An Update-intensive LSM-based R-tree Index
Jaewoo Shin, Jianguo Wang, Walid G. Aref

TL;DR
This paper presents the LSM RUM-tree, an innovative LSM-based R-tree index with an Update Memo that significantly improves performance for update-intensive spatial workloads, achieving up to 9.6x faster updates and 2400x faster queries.
Contribution
It introduces the LSM RUM-tree, a novel LSM-based R-tree with an in-memory Update Memo to efficiently support high-rate updates and queries in spatial databases.
Findings
Up to 9.6x faster update operations.
Up to 2400x faster query processing.
Effective control of Update Memo size for high performance.
Abstract
Many applications require update-intensive workloads on spatial objects, e.g., social-network services and shared-riding services that track moving objects. By buffering insert and delete operations in memory, the Log Structured Merge Tree (LSM) has been used widely in various systems because of its ability to handle write-heavy workloads. While the focus on LSM has been on key-value stores and their optimizations, there is a need to study how to efficiently support LSM-based {\em secondary} indexes (e.g., location-based indexes) as modern, heterogeneous data necessitates the use of secondary indexes. In this paper, we investigate the augmentation of a main-memory-based memo structure into an LSM secondary index structure to handle update-intensive workloads efficiently. We conduct this study in the context of an R-tree-based secondary index. In particular, we introduce the LSM RUM-tree…
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Taxonomy
TopicsData Management and Algorithms · Data Mining Algorithms and Applications · Advanced Database Systems and Queries
