Spatial Join Query Processing in Cloud: Analyzing Design Choices and Performance Comparisons

被引：14

作者：

You, Simin ^{[1
]}

Zhang, Jianting ^{[2
]}

Gruenwald, Le ^{[3
]}

机构：

[1] CUNY Grad Ctr, Dept Comp Sci, New York, NY 10016 USA

[2] CUNY City Coll, Dept Comp Sci, New York, NY 10031 USA

[3] Univ Oklahoma, Dept Comp Sci, Norman, OK 73019 USA

来源：

2015 44TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING WORKSHOPS | 2015年

关键词：

Spatial Join; Query Processing; Cloud Computing; Design; Performance;

D O I：

10.1109/ICPPW.2015.41

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Data volumes of GPS recorded locations and many other types of geospatial data are fast increasing. Processing large-scale spatial joins in Cloud for performance and scalability is becoming increasingly popular. In this study, we compare three leading Cloud-based spatial data management systems, namely HadoopGIS, SpatialHadoop and SpatialSpark, both conceptually through analysis of design choices and empirically through experiments using real world datasets. Using both a workstation serving as a single-node cluster and up to 10 nodes Amazon EC2 clusters, the results show that the combined factors, including Cloud platforms, data access models and the underlying geometry libraries, have significant impacts in their realized performance. While SpatialHadoop generally wins on robustness, SpatialSpark is the clear winner of efficiency due to in-memory processing.

引用

页码：90 / 97

页数：8