$ cat services.json

RabbitMQ Implementation

Set up and configure RabbitMQ for your application.

Deliverables:

Exchange/queue design
Routing configuration
Dead letter handling
High availability setup
Monitoring integration

Message Patterns

Implement common messaging patterns with RabbitMQ.

Deliverables:

Work queues
Pub/sub fanout
Topic routing
RPC patterns
Priority queues

Celery + RabbitMQ

Set up Celery with RabbitMQ as the broker.

Deliverables:

Broker configuration
Task routing
Result backend
Monitoring
Scaling strategy

$ man rabbitmq

RabbitMQ vs Kafka

RabbitMQ - Traditional message queue

Best for: Request/reply, routing, lower throughput
Pros: Flexible routing, mature, easy to use
Cons: Lower throughput than Kafka

Kafka - Event streaming platform

Best for: High throughput, event sourcing, logs
Pros: Massive scale, replay capability
Cons: More complex, overkill for simple queues

RabbitMQ Patterns

Direct Exchange - Route by key Fanout Exchange - Broadcast to all Topic Exchange - Pattern matching Headers Exchange - Route by headers

Reliability patterns:

Acknowledgements
Publisher confirms
Dead letter queues
Message TTL

$ cat README.md

RabbitMQ Patterns

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import pika
from pika.exchange_type import ExchangeType

# Publisher
connection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))
channel = connection.channel()

# Declare exchange and queue
channel.exchange_declare(
    exchange='events',
    exchange_type=ExchangeType.topic,
    durable=True
)

channel.queue_declare(queue='user_events', durable=True)
channel.queue_bind(
    exchange='events',
    queue='user_events',
    routing_key='user.*'
)

# Publish message with confirmation
channel.confirm_delivery()
channel.basic_publish(
    exchange='events',
    routing_key='user.created',
    body='{"user_id": "123"}',
    properties=pika.BasicProperties(
        delivery_mode=2,  # Persistent
        content_type='application/json'
    )
)

# Consumer with acknowledgements
def callback(ch, method, properties, body):
    try:
        process_message(body)
        ch.basic_ack(delivery_tag=method.delivery_tag)
    except Exception as e:
        ch.basic_nack(delivery_tag=method.delivery_tag, requeue=False)

channel.basic_consume(
    queue='user_events',
    on_message_callback=callback,
    auto_ack=False
)

RabbitMQ Architecture

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┌─────────────┐     ┌─────────────────────────────────────┐
│  Producer   │────▶│           RabbitMQ Broker           │
└─────────────┘     │                                      │
                    │  ┌─────────┐      ┌───────────────┐ │
                    │  │Exchange │─────▶│    Queue 1    │ │
                    │  │ (topic) │      └───────────────┘ │
                    │  │         │      ┌───────────────┐ │
                    │  │         │─────▶│    Queue 2    │ │
                    │  └─────────┘      └───────────────┘ │
                    │                   ┌───────────────┐ │
                    │                   │  Dead Letter  │ │
                    │                   └───────────────┘ │
                    └──────────────────────┬──────────────┘
                                           │
                    ┌──────────────────────┼──────────────┐
                    ▼                      ▼              ▼
             ┌──────────┐          ┌──────────┐   ┌──────────┐
             │ Consumer │          │ Consumer │   │ Consumer │
             │    1     │          │    2     │   │    3     │
             └──────────┘          └──────────┘   └──────────┘

$ ls -la projects/

Real-Time Event Processing

@ The Virtulab

Challenge:

Process real-time events from video sessions.

Solution:

RabbitMQ with topic exchanges for event routing, Celery workers for processing.

Result:

Reliable real-time event handling with proper error recovery.

IoT Data Pipeline

@ Spiio

Challenge:

Queue sensor data for async processing.

Solution:

RabbitMQ with work queues, Celery for processing, dead letter handling.

Result:

Scalable async processing of sensor data.

Email Processing Queue

@ Flowrite

Challenge:

Queue email generation requests for async processing.

Solution:

RabbitMQ as Celery broker, priority queues for premium users.

Result:

Smooth user experience, scalable processing.

🐰 RabbitMQ

$ cat services.json

RabbitMQ Implementation

Message Patterns

Celery + RabbitMQ

$ man rabbitmq

RabbitMQ vs Kafka

RabbitMQ Patterns

$ cat README.md

RabbitMQ Patterns

RabbitMQ Architecture

$ ls -la projects/

Real-Time Event Processing

IoT Data Pipeline

Email Processing Queue

$ diff me competitors/

Build Your Message Queue