MatrixOne Introduction

MatrixOne is a distributed, cloud-native database that brings Git-style version control to data. Its storage-compute separation architecture and HSTAP engine support OLTP, OLAP, full-text search, and vector search in one system.

MatrixOne combines MySQL compatibility with AI-native search, built-in multi-account isolation, elastic scaling, and enterprise-grade high availability. It reduces data movement and the operational overhead of maintaining separate transactional, analytical, and search systems.

MatrixOne is designed for scenarios that require real-time data ingestion, large-scale data management, fluctuating workloads, and multi-modal data management. It is particularly suited for environments that combine transactional and analytical workloads, such as generative AI applications, mobile internet applications, IoT data processing, real-time data warehouses, and SaaS platforms.

Key Features

Git for Data

Git for Data applies familiar version-control concepts to database data. Instead of exporting data into a separate system before every experiment or release, you can preserve a known state, create an isolated line of change, inspect the resulting row-level differences, and promote selected changes with SQL.

Capability

What it provides

Snapshots

Create named restore points at cluster, account, database, or table scope. Snapshots can protect a baseline before upgrades, bulk changes, or tests.

Time Travel and PITR

Query or restore historical data by using snapshots and Point-in-Time Recovery (PITR), supporting recovery, auditing, and reproducible analysis.

Data Branches

Create a table or database branch from current data or a named snapshot. The branch keeps its lineage while changes on the source and branch remain isolated.

Diff

Compare two related tables and inspect INSERT, DELETE, and UPDATE differences. Results can be summarized, limited, filtered by columns, or exported.

Pick

Promote only selected rows, primary keys, or changes within a snapshot window instead of merging the complete difference set.

Merge

Apply branch changes to a destination and handle conflicting rows with FAIL, SKIP, or ACCEPT.

Typical Git for Data Workflow

  1. Preserve the baseline with CREATE SNAPSHOT or configure PITR.

  2. Use DATA BRANCH CREATE to create an isolated table or database branch from the current state or a snapshot.

  3. Develop, test, transform, or validate data on the branch without changing the source.

  4. Review row-level changes with DATA BRANCH DIFF.

  5. Promote a subset with DATA BRANCH PICK, or combine the branch with DATA BRANCH MERGE.

  6. Remove an obsolete branch with DATA BRANCH DELETE.

This workflow is useful for release validation, safe data transformations, development and test environments, selective data promotion, incident recovery, and audit-oriented comparisons.

For a deeper look at the concepts, lifecycle, privilege model, and typical workflows, see Git for Data.

Hyper-converged Engine

  • Monolithic Engine

    HTAP data engine that supports a mix of workloads such as TP, AP, time series, and machine learning within a single database.

  • Built-in Streaming Engine

    Built-in stream computing engine that enables real-time data inflow, transformation, and querying.

Cloud & Edge Native

  • Storage-Computation Separation Structure

    Separates the storage, computation, and transaction layers, leveraging a containerized design for ultimate scalability.

  • Multi-Infrastructure Compatibility

    MatrixOne provides industry-leading latency control with optimized consistency protocol.

Extreme Performance

  • High-Performance Execution Engine

    The flexible combination of Compute Node and Transaction node accommodates point queries and batch processing, delivering peak performance for OLTP and OLAP.

  • Enterprise-Grade High Availability

    Establishes a consistently shared log under a leading Multi-Raft replication state machine model. It ensures high cluster availability while preventing data duplication, thus achieving RTO=0.

Ease of Use

  • Built-in Multi-Tenancy Capability

    Offers inherent multi-tenancy, where tenants are isolated from each other, independently scalable yet uniformly manageable. This feature simplifies the complexity of multi-tenancy design in upper-level applications.

  • High Compatibility with MySQL

    MatrixOne exhibits high compatibility with MySQL 8.0, including transmission protocol, SQL syntax, and ecosystem tools, lowering usage and migration barriers.

Cost-Effective

  • Efficient Storage Design

    Employs cost-effective object storage as primary storage. High availability can be achieved through erasure coding technology with only about 150% data redundancy. It also provides high-speed caching capabilities, balancing cost and performance via a multi-tiered storage strategy that separates hot and cold data.

