2024 · Conference paper
Trinity: A Fast Compressed Multi-attribute Data Store
ACM EuroSys, 2024
Best Student Paper Award
Abstract
Attribute-rich machine-generated data drives real-time monitoring, diagnosis, and visualization tools that analyze multiple attributes at once. Trinity facilitates both query and storage efficiency across large volumes of multi-attribute records through MDTrie, a dynamic, succinct, multidimensional data structure combining a Morton-code generalization, a multi-attribute query algorithm, and a self-indexed trie. On real-world use cases, Trinity provides 7.2–59.6 times faster multi-attribute searches, storage comparable to OLAP column stores and 4.8–15.1 times smaller than NoSQL and OLTP systems, and point-query throughput comparable to NoSQL stores and up to 52.5 times higher than OLTP and OLAP systems.
Publication details
- Venue
- ACM EuroSys
- Publication year
- 2024
- Awards
- Best Student Paper Award
BibTeX
@inproceedings{trinity,
author = {Mao, Ziming and Srinivasan, Kiran and Khandelwal, Anurag},
title = {{Trinity: A Fast Compressed Multi-attribute Data Store}},
year = {2024},
mon = {April},
booktitle = {ACM EuroSys},
award = {Best Student Paper Award}
}