Event-Driven Architecture: Kafka vs RabbitMQ

Visualize how async messaging decouples services, handles backpressure, and ensures fault tolerance.

RabbitMQ (Queue Model)

"Smart Broker, Dumb Consumer"

Point-to-Point: Each message is processed by exactly ONE consumer.
Push-based: Broker pushes messages to workers.
Transient: Messages are removed after acknowledgement. Good for task queues (e.g., sending emails).

Kafka (Stream Model)

"Dumb Broker, Smart Consumer"

Pub/Sub: Messages are appended to a Log. Consumers read from their own "Offset".
Pull-based: Consumers ask for data when ready.
Durable: Messages stay for days/weeks. Good for Event Sourcing and Analytics.

Why Event-Driven?

Decoupling

Producers don't need to know if Consumers are online. They just fire and forget.

Backpressure

If Consumers get overwhelmed, the Queue acts as a buffer, preventing system crashes.

Fault Tolerance

If a worker fails (NACK), the message can be retried or sent to a Dead Letter Queue (DLQ).

Implementing Dead Letter Queues: A Deep Dive

What is a Dead Letter Queue?

A Dead Letter Queue (DLQ) is a special queue that stores messages that couldn't be processed successfully. Instead of losing failed messages or blocking the main queue, they're moved to the DLQ for later investigation.

When Messages Go to DLQ

Message format is invalid (parsing fails)
Maximum retry count exceeded
Message TTL (Time-To-Live) expired
Consumer explicitly rejects (NACK) the message

RabbitMQ DLX Configuration

RabbitMQ uses Dead Letter Exchanges (DLX) to route failed messages. Configure at queue creation time using `x-dead-letter-exchange` argument.

// RabbitMQ Queue with DLX
channel.assertQueue('orders', {
  arguments: {
    'x-dead-letter-exchange': 'dlx.exchange',
    'x-dead-letter-routing-key': 'orders.failed',
    'x-message-ttl': 86400000 // 24 hours
  }
});

Kafka Dead Letter Topic Pattern

Kafka doesn't have built-in DLQ, but the pattern is implemented in consumer code. Failed messages are published to a separate `.dlq` topic.

✓ Best Practices

Set alerts on DLQ message count
Include original message metadata
Add failure reason/stack trace
Implement replay mechanism

✗ Common Mistakes

Ignoring DLQ messages
No monitoring or alerting
Infinite retry loops
Losing original message context

Event-Driven Architecture

Message Broker Simulator

Queue (FIFO)

Metrics

Quick Guide: Message Brokers

How It Works

Key Benefits

Real-World Uses