Spatial RDD app

The page outlines the steps to create Spatial RDDs and run spatial queries using Sedona-core.

Set up dependencies

Scala/JavaPython

1. Read Sedona Maven Central coordinates and add Sedona dependencies in build.sbt or pom.xml.
2. Add Apache Spark core, Apache SparkSQL in build.sbt or pom.xml.
3. Please see RDD example project

1. Please read Quick start to install Sedona Python.
2. This tutorial is based on Sedona Core Jupyter Notebook example. You can interact with Sedona Python Jupyter notebook immediately on Binder. Click to interact with Sedona Python Jupyter notebook immediately on Binder.

Initiate SparkContext

ScalaJavaPython

val conf = new SparkConf()
conf.setAppName("SedonaRunnableExample") // Change this to a proper name
conf.setMaster("local[*]") // Delete this if run in cluster mode
// Enable Sedona custom Kryo serializer
conf.set("spark.serializer", classOf[KryoSerializer].getName) // org.apache.spark.serializer.KryoSerializer
conf.set("spark.kryo.registrator", classOf[SedonaKryoRegistrator].getName) // org.apache.sedona.core.serde.SedonaKryoRegistrator
val sc = new SparkContext(conf)

If you add the Sedona full dependencies as suggested above, please use the following two lines to enable Sedona Kryo serializer instead:

conf.set("spark.serializer", classOf[KryoSerializer].getName) // org.apache.spark.serializer.KryoSerializer
conf.set("spark.kryo.registrator", classOf[SedonaVizKryoRegistrator].getName) // org.apache.sedona.viz.core.Serde.SedonaVizKryoRegistrator

SparkConf conf = new SparkConf()
conf.setAppName("SedonaRunnableExample") // Change this to a proper name
conf.setMaster("local[*]") // Delete this if run in cluster mode
// Enable Sedona custom Kryo serializer
conf.set("spark.serializer", KryoSerializer.class.getName) // org.apache.spark.serializer.KryoSerializer
conf.set("spark.kryo.registrator", SedonaKryoRegistrator.class.getName) // org.apache.sedona.core.serde.SedonaKryoRegistrator
SparkContext sc = new SparkContext(conf)

If you use SedonaViz with SedonaRDD, please use the following two lines to enable Sedona Kryo serializer instead:

conf.set("spark.serializer", KryoSerializer.class.getName) // org.apache.spark.serializer.KryoSerializer
conf.set("spark.kryo.registrator", SedonaVizKryoRegistrator.class.getName) // org.apache.sedona.viz.core.Serde.SedonaVizKryoRegistrator

conf.set("spark.serializer", KryoSerializer.getName)
conf.set("spark.kryo.registrator", SedonaKryoRegistrator.getName)
sc = SparkContext(conf=conf)

Warning

Sedona has a suite of well-written geometry and index serializers. Forgetting to enable these serializers will lead to high memory consumption.

Create a SpatialRDD

Create a typed SpatialRDD

Sedona-core provides three special SpatialRDDs: PointRDD, PolygonRDD, and LineStringRDD.

Warning

Typed SpatialRDD has been deprecated for a long time. We do NOT recommend it anymore.

Create a generic SpatialRDD

A generic SpatialRDD is not typed to a certain geometry type and open to more scenarios. It allows an input data file contains mixed types of geometries. For instance, a WKT file contains three types gemetries LineString, Polygon and MultiPolygon.

From WKT/WKB

Geometries in a WKT and WKB file always occupy a single column no matter how many coordinates they have. Sedona provides WktReader and WkbReader to create generic SpatialRDD.

Suppose we have a checkin.tsv WKT TSV file at Path /Download/checkin.tsv as follows:

POINT (-88.331492 32.324142)    hotel
POINT (-88.175933 32.360763)    gas
POINT (-88.388954 32.357073)    bar
POINT (-88.221102 32.35078) restaurant

This file has two columns and corresponding offsets(Column IDs) are 0, 1. Column 0 is the WKT string and Column 1 is the checkin business type.

