Load testing

Inizia esaminando docs/PRDs/182-load-testing.md e il contesto architetturale che comprende SvelteKit, Supabase, Postgres, l’elaborazione dei webhook, l’esecuzione dei workflow e lo streaming dei log in tempo reale. Scegli e configura uno strumento di load testing come JMeter o Locust, quindi documenta gli scenari, i benchmark, le metriche, i colli di bottiglia e i risultati. Il lavoro è considerato completato quando i criteri di accettazione sono soddisfatti, inclusi i test simulati con carico di picco e le ottimizzazioni validate.

📋 Product Requirements Document

PRD: Load testing

Issue: #182
Milestone: Phase 7: Testing
Labels: performance-tests, hacktoberfest

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PRD: Load Testing for MeshHook

Overview

The objective of this Product Requirements Document (PRD) is to outline the approach for conducting comprehensive load testing on the MeshHook platform. This initiative is in alignment with MeshHook's commitment to providing a robust, scalable, and efficient workflow engine capable of handling high volume and concurrency with consistent performance. The purpose of load testing is to validate the system's resilience, performance, and reliability under various stress and load conditions, ensuring that MeshHook meets its performance benchmarks and delivers a seamless experience to its users, even under peak loads.

Functional Requirements

1. Comprehensive Test Plan: Develop an exhaustive load testing plan that encompasses critical functionalities of MeshHook, including but not limited to webhook processing, workflow executions, live log streaming, and database interactions under multi-tenant security constraints.

2. Simulation of Real-world Traffic: Utilize advanced load testing tools and frameworks to simulate real-world traffic patterns, aiming to mimic actual user interactions and workflow executions as closely as possible.

3. Performance Benchmarking: Establish clear performance benchmarks for various operations within MeshHook, such as webhook response times, workflow execution latency, and live log retrieval speeds.

4. Bottleneck Identification: Systematically identify and document any performance bottlenecks, scalability issues, or resource constraints that may hinder MeshHook's ability to perform under high load.

5. Iterative Optimization: Based on the findings from the load tests, iterate on system configurations, code optimizations, and infrastructure adjustments to address identified issues. Repeat testing as necessary to verify improvements.

Non-Functional Requirements

• Scalability: Ensure MeshHook can scale horizontally to accommodate increasing loads, with a focus on stateless architecture and efficient resource management.

• Performance: Target sub-second response times for end-user interactions and workflow executions under peak load conditions, supporting at least 10,000 concurrent workflows.

• Reliability: Achieve a minimum of 99.9% uptime, with the system capable of self-recovery from failures and ensuring data integrity during and after load tests.

• Security: Maintain stringent security measures during load testing to protect sensitive data and prevent unauthorized access, in line with MeshHook's existing security protocols.

Technical Specifications

Architecture Context

MeshHook is built on a serverless architecture leveraging SvelteKit for the frontend and Supabase (including Postgres, Realtime, and Storage) for the backend. The system is designed with scalability in mind, utilizing serverless workers for workflow orchestration and execution. Integration points for load testing include webhook endpoints, workflow processing logic, and live log streaming capabilities.

Implementation Approach

1. Preparation: Review existing architecture and performance metrics to identify critical components and potential bottlenecks.

2. Load Testing Strategy Development: Create detailed testing scenarios that cover all aspects of MeshHook's functionality, focusing on areas most likely to be affected by high load.

3. Tool Selection and Configuration: Choose appropriate load testing tools (e.g., JMeter, Locust.io) and configure them to replicate realistic usage patterns.

4. Testing Execution: Conduct the load tests as per the plan, closely monitoring system performance, resource utilization, and error rates.

5. Analysis and Optimization: Analyze the collected data to pinpoint inefficiencies and implement optimizations. Retest as necessary to confirm performance improvements.

Data Model and API Considerations

• No immediate changes to the data model are envisioned solely for the purpose of load testing. However, insights gained may necessitate schema optimizations or query adjustments.

• API Impact: While existing API endpoints will be the primary focus of load testing, this process may reveal the need for additional endpoints or modifications to existing ones to improve performance or monitoring capabilities.

Acceptance Criteria

• A detailed load testing plan is created and documented.
• Load testing tools and environments are fully configured and prepared.
• The system has been tested under simulated peak load conditions, with performance metrics thoroughly documented.
• Identified bottlenecks and performance issues have been addressed, and optimizations have been validated through subsequent testing rounds.
• MeshHook meets or exceeds all established performance benchmarks under load.

Dependencies

• Availability of load testing tools and resources capable of generating the required load.
• Collaboration with the development and operations teams for access, insights, and rapid adjustments based on test outcomes.
• Adequate provisioning of hardware or cloud resources to mimic expected traffic and load patterns.

Implementation Notes

Development Guidelines

• Document all test cases, configurations, and results for future reference and reproducibility.
• Adhere to MeshHook's coding standards and best practices for any scripts or modifications introduced during the load testing process.

Testing Strategy

• Employ both automated and manual testing approaches, with a focus on automated tools for efficiency and scalability.
• Start with lower load scenarios, gradually increasing to the target peak load to understand system behavior and capacity incrementally.

Security Considerations

• Ensure all testing activities are conducted in a manner that does not compromise the security or integrity of the MeshHook platform or its data.
• Utilize anonymized or synthetic data where possible to prevent exposure of sensitive information.

Monitoring and Observability

• Leverage Supabase Realtime and other observability tools to monitor system performance and resource usage in real-time during load tests.
• Implement detailed logging for all test scenarios to facilitate in-depth analysis and troubleshooting of any issues encountered.

By adhering to the guidelines and requirements outlined in this PRD, the MeshHook team aims to ensure the platform's readiness for high-demand scenarios, maintaining its commitment to providing a high-performance, scalable workflow engine.

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This PRD was AI-generated using gpt-4-turbo-preview from GitHub issue #182
Generated: 2025-10-10

📎 Generated Documentation

• 📄 PRD Document: 182-load-testing.md
• 🎨 PlantUML Diagram: 182-load-testing.puml
• 🖼️ Diagram Image: 182-load-testing.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