RDD

This page explains how to customize the internal algorithms used in Spatial SQL geospatial queries. Such customization requires Spatial RDD, which is not recommended by us.

Convert a Spatial DataFrame to SpatialRDD

Use WherobotsDB DataFrame-RDD Adapter to convert a DataFrame to an SpatialRDD.

Python
Scala
Java

from sedona.spark import *

spatialRDD = Adapter.toSpatialRdd(spatialDf, "usacounty")

var spatialRDD = Adapter.toSpatialRdd(spatialDf, "usacounty")

SpatialRDD spatialRDD = Adapter.toSpatialRdd(spatialDf, "usacounty")

“usacounty” is the name of the geometry column

Only one Geometry type column is allowed per DataFrame.

Write a Spatial Range Query

A spatial range query takes as input a range query window and an SpatialRDD and returns all geometries that have specified relationship with the query window. Assume you now have an SpatialRDD (typed or generic). You can use the following code to issue an Spatial Range Query on it. ==spatialPredicate== can be set to SpatialPredicate.INTERSECTS to return all geometries intersect with query window. Supported spatial predicates are:

CONTAINS: geometry is completely inside the query window
INTERSECTS: geometry have at least one point in common with the query window
WITHIN: geometry is completely within the query window (no touching edges)
COVERS: query window has no point outside of the geometry
COVERED_BY: geometry has no point outside of the query window
OVERLAPS: geometry and the query window spatially overlap
CROSSES: geometry and the query window spatially cross
TOUCHES: the only points shared between geometry and the query window are on the boundary of geometry and the query window
EQUALS: geometry and the query window are spatially equal

Spatial range query is equivalent with a SELECT query with spatial predicate as search condition in Spatial SQL. An example query is as follows:

SELECT *
FROM checkin
WHERE ST_Intersects(checkin.location, queryWindow)

Python
Scala
Java

from sedona.spark.core.geom.envelope import Envelope
from sedona.spark import *

range_query_window = Envelope(-90.01, -80.01, 30.01, 40.01)
consider_boundary_intersection = False  ## Only return gemeotries fully covered by the window
using_index = False
query_result = RangeQuery.SpatialRangeQuery(spatial_rdd, range_query_window, consider_boundary_intersection, using_index)

val rangeQueryWindow = new Envelope(-90.01, -80.01, 30.01, 40.01)
val spatialPredicate = SpatialPredicate.COVERED_BY // Only return gemeotries fully covered by the window
val usingIndex = false
var queryResult = RangeQuery.SpatialRangeQuery(spatialRDD, rangeQueryWindow, spatialPredicate, usingIndex)

Envelope rangeQueryWindow = new Envelope(-90.01, -80.01, 30.01, 40.01)
SpatialPredicate spatialPredicate = SpatialPredicate.COVERED_BY // Only return gemeotries fully covered by the window
boolean usingIndex = false
JavaRDD queryResult = RangeQuery.SpatialRangeQuery(spatialRDD, rangeQueryWindow, spatialPredicate, usingIndex)

WherobotsDB Python users: Please use RangeQueryRaw from the same module if you want to avoid jvm python serde while converting to Spatial DataFrame. It takes the same parameters as RangeQuery but returns reference to jvm rdd which can be converted to dataframe without python - jvm serde using Adapter.

Example:

from sedona.spark.core.geom.envelope import Envelope
from sedona.spark import *

range_query_window = Envelope(-90.01, -80.01, 30.01, 40.01)
consider_boundary_intersection = False  ## Only return gemeotries fully covered by the window
using_index = False
query_result = RangeQueryRaw.SpatialRangeQuery(spatial_rdd, range_query_window, consider_boundary_intersection, using_index)
gdf = Adapter.toDf(query_result, spark, ["col1", ..., "coln"])

Range query window

Besides the rectangle (Envelope) type range query window, WherobotsDB range query window can be Point/Polygon/LineString. The code to create a point, linestring (4 vertices) and polygon (4 vertices) is as follows:

Python
Scala
Java

A Shapely geometry can be used as a query window. To create shapely geometries, please follow the Shapely official docs.

