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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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By contrast, web applications are a managed environment. You don't write a main(), you don't control startup. You configure the Servlet API to tell it about your servlet classes to be instantiated, and their lifecycle life cycle is totally controlled by the servlet container.

Inversion of Control is just a more general application of this approach. The container is ultimately responsible for instantiating and configuring the objects you tell it about, and running their entire lifecycle life cycle of those objects.

Building web Web applications are more complicated to write than monolithic applications, largely because of multithreading. Your code will be servicing many different users simultaneously across many different threads. This tends to complicate the code you write, since some fundamental aspects of object oriented development get called into question: in particular, the use of internal state, (values stored inside instance variables), since in a multi-threaded multithreaded environment, that's no longer the safe place it is in traditional development. Shared objects plus internal state plus multiple threads equals an broken, unpredictable application.

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Using unit tests, in collaboration with tools such as EasyMock, you can have a code base that is easy to maintain, easy to extend, and easy to test. And by factoring out a lot of plumbing code, your code base will not only be easier to work with, it will be smaller.

Living on the Frontier

Coding applications the traditional way is like being a homesteader on the American frontier in the 1800's. You're responsible for every aspect of your house: every board, every nail, every stick of furniture is something you personally created. There is a great comfort in total self reliance. Even if your house is small, the windows are a bit drafty or the floorboards creak a little, you know exactly why things are not-quite perfect.

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To extend the metaphor, a house in a town is not alone and self-reliant the way a frontier house is. The town house is situated on a street, in a neighborhood, within a town. The town provides services (utilities, police, fire control, streets and sewers) to houses in a uniform way. Each house just needs to connect up to those services.

The World of the Container

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".

Image Added

Here !images/ioc-overview.png!IoC OverviewHere 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.

Tapestry IoC's job is to make all of these services available to each other, and to the outside world. The outside world could be a standalone application, or it could be an application built on top of the Tapestry web framework.

Service

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Life Cycle

Tapestry services are lazy, which means they are not fully instantiated until they are absolutely needed. Often, what looks like a service is really a proxy object ... the first time any method of the proxy is invoked, the actual service is instantiated and initialized (Tapestry uses the term realized for this process). Of course, this is all absolutely thread-safe.

Initially a service is defined, meaning some module has defined the service. Later, the service will be virtual, meaning a proxy has been created. This occurs most often because some other service depends on it, but hasn't gotten around to invoking methods on it. Finally, a service that is ready to use is realized. What's nice is that your code neither knows nor cares about the lifecycle life cycle of the service, because of the magic of the proxy.

In fact, when a Tapestry web application starts up, before it services its first request, only about 20% of the services have been realized; the remainder are defined or virtual.

Class vs. Service

A Tapestry service is more than just a class. First of all, it is a combination of an interface that defines the operations of the service, and an implementation class that implements the interface.

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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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In any case, injection "just happens". Tapestry finds the constructor of your class and analyzes the parameters to determine what to pass in. In some cases, it uses just the parameter type to find a match, in other cases, annotations on the parameters may also be used. It also scans through the fields of your service implementation class to identify which should have injected values written into them.

Why can't I just use new?

I've had this question asked me many a time. All these new concepts seem alien. All that XML (in the Spring or HiveMind IoC containers; Tapestry IoC uses no XML) is a burdenThat's a common question. All these concepts seem alien at 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.

This example is built around some work I've done recently involving real-world work that involves a Java Messaging Service queue, part of an application performance monitoring subsystem for a large application. Code inside each server collects performance data of various types and sends it, via a shared JMS queue, to a central server for collection and reporting.

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);
  }
}

IWe've elided 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.

What we're seeing here is that there are multple concerns inside the little bit of code in this example. TableMetricProducer has an unwanted construction concern about which implementation of QueueWriter to instantiate (this shows up as two constructors, rather than just one). QueueWriterImpl has an additional lifecycle life cycle concern, in terms of managing the singleton.

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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, Iwe've elided out 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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The contributeMetricScheduler() method allows the module to contribute into the MetricProducer service's configuration. More testability: the MetricProducer isn't tied to a pre-set list of producers, instead it will have a Collection<MetricProducer> injected into its constructor. Thus, when we're coding the MetricProducerImpl class, we can test it against mock implementations of MetricProducer.

The QueueWriter service in is injected into the contributeMetricScheduler() method. Since there's only one QueueWriter service, Tapestry IoC is able to "find" the correct service based entirely on type. If, eventually, there's more than one QueueWriter service (perhaps pointing at different JMS queues), you would use an annotation on the parameter to help Tapestry connect the parameter to the appropriate service.

Presumably, there 'd would be a couple of other parameters to the contributeMetricScheduler() method, to inject in a database URL or connection pool (that would, in turn, be passed to TableMetricProducer).

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Meanwhile, the QueueWriterImpl class no longer needs the instance variable or getInstance() method, and the TableMetricProducer only needs a single constructor.

Advantages of IoC: Summary

It would be ludicrous for us to claim that applications built without an IoC container are doomed to failure. There is overwhelming evidence that applications have been built without containers and have been perfectly successful.

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

What we're saying is that an IoC container allows you to work faster and smarter.

Many of these traits work together; for example, a more testable application is inherently more robust. Having a test suite makes it easier to maintain and extend your code, because its much easier to see if new features break existing ones. Simpler code plus tests also lowers the cost of entry for new developers coming on board, which allows for more developers to work efficiently on the same code base. The clean separation between interface and implementation also allows multiple developers to work on different aspects of the same code base with a lowered risk of interference and conflict.

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Over a decade ago, when Java first came on the scene, it was the first mainstream language to support garbage collection. This was very controversial: the garbage collector was seen as unnecessary, and a waste of resources. Among C and C++ developers, the attitude was "Why do I need a garbage collector? If I call malloc() I can call free()."

I don't know about you, but I don't think I could ever But now, most developers would never want to go back to a non-garbage collected environment. Having the GC around makes it much easier to code in a way I we find natural: many small related objects working together. It turns out that knowing when to call free() is more difficult than it sounds. The Objective-C language tried to solve this with retain counts on objects and that still lead to memory leaks when it was applied to object graphs rather than object trees.

Roll the clock forward a decade and the common consensus has shifted considerably. Objective-C 2.0 features true garbage collection and GC libraries are available for C and C++. All scripting languages, including Ruby and Python, feature garbage collection as well. A new language without garbage collection is now considered an anomaly.

The point is, the lifecycle life cycle of objects turns out to be far more complicated than it looks at first glance. We've come to accept that our own applications lack the ability to police their objects as they are no longer needed (they literally lack the ability to determine when an object is no longer needed) and the garbage collector, a kind of higher authority, takes over that job very effetivelyeffectively. The end result? Less code and fewer bugs. And a careful study shows that the Java memory allocator and garbage collector (the two are quite intimately tied together) is actually more efficient that than malloc() and free().

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 lifecycle 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!