Simba: Efficient in-memory spatial analytics

Dong Xie, Feifei Li, Bin Yao, Gefei Li, Liang Zhou, Minyi Guo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

120 Scopus citations

Abstract

Large spatial data becomes ubiquitous. As a result, it is critical to provide fast, scalable, and high-throughput spatial queries and analytics for numerous applications in location-based services (LBS). Traditional spatial databases and spatial analytics systems are diskbased and optimized for IO efficiency. But increasingly, data are stored and processed in memory to achieve low latency, and CPU time becomes the new bottleneck. We present the Simba (Spatial In-Memory Big data Analytics) system that offers scalable and efficient in-memory spatial query processing and analytics for big spatial data. Simba is based on Spark and runs over a cluster of commodity machines. In particular, Simba extends the Spark SQL engine to support rich spatial queries and analytics through both SQL and the DataFrame API. It introduces indexes over RDDs in order to work with big spatial data and complex spatial operations. Lastly, Simba implements an effective query optimizer, which leverages its indexes and novel spatial-aware optimizations, to achieve both low latency and high throughput. Extensive experiments over large data sets demonstrate Simba's superior performance compared against other spatial analytics system.

Original languageEnglish (US)
Title of host publicationSIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages1071-1085
Number of pages15
ISBN (Electronic)9781450335317
DOIs
StatePublished - Jun 26 2016
Event2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016 - San Francisco, United States
Duration: Jun 26 2016Jul 1 2016

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
Volume26-June-2016
ISSN (Print)0730-8078

Other

Other2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016
CountryUnited States
CitySan Francisco
Period6/26/167/1/16

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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