Nearest-Neighbour searching

Sedona supports nearest-neighbour searching on geospatial data by providing a geospatial k-Nearest Neighbors (kNN) join method. This method involves identifying the k-nearest neighbors for a given spatial point or region based on geographic proximity, typically using spatial coordinates and a suitable distance metric like Euclidean or great-circle distance.

ST_KNN¶

Introduction: join operation to find the k-nearest neighbors of a point or region in a spatial dataset.

Format: ST_KNN(R: Table, S: Table, k: Integer, use_spheroid: Boolean)

Where R is the queries side table and S is the object side table, K is the number of neighbors. use_spheroid is a boolean value that determines whether to use the spheroid distance or not.

Queries side table contains geometries that are used to find the k-nearest neighbors in the object side table.

When either queries or objects data contain non-point data (geometries), we take the centroid of each geometry.

In case there are ties in the distance, the result will include all the tied geometries only when the following sedona config is set to true:

spark.sedona.join.knn.includeTieBreakers=true

Filter Pushdown Considerations:¶

When using ST_KNN with filters applied to the resulting DataFrame, some of these filters may be pushed down to the object side of the kNN join. This means the filters will be applied to the object side reader before the kNN join is executed. If you want the filters to be applied after the kNN join, ensure that you first materialize the kNN join results and then apply the filters.

For example, you can use the following approach:

Scala Example:

val knnResult = knnJoinDF.cache()
val filteredResult = knnResult.filter(condition)

SQL Example:

CREATE OR REPLACE TEMP VIEW knnResult AS
SELECT * FROM (
  -- Your KNN join SQL here
) AS knnView;
CACHE TABLE knnResult;
SELECT * FROM knnResult WHERE condition;

Handling SQL-Defined Tables in ST_KNN Joins:¶

When creating DataFrames from hard-coded SQL select statements in Sedona, and later using them in ST_KNN joins, Sedona may attempt to optimize the query in a way that bypasses the intended kNN join logic. Specifically, if you create DataFrames with hard-coded SQL, such as:

val df1 = sedona.sql("SELECT ST_Point(0.0, 0.0) as geom1")
val df2 = sedona.sql("SELECT ST_Point(0.0, 0.0) as geom2")

val df = df1.join(df2, expr("ST_KNN(geom1, geom2, 1)"))

Sedona may optimize the join to a form like this:

SELECT ST_KNN(ST_Point(0.0, 0.0), ST_Point(0.0, 0.0), 1)

As a result, the ST_KNN function is handled as a User-Defined Function (UDF) instead of a proper join operation, preventing Sedona from initiating the kNN join execution path. Unlike typical UDFs, the ST_KNN function operates on multiple rows across DataFrames, not just individual rows. When this occurs, the query fails with an UnsupportedOperationException, indicating that the KNN predicate is not supported.

Workaround:

To prevent Spark's optimization from bypassing the kNN join logic, the DataFrames created with hard-coded SQL select statements must be materialized before performing the join. By caching the DataFrames, you can instruct Spark to avoid this undesired optimization:

val df1 = sedona.sql("SELECT ST_Point(0.0, 0.0) as geom1").cache()
val df2 = sedona.sql("SELECT ST_Point(0.0, 0.0) as geom2").cache()

val df = df1.join(df2, expr("ST_KNN(geom1, geom2, 1)"))

Materializing the DataFrames with .cache() ensures that the correct kNN join path is followed in the Spark logical plan and prevents the optimization that would treat ST_KNN as a simple UDF.

SQL Example¶

Suppose we have two tables QUERIES and OBJECTS with the following data:

QUERIES table:

ID  GEOMETRY            NAME
1   POINT(1 1)          station1
2   POINT(10 10)        station2
3   POINT(-0.5 -0.5)    station3

OBJECTS table:

ID  GEOMETRY            NAME
1   POINT(11 5)         bank1
2   POINT(12 1)         bank2
3   POINT(-1 -1)        bank3
4   POINT(-3 5)         bank4
5   POINT(9 8)          bank5
6   POINT(4 3)          bank6
7   POINT(-4 -5)        bank7
8   POINT(4 -2)         bank8
9   POINT(-3 1)         bank9
10  POINT(-7 3)         bank10
11  POINT(11 5)         bank11
12  POINT(12 1)         bank12
13  POINT(-1 -1)        bank13
14  POINT(-3 5)         bank14
15  POINT(9 8)          bank15
16  POINT(4 3)          bank16
17  POINT(-4 -5)        bank17
18  POINT(4 -2)         bank18
19  POINT(-3 1)         bank19
20  POINT(-7 3)         bank20

SELECT
    QUERIES.ID AS QUERY_ID,
    QUERIES.GEOMETRY AS QUERIES_GEOM,
    OBJECTS.GEOMETRY AS OBJECTS_GEOM
FROM QUERIES JOIN OBJECTS ON ST_KNN(QUERIES.GEOMETRY, OBJECTS.GEOMETRY, 4, FALSE)

Output:

+--------+-----------------+-------------+
|QUERY_ID|QUERIES_GEOM     |OBJECTS_GEOM |
+--------+-----------------+-------------+
|3       |POINT (-0.5 -0.5)|POINT (-1 -1)|
|3       |POINT (-0.5 -0.5)|POINT (-1 -1)|
|3       |POINT (-0.5 -0.5)|POINT (-3 1) |
|3       |POINT (-0.5 -0.5)|POINT (-3 1) |
|1       |POINT (1 1)      |POINT (-1 -1)|
|1       |POINT (1 1)      |POINT (-1 -1)|
|1       |POINT (1 1)      |POINT (4 3)  |
|1       |POINT (1 1)      |POINT (4 3)  |
|2       |POINT (10 10)    |POINT (9 8)  |
|2       |POINT (10 10)    |POINT (9 8)  |
|2       |POINT (10 10)    |POINT (11 5) |
|2       |POINT (10 10)    |POINT (11 5) |
+--------+-----------------+-------------+