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Processing JSON in Scala with Jerkson

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. :) Getting set up 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 consoleIf 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.Processing a simple JSON example 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 = 42scala> 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 = 42scala> 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 = 42scala> val barValue = simple('bar').asInstanceOf] barValue: java.util.ArrayList[String] = [a, b, c]scala> val bazValue = simple('baz').asInstanceOf] 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].toList barValue: List[String] = List(a, b, c)scala> val bazValue = simple('baz').asInstanceOf].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.Deserializing into user-defined types 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).A richer JSON example 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. > runMultiple main classes detected, select one to run:[1] SimpleExample [2] MusicJson [3] ConvertXmlToJsonEnter 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 AMWith 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.)Conclusion 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....
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Spring Integration with reCAPTCHA

Sometimes we just need CAPTCHA, that’s a sad fact. Today we will learn how to integrate with reCAPTCHA. Because the topic itself isn’t particularly interesting and advanced, we will overengineer a bit (?) by using Spring Integration to handle low-level details. The decision to use reCAPTCHA by Google was dictated by two factors: (1) it is a moderately good CAPTCHA implementation with decent images with built-in support for visually impaired people and (2) outsourcing CAPTCHA allows us to remain stateless on the server side. Not to mention we help in digitalizing books.The second reason is actually quite important. Typically you have to generate CAPTCHA on the server side and store the expected result e.g. in user session. When the response comes back you compare expected and entered CAPTCHA solution. Sometimes we don’t want to store any state on the server side, not to mention implementing CAPTCHA isn’t particularly rewarding task. So it is nice to have something ready-made and acceptable.The full source code is as always available, we are starting from a simple Spring MVC web application without any CAPTCHA. reCAPTCHA is free but requires registration, so the first step is to sing-up and generate your public/private keys and fill-in app.properties configuration file in our sample project.To display and include reCAPTCHA on your form all you have to do is add JavaScript library:<div id="recaptcha"> </div> ... <script src="http://www.google.com/recaptcha/api/js/recaptcha_ajax.js"></script>And place reCAPTCHA widget anywhere you like:Recaptcha.create("${recaptcha_public_key}", "recaptcha", { theme: "white", lang : 'en' } );The official documentation is very concise and descriptive, so I am not diving into details of that. When you include this widget inside your <form/> you will receive two extra fields when user submits: recaptcha_response_field and recaptcha_challenge_field. The first is the actual text typed by the user and the second is a hidden token generated per request. It is probably used by reCAPTCHA servers as a session key, but we don’t care, all we have to do is passing this fields further to reCAPTCHA server. I will use HttpClient 4 to perform HTTP request to external server and some clever pattern matching in Scala to parse the response:trait ReCaptchaVerifier { def validate(reCaptchaRequest: ReCaptchaSecured): Boolean}@Service class HttpClientReCaptchaVerifier @Autowired()( httpClient: HttpClient, servletRequest: HttpServletRequest, @Value("${recaptcha_url}") recaptchaUrl: String, @Value("${recaptcha_private_key}") recaptchaPrivateKey: String ) extends ReCaptchaVerifier {def validate(reCaptchaRequest: ReCaptchaSecured): Boolean = { val post = new HttpPost(recaptchaUrl) post.setEntity(new UrlEncodedFormEntity(List( new BasicNameValuePair("privatekey", recaptchaPrivateKey), new BasicNameValuePair("remoteip", servletRequest.getRemoteAddr), new BasicNameValuePair("challenge", reCaptchaRequest.recaptchaChallenge), new BasicNameValuePair("response", reCaptchaRequest.recaptchaResponse))) ) val response = httpClient.execute(post) isReCaptchaSuccess(response.getEntity.getContent) }private def isReCaptchaSuccess(response: InputStream) = { val responseLines = Option(response) map { Source.fromInputStream(_).getLines().toList } getOrElse Nil responseLines match { case "true" :: _ => true case "false" :: "incorrect-captcha-sol" :: _=> false case "false" :: msg :: _ => throw new ReCaptchaException(msg) case resp => throw new ReCaptchaException("Unrecognized response: " + resp.toList) } }}class ReCaptchaException(msg: String) extends RuntimeException(msg)The only missing piece is the ReCaptchaSecured trait encapsulating two reCAPTCHA fields mentioned earlier. In order to secure any web form with reCAPTCHA I am simply extending this model:trait ReCaptchaSecured { @BeanProperty var recaptchaChallenge = "" @BeanProperty var recaptchaResponse = "" }class NewComment extends ReCaptchaSecured { @BeanProperty var name = "" @BeanProperty var contents = "" }The whole CommentsController.scala is not that relevant. But the result is! So it works, but obviously it wasn’t really spectacular. What would you say about replacing the low-level HttpClient call with Spring Integration? The ReCaptchaVerifier interface (trait) remains the same so