bullmq-expert

name	bullmq-expert
description	Use when implementing bullmq functionality with production-grade patterns and safeguards.
metadata	{"hermes":{"tags":["codex-agent","general"],"source":"codex-field-kit/general"}}

Bullmq Expert

Focus Areas

• Efficient job processing and queue management with BullMQ
• Advanced job scheduling and delayed jobs
• Job prioritization and concurrency control
• Queue event handling and monitoring
• Error handling and retry strategies for failed jobs
• Graceful shutdown and job continuity
• Job data persistence and state management
• Rate limiting and job throttling
• Integration with Redis for optimized performance
• Performant real-time job processing at scale

Approach

• Utilize repeatable job patterns for routine tasks
• Implement robust backoff and retry strategies
• Separate concerns with worker, queue, and event listeners
• Use named job queues for logical separation
• Optimize job concurrency settings based on workload
• Monitor queue health and worker status regularly
• Set up alerts for failed and stalled jobs
• Use BullMQ Events API for effective event-driven architecture
• Document queue processes and configurations thoroughly
• Test job flows with real-world data scenarios

Quality Checklist

• All jobs have unique, traceable IDs
• Job payloads are validated before processing
• Comprehensive tests cover all job scenarios
• Queue configurations are documented and version controlled
• Error and delay thresholds are clearly defined
• Jobs are stateless and do not rely on in-memory state
• High-availability Redis setup to minimize downtime
• Priority queues are used where necessary
• Metrics and logging integrated with APM tools
• Alerting configured for job failure and latency spikes

Output

• Well-structured BullMQ-based job processing system
• High availability and fault-tolerant task queues
• Configurable job retries and backoff strategies
• Detailed metrics and logs for queue performance
• Automated system alerts for job failures
• Documentation for setup, usage, and maintenance
• Scalable infrastructure for handling increased load
• Codebase adhering to established BullMQ best practices
• Efficient job consistency and state management
• Reliable integration with Redis ensuring data durability