9.2. Spark JTS¶

The Spark JTS module provides a set of User Defined Functions (UDFs) and User Defined Types (UDTs) that enable executing SQL queries in spark that perform geospatial operations on geospatial data types.

GeoMesa SparkSQL support builds upon the DataSet/DataFrame API present in the Spark SQL module to provide geospatial capabilities. This includes custom geospatial data types and functions, the ability to create a DataFrame from a GeoTools DataStore, and optimizations to improve SQL query performance.

This functionality is located in the geomesa-spark/geomesa-spark-jts module:

<dependency>
  <groupId>org.locationtech.geomesa</groupId>
  <artifactId>geomesa-spark-jts_2.11</artifactId>
  // version, etc.
</dependency>

9.2.1. Example¶

The following is a Scala example of loading a DataFrame with user defined types:

import org.locationtech.jts.geom._
import org.apache.spark.sql.types._
import org.locationtech.geomesa.spark.jts._

import spark.implicits._

val schema = StructType(Array(
  StructField("name",StringType, nullable=false),
  StructField("pointText", StringType, nullable=false),
  StructField("polygonText", StringType, nullable=false),
  StructField("latitude", DoubleType, nullable=false),
  StructField("longitude", DoubleType, nullable=false)))

val dataFile = this.getClass.getClassLoader.getResource("jts-example.csv").getPath
val df = spark.read
  .schema(schema)
  .option("sep", "-")
  .option("timestampFormat", "yyyy/MM/dd HH:mm:ss ZZ")
  .csv(dataFile)

val alteredDF = df
  .withColumn("polygon", st_polygonFromText($"polygonText"))
  .withColumn("point", st_makePoint($"latitude", $"longitude"))

Notice how the initial schema does not have a UserDefinedType, but after applying our User Defined Functions to the appropriate columns, we are left with a data frame with geospatial column types.

It is also possible to construct a DataFrame from a list of geospatial objects:

import spark.implicits._
val point = new GeometryFactory().createPoint(new Coordinate(3.4, 5.6))
val df = Seq(point).toDF("point")

9.2.2. Configuration¶

To enable this behavior, import org.locationtech.geomesa.spark.jts._, create a SparkSession` and call ``.withJTS on it. This will register the UDFs and UDTs as well as some catalyst optimizations for these operations. Alternatively you can call initJTS(SQLContext).

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.SQLContext
import org.locationtech.geomesa.spark.jts._

val spark: SparkSession = SparkSession.builder() // ... initialize spark session
spark.withJTS

9.2.3. Geospatial User-defined Types and Functions¶

The Spark JTS module takes several classes representing geometry objects (as described by the OGC OpenGIS Simple feature access common architecture specification and implemented by the Java Topology Suite) and registers them as user-defined types (UDTs) in SparkSQL. For example the Geometry class is registered as GeometryUDT. The following types are registered:

GeometryUDT

PointUDT

LineStringUDT

PolygonUDT

MultiPointUDT

MultiLineStringUDT

MultiPolygonUDT

GeometryCollectionUDT

Spark JTS also implements a subset of the functions described in the OGC OpenGIS Simple feature access SQL option specification as SparkSQL user-defined functions (UDFs). These include functions for creating geometries, accessing properties of geometries, casting Geometry objects to more specific subclasses, outputting geometries in other formats, measuring spatial relationships between geometries, and processing geometries.

For example, the following SQL query

select * from chicago where st_contains(st_makeBBOX(0.0, 0.0, 90.0, 90.0), geom)

uses two UDFs–st_contains and st_makeBBOX–to find the rows in the chicago DataFrame where column geom is contained within the specified bounding box.

The UDFs are also exposed for use with the DataFrame API, meaning the above example is also achievable with the following code:

import org.locationtech.geomesa.spark.jts._
import spark.implicits. _
chicagoDF.where(st_contains(st_makeBBOX(0.0, 0.0, 90.0, 90.0), $"geom"))

A complete list of the implemented UDFs is given in the next section (SparkSQL Functions).

9.2.4. GeoJSON Output¶

The Spark JTS module also provides a means of exporting a DataFrame to a GeoJSON string. This allows for quick visualization of the data in many front-end mapping libraries that support GeoJSON input such as Leaflet or Open Layers.

To convert a DataFrame, import the implicit conversion and invoke the toGeoJSON method.

import org.locationtech.geomesa.spark.jts.util.GeoJSONExtensions._
val df : DataFrame = // Some data frame
val geojsonDf = df.toGeoJSON

Given only the schema, the converter can infer which of the fields holds the geometry, but in the event of multiple geometric fields, it defaults to the first such field. This behavior can be overridden by providing the index (starting from 0) of the desired geometry in the schema. For example, df.toGeoJSON(2) if the desired geometry is the third field of the schema.

If the result can fit in memory, it can then be collected on the driver and written to a file. If not, each executor can write to a distributed file system like HDFS.

val geoJsonString = geojsonDF.collect.mkString("[",",","]")

Note

For this to work, the Data Frame should have a geometry field, meaning its schema should have a StructField that is one of the JTS geometry types provided in this module. It is acceptable, however, if some of the rows have null geometries. In such a case, null will be written as the value of the geometry in GeoJSON.

9.2.5. Building¶

This module can be built and used independently of GeoMesa with the following command:

$ mvn install -pl geomesa-spark/geomesa-spark-jts