Abstract
The proliferation of geospatial applications has tremendously increased the variety, velocity, and volume of spatial data that data stores have to manage. Traditional relational databases reveal limitations in handling such big geospatial data, mainly due to their rigid schema requirements and limited scalability. Numerous NoSQL databases have emerged and actively serve as alternative data stores for big spatial data. Benchmarks play a crucial role in evaluating NoSQL databases and provide decision-makers with trustworthy information to choose the most suitable data store for applications. In this study, we present a framework to evaluate the performance and scalability of geospatial NoSQL databases, called GeoYCSB. We extend YCSB, a de facto benchmark framework for NoSQL systems, by integrating new components to its design architecture and also by implementing geospatial workloads. We use GeoYCSB to evaluate two leading document stores, MongoDB and Couchbase, which support geospatial queries. GeoYCSB is extensible and can be used to evaluate any NoSQL databases for geospatial workloads, provided they support spatial queries.
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