SplinterDB: Closing the Bandwidth Gap for NVMe Key-Value Stores


Alexander Conway, Rutgers University and Vmware Research; Abhishek Gupta, DropBox; Vijay Chidambaram, University of Texas at Austin and VMware Research; Martin Farach-Colton, Rutgers University; Richard Spillane, VMware; Amy Tai and Rob Johnson, VMware Research


Modern NVMe solid state drives offer significantly higher bandwidth and low latency than prior storage devices. Current key-value stores struggle to fully utilize the bandwidth of such devices. This paper presents SplinterDB, a new key-value store explicitly designed for NVMe solid state drives.

SplinterDB is designed around a novel data structure (the STBε-tree), that exposes I/O and CPU concurrency and reduces write amplification without sacrificing query performance. STBε-tree combines ideas from log-structured merge trees and Bε-trees to reduce write amplification and CPU costs of compaction. The SplinterDB memtable and cache are designed to be highly concurrent and to reduce cache misses.

We evaluate SplinterDB on a number of micro- and macro-benchmarks, and show that SplinterDB outperforms RocksDB, a state-of-the-art key-value store, by a factor of 6–10x on insertions and 2–2.6x on point queries, while matching RocksDB on small range queries. Furthermore, SplinterDB reduces write amplification by 2x compared to RocksDB.

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@inproceedings {254368,
author = {Alexander Conway and Abhishek Gupta and Vijay Chidambaram and Martin Farach-Colton and Richard Spillane and Amy Tai and Rob Johnson},
title = {{SplinterDB}: Closing the Bandwidth Gap for {NVMe} {Key-Value} Stores},
booktitle = {2020 USENIX Annual Technical Conference (USENIX ATC 20)},
year = {2020},
isbn = {978-1-939133-14-4},
pages = {49--63},
url = {https://www.usenix.org/conference/atc20/presentation/conway},
publisher = {USENIX Association},
month = jul

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