Abstract
Due to the ubiquity of spatial data applications and the large amounts of spatial data that these applications generate and process, there is a pressing need for scalable spatial query processing. In this paper, we present new techniques for spatial query processing and optimization in an in-memory and distributed setup to address scalability. More specifically, we introduce new techniques for handling query skew that commonly happens in practice, and minimizes communication costs accordingly. We propose a distributed query scheduler that uses a new cost model to minimize the cost of spatial query processing. The scheduler generates query execution plans that minimize the effect of query skew. The query scheduler utilizes new spatial indexing techniques based on bitmap filters to forward queries to the appropriate local nodes. Each local computation node is responsible for optimizing and selecting its best local query execution plan based on the indexes and the nature of the spatial queries in that node. All the proposed spatial query processing and optimization techniques are prototyped inside Spark, a distributed memory-based computation system. Our prototype system is termed LocationSpark. The experimental study is based on real datasets and demonstrates that LocationSpark can enhance distributed spatial query processing by up to an order of magnitude over existing in-memory and distributed spatial systems.