SplinterDB: Closing the Bandwidth Gap for NVMe Key-Value Stores
Alexander Conway, Abhishek Gupta, Vijay Chidambaram, Martin Farach-Colton, Richard P. Spillane, Amy Tai, Rob Johnson
Abstract
Modern NVMe solid state drives offer significantly higher bandwidth and lower latency than prior storage devices. Current key-value stores struggle to fully utilize the bandwidth of such devices. This paper presents SplinterDB, a new keyvalue store explicitly designed for NVMe solid-state-drives.
SplinterDB is designed around a novel data structure (the STB ε -tree) that exposes I/O and CPU concurrency and reduces write amplification without sacrificing query performance. STB ε -tree combines ideas from log-structured merge trees and B ε -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 6-10× on insertions and 2-2.6× on point queries, while matching RocksDB on small range queries. Furthermore, SplinterDB reduces write amplification by 2× compared to RocksDB.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 28b4db67-6f62-4c6e-9a3e-941d21980e5aCited by top-tier papers35
- SpanDB: A Fast, Cost-Effective LSM-tree Based KV Store on Hybrid StorageHao Chen, Chaoyi Ruan, Cheng Li, Xiaosong Ma et al.FAST 2021 · 120 citations
- XRP: In-Kernel Storage Functions with eBPFYuhong Zhong, Haoyu Li, Yu Jian Wu, Ioannis Zarkadas et al.OSDI 2022 · 100 citations
- What Modern NVMe Storage Can Do, And How To Exploit It: High-Performance I/O for High-Performance Storage EnginesGabriel Haas, Viktor LeisVLDB 2023 · 83 citations
- Differentiated Key-Value Storage Management for Balanced I/O PerformanceYongkun Li, Zhen Liu, Patrick P. C. Lee, Jiayu Wu et al.USENIX ATC 2021 · 79 citations
- ChameleonDB: a key-value store for optane persistent memoryWenhui Zhang, Xingsheng Zhao, Song Jiang, Hong JiangEuroSys 2021 · 72 citations
Builds on1
Related papers
- Dynamic read & write optimization with TurtleKVTony Astolfi, Vidya Silai, Darby Huye, Lan Liu et al.VLDB 2026
- TreeLine: An Update-In-Place Key-Value Store for Modern StorageGeoffrey X. Yu, Markos Markakis, Andreas Kipf, Per-Åke Larson et al.VLDB 2023 · 36 citations
- PartitionKV: Redesigning LSM-tree KV Stores on NVMs with Adaptive Partitioning for Reducing Write Stalls and AmplificationXingye Huang, Jinyu Wu, Xiaofang Xia, Jiangtao Cui et al.SIGMOD 2026
- SplinterDB and Maplets: Improving the Tradeoffs in Key-Value Store Compaction PolicyAlex Conway, Martin Farach-Colton, Rob JohnsonSIGMOD 2023 · 20 citations
- ListDB: Union of Write-Ahead Logs and Persistent SkipLists for Incremental Checkpointing on Persistent MemoryWonbae Kim, Chanyeol Park, Dongui Kim, Hyeongjun Park et al.OSDI 2022 · 47 citations
