MTDB:基于 LSM 树的键值存储,使用多树结构提高读取性能

Xinwei Lin, Yubiao Pan, Wenjuan Feng, Huizhen Zhang, Mingwei Lin
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引用次数: 0

摘要

由于 LSM 树的多级结构、第 0 级中无序的 SSTable 文件以及键值对缺乏内存索引结构,传统的基于 LSM 树的键值存储系统面临着严重的读取放大问题。我们观察到,在具有位置特征的工作负载影响下,键值对表现出特定范围的访问强度。针对 LSM 树读取放大的三个原因,我们利用工作负载的特定范围访问强度,提出了一种由 B+ 树、单级热树和基于分区的 0 级 LSM 树组成的多树结构。这样做的目的是提高基于 LSM 树的键值存储系统的读取性能。我们在 LevelDB 的基础上设计了 MTDB 原型。实验结果表明,MTDB 的读取性能是 LevelDB 的 1.62 倍到 2.02 倍,接近或超过了 KVell 和 Bourbon 的读取性能,同时减少了 58.85%-86% 的内存开销。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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MTDB: an LSM-tree-based key-value store using a multi-tree structure to improve read performance

Traditional LSM-tree-based key-value storage systems face significant read amplification issues due to the multi-level structure of LSM-tree, the unordered SSTable files in Level 0, and the lack of an in-memory index structure for key-value pairs. We observed that, under the influence of workloads with locality features, key-value pairs exhibit a range-specific access intensity. Addressing the three reasons for LSM-tree read amplification, we have utilized the range-specific access intensity of workload to propose a multi-tree structure consisting of a B+ tree, a single-level hot tree, and an LSM-tree with partition-based Level 0. This aims to enhance the read performance of LSM-tree-based key-value storage systems. We designed the prototype, MTDB, based on LevelDB. The experimental results show that MTDB’s read performance is 1.62× to 2.02× that of LevelDB, and it approaches or exceeds the read performance of KVell and Bourbon while reducing memory overhead by 58.85%–86%.

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