Quick tip: Using $euclideanDistance with SingleStore Kai for MongoDB

A quick test of a SingleStore Extension Command

Abstract

The announcement of SingleStore Kai for MongoDB provides exciting opportunities for turbocharged JSON analytics. Two vector functions are available in the preview release — $euclideanDistance and $dotProduct. In this short article, we’ll evaluate $euclideanDistance using some example data from a previous article.

The notebook file used in this article is available on GitHub.

Introduction

SingleStoreDB supports a range of vector functions. In a previous article, we used the EUCLIDEAN_DISTANCE and JSON_ARRAY_PACK functions. In another previous article, we used the DOT_PRODUCT and UNHEX functions. In this short article, we’ll use $euclideanDistance from SingleStore Kai for MongoDB.

Create a SingleStoreDB Cloud account

A previous article showed the steps to create a free SingleStoreDB Cloud account. We’ll use the following settings:

• Workspace Group Name: Iris Demo Group
• Cloud Provider: AWS
• Region: US East 1 (N. Virginia)
• Workspace Name: iris-demo
• Size: S-00
• Advanced Settings:
  - SingleStore Kai for MongoDB selected
  - MarTech Application deselected

From the left navigation pane, we’ll select DEVELOP > SQL Editor to create a new database, as follows:

CREATE DATABASE IF NOT EXISTS iris_db;

New notebook

A previous article showed the steps to create a new notebook.

We’ll call the notebook kai_demo, and select a Blank notebook template from the available options.

Fill out the notebook

Create Table

We’ll use the SQL code from a GitHub Gist for our table, as follows:

%%sql

USE iris_db;
DROP TABLE IF EXISTS iris;
CREATE TABLE IF NOT EXISTS iris (
     vector BLOB,
     species VARCHAR(20)
);

Load Data

We’ll now load the data into the table, as follows:

