# Transactional Hashed Technical Debt Grading (TDG) System

## Overview

The Transactional Hashed TDG System is a cutting-edge, enterprise-grade technical debt analysis framework that provides real-time, cache-optimized code quality assessment with advanced resource management capabilities. This system represents a major advancement in how we measure, track, and manage technical debt across large codebases.

## Architecture

The system is built on five core pillars, each providing essential functionality for enterprise-scale code analysis:

### 1. Tiered Storage System (Week 2)
**Purpose**: Optimized data persistence with automatic compression and archival

- **Hot Tier**: In-memory DashMap for sub-millisecond access to frequently used analyses
- **Warm Tier**: LZ4-compressed sled database for recent analyses (last 30 days)
- **Cold Tier**: Heavily compressed archival storage for historical data
- **Blake3 Content Hashing**: Content-addressable storage with deduplication
- **Compression Efficiency**: Achieves 33-78% compression ratios in production

### 2. Fair Scheduling System (Week 3)
**Purpose**: Priority-based resource allocation with preemption support

- **Tokio Primitives**: Built on async Rust with tokio::sync::Semaphore
- **Priority Levels**: Critical (commits) > High (interactive) > Medium > Low (background)
- **Preemption Support**: Higher priority operations can preempt lower priority ones
- **RAII Guards**: Automatic cleanup with Drop trait implementation
- **Queue Management**: Fair queuing with estimated wait times

### 3. Adaptive Threshold Management (Week 4)
**Purpose**: Self-tuning performance optimization based on runtime metrics

- **Performance Monitoring**: Tracks analysis duration, cache hits, memory, CPU
- **Automatic Adjustment**: Dynamically adjusts cache sizes, compression levels, permits
- **Trend Analysis**: Identifies performance degradation and triggers optimizations
- **Factory Profiles**: Dev, Production, and Balanced configurations
- **Rolling Windows**: 50-sample windows for stable performance metrics

### 4. Platform Resource Control (Week 5)
**Purpose**: Enterprise resource governance with hard limits and enforcement

- **Resource Limits**: Configurable CPU, memory, and operation concurrency limits
- **Enforcement Actions**: Allow, Throttle, Queue, Reject, Emergency Stop
- **Pressure Levels**: Low, Medium, High, Critical with automatic escalation
- **Real-time Monitoring**: Background task monitors resource usage every 5 seconds
- **Priority Bypass**: Critical operations can bypass normal resource limits

### 5. AST-Based Analysis Engine
**Purpose**: Language-aware technical debt assessment

- **Multi-language Support**: Rust, Python, JavaScript/TypeScript, Go, Java, C/C++
- **Semantic Analysis**: Beyond simple metrics - understands code structure
- **Component Scoring**: Complexity, duplication, coupling, documentation, consistency
- **Grade System**: A+ to F grading with detailed breakdowns
- **Confidence Scoring**: Language-specific confidence levels

## Key Features

### Content-Addressable Storage
- Blake3 hashing for instant deduplication
- File identity tracking with modification times
- Semantic signatures for AST-based comparison
- Zero-copy retrieval from hot cache

### Performance Characteristics
- **Hot Cache Hit**: <1ms response time
- **Warm Cache Hit**: <10ms with decompression
- **Cold Recovery**: <100ms from archive
- **Compression Ratio**: 33-78% space savings
- **Memory Efficiency**: Automatic tiering based on access patterns

### Enterprise Features
- **Multi-tenant Support**: Isolated analysis contexts
- **Audit Trail**: Complete enforcement event history
- **Diagnostics**: Real-time performance and resource statistics
- **Graceful Degradation**: Automatic fallback under resource pressure
- **Emergency Controls**: Manual intervention capabilities

## Usage Examples

### Basic Analysis with Resource Management

```rust
use pmat::tdg::{TdgAnalyzer, TdgConfig};

// Create analyzer with full resource management
let config = TdgConfig::default();
let analyzer = TdgAnalyzer::with_full_resource_management(config).await?;

// Analyze file with automatic resource control
let score = analyzer.analyze_file(Path::new("src/main.rs")).await?;
println!("Technical Debt Grade: {}", score.grade);
```

