- 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.
|
🗄️ 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. |
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.
docker run -d -p 6001:6001 --name matrixone matrixorigin/matrixone:latestmysql -h127.0.0.1 -P6001 -p111 -uroot -e "create database demo"Install Python SDK:
pip install matrixone-python-sdkVector 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.
Ready to dive deeper? Explore our comprehensive collection of hands-on tutorials and real-world demos:
| 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 |
| 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 |
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.
MatrixOne's architecture is as below:
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.
📖 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.