Use the following code to create a SpatialRDD

ScalaJavaPython

val inputLocation = "/Download/checkin.tsv"
val wktColumn = 0 // The WKT string starts from Column 0
val allowTopologyInvalidGeometries = true // Optional
val skipSyntaxInvalidGeometries = false // Optional
val spatialRDD = WktReader.readToGeometryRDD(sparkSession.sparkContext, inputLocation, wktColumn, allowTopologyInvalidGeometries, skipSyntaxInvalidGeometries)

String inputLocation = "/Download/checkin.tsv"
int wktColumn = 0 // The WKT string starts from Column 0
boolean allowTopologyInvalidGeometries = true // Optional
boolean skipSyntaxInvalidGeometries = false // Optional
SpatialRDD spatialRDD = WktReader.readToGeometryRDD(sparkSession.sparkContext, inputLocation, wktColumn, allowTopologyInvalidGeometries, skipSyntaxInvalidGeometries)

from sedona.core.formatMapper import WktReader
from sedona.core.formatMapper import WkbReader

WktReader.readToGeometryRDD(sc, wkt_geometries_location, 0, True, False)

WkbReader.readToGeometryRDD(sc, wkb_geometries_location, 0, True, False)

From GeoJSON

Geometries in GeoJSON is similar to WKT/WKB. However, a GeoJSON file must be beaked into multiple lines.

Suppose we have a polygon.json GeoJSON file at Path /Download/polygon.json as follows:

{ "type": "Feature", "properties": { "STATEFP": "01", "COUNTYFP": "077", "TRACTCE": "011501", "BLKGRPCE": "5", "AFFGEOID": "1500000US010770115015", "GEOID": "010770115015", "NAME": "5", "LSAD": "BG", "ALAND": 6844991, "AWATER": 32636 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -87.621765, 34.873444 ], [ -87.617535, 34.873369 ], [ -87.6123, 34.873337 ], [ -87.604049, 34.873303 ], [ -87.604033, 34.872316 ], [ -87.60415, 34.867502 ], [ -87.604218, 34.865687 ], [ -87.604409, 34.858537 ], [ -87.604018, 34.851336 ], [ -87.603716, 34.844829 ], [ -87.603696, 34.844307 ], [ -87.603673, 34.841884 ], [ -87.60372, 34.841003 ], [ -87.603879, 34.838423 ], [ -87.603888, 34.837682 ], [ -87.603889, 34.83763 ], [ -87.613127, 34.833938 ], [ -87.616451, 34.832699 ], [ -87.621041, 34.831431 ], [ -87.621056, 34.831526 ], [ -87.62112, 34.831925 ], [ -87.621603, 34.8352 ], [ -87.62158, 34.836087 ], [ -87.621383, 34.84329 ], [ -87.621359, 34.844438 ], [ -87.62129, 34.846387 ], [ -87.62119, 34.85053 ], [ -87.62144, 34.865379 ], [ -87.621765, 34.873444 ] ] ] } },
{ "type": "Feature", "properties": { "STATEFP": "01", "COUNTYFP": "045", "TRACTCE": "021102", "BLKGRPCE": "4", "AFFGEOID": "1500000US010450211024", "GEOID": "010450211024", "NAME": "4", "LSAD": "BG", "ALAND": 11360854, "AWATER": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -85.719017, 31.297901 ], [ -85.715626, 31.305203 ], [ -85.714271, 31.307096 ], [ -85.69999, 31.307552 ], [ -85.697419, 31.307951 ], [ -85.675603, 31.31218 ], [ -85.672733, 31.312876 ], [ -85.672275, 31.311977 ], [ -85.67145, 31.310988 ], [ -85.670622, 31.309524 ], [ -85.670729, 31.307622 ], [ -85.669876, 31.30666 ], [ -85.669796, 31.306224 ], [ -85.670356, 31.306178 ], [ -85.671664, 31.305583 ], [ -85.67177, 31.305299 ], [ -85.671878, 31.302764 ], [ -85.671344, 31.302123 ], [ -85.668276, 31.302076 ], [ -85.66566, 31.30093 ], [ -85.665687, 31.30022 ], [ -85.669183, 31.297677 ], [ -85.668703, 31.295638 ], [ -85.671985, 31.29314 ], [ -85.677177, 31.288211 ], [ -85.678452, 31.286376 ], [ -85.679236, 31.28285 ], [ -85.679195, 31.281426 ], [ -85.676865, 31.281049 ], [ -85.674661, 31.28008 ], [ -85.674377, 31.27935 ], [ -85.675714, 31.276882 ], [ -85.677938, 31.275168 ], [ -85.680348, 31.276814 ], [ -85.684032, 31.278848 ], [ -85.684387, 31.279082 ], [ -85.692398, 31.283499 ], [ -85.705032, 31.289718 ], [ -85.706755, 31.290476 ], [ -85.718102, 31.295204 ], [ -85.719132, 31.29689 ], [ -85.719017, 31.297901 ] ] ] } },
{ "type": "Feature", "properties": { "STATEFP": "01", "COUNTYFP": "055", "TRACTCE": "001300", "BLKGRPCE": "3", "AFFGEOID": "1500000US010550013003", "GEOID": "010550013003", "NAME": "3", "LSAD": "BG", "ALAND": 1378742, "AWATER": 247387 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -86.000685, 34.00537 ], [ -85.998837, 34.009768 ], [ -85.998012, 34.010398 ], [ -85.987865, 34.005426 ], [ -85.986656, 34.004552 ], [ -85.985, 34.002659 ], [ -85.98851, 34.001502 ], [ -85.987567, 33.999488 ], [ -85.988666, 33.99913 ], [ -85.992568, 33.999131 ], [ -85.993144, 33.999714 ], [ -85.994876, 33.995153 ], [ -85.998823, 33.989548 ], [ -85.999925, 33.994237 ], [ -86.000616, 34.000028 ], [ -86.000685, 34.00537 ] ] ] } },
{ "type": "Feature", "properties": { "STATEFP": "01", "COUNTYFP": "089", "TRACTCE": "001700", "BLKGRPCE": "2", "AFFGEOID": "1500000US010890017002", "GEOID": "010890017002", "NAME": "2", "LSAD": "BG", "ALAND": 1040641, "AWATER": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -86.574172, 34.727375 ], [ -86.562684, 34.727131 ], [ -86.562797, 34.723865 ], [ -86.562957, 34.723168 ], [ -86.562336, 34.719766 ], [ -86.557381, 34.719143 ], [ -86.557352, 34.718322 ], [ -86.559921, 34.717363 ], [ -86.564827, 34.718513 ], [ -86.567582, 34.718565 ], [ -86.570572, 34.718577 ], [ -86.573618, 34.719377 ], [ -86.574172, 34.727375 ] ] ] } },

Use the following code to create a generic SpatialRDD:

ScalaJavaPython

val inputLocation = "/Download/polygon.json"
val allowTopologyInvalidGeometries = true // Optional
val skipSyntaxInvalidGeometries = false // Optional
val spatialRDD = GeoJsonReader.readToGeometryRDD(sparkSession.sparkContext, inputLocation, allowTopologyInvalidGeometries, skipSyntaxInvalidGeometries)

String inputLocation = "/Download/polygon.json"
boolean allowTopologyInvalidGeometries = true // Optional
boolean skipSyntaxInvalidGeometries = false // Optional
SpatialRDD spatialRDD = GeoJsonReader.readToGeometryRDD(sparkSession.sparkContext, inputLocation, allowTopologyInvalidGeometries, skipSyntaxInvalidGeometries)

from sedona.core.formatMapper import GeoJsonReader

GeoJsonReader.readToGeometryRDD(sc, geo_json_file_location)