val geometryFactory = new GeometryFactory()
val pointObject = geometryFactory.createPoint(new Coordinate(-84.01, 34.01))

val geometryFactory = new GeometryFactory()
val coordinates = new Array[Coordinate](5)
coordinates(0) = new Coordinate(0,0)
coordinates(1) = new Coordinate(0,4)
coordinates(2) = new Coordinate(4,4)
coordinates(3) = new Coordinate(4,0)
coordinates(4) = coordinates(0) // The last coordinate is the same as the first coordinate in order to compose a closed ring
val polygonObject = geometryFactory.createPolygon(coordinates)

val geometryFactory = new GeometryFactory()
val coordinates = new Array[Coordinate](4)
coordinates(0) = new Coordinate(0,0)
coordinates(1) = new Coordinate(0,4)
coordinates(2) = new Coordinate(4,4)
coordinates(3) = new Coordinate(4,0)
val linestringObject = geometryFactory.createLineString(coordinates)

GeometryFactory geometryFactory = new GeometryFactory()
Point pointObject = geometryFactory.createPoint(new Coordinate(-84.01, 34.01))

GeometryFactory geometryFactory = new GeometryFactory()
Coordinate[] coordinates = new Array[Coordinate](5)
coordinates(0) = new Coordinate(0,0)
coordinates(1) = new Coordinate(0,4)
coordinates(2) = new Coordinate(4,4)
coordinates(3) = new Coordinate(4,0)
coordinates(4) = coordinates(0) // The last coordinate is the same as the first coordinate in order to compose a closed ring
Polygon polygonObject = geometryFactory.createPolygon(coordinates)

GeometryFactory geometryFactory = new GeometryFactory()
val coordinates = new Array[Coordinate](4)
coordinates(0) = new Coordinate(0,0)
coordinates(1) = new Coordinate(0,4)
coordinates(2) = new Coordinate(4,4)
coordinates(3) = new Coordinate(4,0)
LineString linestringObject = geometryFactory.createLineString(coordinates)

Use spatial indexes

WherobotsDB provides two types of spatial indexes, Quad-Tree and R-Tree. Once you specify an index type, WherobotsDB will build a local tree index on each of the SpatialRDD partition. To utilize a spatial index in a spatial range query, use the following code:

Python
Scala
Java

from sedona.spark.core.geom.envelope import Envelope
from sedona.spark import *

range_query_window = Envelope(-90.01, -80.01, 30.01, 40.01)
consider_boundary_intersection = False ## Only return gemeotries fully covered by the window

build_on_spatial_partitioned_rdd = False ## Set to TRUE only if run join query
spatial_rdd.buildIndex(IndexType.QUADTREE, build_on_spatial_partitioned_rdd)

using_index = True

query_result = RangeQuery.SpatialRangeQuery(
        spatial_rdd,
        range_query_window,
        consider_boundary_intersection,
        using_index
)

val rangeQueryWindow = new Envelope(-90.01, -80.01, 30.01, 40.01)
val spatialPredicate = SpatialPredicate.COVERED_BY // Only return gemeotries fully covered by the window

val buildOnSpatialPartitionedRDD = false // Set to TRUE only if run join query
spatialRDD.buildIndex(IndexType.QUADTREE, buildOnSpatialPartitionedRDD)

val usingIndex = true
var queryResult = RangeQuery.SpatialRangeQuery(spatialRDD, rangeQueryWindow, spatialPredicate, usingIndex)

Envelope rangeQueryWindow = new Envelope(-90.01, -80.01, 30.01, 40.01)
SpatialPredicate spatialPredicate = SpatialPredicate.COVERED_BY // Only return gemeotries fully covered by the window

boolean buildOnSpatialPartitionedRDD = false // Set to TRUE only if run join query
spatialRDD.buildIndex(IndexType.QUADTREE, buildOnSpatialPartitionedRDD)

boolean usingIndex = true
JavaRDD queryResult = RangeQuery.SpatialRangeQuery(spatialRDD, rangeQueryWindow, spatialPredicate, usingIndex)

Using an index might not be the best choice all the time because building index also takes time. A spatial index is very useful when your data is complex polygons and line strings.

