Tag Archives: Java

Extending Guice

Guice is a framework that I had been looking forward to trying out for a while, but until recently I never had the opportunity.  Previously I had mostly used Spring (with a dash of PicoContainer), so when I got the opportunity to start using Guice, I naturally had a number of my favourite Spring features in mind as I started using it.  Very quickly I found myself wanting an equivalent of Springs DisposableBean.  Guice is focussed on doing one thing and doing it well, and that thing is dependency injection.  Lifecycle management doesn’t really come into that, so I am not surprised that Guice doesn’t offer native support for disposing of beans.  There is one Guice extension out there, Guiceyfruit, that does offer reasonably complete per scope lifecycle support, however Guiceyfruit requires using a fork of Guice, which didn’t particularly appeal to me.  Besides, Guice is very simple, so I imagined that providing my own simple extensions to it would also be simple.  I was right.

Though, to be honest, while the extensions themselves are simple, it wasn’t that simple to work out how to write them.  On my first attempt, I gave up after Googling and trying things out myself for an hour.  On my second attempt, I almost gave up with this tweet.  But, I stuck with it, and eventually made my breakthrough. The answer was in InjectionListener. This listener is called on every component that Guice manages, including both components that Guice instantiates itself, and components that are provided as instances to Guice.

Supporting Disposables

So, I had my disposable interface:

public interface Disposable {
  void dispose();

and I wanted any component that implemented this interface to have their dispose() method called when my application shut down.  Naturally I had to maintain a list of components to dispose of:

final List<Disposable> disposables = Collections.synchronizedList(new ArrayList());

Thread safety must be taken into consideration, but since I only expected this list to be accessed when my application was starting up and shutting down, a simple synchronized list was suffcient, no need to worry about performant concurrent access.

My InjectionListener is very simple, it just adds disposables to this list after they’ve been injected:

final InjectionListener injectionListener = new InjectionListener<Disposable>() {
  public void afterInjection(Disposable injectee) {

InjectionListener‘s are registered by registering a TypeListener that listens for events on types that Guice encounters.  My type listener checks if the type is Disposable (this actually isn’t necessary because we will register it using a matcher that matches only Disposable types, but it is defensive to do the check), and if so registers the InjectionListener:

TypeListener disposableListener = new TypeListener {
  public <I> void hear(TypeLiteral<I> type, TypeEncounter<I> encounter) {
    if (Disposable.class.isAssignableFrom(type.getRawType())) {
      TypeEncounter<Disposable> disposableEncounter = (TypeEncounter<Disposable>) encounter;

Now I can register my TypeListener.  This is done from a module:

bindListener(new AbstractMatcher<TypeLiteral<?>>() {
      public boolean matches(TypeLiteral<?> typeLiteral) {
        return Disposable.class.isAssignableFrom(typeLiteral.getRawType());
    }, disposableListener);

The last thing I need to do is bind my collection of disposables, so that when my app shuts down, I can dispose of them:

bind((TypeLiteral) TypeLiteral.get(Types.listOf(Disposable.class)))

So now when my app shuts down, I can look up the list of disposables and dispose of them:

for (Disposable disposable : ((List<Disposable>) injector.getInstance(
    Key.get(Types.listOf(Disposable.class)))) {

If you decide to use this code in your own app, please be very wary of a potential memory leak. Any beans that are not singleton scoped will be added to the disposable list each time they are requested (per scope).  For my purposes, all my beans that required being disposed of were singleton scoped, so I didn’t have to worry about this.

Supporting annotation based method invocation scheduling

Happy that I now had a very simple extension with very little code for supporting automatic disposing of beans, I decided to try something a little more complex… scheduling. My app contains a number of simple scheduled tasks, and the amount of boilerplate for scheduling each of these was too much for my liking. My aim was to able to do something like this:

@Schedule(delay = 5L, timeUnit = TimeUnit.MINUTES, initialDelay = 1L)
def cleanUpExpiredData() {

(Yep, this app has a mixture of Scala and Java.) So, I started with my annotation:

public @interface Schedule {
    long delay();
    TimeUnit timeUnit() default TimeUnit.MILLISECONDS;
    long initialDelay() default 0;

