Queue Monitoring

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

Comprehensive guide to monitoring message queues including RabbitMQ, Redis, and Kafka for production systems. This skill covers key metrics collection, health checks, alerting strategies, Grafana dashboards, dead letter queue monitoring, performance troubleshooting, and capacity planning.

Why This Matters

Effective queue monitoring is essential for maintaining reliable, performant message queue systems. Without proper monitoring, queue issues can go undetected until they cause system-wide failures. This skill provides comprehensive patterns for monitoring RabbitMQ, Redis, and Kafka queues in production environments.

Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

Skill Composition

• Depends on: None
• Compatible with: None
• Conflicts with: None
• Related Skills: None

Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions

• Queue systems are accessible via management APIs
• Prometheus and Grafana are available
• Network connectivity to queue systems
• Appropriate permissions for monitoring
• Queue systems are properly configured

Compatibility

• RabbitMQ: 3.8+ with management plugin
• Redis: 6+ with Bull/BullMQ
• Kafka: 2.8+ with JMX metrics
• Prometheus: 2.30+
• Grafana: 8.0+
• Node.js: 14+
• TypeScript: 4.0+

Test Scenario Matrix (QA Strategy)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms
• Memory Management: Use streams/pagination for large data
• Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection
• Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id
• Metrics: Track error_rate, latency, queue_depth
• Error Handling: Standardized error codes, no bare except
• Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• Tests passed + coverage met
• Lint/Typecheck passed
• Logging/Metrics/Trace implemented
• Security checks passed
• Documentation/Changelog updated
• Accessibility/Performance requirements met (if frontend)

Anti-patterns

1. Monitoring everything: Only monitor what matters
2. Too many alerts: Alert on actionable issues only
3. No alert cooldowns: Implement cooldowns to prevent spam
4. Ignoring context: Include context in alerts
5. No dashboards: Create dashboards for visibility
6. No documentation: Document all monitoring setup
7. Not testing alerts: Test alert delivery regularly
8. Ignoring trends: Monitor trends, not just snapshots

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:
  • 2026-02-22: Initial version with complete template structure