Output format

Python
Scala/Java

The output format of the spatial range query is another RDD which consists of GeoData objects.SpatialRangeQuery result can be used as RDD with map or other spark RDD functions. Also it can be used as Python objects when using collect method. Example:

query_result.map(lambda x: x.geom.length).collect()

[
5900840000000045,
5906639999999896,
1110299999999995,
1096700000000084,
1415619999999933,
1386399999999952,
1415619999999933,
1418860000000137,
1392780000000045,
 ...
]

Or transformed to GeoPandas GeoDataFrame

import geopandas as gpd
gpd.GeoDataFrame(
        query_result.map(lambda x: [x.geom, x.userData]).collect(),
        columns=["geom", "user_data"],
        geometry="geom"
)

Write a Spatial KNN Query

A spatial K Nearnest Neighbor query takes as input a K, a query point and an SpatialRDD and finds the K geometries in the RDD which are the closest to he query point. Assume you now have an SpatialRDD (typed or generic). You can use the following code to issue an Spatial KNN Query on it.

Python
Scala
Java

from sedona.spark import *
from shapely.geometry import Point

point = Point(-84.01, 34.01)
k = 1000 ## K Nearest Neighbors
using_index = False
result = KNNQuery.SpatialKnnQuery(object_rdd, point, k, using_index)

val geometryFactory = new GeometryFactory()
val pointObject = geometryFactory.createPoint(new Coordinate(-84.01, 34.01))
val K = 1000 // K Nearest Neighbors
val usingIndex = false
val result = KNNQuery.SpatialKnnQuery(objectRDD, pointObject, K, usingIndex)

GeometryFactory geometryFactory = new GeometryFactory()
Point pointObject = geometryFactory.createPoint(new Coordinate(-84.01, 34.01))
int K = 1000 // K Nearest Neighbors
boolean usingIndex = false
JavaRDD result = KNNQuery.SpatialKnnQuery(objectRDD, pointObject, K, usingIndex)

Spatial KNN query that returns 5 Nearest Neighbors is equal to the following statement in Spatial SQL

SELECT ck.name, ck.rating, ST_Distance(ck.location, myLocation) AS distance
FROM checkins ck
ORDER BY distance DESC
LIMIT 5

Query center geometry

Besides the Point type, WherobotsDB KNN query center can be Polygon and LineString.

Python
Scala/Java

To create Polygon or Linestring object please follow Shapely official docs

Use spatial indexes

To utilize a spatial index in a spatial KNN query, use the following code:

Python
Scala
Java

from sedona.spark import *
from shapely.geometry import Point

point = Point(-84.01, 34.01)
k = 5 ## K Nearest Neighbors

build_on_spatial_partitioned_rdd = False ## Set to TRUE only if run join query
spatial_rdd.buildIndex(IndexType.RTREE, build_on_spatial_partitioned_rdd)

using_index = True
result = KNNQuery.SpatialKnnQuery(spatial_rdd, point, k, using_index)

val geometryFactory = new GeometryFactory()
val pointObject = geometryFactory.createPoint(new Coordinate(-84.01, 34.01))
val K = 1000 // K Nearest Neighbors


val buildOnSpatialPartitionedRDD = false // Set to TRUE only if run join query
objectRDD.buildIndex(IndexType.RTREE, buildOnSpatialPartitionedRDD)

val usingIndex = true
val result = KNNQuery.SpatialKnnQuery(objectRDD, pointObject, K, usingIndex)

GeometryFactory geometryFactory = new GeometryFactory()
Point pointObject = geometryFactory.createPoint(new Coordinate(-84.01, 34.01))
val K = 1000 // K Nearest Neighbors


boolean buildOnSpatialPartitionedRDD = false // Set to TRUE only if run join query
objectRDD.buildIndex(IndexType.RTREE, buildOnSpatialPartitionedRDD)

boolean usingIndex = true
JavaRDD result = KNNQuery.SpatialKnnQuery(objectRDD, pointObject, K, usingIndex)

Only R-Tree index supports Spatial KNN query

Output format

Python
Scala/Java

The output format of the spatial KNN query is a list of GeoData objects. The list has K GeoData objects.Example:

>> result

[GeoData, GeoData, GeoData, GeoData, GeoData]

Write a Spatial Join Query

A spatial join query takes as input two Spatial RDD A and B. For each geometry in A, finds the geometries (from B) covered/intersected by it. A and B can be any geometry type and are not necessary to have the same geometry type. Assume you now have two SpatialRDDs (typed or generic). You can use the following code to issue an Spatial Join Query on them.

