> ## Documentation Index > Fetch the complete documentation index at: https://docs.wherobots.com/llms.txt > Use this file to discover all available pages before exploring further. # Nearest-Neighbor Searching WherobotsDB supports nearest-neighbor 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_sphere: Boolean, search_radius: Double)` * `R` represents the queries side table. * `S` represents the objects side table. * `K` denotes the number of nearest neighbors to retrieve. * `use_sphere` is a boolean value that specifies whether to calculate distances using the sphere model. * `search_radius` is an optional parameter that defines the maximum distance within which neighbors will be searched, without imposing any constraints on its value. 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 ``` The `ST_KNN` join does an inner join for the query side table (Table R). It returns only pairs where there is at least one matching neighbor within the k nearest neighbors. If a query point has no valid neighbor (e.g., because k is too large), it is excluded from the result. ### 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: ```scala theme={"system"} 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: ```sql theme={"system"} 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: ```scala theme={"system"} 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 ``` Example 1: Query Without search\_radius Parameter ```sql theme={"system"} 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) ``` Explanation: This query performs a k-Nearest Neighbor (kNN) join between the QUERIES table and the OBJECTS table. For each geometry in the QUERIES table, the function ST\_KNN identifies the 4 nearest geometries (based on spatial distance) from the OBJECTS table. `FALSE` indicates that the spheroid distance model is not being used. Instead a planar distance model is being applied. Since the `search_radius` parameter is not provided, there are no constraints on the maximum distance between the query and the objects. 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) | +--------+-----------------+-------------+ ``` Example 2: Query With `search_radius` Parameter ```sql theme={"system"} 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, 3.0) ``` Explanation: This query is similar to Example 1 but this example includes an additional parameter, `search_radius = 3.0`. With this parameter, only neighbors within 3.0 units of each query geometry are considered. The `search_radius` acts as a filter, removing any object geometries that are farther than the specified radius. When `search_radius` is applied, the query may return fewer than `K` neighbors if there aren't enough points within the radius. Output: ``` +--------+-----------------+-------------+ |QUERY_ID|QUERIES_GEOM |OBJECTS_GEOM | +--------+-----------------+-------------+ |2 |POINT (10 10) |POINT (9 8) | |2 |POINT (10 10) |POINT (9 8) | |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)| +--------+-----------------+-------------+ ``` In this output: Some object points from the previous result set are missing because they fall outside the 3.0-unit search radius. For some queries (e.g., `QUERY_ID = 2`), fewer than 4 neighbors are returned since not enough points are within the specified radius. The `search_radius` ensures more focused results by excluding distant neighbors, which can be useful for scenarios requiring spatial proximity. ## 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_sphee: Boolean, search_radius: Double)` 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 ```sql theme={"system"} 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.