EUCLIDEAN_DISTANCE

Syntax

vector_expression <-> vector_expression

EUCLIDEAN_DISTANCE(vector_expression, vector_expression)

Arguments

• vector_expression: An expression that evaluates to a vector. Vectors can be stored in SingleStore using the native VECTOR type (Vector Type) or the BLOB type (BLOB Types). SingleStore recommends using the VECTOR type when possible.

• JSON strings are allowed as vector_expressions when the other argument is of type VECTOR.

Remarks

• If the result is infinity, negative infinity, or not a number (NaN), NULL will be returned instead.

• See Using Suffixes for Other Element Types with BLOBs for information on using EUCLIDEAN_DISTANCE with vectors with element types other than 32-bit floating-point numbers.

• The default element type for vector storage and processing is 32-bit floating-point (F32). The EUCLIDEAN_DISTANCE function assumes the vector inputs are encoded as 32-bit floating-point numbers and returns a DOUBLE.

• EUCLIDEAN_DISTANCE(v1, v2) is computationally equivalent to SQRT(DOT_PRODUCT(VECTOR_SUB(v1, v2), VECTOR_SUB(v1, v2))). However, the EUCLIDEAN_DISTANCE() function is more efficient than the latter.

Using EUCLIDEAN_DISTANCE with the VECTOR Data Type

The following example shows the use of  EUCLIDEAN_DISTANCE() to calculate the similarity between a query vector and a set of vectors in a table.

Create a table with a column of type VECTOR, insert data into the table, and then verify the contents of the table.

CREATE TABLE vectors (id int, vec VECTOR(4) not null);

INSERT INTO vectors VALUES (1, '[0.45, 0.55, 0.495, 0.5]');
INSERT INTO vectors VALUES (2, '[0.1, 0.8, 0.2, 0.555]');
INSERT INTO vectors VALUES (3, '[-0.5, -0.03, -0.1, 0.86]');
INSERT INTO vectors VALUES (4, '[0.5, 0.3, 0.807, 0.1]');

SELECT id, vec
FROM vectors
ORDER BY id;

+------+---------------------------------------------------+
| id   | vec                                               |
+------+---------------------------------------------------+
|    1 | [0.449999988,0.550000012,0.495000005,0.5]         |
|    2 | [0.100000001,0.800000012,0.200000003,0.555000007] |
|    3 | [-0.5,-0.0299999993,-0.100000001,0.860000014]     |
|    4 | [0.5,0.300000012,0.806999981,0.100000001]         |
+------+---------------------------------------------------+

Notice that the results of the SELECT do not exactly match the values in the INSERT. This is because elements in the VECTOR data type are stored as floating-point numbers and the values in the INSERT statement are not perfectly representable in floating-point.

After you've created a table and inserted data, set up a query vector. The queries below will evaluate vector similarity between the vectors in the vectors table and @query_vec.

This query finds the similarities between vectors and @query_vec using the EUCLIDEAN_DISTANCE infix operator <->. The ORDER BY clause is included so the order of your results match the results shown below.

Lower values of EUCLIDEAN_DISTANCE indicate higher similarity. The value of EUCLIDEAN_DISTANCE is always greater than 0.

SET @query_vec = ('[0.44, 0.554, 0.34, 0.62]'):>VECTOR(4);

SELECT vec <-> @query_vec AS score
FROM vectors
ORDER BY score ASC;

+---------------------+
| score               |
+---------------------+
| 0.19631861943383927 |
| 0.44714763700937277 |
|  0.7460596366393402 |
|  1.2148481657187131 |
+---------------------+

Using EUCLIDEAN_DISTANCE (<->) in Filters, Joins, and Ordering

EUCLIDEAN_DISTANCE can appear wherever a floating-point expression can be used in a query, including in filters, ordering, joins, and cross products.

The DOT_PRODUCT page shows examples of using DOT_PRODUCT in various ways in SQL queries. EUCLIDEAN_DISTANCE can be used similarly. Remember that sort order is ASC when using EUCLIDEAN_DISTANCE and DESC with DOT_PRODUCT.

Below is one example of using EUCLIDEAN_DISTANCE (<->) in a join query. Both arguments can be table fields or derived from table fields.

