# On Performance Stability in LSM-based Storage Systems (Extended Version)

arXiv: https://arxiv.org/abs/1906.09667 · HTML: https://mlsanity.com/paper/1906.09667/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads](https://mlsanity.com/paper/2308.07013/) (2023)
- [CAMAL: Optimizing LSM-trees via Active Learning](https://mlsanity.com/paper/2409.15130/) (2024)

## Abstract (arXiv)

The Log-Structured Merge-Tree (LSM-tree) has been widely adopted for use in modern NoSQL systems for its superior write performance. Despite the popularity of LSM-trees, they have been criticized for suffering from write stalls and large performance variances due to the inherent mismatch between their fast in-memory writes and slow background I/O operations. In this paper, we use a simple yet effective two-phase experimental approach to evaluate write stalls for various LSM-tree designs. We further explore the design choices of LSM merge schedulers to minimize write stalls given an I/O bandwidth budget. We have conducted extensive experiments in the context of the Apache AsterixDB system and we present the results here.