Python
Scala
Java

from sedona.spark import *

consider_boundary_intersection = False ## Only return geometries fully covered by each query window in queryWindowRDD
using_index = False

object_rdd.analyze()

object_rdd.spatialPartitioning(GridType.KDBTREE)
query_window_rdd.spatialPartitioning(object_rdd.getPartitioner())

result = JoinQuery.SpatialJoinQuery(object_rdd, query_window_rdd, using_index, consider_boundary_intersection)

val spatialPredicate = SpatialPredicate.COVERED_BY // Only return gemeotries fully covered by each query window in queryWindowRDD
val usingIndex = false

objectRDD.analyze()

objectRDD.spatialPartitioning(GridType.KDBTREE)
queryWindowRDD.spatialPartitioning(objectRDD.getPartitioner)

val result = JoinQuery.SpatialJoinQuery(objectRDD, queryWindowRDD, usingIndex, spatialPredicate)

SpatialPredicate spatialPredicate = SpatialPredicate.COVERED_BY // Only return gemeotries fully covered by each query window in queryWindowRDD
val usingIndex = false

objectRDD.analyze()

objectRDD.spatialPartitioning(GridType.KDBTREE)
queryWindowRDD.spatialPartitioning(objectRDD.getPartitioner)

JavaPairRDD result = JoinQuery.SpatialJoinQuery(objectRDD, queryWindowRDD, usingIndex, spatialPredicate)

Spatial join query is equal to the following query in Spatial SQL:

SELECT superhero.name
FROM city, superhero
WHERE ST_Contains(city.geom, superhero.geom);

Find the superheroes in each city

Use spatial partitioning

Sedona spatial partitioning method can significantly speed up the join query. Three spatial partitioning methods are available: KDB-Tree, Quad-Tree and R-Tree. Two SpatialRDD must be partitioned by the same way. If you first partition SpatialRDD A, then you must use the partitioner of A to partition B.

Python
Scala/Java

object_rdd.spatialPartitioning(GridType.KDBTREE)
query_window_rdd.spatialPartitioning(object_rdd.getPartitioner())

objectRDD.spatialPartitioning(GridType.KDBTREE)
queryWindowRDD.spatialPartitioning(objectRDD.getPartitioner)

Python
Scala/Java

query_window_rdd.spatialPartitioning(GridType.KDBTREE)
object_rdd.spatialPartitioning(query_window_rdd.getPartitioner())

queryWindowRDD.spatialPartitioning(GridType.KDBTREE)
objectRDD.spatialPartitioning(queryWindowRDD.getPartitioner)

Use spatial indexes

To utilize a spatial index in a spatial join query, use the following code:

Python
Scala
Java

from sedona.spark import *

object_rdd.spatialPartitioning(GridType.KDBTREE)
query_window_rdd.spatialPartitioning(object_rdd.getPartitioner())

build_on_spatial_partitioned_rdd = True ## Set to TRUE only if run join query
using_index = True
query_window_rdd.buildIndex(IndexType.QUADTREE, build_on_spatial_partitioned_rdd)

result = JoinQuery.SpatialJoinQueryFlat(object_rdd, query_window_rdd, using_index, True)

objectRDD.spatialPartitioning(joinQueryPartitioningType)
queryWindowRDD.spatialPartitioning(objectRDD.getPartitioner)

val buildOnSpatialPartitionedRDD = true // Set to TRUE only if run join query
val usingIndex = true
queryWindowRDD.buildIndex(IndexType.QUADTREE, buildOnSpatialPartitionedRDD)

val result = JoinQuery.SpatialJoinQueryFlat(objectRDD, queryWindowRDD, usingIndex, spatialPredicate)

objectRDD.spatialPartitioning(joinQueryPartitioningType)
queryWindowRDD.spatialPartitioning(objectRDD.getPartitioner)

boolean buildOnSpatialPartitionedRDD = true // Set to TRUE only if run join query
boolean usingIndex = true
queryWindowRDD.buildIndex(IndexType.QUADTREE, buildOnSpatialPartitionedRDD)

JavaPairRDD result = JoinQuery.SpatialJoinQueryFlat(objectRDD, queryWindowRDD, usingIndex, spatialPredicate)

The index should be built on either one of two SpatialRDDs. In general, you should build it on the larger SpatialRDD.

Output format

Python
Scala/Java

Result for this query is RDD which holds two GeoData objects within list of lists. Example:

result.collect()

[[GeoData, GeoData], [GeoData, GeoData] ...]

