Nearest-Neighbour searching
WherobotsDB 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 WherobotsDB config is set to true:
spark.sedona.join.knn.includeTieBreakers=true
Known limitations¶
ST_KNN join is a new syntax we introduced to Spatial SQL and therefore it has a few known limitations. But we are actively working on solving them.
- 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 WherobotsDB, and later using them in ST_KNN joins, WherobotsDB 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)"))
WherobotsDB 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 WherobotsDB 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 the 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 the engine 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 and it 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) |
+--------+-----------------+-------------+
ST_AKNN¶
Introduction: join operation to find the k-nearest neighbors of a point or region in a spatial dataset.
Format: ST_AKNN(R: Table, S: Table, k: Integer, use_spheroid: Boolean)
The ST_AKNN function is similar to ST_KNN, but it uses approximate algorithms to find the k-nearest neighbors. This can be useful for large datasets where exact kNN search is computationally expensive. The trade-off is that approximate algorithms may not always return the exact k-nearest neighbors, but they provide a good approximation in a reasonable amount of time.
Known limitations¶
Similar to ST_KNN, ST_AKNN join is a new syntax we introduced to Spatial SQL.
- Filter Pushdown Considerations
Filter pushdown behavior in ST_AKNN is similar to that in ST_KNN. If filters are applied to the resulting DataFrame, they might be pushed down to the object side reader before the kNN join is executed. To apply filters after the join, ensure you materialize the kNN join results before applying any filters. SQL Example
SELECT
QUERIES.ID AS QUERY_ID,
QUERIES.GEOMETRY AS QUERIES_GEOM,
OBJECTS.GEOMETRY AS OBJECTS_GEOM
FROM QUERIES JOIN OBJECTS ON ST_AKNN(QUERIES.GEOMETRY, OBJECTS.GEOMETRY, 4, FALSE)
- Handling SQL-Defined Tables in ST_AKNN Joins
The same considerations for handling SQL-defined tables in ST_KNN joins apply to ST_AKNN joins. Ensure that DataFrames created from hard-coded SQL select statements are materialized before performing the join to prevent the engine from bypassing the kNN join logic.