GeoSpark extends Apache Spark with a set of out-of-the-box Spatial Resilient Distributed Datasets (SRDDs) that efficiently load, process, and analyze large-scale spatial data across machines.
GeoSpark provides APIs for Apache Spark programmer to easily develop their spatial analysis programs with Spatial Resilient Distributed Datasets (SRDDs) which have in house support for geometrical and distance operations.
Extensive experiments show that GeoSpark has much faster speed than Hadoop-based systems in spatial analysis applications like spatial join, spatial aggregation, and spatial co-location.
GeoSpark is an open source in-memory cluster computing system for processing large-scale spatial data. GeoSpark extends RDDs to form Spatial RDDs (SRDDs) and efficiently partitions SRDD data elements across machines and introduces novel parallelized spatial (geometric operations that follows the Open Geosptial Consortium (OGC) standard) transformations and actions (for SRDD) that provide a more intuitive interface for users to write spatial data analytics programs. Moreover, GeoSpark extends the SRDD layer to execute spatial queries (e.g., Range query, KNN query, and Join query) on large-scale spatial datasets. After geometrical objects are retrieved in the Spatial RDD layer, users can invoke spatial query processing operations provided in the Spatial Query Processing Layer of GeoSpark which runs over the in-memory cluster, decides how spatial object-relational tuples could be stored, indexed, and accessed using SRDDs, and returns the spatial query results required by user.
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