It is possible to do some RDD operation on result data ex. Getting polygon centroid.

result.map(lambda x: x[0].geom.centroid).collect()

The output format of the spatial join query is a PairRDD. In this PairRDD, each object is a pair of two geometries. The left one is the geometry from objectRDD and the right one is the geometry from the queryWindowRDD.

Point,Polygon
Point,Polygon
Point,Polygon
Polygon,Polygon
LineString,LineString
Polygon,LineString
...

Each object on the left is covered/intersected by the object on the right.

Sedona Python users: Please use JoinQueryRaw from the same module for methods

spatialJoin
DistanceJoinQueryFlat
SpatialJoinQueryFlat

For better performance while converting to dataframe with adapter. That approach allows to avoid costly serialization between Python and jvm and in result operating on python object instead of native geometries. Example:

from sedona.spark import *

object_rdd.analyze()

circle_rdd = CircleRDD(object_rdd, 0.1) ## Create a CircleRDD using the given distance
circle_rdd.analyze()

circle_rdd.spatialPartitioning(GridType.KDBTREE)
spatial_rdd.spatialPartitioning(circle_rdd.getPartitioner())

consider_boundary_intersection = False ## Only return gemeotries fully covered by each query window in queryWindowRDD
using_index = False

result = JoinQueryRaw.DistanceJoinQueryFlat(spatial_rdd, circle_rdd, using_index, consider_boundary_intersection)

gdf = Adapter.toDf(result, ["left_col1", ..., "lefcoln"], ["rightcol1", ..., "rightcol2"], spark)

Write a Distance Join Query

RDD distance joins are only reliable for points. For other geometry types, please use Spatial SQL.

A distance join query takes as input two Spatial RDD A and B and a distance. For each geometry in A, finds the geometries (from B) are within the given distance to it. A and B can be any geometry type and are not necessary to have the same geometry type. You can transform your CRS using Spatial SQL ST_Transform. If you don’t want to transform your data and are ok with sacrificing the query accuracy, you can use an approximate degree value for distance. Please use this calculator. Assume you now have two SpatialRDDs (typed or generic). You can use the following code to issue an Distance Join Query on them.

Python
Scala/Java

The output format of the distance join query is a list of lists. Each list contains two GeoData objects. Example:

result.collect()

[[GeoData, GeoData], [GeoData, GeoData] ...]

It is possible to do some RDD operation on result data ex. Getting polygon centroid.

result.map(lambda x: x[0].geom.centroid).collect()

The output format of the distance join query is a PairRDD. In this PairRDD, each object is a pair of two geometries. The left one is the geometry from objectRDD and the right one is the geometry from the circleRDD.

Point,Polygon
Point,Polygon
Point,Polygon
Polygon,Polygon
LineString,LineString
Polygon,LineString
...

Each object on the left is within the given distance to the object on the right.

objectRddA.analyze()

CircleRDD circleRDD = new CircleRDD(objectRddA, 0.1) // Create a CircleRDD using the given distance

circleRDD.spatialPartitioning(GridType.KDBTREE)
objectRddB.spatialPartitioning(circleRDD.getPartitioner)

SpatialPredicate spatialPredicate = SpatialPredicate.COVERED_BY // Only return gemeotries fully covered by each query window in queryWindowRDD
boolean usingIndex = false

JavaPairRDD result = JoinQuery.DistanceJoinQueryFlat(objectRddB, circleRDD, usingIndex, spatialPredicate)

Distance join can only accept COVERED_BY and INTERSECTS as spatial predicates. The rest part of the join query is same as the spatial join query. The details of spatial partitioning in join query is here. The details of using spatial indexes in join query is here. The output format of the distance join query is here.

Distance join query is equal to the following query in Spatial SQL:

SELECT superhero.name
FROM city, superhero
WHERE ST_Distance(city.geom, superhero.geom) <= 10;

Find the superheroes within 10 miles of each city

Convert SpatialRDD to Spatial DataFrame

Use WherobotsDB DataFrame-RDD Adapter to convert a DataFrame to an SpatialRDD.

Python
Scala
Java

from sedona.spark import *

spatialDf = Adapter.toDf(spatialRDD, sedona)

var spatialDf = Adapter.toDf(spatialRDD, sedona)

Dataset<Row> spatialDf = Adapter.toDf(spatialRDD, sedona)

You may also manually specify a schema for the resulting DataFrame in case you require different column names or data types. Note that string schemas and not all data types are supported&mdash. At least one column for the user data must be provided.

