Raible Designs

Building GWT Applications with MVP and Issues with Overlay Types

MVP has recently become a popular strategy for structuring GWT applications. This is largely due to its testability and Ray Ryan's Best Practices For Architecting Your GWT App from this year's Google I/O. GWT, by itself, is simply a widget toolkit and doesn't ship with any sort of MVC (or MVP) framework.

On my current project, we're using GXT, a GWT implementation based on ExtJS. It has its own MVC framework, but it has very little documentation and can be confusing when using it with GWT's History management. At one point, I attempted to make it more understandable by writing a blog entry on GXT's MVC Framework.

One of my initial assignments was to decide if we should use MVP or MVC. Regardless of which one was chosen, I was also tasked with deciding if we should use an existing framework or write our own. After watching Ray Ryan's session on YouTube and recalling my frustration with GXT MVC on my last project, I quickly became convinced MVP was the answer.

To test my "MVP is best for our project" theory, I did a spike to implement it. I used the GWT MVP Example tutorial as a starting point and added the following libraries to my project.

• GWT-Presenter: An implementation of the MVP pattern.
• Google Gin: Dependency Injection based on Google's Guice.
• GWT-Log: A log4j-style logger for GWT.

Implementing the MVP pattern itself was relatively straightforward, but I did encounter a few issues. I'm writing this post to see if anyone has solved these issues.

MVP Implementation Issues
The first issue I ran across was GXT's widgets don't implement standard GWT interfaces. To try and figure out a solution, I posted the following on Twitter:

"Wondering if it's possible to do MVP with GXT since it's buttons don't implement standard GWT interfaces."

The best response I received was from Simon Stewart (founder of the WebDriver project, works for Google):

"Put the GXT buttons in the View. Let that turn DOM events into semantic events."

He also pointed me to his tdd-gwt-gae project which shows many techniques for unit testing (with jMock) and integration testing (with GWTTestCase). Using Simon's examples, I was able to determine an initial strategy for implementing MVP with GXT.

My strategy is instead of getting widgets from the view and adding handlers, you add handlers to to the view and it takes care of adding them to the widgets that should listen for them. This seems to work, but my View interface has a lot of void methods, which is a bit different than standard MVP patterns I've seen.

The 2nd issue I encountered is with unit testing. Unit testing can be be performed on MVP applications by mocking out any dependencies that use JSNI. Classes that use JSNI are not testable with plain ol' JUnit and typically requires you to use GWTTestCase, which can be slow and cumbersome. This isn't to say that GWTTestCase isn't useful, just that it has its place.

When unit testing MVP applications, the recommended practice seems to be you should test presenters and not views. Services fall into a similar "don't unit test" category because they'll need to connect to the server-side, which won't be running in a unit testing environment.

This is where I ran into a major issue that I don't have a solution for.

I'm using Overlay Types (as described in JSON Parsing with JavaScript Overlay Types) to do JSON parsing in callbacks. Since Overlay Types use JSNI, it's not possible to do any JSON parsing in unit tests. The problem with not being able to do any JSON parsing is the callbacks will often call eventBus.fireEvent(GwtEvent) after the JSON parsing has happened. This means I can't fully test the flow of a presenter if event firing happens in a callback.

In attempt to try different mocking techniques for callbacks, I created a test that uses two recommended EasyMock-ing strategies. The first is a "CallbackSuccessMatcher" and is described in more detail in Testing GWT without GwtTestCase. The second technique uses a "CallbackMockSupport" class to allow EasyMock expectations such as expectLastCallAsync() and expectLastCallAsyncSuccess(T). You can read more about this technique in Test driven development for GWT UI code with asynchronous RPC.

Both of these examples use RPC, which typically has callbacks that have an onSuccess(T type) method. It's easy to use these callbacks in unit tests since T is a POJO and the onSuccess() method contains no JSNI code.

Currently, I see a few possible solutions to this problem:

• Figure out a way to detect when unit tests are running and add if/else logic to callbacks.
• Modify presenters and services so a callback can be set that is unit test-friendly.
• Make JSOModel an interface that can be replaced/mocked in tests.

The last solution seems like best one, but I'm also curious to know what others are doing. My hunch is that most GWT apps use RPC and haven't run into this issue.

Concurrency on the JVM Using Scala with Venkat Subramaniam

This evening, I attending the Denver JUG where Venkat Subramaniam was speaking about Scala. Unfortunately, I arrived halfway through his Programming Scala talk and didn't get a chance to learn as much as I wanted to. What I did see made Scala look very powerful and (possibly) easier to learn than Java. Below are my notes from Venkat's talk.

