Work

SuccinctKV: a CPU-efficient LSM-tree Based KV Store with Scan-based Compaction

Year: 2024

Type: article

Abstract: The CPU overhead of the LSM-tree becomes increasingly significant when high-speed storage devices are utilized. In this article, we propose SuccinctKV , a key-value store based on LSM-tree that is optimized to improve CPU efficiency in mixed workload scenarios. To achieve this, SuccinctKV reduces the CPU overhead of compaction by writing scan-sorted data directly to the storage device. SuccinctKV also redesigns the merge-sort operation of the LSM-tree, enhancing CPU locality and reducing the unnecessary CPU overhead of cache accesses and I/O system calls. Additionally, SuccinctKV introduces a scheduler to resolve potential bursty I/O contention by autonomously initiating I/O requests at appropriate times and quickly relieving I/O pressure by terminating background I/O requests. We implement SuccinctKV on RocksDB and conduct extensive experiments to evaluate our proposed methods. The experimental results demonstrate that, compared with RocksDB, SuccinctKV achieves a maximum improvement of 2.6x in scan performance and reduces CPU overhead of compaction by up to 89% under mixed workloads.

Source: ACM Transactions on Architecture and Code Optimization

DOI: 10.1145/3695873

Authors: Yinan Zhang, Shun Yang, Huiqi Hu, Chengcheng Yang, Peng Cai, Xuan Zhou

Institution: East China Normal University

Language: English

Topic: Advanced Data Storage Technologies

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Natural Science Foundation of China

Award: G2489148954