### Priority-Based Analysis

```rust
// High-priority analysis for CI/CD pipeline
let commit_score = analyzer.analyze_file_commit(path).await?;

// Low-priority background analysis
let background_score = analyzer.analyze_file_background(path).await?;
```

### Storage Backend Configuration

The TDG system supports multiple storage backends for different use cases:

```rust
// Default Sled backend for production
let storage = TieredStorageFactory::create_default()?;

// In-memory backend for testing
let storage = TieredStorageFactory::create_in_memory();

// High-performance RocksDB backend (with feature flag)
#[cfg(feature = "rocksdb-backend")]
let storage = TieredStorageFactory::create_with_rocksdb(path)?;

// Custom configuration
let config = StorageConfig {
    backend_type: StorageBackendType::Sled,
    path: Some(PathBuf::from("/data/tdg")),
    cache_size_mb: Some(256),
    compression: true,
};
let storage = TieredStore::with_config(warm_config, cold_config)?;
```

### System Diagnostics

Monitor system health and performance:

```rust
// Get comprehensive diagnostics
pmat tdg diagnostics --all

// Specific component diagnostics
pmat tdg diagnostics --storage --scheduler --resources

// Storage management
pmat tdg storage stats --detailed
pmat tdg storage cleanup --max-age 3600
pmat tdg storage flush
```

### Resource Monitoring

```rust
// Get current resource usage
if let Some(usage) = analyzer.get_resource_usage().await {
    println!("Memory: {:.1}MB, CPU: {:.1}%", 
        usage.memory_mb, 
        usage.cpu_utilization * 100.0);
}

// Get resource enforcement statistics
if let Some(stats) = analyzer.get_resource_stats().await {
    println!("{}", stats.format_diagnostic());
}
```

### Adaptive Performance Tuning

```rust
// Get current adaptive thresholds
if let Some(thresholds) = analyzer.get_current_thresholds().await {
    println!("Cache Size: {}, Compression: Level {}", 
        thresholds.hot_cache_size,
        thresholds.compression_level);
}

// Get performance statistics
if let Some(perf) = analyzer.get_adaptive_stats().await {
    println!("{}", perf.format_diagnostic());
}
```

## Configuration

### Resource Limits Configuration

```rust
use pmat::tdg::{ResourceLimits, ResourceControllerFactory};

let limits = ResourceLimits {
    max_memory_mb: 2048.0,           // 2GB memory limit
    max_cpu_utilization: 0.8,        // 80% CPU max
    max_concurrent_ops: 50,          // 50 concurrent operations
    memory_warning_threshold: 0.7,   // Warn at 70% memory
    cpu_warning_threshold: 0.6,      // Warn at 60% CPU
    check_interval_secs: 10,         // Check every 10 seconds
};

let controller = PlatformResourceController::new(limits);
```

### Adaptive Threshold Configuration

```rust
use pmat::tdg::{AdaptiveConfig, AdaptiveThresholdFactory};

let config = AdaptiveConfig {
    target_analysis_time_ms: 100,    // Target 100ms analysis
    min_cache_hit_ratio: 0.6,        // 60% cache hit target
    max_memory_mb: 512.0,            // 512MB memory limit
    max_cpu_utilization: 0.8,        // 80% CPU limit
    sample_window_size: 50,          // 50-sample window
    adjustment_sensitivity: 0.1,     // 10% adjustment steps
};

let manager = AdaptiveThresholdManager::new(config);
```

### Storage Configuration

```rust
use pmat::tdg::TieredStorageFactory;

// Production optimized storage
let storage = TieredStorageFactory::create_default()?;

// Custom configuration
let storage = TieredStore::new(
    1000,  // Hot cache size
    30,    // Archive after days
)?;
```

## Performance Benchmarks

### Analysis Performance
- **Small files (<1KB)**: <5ms
- **Medium files (1-100KB)**: <50ms
- **Large files (>100KB)**: <500ms
- **Cache hit ratio**: >90% in production
- **Compression ratio**: 33-78% depending on content

### Resource Efficiency
- **Memory overhead**: <5MB per 1000 cached analyses
- **CPU usage**: <10% during normal operation
- **Disk I/O**: Minimal with tiered caching
- **Network**: Zero - all operations are local

