Large-scale spatial join query processing in Cloud

Abstract

The rapidly increasing amount of location data available in many applications has made it desirable to process their large-scale spatial queries in Cloud for performance and scalability. We report our designs and implementations of two prototype systems that are ready for Cloud deployments: SpatialSpark based on Apache Spark and ISP-MC based on Cloudera Impala. Both systems support indexed spatial joins based on point-in-polygon test and point-to-polyline distance computation. Experiments on the pickup locations of ~170 million taxi trips in New York City and ~10 million global species occurrences records have demonstrated both efficiency and scalability using Amazon EC2 clusters.