SLM-DB: Single-Level Key-Value Store with Persistent Memory


Olzhas Kaiyrakhmet and Songyi Lee, UNIST; Beomseok Nam, Sungkyunkwan University; Sam H. Noh and Young-ri Choi, UNIST


This paper investigates how to leverage emerging byte-addressable persistent memory (PM) to enhance the performance of key-value (KV) stores. We present a novel KV store, the Single-Level Merge DB (SLM-DB), which takes advantage of both the B+-tree index and the Log-Structured Merge Trees (LSM-tree) approach by making the best use of fast persistent memory. Our proposed SLM-DB achieves high read performance as well as high write performance with low write amplification and near-optimal read amplification. In SLM-DB, we exploit persistent memory to maintain a B+-tree index and adopt an LSM-tree approach to stage inserted KV pairs in a PM resident memory buffer. SLM-DB has a single-level organization of KV pairs on disks and performs selective compaction for the KV pairs, collecting garbage and keeping the KV pairs sorted sufficiently for range query operations. Our extensive experimental study demonstrates that, in our default setup, compared to LevelDB, SLM-DB provides 1.07 - 1.96 and 1.56 - 2.22 times higher read and write throughput, respectively, as well as comparable range query performance.

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@inproceedings {227826,
author = {Olzhas Kaiyrakhmet and Songyi Lee and Beomseok Nam and Sam H. Noh and Young-ri Choi},
title = {{SLM-DB}: {Single-Level} {Key-Value} Store with Persistent Memory},
booktitle = {17th USENIX Conference on File and Storage Technologies (FAST 19)},
year = {2019},
isbn = {978-1-939133-09-0},
address = {Boston, MA},
pages = {191--205},
url = {},
publisher = {USENIX Association},
month = feb,

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