Concurrency is important these days because we're in a world of multiple processors. When you have multiple threads running at one time, it can become painful. Before Java, you had to learn the API for multi-threading for each different platform. With Java's "Write once, debug everywhere", you only had to learn one API. Unfortunately, it's pretty low level: how to start a thread, manage it, stop it, etc. You also have to remember where to put synchronize in your code.

With Scala, immutability and its Actors make it easy to program concurrent systems. For example, here's a web service that retrieves stock prices in sequential order:

def getyearEndClosing(symbol : String, year : Int) = {
  val url = "http://ichart.finance.yahoo.com/table.csv?s=" + 
    symbol + "&a=11&b=01&c" + year + "&d=11&e=31&f=" + year + "&g=m"
  val data = io.Source.fromURL(url).mkString
  val price = data.split("\n")(1).split(",")(4).toDouble
  Thread.sleep(1000); // slow down internet
  (symbol, price)
}

val symbols = List("APPL", "GOOG", "IBM", "JAVA", "MSFT")

val start = System.nanoTime

val top = (("", 0.0) /: symbols) { (topStock, symbol) => 
  val (sym, price) = getYearEndClosing(symbol, 2008)

  if (topStock._2 < price) (sym, price) else topStock
}

val end = System.nanoTime

println("Top stock is " + top._1 + " with price " + top._2)
println("Time taken " + (end - start)/10000000000.0)

To make this concurrent, we create Actors. Actors are nothing but Threads with a built-in message queue. Actors allow spawning separate threads to retrieve each stock price. Instead of doing:

symbols.foreach { symbol => 
  getYearEndClosing(symbol, 2008)
}

You can add actors:

val caller = self

symbols.foreach { symbol => 
  actor { caller ! getYearEndClosing(symbol, 2008) }
}

Then remove val (sym, price) = getYearEndClosing(symbol, 2008) and replace it with:

  receive {
    case(sym: String, price: Double) =>
      if (topStock._2 < price) (sym, price) else topStock
  }

After making this change, the time to execute the code dropped from ~7 seconds to ~2 seconds. Also, since nothing is mutable in this code, you don't have to worry about concurrency issues.

With Scala, you don't suffer the multiple-inheritance issues you do in Java. Instead you can use Traits to do mixins. For example:

import scala.actors._
import Actor._

class MyActor extends Actor {
  def act() {
    for(i <- 1 to 3) {
    receive {
      case msg => println("Got " + msg)
    }
  }
}

When extending Actor, you have to call MyActor.start to start the Actor. Writing actors this way is not recommended (not sure why, guessing because you have to manually start them).

Venkat is now showing an example that counts prime numbers and he's showing us how it pegs the CPU when counting how many exist between 1 and 1 million (78,499). After adding actor and receive logic, he shows how his Activity Monitor shows 185% CPU usage, indicating that both cores are being used.

What happens when one of the threads crashes and burns? The receive will wait forever. Because of this, using receive is a bad idea. It's much better to use receiveWithin(millis) to set a timeout. Then you can catch the timeout in the receiveWithin block using:

case TIMEOUT => println("Uh oh, timed out")

A more efficient way to use actors is using react instead of receive. With react, threads leave after putting the message on the queue and new threads are started to execute the block when the message is "reacted" to. One thing to remember with react is any code after the react block will never be executed. Just like receiveWithin(millis), you can use reactWithin(millis) to set a timeout.

The major thing I noticed between receive and react is Venkat often had to change the method logic to use react. To solve this, you can use loop (or better yet, loopWhile(condition)) to allow accessing the data outside the react block. In conclusion, reactWithin(millis) is best to use, unless you need to execute code after the react block.

Conclusion
This was a great talk by Venkat. He used TextMate the entire time to author and execute all his Scala examples. Better yet, he never used any sort of presentation. All he had was a "todo" list with topics (that he checked off as he progressed) and a sample.scala file.

Personally, I don't plan on using Scala in the near future, but that's mostly because I'm doing UI development and GWT and JavaScript are my favorite languages for that. On the server-side, I can see how it reduces the amount of Java you need to write (the compiler works for you instead of you working for the compiler). However, my impression is its sweet spot is when you need to easily author an efficient concurrent system.

If you're looking to learn Scala, I've heard Scala by Example (PDF) is a great getting-started resource. From there, I believe Programming in Scala and Venkat's Programming Scala are great books.