the client code doesn’t have to be changed. But we refactor HttpClientReCaptchaVerifier into two separate, small, relatively high-level and abstract classes:@Service class ReCaptchaFormToHttpRequest @Autowired() (servletRequest: HttpServletRequest, @Value("${recaptcha_private_key}") recaptchaPrivateKey: String) {def transform(form: ReCaptchaSecured) = Map( "privatekey" -> recaptchaPrivateKey, "remoteip" -> servletRequest.getRemoteAddr, "challenge" -> form.recaptchaChallenge, "response" -> form.recaptchaResponse).asJava}@Service class ReCaptchaServerResponseToResult {def transform(response: String) = { val responseLines = response.split('\n').toList responseLines match { case "true" :: _ => true case "false" :: "incorrect-captcha-sol" :: _=> false case "false" :: msg :: _ => throw new ReCaptchaException(msg) case resp => throw new ReCaptchaException("Unrecognized response: " + resp.toList) } }}Note that we no longer have to implement ReCaptchaVerifier, Spring Integration will do it for us. We only have to tell how is the framework suppose to use building blocks we have extracted above. I think I haven’t yet described what Spring Integration is and how it works. In few words it is a very pure implementation of enterprise integration patterns (some may call it ESB). The message flows are described using XML and can be embedded inside standard Spring XML configuration:<?xml version="1.0" encoding="UTF-8"?> <beans:beans xmlns:beans="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.springframework.org/schema/integration" xmlns:http="http://www.springframework.org/schema/integration/http" xsi:schemaLocation="http://www.springframework.org/schema/integrationhttp://www.springframework.org/schema/integration/spring-integration.xsdhttp://www.springframework.org/schema/beanshttp://www.springframework.org/schema/beans/spring-beans.xsdhttp://www.springframework.org/schema/integration/httphttp://www.springframework.org/schema/integration/http/spring-integration-http.xsd"><!-- configuration here --></beans:beans>In our case we will describe a message flow from HttpClientReCaptchaVerifier Java interface/Scala trait to the reCAPTCHA server and back. On the way the ReCaptchaSecured object must be translated into HTTP request and the HTTP response should be translated into meaningful result, returned transparently from the interface.<gateway id="ReCaptchaVerifier" service-interface="com.blogspot.nurkiewicz.recaptcha.ReCaptchaVerifier" default-request-channel="reCaptchaSecuredForm"/><channel id="reCaptchaSecuredForm" datatype="com.blogspot.nurkiewicz.web.ReCaptchaSecured"/><transformer input-channel="reCaptchaSecuredForm" output-channel="reCaptchaGoogleServerRequest" ref="reCaptchaFormToHttpRequest"/><channel id="reCaptchaGoogleServerRequest" datatype="java.util.Map"/><http:outbound-gateway request-channel="reCaptchaGoogleServerRequest" reply-channel="reCaptchaGoogleServerResponse" url="${recaptcha_url}" http-method="POST" extract-request-payload="true" expected-response-type="java.lang.String"/><channel id="reCaptchaGoogleServerResponse" datatype="java.lang.String"/><transformer input-channel="reCaptchaGoogleServerResponse" ref="reCaptchaServerResponseToResult"/>Despite the amount of XML, the overall message flow is quite simple. First we define gateway, which is a bridge between Java interface and Spring Integration message flow. The argument of ReCaptchaVerifier.validate() later becomes a message that is sent to reCaptchaSecuredForm channel. From that channel ReCaptchaSecured object is passed to a ReCaptchaFormToHttpRequest transformer. The purpose of the transformer is two translate from ReCaptchaSecured object to Java map representing a set of key-value pairs. Later this map is passed (through reCaptchaGoogleServerRequest channel) to http:outbound-gateway. The responsibility of this component is to translate previously created map into an HTTP request and send it to specified address.When the response comes back, it is sent to reCaptchaGoogleServerResponse channel. There ReCaptchaServerResponseToResult transformer takes action, translating HTTP response to business result (boolean). Finally the transformer result is routed back to the gateway. Everything happens synchronously by default so we can still use simple Java interface for reCAPTCHA validation.Believe it or not, this all works. We no longer use HttpClient (guess everything is better compared to HttpClient 4 API…) and instead of one “huge” class we have a set of smaller, focused, easy to test classes. The framework handles wiring up and the low-level details. Wonderful?Architect’s Dream or Developer’s Nightmare?Let me summarize our efforts by quoting the conclusions from the presentation above: balance architectural benefits with development effectiveness. Spring Integration is capable of receiving data from various heterogeneous sources like JMS, relational database or even FTP, aggregating, splitting, parsing and filtering messages in multiple ways and finally sending them further with the most exotic protocols. Coding all this by hand is a really tedious and error-prone task. On the other hand sometimes we just don’t need all the fanciness and getting our hands dirty (e.g. by doing a manual HTTP request and parsing the response) is much simpler and easier to understand. Before you blindly base your whole architecture either on very high-level abstractions or on hand-coded low-level procedures: think about the consequences and balance. No solution fits all problems. Which version of reCAPTCHA integration do you find better?Reference: Integrating with reCAPTCHA using… Spring Integration from our JCG partner Tomasz Nurkiewicz at the Java and neighbourhood blog....
