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Tapestry IoC Overview

Even today, with the overwhelming success of Spring and the rise of smaller, simpler approaches to building application that stand applications (in sharp contrast to the ultra- heavyweight EJB 2.0 approach), many people still have trouble wrapping their heads around Inversion of Control.

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Inversion of Control builds upon those ideas. The goal is to make coding code more robust (that is, with fewer errors), more reusable and to make code much easier to test.

Most developers are Prior to IoC approaches, most developers were used to a more monolithic design, they have with a few core objects and a main() method somewhere that starts the ball rolling. main() instantiates the first couple of classes, and those classes end up instantiating and using all the other classes in the system.

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So the IoC container is the "town" and in the world of the IoC container, everything has a name, a place, and a relationship to everything else in the container. Tapestry calls this world "The Registry".

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Here we're seeing a few services from the built-in Tapestry IoC module, and a few of the services from the Tapestry web framework module. In fact, there are over 100 services, all interrelated, in the Registry ... and that's before you add your own to the mix. The IoC Registry treats all the services uniformly, regardless of whether they are part of Tapestry, or part of your application, or part of an add-on library.

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Tapestry IoC also has support for other configuration that may be provided to services when they are realized.

Dependency Injection

Main Article: Injection

Inversion of Control refers to the fact that the container, here Tapestry IoC's Registry, instantiates your classes. It decides on when the classes get instantiated.

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That's a common question. All these new concepts seem alien . All that XML (in the Spring or HiveMind IoC containers; Tapestry IoC uses no XML) is a burdenat first. What's wrong with new?

The problem with new is that it rigidly connects one implementation to another implementation. Let's follow a progression that reflects how a lot of projects get written. It will show that in the real world, new is not as simple as it first seems.

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This code is for a metric that periodically counts the number of rows in a key database table. Other implementations of MetricProducer will be responsible for measuring CPU utilization, available disk space, number of requests per second, and so forth.

public class TableMetricProducer implements MetricProducer
{
  . . . 

  public void execute() 
  {

    int rowCount = . . .;
    Metric metric = new Metric("app/clients", System.currentTimeMillis(), rowCount);

    new QueueWriter().sendMetric(metric);
  }
}

We've omitted some of the details (this code will need a database URL or a connection pool to operate), so as to focus on the one method and it's relationship to the QueueWriter class.

Obviously, this code has a problem ... we're creating a new QueueWriter for each metric we write into the queue, and the QueueWriter presumably is going to open the JMS queue fresh each time, an expensive operation. Thus:

public class TableMetricProducer implements MetricProducer
{
  . . . 

  private final QueueWriter queueWriter = new QueueWriter();

  public void execute() 
  {
    int rowCount = . . .;
    Metric metric = new Metric("app/clients", System.currentTimeMillis(), rowCount);

    queueWriter.sendMetric(metric);
  }

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Here's a more immediate problem: JMS connections are really meant to be shared, and we'll have lots of little classes collecting different metrics. So we need to make the QueueWriter shareable:

  private final QueueWriter queueWriter = QueueWriter.getInstance();

... and inside class QueueWriter:

public class QueueWriter
{
  private static QueueWriter instance;

  private QueueWriter()
  {
    ...
  }

  public static getInstance()
  {
    if (instance == null)
    {
      instance = new QueueWriter();
    }
    return instance;
  }
}

Much better! Now all the metric producers running inside all the threads can share a single QueueWriter. Oh wait ...

  public synchronized static getInstance()
  {
    if (instance == null)
    {
      instance = new QueueWriter();
    }
    return instance;
  }

Is that necessary? Yes. Will the code work without it? Yes – 99.9% of the time. In fact, this is a very common error in systems that manually code a lot of these construction patterns: forgetting to properly synchronize access. These things often work in development and testing, but fail (with infuriating infrequency) in production, as it takes two or more threads running simultaneously to reveal the coding error.

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We'll need to change TableMetricProducer to take the QueueWriter as a constructor parameter.

public class TableMetricProducer implements MetricProducer
{
  private final QueueWriter queueWriter;

  /**
   * The normal constructor.
   *
   */
  public TableMetricProducer(. . .)
  {
    this(QueueWriterImpl.getInstance(), . . .);
  }

  /**
   * Constructor used for testing.
   *
   */
  TableMetricProducer(QueueWriter queueWriter, . . .)
  {
    queueWriter = queueWriter;
    . . . 
  }

  public void execute() 
  {
    int rowCount = . . .;
    Metric metric = new Metric("app/clients", System.currentTimeMillis(), rowCount);

   queueWriter.sendMetric(metric);
  }
}

This still isn't ideal, as we still have an explicit linkage between TableMetricProducer and QueueWriterImpl.

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For comparison, lets see what the Tapestry IoC implementation would look like:

public class MonitorModule
{
  public static void bind(ServiceBinder binder)
  {
    binder.bind(QueueWriter.class, QueueWriterImpl.class);
    binder.bind(MetricScheduler.class, MetricSchedulerImpl.class);
  }

  public void contributeMetricScheduler(Configuration<MetricProducer> configuration, QueueWriter queueWriter, . . .)
  {
    configuration.add(new TableMetricProducer(queueWriter, . . .))
  }
}

Again, we've omitted a few details related to the database the TableMetricProducer will point at (in fact, Tapestry IoC provides a lot of support for configuration of this type as well, which is yet another concern).

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What we are saying is that IoC techniques and discipline will lead to applications that are:

• More testable – smaller, simpler classes; coding to interfaces allows use of mock implementations
• More robust – smaller, simpler classes; use of final variables; thread safety baked in
• More scalable – thread safety baked in
• Easier to maintain – less code, simpler classes
• Easier to extend – new features are often additions (new services, new contributions) rather than changes to existing classes

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So we've come to accept that the death concern is better handled outside of our own code. The use of Inversion of Control is simply the flip side of that: the life cycle and construction concerns are also better handled by an outside authority as well: the IoC container. These concerns govern when a service is realized and how its dependencies and configuration are injected. As with the garbage collector, ceding these chores to the container results in less code and fewer bugs, and lets you concentrate on the things that should matter to you: your business logic, your application – and not a whole bunch of boilerplate plumbing!

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