Dragonfly has introduced a native support system for geospatial indexing and querying, leveraging the R-Tree data structure to efficiently handle geographical data. R-Trees are designed for multi-dimensional data, allowing for effective range and nearest-neighbor searches by organizing data hierarchically and minimizing unnecessary search space. Dragonfly's implementation involves using the Boost Geometry library with a linear split strategy for its R-Tree configuration, which allows for simple insertion and deletion operations. However, to enable radius-based searches, Dragonfly developed a custom approach involving the construction of a temporary polygon and a minimum bounding box, followed by Haversine distance calculations. Performance benchmarks show Dragonfly outperforming Redis in terms of request processing speed and scalability, making it a strong candidate for high-volume geospatial search applications. As data volumes grow, Dragonfly's combination of R-Tree indexing and multi-threaded processing offers significant advantages, positioning it as a more efficient and scalable solution compared to RediSearch.