Learning Multi-Dimensional Indexes
Name
1912.01668.pdf
Description
Submitted version
Size
2.51 MB
Format
Unknown
Checksum (MD5)
de5613d8d89deb958d702c8a7168206e
Author(s)
Nathan, Vikram
Ding, Jialin
Alizadeh Attar, Mohammadreza
Kraska, Tim
Date Issued
2020
Journal
Proceedings of the ACM SIGMOD International Conference on Management of Data
Publisher
ACM
Version
Original manuscript
Abstract
© 2020 Association for Computing Machinery. Scanning and filtering over multi-dimensional tables are key operations in modern analytical database engines. To optimize the performance of these operations, databases often create clustered indexes over a single dimension or multi-dimensional indexes such as R-Trees, or use complex sort orders (e.g., Z-ordering). However, these schemes are often hard to tune and their performance is inconsistent across different datasets and queries. In this paper, we introduce Flood, a multi-dimensional in-memory read-optimized index that automatically adapts itself to a particular dataset and workload by jointly optimizing the index structure and data storage layout. Flood achieves up to three orders of magnitude faster performance for range scans with predicates than state-of-the-art multi-dimensional indexes or sort orders on real-world datasets and workloads. Our work serves as a building block towards an end-to-end learned database system.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3318464.3380579
