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RabbitMQ Messaging & Async Systems · Aula

Filas de trabalho: distribuição justa

Implemente filas de trabalho para distribuir tarefas entre vários consumidores usando uma estratégia de distribuição circular. Aprenda a processar tarefas demoradas de forma assíncrona.

Filas de trabalho: distribuição justa é uma aula grátis de RabbitMQ Messaging & Async Systems no CoddyKit. Esta é a aula 2 de 4. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de RabbitMQ Messaging & Async Systems, e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de RabbitMQ Messaging & Async Systems inclui 4 aulas no total.

Partes desta aula ainda não foram traduzidas e aparecem em inglês.

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!

Perguntas Frequentes

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O que vou aprender em “Filas de trabalho: distribuição justa”?

Implemente filas de trabalho para distribuir tarefas entre vários consumidores usando uma estratégia de distribuição circular. Aprenda a processar tarefas demoradas de forma assíncrona. Você pratica RabbitMQ Messaging & Async Systems com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.

Preciso ter experiência prévia para começar RabbitMQ Messaging & Async Systems?

Nenhuma experiência prévia é necessária. RabbitMQ Messaging & Async Systems no CoddyKit é estruturado para alunos iniciantes até avançados, então você pode começar aqui ou desde o início e aprender no seu ritmo. Esta é a aula 2 de 4.

Quanto tempo leva a aula “Filas de trabalho: distribuição justa”?

A maioria das aulas CoddyKit leva cerca de 5–10 minutos. Cada uma é compacta e interativa, então você faz progresso constante e retoma exatamente de onde parou entre web e app.

Posso escrever e executar código nesta aula de RabbitMQ Messaging & Async Systems?

Sim. Cada aula de RabbitMQ Messaging & Async Systems inclui um editor de código integrado, então você escreve e executa código real direto no navegador e recebe feedback de IA instantaneamente — nenhuma configuração local necessária.

Todas as aulas deste curso

  1. Olá, mundo: fila simples
  2. Filas de trabalho: distribuição justa
  3. Confirmações e durabilidade de mensagens
  4. Publicação e assinatura com exchanges fanout
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