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GeoSpark is a cluster computing system for processing large-scale spatial data. GeoSpark extends Apache Spark / SparkSQL with a set of out-of-the-box Spatial Resilient Distributed Datasets (SRDDs)/ SpatialSQL that efficiently load, process, and analyze large-scale spatial data across machines.

GeoSpark contains several modules:

Name API Spark compatibility Introduction
Core RDD Spark 2.X/1.X SpatialRDDs and Query Operators.
SQL SQL/DataFrame SparkSQL 2.1+ SQL interfaces for GeoSpark core.
Viz RDD, SQL/DataFrame RDD - Spark 2.X/1.X, SQL - Spark 2.1+ Visualization for Spatial RDD and DataFrame.
Zeppelin Apache Zeppelin Spark 2.1+, Zeppelin 0.8.1+ GeoSpark plugin for Apache Zeppelin


GeoSpark Downloads on Maven Central

GeoSpark ecosystem has around 8K - 10K downloads per month.


  • Spatial RDD
  • Spatial SQL
    FROM city, superhero
    WHERE ST_Contains(city.geom, superhero.geom)
    AND = 'Gotham';
  • Complex geometries / trajectories: point, polygon, linestring, multi-point, multi-polygon, multi-linestring, GeometryCollection
  • Various input formats: CSV, TSV, WKT, WKB, GeoJSON, NASA NetCDF/HDF, Shapefile (.shp, .shx, .dbf): extension must be in lower case
  • Spatial query: range query, range join query, distance join query, K Nearest Neighbor query
  • Spatial index: R-Tree, Quad-Tree
  • Spatial partitioning: KDB-Tree, Quad-Tree, R-Tree, Voronoi diagram, Hilbert curve, Uniform grids
  • Coordinate Reference System / Spatial Reference System Transformation: for exmaple, from WGS84 (EPSG:4326, degree-based), to EPSG:3857 (meter-based)
  • High resolution map: Scatter plot, heat map, choropleth map

GeoSpark language support

Language Supported GeoSpark modules Tutorial
Scala RDD, SQL, Viz, Zeppelin RDD, SQL, Viz, Zeppelin
Java RDD, SQL, Viz RDD, SQL, Viz
SQL SQL, Viz, Zeppelin SQL, Viz, Zeppelin

GeoSpark Visualization Extension (GeoSparkViz)

GeoSparkViz is a large-scale in-memory geospatial visualization system.

GeoSparkViz provides native support for general cartographic design by extending GeoSpark to process large-scale spatial data. It can visulize Spatial RDD and Spatial Queries and render super high resolution image in parallel.

More details are available here: Visualize Spatial DataFrame/RDD

GeoSparkViz Gallery

Watch the high resolution version on a real map


GeoSpark development team has published many papers about GeoSpark. Please read Publications.

GeoSpark received an evaluation from PVLDB 2018 paper "How Good Are Modern Spatial Analytics Systems?" Varun Pandey, Andreas Kipf, Thomas Neumann, Alfons Kemper (Technical University of Munich), quoted as follows:

GeoSpark comes close to a complete spatial analytics system. It also exhibits the best performance in most cases.