GitHub - matrixorigin/matrixone: AI-native HTAP database with Git-for-Data and built-in vector search, serving as the data and memory backbone for intelligent agents and applications.

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What is MatrixOne?

MatrixOne is the industry's first database to bring Git-style version control to data, combined with MySQL compatibility, AI-native capabilities, and cloud-native architecture.

At its core, MatrixOne is a HTAP (Hybrid Transactional/Analytical Processing) database with a hyper-converged HSTAP engine that seamlessly handles transactional (OLTP), analytical (OLAP), full-text search, and vector search workloads in a single unified system—no data movement, no ETL, no compromises.

🎬 Git for Data - The Game Changer

Just as Git revolutionized code management, MatrixOne revolutionizes data management. Manage your database like code:

📸 Instant Snapshots - Zero-copy snapshots in milliseconds, no storage explosion
⏰ Time Travel - Query data as it existed at any point in history
🔀 Branch & Merge - Test migrations and transformations in isolated branches
↩️ Instant Rollback - Restore to any previous state without full backups
🔍 Complete Audit Trail - Track every data change with immutable history

Why it matters: Data mistakes are expensive. Git for Data gives you the safety net and flexibility developers have enjoyed with Git—now for your most critical asset: your data.

🎯 Built for the AI Era

🗄️ MySQL-Compatible

Drop-in replacement for MySQL. Use existing tools, ORMs, and applications without code changes. Seamless migration path.

🤖 AI-Native

Built-in vector search (IVF/HNSW) and full-text search. Build RAG apps and semantic search directly—no external vector databases needed.

☁️ Cloud-Native

Storage-compute separation. Deploy anywhere. Elastic scaling. Kubernetes-native. Zero-downtime operations.

🚀 One Database for Everything

The typical modern data stack:

🗄️ MySQL for transactions → 📊 ClickHouse for analytics → 🔍 Elasticsearch for search → 🤖 Pinecone for AI

The problem: 4 databases · Multiple ETL jobs · Hours of data lag · Sync nightmares

MatrixOne replaces all of them:

🎯 One database with native OLTP, OLAP, full-text search, and vector search. Real-time. ACID compliant. No ETL.

⚡️ Get Started in 60 Seconds

1️⃣ Launch MatrixOne

docker run -d -p 6001:6001 --name matrixone matrixorigin/matrixone:latest

2️⃣ Create Database

mysql -h127.0.0.1 -P6001 -p111 -uroot -e "create database demo"

3️⃣ Connect & Query

Install Python SDK:

pip install matrixone-python-sdk

Vector search:

from matrixone import Client
from matrixone.orm import declarative_base
from sqlalchemy import Column, Integer, String, Text
from matrixone.sqlalchemy_ext import create_vector_column

# Create client and connect
client = Client()
client.connect(database='demo')

# Define model using MatrixOne ORM
Base = declarative_base()

class Article(Base):
    __tablename__ = 'articles'
    id = Column(Integer, primary_key=True, autoincrement=True)
    title = Column(String(200), nullable=False)
    content = Column(Text, nullable=False)
    embedding = create_vector_column(8, "f32")

# Create table using client API
client.create_table(Article)

# Insert some data using client API
articles = [
    {'title': 'Machine Learning Guide',
     'content': 'Comprehensive machine learning tutorial...',
     'embedding': [0.1, 0.2, 0.3, 0.15, 0.25, 0.35, 0.12, 0.22]},
    {'title': 'Python Programming',
     'content': 'Learn Python programming basics',
     'embedding': [0.2, 0.3, 0.4, 0.25, 0.35, 0.45, 0.22, 0.32]},
]
client.batch_insert(Article, articles)

client.vector_ops.create_ivf(
    Article,
    name='idx_embedding',
    column='embedding',
    lists=100,
    op_type='vector_l2_ops'
)

query_vector = [0.2, 0.3, 0.4, 0.25, 0.35, 0.45, 0.22, 0.32]
results = client.query(
    Article.title,
    Article.content,
    Article.embedding.l2_distance(query_vector).label("distance"),
).filter(Article.embedding.l2_distance(query_vector) < 0.1).execute()
for row in results.rows:
    print(f"Title: {row[0]}, Content: {row[1][:50]}...")

# Cleanup
client.drop_table(Article)  # Use client API
client.disconnect()

Fulltext Search:

...
from matrixone.sqlalchemy_ext import boolean_match

# Create fulltext index using SDK 
client.fulltext_index.create(
    Article,name='ftidx_content',columns=['title', 'content']
)

# Boolean search with must/should operators
results = client.query(
    Article.title,
    Article.content,
    boolean_match('title', 'content')
        .must('machine')
        .must('learning')
        .must_not('basics')
).execute()

# Results is a ResultSet object
for row in results.rows:
    print(f"Title: {row[0]}, Content: {row[1][:50]}...")
...

That's it! 🎉 You're now running a production-ready database with Git-like snapshots, vector search, and full ACID compliance.

💡 Want more control? Check out the Installation & Deployment section below for production-grade installation options.