Warning

The way that Sedona reads JSON file is different from SparkSQL

From Shapefile

ScalaJavaPython

val shapefileInputLocation="/Download/myshapefile"
val spatialRDD = ShapefileReader.readToGeometryRDD(sparkSession.sparkContext, shapefileInputLocation)

String shapefileInputLocation="/Download/myshapefile"
SpatialRDD spatialRDD = ShapefileReader.readToGeometryRDD(sparkSession.sparkContext, shapefileInputLocation)

from sedona.core.formatMapper.shapefileParser import ShapefileReader

ShapefileReader.readToGeometryRDD(sc, shape_file_location)

Note

The file extensions of .shp, .shx, .dbf must be in lowercase. Assume you have a shape file called myShapefile, the file structure should be like this:

- shapefile1
- shapefile2
- myshapefile
    - myshapefile.shp
    - myshapefile.shx
    - myshapefile.dbf
    - myshapefile...
    - ...

If the file you are reading contains non-ASCII characters you'll need to explicitly set the encoding via sedona.global.charset system property before creating your Spark context.

Example:

System.setProperty("sedona.global.charset", "utf8")

val sc = new SparkContext(...)

From SedonaSQL DataFrame

Note

More details about SedonaSQL, please read the SedonaSQL tutorial.

To create a generic SpatialRDD from CSV, TSV, WKT, WKB and GeoJSON input formats, you can use SedonaSQL.

We use checkin.csv CSV file as the example. You can create a generic SpatialRDD using the following steps:

1. Load data in SedonaSQL.

   var df = sparkSession.read.format("csv").option("header", "false").load(csvPointInputLocation)
   df.createOrReplaceTempView("inputtable")
   

2. Create a Geometry type column in SedonaSQL

   var spatialDf = sparkSession.sql(
       """
           |SELECT ST_Point(CAST(inputtable._c0 AS Decimal(24,20)),CAST(inputtable._c1 AS Decimal(24,20))) AS checkin
           |FROM inputtable
       """.stripMargin)
   

3. Use SedonaSQL DataFrame-RDD Adapter to convert a DataFrame to an SpatialRDD

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

"checkin" is the name of the geometry column

For WKT/WKB/GeoJSON data, please use ST_GeomFromWKT / ST_GeomFromWKB / ST_GeomFromGeoJSON instead.

Transform the Coordinate Reference System

Sedona doesn't control the coordinate unit (degree-based or meter-based) of all geometries in an SpatialRDD. The unit of all related distances in Sedona is same as the unit of all geometries in an SpatialRDD.

To convert Coordinate Reference System of an SpatialRDD, use the following code:

ScalaJavaPython

val sourceCrsCode = "epsg:4326" // WGS84, the most common degree-based CRS
val targetCrsCode = "epsg:3857" // The most common meter-based CRS
objectRDD.CRSTransform(sourceCrsCode, targetCrsCode, false)

String sourceCrsCode = "epsg:4326" // WGS84, the most common degree-based CRS
String targetCrsCode = "epsg:3857" // The most common meter-based CRS
objectRDD.CRSTransform(sourceCrsCode, targetCrsCode, false)

sourceCrsCode = "epsg:4326" // WGS84, the most common degree-based CRS
targetCrsCode = "epsg:3857" // The most common meter-based CRS
objectRDD.CRSTransform(sourceCrsCode, targetCrsCode, False)

false in CRSTransform(sourceCrsCode, targetCrsCode, false) means that it will not tolerate Datum shift. If you want it to be lenient, use true instead.

