Relational databases have been around for a long time and Spatial databases have exploited this feature for close to two decades. The recent past has seen the development of NoSQL non-relational databases, which are now being adopted for spatial object storage and handling too. And this is gaining ground in the context of increased shift towards GeoSpatial Web Services on both the Web and mobile platforms especially in the usercentric services, where there is a need to improve the query response time. While SQL databases face scalability and agility challenges and fail to take the advantage of the cheap memory and processing power available these days, NoSQL databases can handle the rise in the data storage and frequency at which it is accessed and processed - which are essential features needed in geospatial scenarios, which do not deal with a fixed schema(geometry) and fixed data size. This paper attempts to evaluate the performance of an existing NoSQL database \u27MongoDB\u27 with its inbuilt spatial functions with that of a SQL database with spatial extension \u27PostGIS\u27 for two primitive spatial problems - LineIntersection and Point Containment problem, across a range of datasets, with varying features counts. For LineIntersection function, the dataset consisted of two independent layers of horizontal lines and vertical lines with incremental lengths and their size varied from ten lines to million lines in each layer and another dataset with two layers, one of random lines of variable size and shape and another layer of a single line which is intersecting many lines of layer1. For Point Containment problem, the dataset consists of two layers, one of polygons in space of different shape and size and another layer of random points in the space, some inside the polygons and some outside. All the data in the analysis was processed In-memory and no secondary memory was used. Initial results suggest that MongoDB performs better by an average factor of 25x for Line Intersection Problem and 10x for Point Containment Problem which increases exponentially as the data size increases in both indexed and nonindexed operations. Given these results NoSQL databases may be better suited for simultaneous multiple-user query systems including Web-GIS and mobile-GIS. Further studies are required to understand the full potential of NoSQL databases across various geometries and spatial query types
