HotRing: A Hotspot-Aware In-Memory Key-Value Store

Authors: 

Jiqiang Chen, Liang Chen, Sheng Wang, Guoyun Zhu, Yuanyuan Sun, Huan Liu, and Feifei Li, Alibaba Group

Abstract: 

Due to travel restrictions, the authors could not attend the conference and their work was presented by Le Cai, Alibaba Group.

In-memory key-value stores (KVSes) are widely used to cache hot data, in order to solve the hotspot issue in disk-based storage or distributed systems. The hotspot issue inside in-memory KVSes is however being overlooked. Due to the recent trend that hotspot issue becomes more serious, the lack of hotspot-awareness in existing KVSes make them poorly performed and unreliable on highly skewed workloads. In this paper, we explore hotspot-aware designs for in-memory index structures in KVSes. We first analyze the potential benefits from ideal hotspot-aware indexes, and discuss challenges (i.e., hotspot shift and concurrent access issues) in effectively leveraging hotspot-awareness. Based on these insights, we propose a novel hotspot-aware KVS, named HotRing, that is optimized for massively concurrent accesses to a small portion of items. HotRing is based on an ordered-ring hash index structure, which provides fast access to hot items by moving head pointers closer to them. It also applies a lightweight strategy to detect hotspot shifts at run-time. HotRing comprehensively adopts lock-free structures in its design, for both common operations (i.e., read, update) and HotRing-specific operations (i.e., hotspot shift detection, head pointer movement and ordered-ring rehash), so that massively concurrent requests can better leverage multi-core architectures. The extensive experiments show that our approach is able to achieve 2.58× improvement compared to other in-memory KVSes on highly skewed workloads.

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BibTeX
@inproceedings {246162,
author = {Jiqiang Chen and Liang Chen and Sheng Wang and Guoyun Zhu and Yuanyuan Sun and Huan Liu and Feifei Li},
title = {HotRing: A Hotspot-Aware In-Memory Key-Value Store},
booktitle = {18th {USENIX} Conference on File and Storage Technologies ({FAST} 20)},
year = {2020},
isbn = {978-1-939133-12-0},
address = {Santa Clara, CA},
pages = {239--252},
url = {https://www.usenix.org/conference/fast20/presentation/chen-jiqiang},
publisher = {{USENIX} Association},
month = feb,
}

Presentation Video