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
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:
CREATE DATABASE IF NOT EXISTS iris_db;
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:
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(connection_url_kai)
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
Example Queries
Query 1
Here is the first SQL query we used in a previous article:
USE iris_db;
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:
vector = [5.1, 3.5, 1.4, 0.2]
vector_bytes = struct.pack('f' * len(vector), *vector)
query = {
"$expr": {
"$eq": [
{ "$euclideanDistance": ["$vector", vector_bytes] },
0
]
}
}
projection = { "species": 1 }
document = db.iris.find_one(query, projection)
species = document["species"]
print(species)
Since the vector we used was stored in the database and we are looking for a single match, the result should be:
Iris-virginica
Query 2
Here is the second SQL query we used in a previous article, looking for other nearby flowers:
USE iris_db;
SELECT EUCLIDEAN_DISTANCE(vector, JSON_ARRAY_PACK('[5.9,3,5.1,1.8]')) AS euclidean_distance, species
FROM iris
ORDER BY euclidean_distance
LIMIT 5;
The result was:
+---------------------+----------------+
| euclidean_distance | species |
+---------------------+----------------+
| 0 | Iris-virginica |
| 0.28284244589567653 | Iris-virginica |
| 0.31622746208231284 | Iris-virginica |
| 0.3316624219760969 | Iris-virginica |
| 0.3316624219760969 | Iris-virginica |
+---------------------+----------------+
Here is one solution using SingleStore Kai:
pipeline = [{
"$project": {
"euclidean_distance": {
"$euclideanDistance": [ "$vector", vector_bytes ] },
"species": "$species" } }, {
"$sort": {
"euclidean_distance": 1 } }, {
"$limit": 5 }
]
cursor = db.iris.aggregate(pipeline)
table = []
for document in cursor:
species = document["species"]
euclidean_distance = document["euclidean_distance"]
table.append([euclidean_distance, species])
print(tabulate(table, headers = ["euclidean_distance", "species"]))
The result should be:
euclidean_distance species
-------------------- --------------
0 Iris-virginica
0.282842 Iris-virginica
0.316227 Iris-virginica
0.331662 Iris-virginica
0.331662 Iris-virginica
Query 3
Here is the third SQL query we used in a previous article, using some fictitious data values to make a prediction:
USE iris_db;
SELECT EUCLIDEAN_DISTANCE(vector, JSON_ARRAY_PACK('[5.2,3.6,1.5,0.3]')) AS euclidean_distance, species
FROM iris
ORDER BY euclidean_distance
LIMIT 5;
The result was:
+---------------------+-------------+
| euclidean_distance | species |
+---------------------+-------------+
| 0.14142129538778386 | Iris-setosa |
| 0.1732049874122573 | Iris-setosa |
| 0.17320510570613526 | Iris-setosa |
| 0.17320538530952567 | Iris-setosa |
| 0.19999992325900512 | Iris-setosa |
+---------------------+-------------+
Here is one solution using SingleStore Kai:
vector = [5.2, 3.6, 1.5, 0.3]
vector_bytes = struct.pack('f' * len(vector), *vector)
pipeline = [{
"$project": {
"euclidean_distance": {
"$euclideanDistance": [ "$vector", vector_bytes ] },
"species": "$species" } }, {
"$sort": {
"euclidean_distance": 1 } }, {
"$limit": 5 }
]
cursor = db.iris.aggregate(pipeline)
table = []
for document in cursor:
species = document["species"]
euclidean_distance = document["euclidean_distance"]
table.append([euclidean_distance, species])
print(tabulate(table, headers = ["euclidean_distance", "species"]))
The result should be:
euclidean_distance species
-------------------- -----------
0.141421 Iris-setosa
0.173205 Iris-setosa
0.173205 Iris-setosa
0.173205 Iris-setosa
0.2 Iris-setosa
Query 4
Finally, here is the fourth SQL query we used in a previous article:
USE iris_db;
SELECT species
FROM iris
ORDER BY EUCLIDEAN_DISTANCE(vector, JSON_ARRAY_PACK('[5.2,3.6,1.5,0.3]'))
LIMIT 1;
The output was:
+-------------+
| species |
+-------------+
| Iris-setosa |
+-------------+
Here is one solution using SingleStore Kai:
pipeline = [{
"$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.
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