Warning

CRS transformation should be done right after creating each SpatialRDD, otherwise it will lead to wrong query results. For instance, use something like this:

ScalaJavaPython

val objectRDD = WktReader.readToGeometryRDD(sparkSession.sparkContext, inputLocation, wktColumn, allowTopologyInvalidGeometries, skipSyntaxInvalidGeometries)
objectRDD.CRSTransform("epsg:4326", "epsg:3857", false)

SpatialRDD objectRDD = WktReader.readToGeometryRDD(sparkSession.sparkContext, inputLocation, wktColumn, allowTopologyInvalidGeometries, skipSyntaxInvalidGeometries)
objectRDD.CRSTransform("epsg:4326", "epsg:3857", false)

objectRDD = WktReader.readToGeometryRDD(sparkSession.sparkContext, inputLocation, wktColumn, allowTopologyInvalidGeometries, skipSyntaxInvalidGeometries)
objectRDD.CRSTransform("epsg:4326", "epsg:3857", False)

The details CRS information can be found on EPSG.io

Read other attributes in an SpatialRDD

Each SpatialRDD can carry non-spatial attributes such as price, age and name.

The other attributes are combined together to a string and stored in UserData field of each geometry.

To retrieve the UserData field, use the following code:

ScalaJavaPython

val rddWithOtherAttributes = objectRDD.rawSpatialRDD.rdd.map[String](f=>f.getUserData.asInstanceOf[String])

SpatialRDD<Geometry> spatialRDD = Adapter.toSpatialRdd(spatialDf, "arealandmark");
spatialRDD.rawSpatialRDD.map(obj -> {return obj.getUserData();});

rdd_with_other_attributes = object_rdd.rawSpatialRDD.map(lambda x: x.getUserData())

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

Note

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)

ScalaJavaPython

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)

from sedona.core.geom.envelope import Envelope
from sedona.core.spatialOperator import RangeQuery

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)

Note

Sedona 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.core.geom.envelope import Envelope
from sedona.core.spatialOperator import RangeQueryRaw
from sedona.utils.adapter import Adapter

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, Sedona range query window can be Point/Polygon/LineString.

The code to create a point, linestring (4 vertexes) and polygon (4 vertexes) is as follows:

ScalaJavaPython

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)

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

Use spatial indexes

Sedona provides two types of spatial indexes, Quad-Tree and R-Tree. Once you specify an index type, Sedona 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:

ScalaJavaPython

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)

from sedona.core.geom.envelope import Envelope
from sedona.core.enums import IndexType
from sedona.core.spatialOperator import RangeQuery

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
)

Tip

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

Scala/JavaPython

The output format of the spatial range query is another SpatialRDD.

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()

[
 1.5900840000000045,
 1.5906639999999896,
 1.1110299999999995,
 1.1096700000000084,
 1.1415619999999933,
 1.1386399999999952,
 1.1415619999999933,
 1.1418860000000137,
 1.1392780000000045,
 ...
]

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.

ScalaJavaPython

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)

from sedona.core.spatialOperator import KNNQuery
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)

Note

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, Sedona KNN query center can be Polygon and LineString.

Scala/JavaPython

To learn how to create Polygon and LineString object, see Range query window.

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:

ScalaJavaPython

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)

from sedona.core.spatialOperator import KNNQuery
from sedona.core.enums import IndexType
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)

Warning

Only R-Tree index supports Spatial KNN query

Output format

Scala/JavaPython

The output format of the spatial KNN query is a list of geometries. The list has K geometry objects.

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.

ScalaJavaPython

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)

from sedona.core.enums import GridType
from sedona.core.spatialOperator import JoinQuery

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)

Note

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.

Scala/JavaPython

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

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

Or

Scala/JavaPython

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

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

Use spatial indexes

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

ScalaJavaPython

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)

from sedona.core.enums import GridType
from sedona.core.enums import IndexType
from sedona.core.spatialOperator import JoinQuery

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)

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

Output format

Scala/JavaPython

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.

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()

Note

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.core.SpatialRDD import CircleRDD
from sedona.core.enums import GridType
from sedona.core.spatialOperator import JoinQueryRaw

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

Warning

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. The unit of the distance is explained here.

