MatrixOne v25.3.0.0 Release Note
We are thrilled to announce the official release of MatrixOne v25.3.0.0 on Augest 26, 2025!
What is MatrixOne?
MatrixOne is an AI-driven cloud-native hyper-converged database that adopts a storage-compute separation architecture and fully leverages cloud infrastructure. It is MySQL-compatible and supports hybrid workload scenarios. By combining vector data types and full-text search capabilities, MatrixOne efficiently handles multi-modal data querying and management for generative AI applications.
Feature Overview
MatrixOne's latest release introduces dual capabilities in data replication and performance optimization. The new CREATE CLONE feature enables efficient cross-tenant replication of tables and database structures with data, ensuring data consistency through snapshot mechanisms, providing enterprise-grade solutions for data migration and backup recovery. Meanwhile, this version achieves significant optimizations in query performance, concurrent processing capabilities, and resource utilization, comprehensively enhancing the operational efficiency and stability of the distributed database, delivering an exceptional data management experience for users.
Use Cases
MatrixOne is suitable for the following application scenarios. We warmly welcome users with the following business pain points and needs to contact us for trial testing.
- Generative AI Scenarios: MatrixOne's hyper-converged database provides robust multi-modal data support, real-time retrieval, and intelligent data processing capabilities for generative AI, forming the core infrastructure for generative AI applications. In multi-modal scenarios such as text generation and image generation, MatrixOne ensures rapid responses and high-quality generative results on large-scale datasets through efficient data management, vector and hybrid search, Python UDF-supported data cleaning and preprocessing, and GPU-accelerated real-time inference. Whether handling large-scale data access and storage or online inference and dynamic feedback, MatrixOne delivers stable, low-latency support for generative AI applications, helping enterprises quickly deploy, iterate, and optimize generative AI solutions.
- Time-Series Data Applications: In modern IoT applications, billions of devices and sensors continuously collect and transmit data, including industrial production lines, smart grids, smart city infrastructure, and autonomous vehicles, generating terabytes of real-time data daily. MatrixOne's hyper-converged database provides efficient real-time data processing capabilities for IoT scenarios, supporting millisecond-level high-concurrency writes and fast retrieval while offering superior scalability to handle peak loads. Its real-time analytics capabilities enable businesses to quickly derive critical insights from massive IoT data. Seamless integration with machine learning models allows real-time data streams to feed directly into models for prediction and anomaly detection, making it ideal for industrial predictive maintenance, energy efficiency optimization, and intelligent monitoring applications, fully meeting IoT needs for high throughput, low latency, and intelligent data management.
- Hybrid Workload Support: In enterprise OA, ERP, and CRM systems, traditional single-machine databases often struggle to meet performance demands during peak periods as data volume and business complexity grow. Critical timeframes, such as month-end or quarter-end, typically require high-frequency analysis and real-time statistical reporting for decision-making. Many enterprises resort to standalone analytical databases or sharding to alleviate query loads on primary databases. MatrixOne's hybrid workload support eliminates the need for additional systems by enabling both operational and analytical needs within a single database, ensuring rapid responses under high concurrency through real-time data analytics. Its scalability allows seamless expansion as business grows, maintaining efficient real-time queries and statistics even with large-scale data growth, ensuring real-time, continuous, and efficient data-driven decision-making while enhancing flexibility in data management.
- Enterprise SaaS Scenarios: With the rapid growth of enterprise SaaS applications, SaaS development must address multi-tenant model requirements. Traditional approaches often force a choice between shared database instances for multiple tenants or dedicated instances per tenant, creating trade-offs between management costs and tenant isolation. MatrixOne natively supports multi-tenancy, providing workload isolation and independent scalability between tenants while offering unified management. This architecture effectively reduces management costs, ensures data isolation, and improves operational efficiency, fully meeting SaaS needs for cost control, ease of management, and isolation, making it an ideal database choice for SaaS applications.
New Features
Key New Features
Data Clone Support
MatrixNow officially supports the CREATE CLONE feature, enabling efficient replication of tables and databases—including both structure and data—within the same tenant or across tenants. Leveraging a snapshot mechanism, this feature guarantees point-in-time data consistency, providing a convenient and reliable solution for data backup and recovery, test environment setup, and cross-tenant data migration. With concise SQL syntax and strict permission controls, it significantly enhances the flexibility and efficiency of data management.
Other New Features
- Added support for the
strcmpfunction. - Implemented
RIGHT DEDUPjoin to reduce memory usage during large-scale data insertion.