fortunately, if you have defined your own case class with the appropriate fields, you can provide that as the expected type instead. For example, here’s a simple case class that will do the trick.
case class Simple(val foo: String, val bar: List[String], val baz: Map[String,Int])
Clearly this has all the right fields (with variables named the same as the fields in the JSON example), and the variables have the types we’d like them to have.
Unfortunately, due to class loading issues with SBT, we cannot carry on the rest of this exercise solely in the REPL and must define this class in code. This code can be compiled and then used in the REPL or by other code. To do this, save the following as ~/json-tutorial/Simple.scala.
case class Simple(val foo: String, val bar: List[String], val baz: Map[String,Int])
object SimpleExample {
def main(args: Array[String]) {
import com.codahale.jerkson.Json._
val simpleJson = '''{'foo':42, 'bar':['a','b','c'], 'baz':{'x':1,'y':2}}'''
val simpleObject = parse[Simple](simpleJson)
println(simpleObject)
}
}
Then exit the Scala REPL session you were in for the previous section using the command :quit, and do the following. (If anything has gone amiss you can restart SBT (with runSbt) and do the following commands.)
> compile [info] Compiling 1 Scala source to /Users/jbaldrid/json-tutorial/target/scala-2.9.2/classes... [success] Total time: 2 s, completed May 11, 2012 9:24:00 PM > run [info] Running SimpleExample SimpleExample Simple(42,List(a, b, c),Map(x -> 1, y -> 2)) [success] Total time: 1 s, completed May 11, 2012 9:24:03 PM
You can make changes to the code in Simple.scala, compile it again (you don’t need to exit SBT to do so), and run it again. Also, now that you’ve compiled, if you start up the Scala REPL using the console action, then the Simple class is now available to you and you can carry on working in the REPL. For example, here are the same statements that are used in the SimpleExample main method given previously.
scala> import com.codahale.jerkson.Json._
import com.codahale.jerkson.Json._
scala> val simpleJson = '''{'foo':42, 'bar':['a','b','c'], 'baz':{'x':1,'y':2}}'''
simpleJson: java.lang.String = {'foo':42, 'bar':['a','b','c'], 'baz':{'x':1,'y':2}}
scala> val simpleObject = parse[Simple](simpleJson)
simpleObject: Simple = Simple(42,List(a, b, c),Map(x -> 1, y -> 2))
scala> println(simpleObject)
Simple(42,List(a, b, c),Map(x -> 1, y -> 2))
Another nice feature of JSON serialization is that if the JSON string has more information than you need to construct the object want to build from it, it is ignored. For example, consider deserializing the following example, which has an extra field eca in the JSON representation.
scala> val ecaJson = '''{'foo':42, 'bar':['a','b','c'], 'baz':{'x':1,'y':2}, 'eca': true}'''
ecaJson: java.lang.String = {'foo':42, 'bar':['a','b','c'], 'baz':{'x':1,'y':2}, 'eca': true}
scala> val noEcaSimpleObject = parse[Simple](ecaJson)
noEcaSimpleObject: Simple = Simple(42,List(a, b, c),Map(x -> 1, y -> 2))
The eca information silently slips away and we still get a Simple object with all the information we need. This property is very handy for ignoring irrelevant information, which I’ll show to be quite useful in a follow-up post on processing JSON formatted tweets from Twitter’s API.
Another thing to note about the above example is that the Boolean values true and false are valid JSON (they are not quoted strings, but actual Boolean values). Parsing a Boolean is even quite forgiving as Jerkson will give you a Boolean even when it is defined as a String.
scala> parse[Map[String,Boolean]]('''{'eca':true}''')
res0: Map[String,Boolean] = Map(eca -> true)
scala> parse[Map[String,Boolean]]('''{'eca':'true'}''')
res1: Map[String,Boolean] = Map(eca -> true)
And it will convert a Boolean into a String if you happen to ask it to do so.
scala> parse[Map[String,String]]('''{'eca':true}''')
res2: Map[String,String] = Map(eca -> true)
But it (sensibly) won’t convert any String other than true or false into a Boolean.
scala> parse[Map[String,Boolean]]('''{'eca':'brillig'}''')
com.codahale.jerkson.ParsingException: Can not construct instance of boolean from String value 'brillig': only 'true' or 'false' recognized
at [Source: java.io.StringReader@6b2739b8; line: 1, column: 2]
<...stacktrace...>
And it doesn’t admit unquoted values other than a select few, including true and false.
scala> parse[Map[String,String]]('''{'eca':brillig}''')
com.codahale.jerkson.ParsingException: Malformed JSON. Unexpected character ('b' (code 98)): expected a valid value (number, String, array, object, 'true', 'false' or 'null') at character offset 7.
<...stacktrace...>
In other words, your JSON needs to be grammatical. Generating JSON from an object
If you have an object in hand, it is very easy to create JSON from it (serialize) using the generate method.
scala> val simpleJsonString = generate(simpleObject)
simpleJsonString: String = {'foo':'42','bar':['a','b','c'],'baz':{'x':1,'y':2}}
This is much easier than the XML solution, which required explicitly declaring how an object was to be turned into XML elements. The restriction is that any such objects must be instances of a case class. If you don’t have a case class, you’ll need to do some special handling (not discussed in this tutorial).
In the vein of the previous tutorial on XML, I’ve created the JSON corresponding to the music XML example used there. You can find it as the Github gist music.json:
https://gist.github.com/2668632
Save that file as /tmp/music.json.
Tip: you can easily format condensed JSON to be more human-readable by using the mjson tool in Python.
