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