Run success/failure rates

📋 Product Requirements Document

PRD: Run success/failure rates

Issue: #167
Milestone: Phase 6: Observability
Labels: metrics, hacktoberfest

---

PRD: Run Success/Failure Rates

Issue: #167
Milestone: Phase 6: Observability
Labels: metrics, hacktoberfest

---

1. Overview

This PRD outlines the implementation of run success and failure rates to enhance MeshHook's observability features. Aligning with MeshHook's v1 goals, this feature aims to provide users with immediate insights into the health and performance of their workflows, thus enabling more efficient debugging and optimization processes. By tracking and visualizing these metrics, users can assess the reliability of their workflows over time and make data-driven decisions to improve their configurations.

2. Functional Requirements

1. Metric Calculation: Implement logic to calculate and store success and failure rates of workflow runs within the database.
2. Dashboard Visualization: Display these metrics on the MeshHook dashboard, offering insights at both individual workflow and aggregate levels.
3. Real-Time Metrics: Ensure the metrics reflect real-time data, updating immediately after each workflow run completes.
4. Historical Trend Analysis: Enable users to view historical trends of success and failure rates over selectable time periods.
5. Customizable Views: Allow users to filter and sort metrics based on time ranges, specific workflows, or other criteria.
6. Threshold-Based Notifications: Create a notification system that alerts users when the success or failure rates of workflows breach predefined thresholds.

3. Non-Functional Requirements

• Performance: Ensure the metrics computation and dashboard updates are optimized for performance, causing no noticeable delay in workflow execution or dashboard interaction.
• Reliability: The metrics must accurately represent the true outcomes of workflow runs, with a robust design that minimizes the risk of data loss or inaccuracies.
• Security: Adhere to MeshHook's multi-tenant RLS security model, ensuring that users can only access metrics relevant to their permissions.
• Maintainability: Craft the solution with maintainability in mind, employing clean, modular code that integrates seamlessly with the existing codebase.

4. Technical Specifications

Architecture Considerations

• Integration with Workflow Execution Engine: To capture the outcome of each workflow run.
• Supabase Realtime Integration: For pushing real-time updates to the dashboard.
• SvelteKit Dashboard Enhancements: To visualize the metrics data effectively for the end-users.

Implementation Approach

1. Analysis Phase:
   • Identify optimal points within the workflow execution engine to hook in the success/failure tracking logic.
2. Design Phase:
   • Data Model Update: Introduce a workflow_run_metrics table to store aggregated metrics data.
   • API Design: Define internal RESTful API endpoints for fetching and updating metrics data.
   • UI Components: Design and prototype dashboard components for metrics visualization.
3. Implementation Phase:
   • Modify the workflow execution logic to update success/failure metrics upon each run’s completion.
   • Implement the backend API for metrics retrieval.
   • Develop the frontend components and integrate them into the existing dashboard.
4. Testing & Documentation:
   • Write comprehensive tests covering new logic and interactions.
   • Update developer and user documentation to reflect the new features.

Data Model Changes

• New Table: workflow_run_metrics
  • Columns: workflow_id (FK), success_count INT, failure_count INT, last_updated TIMESTAMP.

API Endpoints

• GET /api/metrics/workflow/{workflow_id}: Fetch success and failure rates for a given workflow.
• GET /api/metrics/workflows/summary: Fetch a summary of success and failure rates across all workflows.

5. Acceptance Criteria

• Success and failure rates are accurately tracked and updated after each workflow run.
• The dashboard displays these metrics in real-time, with support for historical data viewing.
• Users can filter and sort metrics based on their needs.
• Alerting mechanism works as expected, notifying users of significant changes in success/failure rates.
• The implementation adheres to performance, security, and maintainability standards.
• All new features are fully documented and tested.

6. Dependencies and Prerequisites

• Access to the workflow execution engine for integrating success/failure tracking.
• Supabase Realtime setup for live dashboard updates.
• Existing SvelteKit dashboard infrastructure for UI enhancements.

7. Implementation Notes

Development Guidelines

• Follow MeshHook’s coding standards and conventions.
• Write clean, well-documented, and modular code.
• Prioritize security and efficiency in all implementations.

Testing Strategy

• Develop unit tests for all new logic and data handling.
• Implement integration tests to ensure seamless interaction between the metrics feature and existing systems.
• Conduct end-to-end tests focusing on user interactions with the dashboard.

Security Considerations

• Ensure new API endpoints require authentication and respect RLS policies.
• Validate input data rigorously to prevent injection attacks or data leaks.

Monitoring & Observability

• Integrate monitoring tools to track the performance and reliability of the metrics calculation and API endpoints.
• Log operational metrics for the metrics feature to facilitate quick troubleshooting.

By adhering to these specifications, MeshHook will enhance its observability capabilities, providing users with valuable insights into their workflows’ performance and reliability.

---

This PRD was AI-generated using gpt-4-turbo-preview from GitHub issue #167
Generated: 2025-10-10

📎 Generated Documentation

• 📄 PRD Document: 167-run-success-failure-rates.md
• 🎨 PlantUML Diagram: 167-run-success-failure-rates.puml
• 🖼️ Diagram Image: 167-run-success-failure-rates.png

---

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

首先定位 PRD 中描述的工作流执行引擎、仪表板基础设施，以及现有的数据库和 API 模式。当运行结果被存储、指定的指标端点和仪表板视图支持实时和历史筛选、阈值通知正常工作，并且该功能经过测试和文档化后，这项工作就完成了。