Vchaudhari17/RecursiveModelIndex

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:

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

{
  "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

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

License

MIT License