The price of generality in spatial indexing

Efficient indexing can significantly speed up the processing of large volumes of spatial data in many BigData applications. Many new emerging spatial applications (e.g., biomedical imaging, genome analysis, etc.) have varying indexing requirements, thus, a unified indexing infrastructure for implementing new indexing schemes without requiring knowledge of database internals is beneficial. However, designing a generic indexing framework is a challenging task. We study the issues with general indexing schemes, such as the GiST (used in PostGIS) and expose the tradeoff between generality and performance, showing that generality can be severely detrimental to performance if the abstractions are not carefully designed. Our experiments indicate that the GiST framework, as implemented in PostgreSQL/PostGIS, performs 4.5-6x slower for filtering records through the index, compared to a custom R-tree implementation. We also isolate the GiST-specific overhead by implementing the framework outside the DBMS, showing that the GiST-based R-tree is up to 2x slower than the raw R-tree algorithm that it uses internally. We conclude that although a generic framework for a wide range of spatial BigData application domains is desirable, implementers of new frameworks need to be careful in designing the abstractions to avoid paying a hefty performance penalty.