Vector Databases · matrixorigin

matrixone

MatrixOne is an open-source HTAP database written in Go that combines transactional (OLTP) and analytical (OLAP) workloads in one system, with built-in vector search, full-text search, and Git-style data versioning. It is MySQL-compatible, cloud-native, and positioned as an AI-native database for modern applications.

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Source: GitHub — github.com/matrixorigin/matrixone

1.9k

GitHub stars

302

Forks

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	matrixorigin/matrixone
Owner	matrixorigin
Primary language	Go
License	Apache-2.0 — OSI-approved
Stars	1.9k
Forks	302
Open issues	736
Latest release	v4.0.0-rc4 (2026-06-30)
Last updated	2026-07-08
Source	https://github.com/matrixorigin/matrixone

What matrixone is

MatrixOne is a hyper-converged HSTAP engine supporting OLTP, OLAP, full-text search, and vector search (IVF/HNSW) in a single unified architecture. It implements Git-for-Data version control with zero-copy snapshots and time-travel queries, runs in Go, separates storage from compute, and maintains MySQL protocol compatibility for drop-in migration.

Quickstart

Get the matrixone source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/matrixorigin/matrixone.git2cd matrixone3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

AI/RAG Applications with Semantic Search

Build retrieval-augmented generation systems with native vector search (IVF/HNSW) and full-text search without external vector databases or search engines. Unified schema simplifies data lineage and reduces operational complexity.

Data-Heavy Applications Requiring ACID + Analytics

Applications that need real-time transactions, analytical queries on the same dataset, and audit trails benefit from single-system HTAP without ETL pipelines. Git-for-Data simplifies rollback and data recovery during development or incidents.

Cloud-Native Deployments with Elastic Scaling

Teams deploying on Kubernetes with storage-compute separation needs can leverage cloud-native architecture. Ideal for workloads with variable transaction and analytical demands that benefit from independent scaling and zero-downtime operations.

Implementation considerations

RC4 status means API stability is not guaranteed; plan for potential breaking changes in v4.0.0 final release and monitor release notes carefully.
Verify MySQL compatibility depth; not all MySQL features may be supported. Test existing tools, ORMs, and queries in a pre-production environment.
Git-for-Data versioning adds operational responsibility: understand snapshot retention policies, storage overhead, and rollback time guarantees for your use case.
Cloud-native deployment assumes familiarity with Kubernetes and storage-compute separation patterns; on-premises or traditional VM deployments may require additional tooling.
Full-text and vector search require schema design choices (index types, lists parameter for IVF); optimize during schema design, not retrofitting.

When to avoid it — and what to weigh

Mature, Mission-Critical Production Systems — Latest release is v4.0.0-rc4 (release candidate). Not yet at stable v4.0.0. Production adoption risk is higher than battle-tested databases. Verify stability and support SLAs before migrating critical workloads.
Dependency on Proprietary Database Features — If your application relies on Oracle, SQL Server, PostgreSQL-specific syntax, or vendor-locked features, MySQL compatibility alone may not be sufficient. Migration effort could be substantial.
Minimal Engineering Resources or Low Risk Tolerance — Project has 736 open issues and is actively evolving. Requires engineering capacity to monitor releases, test updates, and adapt to API/behavior changes. Not a plug-and-forget database.
Extreme Scale or Specialized Workloads — No performance benchmarks, scale limits, or adoption numbers provided. Unknown how MatrixOne performs against industry standards at extreme scale or under specialized constraints (e.g., sub-millisecond latency, exabyte-scale storage).

License & commercial use

MatrixOne is licensed under Apache License 2.0 (Apache-2.0), a permissive open-source license that allows commercial use, modification, and distribution with minimal restrictions. Attribution and license inclusion are required; no warranty is provided.

Apache-2.0 is a widely recognized permissive OSI license that explicitly permits commercial use, including proprietary modifications and SaaS offerings. However, the project is pre-release (RC4), and commercial deployment risk assessment depends on your risk tolerance and the nature of your business. Verify support channels, SLAs, and liability terms independently; Apache-2.0 provides no warranties or indemnification.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	Medium

Security considerations

No explicit security audit, vulnerability disclosure policy, or threat model is mentioned in provided data. Standard considerations for database deployments apply: authenticate and encrypt network traffic (MySQL protocol supports SSL/TLS, verify MatrixOne support), implement access controls, and audit data changes (Git-for-Data provides audit trail, but its cryptographic guarantees are unknown). RC status suggests security hardening may still be in progress; perform threat modeling and penetration testing before production deployment. No claims about FIPS compliance, regulatory certifications, or supply chain security are provided.

Alternatives to consider

PostgreSQL + pgvector + Elasticsearch

Stable, battle-tested ecosystem with strong OLTP foundation, widely-adopted vector search extension (pgvector), and dedicated full-text search alternatives. Requires ETL and separate systems but mature and lower operational risk.

ClickHouse

Purpose-built OLAP database with exceptional analytical performance and vector search support. Excellent for analytics-heavy workloads but requires separate OLTP system and custom hybrid management.

MongoDB Atlas with Vectorized Search

Document-oriented with native vector search, flexible schema, and cloud-managed operations. Better for schema-flexible workloads but less suited for traditional relational ACID transactions and complex analytical queries.

Software development agency

Build on matrixone with DEV.co software developers

MatrixOne combines OLTP, OLAP, vector search, and data versioning in one unified database. Ideal for AI applications, real-time analytics, and cloud-native deployments. Start with the Docker quick-start and review our architecture docs to assess fit for your workloads.

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matrixone FAQ

Is MatrixOne production-ready?

Latest release is v4.0.0-rc4 (release candidate). Not yet at stable v4.0.0. Pre-release status carries higher risk; verify stability, support SLAs, and test thoroughly before production deployment. Suitable for new projects with engineering capacity to manage evolving software.

Can I migrate from MySQL without code changes?

MatrixOne claims MySQL compatibility and drop-in replacement capability. However, depth of compatibility is not benchmarked in provided data. Test existing queries, ORM code, and tools in a staging environment; some MySQL-specific features or syntax may not be supported.

What is 'Git for Data' and why does it matter?

Git-for-Data provides version control for databases: zero-copy snapshots, time-travel queries, branching/merging, and instant rollback. It reduces data recovery costs and enables safe testing of migrations. Adds operational complexity; understand snapshot retention, storage overhead, and rollback guarantees for your use case.

Do I need a separate vector database or search engine?

MatrixOne includes built-in vector search (IVF/HNSW) and full-text search. For simple RAG or semantic search, separate systems may not be needed. However, test search performance, latency, and scaling characteristics against your requirements; no benchmarks are provided.

Software developers & web developers for hire

DEV.co helps companies turn open-source tools like matrixone into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your vector databases stack.

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