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Apache Sedona  (formerly GeoSpark) is a new in-memory cluster computing system for processing large-scale spatial data, which extends the core of Apache Spark to support spatial datatypes, partitioning techniques, spatial indexes, and spatial operations (e.g. spatial range, nearest neighbor, and spatial join queries). Distance-based Join Queries (DJQs), like nearest neighbor join ( k NNJQ) or closest pairs queries ( k CPQ), are not supported by it. Therefore, in this paper, we investigate how to design and implement efficient DJQ distributed algorithms in  Apache Sedona , using the most appropriate spatial partitioning and other optimization techniques. The results of an extensive set of experiments with real-world datasets are presented, demonstrating that the proposed  k NNJQ and  k CPQ distributed algorithms are efficient, scalable, and robust in  Apache Sedona . Finally,  Sedona  is also compared to other similar cluster computing systems, showing the best performance for  k CPQ and competitive results for  k NNJQ.

Efficient distributed algorithms for distance join queries in spark-based spatial analytics systems