# LearnedKV: Integrating LSM and Learned Index for Superior Performance on Storage

arXiv: https://arxiv.org/abs/2406.18892 · HTML: https://mlsanity.com/paper/2406.18892/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [The Case for Learned Index Structures](https://mlsanity.com/paper/1712.01208/) (2017)
- [RadixSpline: A Single-Pass Learned Index](https://mlsanity.com/paper/2004.14541/) (2020)
- [FITing-Tree: A Data-aware Index Structure](https://mlsanity.com/paper/1801.10207/) (2018)
- [SOSD: A Benchmark for Learned Indexes](https://mlsanity.com/paper/1911.13014/) (2019)
- [The PGM-index: a multicriteria, compressed and learned approach to data indexing](https://mlsanity.com/paper/1910.06169/) (2019)
- [Learned Indexes for a Google-scale Disk-based Database](https://mlsanity.com/paper/2012.12501/) (2020)
- [Updatable Learned Indexes Meet Disk-Resident DBMS -- From Evaluations to Design Choices](https://mlsanity.com/paper/2305.01237/) (2023)
- [Enabling Efficient Updates in KV Storage via Hashing: Design and Performance Evaluation](https://mlsanity.com/paper/1811.10000/) (2018)
- [Lethe: A Tunable Delete-Aware LSM Engine (Updated Version)](https://mlsanity.com/paper/2006.04777/) (2020)

## Abstract (arXiv)

We present LearnedKV, a novel tiered key-value store that seamlessly integrates a Log-Structured Merge (LSM) tree with a Learned Index to achieve superior read and write performance on storage systems. While existing approaches use learned indexes primarily as auxiliary components within LSM trees, LearnedKV employs a two-tier design where the LSM tree handles recent write operations while a separate Learned Index accelerates read performance. Our design includes a non-blocking conversion mechanism that efficiently transforms LSM data into a Learned Index during garbage collection, maintaining high performance without interrupting operations. LearnedKV dramatically reduces LSM size through this tiered approach, leading to significant performance gains in both reads and writes. Extensive evaluations across diverse workloads show that LearnedKV outperforms state-of-the-art LSM-based solutions by up to 4.32x for read operations and 1.43x for writes. The system demonstrates robust performance across different data distributions, access patterns, and storage media including both SSDs and HDDs.