Tsunami: a learned multi-dimensional index for correlated data and skewed workloads
Name
3425879.3425880.pdf
Description
Published version
Size
1.91 MB
Format
Adobe PDF
Checksum (MD5)
1a116a94ce96e3e84eb9efd0d5de76a2
Author(s)
Ding, Jialin
Nathan, Vikram
Alizadeh, Mohammad
Kraska, Tim
Date Issued
2020
Journal
Proceedings of the VLDB Endowment
Publisher
VLDB Endowment
Version
Final published version
Abstract
© 2020, VLDB Endowment. All rights reserved. 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 6× faster query performance and up to 8× smaller index size than existing learned multi-dimensional indexes, in addition to up to 11× faster query performance and 170× smaller index size than optimally-tuned traditional indexes.
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-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.14778/3425879.3425880
