Sphinx: distributed execution of interactive SQL queries on big spatial data

Abstract

This paper presents Sphinx, a full-fledged distributed system which uses a standard SQL interface to process big spatial data. Sphinx adds spatial data types, indexes and query processing, inside the code-base of Cloudera Impala for efficient processing of spatial data. In particular, Sphinx is composed of four main components, 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 traditional Impala.