Use the vectors table and the @query_vec created above, in addition to the new table created below, when running the following query.

CREATE TABLE vectors_2 (id_2 int, vec_2 VECTOR(4) not null);

INSERT INTO vectors_2 VALUES (5, '[0.4, 0.49, 0.16, 0.555]'); 
INSERT INTO vectors_2 VALUES (6, '[-0.01, -0.1, -0.2, 0.975]');

SELECT v1.id, v2.id_2, v1.vec <-> v2.vec_2 AS score
FROM vectors v1, vectors_2 v2
WHERE v1.vec <-> v2.vec_2 < 0.7
ORDER BY score ASC;

+------+------+---------------------+
| id   | id_2 | score               |
+------+------+---------------------+
|    1 |    5 | 0.34835327652980475 |
|    2 |    5 |  0.4332435843558954 |
|    3 |    6 |  0.5179044162849211 |
+------+------+---------------------+

Automatic Type Conversions

As described in Remarks, in some cases, SingleStore will do automatic type conversions between JSON strings, BLOBs, and VECTORs. Three examples of those automatic type conversions are provided below.

SELECT vec, vec <-> '[0.44, 0.554, 0.34, 0.62]' AS score
FROM vectors
ORDER BY score ASC;

+---------------------------------------------------+---------------------+
| vec                                               | score               |
+---------------------------------------------------+---------------------+
| [0.449999988,0.550000012,0.495000005,0.5]         | 0.19631861943383927 |
| [0.100000001,0.800000012,0.200000003,0.555000007] | 0.44714763700937277 |
| [0.5,0.300000012,0.806999981,0.100000001]         |  0.7460596366393402 |
| [-0.5,-0.0299999993,-0.100000001,0.860000014]     |  1.2148481657187131 |
+---------------------------------------------------+---------------------+

CREATE TABLE vectors_i16(id INT, vec VECTOR(3, I16));

INSERT INTO vectors_i16 VALUES(1, '[1, 2, 3]');
INSERT INTO vectors_i16 VALUES(2, '[4, 5, 6]');
INSERT INTO vectors_i16 VALUES(3, '[1, 4, 8]');

SELECT id, '[3, 2, 1]' <-> vectors_i16.vec AS score
FROM vectors_i16
ORDER BY score ASC;

+------+--------------------+
| id   | score              |
+------+--------------------+
|    1 | 2.8284271247461903 |
|    2 |  5.916079783099616 |
|    3 |   7.54983443527075 |
+------+--------------------+

CREATE TABLE vectors_b (id int, vec BLOB not null);

INSERT INTO vectors_b VALUES (1, JSON_ARRAY_PACK('[0.1, 0.8, 0.2, 0.555]')); 
INSERT INTO vectors_b VALUES (2, JSON_ARRAY_PACK('[0.45, 0.55, 0.495, 0.5]'));

SET @query_vec = '[0.44, 0.554, 0.34, 0.62]':>VECTOR(4);

SELECT id, @query_vec <*> vectors_b.vec AS score
FROM vectors_b
ORDER BY score DESC;

+------+--------------------+
| id   | score              |
+------+--------------------+
|    2 | 0.9810000061988831 |
|    1 | 0.8993000388145447 |
+------+--------------------+

Using EUCLIDEAN_DISTANCE with Vectors as BLOBs

The following example shows the use of  EUCLIDEAN_DISTANCE() to calculate the similarity between a query vector and a set of vectors in a table with the vectors stored as BLOBs.

Create a table with a column of type BLOB to store the vectors. The second column in this table, with column name vec and type BLOB, will store the vectors. This example demonstrates storing vector data using BLOBs, hence the column of type BLOB named vec.

Then insert data using the JSON_ARRAY_PACK() built-in function to easily insert properly formatted vectors.

