DevOps AI Toolkit

# PRD-51: Capability Scanning Performance Optimization **GitHub Issue**: [#51](https://github.com/vfarcic/dot-ai/issues/51) **Status**: Superseded **Created**: 2025-01-08 **Closed**: 2025-11-19 **Owner**: TBD ## Work Log ### 2025-11-19: PRD Closure - Superseded **Status**: Superseded by [PRD #216](./216-controller-based-autonomous-capability-scanning.md) **Closure Summary**: This PRD focused on optimizing the performance of the synchronous, client-side capability scanning process. However, the project direction has shifted towards an autonomous, event-driven architecture defined in **PRD #216: Controller-Based Autonomous Capability Scanning**. **Reasoning**: 1. **Architectural Shift**: Moving scanning to a background controller eliminates the user-facing latency issue entirely. Users will no longer wait for scans to complete interactively; the data will be pre-populated and kept fresh by the controller. 2. **Better Optimization**: Event-driven scanning (processing only changed resources) is inherently more efficient and scalable than optimizing a full-cluster polling mechanism. 3. **Effort Conservation**: optimizing the legacy synchronous scanning capability would be wasted effort given the move to the controller-based model. All performance and user experience goals from this PRD are better served by the implementation of PRD #216. ## Executive Summary Cluster capability scanning is currently a performance bottleneck that impacts user experience with AI recommendations. Users must wait for initial capability scans to complete before they can access recommendation features, creating friction in the user journey. This PRD focuses on optimizing capability scanning performance to reduce wait times and improve overall system responsiveness. ## Problem Statement ### Current State - Initial capability scanning is required before users can access AI-powered recommendations - Scanning performance creates noticeable delays in user workflows - Performance bottlenecks may discourage adoption of recommendation features - Current scanning approach may not scale efficiently with cluster size or resource count ### Impact - **User Experience**: Delayed access to core recommendation functionality - **Adoption**: Performance friction may reduce feature adoption - **Scalability**: Current approach may not handle large clusters efficiently - **System Resources**: Inefficient scanning may consume unnecessary compute/API resources ### Success Criteria - Measurably faster capability scanning times - Improved user experience during initial setup and rescanning operations - Better resource utilization during scanning operations - Maintained accuracy and completeness of capability discovery ## Solution Overview This PRD focuses on optimizing the capability scanning pipeline to achieve significant performance improvements. The exact optimization strategies will be determined during implementation based on profiling and analysis of current bottlenecks. ### Approach - Analyze current scanning performance bottlenecks - Implement evidence-based optimizations - Maintain compatibility with existing capability data structures - Ensure no regression in scanning accuracy or completeness ### Out of Scope - Changes to capability data models or storage formats - Modifications to recommendation algorithms that consume capability data - Broader cluster discovery optimizations beyond capability scanning ## User Stories & Requirements ### Primary User Stories **As a DevOps engineer using dot-ai recommendations** - I want capability scanning to complete quickly so I can get recommendations without significant wait times - I want scanning progress to be visible so I understand what's happening during longer operations - I want rescanning to be efficient so I can refresh capabilities as needed **As a platform team deploying dot-ai** - I want scanning to scale efficiently across different cluster sizes - I want scanning to use resources efficiently without impacting cluster performance - I want scanning reliability to be maintained even with performance optimizations ### Functional Requirements #### Core Performance Requirements - [ ] Achieve measurable improvement in capability scanning speed - [ ] Maintain accuracy and completeness of capability discovery - [ ] Preserve existing capability data formats and interfaces - [ ] Support progress tracking during scanning operations #### Compatibility Requirements - [ ] Maintain compatibility with existing Vector DB storage - [ ] Preserve existing MCP tool interfaces for capability management - [ ] Support existing capability search and retrieval patterns - [ ] Maintain existing error handling and recovery mechanisms #### Operational Requirements - [ ] Provide clear scanning progress feedback to users - [ ] Handle scanning failures gracefully without data