可更新学习型索引遇上磁盘驻留数据库管理系统——从评估到设计选择
Updatable Learned Indexes Meet Disk-Resident DBMS -- From Evaluations to Design Choices
AI总结：
本研究在磁盘环境下实现并对比四种前沿可更新学习型索引与B+-树的性能，总结其优劣与五类共性缺陷，提出四项指导磁盘可更新学习型索引设计的原则。
摘要：
尽管近年来已有多种可更新学习型索引被提出，但它们在磁盘环境下能否超越传统方法仍未可知。本研究重新梳理并在磁盘上实现了四种最先进的可更新学习型索引，在多种设置下将它们与B+-树进行对比。通过评估，我们得出几项关键结论：1）总体而言，B+-树在多种工作负载类型和数据集下均表现良好。2）在特定工作负载下，学习型索引的性能可能优于磁盘上的B+-树或其他学习型索引。例如，PGM在只写工作负载中表现最佳，而LIPP在只读查找工作负载中显著优于其他索引。我们进一步开展了详细的性能分析，揭示这些学习型索引在磁盘上的优势与不足。此外，我们将观察到的共性缺陷归纳为五大类，并提出四项设计原则，以指导未来磁盘驻留可更新学习型索引的设计：（1）降低索引的树高度；（2）采用更优的数据结构以降低操作开销；（3）提升扫描操作的效率；（4）实现更高效的存储布局。
英文摘要：
Although many updatable learned indexes have been proposed in recent years, whether they can outperform traditional approaches on disk remains unknown. In this study, we revisit and implement four state-of-the-art updatable learned indexes on disk, and compare them against the B+-tree under a wide range of settings. Through our evaluation, we make some key observations: 1) Overall, the B+-tree performs well across a range of workload types and datasets. 2) A learned index could outperform B+-tree or other learned indexes on disk for a specific workload. For example, PGM achieves the best performance in write-only workloads while LIPP significantly outperforms others in lookup-only workloads. We further conduct a detailed performance analysis to reveal the strengths and weaknesses of these learned indexes on disk. Moreover, we summarize the observed common shortcomings in five categories and propose four design principles to guide future design of on-disk, updatable learned indexes: (1) reducing the index's tree height, (2) better data structures to lower operation overheads, (3) improving the efficiency of scan operations, and (4) more efficient storage layout.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
