Add cosine_distance for sparse vectors

core/trino-main/src/main/java/io/trino/operator/scalar/MathFunctions.java

@@ -1404,6 +1404,24 @@ public static Double cosineSimilarity(
         return dotProduct / (normLeftMap * normRightMap);
     }
 
+    @Description("Calculates the cosine distance between the give sparse vectors")
+    @ScalarFunction
+    @SqlType(StandardTypes.DOUBLE)
+    public static double cosineDistance(
+                    @OperatorDependency(
+                    operator = IDENTICAL,
+                    argumentTypes = {"varchar", "varchar"},
+                    convention = @Convention(arguments = {BLOCK_POSITION, BLOCK_POSITION}, result = NULLABLE_RETURN)) BlockPositionIsIdentical varcharIdentical,
+            @OperatorDependency(
+                    operator = HASH_CODE,
+                    argumentTypes = "varchar",
+                    convention = @Convention(arguments = BLOCK_POSITION, result = FAIL_ON_NULL)) BlockPositionHashCode varcharHashCode,
+            @SqlType("map(varchar,double)") SqlMap leftMap,
+            @SqlType("map(varchar,double)") SqlMap rightMap)
+    {
+        return 1.0 - cosineSimilarity(varcharIdentical, varcharHashCode, leftMap, rightMap);
+    }
+
     private static double mapDotProduct(BlockPositionIsIdentical varcharIdentical, BlockPositionHashCode varcharHashCode, SqlMap leftMap, SqlMap rightMap)
     {
         int leftRawOffset = leftMap.getRawOffset();

core/trino-main/src/test/java/io/trino/operator/scalar/TestMathFunctions.java

@@ -3457,6 +3457,25 @@
                 .isNull(DOUBLE);
     }
 
+    @Test
+    public void testCosineDistance()
+    {
+        assertThat(assertions.function("cosine_distance", "map(ARRAY['a', 'b'], ARRAY[1.0E0, 2.0E0])", "map(ARRAY['c', 'b'], ARRAY[1.0E0, 3.0E0])"))
+                .isEqualTo(1 - (2 * 3 / (Math.sqrt(5) * Math.sqrt(10))));
+
+        assertThat(assertions.function("cosine_distance", "map(ARRAY['a', 'b', 'c'], ARRAY[1.0E0, 2.0E0, -1.0E0])", "map(ARRAY['c', 'b'], ARRAY[1.0E0, 3.0E0])"))
+                .isEqualTo(1 - ((2 * 3 + -1 * 1) / (Math.sqrt(1 + 4 + 1) * Math.sqrt(1 + 9))));
+
+        assertThat(assertions.function("cosine_distance", "map(ARRAY['a', 'b', 'c'], ARRAY[1.0E0, 2.0E0, -1.0E0])", "map(ARRAY['d', 'e'], ARRAY[1.0E0, 3.0E0])"))
+                .isEqualTo(1.0);
+
+        assertThat(assertions.function("cosine_distance", "null", "map(ARRAY['c', 'b'], ARRAY[1.0E0, 3.0E0])"))
+                .isNull();
+
+        assertThat(assertions.function("cosine_distance", "map(ARRAY['a', 'b'], ARRAY[1.0E0, null])", "map(ARRAY['c', 'b'], ARRAY[1.0E0, 3.0E0])"))
+                .isNull();
+    }
+
     @Test
     public void testInverseNormalCdf()
     {

docs/src/main/sphinx/functions/math.md

@@ -205,6 +205,16 @@ SELECT cosine_distance(ARRAY[1.0, 2.0], ARRAY[3.0, 4.0]);
 ```
 :::
 
+:::{function} cosine_distance(x, y) -> double
+:no-index:
+Calculates the cosine distance between two sparse vectors:
+
+```sql
+SELECT cosine_distance(MAP(ARRAY['a'], ARRAY[1.0]), MAP(ARRAY['a'], ARRAY[2.0]));
+-- 0.0
+```
+:::
+
 :::{function} cosine_similarity(array(double), array(double)) -> double
 Calculates the cosine similarity of two dense vectors: