Optimizing Spatial Queries in NoSQL Databases: A Comparative Analysis of B-Tree, Hashed, and Geospatial Indexing Techniques

The explosive growth of location-based services and geospatial applications has fueled the demand for efficient spatial queries within NoSQL databases. While various indexing techniques exist, their performance and suitability for different query types remain debatable. This paper presents a comparative analysis of B-Tree, Hashed, and Geospatial indexing techniques in the context of NoSQL databases, focusing on real-world datasets and use cases like nearest neighbor search and range queries. Through comprehensive benchmarks and in-depth analysis, we evaluate the efficiency of each approach in terms of query execution time, storage overhead, and scalability. Our findings reveal that Geospatial indexes excel in nearest neighbor searches and distance-based queries, while Hashed indexes exhibit exceptional performance for point in-polygon and range queries. B-Trees offer a balanced solution for general spatial queries but struggle with high dimensional data and complex operations. This study provides valuable insights for developers and architects choosing the optimal indexing technique for their specific NoSQL-based geospatial applications.