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The Visitor Pattern Re-visited

The visitor pattern is one of the most overrated and yet underestimated patterns in object-oriented design. Overrated, because it is often chosen too quickly (possibly by an architecture astronaut), and then bloats an otherwise very simple design, when added in the wrong way. Underestimated, because it can be very powerful, if you don’t follow the school-book example. Let’s have a look in detail. Problem #1: The naming Its biggest flaw (in my opinion) is its naming itself. The “visitor” pattern. When we google it, we most likely find ourselves on the related Wikipedia article, showing funny images like this one:Wikipedia Visitor Pattern example Right. For the 98% of us thinking in wheels and engines and bodies in their every day software engineering work, this is immediately clear, because we know that the mechanic billing us several 1000$ for mending our car will first visit the wheels, then the engine, before eventually visiting our wallet and accepting our cash. If we’re unfortunate, he’ll also visit our wife while we’re at work, but she’ll never accept, that faithful soul. But what about the 2% that solve other problems in their worklife? Like when we code complex data structures for E-Banking systems, stock exchange clients, intranet portals, etc. etc. Why not apply a visitor pattern to a truly hierarchical data structure? Like folders and files? (ok, not so complex after all) OK, so we’ll “visit” folders and every folder is going to let its files “accept” a “visitor” and then we’ll let the visitor “visit” the files, too. What?? The car lets its parts accept the visitor and then let the visitor visit itself? The terms are misleading. They’re generic and good for the design pattern. But they will kill your real-life design, because no one thinks in terms of “accepting” and “visiting”, when in fact, you read/write/delete/modify your file system. Problem #2: The polymorphism This is the part that causes even more headache than the naming, when applied to the wrong situation. Why on earth does the visitor know everyone else? Why does the visitor need a method for every involved element in the hierarchy? Polymorphism and encapsulation claim that the implementation should be hidden behind an API. The API (of our data structure) probably implements the composite pattern in some way, i.e. its parts inherit from a common interface. OK, of course, a wheel is not a car, neither is my wife a mechanic. But when we take the folder/file structure, aren’t they all java.util.File objects? Understanding the problem The actual problem is not the naming and horrible API verbosity of visiting code, but the mis-understanding of the pattern. It’s not a pattern that is best suited for visiting large and complex data structures with lots of objects of different types. It’s the pattern that is best suited for visiting simple data structures with few different types, but visiting them with hundreds of visitors. Take files and folders. That’s a simple data structure. You have two types. One can contain the other, both share some properties. Various visitors could be:CalculateSizeVisitor FindOldestFileVisitor DeleteAllVisitor FindFilesByContentVisitor ScanForVirusesVisitor … you name itI still dislike the naming, but the pattern works perfectly in this paradigm. So when is the visitor pattern “wrong”? I’d like to give the jOOQ QueryPart structure as an example. There are a great many of them, modelling various SQL query constructs, allowing jOOQ to build and execute SQL queries of arbitrary complexity. Let’s name a few examples:ConditionCombinedCondition NotCondition InCondition BetweenConditionFieldTableField Function AggregateFunction BindValueFieldListThere are many more. Each one of them must be able to perform two actions: render SQL and bind variables. That would make two visitors each one knowing more than… 40-50 types…? Maybe in the faraway future, jOOQ queries will be able to render JPQL or some other query type. That would make 3 visitors against 40-50 types. Clearly, here, the classic visitor pattern is a bad choice. But I still want to “visit” the QueryParts, delegating rendering and binding to lower levels of abstraction. How to implement this, then? It’s simple: Stick with the composite pattern! It allows you to add some API elements to your data structure, that everyone has to implement. So by intuition, step 1 would be this interface QueryPart { // Let the QueryPart return its SQL String getSQL();// Let the QueryPart bind variables to a prepared // statement, given the next bind index, returning // the last bind index int bind(PreparedStatement statement, int nextIndex); } With this API, we can easily abstract a SQL query and delegate the responsibilities to lower-level artefacts. A BetweenCondition for instance. It takes care of correctly ordering the parts of a [field] BETWEEN [lower] AND [upper] condition, rendering syntactically correct SQL, delegating parts of the tasks to its child-QueryParts: class BetweenCondition { Field field; Field lower; Field upper;public String getSQL() { return field.getSQL() + ' between ' + lower.getSQL() + ' and ' + upper.getSQL(); }public int bind(PreparedStatement statement, int nextIndex) { int result = nextIndex;result = field.bind(statement, result); result = lower.bind(statement, result); result = upper.bind(statement, result);return result; } } Whereas BindValue on the other hand, would mainly take care of variable binding class BindValue { Object value;public String getSQL() { return '?'; }public int bind(PreparedStatement statement, int nextIndex) { statement.setObject(nextIndex, value); return nextIndex + 1; } } Combined, we can now easily create conditions of this form: ? BETWEEN ? AND ?. When more QueryParts are implemented, we could also imagine things like MY_TABLE.MY_FIELD BETWEEN ? AND (SELECT ? FROM DUAL), when appropriate Field implementations are available. That’s what makes the composite pattern so powerful, a common API and many components encapsulating behaviour, delegating parts of the behaviour to sub-components. Step 2 takes care of API evolution The composite pattern that we’ve seen so far is pretty intuitive, and yet very powerful. But sooner or later, we will need more parameters, as we find out that we want to pass state from parent QueryParts to their children. For instance, we want to be able to inline some bind values for some clauses. Maybe, some SQL dialects do not allow bind values in the BETWEEN clause. How to handle that with the current API? Extend it, adding a “boolean inline” parameter? No! That’s one of the reasons why the visitor pattern