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2026-03-29

Comparing Performance After Optimization in django-mariadb-vector

Since version v0.2.0, the 'django-mariadb-vector' library includes several optimization options. Here are the results from performance tests.

Performance results

Note: Performance was measured using 20,000 iterations (3 runs) in `tests/measure_performance.py`, with randomly generated vectors of dimension 3072.

Benefits 'orlson' vs 'json'

Up to ~20× faster compared to the standard `json` library on generate vector data
Up to ~8× faster compared to the standard `json` library on response vector data

Benefits of 'binary' on response vector data

Up to ~16× faster compared to the standard `json` library
About ~2× faster compared to `orjson`

Testing output

Warming for 3 seconds

decode_binary start testing...
binary: 1.8913245666384075 [1.9641265999525785, 1.8629208999918774, 1.846926199970767]

decode_orjson_str start testing...
json_str: 3.804304266697727 [4.095871299970895, 3.7040354000637308, 3.6130061000585556]

decode_json_str start testing...
json_str: 30.897333099972457 [30.78853879997041, 31.119306599954143, 30.784153899992816]
* decode binary is fastest than json in 16.34 times
* decode binary is fastest than orjson in 2.01 times
* decode orjson is fastest than json in 8.12 times

encode_orjson_str start testing...
orjson_str: 2.32788303331472 [2.455615399987437, 2.2925777999917045, 2.235455899965018]

encode_json_str start testing...
json_str: 51.04346506666237 [50.961705199908465, 50.88195970002562, 51.28673030005302]
* encode orjson is fastest than json in 21.93 times

Reference

🔗 DEMO Django application of usage library 'django-mariadb-vector' (MariaDB Vector)

🔗 Django MariaDB Vector package on pypi - 'django-mariadb-vector'

🔗 Repo library: https://github.com/lexxai/django-mariadb-vector

🔗 Repo Demo: https://github.com/lexxai/django-mariadb-vector-demo

2026-03-28

DEMO Django application of usage library 'django-mariadb-vector' (MariaDB Vector)

pip install django-mariadb-vector

📒 Django MariaDB Vector DEMO application

A minimal demo project showing how to build article recommendations using vector similarity in Django with MariaDB as the database.

The app stores articles, embeds their content into vectors, and then finds similar articles based on vector distance.

🔗 Repo: https://github.com/lexxai/django-mariadb-vector-demo
🔗 Examples: https://github.com/lexxai/django-mariadb-vector-demo/tree/main/docs

🔗 Repo library: https://github.com/lexxai/django-mariadb-vector

Features

Django application using MariaDB as the primary database
Article model with text content
Vector-based similarity search for recommendations
Simple UI:
List of all articles

"Similar articles" view for a selected article
Admin interface to add and manage articles

Django MariaDB Vector package on pypi - 'django-mariadb-vector'

pip install django-mariadb-vector

📒 Django MariaDB Vector

The Vector field, introduced in MariaDB 11.7, now has a simple library called 'django-mariadb-vector' that adds Django ORM support for it.

Created by me and shared for everyone to use.

🔸 pip install django-mariadb-vector

🔗 Repo: https://github.com/lexxai/django-mariadb-vector
🔗 Demo: https://github.com/lexxai/django-mariadb-vector-demo
🔗 Examples: https://github.com/lexxai/django-mariadb-vector-demo/tree/main/docs

MariaDB introduced native vector support, allowing you to store embeddings and perform similarity search directly in the database.

However, Django currently lacks: