LMG Index: A Robust Learned Index for Multi-Dimensional Performance Balance
Published:Dec 31, 2025 12:25
•2 min read
•ArXiv
Analysis
This paper introduces LMG Index, a learned indexing framework designed to overcome the limitations of existing learned indexes by addressing multiple performance dimensions (query latency, update efficiency, stability, and space usage) simultaneously. It aims to provide a more balanced and versatile indexing solution compared to approaches that optimize for a single objective. The core innovation lies in its efficient query/update top-layer structure and optimal error threshold training algorithm, along with a novel gap allocation strategy (LMG) to improve update performance and stability under dynamic workloads. The paper's significance lies in its potential to improve database performance across a wider range of operations and workloads, offering a more practical and robust indexing solution.
Key Takeaways
- •LMG Index is a learned indexing framework designed for balanced performance across multiple dimensions.
- •It uses an efficient query/update top-layer structure and an optimal error threshold training algorithm.
- •LMG, a variant of LMIndex, employs a gap allocation strategy to improve update performance and stability.
- •Evaluations show LMG outperforms existing methods in various aspects, including query speed, update efficiency, and space usage.
Reference
“LMG achieves competitive or leading performance, including bulk loading (up to 8.25x faster), point queries (up to 1.49x faster), range queries (up to 4.02x faster than B+Tree), update (up to 1.5x faster on read-write workloads), stability (up to 82.59x lower coefficient of variation), and space usage (up to 1.38x smaller).”