$ cat /tmp/music.json | python -mjson.tool
[
{
'albums': [
{
'description': '\n\tThe King of Limbs is the eighth studio album by English rock band Radiohead, produced by Nigel Godrich. It was self-released on 18 February 2011 as a download in MP3 and WAV formats, followed by physical CD and 12\' vinyl releases on 28 March, a wider digital release via AWAL, and a special \'newspaper\' edition on 9 May 2011. The physical editions were released through the band's Ticker Tape imprint on XL in the United Kingdom, TBD in the United States, and Hostess Entertainment in Japan.\n ',
'songs': [
{
'length': '5:15',
'title': 'Bloom'
},
<...etc...>
Next, save the following code as ~/json-tutorial/MusicJson.scala.
package music {
case class Song(val title: String, val length: String) {
@transient lazy val time = {
val Array(minutes, seconds) = length.split(':')
minutes.toInt*60 + seconds.toInt
}
}
case class Album(val title: String, val songs: Seq[Song], val description: String) {
@transient lazy val time = songs.map(_.time).sum
@transient lazy val length = (time / 60)+':'+(time % 60)
}
case class Artist(val name: String, val albums: Seq[Album])
}
object MusicJson {
def main(args: Array[String]) {
import com.codahale.jerkson.Json._
import music._
val jsonInput = io.Source.fromFile('/tmp/music.json').mkString
val musicObj = parse[List[Artist]](jsonInput)
println(musicObj)
}
}A couple of quick notes. The Song, Album, and Artist classes are the same as I used in the previous tutorial on XML processing, with two changes. The first is that I’ve wrapped them in a package music. This is only necessary to get around an issue with running Jerkson in SBT as we are doing here. The other is that the fields that are not in the constructor are marked as @transient: this ensures that they are not included in the output when we generate JSON from objects of these classes. An example showing how this matters is the way that I created the music.json file: I read in the XML as in the previous tutorial and then use Jerkson to generate the JSON — without the @transient annotation, those fields are included in the output. For reference, here’s the code to do the conversion from XML to JSON (which you can add to MusicJson.scala if you like).
object ConvertXmlToJson {
def main(args: Array[String]) {
import com.codahale.jerkson.Json._
import music._
val musicElem = scala.xml.XML.loadFile('/tmp/music.xml')
val artists = (musicElem \ 'artist').map { artist =>
val name = (artist \ '@name').text
val albums = (artist \ 'album').map { album =>
val title = (album \ '@title').text
val description = (album \ 'description').text
val songList = (album \ 'song').map { song =>
Song((song \ '@title').text, (song \ '@length').text)
}
Album(title, songList, description)
}
Artist(name, albums)
}
val musicJson = generate(artists)
val output = new java.io.BufferedWriter(new java.io.FileWriter(new java.io.File('/tmp/music.json')))
output.write(musicJson)
output.flush
output.close
}
}
There are other serialization strategies (e.g. binary serialization of objects), and the @transient annotation is similarly respected by them.
Given the code in MusicJson.scala, we can now compile and run it. In SBT, you can either do run or run-main. If you choose run and there are more than one main methods in your project, SBT will give you a choice.
> run Multiple main classes detected, select one to run: [1] SimpleExample [2] MusicJson [3] ConvertXmlToJson Enter number: 2 [info] Running MusicJson List(Artist(Radiohead,List(Album(The King of Limbs,List(Song(Bloom,5:15), Song(Morning Mr Magpie,4:41), Song(Little by Little,4:27), Song(Feral,3:13), Song(Lotus Flower,5:01), Song(Codex,4:47), Song(Give Up the Ghost,4:50), Song(Separator,5:20)), The King of Limbs is the eighth studio album by English rock band Radiohead, produced by Nigel Godrich. It was self-released on 18 February 2011 as a download in MP3 and WAV formats, followed by physical CD and 12' vinyl releases on 28 March, a wider digital release via AWAL, and a special 'newspaper' edition on 9 May 2011. The physical editions were released through the band's Ticker Tape imprint on XL in the United Kingdom, TBD in the United States, and Hostess Entertainment in Japan. ), Album(OK Computer,List(Song(Airbag,4:44), Song(Paranoid <...more printed output...> [success] Total time: 3 s, completed May 12, 2012 11:52:06 AM
With run-main, you just explicitly provide the name of the object whose main method you wish to run.
> run-main MusicJson [info] Running MusicJson <...same output as above...>
So, either way, we have successfully de-serialized the JSON description of the music data. (You can also get the same result by entering the code of the main method of MusicJson into the REPL when you run it from the SBT console.)
This tutorial has shown how easy it is to serialize (generate) and deserialize (parse) objects to and from JSON format. Hopefully, this has demonstrated the relative ease of doing this with the Jerkson library and Scala, and especially the relative ease in comparison with working with XML for similar purposes.
In addition to this ease, JSON is generally more compact than the equivalent XML. However, it still is far from being a truly compressed format, and there is a lot of obvious “waste”, like having the field names repeated again and again for each object. This matters a lot when data is represented as JSON strings and is being sent over networks and/or used in distributed processing frameworks like Hadoop. The Avro file format is an evolution of JSON that performs such compression: it includes a schema with each file and then each object is represented in a binary format that only specifies the data and not the field names. In addition to being more compact, it retains the properties of being easily splittable, which matters a great deal for processing large files in Hadoop.
Reference: Processing JSON in Scala with Jerkson from our JCG partner Jason Baldridge at the Bcomposes blog.
