using-message-queues

# Message Queues

Implement asynchronous communication patterns for event-driven architectures, background job processing, and service decoupling.

## When to Use This Skill

Use message queues when:
- **Long-running operations** block HTTP requests (report generation, video processing)
- **Service decoupling** required (microservices, event-driven architecture)
- **Guaranteed delivery** needed (payment processing, order fulfillment)
- **Event streaming** for analytics (log aggregation, metrics pipelines)
- **Workflow orchestration** for complex processes (multi-step sagas, human-in-the-loop)
- **Background job processing** (email sending, image resizing)

## Broker Selection Decision Tree

Choose message broker based on primary need:

### Event Streaming / Log Aggregation
**→ Apache Kafka**
- Throughput: 500K-1M msg/s
- Replay events (event sourcing)
- Exactly-once semantics
- Long-term retention
- Use: Analytics pipelines, CQRS, event sourcing

### Simple Background Jobs
**→ Task Queues**
- **Python** → Celery + Redis
- **TypeScript** → BullMQ + Redis
- **Go** → Asynq + Redis
- Use: Email sending, report generation, webhooks

### Complex Workflows / Sagas
**→ Temporal**
- Durable execution (survives restarts)
- Saga pattern support
- Human-in-the-loop workflows
- Use: Order processing, AI agent orchestration

### Request-Reply / RPC Patterns
**→ NATS**
- Built-in request-reply
- Sub-millisecond latency
- Cloud-native, simple operations
- Use: Microservices RPC, IoT command/control

### Complex Message Routing
**→ RabbitMQ**
- Exchanges (direct, topic, fanout, headers)
- Dead letter exchanges
- Message TTL, priorities
- Use: Multi-consumer patterns, pub/sub

### Already Using Redis
**→ Redis Streams**
- No new infrastructure
- Simple consumer groups
- Moderate throughput (100K+ msg/s)
- Use: Notification queues, simple job queues

## Performance Comparison

| Broker | Throughput | Latency (p99) | Best For |
|--------|-----------|---------------|----------|
| **Kafka** | 500K-1M msg/s | 10-50ms | Event streaming |
| **NATS JetStream** | 200K-400K msg/s | Sub-ms to 5ms | Cloud-native microservices |
| **RabbitMQ** | 50K-100K msg/s | 5-20ms | Task queues, complex routing |
| **Redis Streams** | 100K+ msg/s | Sub-ms | Simple queues, caching |

## Quick Start Examples

### Kafka Producer/Consumer (Python)
See `examples/kafka-python/` for working code.

```python
from confluent_kafka import Producer, Consumer

# Producer
producer = Producer({'bootstrap.servers': 'localhost:9092'})
producer.produce('orders', key='order_123', value='{"status": "created"}')
producer.flush()

# Consumer
consumer = Consumer({
    'bootstrap.servers': 'localhost:9092',
    'group.id': 'order-processors',
    'auto.offset.reset': 'earliest'
})
consumer.subscribe(['orders'])

while True:
    msg = consumer.poll(1.0)
    if msg is not None:
        process_order(msg.value())
```

### Celery Background Jobs (Python)
See `examples/celery-image-processing/` for full implementation.

```python
from celery import Celery

app = Celery('tasks', broker='redis://localhost:6379')

@app.task(bind=True, max_retries=3)
def process_image(self, image_url: str):
    try:
        result = expensive_image_processing(image_url)
        return result
    except RecoverableError as e:
        raise self.retry(exc=e, countdown=60)
```

### BullMQ Job Processing (TypeScript)
See `examples/bullmq-webhook-processor/` for full implementation.

```typescript
import { Queue, Worker } from 'bullmq'

const queue = new Queue('webhooks', {
  connection: { host: 'localhost', port: 6379 }
})

// Enqueue job
await queue.add('send-webhook', {
  url: 'https://example.com/webhook',
  payload: { event: 'order.created' }
})

// Process jobs
const worker = new Worker('webhooks', async job => {
  await fetch(job.data.url, {
    method: 'POST',
    body: JSON.stringify(job.data.payload)
  })
}, { connection: { host: 'localhost', port: 6379 } })
```

### Temporal Workflow Orchestration
See `examples/temporal-order-saga/` for saga pattern implementation.

```python
from temporalio import workflow, activity
from datetime import timedelta

@workflow.defn
class OrderSagaWorkflow:
    @workflow.run
    async def run(self, order_id: str) -> str:
        # Step 1: Reserve inventory
        inventory_id = await workflow.execute_activity(
            reserve_inventory,
            order_id,
            start_to_close_timeout=timedelta(seconds=10),
        )

        # Step 2: Charge payment
        payment_id = await workflow.execute_activity(
            charge_payment,
            order_id,
            start_to_close_timeout=timedelta(seconds=30),
        )

        return f"Order {order_id} completed"
```

## Core Patterns

### Event Naming Convention
Use: `Domain.Entity.Action.Version`

Examples:
- `order.created.v1`
- `user.profile.updated.v2`
- `payment.failed.v1`

### Event Schema Structure
```json
{
  "event_type": "order.created.v2",
  "event_id": "uuid-here",
  "timestamp": "2025-12-02T10:00:00Z",
  "version": "2.0",
  "data": {
    "order_id": "ord_123",
    "customer_id": "cus_456"
  },
  "metadata": {
    "producer": "order-service",
    "trace_id": "abc123",
    "correlation_id": "xyz789"
  }
}
```

### Dead Letter Queue Pattern
Route failed messages to dead letter queue (DLQ) after max retries:

```python
@app.task(bind=True, max_retries=3)
def process_order(self, order_id: str):
    try:
        result = perform_processing(order_id)
        return result
    except UnrecoverableError as e:
        send_to_dlq(order_id, str(e))
        raise Reject(e, requeue=False)
```

### Idempotency for Exactly-Once Processing
```python
@app.post("/process")
async def process_payment(
    payment_data: dict,
    idempotency_key: str = Header(None)
):
    # Check if already processed
    cached_result = redis_client.get(f"idempotency:{idempotency_key}")
    if cached_result:
        return {"status": "already_processed"}

    result = process_payment_logic(payment_dat