About Tomasz Nurkiewicz

Java EE developer, Scala enthusiast. Enjoying data analysis and visualization. Strongly believes in the power of testing and automation.

Implementing custom Future

Last time we learned the principles behind java.util.concurrent.Future<T>. We also discovered that Future<T> is typically returned by libraries or frameworks. But there is nothing stopping us from implementing it all by ourselves when it makes sense. It is not particularly complex and may significantly improve your design. I did my best to pick interesting use case for our example.

JMS (Java Message Service) is a standard Java API for sending asynchronous messages. When we think about JMS, we immediately see a client sending a message to a server (broker) in a fire and forget manner. But it is equally common to implement request-reply messaging pattern on top of JMS. The implementation is fairly simple: you send a request message (of course asynchronously) to an MDB on the other side.
 
MDB processes the request and sends a reply back either to hardcoded reply queue or to an arbitrary queue chosen by the client and sent along with the message in JMSReplyTo property. The second scenario is much more interesting. Client can create a temporary queue and use it as a reply queue when sending a request. This way each request/reply pair uses different reply queue, this there is no need for correlation ID, selectors, etc.

There is one catch, however. Sending a message to JMS broker is simple and asynchronous. But receiving reply is much more cumbersome. You can either implement MessageListener to consume one, single message or use blocking MessageConsumer.receive(). First approach is quite heavyweight and hard to use in practice. Second one defeats the purpose of asynchronous messaging. You can also poll the reply queue with some interval, which sounds even worse.

Knowing the Future abstraction by now you should have some design idea. What if we can send a request message and get a Future<T> back, representing reply message that didn’t came yet? This Future abstraction should handle all the logic and we can safely use it as a handle to future outcome. Here is the plumbing code used to create temporary queue and send request:

private <T extends Serializable> Future<T> asynchRequest(ConnectionFactory connectionFactory, Serializable request, String queue) throws JMSException {
    Connection connection = connectionFactory.createConnection();
    connection.start();
    final Session session = connection.createSession(false, Session.AUTO_ACKNOWLEDGE);
    final Queue tempReplyQueue = session.createTemporaryQueue();
    final ObjectMessage requestMsg = session.createObjectMessage(request);
    requestMsg.setJMSReplyTo(tempReplyQueue);
    sendRequest(session.createQueue(queue), session, requestMsg);
    return new JmsReplyFuture<T>(connection, session, tempReplyQueue);
}

asynchRequest() method simply takes a ConnectionFactory to JMS broker and arbitrary piece of data. This object will be sent to queue using ObjectMessage. Last line is crucial – we return our custom JmsReplyFuture<T> that will represent not-yet-received reply. Notice how we pass temporary JMS queue to both JMSReplyTo property and our Future. Implementation of the MDB side is not that important. Needless to say it is suppose to send a reply back to designated queue:

final ObjectMessage reply = session.createObjectMessage(...);
session.createProducer(request.getJMSReplyTo()).send(reply);

So let’s dive into the implementation of JmsReplyFuture<T>. I made an assumption that both request and reply are ObjectMessages. It’s not very challenging to use a different type of message. First of all let us see how receiving messages from reply channel is set up:

public class JmsReplyFuture<T extends Serializable> implements Future<T>, MessageListener {
 
    //...
 
    public JmsReplyFuture(Connection connection, Session session, Queue replyQueue) throws JMSException {
        this.connection = connection;
        this.session = session;
        replyConsumer = session.createConsumer(replyQueue);
        replyConsumer.setMessageListener(this);
    }
 
    @Override
    public void onMessage(Message message) {
        //...
    }
 
}

As you can see JmsReplyFuture implements both Future<T> (where T is expected type of object wrapped inside ObjectMessage) and JMS MessageListener. In the constructor we simply start listening on replyQueue. From our design assumptions we know that there will be at most one message there because reply queue is temporary throw away queue. In the previous article we learned that Future.get() should block while waiting for a result. On the other hand onMessage() is a callback method called from some internal JMS client thread/library. Apparently we need some shared variable/lock to let waiting get() know that reply arrived. Preferably our solution should be lightweight and not introduce any latency so busy waiting on volatile variable is a bad idea. Initially I though about Semaphore that I would use to unblock get() from onMessage(). But I would still need some shared variable to hold the actual reply object. So I came up with an idea of using ArrayBlockingQueue. It might sound strange to use a queue when we know there will be no more that one item. But it works perfectly, utilizing good old producer-consumer pattern: Future.get() is a consumer blocking on an empty queue’s poll() method. On the other hand onMessage() is a producer, placing reply message in that queue and immediately unblocking consumer. Here is how it looks:

public class JmsReplyFuture<T extends Serializable> implements Future<T>, MessageListener {
 
    private final BlockingQueue<T> reply = new ArrayBlockingQueue<>(1);
 
    //...
 
