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<a href="https://github.com/apache/sedona/edit/master/docs/api/sql/NearestNeighbourSearching.md" title="Edit this page" class="md-content__button md-icon" rel="edit">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M10 20H6V4h7v5h5v3.1l2-2V8l-6-6H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h4zm10.2-7c.1 0 .3.1.4.2l1.3 1.3c.2.2.2.6 0 .8l-1 1-2.1-2.1 1-1c.1-.1.2-.2.4-.2m0 3.9L14.1 23H12v-2.1l6.1-6.1z"/></svg>
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<h1>Nearest-Neighbour searching</h1>
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<p>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.</p>
<h2 id="st_knn">ST_KNN<a class="headerlink" href="#st_knn" title="Permanent link">&para;</a></h2>
<p>Introduction: join operation to find the k-nearest neighbors of a point or region in a spatial dataset.</p>
<p>Format: <code>ST_KNN(R: Table, S: Table, k: Integer, use_spheroid: Boolean)</code></p>
<p>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.</p>
<p>Queries side table contains geometries that are used to find the k-nearest neighbors in the object side table.</p>
<p>When either queries or objects data contain non-point data (geometries), we take the centroid of each geometry.</p>
<p>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:</p>
<p><strong>Note for Inner Join:</strong></p>
<ul>
<li>The <code>ST_KNN</code> join only supports left inner join.</li>
<li>It returns only pairs where there is at least one matching neighbor within the k nearest neighbors.</li>
<li>If a query point has no valid neighbor (e.g., because k is too large), it is excluded from the result.</li>
</ul>
<div class="highlight"><pre><span></span><code>spark.sedona.join.knn.includeTieBreakers=true
</code></pre></div>
<h3 id="filter-pushdown-considerations">Filter Pushdown Considerations:<a class="headerlink" href="#filter-pushdown-considerations" title="Permanent link">&para;</a></h3>
<p>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.</p>
<p>For example, you can use the following approach:</p>
<p>Scala Example:</p>
<div class="highlight"><pre><span></span><code>val knnResult = knnJoinDF.cache()
val filteredResult = knnResult.filter(condition)
</code></pre></div>
<p>SQL Example:</p>
<div class="highlight"><pre><span></span><code>CREATE OR REPLACE TEMP VIEW knnResult AS
SELECT * FROM (
-- Your KNN join SQL here
) AS knnView;
CACHE TABLE knnResult;
SELECT * FROM knnResult WHERE condition;
</code></pre></div>
<h3 id="optimization-barrier">Optimization Barrier<a class="headerlink" href="#optimization-barrier" title="Permanent link">&para;</a></h3>
<p>Use the <code>barrier</code> function to prevent filter pushdown and control predicate evaluation order in complex spatial joins. This function creates an optimization barrier by evaluating boolean expressions at runtime.</p>
<p>The <code>barrier</code> function takes a boolean expression as a string, followed by pairs of variable names and their values that will be substituted into the expression:</p>
<div class="highlight"><pre><span></span><code><span class="n">barrier</span><span class="p">(</span><span class="n">expression</span><span class="p">,</span><span class="w"> </span><span class="n">var_name1</span><span class="p">,</span><span class="w"> </span><span class="n">var_value1</span><span class="p">,</span><span class="w"> </span><span class="n">var_name2</span><span class="p">,</span><span class="w"> </span><span class="n">var_value2</span><span class="p">,</span><span class="w"> </span><span class="p">...)</span>
</code></pre></div>
<p>The placement of filters relative to KNN joins changes the semantic meaning of the query:</p>
<ul>
<li><strong>Filter before KNN</strong>: First filters the data, then finds K nearest neighbors from the filtered subset. This answers "What are the K nearest high-rated restaurants?"</li>
<li><strong>Filter after KNN</strong>: First finds K nearest neighbors from all data, then filters those results. This answers "Of the K nearest restaurants, which ones are high-rated?"</li>
</ul>
<h3 id="example">Example<a class="headerlink" href="#example" title="Permanent link">&para;</a></h3>
