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RabbitMQ Messaging & Async Systems · Pelajaran

Antrean Kerja: Distribusi Adil

Terapkan antrean kerja untuk mendistribusikan tugas di antara beberapa konsumen menggunakan strategi distribusi round-robin. Pelajari cara memproses tugas yang memerlukan waktu secara asinkron.

Antrean Kerja: Distribusi Adil adalah pelajaran RabbitMQ Messaging & Async Systems gratis di CoddyKit. Ini adalah pelajaran 2 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar RabbitMQ Messaging & Async Systems, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus RabbitMQ Messaging & Async Systems mencakup 4 pelajaran total.

Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.

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!

Pertanyaan yang Sering Diajukan

Apakah pelajaran “Antrean Kerja: Distribusi Adil” gratis?

Ya — teks lengkap “Antrean Kerja: Distribusi Adil” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus RabbitMQ Messaging & Async Systems, upgrade ke CoddyKit PRO. Kursus RabbitMQ Messaging & Async Systems mencakup 4 pelajaran total.

Apa yang akan aku pelajari di “Antrean Kerja: Distribusi Adil”?

Terapkan antrean kerja untuk mendistribusikan tugas di antara beberapa konsumen menggunakan strategi distribusi round-robin. Pelajari cara memproses tugas yang memerlukan waktu secara asinkron. Kamu berlatih RabbitMQ Messaging & Async Systems dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.

Apakah aku perlu pengalaman untuk memulai RabbitMQ Messaging & Async Systems?

Tidak diperlukan pengalaman sebelumnya. RabbitMQ Messaging & Async Systems di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 2 dari 4.

Berapa lama pelajaran “Antrean Kerja: Distribusi Adil” memakan waktu?

Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.

Bisakah aku menulis dan menjalankan kode dalam pelajaran RabbitMQ Messaging & Async Systems ini?

Ya. Setiap pelajaran RabbitMQ Messaging & Async Systems menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.

Semua pelajaran dalam kursus ini

  1. Halo Dunia: Antrean Sederhana
  2. Antrean Kerja: Distribusi Adil
  3. Konfirmasi Pesan dan Persistensi
  4. Publish/Subscribe dengan Exchange Fanout
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