PinK: High-speed In-storage Key-value Store with Bounded Tails


Junsu Im and Jinwook Bae, DGIST; Chanwoo Chung and Arvind, Massachusetts Institute of Technology; Sungjin Lee, DGIST
Awarded Best Paper!


Key-value store based on a log-structured merge-tree (LSM-tree) is preferable to hash-based KV store because an LSM-tree can support a wider variety of operations and show better performance, especially for writes. However, LSM-tree is difficult to implement in the resource constrained environment of a key-value SSD (KV-SSD) and consequently, KV-SSDs typically use hash-based schemes. We present PinK, a design and implementation of an LSM-tree-based KV-SSD, which compared to a hash-based KV-SSD, reduces 99$^{th}$ percentile tail latency by 73%, improves average read latency by 42% and shows 37% higher throughput. The key idea in improving the performance of an LSM-tree in a resource constrained environment is to avoid the use of Bloom filters and instead, use a small amount of DRAM to keep/pin the top levels of the LSM-tree.

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@inproceedings {254386,
author = {Junsu Im and Jinwook Bae and Chanwoo Chung and Arvind and Sungjin Lee},
title = {PinK: High-speed In-storage Key-value Store with Bounded Tails},
booktitle = {2020 {USENIX} Annual Technical Conference ({USENIX} {ATC} 20)},
year = {2020},
isbn = {978-1-939133-14-4},
pages = {173--187},
url = {},
publisher = {{USENIX} Association},
month = jul,

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