Queue health monitoring

[ralyodio]

📋 Product Requirements Document

PRD: Queue health monitoring

Issue: #203
Milestone: Phase 9: Deployment & Operations
Labels: monitoring, hacktoberfest

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PRD: Queue Health Monitoring

Overview

The Queue Health Monitoring feature is an essential addition to MeshHook, a webhook-first, deterministic, Postgres-native workflow engine. This feature is designed to enhance the visibility and control over the queue states, ensuring the high availability and performance of MeshHook's processing capabilities. By monitoring key metrics in real-time, identifying issues proactively, and resolving them promptly, we aim to maintain and improve the system's reliability and efficiency.

Objectives

1. Implement a comprehensive queue health monitoring system.
2. Provide real-time visibility into the queue's performance and health.
3. Enable the proactive detection and resolution of potential issues to minimize downtime and maintain system performance.

Functional Requirements

1. Queue Metrics Collection: The system must automatically collect and store key metrics, including but not limited to:
   • Queue length
   • Processing time per job
   • Error rates and types
   • Success rates
2. Real-Time Monitoring Dashboard: Develop a dashboard using SvelteKit that displays real-time metrics, allowing quick identification and response to issues.
3. Alerting Mechanism: An alerting system must be in place to notify administrators via email or SMS about abnormal queue conditions based on configurable thresholds.
4. Documentation: Comprehensive documentation covering the setup, configuration, and operational guidelines of the queue monitoring system must be provided.

Non-Functional Requirements

1. Performance: The monitoring system should have a sub-second impact on queue operations, ensuring minimal latency.
2. Reliability: The system should aim for 99.9% uptime, guaranteeing continuous operation.
3. Security: Must adhere to MeshHook’s security guidelines, ensuring the monitoring data is securely stored and accessed.
4. Maintainability: The code should be clean, modular, well-documented, and easy to update or enhance.

Technical Specifications

Architecture Context

MeshHook utilizes pg-boss or pgmq for queue management within a Supabase-hosted Postgres environment. The monitoring system should integrate with these components and leverage Supabase Realtime for live data feeds, ensuring minimal performance impact on queue operations.

Implementation Approach

1. Analysis: Review the existing queue management setup to identify and define critical metrics for monitoring.
2. Integration Design: Design the integration with Supabase Realtime to fetch queue metrics efficiently, ensuring the system’s performance is not compromised.
3. Dashboard Development: Develop the monitoring dashboard using SvelteKit, incorporating it into MeshHook’s existing web interface for seamless user experience.
4. Alerting System: Implement alerting mechanisms using Supabase functions or external services like AWS Lambda for real-time notifications based on predefined thresholds.
5. Testing: Perform comprehensive testing, including performance impact and load testing, to validate the monitoring system’s effectiveness and reliability.
6. Deployment: Deploy the monitoring system incrementally, validating its functionality in a controlled test environment before production rollout.

Data Model

No significant changes to the existing data model are anticipated. Metrics will be collected from current queue tables and logs without necessitating schema modifications.

API Endpoints

No additional API endpoints are required. The system will utilize existing Supabase Realtime subscriptions and internal MeshHook APIs for metrics collection and alert functionalities.

Acceptance Criteria

• The dashboard displays real-time queue metrics with a latency of no more than 10 seconds.
• An alerting mechanism is operational, capable of notifying administrators based on predefined criteria.
• The monitoring system has a negligible impact on queue performance, evidenced by a less than 1% increase in processing time.
• Documentation is provided, covering all aspects of the monitoring system in detail.
• All security measures are in place, with strict access controls and data protection as per MeshHook standards.

Dependencies

• Access to the existing queue management setup using pg-boss or pgmq.
• Availability of Supabase project services, including Realtime and Postgres.

Implementation Notes

Development Guidelines

• Adhere to MeshHook's established coding standards for SvelteKit and Supabase integration.
• Prioritize security in all aspects of the monitoring system, especially in dashboard access and data handling.
• Ensure the code is modular, well-documented, and maintainable.

Testing Strategy

• Develop unit tests for new components and integration tests to ensure system-wide functionality.
• Conduct load testing to assess the monitoring system's impact on queue performance.

Security Considerations

• Implement Row-Level Security (RLS) for dashboard access, allowing users to see data only for their respective projects.
• Ensure the alerting mechanism is secure, preventing unauthorized access and guaranteeing the integrity of the notification process.

Monitoring & Observability

• Use Supabase Realtime for live monitoring, ensuring real-time visibility into queue metrics.
• Implement comprehensive logging for the monitoring system to aid in debugging and performance tracking.

By following this PRD, MeshHook will enhance its operational capabilities, ensuring that its queue system remains robust, reliable, and efficient.

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This PRD was AI-generated using gpt-4-turbo-preview from GitHub issue #203
Generated: 2025-10-10

📎 Generated Documentation

• 📄 PRD Document: 203-queue-health-monitoring.md
• 🎨 PlantUML Diagram: 203-queue-health-monitoring.puml
• 🖼️ Diagram Image: 203-queue-health-monitoring.png

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This issue body was auto-generated from the PRD. Original issue content is preserved in the PRD document.
Last updated: 2025-10-10