Performance benchmarks

[ralyodio]

📋 Product Requirements Document

PRD: Performance benchmarks

Issue: #219
Milestone: Phase 10: Polish & Launch
Labels: launch-prep, hacktoberfest

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PRD: Performance Benchmarks for MeshHook

1. Overview

Objective

The primary objective is to establish a comprehensive performance benchmarking system for MeshHook that ensures its components meet and exceed the required performance standards for webhook processing, workflow execution, and multi-tenant security operations. This will enable the identification and elimination of bottlenecks, ensuring MeshHook's architecture can scale efficiently to handle growing user demands.

Alignment with Project Goals

This benchmarking initiative is aligned with MeshHook's overarching goal to deliver a high-performance, durable, and visually intuitive workflow engine. By implementing a robust performance benchmarking framework, MeshHook aims to enhance user satisfaction, improve reliability, and uphold its commitment to being a leading open-source workflow solution in the market.

2. Functional Requirements

1. Benchmark Development:

   • Develop benchmarks to evaluate the performance of webhook trigger processing, focusing on achieving minimal latency and high throughput capabilities.
   • Benchmark the Visual DAG builder for responsiveness and interaction latency under various load conditions.
   • Design workflow execution time benchmarks that cater to diverse complexities and sizes, ensuring efficient execution across all scenarios.
   • Establish benchmarks for live log delivery performance, prioritizing real-time updates with minimal latency.
   • Assess the performance impact of MeshHook's multi-tenant RLS security model, ensuring that security mechanisms do not adversely affect overall performance.

2. Automation and Integration:

   • Implement automated benchmarking tools that simulate real-world usage scenarios to accurately measure MeshHook's performance.
   • Integrate benchmarking processes within MeshHook's CI/CD pipeline to enable continuous performance evaluation and monitoring.

3. Metric Collection and Analysis:

   • Systematically collect a wide range of performance metrics, including execution times, resource consumption, throughput rates, and more.
   • Analyze collected metrics to identify areas of inefficiency, potential bottlenecks, and opportunities for performance optimization.

4. Baseline Metrics and Regression Detection:

   • Establish baseline performance metrics for all critical MeshHook functionalities.
   • Implement an automated system for detecting performance regressions, alerting the development team to any deviations from the established baselines.

3. Non-Functional Requirements

• Performance: MeshHook must achieve and consistently maintain sub-second response times for all user-facing operations, even under peak load conditions.
• Reliability: The benchmarking suite, along with all MeshHook components, should demonstrate a minimum of 99.9% uptime.
• Security: Ensure that benchmarking activities adhere to MeshHook's security protocols, preventing the introduction of new vulnerabilities.
• Maintainability: Benchmarking code, documentation, and tools must be clearly organized, easily understandable, and maintain MeshHook’s coding standards for future enhancements.

4. Technical Specifications

Architecture Context

MeshHook leverages a SvelteKit/Svelte 5 frontend and utilizes Supabase for real-time functionalities and Postgres operations. The performance benchmarks must integrate seamlessly with this existing architecture, focusing on webhook processing efficiency, DAG builder responsiveness, and workflow execution performance.

Implementation Approach

1. Preliminary Analysis: Conduct an in-depth analysis of MeshHook's codebase and architecture to identify performance-critical paths.
2. Benchmark Design: Create detailed benchmark tests covering all essential MeshHook functionalities.
3. Tool Selection and Development: Choose or develop benchmarking tools tailored to MeshHook’s specific needs.
4. Benchmark Execution: Run benchmarks to gather extensive performance data.
5. Data Analysis and Optimization: Analyze data to locate performance bottlenecks and outline optimization strategies.
6. Integration and Automation: Ensure benchmarks are integrated into the CI/CD pipeline for regular performance assessment.
7. Documentation: Document the benchmarking methodology, tools, and outcomes comprehensively.

Data Model and API Endpoints

• Data Model Changes: No specific changes to the data model are required for the benchmarking processes.
• API Endpoints: Current API endpoints' performance will be assessed; no additional endpoints are needed for benchmarking activities.

5. Acceptance Criteria

• Benchmarks for key components are successfully implemented and documented.
• Integration of benchmark results into the CI/CD pipeline for continuous performance monitoring is achieved.
• Performance baselines are established, with comparative analysis against new metrics.
• Identification and documentation of performance optimization opportunities.
• Comprehensive documentation of the benchmarking process, tools, and findings.

6. Dependencies and Prerequisites

• Complete access to the MeshHook codebase and deployment infrastructure.
• Selection or development of appropriate benchmarking tools.
• Capability to integrate benchmarking results within the current CI/CD pipeline.

7. Implementation Notes

Development Guidelines

• Follow modern JavaScript best practices and adhere to MeshHook's coding standards.
• Benchmarking code must be clean, maintainable, and accompanied by detailed comments.
• Apply Test-Driven Development (TDD) principles where applicable to the development of benchmarking code.

Testing Strategy

• Ensure benchmarks are rigorously tested for accuracy and reliability.
• Validate that performance optimizations do not introduce regressions or negatively affect other functionalities.

Security Considerations

• Conduct benchmarking in alignment with MeshHook's security guidelines, ensuring the protection of sensitive data throughout the testing process.

Monitoring and Observability

• Implement detailed logging for benchmarking activities to support in-depth analysis and troubleshooting.
• Closely monitor system performance and resource usage during benchmark tests to detect any unexpected issues or impacts.

Adhering to this PRD will lay a solid foundation for MeshHook's performance capabilities, enabling continuous performance improvements and ensuring the platform remains highly efficient and scalable.

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This PRD was AI-generated using gpt-4-turbo-preview from GitHub issue #219
Generated: 2025-10-10

📎 Generated Documentation

• 📄 PRD Document: 219-performance-benchmarks.md
• 🎨 PlantUML Diagram: 219-performance-benchmarks.puml
• 🖼️ Diagram Image: 219-performance-benchmarks.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