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2024 · Conference paper

Trinity: A Fast Compressed Multi-attribute Data Store

Ziming Mao, Kiran Srinivasan and Anurag Khandelwal

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}
}