rdd.md

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

Set up dependencies

Please refer to Set up dependencies to set up dependencies.

Create Sedona config

Please refer to Create Sedona config to create a Sedona config.

Initiate SedonaContext

Please refer to Initiate SedonaContext to initiate a SedonaContext.

Create a SpatialRDD from SedonaSQL DataFrame

Please refer to Create a Geometry type column to create a Geometry type column. Then you can create a SpatialRDD from the DataFrame.

=== "Scala"

```scala
var spatialRDD = StructuredAdapter.toSpatialRdd(spatialDf, "usacounty")
```

=== "Java"

```java
SpatialRDD spatialRDD = StructuredAdapter.toSpatialRdd(spatialDf, "usacounty")
```

=== "Python"

```python
from sedona.spark import StructuredAdapter

spatialRDD = StructuredAdapter.toSpatialRdd(spatialDf, "usacounty")
```

"usacounty" is the name of the geometry column. It is an optional parameter. If you don't provide it, the first geometry column will be used.

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.

By default, this function uses lon/lat order since v1.5.0. Before, it used lat/lon order. You can use ==spatialRDD.flipCoordinates== to swap X and Y.

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

=== "Scala"

```scala
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)
```

=== "Java"

```java
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)
```

=== "Python"

```python
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:

=== "Scala"

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

=== "Java"

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

=== "Python"

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

The details CRS information can be found on EPSG.io

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 a SpatialRDD (typed or generic). You can use the following code to issue a 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: sql SELECT * FROM checkin WHERE ST_Intersects(checkin.location, queryWindow)

=== "Scala"

```scala
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)
```

=== "Java"

```java
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)
```

=== "Python"

```python
from sedona.spark import Envelope
from sedona.spark 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:
```python
from sedona.spark import Envelope
from sedona.spark import RangeQueryRaw
from sedona.spark 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 = StructuredAdapter.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 vertices) and polygon (4 vertices) is as follows:

=== "Scala"

```scala
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)
```

=== "Java"

```java
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)
```

=== "Python"

A Shapely geometry can be used as a query window. To create shapely geometries, please follow [Shapely official docs](https://shapely.readthedocs.io/en/stable/manual.html)

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:

=== "Scala"

```scala
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)
```

=== "Java"

```java
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)
```

=== "Python"

```python
from sedona.spark import Envelope
from sedona.spark import IndexType
from sedona.spark 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/Java"

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

=== "Python"

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:

```python
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

```python
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 Nearest Neighbor query takes as input a K, a query point and a SpatialRDD and finds the K geometries in the RDD which are the closest to the query point.

Assume you now have a SpatialRDD (typed or generic). You can use the following code to issue a Spatial KNN Query on it.

=== "Scala"

```scala
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)
```

=== "Java"

```java
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)
```

=== "Python"

```python
from sedona.spark 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 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/Java"

To learn how to create Polygon and LineString object, see [Range query window](#range-query-window).

=== "Python"

To create Polygon or Linestring object please follow [Shapely official docs](https://shapely.readthedocs.io/en/stable/manual.html)

Use spatial indexes

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

=== "Scala"

```scala
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)
```

=== "Java"

```java
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)
```

=== "Python"

```python
from sedona.spark import KNNQuery
from sedona.spark 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/Java"

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

=== "Python"

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

Example:
```python
>> 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 a Spatial Join Query on them.

=== "Scala"

```scala
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)
```

=== "Java"

```java
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)
```

=== "Python"

```python
from sedona.spark import GridType
from sedona.spark 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: 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/Java"

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

=== "Python"

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

Or

=== "Scala/Java"

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

=== "Python"

```python
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:

=== "Scala"

```scala
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)
```

=== "Java"

```java
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)
```

=== "Python"

```python
from sedona.spark import GridType
from sedona.spark import IndexType
from sedona.spark 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/Java"

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.

=== "Python"

Result for this query is RDD which holds two GeoData objects within list of lists.
Example:
```python
result.collect()
```

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

It is possible to do some RDD operation on result data ex. Getting polygon centroid.
```python
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:
```python
from sedona.spark import CircleRDD
from sedona.spark import GridType
from sedona.spark import JoinQueryRaw
from sedona.spark import StructuredAdapter

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 = StructuredAdapter.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 a Distance Join Query on them.

=== "Scala"

```scala
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)
```

=== "Java"

```java
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)
```

=== "Python"

```python
from sedona.spark import CircleRDD
from sedona.spark import GridType
from sedona.spark 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: 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.

Save an SpatialRDD (not indexed)

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

=== "Scala/Java"

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

=== "Python"

```python
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.

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==. Use the following code to reload the SpatialRDD:

=== "Scala"

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

=== "Java"

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

=== "Python"

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

Use the following code to reload the indexed SpatialRDD:

=== "Scala"

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

=== "Java"

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

=== "Python"

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