%%sql

USE iris_db;
INSERT INTO iris VALUES
(JSON_ARRAY_PACK('[5.1,3.5,1.4,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.9,3,1.4,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.7,3.2,1.3,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.6,3.1,1.5,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5,3.6,1.4,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.4,3.9,1.7,0.4]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.6,3.4,1.4,0.3]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5,3.4,1.5,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.4,2.9,1.4,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.9,3.1,1.5,0.1]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.4,3.7,1.5,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.8,3.4,1.6,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.8,3,1.4,0.1]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.3,3,1.1,0.1]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.8,4,1.2,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.7,4.4,1.5,0.4]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.4,3.9,1.3,0.4]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.1,3.5,1.4,0.3]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.7,3.8,1.7,0.3]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.1,3.8,1.5,0.3]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.4,3.4,1.7,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.1,3.7,1.5,0.4]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.6,3.6,1,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.1,3.3,1.7,0.5]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.8,3.4,1.9,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5,3,1.6,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5,3.4,1.6,0.4]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.2,3.5,1.5,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.2,3.4,1.4,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.7,3.2,1.6,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.8,3.1,1.6,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.4,3.4,1.5,0.4]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.2,4.1,1.5,0.1]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.5,4.2,1.4,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.9,3.1,1.5,0.1]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5,3.2,1.2,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.5,3.5,1.3,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.9,3.1,1.5,0.1]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.4,3,1.3,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.1,3.4,1.5,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5,3.5,1.3,0.3]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.5,2.3,1.3,0.3]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.4,3.2,1.3,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5,3.5,1.6,0.6]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.1,3.8,1.9,0.4]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.8,3,1.4,0.3]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.1,3.8,1.6,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[4.6,3.2,1.4,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5.3,3.7,1.5,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[5,3.3,1.4,0.2]'),'Iris-setosa'),
(JSON_ARRAY_PACK('[7,3.2,4.7,1.4]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.4,3.2,4.5,1.5]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.9,3.1,4.9,1.5]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.5,2.3,4,1.3]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.5,2.8,4.6,1.5]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.7,2.8,4.5,1.3]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.3,3.3,4.7,1.6]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[4.9,2.4,3.3,1]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.6,2.9,4.6,1.3]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.2,2.7,3.9,1.4]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5,2,3.5,1]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.9,3,4.2,1.5]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6,2.2,4,1]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.1,2.9,4.7,1.4]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.6,2.9,3.6,1.3]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.7,3.1,4.4,1.4]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.6,3,4.5,1.5]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.8,2.7,4.1,1]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.2,2.2,4.5,1.5]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.6,2.5,3.9,1.1]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.9,3.2,4.8,1.8]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.1,2.8,4,1.3]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.3,2.5,4.9,1.5]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.1,2.8,4.7,1.2]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.4,2.9,4.3,1.3]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.6,3,4.4,1.4]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.8,2.8,4.8,1.4]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.7,3,5,1.7]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6,2.9,4.5,1.5]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.7,2.6,3.5,1]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.5,2.4,3.8,1.1]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.5,2.4,3.7,1]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.8,2.7,3.9,1.2]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6,2.7,5.1,1.6]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.4,3,4.5,1.5]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6,3.4,4.5,1.6]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.7,3.1,4.7,1.5]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.3,2.3,4.4,1.3]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.6,3,4.1,1.3]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.5,2.5,4,1.3]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.5,2.6,4.4,1.2]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.1,3,4.6,1.4]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.8,2.6,4,1.2]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5,2.3,3.3,1]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.6,2.7,4.2,1.3]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.7,3,4.2,1.2]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.7,2.9,4.2,1.3]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.2,2.9,4.3,1.3]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.1,2.5,3,1.1]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[5.7,2.8,4.1,1.3]'),'Iris-versicolor'),
(JSON_ARRAY_PACK('[6.3,3.3,6,2.5]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[5.8,2.7,5.1,1.9]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[7.1,3,5.9,2.1]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.3,2.9,5.6,1.8]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.5,3,5.8,2.2]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[7.6,3,6.6,2.1]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[4.9,2.5,4.5,1.7]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[7.3,2.9,6.3,1.8]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.7,2.5,5.8,1.8]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[7.2,3.6,6.1,2.5]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.5,3.2,5.1,2]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.4,2.7,5.3,1.9]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.8,3,5.5,2.1]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[5.7,2.5,5,2]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[5.8,2.8,5.1,2.4]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.4,3.2,5.3,2.3]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.5,3,5.5,1.8]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[7.7,3.8,6.7,2.2]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[7.7,2.6,6.9,2.3]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6,2.2,5,1.5]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.9,3.2,5.7,2.3]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[5.6,2.8,4.9,2]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[7.7,2.8,6.7,2]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.3,2.7,4.9,1.8]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.7,3.3,5.7,2.1]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[7.2,3.2,6,1.8]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.2,2.8,4.8,1.8]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.1,3,4.9,1.8]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.4,2.8,5.6,2.1]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[7.2,3,5.8,1.6]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[7.4,2.8,6.1,1.9]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[7.9,3.8,6.4,2]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.4,2.8,5.6,2.2]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.3,2.8,5.1,1.5]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.1,2.6,5.6,1.4]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[7.7,3,6.1,2.3]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.3,3.4,5.6,2.4]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.4,3.1,5.5,1.8]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6,3,4.8,1.8]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.9,3.1,5.4,2.1]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.7,3.1,5.6,2.4]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.9,3.1,5.1,2.3]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[5.8,2.7,5.1,1.9]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.8,3.2,5.9,2.3]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.7,3.3,5.7,2.5]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.7,3,5.2,2.3]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.3,2.5,5,1.9]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.5,3,5.2,2]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[6.2,3.4,5.4,2.3]'),'Iris-virginica'),
(JSON_ARRAY_PACK('[5.9,3,5.1,1.8]'),'Iris-virginica');

Install Libraries

We’ll install a library that we’ll use later:

!pip install tabulate --quiet

Import Libraries

Next, we’ll import some libraries, as follows:

import pymongo
import struct

from pymongo import MongoClient
from tabulate import tabulate

Connect to SingleStore Kai

We’ll now connect to our system, as follows:

client = MongoClient("mongodb://admin:<password>@<host>:27017/?authMechanism=PLAIN&tls=true&loadBalanced=true")

We’ll replace the <password> and <host> with the values from our SingleStoreDB Cloud account.

We’ll switch to the Iris database and list the collections, as follows:

db = client["iris_db"]

for coll in db.list_collection_names():
    print(coll)

The output should be as follows:

iris

Let’s now take a vector from our list and convert it into bytes:

vector = [5.1, 3.5, 1.4, 0.2]

vector_bytes = struct.pack('f' * len(vector), *vector)

print(vector_bytes)

The result should be as follows:

b'\xcd\xcc\xbc@\x00\x00@@33\xa3@ff\xe6?'

