Evaluation of Performance Metrics in GeoRediSpark Framework for GeoSpatial Query Processing

  • G. V. N. Priyanka
  • S. VasaviEmail author
  • A. Anu Gokhale
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)


Now-a-days we are moving towards digitization and making all our devices producing bigdata. This bigdata has variety of data and has paved the way to the emergence of NoSQL databases, like Cassandra, MongoDB, Redis. Bigdata such as geospatial data requires geospatial analytics in applications such as tourism, marketing, rural development. Spark framework provides operators for storing and processing distributed data. Our earlier work proposed “GeoRediSpark” to integrate Redis with Spark. Redis is a key-value store that uses in-memory store, hence integrating Redis with Spark can extend the real-time processing of geospatial data. The paper investigated on storage and retrieval of Redis built in geospatial queries and added two new geospatial operators namely GeoWithin and GeoIntersect to enhance the capabilities of Redis. Hashed indexing is used to improve the processing performance. Comparison on Redis metrics on three benchmark datasets is made in this paper. Hashset is used to display geographic data. Output of geospatial queries is visualized in specific to type of place and nature of query using Tableau.


Geospatial data Consistent hashing Location-based query Master-executor daemon REmote DIctionary Server (Redis) metrics Hashset GeoHash Resilient Distributed Datasets (RDD) 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • G. V. N. Priyanka
    • 1
  • S. Vasavi
    • 1
    Email author
  • A. Anu Gokhale
    • 2
  1. 1.VR Siddhartha Engineering CollegeVijayawadaIndia
  2. 2.Illinois State UniversityNormalUSA

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