Brummer MCP Server

# Phase 3 Execution Plan: Atomic Operations Implementation **Created**: January 31, 2025 **Status**: Ready for Implementation **Approach**: Atomic pointer swapping + sync.Map **Expected Timeline**: 3 weeks ## Executive Summary After prototype testing revealed that channels are 15-67x slower than mutexes, we discovered that atomic operations are 30-300x FASTER than mutexes. This validates pivoting to an atomic-based approach for Phase 3. ## Implementation Phases ### Phase 3A: Atomic Process State (Week 1) #### Tasks 1. **Create immutable ProcessState struct** - All process fields in single immutable object - Efficient copying for updates - Memory-aligned for atomic operations 2. **Implement AtomicProcess wrapper** - Atomic pointer to ProcessState - Lock-free GetState() method - CAS-based update methods 3. **Add atomic fast-path to existing Process** - Maintain backward compatibility - Add GetStateAtomic() alongside existing getters - Dual-write to both mutex and atomic state 4. **Update hot-path code** - MCP tools (scripts_status, etc.) - TUI refresh loops - High-frequency status checks #### Success Criteria - 30x performance improvement on state reads - Zero breaking changes to existing API - All tests pass with atomic operations ### Phase 3B: sync.Map Process Registry (Week 2) #### Tasks 1. **Replace processes map in Manager** - Migrate from `map[string]*Process` to `sync.Map` - Update all registry operations - Maintain existing Manager API 2. **Optimize GetAllProcesses()** - Use sync.Map.Range() efficiently - Consider caching for repeated calls - Benchmark against current implementation 3. **Update process lifecycle methods** - AddProcess to use sync.Map.Store() - RemoveProcess to use sync.Map.Delete() - FindProcess to use sync.Map.Load() 4. **Add benchmarks** - Registry operations under load - Concurrent access patterns - Memory usage comparison #### Success Criteria - 3x improvement in registry lookups - No degradation in Range operations - Maintain safe concurrent access ### Phase 3C: Integration and Optimization (Week 3) #### Tasks 1. **Profile end-to-end performance** - Run production-like workloads - Identify any new bottlenecks - Measure overall improvement 2. **Optimize memory allocations** - Pool ProcessState objects if needed - Reduce GC pressure - Profile allocation patterns 3. **Create migration guide** - Document new patterns - Provide examples - Update architecture docs 4. **Add feature flags** - Allow gradual rollout - A/B testing capability - Emergency rollback option #### Success Criteria - 10x overall performance improvement in concurrent scenarios - Memory usage within 10% of current - Comprehensive documentation ## Code Architecture ### Immutable ProcessState ```go type ProcessState struct { ID string Name string Script string Status ProcessStatus StartTime time.Time EndTime *time.Time ExitCode *int // Additional fields as needed } ``` ### Atomic Process Enhancement ```go type Process struct { // Existing fields mu sync.RWMutex // New atomic state atomicState unsafe.Pointer // *ProcessState // Keep existing fields for compatibility ID string Status ProcessStatus // ... } // Fast atomic read func (p *Process) GetStateAtomic() *ProcessState { return (*ProcessState)(atomic.LoadPointer(&p.atomicState)) } // Backward compatible getter func (p *Process) GetStatus() ProcessStatus { // Can migrate to atomic gradually if state := p.GetStateAtomic(); state != nil { return state.Status } // Fallback to mutex p.mu.RLock() defer p.mu.RUnlock() return p.Status } ``` ### Manager with sync.Map ```go type Manager struct { processes sync.Map // Instead of map[string]*Process eventBus *events.EventBus // Other fields unchanged } func (m *Manager) GetProcess(id string) *Process { if val, ok := m.processes.Load(id); ok { return val.(*Process) } return nil } ``` ## Risk Mitigation ### Technical Risks 1. **Memory ordering issues** - Use atomic package correctly - Test on different architectures - Add memory barrier documentation 2. **ABA problem in CAS** - Use unique state pointers - Consider version numbers if needed - Test under high contention 3. **sync.Map limitations** - Not optimal for frequent writes - Test write-heavy workloads - Have fallback plan ### Mitigation Strategies - Gradual rollout with monitoring - Comprehensive benchmark suite - Keep mutex implementation as fallback - Feature flags for quick rollback ## Measurement Plan ### Performance Metrics - Operation latency (p50, p95, p99) - Throughput under load - CPU usage comparison - Memory allocation rate ### Correctness Metrics - Race detector runs - Stress test results - Data consistency checks - API compatibility tests ## Timeline ### Week 1: January 31 - February 6 - Implement atomic ProcessState - Add to existing Process struct - Update hot-path code - Benchmark improvements ### Week 2: February 7 - February 13 - Implement sync.Map registry - Update Manager methods - Integration testing - Performance profiling ### Week 3: February 14 - February 20 - End-to-end optimization - Documentation - Migration guide - Production readiness ## Expected Outcomes ### Performance Improvements - **State reads**: 30-300x faster - **Registry lookups**: 3-5x faster - **Concurrent operations**: 10-50x better scaling - **Memory**: Similar or slightly higher (acceptable trade-off) ### Architecture Benefits - True lock-free read operations - Better CPU cache utilization - Reduced contention under load - Simpler reasoning about concurrency ## Conclusion The atomic operations approach offers dramatic performance improvements while maintaining API compatibility and correctness. This is a much more promising direction than the channel-based approach and aligns with modern CPU architectures and Go's atomic primitives. **Recommendation**: Proceed with implementation starting with Phase 3A.