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.

ScalaJavaPython

objectRddA.analyze()

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

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

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

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

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)

from sedona.core.SpatialRDD import CircleRDD
from sedona.core.enums import GridType
from sedona.core.spatialOperator import JoinQuery

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 = JoinQuery.DistanceJoinQueryFlat(spatial_rdd, circle_rdd, using_index, consider_boundary_intersection)

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.

Note

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

Save to permanent storage

You can always save an SpatialRDD back to some permanent storage such as HDFS and Amazon S3. You can save distributed SpatialRDD to WKT, GeoJSON and object files.

Note

Non-spatial attributes such as price, age and name will also be stored to permanent storage.

Save an SpatialRDD (not indexed)

Typed SpatialRDD and generic SpatialRDD can be saved to permanent storage.

Save to distributed WKT text file

Use the following code to save an SpatialRDD as a distributed WKT text file:

objectRDD.rawSpatialRDD.saveAsTextFile("hdfs://PATH")
objectRDD.saveAsWKT("hdfs://PATH")

Save to distributed WKB text file

Use the following code to save an SpatialRDD as a distributed WKB text file:

objectRDD.saveAsWKB("hdfs://PATH")

Save to distributed GeoJSON text file

Use the following code to save an SpatialRDD as a distributed GeoJSON text file:

objectRDD.saveAsGeoJSON("hdfs://PATH")

Save to distributed object file

Use the following code to save an SpatialRDD as a distributed object file:

Scala/JavaPython

objectRDD.rawSpatialRDD.saveAsObjectFile("hdfs://PATH")

object_rdd.rawJvmSpatialRDD.saveAsObjectFile("hdfs://PATH")

Note

Each object in a distributed object file is a byte array (not human-readable). This byte array is the serialized format of a Geometry or a SpatialIndex.

Save an SpatialRDD (indexed)

Indexed typed SpatialRDD and generic SpatialRDD can be saved to permanent storage. However, the indexed SpatialRDD has to be stored as a distributed object file.

Save to distributed object file

Use the following code to save an SpatialRDD as a distributed object file:

objectRDD.indexedRawRDD.saveAsObjectFile("hdfs://PATH")

Save an SpatialRDD (spatialPartitioned W/O indexed)

A spatial partitioned RDD can be saved to permanent storage but Spark is not able to maintain the same RDD partition Id of the original RDD. This will lead to wrong join query results. We are working on some solutions. Stay tuned!

Reload a saved SpatialRDD

You can easily reload an SpatialRDD that has been saved to a distributed object file.

Load to a typed SpatialRDD

Warning

Typed SpatialRDD has been deprecated for a long time. We do NOT recommend it anymore.

Load to a generic SpatialRDD

Use the following code to reload the SpatialRDD:

ScalaJavaPython

var savedRDD = new SpatialRDD[Geometry]
savedRDD.rawSpatialRDD = sc.objectFile[Geometry]("hdfs://PATH")

SpatialRDD savedRDD = new SpatialRDD<Geometry>
savedRDD.rawSpatialRDD = sc.objectFile<Geometry>("hdfs://PATH")

saved_rdd = load_spatial_rdd_from_disc(sc, "hdfs://PATH", GeoType.GEOMETRY)

Use the following code to reload the indexed SpatialRDD:

ScalaJavaPython

var savedRDD = new SpatialRDD[Geometry]
savedRDD.indexedRawRDD = sc.objectFile[SpatialIndex]("hdfs://PATH")

SpatialRDD savedRDD = new SpatialRDD<Geometry>
savedRDD.indexedRawRDD = sc.objectFile<SpatialIndex>("hdfs://PATH")

saved_rdd = SpatialRDD()
saved_rdd.indexedRawRDD = load_spatial_index_rdd_from_disc(sc, "hdfs://PATH")

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Last update: March 20, 2023 03:44:37