corruption - [ ] Support interruption and resumption of scanning operations - [ ] Maintain logging and monitoring for scanning performance ## Technical Approach ### Discovery Phase - Profile current scanning operations to identify specific bottlenecks - Analyze resource usage patterns during scanning - Measure baseline performance across different cluster configurations - Identify optimization opportunities in the scanning pipeline ### Implementation Principles - Evidence-based optimization based on profiling data - Incremental improvements with measurable results - Backward compatibility with existing systems - Comprehensive testing to ensure no accuracy regression ### Performance Metrics - Scanning time reduction (baseline vs. optimized) - Resource utilization efficiency - API call optimization ratios - Success/failure rates during scanning ## Milestones & Timeline ### Phase 1: Analysis & Baseline (Week 1-2) - [ ] **Performance Profiling Complete**: Comprehensive analysis of current scanning bottlenecks with baseline metrics - [ ] **Optimization Strategy Defined**: Clear plan for performance improvements based on profiling data ### Phase 2: Core Optimization Implementation (Week 3-4) - [ ] **Primary Bottleneck Optimized**: Implementation of highest-impact performance improvement identified in analysis - [ ] **Performance Testing Validated**: Measurable improvement demonstrated with comprehensive test coverage ### Phase 3: Additional Optimizations (Week 5-6) - [ ] **Secondary Optimizations Implemented**: Additional performance improvements based on continued profiling - [ ] **Integration Testing Complete**: Full compatibility validation with existing systems and workflows ### Phase 4: Production Readiness (Week 7-8) - [ ] **Documentation Updated**: User and developer documentation reflects performance improvements and new capabilities - [ ] **Monitoring & Observability Ready**: Performance metrics and monitoring capabilities deployed for production use ### Phase 5: Deployment & Validation (Week 9-10) - [ ] **Production Deployment Complete**: Optimization changes deployed and validated in production environment - [ ] **User Experience Validated**: Confirmed improvement in user experience with real-world usage patterns ## Dependencies & Risks ### Dependencies - Existing Vector DB and embedding service infrastructure - Current capability inference and storage systems - MCP tool interface compatibility - Claude AI integration for capability analysis ### Technical Risks - **Performance Regression**: Optimizations might introduce unexpected bottlenecks - **Data Consistency**: Changes might affect capability data accuracy or completeness - **Integration Compatibility**: Performance changes might break existing integrations ### Mitigation Strategies - Comprehensive testing with real cluster data before deployment - Gradual rollout with performance monitoring and rollback capabilities - Baseline performance measurements to validate improvements ## Success Metrics ### Primary Metrics - **Scanning Time Improvement**: Measurable reduction in time to complete capability scanning - **User Experience Score**: Reduced friction in accessing recommendation features - **Resource Efficiency**: Improved resource utilization during scanning operations ### Secondary Metrics - **Adoption Metrics**: Increased usage of recommendation features post-optimization - **Error Rates**: Maintained or improved scanning success rates - **System Performance**: No negative impact on overall system performance ## Documentation Requirements ### User Documentation - Updated capability scanning guides with performance expectations - Troubleshooting guides for scanning performance issues - Best practices for large cluster scanning operations ### Developer Documentation - Performance profiling and optimization methodology - Architectural changes and implementation details - Integration testing approaches for performance validation ## Future Considerations ### Potential Enhancements - Real-time capability updates for dynamic cluster changes - Predictive scanning based on cluster usage patterns - Advanced caching strategies for frequently accessed capabilities ### Scaling Considerations - Multi-cluster scanning optimization - Distributed scanning approaches for very large environments - Integration with cluster-native monitoring for scanning triggers --- ## Work Log ### 2025-01-08 - **PRD Created**: Initial PRD structure established - **GitHub Issue**: Created issue #51 for tracking - **Status**: Ready for planning and analysis phase --- ## References - [GitHub Issue #51](https://github.com/vfarcic/dot-ai/issues/51) - [Existing Capability Scanning Implementation](../src/tools/organizational-data.ts) - [Vector DB Integration](../src/core/capability-vector-service.ts) - [Embedding Service](../src/core/embedding-service.ts)