Sphinx: Empowering Impala for Efficient Execution of SQL Queries on Big Spatial Data

This paper presents Sphinx, a full-fledged open-source system for big spatial data which overcomes the limitations of existing systems by adopting a standard SQL interface, and by providing a high efficient core built inside the core of the Apache Impala system. Sphinx is composed of four main layers, namely, query parser, indexer, query planner, and query executor. The query parser injects spatial data types and functions in the SQL interface of Sphinx. The indexer creates spatial indexes in Sphinx by adopting a two-layered index design. The query planner utilizes these indexes to construct efficient query plans for range query and spatial join operations. Finally, the query executor carries out these plans on big spatial datasets in a distributed cluster. A system prototype of Sphinx running on real datasets shows up-to three orders of magnitude performance improvement over plain-vanilla Impala, SpatialHadoop, and PostGIS.