Example Queries

Query 1

Here is the first SQL query we used in a previous article:

SELECT species
FROM iris
WHERE EUCLIDEAN_DISTANCE(vector, JSON_ARRAY_PACK('[5.9,3,5.1,1.8]')) = 0;

The result was:

+----------------+
| species        |
+----------------+
| Iris-virginica |
+----------------+

Here is one solution using SingleStore Kai:

query = {
    "$expr": {
        "$eq": [
            { "$euclideanDistance": ["$vector", vector_bytes] },
            0
        ]
    }
}

projection = { "species": 1 }

document = db.iris.find_one(query, projection)

species = document["species"]

print(species)

Since the vector we used was stored in the database and we are looking for a single match, the result should be:

Iris-virginica

Query 2

Here is the second SQL query we used in a previous article, looking for other nearby flowers:

SELECT EUCLIDEAN_DISTANCE(vector, JSON_ARRAY_PACK('[5.9,3,5.1,1.8]')) AS euclidean_distance, species
FROM iris
ORDER BY euclidean_distance
LIMIT 5;

The result was:

+---------------------+----------------+
| euclidean_distance  | species        |
+---------------------+----------------+
|                   0 | Iris-virginica |
| 0.28284244589567653 | Iris-virginica |
| 0.31622746208231284 | Iris-virginica |
|  0.3316624219760969 | Iris-virginica |
|  0.3316624219760969 | Iris-virginica |
+---------------------+----------------+

Here is one solution using SingleStore Kai:

pipeline = [{
    "$project": {
        "euclidean_distance": {
            "$euclideanDistance": [ "$vector", vector_bytes ] },
        "species": "$species" } }, {
    "$sort": {
        "euclidean_distance": 1 } }, {
    "$limit": 5 }
]

cursor = db.iris.aggregate(pipeline)

table = []

for document in cursor:
    species = document["species"]
    euclidean_distance = document["euclidean_distance"]
    table.append([euclidean_distance, species])

print(tabulate(table, headers = ["euclidean_distance", "species"]))

The result should be:

  euclidean_distance  species
--------------------  --------------
            0         Iris-virginica
            0.282842  Iris-virginica
            0.316227  Iris-virginica
            0.331662  Iris-virginica
            0.331662  Iris-virginica

Query 3

Here is the third SQL query we used in a previous article, using some fictitious data values to make a prediction:

SELECT EUCLIDEAN_DISTANCE(vector, JSON_ARRAY_PACK('[5.2,3.6,1.5,0.3]')) AS euclidean_distance, species
FROM iris
ORDER BY euclidean_distance
LIMIT 5;

The result was:

+---------------------+-------------+
| euclidean_distance  | species     |
+---------------------+-------------+
| 0.14142129538778386 | Iris-setosa |
|  0.1732049874122573 | Iris-setosa |
| 0.17320510570613526 | Iris-setosa |
| 0.17320538530952567 | Iris-setosa |
| 0.19999992325900512 | Iris-setosa |
+---------------------+-------------+

Here is one solution using SingleStore Kai:

vector = [5.2, 3.6, 1.5, 0.3]

vector_bytes = struct.pack('f' * len(vector), *vector)

pipeline = [{
    "$project": {
        "euclidean_distance": {
            "$euclideanDistance": [ "$vector", vector_bytes ] },
        "species": "$species" } }, {
    "$sort": {
        "euclidean_distance": 1 } }, {
    "$limit": 5 }
]

cursor = db.iris.aggregate(pipeline)

table = []

for document in cursor:
    species = document["species"]
    euclidean_distance = document["euclidean_distance"]
    table.append([euclidean_distance, species])

print(tabulate(table, headers = ["euclidean_distance", "species"]))

The result should be:

  euclidean_distance  species
--------------------  -----------
            0.141421  Iris-setosa
            0.173205  Iris-setosa
            0.173205  Iris-setosa
            0.173205  Iris-setosa
            0.2       Iris-setosa

Query 4

Finally, here is the fourth SQL query we used in a previous article:

SELECT species
FROM iris
ORDER BY EUCLIDEAN_DISTANCE(vector, JSON_ARRAY_PACK('[5.2,3.6,1.5,0.3]'))
LIMIT 1;

The output was:

+-------------+
| species     |
+-------------+
| Iris-setosa |
+-------------+

Here is one solution using SingleStore Kai:

pipeline = [{
    "$project": {
        "euclidean_distance": {
            "$euclideanDistance": [ "$vector", vector_bytes ] },
        "species": "$species" } }, {
    "$sort": {
        "euclidean_distance": 1 } }, {
    "$limit": 1 }
]

cursor = db.iris.aggregate(pipeline)

table = []

for document in cursor:
    species = document["species"]
    table.append([species])

print(tabulate(table, headers = ["species"]))

The result should be:

species
-----------
Iris-setosa

Comparing the SQL results with the SingleStore Kai results, we can see that the $euclideanDistance function in SingleStore Kai is working as expected.

Summary

In this short article, we tested SQL against SingleStore Kai using the $euclideanDistance function. In future articles, we’ll try the additional functionality of this new product offering. Stay tuned.