R*-Grove: Balanced Spatial Partitioning for Large-Scale Datasets

Abstract

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The rapid growth of big spatial data urged the research community to develop several big spatial data systems. Regardless of their architecture, one of the fundamental requirements of all these systems is to spatially partition the data efficiently across machines. The core challenges of big spatial partitioning are building high spatial quality partitions while simultaneously taking advantages of distributed processing models by providing load balanced partitions. Previous works on big spatial partitioning are to reuse existing index search trees as-is, e.g., the R-tree family, STR, Kd-tree, and Quad-tree, by building a temporary tree for a sample of the input and use its leaf nodes as partition boundaries. However, we show in this paper that none of those techniques has addressed the mentioned challenges completely. This paper proposes a novel partitioning method, termed R*-Grove, which can partition very large spatial datasets into high quality partitions with excellent load balance and block utilization. This appealing property allows R*-Grove to outperform existing techniques in spatial query processing. R*-Grove can be easily integrated into any big data platforms such as Apache Spark or Apache Hadoop. Our experiments show that R*-Grove outperforms the existing partitioning techniques for big spatial data systems. With all the proposed work publicly available as open source, we envision that R*-Grove will be adopted by the community to better serve big spatial data research.

Keywords

• big spatial data
• partitioning
• R*-Grove
• index optimization
• query processing