was invented. To keep the API of the composite structure elements simple (they only have to implement “accept”). But in this case, much better than implementing a true visitor pattern is to replace parameters by a “context”: interface QueryPart { // The QueryPart now renders its SQL to the context void toSQL(RenderContext context);// The QueryPart now binds its variables to the context void bind(BindContext context); } The above contexts would contain properties like these (setters and render methods return the context itself, to allow for method chaining): interface RenderContext { // Whether we're inlining bind variables boolean inline(); RenderContext inline(boolean inline);// Whether fields should be rendered as a field declaration // (as opposed to a field reference). This is used for aliased fields boolean declareFields(); RenderContext declareFields(boolean declare);// Whether tables should be rendered as a table declaration // (as opposed to a table reference). This is used for aliased tables boolean declareTables(); RenderContext declareTables(boolean declare);// Whether we should cast bind variables boolean cast();// Render methods RenderContext sql(String sql); RenderContext sql(char sql); RenderContext keyword(String keyword); RenderContext literal(String literal);// The context's 'visit' method RenderContext sql(QueryPart sql); }The same goes for the BindContext. As you can see, this API is quite extensible, new properties can be added, other common means of rendering SQL can be added, too. But the BetweenCondition does not have to surrender its encapsulated knowledge about how to render its SQL, and whether bind variables are allowed or not. It’ll keep that knowledge to itself: class BetweenCondition { Field field; Field lower; Field upper;// The QueryPart now renders its SQL to the context public void toSQL(RenderContext context) { context.sql(field).keyword(' between ') .sql(lower).keyword(' and ') .sql(upper); }// The QueryPart now binds its variables to the context public void bind(BindContext context) { context.bind(field).bind(lower).bind(upper); } } Whereas BindValue on the other hand, would mainly take care of variable binding class BindValue { Object value;public void toSQL(RenderContext context) { context.sql('?'); }public void bind(BindContext context) { context.statement().setObject(context.nextIndex(), value); } }Conclusion: Name it Context-Pattern, not Visitor-Pattern Be careful when jumping quickly to the visitor pattern. In many many cases, you’re going to bloat your design, making it utterly unreadable und difficult to debug. Here are the rules to remember, summed up:If you have many many visitors and a relatively simple data structure (few types), the visitor pattern is probably OK. If you have many many types and a relatively small set of visitors (few behaviours), the visitor pattern is overkill, stick with the composite pattern To allow for simple API evolution, design your composite objects to have methods taking a single context parameter. All of a sudden, you will find yourself with an “almost-visitor” pattern again, where context=visitor, “visit” and “accept”=”your proprietary method namesThe “Context Pattern” is at the same time intuitive like the “Composite Pattern”, and powerful as the “Visitor Pattern”, combining the best of both worlds. Reference: The Visitor Pattern Re-visited from our JCG partner Lukas Eder at the JAVA, SQL, AND JOOQ blog....
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Mocks And Stubs – Understanding Test Doubles With Mockito

Introduction A common thing I come across is that teams using a mocking framework assume they are mocking. They are not aware that Mocks are just one of a number of ‘Test Doubles’ which Gerard Meszaros has categorised at xunitpatterns.com. It’s important to realise that each type of test double has a different role to play in testing. In the same way that you need to learn different patterns or refactoring’s, you need to understand the primitive roles of each type of test double. These can then be combined to achieve your testing needs. I’ll cover a very brief history of how this classification came about, and how each of the types differs. I’ll do this using some short, simple examples in Mockito. A Very Brief History For years people have been writing lightweight versions of system components to help with testing. In general it was called stubbing. In 2000′ the article ‘Endo-Testing: Unit Testing with Mock Objects’ introduced the concept of a Mock Object. Since then Stubs, Mocks and a number of other types of test objects have been classified by Meszaros as Test Doubles. This terminology has been referenced by Martin Fowler in ‘Mocks Aren’t Stubs’ and is being adopted within the Microsoft community as shown in ‘Exploring The Continuum of Test Doubles’ A link to each of these important papers are shown in the reference section. Categories of test doublesThe diagram above shows the commonly used types of test double. The following URL gives a good cross reference to each of the patterns and their features as well as alternative terminology. http://xunitpatterns.com/Test%20Double.html Mockito Mockito is a test spy framework and it is very simple to learn. Notable with Mockito is that expectations of any mock objects are not defined before the test as they sometimes are in other mocking frameworks. This leads to a more natural style(IMHO) when beginning mocking. The following examples are here purely to give a simple demonstration of using Mockito to implement the different types of test doubles. There are a much larger number of specific examples of how to use Mockito on the website. http://docs.mockito.googlecode.com/hg/latest/org/mockito/Mockito.html Test Doubles with Mockito Below are some basic examples using Mockito to show the role of each test double as defined by Meszaros. I’ve included a link to the main definition for each so you can get more examples and a complete definition. Dummy Object http://xunitpatterns.com/Dummy%20Object.html This is the simplest of all of the test doubles. This is an object that has no implementation which is used purely to populate arguments of method calls which are irrelevant to your test. For example, the code below uses a lot of code to create the customer which is not important to the test. The test couldn’t care less which customer is added, as long as the customer count comes back as one. public Customer createDummyCustomer() { County county = new County('Essex'); City city = new City('Romford', county); Address address = new Address('1234 Bank Street', city); Customer customer = new Customer('john', 'dobie', address); return customer; }@Test public void