GIS extension for SparkSQL
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SparkGIS adds GIS functionalities to SparkSQL through:

  • a user-defined type (UDT): GeometryType
  • a class representing values of such type: Geometry
  • a set of user-defined Functions (UDF) and predicates operating on one or two Geometry values

Creating Geometry values

To create values, use factory methods in the Geometry object:

  import Geometry.WGS84
  val pt = Geometry.point(12.5, 14.6)
  val ln = Geometry.line((0,0), (20,50))
  val collection = Geometry.collection(pt, ln)

Each factory method has two argument lists:

  • the first one is the set of 2D coordinates describing the geometry (one, many, many sequences, depending on the geometry type)
  • the second one is the coordinate reference system id; an implicit value Geometry.WGS84 is provided for this

You can also create Geometry values from WKB (Well Known Binary), WKT (Well Known Text), and GeoJSON formats:

  val mp = Geometry.fromString("MULTIPOINT ((1 1), (2 2))")
  val ml = Geometry.fromGeoJSON("{\"type\":\"MultiLineString\",\"coordinates\":[[[12,13],[15,20]],[[7,9],[11,17]]]}}")

Defining table schemas

Simply use the GeometryType instance as a type:

  val schema = StructType(Seq(
    StructField("id", IntegerType),
    StructField("geo", GeometryType.Instance)

Creating RDDs

The GeometryType is able to produce Geometry values from any supported serialization format ("WKB, WKT, GeoJSON) as well as from schema-less JSON RDDs. So simply load your data and apply the schema as shown below:

  // using GeoJSON
  val data = Seq(
  val rdd = sc.parallelize(data)
  val df = sqlContext.jsonRDD(rdd, schema)

  // or other means
  val data = Seq(
    Row(1, Geometry.point(1,1)),
    Row(2, Geometry.fromString("MULTIPOINT ((1 1), (2 2))"),
  val rdd = sc.parallelize(data)
  val df = sqlContext.createDataFrame(rdd, schema)

Using functions

Each function is defined as a method of the Functions object and can be used freely in any suitable context. Moreover, they can be registered in the SQLContext and used inside SparkSQL queries:

  result = sqlContext.sql("SELECT ST_Length(geo) FROM features")

Using geometry methods

GIS functions are just aliasing methods from the class hierarchy rooting at GisGeometry. GisGeometry hierarchy wraps GeoTools classes to provide a consistent interface - like returning options instead of magic values or to model the absence of some property for some geometry type.

An instance of GisGeometry is wrapped by the SparkSQL Geometry type; the easiest way to access it and invoke its methods is by importing Geometry.ImplicitConversions:

  import Geometry.ImplicitConversions._

  val l = Geometry.line((10.0,10.0), ...)
  if (!l.isEmpty) {
    val p: Option[Geometry] = l.startPoint

Some method is also aliased as operator by GeometryOperators implicit class:

  import GeometryOperators._
  import Geometry.ImplicitConversions._

  val l1 = Geometry.line((10.0,10.0), (20.0,20.0), (10.0,30.0))
  val l2 = Geometry.line((20.0,20.0), (30.0,30.0), (40.0,40.0))
  if ((l1 <-> l2) < 50.0) { // distance less than 50

Build, test and doc

The project uses Maven as build system, so you should be comfortable with it. If not, install Maven 3, cd in your SparkGIS directory and

  mvn package -DskipTests
  mvn test
  mvn scala:doc

You'll find the jar under the target directory, have run all available tests, and generated the documentation under target/site/scaladocs.


The Geometry value class is written on top of the GeoTools library.

UDFs aim to adhere to OGC Simple Feature Access recommendation. Name and documentation of GIS functions have been copied from PostGIS.


In order to work within jsonRDDs, Spark >= 1.4 is needed.



  • Abandoned ESRI Geometry library in favor of GeoTools
  • Moved to Scala 2.11
  • Moved to Spark 1.6
  • Added ST_Perimeter
  • Added tolerance argument to ST_Simplify
  • SRID in factory methods is now an implicit argument