The main difference this time is that I’m not listening for events on a particular type, but rather I want to check all types to see if they have a @Schedule annotated method. This is a little more involved, so I’m going to have a scheduler service that does this checking and the scheduling. Additionally it will make use of the disposable support that I just implemented:

public class SchedulerService implements Disposable {
  private final ScheduledExcecutorService executor = Executors.newSingleThreadScheduledExecutor();

  public boolean hasScheduledMethod(Class clazz) {
    for (Method method : clazz.getMethods()) {
      Schedule schedule = method.getAnnotation(Schedule.class);
      if (schedule != null) {
        return true;
    return false;

  public void schedule(Object target) {
    for (final Method method : target.getClass().getMethods()) {
      Schedule schedule = method.getAnnotation(Schedule.class);
      if (schedule != null) {
        schedule(target, method, schedule);

  private void schedule(final Object target, final Method method, Schedule schedule) {
    executor.scheduleWithFixedDelay(new Runnable() {
      public void run() {
      }, schedule.initialDelay(), schedule.delay(), schedule.timeUnit());

  public void dispose() {

Now in my module I instantiate one of these services:

final SchedulerService schedulerService = new SchedulerService();

I then implement my InjectionListener:

final InjectionListener injectionListener = new InjectionListener() {
  public void afterInjection(Object injectee) {

and my TypeListener:

TypeListener typeListener = new TypeListener() {
  public <I> void hear(TypeLiteral<I> type, TypeEncounter<I> encounter) {
    if (schedulerService.hasScheduledMethod(type.getRawType()))  {

And then all I have to do is register my type listener, and also my scheduler service (so that it gets disposed properly):

  bindListener(Matchers.any(), typeListener);


Although Guice doesn’t come with as much as Spring does out of the box, it is very simple to extend to meet your own requirements. If I were needing many more container like features, then maybe Spring would be a better tool for the job, but when I’m just after a dependency injection framework with a little sugar on top, Guice is a very nice and much lighter weight solution.

You did what in Scala?

This morning when I got into work, the first thing that anyone said to me was “you did what in Scala?”  Not the usual greeting I get in the morning… clearly I had stirred something up.  I knew exactly what this person was talking about, the evening before I committed some code, and then tweeted this:

Just added scala for the first time to an existing Java project. Not too shaby.

As soon as I saw the build passed on our CI server, I went home, but it caught the attention of my product manager, and he was very intrigued.  What I had in fact done was started writing unit tests for an existing Java service that I was working on in Scala.  Why did I do this?  A number of reasons:

  1. I’ve been meaning to learn Scala for at least a year.
  2. I’ve seen Scala unit tests before, and they look very cool, they’re very good at minimising boilerplate, and very easy to read and understand.
  3. At VZ, we are free to make sensible technology choices.  This ranges from what libraries we use, to what databases we use, to what languages we use.  Nothing is off limits, as long as we can provide a good argument as to why it’s better than the alternatives.  And when we do that, our managers trust us.

My product manager of course had no problems with me using Scala, we have another project here that uses Scala and he thought I meant I had done some work on that, and was interested in knowing why.  After explaining that I had actually added Scala to the project I was supposed to be working on, he was completely fine, and that’s one of the things I love about working for VZ, we have the freedom to make our own decisions.

For those that are not familiar with Scala, here is a quick overview of how I introduced Scala into my existing Java project.

First, I did my research.  What unit testing frameworks are there in Scala?  You’ll quickly find that there are two popular frameworks, one called specs, and another called ScalaTest.  ScalaTest supports a number of different testing styles, including TDD and BDD, while specs only supports BDD.  I only wanted BDD, so both were equal to me at this point.  Further research showed that specs has good integration with my favourite mocking framework, Mockito, so I went with specs.  I suggest you do your own research for your own purposes, my comparison here is far from complete.