addCustomerTest() { Customer dummy = createDummyCustomer(); AddressBook addressBook = new AddressBook(); addressBook.addCustomer(dummy); assertEquals(1, addressBook.getNumberOfCustomers()); }We actually don’t care about the contents of customer object – but it is required. We can try a null value, but if the code is correct you would expect some kind of exception to be thrown. @Test(expected=Exception.class) public void addNullCustomerTest() { Customer dummy = null; AddressBook addressBook = new AddressBook(); addressBook.addCustomer(dummy); }To avoid this we can use a simple Mockito dummy to get the desired behaviour. @Test public void addCustomerWithDummyTest() { Customer dummy = mock(Customer.class); AddressBook addressBook = new AddressBook(); addressBook.addCustomer(dummy); Assert.assertEquals(1, addressBook.getNumberOfCustomers()); }It is this simple code which creates a dummy object to be passed into the call. Customer dummy = mock(Customer.class); Don’t be fooled by the mock syntax – the role being played here is that of a dummy, not a mock. It’s the role of the test double that sets it apart, not the syntax used to create one. This class works as a simple substitute for the customer class and makes the test very easy to read. Test stub http://xunitpatterns.com/Test%20Stub.html The role of the test stub is to return controlled values to the object being tested. These are described as indirect inputs to the test. Hopefully an example will clarify what this means. Take the following code public class SimplePricingService implements PricingService { PricingRepository repository;public SimplePricingService(PricingRepository pricingRepository) { this.repository = pricingRepository; }@Override public Price priceTrade(Trade trade) { return repository.getPriceForTrade(trade); }@Override public Price getTotalPriceForTrades(Collection trades) { Price totalPrice = new Price(); for (Trade trade : trades) { Price tradePrice = repository.getPriceForTrade(trade); totalPrice = totalPrice.add(tradePrice); } return totalPrice; } TheSimplePricingServicehas one collaborating object which is the trade repository. The trade repository provides trade prices to the pricing service through the getPriceForTrade method. For us to test the businees logic in the SimplePricingService, we need to control these indirect inputs i.e. inputs we never passed into the test. This is shown below.In the following example we stub the PricingRepository to return known values which can be used to test the business logic of the SimpleTradeService. @Test public void testGetHighestPricedTrade() throws Exception { Price price1 = new Price(10); Price price2 = new Price(15); Price price3 = new Price(25); PricingRepository pricingRepository = mock(PricingRepository.class); when(pricingRepository.getPriceForTrade(any(Trade.class))) .thenReturn(price1, price2, price3); PricingService service = new SimplePricingService(pricingRepository); Price highestPrice = service.getHighestPricedTrade(getTrades()); assertEquals(price3.getAmount(), highestPrice.getAmount()); }Saboteur Example There are 2 common variants of Test Stubs: Responder’s and Saboteur’s. Responder’s are used to test the happy path as in the previous example. A saboteur is used to test exceptional behaviour as below. @Test(expected=TradeNotFoundException.class) public void testInvalidTrade() throws Exception {Trade trade = new FixtureHelper().getTrade(); TradeRepository tradeRepository = mock(TradeRepository.class);when(tradeRepository.getTradeById(anyLong())) .thenThrow(new TradeNotFoundException());TradingService tradingService = new SimpleTradingService(tradeRepository); tradingService.getTradeById(trade.getId()); }Mock Object http://xunitpatterns.com/Mock%20Object.html Mock objects are used to verify object behaviour during a test. By object behaviour I mean we check that the correct methods and paths are excercised on the object when the test is run. This is very different to the supporting role of a stub which is used to provide results to whatever you are testing. In a stub we use the pattern of defining a return value for a method. when(customer.getSurname()).thenReturn(surname);In a mock we check the behaviour of the object using the following form. verify(listMock).add(s);Here is a simple example where we want to test that a new trade is audited correctly. Here is the main code. public class SimpleTradingService implements TradingService{TradeRepository tradeRepository; AuditService auditService; public SimpleTradingService(TradeRepository tradeRepository, AuditService auditService) { this.tradeRepository = tradeRepository; this.auditService = auditService; }public Long createTrade(Trade trade) throws CreateTradeException { Long id = tradeRepository.createTrade(trade); auditService.logNewTrade(trade); return id; }The test below creates a stub for the trade repository and mock for the AuditService We then call verify on the mocked AuditService to make sure that the TradeService calls it’s logNewTrade method correctly @Mock TradeRepository tradeRepository; @Mock AuditService auditService; @Test public void testAuditLogEntryMadeForNewTrade() throws Exception { Trade trade = new Trade('Ref 1', 'Description 1'); when(tradeRepository.createTrade(trade)).thenReturn(anyLong()); TradingService tradingService = new SimpleTradingService(tradeRepository, auditService); tradingService.createTrade(trade); verify(auditService).logNewTrade(trade); }The following line does the checking on the mocked AuditService. verify(auditService).logNewTrade(trade); This test allows us to show that the audit service behaves correctly when creating a trade. Test Spy http://xunitpatterns.com/Test%20Spy.html It’s worth having a look at the above link for the strict definition of a Test Spy. However in Mockito I like to use it to allow you to wrap a real object and then verify or modify it’s behaviour to support your testing. Here is an example were we check the standard behaviour of a List. Note that we can both verify that the add method is called and also assert that the item was added to the list. @Spy List listSpy = new ArrayList();@Test public void testSpyReturnsRealValues() throws Exception { String s = 'dobie'; listSpy.add(new String(s));verify(listSpy).add(s); assertEquals(1, listSpy.size()); }Compare this with using a mock object where only the method call can be validated. Because we only mock the behaviour of the list, it does not record that the item has been added and returns the default value of zero when we call the size() method. @Mock List listMock = new ArrayList ();@Test public void testMockReturnsZero() throws Exception { String s = 'dobie';listMock.add(new