Caching strategy

[ralyodio]

📋 Product Requirements Document

PRD: Caching strategy

Issue: #211
Milestone: Phase 10: Polish & Launch
Labels: performance-optimization, hacktoberfest

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PRD: Caching Strategy for MeshHook

Overview

The caching strategy for MeshHook is a crucial enhancement aimed at bolstering the platform's performance by minimizing direct database interactions, thereby reducing load and optimizing response times for a seamless user experience. This strategy aligns with MeshHook's overarching goals of providing a robust, efficient, and secure workflow engine by implementing a caching layer that supports quick data retrieval while ensuring data consistency and integrity.

Functional Requirements

1. Caching Layer Integration: Deploy a caching layer to intercept and minimize repetitive database queries, primarily targeting high-frequency data such as workflow definitions, execution logs, and node configurations.

2. Data Invalidation and Consistency: Implement a comprehensive invalidation strategy to maintain cache consistency with the database, accounting for data modifications including updates, deletions, and insertions.

3. Cache Management: Develop interfaces or tools for cache administration, enabling configuration of TTL values, eviction policies, and manual cache flushing or invalidation.

4. Integration Compatibility: Ensure the caching solution seamlessly integrates with MeshHook's existing infrastructure, adhering to the architectural design and data flow.

Non-Functional Requirements

1. Performance Enhancement: Achieve a marked reduction in database query and server response times, contributing to an improved user experience.

2. System Reliability: Incorporate redundancy and recovery mechanisms to maintain operational continuity in case of cache misses or failures in the caching service.

3. Security Assurance: Implement the caching solution without introducing new security risks, especially concerning sensitive data handling and access control mechanisms.

4. Maintainability and Scalability: Design the caching logic to be modular, well-documented, and scalable, facilitating future enhancements or modifications with minimal effort.

Technical Specifications

Architecture Context

Given MeshHook's use of SvelteKit for its frontend and API services and Supabase for backend operations, including Postgres for data storage, the caching strategy must be compatible with these technologies. It should facilitate efficient communication between SvelteKit components and the Postgres database, leveraging the capabilities of a caching technology like Redis or Memcached.

Implementation Approach

1. Caching Technology Selection: Assess and choose a caching technology (Redis, Memcached) that aligns with our performance, scalability, and deployment requirements.

2. Identifying Caching Candidates: Analyze MeshHook’s operations to determine which data retrieval processes are candidates for caching, prioritizing those with high access frequencies and read-heavy operations.

3. Caching Logic Development: Implement the caching mechanism, ensuring it populates the cache on misses and effectively invalidates entries on data updates to maintain consistency.

4. Cache Invalidation Strategy: Formulate and implement a strategy for invalidating cache entries in sync with data changes in the database, to prevent stale data issues.

5. Monitoring and Tools: Set up monitoring for the caching system to track metrics like hit/miss ratios and develop tools for cache management, including flushing and parameter tuning.

Data Model Changes

No immediate changes to the existing data models are required for caching. However, future considerations may include introducing models for caching metrics or performance data.

API Endpoints

This caching strategy does not necessitate new API endpoints but will enhance the efficiency of existing endpoints by reducing database load.

Acceptance Criteria

• Successful integration of the caching layer with MeshHook's architecture without impacting existing functionalities.
• Demonstrable reduction in database query loads for operations identified as high-traffic.
• Effective cache invalidation mechanism ensuring data consistency across MeshHook.
• Deployment of cache management tools for monitoring and configuration purposes.
• Documentation updated to reflect the caching strategy, including setup, configuration, and management guidelines.

Dependencies

• Choice of caching technology that is compatible with MeshHook's stack.
• Comprehensive analysis of MeshHook’s operations to identify optimal caching opportunities.

Implementation Notes

Development Guidelines

• Adhere to MeshHook’s coding standards and architectural patterns.
• Ensure secure handling of potentially sensitive data in cache, maintaining multi-tenant data isolation.
• Utilize environment variables for caching configurations to simplify adjustments across various deployment environments.

Testing Strategy

• Implement comprehensive unit and integration tests for the caching logic and invalidation mechanisms.
• Perform benchmarking and performance tests to evaluate the impact of caching on operation times.

Security Considerations

• Enforce strict access controls and isolation in the caching layer, especially in multi-tenant setups.
• Ensure secure handling of any cached sensitive information, in line with MeshHook’s security practices.

Monitoring & Observability

• Integrate monitoring tools to track cache performance metrics such as hit/miss ratios and eviction rates.
• Log cache-related errors or anomalies for prompt investigation and resolution.

Implementing this caching strategy will significantly enhance MeshHook’s performance and scalability, ensuring a more efficient and responsive platform for users while maintaining high standards of security and data integrity.

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This PRD was AI-generated using gpt-4-turbo-preview from GitHub issue #211
Generated: 2025-10-10

📎 Generated Documentation

• 📄 PRD Document: 211-caching-strategy.md
• 🎨 PlantUML Diagram: 211-caching-strategy.puml
• 🖼️ Diagram Image: 211-caching-strategy.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

[pranjalarora98]

@ralyodio can you assign me this task please!! I would like to work on it actively.thanks