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Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in a Colossal Configuration Space

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Siqiang LuoAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 2, Issue 3

Article No.: 175, Pages 1 - 26

https://doi.org/10.1145/3654978

Published: 30 May 2024 Publication History

Abstract

Mainstream LSM-tree-based key-value stores face challenges in optimizing performance for point lookup, range lookup, and update operations concurrently due to their constrained configurations. They typically follow fixed patterns to specify the level capacity and the number of sorted runs per-level. This confines their designs to a restricted space, limiting opportunities for broader optimizations.

To address this challenge, we consider a more flexible configuration that enables independent adjustments of the number of runs per-level, size ratio, and Bloom filter settings at each LSM-tree level. By carefully analyzing the cost of each operation based on the new design space, we unveil two critical insights for optimizing the tradeoff among the three operations. Firstly, achieving efficient point lookup requires a large last level. Secondly, there is a specific correlation between the number of runs per level and size ratio that is advantageous for overall update and range lookup performance.

Based on these insights, we introduce Moose, a structure delivering an impressive overall performance for point lookup, range lookup, and update concurrently. Furthermore, we also introduce a new framework, Smoose, to navigate the design space for adapting specific workloads. We implemented Moose and Smoose on top of RocksDB and experimental results demonstrate that our proposed approach outperforms state-of-the-art LSM-tree structures across diverse workloads.

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Cited By

Chen GHe ZLi MLuo S(2024)Oasis: An Optimal Disjoint Segmented Learned Range FilterProceedings of the VLDB Endowment10.14778/3659437.365944717:8(1911-1924)Online publication date: 31-May-2024
https://dl.acm.org/doi/10.14778/3659437.3659447
Yu WLuo SYu ZCong G(2024)CAMAL: Optimizing LSM-trees via Active LearningProceedings of the ACM on Management of Data10.1145/36771382:4(1-26)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3677138

Index Terms

Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in a Colossal Configuration Space
1. Information systems
  1. Data management systems
    1. Data structures
      1. Data layout
        Record and block layout

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 2, Issue 3

SIGMOD

June 2024

1953 pages

EISSN:2836-6573

DOI:10.1145/3670010

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Published: 30 May 2024

Published in PACMMOD Volume 2, Issue 3

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Cited By

Chen GHe ZLi MLuo S(2024)Oasis: An Optimal Disjoint Segmented Learned Range FilterProceedings of the VLDB Endowment10.14778/3659437.365944717:8(1911-1924)Online publication date: 31-May-2024
https://dl.acm.org/doi/10.14778/3659437.3659447
Yu WLuo SYu ZCong G(2024)CAMAL: Optimizing LSM-trees via Active LearningProceedings of the ACM on Management of Data10.1145/36771382:4(1-26)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3677138

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