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- Title
Comparison of LSM indexing techniques for storing spatial data.
- Authors
Mao, Qizhong; Qader, Mohiuddin Abdul; Hristidis, Vagelis
- Abstract
In the pre-big data era, many traditional databases supported spatial queries via spatial indexes. However, modern applications are seeing a rapid increase of the volume and ingestion rate of spatial data. Log-structured Merge (LSM) tree is used by many big data systems as their storage structure in order to support write-intensive large-volume workloads, which are usually only optimized for single-dimensional data. Research has studied how spatial indexes can be supported on LSM systems, but focused mainly on the local index organization, that is, how data is organized inside a single LSM component. This paper studies various aspects of LSM spatial indexing, including spatial merge policies, which determine when and how spatial components are merged. Three stack-based and one leveled merge policies have been studied, which have been implemented on a common big data system Apache AsterixDB. The write and read performance on various workloads is evaluated, and our findings and recommendations are discussed. A key finding is that Leveled policies underperform other stack-based merge policies for most types of spatial workloads.
- Subjects
DATA warehousing; BIG data; COMMUNITY organization; INDEXING; NONRELATIONAL databases
- Publication
Journal of Big Data, 2023, Vol 10, Issue 1, p1
- ISSN
2196-1115
- Publication type
Article
- DOI
10.1186/s40537-023-00734-3