6ed69a3d48
Major changes: - Organized Python v1 implementation into v1/ subdirectory - Created Rust workspace with 9 modular crates: - wifi-densepose-core: Core types, traits, errors - wifi-densepose-signal: CSI processing, phase sanitization, FFT - wifi-densepose-nn: Neural network inference (ONNX/Candle/tch) - wifi-densepose-api: Axum-based REST/WebSocket API - wifi-densepose-db: SQLx database layer - wifi-densepose-config: Configuration management - wifi-densepose-hardware: Hardware abstraction - wifi-densepose-wasm: WebAssembly bindings - wifi-densepose-cli: Command-line interface Documentation: - ADR-001: Workspace structure - ADR-002: Signal processing library selection - ADR-003: Neural network inference strategy - DDD domain model with bounded contexts Testing: - 69 tests passing across all crates - Signal processing: 45 tests - Neural networks: 21 tests - Core: 3 doc tests Performance targets: - 10x faster CSI processing (~0.5ms vs ~5ms) - 5x lower memory usage (~100MB vs ~500MB) - WASM support for browser deployment
163 lines
3.5 KiB
Markdown
163 lines
3.5 KiB
Markdown
# bottleneck detect
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Analyze performance bottlenecks in swarm operations and suggest optimizations.
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## Usage
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```bash
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npx claude-flow bottleneck detect [options]
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```
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## Options
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- `--swarm-id, -s <id>` - Analyze specific swarm (default: current)
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- `--time-range, -t <range>` - Analysis period: 1h, 24h, 7d, all (default: 1h)
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- `--threshold <percent>` - Bottleneck threshold percentage (default: 20)
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- `--export, -e <file>` - Export analysis to file
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- `--fix` - Apply automatic optimizations
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## Examples
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### Basic bottleneck detection
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```bash
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npx claude-flow bottleneck detect
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```
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### Analyze specific swarm
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```bash
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npx claude-flow bottleneck detect --swarm-id swarm-123
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```
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### Last 24 hours with export
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```bash
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npx claude-flow bottleneck detect -t 24h -e bottlenecks.json
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```
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### Auto-fix detected issues
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```bash
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npx claude-flow bottleneck detect --fix --threshold 15
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```
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## Metrics Analyzed
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### Communication Bottlenecks
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- Message queue delays
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- Agent response times
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- Coordination overhead
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- Memory access patterns
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### Processing Bottlenecks
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- Task completion times
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- Agent utilization rates
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- Parallel execution efficiency
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- Resource contention
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### Memory Bottlenecks
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- Cache hit rates
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- Memory access patterns
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- Storage I/O performance
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- Neural pattern loading
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### Network Bottlenecks
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- API call latency
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- MCP communication delays
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- External service timeouts
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- Concurrent request limits
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## Output Format
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```
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🔍 Bottleneck Analysis Report
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━━━━━━━━━━━━━━━━━━━━━━━━━━━
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📊 Summary
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├── Time Range: Last 1 hour
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├── Agents Analyzed: 6
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├── Tasks Processed: 42
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└── Critical Issues: 2
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🚨 Critical Bottlenecks
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1. Agent Communication (35% impact)
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└── coordinator → coder-1 messages delayed by 2.3s avg
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2. Memory Access (28% impact)
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└── Neural pattern loading taking 1.8s per access
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⚠️ Warning Bottlenecks
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1. Task Queue (18% impact)
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└── 5 tasks waiting > 10s for assignment
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💡 Recommendations
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1. Switch to hierarchical topology (est. 40% improvement)
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2. Enable memory caching (est. 25% improvement)
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3. Increase agent concurrency to 8 (est. 20% improvement)
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✅ Quick Fixes Available
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Run with --fix to apply:
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- Enable smart caching
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- Optimize message routing
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- Adjust agent priorities
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```
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## Automatic Fixes
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When using `--fix`, the following optimizations may be applied:
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1. **Topology Optimization**
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- Switch to more efficient topology
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- Adjust communication patterns
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- Reduce coordination overhead
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2. **Caching Enhancement**
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- Enable memory caching
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- Optimize cache strategies
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- Preload common patterns
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3. **Concurrency Tuning**
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- Adjust agent counts
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- Optimize parallel execution
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- Balance workload distribution
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4. **Priority Adjustment**
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- Reorder task queues
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- Prioritize critical paths
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- Reduce wait times
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## Performance Impact
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Typical improvements after bottleneck resolution:
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- **Communication**: 30-50% faster message delivery
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- **Processing**: 20-40% reduced task completion time
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- **Memory**: 40-60% fewer cache misses
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- **Overall**: 25-45% performance improvement
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## Integration with Claude Code
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```javascript
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// Check for bottlenecks in Claude Code
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mcp__claude-flow__bottleneck_detect {
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timeRange: "1h",
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threshold: 20,
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autoFix: false
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}
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```
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## See Also
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- `performance report` - Detailed performance analysis
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- `token usage` - Token optimization analysis
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- `swarm monitor` - Real-time monitoring
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- `cache manage` - Cache optimization
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