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

Quick Start Examples

Kafka Producer/Consumer (Python)

See examples/kafka-python/ for working code.

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.

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.

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.

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

{
  "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:

@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

@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_data)
    redis_client.setex(f"idempotency:{idempotency_key}", 86400, result)
    return {"status": "processed", "result": result}

Frontend Integration

Job Status Updates via SSE

# FastAPI endpoint for real-time job status
@app.get("/status/{task_id}")
async def task_status_stream(task_id: str):
    async def event_generator():
        while True:
            task = celery_app.AsyncResult(task_id)

            if task.state == 'PROGRESS':
                yield {"event": "progress", "data": task.info.get('progress', 0)}
            elif task.state == 'SUCCESS':
                yield {"event": "complete", "data": task.result}
                break

            await asyncio.sleep(0.5)

    return EventSourceResponse(event_generator())

React Component

export function JobStatus({ jobId }: { jobId: string }) {
  const [progress, setProgress] = useState(0)

  useEffect(() => {
    const eventSource = new EventSource(`/api/status/${jobId}`)

    eventSource.addEventListener('progress', (e) => {
      setProgress(JSON.parse(e.data))
    })

    eventSource.addEventListener('complete', (e) => {
      toast({ title: 'Job complete', description: JSON.parse(e.data) })
      eventSource.close()
    })

    return () => eventSource.close()
  }, [jobId])

  return <ProgressBar value={progress} />
}

Detailed Guides

For comprehensive documentation, see reference files:

Broker-Specific Guides

• Kafka: See references/kafka.md for partitioning, consumer groups, exactly-once semantics
• RabbitMQ: See references/rabbitmq.md for exchanges, bindings, routing patterns
• NATS: See references/nats.md for JetStream, request-reply patterns
• Redis Streams: See references/redis-streams.md for consumer groups, acknowledgments

Task Queue Guides

• Celery: See references/celery.md for periodic tasks, canvas (workflows), monitoring
• BullMQ: See references/bullmq.md for job prioritization, flows, Bull Board monitoring
• Temporal: See references/temporal-workflows.md for saga patterns, signals, queries

Pattern Guides

• Event Patterns: See references/event-patterns.md for event sourcing, CQRS, outbox pattern

Common Anti-Patterns to Avoid

1. Synchronous API for Long Operations

# ❌ BAD: Blocks request thread
@app.post("/generate-report")
def generate_report(user_id: str):
    report = expensive_computation(user_id)  # 5 minutes!
    return report

# ✅ GOOD: Enqueue background job
@app.post("/generate-report")
async def generate_report(user_id: str):
    task = generate_report_task.delay(user_id)
    return {"task_id": task.id}

2. Non-Idempotent Consumers

# ❌ BAD: Processes duplicates
@app.task
def send_email(email: str):
    send_email_service(email)  # Sends twice if retried!

# ✅ GOOD: Idempotent with deduplication
@app.task
def send_email(email: str, idempotency_key: str):
    if redis.exists(f"sent:{idempotency_key}"):
        return "already_sent"
    send_email_service(email)
    redis.setex(f"sent:{idempotency_key}", 86400, "1")

3. Ignoring Dead Letter Queues

# ❌ BAD: Failed messages lost forever
@app.task(max_retries=3)
def risky_task(data):
    process(data)  # If all retries fail, data disappears

# ✅ GOOD: DLQ for manual inspection
@app.task(max_retries=3)
def risky_task(data):
    try:
        process(data)
    except Exception as e:
        if self.request.retries >= 3:
            send_to_dlq(data, str(e))
        raise

4. Using Kafka for Request-Reply

# ❌ BAD: Kafka is not designed for RPC
def get_user_profile(user_id: str):
    kafka_producer.send("user_requests", {"user_id": user_id})
    # How to correlate response? Kafka is asynchronous!

# ✅ GOOD: Use NATS request-reply or HTTP/gRPC
response = await nats.request("user.profile", user_id.encode())

Library Recommendations

Context7 Research

Confluent Kafka (Python)

• Context7 ID: /confluentinc/confluent-kafka-python
• Trust Score: 68.8/100
• Code Snippets: 192+
• Production-ready Python Kafka client

Temporal

• Context7 ID: /websites/temporal_io
• Trust Score: 80.9/100
• Code Snippets: 3,769+
• Workflow orchestration for durable execution

Installation

Python:

pip install confluent-kafka celery[redis] temporalio aio-pika redis

TypeScript/Node.js:

npm install kafkajs bullmq @temporalio/client amqplib ioredis

Rust:

cargo add rdkafka lapin async-nats redis

Go:

go get github.com/confluentinc/confluent-kafka-go
go get github.com/hibiken/asynq
go get go.temporal.io/sdk

Utilities

Use scripts for setup automation:

• Kafka setup: Run python scripts/kafka_producer_consumer.py for test utilities
• Schema validation: Run python scripts/validate_message_schema.py to validate event schemas

Related Skills

• api-patterns: API design for async job submission
• realtime-sync: WebSocket/SSE for job status updates
• feedback: Toast notifications for job completion
• databases-*: Persistent storage for event logs Message Queues v1.1 - Enhanced

🔄 Workflow

Kaynak: Enterprise Integration Patterns & Confluent Kafka Guide

Aşama 1: Design Phase

• Pattern Selection: Point-to-Point (Queue) mi Pub-Sub (Topic) mi karar ver.
• Schema Registry: Mesaj formatını (Avro/Protobuf) ve versiyonlamayı baştan yap.
• Partitioning: Veri dağılımını (Ordering garantisi için Key seçimi) planla.

Aşama 2: Implementation Checklist

• Idempotency: Consumer tarafında "Exactly-Once" veya "At-Least-Once" stratejisini kur.
• DLQ: İşlenemeyen mesajlar için Dead Letter Queue ve Alarm kur.
• Backpressure: Consumer yavaşlarsa Producer'ı yavaşlatacak mekanizmayı düşün.

Aşama 3: Operations

• Lag Monitoring: Consumer Lag (üretim hızı vs tüketim hızı) metriğini izle.
• Retention: Disk doluluğunu önlemek için retention policy (süre veya boyut) ayarla.

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