    @Override
    public T get() throws InterruptedException, ExecutionException {
        return this.reply.take();
    }
 
    @Override
    public T get(long timeout, TimeUnit unit) throws InterruptedException, ExecutionException, TimeoutException {
        final T replyOrNull = reply.poll(timeout, unit);
        if (replyOrNull == null) {
            throw new TimeoutException();
        }
        return replyOrNull;
    }
 
    @Override
    public void onMessage(Message message) {
        final ObjectMessage objectMessage = (ObjectMessage) message;
        final Serializable object = objectMessage.getObject();
        reply.put((T) object);
        //...
    }
 
}

The implementation is still not complete, but it covers most important concepts. Notice how nicely BlockingQueue.poll(long, TimeUnit) method fits into Future.get(long, TimeUnit). Unfortunately, even though they come from the same package and were developed more or less in the same time, one method returns null upon timeout while the other should throw an exception. Easy to fix.

Also notice how easy the implementation of onMessage() became. We just place newly received message in a BlockingQueue reply and the collection does all the synchronization for us. We are still missing some less significant, but still important details – cancelling and clean up. Without going much into details, here is a full implementation:

public class JmsReplyFuture<T extends Serializable> implements Future<T>, MessageListener {
 
    private static enum State {WAITING, DONE, CANCELLED}
 
    private final Connection connection;
    private final Session session;
    private final MessageConsumer replyConsumer;
    private final BlockingQueue<T> reply = new ArrayBlockingQueue<>(1);
    private volatile State state = State.WAITING;
 
    public JmsReplyFuture(Connection connection, Session session, Queue replyQueue) throws JMSException {
        this.connection = connection;
        this.session = session;
        replyConsumer = session.createConsumer(replyQueue);
        replyConsumer.setMessageListener(this);
    }
 
    @Override
    public boolean cancel(boolean mayInterruptIfRunning) {
        try {
            state = State.CANCELLED;
            cleanUp();
            return true;
        } catch (JMSException e) {
            throw Throwables.propagate(e);
        }
    }
 
    @Override
    public boolean isCancelled() {
        return state == State.CANCELLED;
    }
 
    @Override
    public boolean isDone() {
        return state == State.DONE;
    }
 
    @Override
    public T get() throws InterruptedException, ExecutionException {
        return this.reply.take();
    }
 
    @Override
    public T get(long timeout, TimeUnit unit) throws InterruptedException, ExecutionException, TimeoutException {
        final T replyOrNull = reply.poll(timeout, unit);
        if (replyOrNull == null) {
            throw new TimeoutException();
        }
        return replyOrNull;
    }
 
    @Override
    public void onMessage(Message message) {
        try {
            final ObjectMessage objectMessage = (ObjectMessage) message;
            final Serializable object = objectMessage.getObject();
            reply.put((T) object);
            state = State.DONE;
            cleanUp();
        } catch (Exception e) {
            throw Throwables.propagate(e);
        }
    }
 
    private void cleanUp() throws JMSException {
        replyConsumer.close();
        session.close();
        connection.close();
    }
}

I use special State enum to hold the information about state. I find it much more readable compared to complex conditions based on multiple flags, null checks, etc. Second thing to keep in mind is cancelling. Fortunately it’s quite simple. We basically close the underlying session/connection. It has to remain open throughout the course of whole request/reply message exchange, otherwise temporary JMS reply queue disappears. Note that we cannot easily inform broker/MDB that we are no longer interested about the reply. We simply stop listening for it, but MDB will still process request and try to send a reply to no longer existing temporary queue.

So how does this all look in practice? Say we have an MDB that receives a number and returns a square of it. Imagine the computation takes a little bit of time so we start it in advance, do some work in the meantime and later retrieve the results. Here is how such a design might look like:

final Future<Double> replyFuture = asynchRequest(connectionFactory, 7, "square");
//do some more work
final double resp = replyFuture.get();      //49

Where "square" is the name of request queue. If we refactor it and use dependency injection we can further simplify it to something like:

final Future<Double> replyFuture = calculator.square(7);
//do some more work
final double resp = replyFuture.get();      //49

You know what’s best about this design? Even though we are exploiting quite advanced JMS capabilities, there is no JMS code here. Moreover we can later replace calculator with a different implementation, using SOAP or GPU. As far as the client code is concerned, we still use Future<Double> abstraction. Computation result that is not yet available. The underlying mechanism is irrelevant. That is the beauty of abstraction.

Obviously this implementation is not production ready (by far). But even worse, it misses some essential features. We still call blocking Future.get() at some point. Moreover there is no way of composing/chaining futures (e.g. when the response arrives, send another message) or waiting for the fastest future to complete. Be patient!
 

Reference: Implementing custom Future from our JCG partner Tomasz Nurkiewicz at the NoBlogDefFound blog.

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