Execution time statistics

[ralyodio]

📋 Product Requirements Document

PRD: Execution time statistics

Issue: #168
Milestone: Phase 6: Observability
Labels: metrics, hacktoberfest

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PRD: Execution Time Statistics

Issue: #168
Milestone: Phase 6: Observability
Labels: metrics, hacktoberfest
Project: MeshHook

Overview

The integration of execution time statistics is a strategic enhancement aimed at bolstering MeshHook's observability capabilities. This feature is designed to provide granular insights into the performance of workflow runs, facilitating the identification and rectification of inefficiencies within the system. By offering a detailed analysis of execution times, MeshHook will empower users and administrators to streamline workflows, thereby reinforcing the platform's commitment to delivering a high-performance, reliable workflow engine.

Functional Requirements

1. Execution Time Measurement: Implement functionality to accurately measure and log the execution time of each workflow run from start to finish.
2. Statistics Aggregation: Aggregate execution time data to compute and store key metrics, including average, median, minimum, maximum, and specified percentiles (e.g., 95th, 99th) for each workflow.
3. Persistent Storage: Ensure execution time metrics are durably stored in the database, with efficient indexing strategies to support rapid data retrieval.
4. API Endpoint: Develop a RESTful API endpoint to provide access to execution time statistics, supporting queries by workflow ID, date range, and other pertinent filters.
5. UI Enhancements: Augment the existing UI to include visualizations of execution time statistics, allowing users to easily monitor performance metrics within the workflow dashboard.
6. Comprehensive Documentation: Update technical documentation and user guides to accurately reflect the availability and usage of execution time statistics.

Non-Functional Requirements

• Performance: Execution time statistics retrieval operations must be optimized to ensure response times do not exceed 300ms under standard usage conditions.
• Reliability: Implement fault-tolerant mechanisms to guarantee that the collection and storage of execution time data do not adversely affect workflow execution.
• Security: Integration of execution time statistics must comply with MeshHook's multi-tenant RLS security framework, ensuring that data access is appropriately restricted.
• Maintainability: Code contributed to support execution time statistics should adhere to MeshHook's coding standards and best practices, facilitating future maintenance and enhancements.

Technical Specifications

Architecture Context

• Integration Points:
  • Integrate timing mechanisms within the Orchestrator worker component to capture execution durations.
  • Modify the database schema to accommodate the storage of execution time metrics.
  • Update the UI component to display execution time statistics, leveraging SvelteKit for development.

Implementation Approach

1. Schema Modification: Update the schema.sql file to include a new table or extend an existing table dedicated to storing execution time metrics.
2. Timing Logic Implementation: Embed timing logic at the beginning and end of the workflow execution process within the Orchestrator component to measure execution durations.
3. Aggregation Logic: Develop server-side logic to calculate and store aggregated execution time metrics, utilizing efficient PostgreSQL aggregate functions.
4. API Endpoint Development: Implement a new RESTful API endpoint, /api/v1/statistics/execution-times, for fetching execution time statistics with support for various filters.
5. UI Updates: Leverage Svelte to integrate execution time visualizations into the workflow dashboard, ensuring a seamless user experience.
6. Testing: Ensure comprehensive test coverage for all new functionality through unit and integration tests.
7. Documentation Update: Revise API and user documentation to include details on the execution time statistics feature.

Data Model

• Table: execution_time_statistics
  • id: SERIAL PRIMARY KEY
  • workflow_id: INTEGER (Foreign Key)
  • run_id: INTEGER (Foreign Key)
  • execution_time_ms: INTEGER
  • created_at: TIMESTAMP WITH TIME ZONE

API Endpoints

• GET /api/v1/statistics/execution-times
  • Parameters: workflow_id (optional), start_date (optional), end_date (optional)
  • Response: JSON object containing aggregated execution time statistics according to the specified filters.

Acceptance Criteria

• Execution times are precisely measured and logged for each workflow run.
• Execution time metrics (average, median, min, max, percentiles) are accurately calculated and stored.
• The API endpoint for execution time statistics is fully functional, returning data within 300ms under typical conditions.
• UI enhancements for execution time statistics are implemented in a user-friendly manner, consistent with MeshHook's design ethos.
• All newly introduced code achieves greater than 90% test coverage.
• Documentation accurately reflects the execution time statistics functionality.
• No regressions in existing MeshHook features or performance.

Dependencies

• Access to MeshHook's code repository and development environment.
• Supabase access for database schema updates.
• SvelteKit setup for UI development.

Implementation Notes

Development Guidelines

• Utilize asynchronous programming patterns (async/await) for all new asynchronous operations.
• Adhere to MeshHook's established coding conventions and standards to maintain codebase consistency.
• Optimize database interactions for performance, particularly in relation to the aggregation and retrieval of execution time metrics.

Testing Strategy

• Unit Tests: Focus on isolated testing of the execution time measurement and aggregation logic.
• Integration Tests: Ensure comprehensive coverage of the end-to-end process, from workflow execution through to statistics retrieval via the API.

Security Considerations

• Confirm that the execution time statistics feature fully complies with MeshHook's RLS policies, ensuring data access controls are enforced.

Monitoring & Observability

• Implement logging mechanisms within the execution time tracking functionality to facilitate monitoring and troubleshooting.

Related Documentation

• Main PRD
• Architecture Overview
• Security Guidelines

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This PRD was AI-generated using gpt-4-turbo-preview from GitHub issue #168
Generated: 2025-10-10

📎 Generated Documentation

• 📄 PRD Document: 168-execution-time-statistics.md
• 🎨 PlantUML Diagram: 168-execution-time-statistics.puml
• 🖼️ Diagram Image: 168-execution-time-statistics.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