String(s));verify(listMock).add(s); assertEquals(0, listMock.size()); } Another useful feature of the testSpy is the ability to stub return calls. When this is done the object will behave as normal until the stubbed method is called. In this example we stub the get method to always throw a RuntimeException. The rest of the behaviour remains the same. @Test(expected=RuntimeException.class) public void testSpyReturnsStubbedValues() throws Exception { listSpy.add(new String('dobie')); assertEquals(1, listSpy.size()); when(listSpy.get(anyInt())).thenThrow(new RuntimeException()); listSpy.get(0); }In this example we again keep the core behaviour but change the size() method to return 1 initially and 5 for all subsequent calls. public void testSpyReturnsStubbedValues2() throws Exception { int size = 5; when(listSpy.size()).thenReturn(1, size); int mockedListSize = listSpy.size(); assertEquals(1, mockedListSize); mockedListSize = listSpy.size(); assertEquals(5, mockedListSize);mockedListSize = listSpy.size(); assertEquals(5, mockedListSize); }This is pretty Magic! Fake Object http://xunitpatterns.com/Fake%20Object.html Fake objects are usually hand crafted or light weight objects only used for testing and not suitable for production. A good example would be an in-memory database or fake service layer. They tend to provide much more functionality than standard test doubles and as such are probably not usually candidates for implementation using Mockito. That’s not to say that they couldn’t be constructed as such, just that its probably not worth implementing this way. Reference: Mocks And Stubs – Understanding Test Doubles With Mockito from our JCG partner John Dobie at the Agile Engineering Techniques blog....
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Android: Facebook’s Notification Widget

Have you ever checked out the Facebook app? When you click on the Notifications button at the top, the app creates a nice overlaid window that contains a scrolling list view of all your info.It doesn’t dim out the background, and it also disappears if you click anywhere on the screen that isn’t in the overlaid window itself. The overlay is a great idea to add some polish to a UI.The other day I began trying to figure out how Facebook does this overlay. A good way to reproduce this type of overlay is to actually use a Transparent Activity. You can define a Transparent Activity in the Android Manifest as follows:<activity Android:name=".OverlayActivity" android:theme="@android:style/Theme.Translucent.NoTitleBar" > </activity>This gives you an empty activity. So when this activity is started, it looks as if nothing has happened, and the user can no longer click on the UI elements from the previous activity. Now that we have an activity that lets the background of the last activity be visible, we need to design the overlay itself in XML. In Facebook’s case, this would be a ListView and the rectangle graphic. The layout would also need to be positioned correctly. Notice in the Facebook image above how the image is positioned in such a way that the rectangle pointer is directly below the button that activated it.The beauty of using this implementation is that the overlay’s functionality is completely segregated because it is housed in a new activity. In the OverlayActivity, we simply generate our list and set up the click handlers for the list. There are just a few more tricks left to get all of the functionality that Facebook has added.To make the overlay disappear when any area outside of the overlay is clicked, some work needs to be done in the Overlay’s layout. This involves setting up a click handler to cover all of the area that the Overlay’s visible widgets aren’t using. By setting the click handler in the root layout close the activity on click, we have our desired functionality. This works well in Android because any click handlers set up on other views (in Facebook’s case, the ListView) would override the root’s click handler.To do one step above Facebook’s implementation, animation could be added to provide transitions for when the overlay is shown and removed. For example, we could easily add fade in and fade out animations that would make the overlay look even sharper. Since we have an activity, it’s very easy to add any kind of animation to the overlay. For the animation fun, check out the Alpha animation in Android.Reference: Implementing Facebook’s Notification Widget in Android from our JCG partner Isaac Taylor at the Programming Mobile blog....
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MongoDB performance testing

So, this morning I was hacking around in the mongo shell. I had come up with three different ways to aggregate the data I wanted, but wasn’t sure about which one I should subsequently port to code to use within my application. So how would I decide on which method to implement? Well, lets just chose the one that performs the best. Ok, how do I do that? Hmmm. I could download and install some of the tools out there, or I could just wrap the shell code in a function and add some timings. OR, I could use the same tool that I use to performance test everything else; JMeter. To me it was a no brainer. So how do we do it? There is a full tutorial here. Simply put, you need to do the following:Create a Sampler class. Create a BeanInfo class. Create a properties file. Bundle up into a jar and drop into the apache-jmeter-X.X\lib\ext folder Update search_paths=../lib/ext/mongodb.jar in jmeter.properties if you place the jar anywhere else.How I did it I tend to have a scratch pad project set up in my IDE, so I decided just to go with that. Just to be on the safe side, I imported all the dependencies from:apache-jmeter-X.X\lib apache-jmeter-X.X\lib\ext apache-jmeter-X.X\lib\junitI then created the two class and the properties file. I then exported the jar to apache-jmeter-X.X\lib\ext, and fired up jmeter. Go throunullgh the normal steps to set the test plan up:Right click Test Plan and add a Thread Group. Right click the Thread Group and add a Sampler, in this case a MongoDB Script Sampler. Add your script to the textarea; db.YOUR_COLLECTION_NAME.insert({“jan” : “thinks he is great”}) Run the testHappy days. You can then use JMeter as you would for any other sampler. Future enhancements This is just a hack that took me 37 minutes to get running, plus 24 minutes if you include this post. This can certainly be extended to allow you to enter the replicaset config details for instance and to pull the creation of the connection out so we’re not initiating this each time run a test. Reference: Performance testing MongoDB from our JCG partner Jan Ettles at the Exceptionally exceptional exceptions blog....
