Quick tip: Using $euclideanDistance with SingleStore Kai for MongoDB
Tags: singlestoredb, mongodb, euclideandistance, pymongo
Published: 2023-05-20 04:16:52

## Abstract
The announcement of [SingleStore Kai for MongoDB](https://www.singlestore.com/blog/introducing-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](https://medium.com/@VeryFatBoy/quick-tip-singlestore-dbs-euclidean-distance-and-json-array-pack-functions-a3adc5040f82).

The notebook file used in this article is available on [GitHub](https://github.com/VeryFatBoy/singlestore-kai-euclidean-example/blob/main/kai_demo.ipynb).

## Introduction
SingleStoreDB supports a range of [vector functions](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions.html). In [a previous article](https://medium.com/@VeryFatBoy/quick-tip-singlestore-dbs-euclidean-distance-and-json-array-pack-functions-a3adc5040f82), we used the `EUCLIDEAN_DISTANCE` and `JSON_ARRAY_PACK` functions. In [another previous article](https://medium.com/@VeryFatBoy/using-singlestore-spark-and-alternating-least-squares-als-to-build-a-movie-recommender-system-6e74f4e5908d), 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](https://dev.to/singlestore/quick-tip-using-dbt-with-singlestoredb-161g) 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:

```sql
CREATE DATABASE IF NOT EXISTS iris_db;
```

## New notebook
A [previous article](https://dev.to/singlestore/quick-tip-using-the-built-in-notebooks-with-singlestoredb-hj2) 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](https://gist.github.com/VeryFatBoy/be7d0391eeeb4874a523cac613db3867) for our table, as follows:

```sql
%%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
%%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:

```shell
!pip install tabulate --quiet
```

### Import Libraries
Next, we'll import some libraries, as follows:

```python
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:

```python
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:

```python
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:

```python
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:

```sql
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:

```python
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:

```sql
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:

```python
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:

```sql
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:

```python
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:

```sql
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:

```python
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.