RayBooster : A Ray Tracing Engine to Accelerate SedonaDB

Hardware acceleration is becoming increasingly critical for spatial databases, as their workloads are geometrically complex, data-intensive, and subject to growing real-time requirements. Building on our prior evidence that Ray Tracing (RT) cores can significantly accelerate spatial queries through dedicated hardware support, this paper presents RayBooster , the first solution that incorporates RT-core acceleration into a production-grade geospatial database system, Apache SedonaDB. Our approach delivers substantial performance improvements cost-effectively, focusing on spatial joins, which dominate execution time in spatial databases. To enable this integration, we bridge the mismatch between spatial query engines and RT hardware through three key systematic designs. First, to overcome the lack of random access in Well-Known Binary format, we design a GPU-optimized Structure of Arrays storage layout. Second, we eliminate indexing bottlenecks by constructing a monolithic Bounding Volume Hierarchy tree that encodes geometry IDs into the Z-axis, bypassing micro-index overhead. Third, to manage the diverse geometry types, our unified RelateEngine utilizes RT cores to compute the Dimensionally Extended 9-Intersection Model as a universal topological descriptor. Furthermore, we implement a memory-aware execution strategy to mitigate out-of-memory failures through robust resource management. Seamlessly integrated as an extension to SedonaDB, RayBooster delivers up to a 5.93× performance speedup on SpatialBench and provides a 59.02% reduction in operational costs, effectively transforming these idle RT units into an efficient engine for spatial analytics.