<p>Find the 3 nearest high-rated restaurants to luxury hotels, ensuring the KNN join completes before filtering.</p>
<div class="highlight"><pre><span></span><code><span class="k">SELECT</span>
<span class="w"> </span><span class="n">h</span><span class="p">.</span><span class="n">name</span><span class="w"> </span><span class="k">AS</span><span class="w"> </span><span class="n">hotel</span><span class="p">,</span>
<span class="w"> </span><span class="n">r</span><span class="p">.</span><span class="n">name</span><span class="w"> </span><span class="k">AS</span><span class="w"> </span><span class="n">restaurant</span><span class="p">,</span>
<span class="w"> </span><span class="n">r</span><span class="p">.</span><span class="n">rating</span>
<span class="k">FROM</span><span class="w"> </span><span class="n">hotels</span><span class="w"> </span><span class="k">AS</span><span class="w"> </span><span class="n">h</span>
<span class="k">INNER</span><span class="w"> </span><span class="k">JOIN</span><span class="w"> </span><span class="n">restaurants</span><span class="w"> </span><span class="k">AS</span><span class="w"> </span><span class="n">r</span>
<span class="k">ON</span><span class="w"> </span><span class="n">ST_KNN</span><span class="p">(</span><span class="n">h</span><span class="p">.</span><span class="n">geometry</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">.</span><span class="n">geometry</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="k">false</span><span class="p">)</span>
<span class="k">WHERE</span><span class="w"> </span><span class="n">barrier</span><span class="p">(</span><span class="s1">&#39;rating &gt; 4.0 AND stars &gt;= 4&#39;</span><span class="p">,</span>
<span class="w"> </span><span class="s1">&#39;rating&#39;</span><span class="p">,</span><span class="w"> </span><span class="n">r</span><span class="p">.</span><span class="n">rating</span><span class="p">,</span>
<span class="w"> </span><span class="s1">&#39;stars&#39;</span><span class="p">,</span><span class="w"> </span><span class="n">h</span><span class="p">.</span><span class="n">stars</span><span class="p">)</span>
</code></pre></div>
<p>With the barrier function, this query first finds the 3 nearest restaurants to each hotel (regardless of rating), then filters to keep only those pairs where the restaurant has rating &gt; 4.0 and the hotel has stars &gt;= 4. Without the barrier, an optimizer might push the filters down, changing the query to first filter for high-rated restaurants and luxury hotels, then find the 3 nearest among those filtered sets.</p>
<h3 id="handling-sql-defined-tables-in-st_knn-joins">Handling SQL-Defined Tables in ST_KNN Joins:<a class="headerlink" href="#handling-sql-defined-tables-in-st_knn-joins" title="Permanent link">&para;</a></h3>
<p>When creating DataFrames from hard-coded SQL select statements in Sedona, and later using them in <code>ST_KNN</code> 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:</p>
<div class="highlight"><pre><span></span><code><span class="kd">val</span><span class="w"> </span><span class="n">df1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="n">sql</span><span class="p">(</span><span class="s">&quot;SELECT ST_Point(0.0, 0.0) as geom1&quot;</span><span class="p">)</span>
<span class="kd">val</span><span class="w"> </span><span class="n">df2</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="n">sql</span><span class="p">(</span><span class="s">&quot;SELECT ST_Point(0.0, 0.0) as geom2&quot;</span><span class="p">)</span>
<span class="kd">val</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">df1</span><span class="p">.</span><span class="n">join</span><span class="p">(</span><span class="n">df2</span><span class="p">,</span><span class="w"> </span><span class="n">expr</span><span class="p">(</span><span class="s">&quot;ST_KNN(geom1, geom2, 1)&quot;</span><span class="p">))</span>
</code></pre></div>
<p>Sedona may optimize the join to a form like this:</p>
