# Using Message Queues

Async communication patterns using message brokers and task queues.

- Repository: https://github.com/ancoleman/ai-design-components (526 stars)
- Category: Engineering
- License: MIT
- Last updated: 2025-12-11
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add ancoleman/ai-design-components --skill using-message-queues -g -a claude-code -y`
Codex: `npx skills add ancoleman/ai-design-components --skill using-message-queues -g -a codex -y`
Cursor: `npx skills add ancoleman/ai-design-components --skill using-message-queues -g -a cursor -y`
Gemini CLI: `npx skills add ancoleman/ai-design-components --skill using-message-queues -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add ancoleman/ai-design-components`

## Description

Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing patterns.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

The rest: https://github.com/ancoleman/ai-design-components/blob/HEAD/skills/using-message-queues/SKILL.md