SpanDB: A Fast, Cost-Effective LSM-tree Based KV Store on Hybrid Storage

Authors: 

Hao Chen, University of Science and Technology of China & Qatar Computing Research Institute, HBKU; Chaoyi Ruan and Cheng Li, University of Science and Technology of China; Xiaosong Ma, Qatar Computing Research Institute, HBKU; Yinlong Xu, University of Science and Technology of China & Anhui Province Key Laboratory of High Performance Computing

Abstract: 

Key-Value (KV) stores support many crucial applications and services. They perform fast in-memory processing, but are still often limited by I/O performance. The recent emergence of high-speed commodity NVMe SSDs has propelled new KV system designs that take advantage of their ultra-low latency and high bandwidth. Meanwhile, to switch to entirely new data layouts and scale up entire databases to high-end SSDs requires considerable investment. As a compromise, we propose SpanDB, an LSM-tree-based KV store that adapts the popular RocksDB system to utilize selective deployment of high-speed SSDs. SpanDB allows users to host the bulk of their data on cheaper and larger SSDs, while relocating write-ahead logs (WAL) and the top levels of the LSM-tree to a much smaller and faster NVMe SSD. To better utilize this fast disk, SpanDB provides high-speed, parallel WAL writes via SPDK, and enables asynchronous request processing to mitigate inter-thread synchronization over-head and work efficiently with polling-based I/O. Our evaluation shows that SpanDB simultaneously improves RocksDB’s throughput by up to 8.8x and reduces its latency by 9.5- 58.3%. Compared with KVell, a system designed for high-end SSDs, SpanDB achieves 96-140% of its throughput, with a 2.3-21.6x lower latency, at a cheaper storage configuration.

FAST '21 Open Access Sponsored by NetApp

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

BibTeX
@inproceedings {264834,
author = {Hao Chen and Chaoyi Ruan and Cheng Li and Xiaosong Ma and Yinlong Xu},
title = {{SpanDB}: A Fast, {Cost-Effective} {LSM-tree} Based {KV} Store on Hybrid Storage},
booktitle = {19th USENIX Conference on File and Storage Technologies (FAST 21)},
year = {2021},
isbn = {978-1-939133-20-5},
pages = {17--32},
url = {https://www.usenix.org/conference/fast21/presentation/chen-hao},
publisher = {USENIX Association},
month = feb
}

Presentation Video