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<p>With the proliferation of cloud computing and distributed systems, efficient data indexing has become a cornerstone of large-scale data processing. While traditional index structures have long served as the backbone of database systems, they increasingly suffer from severe performance bottlenecks, including high lock contention and limited scalability—in modern high-concurrency environments. Consequently, the “Learned Index” paradigm has emerged as a transformative solution, leveraging machine learning models to approximate key distributions and accelerating query performance. However, despite their high lookup efficiency, existing learned indexes struggle to maintain stability in dynamic, large-scale scenarios: they typically rely on rigid array-based layouts that necessitate frequent, expensive model retraining or blocking structural adjustments to accommodate data drifts, and crucially, they lack native mechanisms to verify data integrity, thereby introducing prohibitive maintenance overheads and trust risks that compromise their viability in real-time, trusted cloud systems. To address these challenges in concurrent and distributed scenarios, this paper proposes FineStore-SL, an enhanced learning index designed to improve the performance and stability of cloud databases. FineStore-SL is built upon the FineStore architecture, optimizing it by replacing its original level-bin structure with a Skip List data structure, which utilizes multi-level indexing and randomized hierarchical levels to improve data processing efficiency. In addition, FineStore-SL incorporates a hash-based verification mechanism for consistency checks, ensuring data integrity while maintaining robustness against dynamic changes in distributed cloud environments. Extensive experiments demonstrate that FineStore-SL significantly outperforms state-of-the-art baselines in high-concurrency and dynamic scenarios, achieving nearly 2× higher throughput than the best-performing concurrent learned index in complex maintenance operations such as updates and deletions. Furthermore, the introduction of the hash-based verification mechanism incurs a negligible overhead of less than 2%.</p>

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FineStore-SL: An efficient and verifiable learning index based on skip list

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Xiuxia Tian,
  • Chunhui Wang

摘要

With the proliferation of cloud computing and distributed systems, efficient data indexing has become a cornerstone of large-scale data processing. While traditional index structures have long served as the backbone of database systems, they increasingly suffer from severe performance bottlenecks, including high lock contention and limited scalability—in modern high-concurrency environments. Consequently, the “Learned Index” paradigm has emerged as a transformative solution, leveraging machine learning models to approximate key distributions and accelerating query performance. However, despite their high lookup efficiency, existing learned indexes struggle to maintain stability in dynamic, large-scale scenarios: they typically rely on rigid array-based layouts that necessitate frequent, expensive model retraining or blocking structural adjustments to accommodate data drifts, and crucially, they lack native mechanisms to verify data integrity, thereby introducing prohibitive maintenance overheads and trust risks that compromise their viability in real-time, trusted cloud systems. To address these challenges in concurrent and distributed scenarios, this paper proposes FineStore-SL, an enhanced learning index designed to improve the performance and stability of cloud databases. FineStore-SL is built upon the FineStore architecture, optimizing it by replacing its original level-bin structure with a Skip List data structure, which utilizes multi-level indexing and randomized hierarchical levels to improve data processing efficiency. In addition, FineStore-SL incorporates a hash-based verification mechanism for consistency checks, ensuring data integrity while maintaining robustness against dynamic changes in distributed cloud environments. Extensive experiments demonstrate that FineStore-SL significantly outperforms state-of-the-art baselines in high-concurrency and dynamic scenarios, achieving nearly 2× higher throughput than the best-performing concurrent learned index in complex maintenance operations such as updates and deletions. Furthermore, the introduction of the hash-based verification mechanism incurs a negligible overhead of less than 2%.