Framework for Visualization of GeoSpatial Query Processing by Integrating Redis With Spark

Nowadays we are moving towards digitization and making all our devices produce a variety of data, this has paved the way to the emergence of NoSQL databases like Cassandra, MongoDB, and Redis. Big data such as geospatial data allows for geospatial analytics in applications such as tourism, marketing, and rural development. Spark frameworks provide operators storage and processing of distributed data. This article proposes “GeoRediSpark” to integrate Redis with Spark. Redis is a key-value store that uses an in-memory store, hence integrating Redis with Spark can extend the real-time processing of geospatial data. The article investigates storage and retrieval of the Redis built-in geospatial queries and has added two new geospatial operators, GeoWithin and GeoIntersect, to enhance the capabilities of Redis. Hashed indexing is used to improve the processing performance. A comparison on Redis metrics with three benchmark datasets is made. Hashset is used to display geographic data. The output of geospatial queries is visualized to the type of place and the nature of the query using Tableau.