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• What is MatrixOne
• Get Started in 60 Seconds
• Tutorials & Demos
• Installation & Deployment
• Architecture
• Python SDK
• Contributing
• License

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.

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.

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.

MatrixOne

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

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

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 →

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

📖 View All Tutorials →

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

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 →

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

📖 Complete Build from Source Guide →

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

📖 All Installation Options →

MatrixOne's architecture is as below:

MatrixOne

For more details, you can checkout MatrixOne Architecture Design.

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 Documentation Status

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

📦 Installation: pip install matrixone-python-sdk

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

MatrixOne is licensed under the Apache License, Version 2.0.