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Contents ======== * [What is MatrixOne](#what-is-matrixone) * [Get Started in 60 Seconds](#️-get-started-in-60-seconds) * [Tutorials & Demos](#-tutorials--demos) * [Installation & Deployment](#️-installation--deployment) * [Architecture](#architecture) * [Python SDK](#python-sdk) * [Citing MatrixOne](#citation) * [Contributing](#contributing) * [License](#license) ## 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 brings Git-style workflows to data management. The design behind this capability is detailed in the arXiv paper [Version Control System for Data with MatrixOne](https://arxiv.org/abs/2604.03927), and in practice it lets you **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.

MatrixOne

## ⚡️ Get Started in 60 Seconds ### 1️⃣ Launch MatrixOne ```bash docker run -d -p 6001:6001 --name matrixone matrixorigin/matrixone:latest ``` ### 2️⃣ Create Database ```bash mysql -h127.0.0.1 -P6001 -p111 -uroot -e "create database demo" ``` ### 3️⃣ Connect & Query **Install Python SDK:** ```bash pip install matrixone-python-sdk ``` **Vector search:** ```python 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:** ```python ... 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](#️-installation--deployment) section below for production-grade installation options. 📖 **[Python SDK Documentation →](https://github.com/matrixorigin/matrixone/blob/HEAD/clients/python/README.md)** ## 📚 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](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/develop-java-crud-demo/) | Java | Java application development | | [SpringBoot and JPA CRUD Demo](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/springboot-hibernate-crud-demo/) | Java | SpringBoot with Hibernate/JPA | | [PyMySQL CRUD Demo](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/develop-python-crud-demo/) | Python | Basic database operations with Python | | [SQLAlchemy CRUD Demo](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/sqlalchemy-python-crud-demo/) | Python | Python with SQLAlchemy ORM | | [Django CRUD Demo](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/django-python-crud-demo/) | Python | Django web framework | | [Golang CRUD Demo](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/develop-golang-crud-demo/) | Go | Go application development | | [Gorm CRUD Demo](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/gorm-golang-crud-demo/) | Go | Go with Gorm ORM | | [C# CRUD Demo](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/c-net-crud-demo/) | C# | .NET application development | | [TypeScript CRUD Demo](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/typescript-crud-demo/) | TypeScript | TypeScript application development | ### 🚀 Advanced Features Tutorials | Tutorial | Use Case | Related MatrixOne Features | |----------|----------|---------------------------| | [Pinecone-Compatible Vector Search](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/pinecone-vector-demo/) | AI & Search | vector search, Pinecone-compatible API | | [IVF Index Health Monitoring](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/ivf-index-health-demo/) | AI & Search | vector search, IVF index | | [HNSW Vector Index](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/hnsw-vector-demo/) | AI & Search | vector search, HNSW index | | [Fulltext Natural Search](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/fulltext-natural-search-demo/) | AI & Search | fulltext search, natural language | | [Fulltext Boolean Search](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/fulltext-boolean-search-demo/) | AI & Search | fulltext search, boolean operators | | [Fulltext JSON Search](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/fulltext-json-search-demo/) | AI & Search | fulltext search, JSON data | | [Hybrid Search](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/hybrid-search-demo/) | AI & Search | hybrid search, vector + fulltext + SQL | | [RAG Application Demo](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/rag-demo/) | AI & Search | RAG, vector search, fulltext search | | [Picture(Text)-to-Picture Search](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/search-picture-demo/) | AI & Search | multimodal search, image similarity | | [Dify Integration Demo](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/dify-mo-demo/) | AI & Search | AI platform integration | | [HTAP Application Demo](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/htap-demo/) | Performance | HTAP, real-time analytics | | [Instant Clone for Multi-Team Development](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/efficient-clone-demo/) | Performance | instant clone, Git for Data | | [Safe Production Upgrade with Instant Rollback](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/snapshot-rollback-demo/) | Performance | snapshot, rollback, Git for Data | 📖 **[View All Tutorials →](https://docs.matrixorigin.cn/en/v25.3.0.2/MatrixOne/Tutorial/snapshot-rollback-demo/)** ## 🛠️ 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. ```bash # 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 →](https://github.com/matrixorigin/matrixone/blob/HEAD/etc/DEV_README.md)** - 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](https://github.com/matrixorigin/mo_ctl_standalone) tool. Handles installation, upgrades, backups, and health monitoring automatically. 📖 **[Complete mo_ctl Installation Guide →](https://github.com/matrixorigin/matrixone/blob/HEAD/INSTALLATION.md#using-moctl-tool)** ### ⚙️ Building from Source Build MatrixOne from source for development, customization, or contributing. Requires Go 1.26.4 or later, GCC/Clang, Git, and Make. 📖 **[Complete Build from Source Guide →](https://github.com/matrixorigin/matrixone/blob/HEAD/BUILD.md)** ### 🐳 Other Methods Docker standalone, Kubernetes, binary packages, and more deployment options. 📖 **[All Installation Options →](https://github.com/matrixorigin/matrixone/blob/HEAD/INSTALLATION.md)** ## 🔎 Architecture MatrixOne's architecture is as below:

MatrixOne

For more details, you can checkout [MatrixOne Architecture Design](https://docs.matrixorigin.cn/en/latest/MatrixOne/Overview/architecture/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](https://matrixone.readthedocs.io/)** [![Documentation Status](https://app.readthedocs.org/projects/matrixone/badge/?version=latest)](https://matrixone.readthedocs.io/en/latest/) 📖 **[Python SDK README](https://github.com/matrixorigin/matrixone/blob/HEAD/clients/python/README.md)** - Full features, installation, and usage guide 📦 **Installation**: `pip install matrixone-python-sdk` ## Citing MatrixOne If you use MatrixOne in academic work or refer to its Git for Data design, please cite: ```bibtex @misc{gou2026versioncontrolsystemdata, title={Version Control System for Data with MatrixOne}, author={Gou, Hongshen and Tian, Feng and Wang, Long and Deng, Nan and Xu, Peng}, year={2026}, eprint={2604.03927}, archivePrefix={arXiv}, primaryClass={cs.DB}, doi={10.48550/arXiv.2604.03927}, url={https://arxiv.org/abs/2604.03927} } ``` ## 🙌 Contributing Contributions to MatrixOne are welcome from everyone. See [Contribution Guide](https://docs.matrixorigin.cn/en/latest/MatrixOne/Contribution-Guide/make-your-first-contribution/) 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

## License MatrixOne is licensed under the [Apache License, Version 2.0](https://github.com/matrixorigin/matrixone/blob/HEAD/LICENSE).