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Android Emulator with Nexus One skin

I know it doesn’t really increase your value during development. It’s just for fun but those of you that like it, here are some links that point to Nexus One skins that can be used on your local dev emulator.http://www.tughi.com/2010/01/19/nexus-one-skin/ http://timhoeck.com/2010/01/16/nexus-one-emulator-skin/Update: The Nexus S skin is available at here. Installation is easy. Just extract it to <android-sdk-install-root>/platforms/<your-platform>/skins and then use it. The result is pretty nice :)Update What I forgot to mention. If you have problems with the size of the emulator that is going to be started, you may want to add a -scale to your emulator run arguments. In Eclipse you add it under your Run Configurations:Reference: Tune your emulator with a pretty Nexus One skin! from our NGC partner Juri Strumpflohner at the Juri Strumpflohner’s TechBlog blog....
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Statement driven development

Today most methodologies used model oriented approach. It can be domain-driven or reverse-engineering, one common point is that they start from a static definition of a model. For domain-driven, it is the domain issued by the development language itself. For the reverse-engineering it starts from a static model structure definition ex: a xsd, wsdl, database schema.  Statement driven development focuses on the actions the developer can run on some models and not the models themselves.  Example: a select statement fetching all the group members a user with group-administrator-role can manage.  Here the statement can be materialized into a search query with sql as implementation. The important thing for the developer is the query and its I/O not really the underneath model and … not the decorative technology.Why Statement-Driven-DevelopmentCouple of articles and limitations make me think that statement driven approach can be a flexible and fast alternative for developpers. Usually after defining an abstract model such as ORM, the user have to write his UC statements. This means that there is still this second step to perform.The model is sometimes/often overkill. As a developer, you do not have to apprehend the entire model complexity before yielding productivity.Limitation on standard Like in the JOOQ article about function and stored procedure extracting the meta-information can reach some limits when entering in vendor specific particularities.Native DSL There is a trend that states that SQL is the only DSL you need. Why should you wrap it with other technology abstraction that limit its power?Bye-Bye Domain Objects …Welcome DTO.  I/O are by essence DTO (why should they be the exact reflection of a persistence layer?). This situation is just a specific exception.  This exception is widely used by multiple apps/framework (strong resuability but limitations).  Remark: This article is not there to cover the ‘everlasting-debate’ DO vs. DTO. SDD just brings a new approach that do not exclude but complement the DDD/Rev-Eng one.  Concretely MinuteProject for release 0.8.1+ (mid-may-2012) will offer Statement-Driven-Development facilities.The user will focus one SQL statement. The output will be deduce by the execution of it. The input will be easily configurable.Example Here is a simple configuration. Statement-model is the new node.<model> ... <statement-model> <queries> <query name="get address street"> <query-body><value><![CDATA[select * from address where addressid > ?]]></value></query-body> <query-params> <query-param name="identifier_Address" is-mandatory="false" type="INT" sample="1"></query-param> </query-params> </query> </queries> </statement-model> </model>This configuration should be enough to get:Input bean Output bean Output list bean All the technology decoration ex for REST CXF:Resource bean with REST path spring config Native DAODemonstration will be shipped in Minuteproject next release (0.8.1).Integrating with REST is pretty much statement-driven: basically you just need to know the URL + its I/O.Conclusion  With SDD you focus on the statement and the I/O. Minuteproject simplifies the rest (technology wrapper).Reference: Statement driven development from our JCG partner Florian Adler at the minuteproject blog blog....