<div class="highlight"><pre><span></span><code><span class="k">SELECT</span><span class="w"> </span><span class="n">ST_KNN</span><span class="p">(</span><span class="n">ST_Point</span><span class="p">(</span><span class="mi">0</span><span class="p">.</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">.</span><span class="mi">0</span><span class="p">),</span><span class="w"> </span><span class="n">ST_Point</span><span class="p">(</span><span class="mi">0</span><span class="p">.</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">.</span><span class="mi">0</span><span class="p">),</span><span class="w"> </span><span class="mi">1</span><span class="p">)</span>
</code></pre></div>
<p>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.</p>
<p>Workaround:</p>
<p>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:</p>
<div class="highlight"><pre><span></span><code><span class="kd">val</span><span class="w"> </span><span class="n">df1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="n">sql</span><span class="p">(</span><span class="s">&quot;SELECT ST_Point(0.0, 0.0) as geom1&quot;</span><span class="p">).</span><span class="n">cache</span><span class="p">()</span>
<span class="kd">val</span><span class="w"> </span><span class="n">df2</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="n">sql</span><span class="p">(</span><span class="s">&quot;SELECT ST_Point(0.0, 0.0) as geom2&quot;</span><span class="p">).</span><span class="n">cache</span><span class="p">()</span>
<span class="kd">val</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">df1</span><span class="p">.</span><span class="n">join</span><span class="p">(</span><span class="n">df2</span><span class="p">,</span><span class="w"> </span><span class="n">expr</span><span class="p">(</span><span class="s">&quot;ST_KNN(geom1, geom2, 1)&quot;</span><span class="p">))</span>
</code></pre></div>
<p>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.</p>
<h3 id="sql-example">SQL Example<a class="headerlink" href="#sql-example" title="Permanent link">&para;</a></h3>
<p>Suppose we have two tables <code>QUERIES</code> and <code>OBJECTS</code> with the following data:</p>
<p>QUERIES table:</p>
<div class="highlight"><pre><span></span><code>ID GEOMETRY NAME
1 POINT(1 1) station1
2 POINT(10 10) station2
3 POINT(-0.5 -0.5) station3
</code></pre></div>
<p>OBJECTS table:</p>
<div class="highlight"><pre><span></span><code>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
</code></pre></div>
<div class="highlight"><pre><span></span><code><span class="k">SELECT</span>
<span class="w"> </span><span class="n">QUERIES</span><span class="p">.</span><span class="n">ID</span><span class="w"> </span><span class="k">AS</span><span class="w"> </span><span class="n">QUERY_ID</span><span class="p">,</span>
<span class="w"> </span><span class="n">QUERIES</span><span class="p">.</span><span class="n">GEOMETRY</span><span class="w"> </span><span class="k">AS</span><span class="w"> </span><span class="n">QUERIES_GEOM</span><span class="p">,</span>
<span class="w"> </span><span class="n">OBJECTS</span><span class="p">.</span><span class="n">GEOMETRY</span><span class="w"> </span><span class="k">AS</span><span class="w"> </span><span class="n">OBJECTS_GEOM</span>
<span class="k">FROM</span><span class="w"> </span><span class="n">QUERIES</span><span class="w"> </span><span class="k">JOIN</span><span class="w"> </span><span class="n">OBJECTS</span><span class="w"> </span><span class="k">ON</span><span class="w"> </span><span class="n">ST_KNN</span><span class="p">(</span><span class="n">QUERIES</span><span class="p">.</span><span class="n">GEOMETRY</span><span class="p">,</span><span class="w"> </span><span class="n">OBJECTS</span><span class="p">.</span><span class="n">GEOMETRY</span><span class="p">,</span><span class="w"> </span><span class="mi">4</span><span class="p">,</span><span class="w"> </span><span class="k">FALSE</span><span class="p">)</span>
</code></pre></div>
<p>Output:</p>
<div class="highlight"><pre><span></span><code>+--------+-----------------+-------------+
|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) |
+--------+-----------------+-------------+
</code></pre></div>
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