# Performance Design

Performance considerations and optimizations in Metric Engine.

## Design Principles

- **Lazy Evaluation**: Calculations run when results are needed
- **Caching**: Expensive operations cached automatically
- **Vectorization**: Bulk operations for large datasets
- **Memory Efficiency**: Minimal object overhead

## Optimization Strategies

### Value Creation
- Object pooling for common values
- Immutable values for safe sharing
- Lightweight unit representations

### Calculations
- JIT compilation for hot paths
- Vectorized operations with NumPy
- Parallel processing for independent calculations

### Policy Resolution
- Policy caching per thread
- Fast path for default policies
- Minimal overhead in calculation loops

## Benchmarks

Target performance goals:
- Value creation: < 1µs
- Simple operations: < 5µs
- Complex calculations: < 100µs
- Bulk operations: 1M values/second

## Memory Usage

- Value objects: ~64 bytes
- Unit objects: ~32 bytes (shared)
- Policy objects: ~128 bytes (cached)

## Profiling

Built-in profiling support for:
- Calculation timing
- Memory usage
- Cache hit rates
- Policy resolution overhead