FPGA-based Compaction Engine for Accelerating LSM-tree Key-Value Stores

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 36th International Conference on Data Engineering
Subtitle of host publicationICDE 2020
PublisherIEEE
Pages1261-1272
Number of pages12
ISBN (Electronic)9781728129037
ISBN (Print)9781728129044
Publication statusPublished - Apr 2020

Publication series

NameProceedings - International Conference on Data Engineering
Volume2020-April
ISSN (Print)1084-4627
ISSN (Electronic)2375-026X

Conference

Title36th IEEE International Conference on Data Engineering
PlaceUnited States
CityDallas
Period20 - 24 April 2020

Abstract

With the rapid growth of big data, LSM-tree based key-value stores are widely applied due to its high efficiency in write performance. Compaction plays a critical role in LSM-tree, which merges old data and could significantly reduce the overall throughput of the whole system especially for write-intensive workloads. Hardware acceleration for database is a popular trend in recent years. In this paper, we design and implement an FPGA-based compaction engine to accelerate compaction in LSM-tree based key-value stores. To take full advantage of the pipeline mechanism on FPGA, the key-value separation and index-data block separation strategies are proposed. In order to improve the compaction performance, the bandwidth of FPGA-chip is fully utilized. In addition, the proposed acceleration engine is integrated with a classic LSM-tree based store without modifications on the original storage format. The experimental results demonstrate that the proposed FPGA-based compaction engine can achieve up to 92.0x acceleration ratio compared with CPU baseline, and achieve up to 6.4x improvement on the throughput of random writes.

Research Area(s)

  • Compaction, FPGA, Key-value, LSM-tree

Citation Format(s)

FPGA-based Compaction Engine for Accelerating LSM-tree Key-Value Stores. / SUN, Xuan; YU, Jinghuan; ZHOU, Zimeng; XUE, Chun Jason.

Proceedings - 2020 IEEE 36th International Conference on Data Engineering: ICDE 2020. IEEE, 2020. p. 1261-1272 00113 (Proceedings - International Conference on Data Engineering; Vol. 2020-April).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)