CREATE TABLE vectors_b (id int, vec BLOB not null);

INSERT INTO vectors_b VALUES (1, JSON_ARRAY_PACK('[0.1, 0.8, 0.2, 0.555]')); 
INSERT INTO vectors_b VALUES (2, JSON_ARRAY_PACK('[0.45, 0.55, 0.495, 0.5]'));

To demonstrate the contents of the table, use the JSON_ARRAY_UNPACK() function to return the table elements in JSON format:

SELECT JSON_ARRAY_UNPACK(vec) FROM vectors_b;

+---------------------------------------------------+
| JSON_ARRAY_UNPACK(vec)                            |
+---------------------------------------------------+
| [0.449999988,0.550000012,0.495000005,0.5]         |
| [0.100000001,0.800000012,0.200000003,0.555000007] |
+---------------------------------------------------+

You can also use the HEX() built-in function to return a printable form of the binary data:

SELECT HEX(vec) FROM vectors_b;

+----------------------------------+
| HEX(vec)                         |
+----------------------------------+
| 6666E63ECDCC0C3FA470FD3E0000003F |
| CDCCCC3DCDCC4C3FCDCC4C3E7B140E3F |
+----------------------------------+

Query the table using the EUCLIDEAN_DISTANCE() function in a SELECT statement.

The SQL below sets up query vector (@query_vec) and then calculates the EUCLIDEAN_DISTANCE of the query vector and the vectors in the vectors_b table.

SET @query_vec = JSON_ARRAY_PACK('[0.44, 0.554, 0.34, 0.62]');

SELECT EUCLIDEAN_DISTANCE(vec, @query_vec) AS score 
FROM vectors_b;

+---------------------+
| score               |
+---------------------+
| 0.19631861943383927 |
| 0.44714763700937277 |
+---------------------+

EUCLIDEAN_DISTANCE() with JSON_ARRAY_PACK()

The JSON_ARRAY_PACK() function makes it easier to input properly-formatted vectors. JSON_ARRAY_PACK() should be used when loading vectors into tables as is shown in the example below. That is, vectors should be formatted with JSON_ARRAY_PACK() at the time they are loaded into a table so that the data stored in the BLOB attribute in the table is in packed binary format. SingleStore does not recommend storing vectors as JSON strings in tables, doing so will have a negative performance impact.

JSON_ARRAY_PACK() should not normally be used as an argument to the EUCLIDEAN_DISTANCE function except when JSON_ARRAY_PACK() is being used to build a constant vector value as is shown in the query below.

SELECT EUCLIDEAN_DISTANCE(JSON_ARRAY_PACK('[0.44, 0.554, 0.34, 0.62]'), vec) AS score
FROM vectors_b
ORDER BY score ASC;

+---------------------+
| score               |
+---------------------+
| 0.19631861943383927 |
| 0.44714763700937277 |
+---------------------+

EUCLIDEAN_DISTANCE on BLOBs with 16-bit Integers

Below is an example of using JSON_ARRAY_PACK and EUCLIDEAN_DISTANCE with 16-bit signed integers.

CREATE TABLE vectors_b_i (id int, vec BLOB not null);

INSERT INTO vectors_b_i VALUES (1, JSON_ARRAY_PACK_I16('[1, 3, 2, 5]')); 
INSERT INTO vectors_b_i VALUES(2, JSON_ARRAY_PACK_I16('[23, 4, 1, 8]'));

SET @qv = JSON_ARRAY_PACK_I16('[1, 2, 3, 4]');

SELECT EUCLIDEAN_DISTANCE_I16(@qv, vec) as EuclidDist
FROM vectors_b_i;

+--------------------+
| EuclidDist         |
+--------------------+
| 22.538855339169288 |
| 1.7320508075688772 |
+--------------------+

The result is an integer as indicated by the _I16 suffix.

When using suffixed versions of EUCLIDEAN_DISTANCE, the return type will be the type of the suffix.

Formatting Binary Vector Data for BLOBs

When using the BLOB type for vector operations, vector data can be formatted using JSON_ARRAY_PACK. If your vector data is already in a packed binary format, you can load that data into the BLOBs. The data must be encoded as a BLOB containing packed numbers in little-endian byte order. Vectors stored as BLOBs can be of any length; however, the input blob length must be divisible by the size of the packed vector elements (1, 2, 4 , or 8 bytes, depending on the vector element).

Related Topics

• Vector Type

• Vector Indexing

• Working with Vector Data

• Inserting Vectors as Blobs from an External Application

• JSON_ARRAY_PACK

• JSON_ARRAY_UNPACK

• Vector Normalization

• DOT_PRODUCT