Error rate tracking

[ralyodio]

📋 Product Requirements Document

PRD: Error rate tracking

Issue: #170
Milestone: Phase 6: Observability
Labels: metrics, hacktoberfest

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PRD: Error Rate Tracking for MeshHook

1. Overview

The purpose of this task is to integrate a sophisticated error rate tracking system into MeshHook. This system is designed to enhance the observability layer of the platform, allowing for real-time monitoring, classification, and analysis of errors across the system. This aligns with MeshHook's goals of providing robust, reliable workflow automation services by enabling proactive issue detection and resolution, thereby reducing downtime and improving user satisfaction.

2. Functional Requirements

1. Automated Error Tracking: Automatically capture and log errors from all components of the MeshHook system, including webhook triggers, workflow executions, and internal system errors.
2. Error Classification: Classify errors based on type (e.g., system, execution, input) and severity levels (low, medium, high) to facilitate prioritized troubleshooting and remediation.
3. Real-Time Monitoring and Visualization: Provide a real-time view of error occurrences, patterns, and trends through an intuitive dashboard, enabling immediate awareness and response.
4. Configurable Alerting Mechanism: Implement a flexible alerting system that can notify administrators or designated users via email or Slack when error rates exceed configurable thresholds.
5. Historical Error Analysis: Enable access to historical error data for trend analysis and to inform system improvements or bug fixes.

3. Non-Functional Requirements

• Performance: The error tracking system must operate with minimal overhead to avoid impacting the overall performance of MeshHook workflows.
• Reliability: Target 99.9% availability for the error tracking and reporting functionality, with robust error handling and failover mechanisms.
• Security: Ensure that error logs and metrics are stored securely, with access restricted based on user roles and that sensitive data is appropriately masked or anonymized.
• Maintainability: Adopt MeshHook's coding standards for clear, well-documented, and modular code to facilitate ease of maintenance and future enhancements.

4. Technical Specifications

Architecture Context

MeshHook's architecture consists of SvelteKit for the SSR/API layer, Supabase for database and real-time functionalities, and a distributed system of workers for executing workflows. The error rate tracking system will be integrated as follows:

• Workers: Enhanced to capture and report errors into the tracking system.
• Supabase (Postgres): Utilized for storing error logs and metrics, leveraging the real-time capabilities for live monitoring.
• SvelteKit: Extended to serve the error rate dashboards and alert configuration UI.

Implementation Approach

1. Schema Definition: Define a PostgreSQL schema for error metrics, including fields for error type, severity, source, and a JSONB field for additional metadata.
2. Error Logging Enhancements: Modify existing error handling in the workers and SvelteKit layers to log detailed error information, including categorization by type and severity.
3. Dashboard and Alerting UI: Develop a dashboard using SvelteKit for visualizing error rates and trends, and a UI for configuring alert thresholds and notification channels.
4. Supabase Realtime Configuration: Configure Supabase Realtime to stream error events to the dashboard for live monitoring.
5. Alerting System Implementation: Integrate with external services (e.g., Slack, email) to dispatch alerts based on user-configured thresholds.

Data Model Changes

• New Table: ErrorMetrics
  • error_id: UUID, Primary Key
  • timestamp: TIMESTAMP WITH TIME ZONE
  • source: VARCHAR (worker, webhook, system)
  • error_type: VARCHAR
  • severity: ENUM ('low', 'medium', 'high')
  • workflow_id: UUID, Foreign Key (optional)
  • metadata: JSONB

API Endpoints

• GET /api/error-metrics: Retrieve error metrics with support for filtering by time range, source, type, and severity.
• POST /api/error-events: Internal endpoint for logging errors, accepting JSON payloads conforming to the ErrorMetrics schema.

5. Acceptance Criteria

• Error logging captures all specified data points with correct classification by type and severity.
• Error metrics are stored securely in Supabase with minimal latency.
• Real-time monitoring dashboard is responsive and accurately reflects current and historical error data.
• Alerting system is configurable and reliably sends notifications within 1 minute of threshold breaches.
• Documentation for error tracking system is comprehensive, covering schema, UI usage, and alert configuration.

6. Dependencies

• Supabase for database and real-time functionality.
• Slack/email integration for alert notifications.
• Existing MeshHook infrastructure and data models.

7. Implementation Notes

Development Guidelines

• Follow MeshHook’s coding and security standards, including thorough code reviews and adherence to best practices for secure coding.
• Implement features in a modular fashion to facilitate future extension and maintenance.

Testing Strategy

• Unit Tests: Cover new functions and components related to error logging and classification.
• Integration Tests: Ensure correct interaction between error tracking components and other MeshHook systems.
• E2E Tests: Validate the overall error tracking flow, including logging, visualization, and alerting.

Security Considerations

• Error data must be encrypted in transit and at rest.
• Apply strict RBAC policies to error data access, ensuring only authorized users can view or modify error metrics and alerts.

Monitoring & Observability

• Incorporate monitoring for the error tracking system itself, ensuring its components are performing as expected and alerting on any issues.

By following this PRD, MeshHook will gain a comprehensive error rate tracking system that not only enhances its observability and reliability but also provides actionable insights for continuous improvement.

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This PRD was AI-generated using gpt-4-turbo-preview from GitHub issue #170
Generated: 2025-10-10

📎 Generated Documentation

• 📄 PRD Document: 170-error-rate-tracking.md
• 🎨 PlantUML Diagram: 170-error-rate-tracking.puml
• 🖼️ Diagram Image: 170-error-rate-tracking.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