Next, since I’m using Maven, I needed to add Scala to my maven project.  I found a blog post that explained how to add Scala to a maven project in 4 steps, and I was able to build my project in no time.  I also added a dependency on the specs library, and configured the Maven surefire plugin to run any classes ending in Test or Spec, as per the instructions for integrating with Maven and JUnit in the specs documentation.  I use IntelliJ IDEA as my IDE, so I searched for a Scala plugin in my preferences, found one, installed it, and after a restart IDEA had Scala support.  The IDEA instructions say that you need to install the Scala SDK, but since I was using Maven, I could just add the scala compiler as a provided maven dependency, then go to the Scala compiler preferences and point IDEA at that dependency.

Finally I had to write my tests.  Below is the first test that I wrote.  If you’re a Scala guru, I’m sure you’ll see things that I could have done simpler or that I haven’t followed conventions for, so I’m happy for you point them out to me, I’m still learning.

class WorkResultHandlerSpec extends SpecificationWithJUnit with Mockito {
  "Work result handler" should {
    val tracker = mock[WorkResultTracker]
    val handlerChain = mock[HandlerChain]
    val workUnit = WorkUnit.builder(JobType.TEST_MESSAGE, null).build
    val job = Job.builder(JobType.TEST_MESSAGE).build
    var handler = new WorkResultHandler(tracker)

    "call handler chain only once" in {
      handler.handle(job, workUnit, handlerChain)
      there was one(handlerChain).passToNextHandler(job, workUnit)

    "pass the result to the tracker" in {
      val workResult = WorkResult.success
      handlerChain.passToNextHandler(job, workUnit) returns workResult
      handler.handle(job, workUnit, handlerChain)
      there was one(tracker).trackWorkResult(JobType.TEST_MESSAGE, workResult)

    "return the result" in {
      val workResult = WorkResult.success
      handlerChain.passToNextHandler(job, workUnit) returns workResult
      handler.handle(job, workUnit, handlerChain) mustEq workResult

    "track an exception as a failure" in {
      handlerChain.passToNextHandler(job, workUnit) throws new RuntimeException("Something bad happened")
      val workResult = handler.handle(job, workUnit, handlerChain)
      workResult.getStatus.isSuccess must_== false
      workResult.getMessage mustEq "Something bad happened"
      there was one(tracker).trackWorkResult(JobType.TEST_MESSAGE, workResult)

Unit testing Java mail code

GreenMail is a mail testing tool that has been around for a long time, but I’m surprised by how few people actually know about it.  I’ve found it very useful on many occasions for testing my mail sending code, both in integration/acceptance style tests, and also in unit tests.  In this post I’m going to give a short introduction to using GreenMail with JUnit, and then I’ll add a number of advanced tips that I’ve learned from my experience to help you effectively test your code. For my examples I’m going to be testing a class that I’ve written called MailSender.

Using GreenMail with JUnit

GreenMail starts as a server listening on ports for SMTP/POP/IMAP connections, these are naturally handled by background threads. In order to use GreenMail in unit testing, you need to start it up before each test and shut it down again after.  GreenMail is incredibly fast to startup and shutdown so there’s no worries about performance of your tests here.

private static final int SMTP_TEST_PORT = 3025;
private GreenMail greenMail;
private MailSender mailSender;

public void setUp() throws Exception {
    greenMail = new GreenMail(new ServerSetup(SMTP_TEST_PORT, null, "smtp"));
    mailSender = new MailSender("localhost", SMTP_TEST_PORT);
public void tearDown() throws Exception {

Now I’m ready to write my tests.  Here’s an example test:

public void sendShouldSetTheRightText() throws Exception {
    mailSender.send("Hello World!");
    assertThat((String) greenMail.getReceivedMessages()[0].getContent(),
        equalTo("Hello World!"));

The getReceivedMessages() method returns an array of javax.mail.internet.MimeMessage, each of which contains all the mime information from each sent email. Note that in this case, I’m assuming that everything is happening synchronously, which it is. Sometimes though (particularly in integration tests) mail is put onto a queue and sent asynchronously. GreenMail offers a handy method for dealing with this, waitForIncomingMail(), which accepts a timeout and a number of emails you want to wait for, and returns a boolean letting you know whether the mail you were waiting for arrived.

