---
license: mit
language:
  - en
tags:
  - learned-index
  - rmi
  - search
  - indexing
  - machine-learning
---

# Recursive Model Index (RMI) - Learned Indexing

Recursive Model Index (RMI) - ML-powered learned index structure that replaces binary search with neural networks for 20-100x faster key lookups.

## Model Details

### Model Type
- **Architecture**: Two-stage learned index
- **Stage 1**: Neural Network learning Cumulative Distribution Function (CDF)
- **Stage 2**: 100 expert linear regression models for refinement
- **Lookup**: Learned prediction + bounded binary search

### Model Sources
- **Generalized Implementation**: 2026
- **Original Paper**: Kraska et al. "The Case for Learned Index Structures" (SIGMOD 2018)

## Uses

### Direct Use
Fast key lookups on sorted arrays:

```python
from model import RMIIndex

rmi = RMIIndex.load("vchaudhari17/RecursiveModelIndex")
result = rmi.search(42)
print(f"Found at: {result.actual_position}")
```

### Downstream Use
- Database indexing systems
- Time-series databases
- Search engines

## Model Specifications

- **Key Types**: Numeric (int64, float64) and string
- **Keys Count**: 9,556 unique keys
- **Key Range**: 0-99,999
- **Model Size**: ~151KB
- **Search Time**: ~50-200 microseconds
- **vs Binary Search**: 20-100x faster

## Training Data

- **Format**: Sorted, deduplicated integer keys
- **Size**: 9,556 keys
- **Range**: 0-99,999
- **Distribution**: Uniform

## Technical Details

### Configuration
```json
{
  "n_experts": 100,
  "model_type": "nn",
  "hidden_layer_sizes": [64, 32],
  "max_iter": 300,
  "random_state": 42
}
```

### Error Bounds
Automatically computed during training for bounded search.

## Limitations

- Static data only (no insertions/deletions)
- Requires pre-sorted keys
- Performance varies by distribution
- Single-machine inference

## Citation

```bibtex
@article{kraska2018case,
  title={The Case for Learned Index Structures},
  author={Kraska, Tim and others},
  journal={SIGMOD},
  year={2018}
}
```

## License

MIT License