Tsunami：一种相关数据和不对称工作负载的学习多维索引
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads
摘要：
基于谓词过滤数据是任何现代数据仓库的最基本操作之一。加速过滤器表达式执行的技术包括聚集索引、专用排序顺序（例如，Z顺序）、多维索引以及用于高选择性查询的二级索引。然而，这些方案很难调整，并且它们的性能不一致。最近关于学习多维索引的工作引入了自动优化特定数据集和工作负载的索引的想法。然而，在存在相关数据和倾斜查询工作负载的情况下，这项工作的性能会受到影响，这两种情况在实际应用程序中都很常见。在本文中，我们介绍了Tsunami，它解决了这些限制，实现了比现有学习多维索引快6倍的查询性能和小8倍的索引大小，以及比优化的传统索引快11倍的查询性能和小170倍的索引大小。
英文摘要：
Filtering data based on predicates is one of the most fundamental operations for any modern data warehouse. Techniques to accelerate the execution of filter expressions include clustered indexes, specialized sort orders (e.g., Z-order), multi-dimensional indexes, and, for high selectivity queries, secondary indexes. However, these schemes are hard to tune and their performance is inconsistent. Recent work on learned multi-dimensional indexes has introduced the idea of automatically optimizing an index for a particular dataset and workload. However, the performance of that work suffers in the presence of correlated data and skewed query workloads, both of which are common in real applications. In this paper, we introduce Tsunami, which addresses these limitations to achieve up to 6X faster query performance and up to 8X smaller index size than existing learned multi-dimensional indexes, in addition to up to 11X faster query performance and 170X smaller index size than optimally-tuned traditional indexes.
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。
