RabbitMQ Messaging & Async Systems · Lesson

Work Queues: Fair Dispatch

Implement work queues to distribute tasks among multiple consumers using a round-robin dispatch strategy. Learn how to process time-consuming tasks asynchronously.

Work Queues: Fair Dispatch is a free RabbitMQ Messaging & Async Systems lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the RabbitMQ Messaging & Async Systems learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Meet Work Queues

Welcome to Work Queues! In distributed systems, you often have tasks that take time to complete, like processing an image or generating a report.

Work Queues are a pattern that helps distribute these time-consuming tasks among multiple workers (consumers) efficiently, preventing any single worker from getting overloaded.

Why Use Work Queues?

Imagine you have many jobs to do, but only one employee. If all jobs go to that single employee, they'll get overwhelmed and tasks will pile up.

Load Balancing: Work queues allow you to add more workers to share the load.
Asynchronous Processing: The producer doesn't wait for a task to finish, it just adds it to the queue.
Reliability: If one worker fails, others can pick up tasks.

How Work Queues Operate

The setup for a work queue is simple:

One producer sends messages (tasks) to a single queue.
Multiple consumers listen to this same queue.
RabbitMQ ensures that each message is delivered to only one of the waiting consumers.

This way, tasks are never duplicated and are processed in parallel.

Fair Dispatch: Round-Robin

By default, RabbitMQ distributes messages to consumers using a round-robin strategy. This means messages are sent to consumers in a rotating fashion:

Consumer 1 gets the first message.
Consumer 2 gets the second message.
Consumer 1 gets the third message, and so on.

This aims for an even distribution of tasks among all active consumers.

The Task Producer

Let's create a producer that sends 10 tasks to our queue. Each task will be a simple string like 'Processing image 1'.

Run this code once to populate the queue with tasks.

import com.rabbitmq.client.Channel;
import com.rabbitmq.client.Connection;
import com.rabbitmq.client.ConnectionFactory;

public class TaskProducer {
    private final static String QUEUE_NAME = "task_queue";

    public static void main(String[] argv) throws Exception {
        ConnectionFactory factory = new ConnectionFactory();
        factory.setHost("localhost"); // Assuming RabbitMQ is local

        try (Connection connection = factory.newConnection();
             Channel channel = connection.createChannel()) {

            channel.queueDeclare(QUEUE_NAME, false, false, false, null);

            for (int i = 0; i < 10; i++) {
                String message = "Processing image " + (i + 1);
                channel.basicPublish("", QUEUE_NAME, null, message.getBytes("UTF-8"));
                System.out.println(" [x] Sent '" + message + "'");
            }
            System.out.println(" [x] All tasks sent.");
        }
    }
}

Producer Code Breakdown

What's happening in our producer code?

We establish a connection and a channel to interact with RabbitMQ.
channel.queueDeclare(QUEUE_NAME, false, false, false, null); ensures the queue exists. The false flags keep it non-durable and non-exclusive for simplicity here.
A loop sends 10 messages to the task_queue. Each message represents a distinct task.

Our First Task Consumer

Now, let's create a consumer, which we'll call a 'worker'. This worker will listen for tasks from the task_queue.

We'll simulate a long-running task using Thread.sleep(). Run this code in one terminal window.

import com.rabbitmq.client.Channel;
import com.rabbitmq.client.Connection;
import com.rabbitmq.client.ConnectionFactory;
import com.rabbitmq.client.DeliverCallback;

public class TaskConsumer {
    private final static String QUEUE_NAME = "task_queue";

    public static void main(String[] argv) throws Exception {
        ConnectionFactory factory = new ConnectionFactory();
        factory.setHost("localhost"); // Assuming RabbitMQ is local

        Connection connection = factory.newConnection();
        Channel channel = connection.createChannel();

        channel.queueDeclare(QUEUE_NAME, false, false, false, null);
        System.out.println(" [*] Waiting for messages. To exit press CTRL+C");

        DeliverCallback deliverCallback = (consumerTag, delivery) -> {
            String message = new String(delivery.getBody(), "UTF-8");
            System.out.println(" [x] Received '" + message + "'");
            try {
                // Simulate long-running task
                Thread.sleep(1000); // 1 second per task
            } catch (InterruptedException e) {
                Thread.currentThread().interrupt();
            } finally {
                System.out.println(" [x] Done with '" + message + "'");
            }
        };
        channel.basicConsume(QUEUE_NAME, true, deliverCallback, consumerTag -> {});
    }
}

Consumer Code Breakdown

Let's look at the consumer's logic:

Similar to the producer, it connects and declares the queue.
A DeliverCallback defines the actions when a message arrives.
Inside the callback, Thread.sleep(1000) simulates a 1-second task.
channel.basicConsume(QUEUE_NAME, true, deliverCallback, ...) starts consuming. The true means messages are automatically acknowledged after delivery.

Scale with Multiple Workers

Here's the core demonstration of work queues:

1. Run the TaskConsumer code in two separate terminal windows (or instances).

2. Then, run the TaskProducer code once.

You will observe that the 10 tasks are divided between your two worker instances, each processing roughly 5 tasks due to RabbitMQ's round-robin dispatch.

Work Queue Quiz

Imagine you have a single RabbitMQ queue and two consumers (Worker A and Worker B) listening to it. A producer sends 4 messages (M1, M2, M3, M4) to this queue.

Which statement accurately describes how the messages are typically distributed using RabbitMQ's default fair dispatch?

Work Queues: Key Takeaways

You've successfully implemented Work Queues, a fundamental pattern for distributing tasks across multiple consumers!

Work queues enable asynchronous processing and prevent single points of failure.
RabbitMQ's default round-robin dispatch strategy ensures tasks are distributed fairly.
By running multiple consumer instances, you can easily scale your task processing capacity.

Next, we'll dive into making your message handling even more robust with acknowledgements and message durability!

Frequently asked questions

Is the “Work Queues: Fair Dispatch” lesson free?

Yes — the full text of “Work Queues: Fair Dispatch” is free to read here on the web, and the RabbitMQ Messaging & Async Systems course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the RabbitMQ Messaging & Async Systems course, upgrade to CoddyKit PRO.

What will I learn in “Work Queues: Fair Dispatch”?

Implement work queues to distribute tasks among multiple consumers using a round-robin dispatch strategy. Learn how to process time-consuming tasks asynchronously. You practise RabbitMQ Messaging & Async Systems with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start RabbitMQ Messaging & Async Systems?

No prior experience is required. RabbitMQ Messaging & Async Systems on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Work Queues: Fair Dispatch” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this RabbitMQ Messaging & Async Systems lesson?

Yes. Every RabbitMQ Messaging & Async Systems lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

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