# Performance Benchmarks

Performance varies depending on the specific operations and database backend. Here are some general observations based on benchmarks:

## Small Dataset Operations (1,000 records)

| Operation | rhosocial ActiveRecord | SQLAlchemy | Django ORM | Peewee |
|-----------|-------------------|------------|------------|--------|
| Simple Retrieval | Fast | Medium | Fast | Fastest |
| Complex Query | Medium | Fast | Medium | Fast |
| Bulk Insert | Fast | Fast | Fast | Fast |
| Single Insert | Fast | Slow | Medium | Fastest |
| Validation-heavy | Fast | Slow | Medium | Slow |

## Large Dataset Operations (100,000+ records)

| Operation | rhosocial ActiveRecord | SQLAlchemy | Django ORM | Peewee |
|-----------|-------------------|------------|------------|--------|
| Simple Retrieval | Medium | Fast | Slow | Medium |
| Complex Query | Medium | Fastest | Slow | Fast |
| Bulk Insert | Fast | Fast | Medium | Fast |
| Single Insert | Medium | Slow | Slow | Fast |
| Validation-heavy | Medium | Slow | Slow | Slow |

## Memory Usage (relative comparison)

| Scenario | rhosocial ActiveRecord | SQLAlchemy | Django ORM | Peewee |
|----------|-------------------|------------|------------|--------|
| Loading 10,000 records | Medium | High | High | Low |
| Complex object hierarchy | Medium | High | High | Low |
| Validation overhead | Medium | Low | Low | Low |

## Asynchronous Performance

| Scenario | rhosocial ActiveRecord | SQLAlchemy | Django ORM | Peewee-Async |
|----------|-------------------|------------|------------|--------------|
| Concurrent operations | Excellent | Good | Limited | Good |
| Connection efficiency | Excellent | Good | Medium | Good |
| Resource utilization | Efficient | Medium | Inefficient | Medium |

## Key Observations

- Peewee generally has the lowest memory footprint due to its lightweight design
- SQLAlchemy excels at complex queries due to its query optimization
- rhosocial ActiveRecord provides balanced performance with validation benefits
- Django ORM can be slower with large datasets but performs well for typical web app loads
- The Pydantic validation in rhosocial ActiveRecord adds some overhead but prevents data issues early
- In async scenarios, rhosocial ActiveRecord's design provides excellent performance for concurrent operations