Scala

val schema = StructType(Array(
    StructField("county", GeometryUDT, nullable = true),
    StructField("name", StringType, nullable = true),
    StructField("price", DoubleType, nullable = true),
    StructField("age", IntegerType, nullable = true)
))
val spatialDf = Adapter.toDf(spatialRDD, schema, sedona)

Convert SpatialPairRDD to Spatial DataFrame

PairRDD is the result of a spatial join query or distance join query. Spatial SQL DataFrame-RDD Adapter can convert the result to a DataFrame. But you need to provide the name of other attributes.

Python
Scala
Java

from sedona.spark import *

joinResultDf = Adapter.toDf(jvm_sedona_rdd, ["poi_from_id", "poi_from_name"], ["poi_to_id", "poi_to_name"], spark))

var joinResultDf = Adapter.toDf(joinResultPairRDD, Seq("left_attribute1", "left_attribute2"), Seq("right_attribute1", "right_attribute2"), sedona)

import scala.collection.JavaConverters;

List leftFields = new ArrayList<>(Arrays.asList("c1", "c2", "c3"));
List rightFields = new ArrayList<>(Arrays.asList("c4", "c5", "c6"));
Dataset joinResultDf = Adapter.toDf(joinResultPairRDD, JavaConverters.asScalaBuffer(leftFields).toSeq(), JavaConverters.asScalaBuffer(rightFields).toSeq(), sedona);

or you can use the attribute names directly from the input RDD

Python
Scala
Java

from sedona.spark import *

joinResultDf = Adapter.toDf(result_pair_rdd, leftRdd.fieldNames, rightRdd.fieldNames, spark)

import scala.collection.JavaConversions._
var joinResultDf = Adapter.toDf(joinResultPairRDD, leftRdd.fieldNames, rightRdd.fieldNames, sedona)

import scala.collection.JavaConverters;
Dataset joinResultDf = Adapter.toDf(joinResultPairRDD, JavaConverters.asScalaBuffer(leftRdd.fieldNames).toSeq(), JavaConverters.asScalaBuffer(rightRdd.fieldNames).toSeq(), sedona);

You may also manually specify a schema for the resulting DataFrame in case you require different column names or data types. Note that string schemas and not all data types are supported&mdash. Columns for the left and right user data must be provided.

Scala

val schema = StructType(Array(
    StructField("leftGeometry", GeometryUDT, nullable = true),
    StructField("name", StringType, nullable = true),
    StructField("price", DoubleType, nullable = true),
    StructField("age", IntegerType, nullable = true),
    StructField("rightGeometry", GeometryUDT, nullable = true),
    StructField("category", StringType, nullable = true)
))
val joinResultDf = Adapter.toDf(joinResultPairRDD, schema, sedona)

Getting Started

Spatial Queries

WherobotsDB

Data Connections

RasterFlow

Advanced Topics

GeoStats, Map Matching & RasterFlow

Convert a Spatial DataFrame to SpatialRDD

Write a Spatial Range Query

Range query window

Use spatial indexes

Output format

Write a Spatial KNN Query

Query center geometry

Use spatial indexes

Output format

Write a Spatial Join Query

Use spatial partitioning

Use spatial indexes

Output format

Write a Distance Join Query

Convert SpatialRDD to Spatial DataFrame

Convert SpatialPairRDD to Spatial DataFrame

​Convert a Spatial DataFrame to SpatialRDD

​Write a Spatial Range Query

​Range query window

​Use spatial indexes

​Output format

​Write a Spatial KNN Query

​Query center geometry

​Use spatial indexes

​Output format

​Write a Spatial Join Query

​Use spatial partitioning

​Use spatial indexes

​Output format

​Write a Distance Join Query

​Convert SpatialRDD to Spatial DataFrame

​Convert SpatialPairRDD to Spatial DataFrame

Convert a Spatial DataFrame to SpatialRDD

Write a Spatial Range Query

Range query window

Use spatial indexes

Output format

Write a Spatial KNN Query

Query center geometry

Use spatial indexes

Output format

Write a Spatial Join Query

Use spatial partitioning

Use spatial indexes

Output format

Write a Distance Join Query

Convert SpatialRDD to Spatial DataFrame

Convert SpatialPairRDD to Spatial DataFrame