📖 Python SDK Documentation →

📚 Tutorials & Demos

Ready to dive deeper? Explore our comprehensive collection of hands-on tutorials and real-world demos:

🎯 Getting Started Tutorials

Tutorial	Language/Framework	Description
Java CRUD Demo	Java	Java application development
SpringBoot and JPA CRUD Demo	Java	SpringBoot with Hibernate/JPA
PyMySQL CRUD Demo	Python	Basic database operations with Python
SQLAlchemy CRUD Demo	Python	Python with SQLAlchemy ORM
Django CRUD Demo	Python	Django web framework
Golang CRUD Demo	Go	Go application development
Gorm CRUD Demo	Go	Go with Gorm ORM
C# CRUD Demo	C#	.NET application development
TypeScript CRUD Demo	TypeScript	TypeScript application development

🚀 Advanced Features Tutorials

Tutorial	Use Case	Related MatrixOne Features
Pinecone-Compatible Vector Search	AI & Search	vector search, Pinecone-compatible API
IVF Index Health Monitoring	AI & Search	vector search, IVF index
HNSW Vector Index	AI & Search	vector search, HNSW index
Fulltext Natural Search	AI & Search	fulltext search, natural language
Fulltext Boolean Search	AI & Search	fulltext search, boolean operators
Fulltext JSON Search	AI & Search	fulltext search, JSON data
Hybrid Search	AI & Search	hybrid search, vector + fulltext + SQL
RAG Application Demo	AI & Search	RAG, vector search, fulltext search
Picture(Text)-to-Picture Search	AI & Search	multimodal search, image similarity
Dify Integration Demo	AI & Search	AI platform integration
HTAP Application Demo	Performance	HTAP, real-time analytics
Instant Clone for Multi-Team Development	Performance	instant clone, Git for Data
Safe Production Upgrade with Instant Rollback	Performance	snapshot, rollback, Git for Data

📖 View All Tutorials →

🛠️ Installation & Deployment

MatrixOne supports multiple installation methods. Choose the one that best fits your needs:

🐳 Local Multi-CN Development

Run a complete distributed cluster locally with multiple CN nodes, load balancing, and easy configuration management.

# Quick start
make dev-build && make dev-up

# Connect via proxy (load balanced)
mysql -h 127.0.0.1 -P 6001 -u root -p111

# Configure specific service (interactive editor)
make dev-edit-cn1          # Edit CN1 config
make dev-restart-cn1       # Restart only CN1 (fast!)

📖 Complete Development Guide → - Comprehensive guide covering standalone setup, multi-CN clusters, monitoring, metrics, configuration, and all make dev-* commands

🎯 Using mo_ctl Tool (Recommended for Production)

One-command deployment and lifecycle management with the official mo_ctl tool. Handles installation, upgrades, backups, and health monitoring automatically.

📖 Complete mo_ctl Installation Guide →

⚙️ Building from Source

Build MatrixOne from source for development, customization, or contributing. Requires Go 1.22, GCC/Clang, Git, and Make.

📖 Complete Build from Source Guide →

🐳 Other Methods

Docker standalone, Kubernetes, binary packages, and more deployment options.

📖 All Installation Options →

🔎 Architecture

MatrixOne's architecture is as below:

For more details, you can checkout MatrixOne Architecture Design.

🐍 Python SDK

MatrixOne provides a comprehensive Python SDK for database operations, vector search, fulltext search, and advanced features like snapshots, PITR, and account management.

Key Features: High-performance async/await support, vector similarity search with IVF/HNSW indexing, fulltext search, metadata analysis, and complete type safety.

📚 Complete Documentation

📖 Python SDK README - Full features, installation, and usage guide

📦 Installation: pip install matrixone-python-sdk

🙌 Contributing

Contributions to MatrixOne are welcome from everyone.
See Contribution Guide for details on submitting patches and the contribution workflow.

👏 All contributors

_Nnsgmsone	_XuPeng-SH	_Fagongzi	_Reusee	_Ouyuanning	_Daviszhen
_{BRong Njam}	_Maomao	_{Iamlinjunhong}	_{Jiangxinmeng1}	_Jianwan0214	_GreatRiver
_{Wei Ziran}	_Chenmingsong	_Dengn	_Aptend	_Lni	_Jackson
_YANGGMM	_Qingxinhome	_Nitao	_{BroccoliSpicy}	_Mooleetzi	_Fengttt
_Kutori	_Prinz	_Brown	_Dongdongyang	_{Boyu Tan}	_{Cui Guoke}
_{Jin Hai}	_Matthew	_Bbbearxyz	_{Tianyahui-python}	_Wanglei	_Triump2020
_Heni02	_Wanhanbo	_IceTTTT	_LiuBo	_刘陶峰	_Ariznawlll
_GoodMan-code	_Yingfeng	_Mklzl	_Jensen	_DomingoZhang	_{Arjun Sunil Kumar}
_{Nuo Xu}	_Aressu1985	_Mo-bot	_Zengyan1	_Aylei	_NoneBack
_Otter	_{Richelle Guice}	_Ryan	_Elijah	_{MatrixAdventurer}	_NTH19
_Anitajjx	_Whileskies	_BePPPower	_{Jiajun Huang}	_Morranto	_Y7n05h
_Songjiayang	_Abirdcfly	_{Binglin Chang}	_Charlie17Li	_{DanielZhangQD}	_{Eng Zer Jun}
_{Eric Shen}	_Fungx	_Kirito	_{Jason Peng}	_O2	_RinChanNOW!
_TheR1sing3un	_{XuQing Chai}	_{Yiliang Qiu}	_{ZeYu Zhao}	_{ZheNing Hu}	_{Zijie Lu}
_{Zoran Pandovski}	_Ajian	_Binxxi	_Coderzc	_ColdWater	_Dr-lab
_Florashi181	_Hiyoyolumi	_Jinfu	_{Liuguangliang}	_Lokax	_Lyfer233
_Sundyli	_{Supermario1990}	_Tjie	_Toms	_Wuliuqii	_Xiyuedong
_Yclchuxue	_Zt