具有精确位置的可更新学习索引
Updatable Learned Index with Precise Positions
摘要：
索引在现代数据库引擎中起着加速查询处理的重要作用。“学习索引”这一新的范式极大地改变了DBMS中索引结构的设计方式。关键的见解是，索引可以被视为预测数据集中查找键位置的学习模型。虽然这些研究在查找时间和索引大小方面都显示出有希望的结果，但它们不能有效地支持更新操作。虽然最近的研究已经提出了一些初步的方法来支持更新，他们是牺牲的查找性能的代价，因为他们遭受的开销所带来的不精确的预测在叶节点。在本文中，我们提出了LIPP，一个全新的框架学习索引来解决这些问题。与最先进的索引结构类似，LIPP能够支持各种索引操作，即查找查询，范围查询，插入，删除，更新和批量加载。同时，克服了以往研究的局限性，在处理更新操作时对树结构进行适当的扩展，消除了叶节点中模型预测位置的偏差。此外，我们进一步提出了一种动态调整策略，以确保树索引的高度是紧密有界的，并提供了全面的理论分析来说明它。结果表明，我们的方法始终优于最先进的解决方案，对于具有不同索引操作的更广泛类别的工作负载，实现高达4倍的性能。
英文摘要：
Index plays an essential role in modern database engines to accelerate the query processing. The new paradigm of "learned index" has significantly changed the way of designing index structures in DBMS. The key insight is that indexes could be regarded as learned models that predict the position of a lookup key in the dataset. While such studies show promising results in both lookup time and index size, they cannot efficiently support update operations. Although recent studies have proposed some preliminary approaches to support update, they are at the cost of scarifying the lookup performance as they suffer from the overheads brought by imprecise predictions in the leaf nodes. In this paper, we propose LIPP, a brand new framework of learned index to address such issues. Similar with state-of-the-art learned index structures, LIPP is able to support all kinds of index operations, namely lookup query, range query, insert, delete, update and bulkload. Meanwhile, we overcome the limitations of previous studies by properly extending the tree structure when dealing with update operations so as to eliminate the deviation of location predicted by the models in the leaf nodes. Moreover, we further propose a dynamic adjustment strategy to ensure that the height of the tree index is tightly bounded and provide comprehensive theoretical analysis to illustrate it. We conduct an extensive set of experiments on several real-life and synthetic datasets. The results demonstrate that our method consistently outperforms state-of-the-art solutions, achieving by up to 4x for a broader class of workloads with different index operations.
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。