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Building security into a development team

Getting application developers to understand and take responsibility for software security is difficult. Bootstrapping an Appsec program requires that you get the team up to speed quickly on security risks and what problems they need to look for, how to find and fix and prevent these problems, what tools to use, and convince them that they need to take security seriously. One way to do this is to train everyone on the development team on software security.But at RSA 2011, Caleb Sima’s presentation Don’t Teach Developers Security challenged the idea that training application developers on software security will make a meaningful difference. He points out (rightly) that you can’t teach most developers anything useful about secure software development in a few hours (which as much Appsec training as most developers will get anyways). At best training like this is a long-term investment that will only pay off with reinforcement and experience – the first step on a long road. Most developers (he suggests as many as 90 out of 100) won’t take a strong interest in software security regardless. They are there to build stuff, that’s what they get paid for, that’s what they care about and that’s what they do best. Customers love them and managers (like me) love them too because they deliver, and that’s what we want them spending their time doing. We don’t want or need them to become AppSec experts. Only a few senior, experienced developers will “get” software security and understand or care about all of the details, and in most cases this is enough. The rest of the team can focus on writing good defensive code and using the right frameworks and libraries properly.Caleb Sima recommends starting an Appsec program by working with QA. Get an application security assessment: a pen test or a scan to identify security vulnerabilities in the app. Identify the top 2 security issues found. Then train the test team on these issues, what they look like, how to test for them, what tools to use. It’s not practical to expect a software tester to become a pen testing expert, but they can definitely learn how to effectively test for specific security issues. When they find security problems they enter them as bugs like any other bug, and then it’s up to development to fix the bugs.Get some wins this way first. Then extend security into the development team. Assign one person as a security controller for each application: a senior developer who understands the code and who has the technical skills and experience to take on security problems. Give them extra Appsec training and the chance to play a leadership role. It’s their job to assess technical risks for security issues. They decide on what tools the team will use to test for security problems, recommend libraries and frameworks for the team to use, and help the rest of the team to write secure code.What worked for usLooking back on what worked for our Appsec program, we learned similar lessons and took some of the same steps.While we were still in startup, I asked one of our senior developers to run an internal security assessment and make sure that our app was built in a secure way. I gave him extra time to learn about secure development and Appsec, and gave him a chance to take on a leadership role for the team. When we brought expert consultants in to do additional assessments (a secure design review and code review and pen testing) he took the lead on working with them and made sure that he understood what they were doing and what they found and what we needed to do about it. He selected a static analysis tool and got people to use it. He ensured that our framework code was secure and used properly, and he reviewed the rest of the team’s code for security and reliability problems. Security wasn’t his entire job, but it was an important part of what he did. When he eventually left the team, another senior developer took on this role. Most development teams have at least 1 developer who the rest of the team respects and looks to for help on how to use the language and platform correctly. Someone who cares about how to write good code and who is willing to help others with tough coding problems and troubleshooting. Who handles the heavy lifting on frameworks or performance engineering work. This is the developer that you need to take on your core security work. Someone who likes to learn about technical stuff and who picks new things up quickly, who understands and likes hard technical stuff (like crypto and session management), who makes sure that things get done right.Without knowing it we ended up following a model similar to Adobe’s “security ninja” program, although on a micro-scale. Most developers on the team are white belts or yellow belts with some training in secure software development and defensive programming. Our security lead is the black belt, with deeper technical experience and extra training and responsibility for leading software security for the application. Although we depended on external consultants for the initial assessments and to help us lay out a secure development roadmap, we have been able to take responsibility for secure development into the development team. Security is a part of what they do and how they design and build software today.This model works and it scales. If as a manager you look at security as an important and fundamental technical problem that needs to be solved (rather than a pain-in-the-ass that needs to be gotten over), then you will find that your senior technical people will take it seriously. And if your best technical people take security seriously, then the rest of the team will too.Reference: Building security into a development team from our JCG partner Jim Bird at the Building Real Software blog....
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MapReduce for dummies

Continuing the coverage on Hadoop component, we will go through the MapReduce component. MapReduce is a concept that has been programming model of LISP. But before we jump into MapReduce, lets start with an example to understand how MapReduce works. Given a couple of sentences, write a program that counts the number of words.Now, the traditional thinking when solving this problem is read a word, check whether the word is one of the stop words, if not , add the word in a HashMap with key as the word and set the value to number of occurrences. If the word is not found in HashMap, then add the word and set the value to 1. If the word is found, then increment the value and word the same in HashMap. Now, in this scenario, the program is processing the sentence in a serial fashion. Now, imagine if instead of a sentence, we need to count the number of words in encylopedia. Serial processing of this amount of data is time consuming. So, question is is there another algorithm we can use to speed up the processing. Lets take the same problem and divide the same into 2 steps. In the first step, we take each sentence each and map the number of words in that sentence.Once, the words have been mapped, lets move to the next step. In this step, we combine (reduce) the maps from two sentences into a single map.That’s it, we have just seen how, individual sentences can be mapped individually and then once mapped, can be reduced to a single resulting map.Advantage of the MapReduce approach isThe whole process got distributed in small tasks that will help in faster completion of the job Both the steps can be broken down into tasks. In the first, instance, run multiple map tasks, once the mapping is done, run multiple reduce tasks to combine the results and finally aggregate the resultsNow, imagine this MapReduce paradigm working on the HDFS. HDFS has data nodes that splits and store the files in blocks. Now, if map the tasks on each of the data nodes, then we can easily leverage the compute power of those data node machines.So, each of the data nodes, can run tasks (map or reduce) which are the essence of the MapReduce. As each data nodes stores data for multiple files, multiple tasks might be running at the same time for different data blocks. To control the MapReduce tasks, there are 2 processes that need to be understoodJobTracker – The JobTracker is the service within Hadoop that farms out MapReduce tasks to specific nodes in the cluster, ideally the nodes that have the data, or at least are in the same rack. TaskTracker – TaskTracker is a process that starts and tracks MapReduce Tasks in a cluster. It contacts the JobTracker for Task assignments and reporting results.These Trackers are part of the Hadoop itself and can be tracked easily viahttp://<host-name>:50030/ – web UI for MapReduce job tracker(s) http://<host-name>:50060/ – web UI for task tracker(s)Reference: MapReduce for dummies from our JCG partner Munish K Gupta at the Tech Spot blog....
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