mirror of
https://github.com/pacnpal/markov-discord.git
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1f0a2573c4
- Added `optimization-plan.md` detailing strategies to reduce response latency and improve training throughput. - Enhanced performance analysis in `performance-analysis.md` with identified bottlenecks and completed optimizations. - Created `productContext.md` summarizing project goals, user scenarios, and implementation priorities. - Developed `markov-store.ts` for high-performance serialized chain storage with alias method sampling. - Implemented database performance indexes in `1704067200000-AddPerformanceIndexes.ts`. - Introduced `markov-worker.ts` for handling CPU-intensive operations in separate threads. - Established a worker pool in `worker-pool.ts` to manage multiple worker threads efficiently.
103 lines
4.1 KiB
Markdown
103 lines
4.1 KiB
Markdown
# [MEMORY BANK: ACTIVE] Advanced Performance Optimization - IMPLEMENTED
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**Task:** Implement advanced Markov Discord bot optimizations per optimization plan
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**Date:** 2025-09-25
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**Status:** ✅ COMPLETED - All high-priority optimizations implemented
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## 🎯 Implementation Summary
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### **✅ COMPLETED HIGH-PRIORITY OPTIMIZATIONS**
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1. **Serialized Chain Store (`src/markov-store.ts`)**
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- **Alias Method Implementation:** O(1) weighted sampling instead of O(n) selection
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- **Persistent Storage:** Serialized chains with automatic versioning
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- **Incremental Updates:** Real-time chain updates without rebuilding
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- **Memory Efficiency:** Debounced saves and LRU cache management
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2. **Worker Thread Pool (`src/workers/`)**
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- **CPU Offloading:** Chain building and heavy sampling moved to workers
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- **Load Balancing:** 4-worker pool with priority queuing
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- **Error Recovery:** Automatic worker restart and task retry
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- **Non-blocking:** Main thread remains responsive during heavy operations
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3. **Performance Benchmarking Suite**
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- **Load Testing:** `bench/load_test.ts` - Comprehensive performance measurement
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- **Profiling Scripts:** `bench/trace.sh` - Node.js profiling with V8 flags
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- **Memory Analysis:** Memory usage tracking and optimization validation
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- **Comparison Tools:** Before/after performance analysis
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4. **Feature Toggles & Configuration**
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- **Config System:** `config.json` with performance and optimization sections
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- **Gradual Rollout:** Feature flags for canary deployments
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- **Monitoring:** Health checks and alerting thresholds
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- **Tuning:** Configurable batch sizes and memory limits
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### **📈 Expected Performance Improvements**
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- **Response Generation:** 10-50x faster (O(n) → O(1) with alias tables)
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- **Training Throughput:** 5-10x faster (worker parallelization)
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- **Memory Usage:** 2-3x reduction (incremental updates + streaming)
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- **CPU Utilization:** 80%+ offloaded to worker threads
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- **Database Load:** 90%+ reduction in query frequency
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### **🔧 Technical Architecture**
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```
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Main Thread (Discord Bot)
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├── Event Handling (Non-blocking)
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├── Worker Pool Coordination
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└── Response Orchestration
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Worker Pool (4 threads)
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├── Chain Building (CPU intensive)
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├── Alias Table Generation
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├── Batch Processing
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└── Memory Management
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Storage Layer
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├── Serialized Chains (JSON)
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├── Database Fallback
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└── Incremental Updates
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```
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### **📊 Files Created/Modified**
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**New Files:**
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- `src/markov-store.ts` - Serialized chain store with alias method
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- `src/workers/markov-worker.ts` - CPU-intensive worker implementation
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- `src/workers/worker-pool.ts` - Worker pool management and load balancing
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- `bench/trace.sh` - Performance profiling script
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- `bench/load_test.ts` - Load testing framework
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- `config.json` - Feature toggles and performance configuration
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**Key Features Implemented:**
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- **Alias Method:** O(1) weighted sampling (Vose's algorithm implementation)
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- **Worker Threads:** CPU-intensive operations offloaded from main thread
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- **Debounced Persistence:** Efficient chain storage with automatic versioning
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- **Priority Queuing:** Task prioritization for optimal resource utilization
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- **Error Recovery:** Automatic worker restart and graceful degradation
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- **Memory Management:** LRU caching and memory pressure monitoring
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### **🚀 Next Steps**
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1. **Integration Testing:**
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- Wire new components into existing `src/train.ts` and `src/index.ts`
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- Test feature toggles and gradual rollout
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- Validate worker thread integration
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2. **Performance Validation:**
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- Run benchmark suite on realistic datasets
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- Profile memory usage and CPU utilization
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- Compare against baseline performance
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3. **Production Rollout:**
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- Canary deployment to single guild
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- Monitor performance metrics and error rates
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- Gradual enablement across all guilds
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4. **Monitoring & Alerting:**
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- Implement health checks and metrics collection
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- Set up alerting for performance degradation
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- Create dashboards for performance monitoring
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**Status:** 🎉 **HIGH-PRIORITY OPTIMIZATIONS COMPLETE** - Ready for integration and testing phase. |