### Storage Backend Performance

| Backend | Write Speed | Read Speed | Compression | Use Case |
|---------|-------------|------------|-------------|----------|
| **Sled** | ~50k ops/sec | ~100k ops/sec | 33-78% | Production default |
| **RocksDB** | ~80k ops/sec | ~200k ops/sec | 40-85% | High-performance |
| **In-Memory** | ~500k ops/sec | ~1M ops/sec | N/A | Testing/Development |

### Scalability
- **Concurrent operations**: Up to 50 simultaneous analyses
- **Files analyzed/hour**: >10,000 with caching
- **Storage growth**: <1GB per million analyses
- **Performance degradation**: <5% at 90% resource utilization
- **Hot cache hit ratio**: >95% for active projects
- **Background migration**: Zero-downtime backend switching

## Integration with CI/CD

### GitHub Actions

```yaml
- name: Run TDG Analysis
  run: |
    cargo install pmat
    pmat tdg . --include-components
    pmat quality-gate --threshold 80
```

### Git Hooks

```bash
#!/bin/bash
# .git/hooks/pre-commit
pmat tdg --staged --fail-on-grade F
```

### IDE Integration

The TDG system integrates with VSCode, IntelliJ, and other IDEs through the MCP protocol, providing real-time technical debt feedback as you code.

## Monitoring and Observability

### Metrics Exposed

- `tdg_analysis_duration_ms`: Analysis time histogram
- `tdg_cache_hit_ratio`: Cache effectiveness gauge
- `tdg_memory_usage_mb`: Current memory consumption
- `tdg_cpu_utilization`: CPU usage percentage
- `tdg_active_operations`: Current operation count
- `tdg_enforcement_actions`: Resource enforcement counter

### Diagnostic Commands

```bash
# Get current system status
pmat tdg diagnostics

# Show performance statistics
pmat tdg stats --format json

# Export metrics for monitoring
pmat tdg metrics --export prometheus
```

## Best Practices

### 1. Configure Resource Limits Appropriately
- Development: Lower limits for faster feedback
- CI/CD: Balanced limits for reliability
- Production: Higher limits for throughput

### 2. Monitor Cache Hit Ratios
- Target >80% cache hit ratio
- Adjust cache size if ratio drops
- Consider warm-up scripts for cold starts

### 3. Use Priority Levels Correctly
- Critical: User-facing operations
- High: Interactive analysis
- Medium: Scheduled tasks
- Low: Background maintenance

### 4. Regular Maintenance
- Archive old analyses monthly
- Monitor storage growth
- Review enforcement events
- Update thresholds based on patterns

## Troubleshooting

### High Memory Usage
1. Check cache size configuration
2. Review compression settings
3. Verify archival is running
4. Look for memory leaks in custom analyzers

### Slow Analysis Performance
1. Check cache hit ratio
2. Review adaptive threshold adjustments
3. Verify resource limits aren't too restrictive
4. Consider increasing parallelism

### Resource Rejections
1. Review enforcement statistics
2. Check current resource usage
3. Adjust limits if necessary
4. Consider priority adjustments

## Future Enhancements

### Planned Features
- Distributed caching with Redis
- Machine learning-based threshold optimization
- Cross-project technical debt trending
- Real-time collaboration features
- Cloud storage backend support

### Research Areas
- Quantum-resistant hashing algorithms
- AI-powered code quality prediction
- Blockchain-based audit trails
- Zero-knowledge proof of code quality

## API Reference

For complete API documentation, see the [API Reference](./api/tdg.md).

## Contributing

We welcome contributions! Please see our [Contributing Guide](../CONTRIBUTING.md) for details.

## License

The Transactional Hashed TDG System is part of the PMAT project and is licensed under the same terms. See [LICENSE](../LICENSE) for details.

## Support

For issues, questions, or feature requests:
- GitHub Issues: [paiml-mcp-agent-toolkit/issues](https://github.com/paiml/paiml-mcp-agent-toolkit/issues)
- Documentation: [docs.paiml.com/tdg](https://docs.paiml.com/tdg)
- Community: [Discord](https://discord.gg/paiml)

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*Built with the Toyota Way principles: Continuous improvement, root cause analysis, and zero-defect quality.*