Selecting a good port

The above code works fine if you know that port 3025 is free. But you don’t always know that, particularly if you’re running your tests on a CI server, that CI server may be running another job that is trying to use the same port. I’ve found the following code useful for picking a good port:

private static int findAvailablePort(int min, int max) {
    for (int port = min; port < max; port++) {
        try {
            new ServerSocket(port).close();
            return port;
        } catch (IOException e) {
            // Must already be taken
    throw new IllegalStateException("Could not find available port in range "
            + min + " to " + max);

I can now specify a range of ports and my code will choose one that is free.

Checking who the mail was actually sent to

As you know, mime headers can often lie. Just like the header on a letter might be different to the name on the envelope, the To header set in a mime header might be different to that sent to the SMTP server. This is also known as bccing, and it may be the case that your code uses this. If that is the case, you can’t run assertions on the To field to ensure that the email was actually sent to the right person (although, due to this bug in GreenMail you actually can, but you can’t know whether they weren’t added to the To header by your code). However, GreenMail provides a way to get mail that was actually delivered to a particular user. The first thing you need to do is get a reference to the object for that user, this is most easily done by setting their password, I do this in the setUp() method:

greenMailUser = greenMail.setUser("recipient@example.org", null);

Now when it comes to writing my tests, I can look up that users inbox and find the messages that are in it:

MailFolder inbox = greenMail.getManagers().getImapHostManager().getInbox(greenMailUser);
List<StoredMessage> messages = inbox.getMessages();
if (!messages.isEmpty()) {
} else {
    fail("No email for user arrived")

Checking the envelope from address

A similar issue to bccing is telling the SMTP server that you’re sending from a different email address to the email address you put in the mime headers. This is also known as the envelope from or bounce address, and it’s typically used for error reporting, for example, to tell the sender that the mailbox doesn’t exist. This can be set in Java by creating an instance of com.sun.mail.smtp.SMTPMessage instead of MimeMessage and setting it using the setEnvelopeFrom() method. This is not a mime header that we can just check, however SMTP servers are required to put this address into the email in the mime header Return-Path. GreenMail does this to. So to assert that the correct envelope from address was set:

String returnPathHeader = message.getHeader("Return-Path", ",");
assertThat(returnPathHeader, notNullValue());
InternetAddress returnPath = InternetAddress.parse(returnPathHeader)[0];
assertThat(returnPath.getAddress(), equalTo("bounce-notify@example.org"));

That concludes my post on using GreenMail, if you have any other helpful tips for using GreenMail, please share them as comments here!

Webserver Scalability and Reliability

Everybody knows Apache and Tomcat but when I try to talk about such strange things like Yaws or Mochiweb nobody knows what I actually want. These two are HTTP server implementations written in the old fashioned functional language Erlang and running on the famous Open Telecom Platform or OTP. Erlang/OTP was developed in the late 80s as a fault tolerant and highly scalable system for telecom applications. Nowadays in the social networking community it is daily business to serve some 10.000s of PHP requests per second. So we are facing problems telcos have for a long time.

Apache is the canonical web server to serve PHP to the world. Thinking about technological alternatives in the backend domain we have a look at both Java and Erlang. A rather quick and easy test to compare the scalability of the technologies was to setup web servers delivering the same static document. The image below shows the results.

Web Server Scaling by Concurrency

Apache and Tomcat scale nearly linearly up to a concurrency of 1000. We find that Mochiweb has a breakdown when concurrency reaches 300 but afterwards still scales linearly as Yaws does at a lower level. Absolute performance is less interesting here. What counts is the scaling behavior. Mochiweb for example is not designed first hand to deliver static content but to act as a HTTP endpoint for arbitrary OTP applications. Neither Yaws nor Mochiweb seem to cache documents in the default setup. Also, we did not use HiPE.

Unfortunally we had not yet the chance to verify the measurements given in this post: http://www.sics.se/~joe/apachevsyaws.html where Yaws still scales linearly (in the average) when Apache has long gone away, at concurrency of 50.000 or even 80.000 and clearly seems to survive DDoS attacks.

In this picture Erlang/OTP may not be a recommendation for classical web delivery but to build reliable services either to provide internal or external API endpoints. An interesing alternative at least.