  • Flexible Resource Allocation

    Users can adjust the resource allocation ratio for OLTP and OLAP according to business conditions, maximizing resource utilization.

Enterprise-Level Security and Compliance

  MatrixOne employs Role-Based Access Control (RBAC), TLS connections, and data encryption to establish a multi-tiered security defense system, safeguarding enterprise-level data security and compliance.

User Values

  • Simplify Database Management and Maintenance

    With business evolution, the number of data engines and middleware enterprises employ increases. Each data engine relies on 5+ essential components and stores 3+ data replicas. Each engine must be independently installed, monitored, patched, and upgraded. This results in high and uncontrollable data engine selection, development, and operations costs. Under MatrixOne's unified architecture, users can employ a single database to serve multiple data applications, reducing the number of introduced data components and technology stacks by 80% and significantly simplifying database management and maintenance costs.

  • Reduce Data Fragmentation and Inconsistency

    Data flow and copy between databases make data sync and consistency increasingly tricky. The unified and incrementally materialized view of MatrixOne allows the downstream to support real-time upstream updates and achieve end-to-end data processing without redundant ETL processes.

  • Decoupling Data Architecture From Infrastructure

    Currently, the architecture design across different infrastructures is complicated, causing new data silos between cloud and edge, cloud and on-premise. MatrixOne is designed with a unified architecture to support simplified data management and operations across different infrastructures.

  • Extremely Fast Complex Query Performance

    Poor business agility results from slow, complex queries and redundant intermediate tables in current data warehousing solutions. MatrixOne supports blazing-fast experience even for star and snowflake schema queries, improving business agility with real-time analytics.

  • An Solid OLTP-like OLAP Experience

    Current data warehousing solutions have the following problems: high latency and absence of immediate visibility for data updates. MatrixOne brings OLTP (Online Transactional Processing) level consistency and high availability to CRUD operations in OLAP (Online Analytical Processing).

  • Seamless and Non-disruptive Scaling

    It is challenging to balance performance and scalability to achieve an optimum price-performance ratio in current data warehousing solutions. MatrixOne’s disaggregated storage and compute architecture makes it fully automated and efficient to scale in/out and up/down without disrupting applications.

Scenarios

  • Traditional Application System Scenarios Requiring Scalability and Analytical Reporting Capabilities

    With the expansion of businesses, the performance needs of traditional single-machine databases cannot meet enterprises’ usual systems such as OA, ERP, CRM, etc., especially during special periods requiring business analysis. Many enterprises equip a separate analytical database system to meet statistical report needs at essential points like month-end, quarter-end, etc. Using MatrixOne; these needs can be met with a single database system, providing strong scalability that seamlessly scales with business growth.

  • Dashboard/BI Report Scenarios Requiring Real-Time Analytical Capabilities

    For typical OLAP-type applications in enterprises, such as dashboards, BI reports, etc., analyzing massive amounts of data often leads to a performance bottleneck when the data volume is significantly large, resulting in poor timeliness. MatrixOne’s robust analysis performance and scalability enable the acceleration of various complex and large-scale SQL queries, providing a near-instant experience and enhancing enterprise decision-making analysis’s agility.

  • Data Platform Scenarios for Real-Time Influx and Processing of Massive Heterogeneous Data Applications

    With the extensive use of sensor and network technology, numerous IoT devices generate substantial data, such as manufacturing factory production lines, new energy vehicles, city security surveillance cameras, etc. Their scale can easily reach hundreds of TB or even PB levels. The demand for digitization also requires enterprises to store and use these data increasingly. Traditional database solutions cannot meet the requirements of such massive and large-scale real-time data writing and processing applications. MatrixOne’s powerful streaming data writing and processing capabilities and robust scalability can adapt to any load and data volume scale, fully meeting these requirements.

  • Data Middle-End Scenarios Where Various Internal Enterprise Data Converges

    Medium and large enterprises often have multiple business systems. To fully understand the enterprise’s overall status, many enterprises build a data middle-end that connects data sources from various systems. Traditional solutions to carry the data middle-end are based on the Hadoop system, which is complex, posing high development and operation thresholds for many enterprises. MatrixOne’s one-stop HTAP architecture makes using a data middle-end as convenient as using MySQL, significantly reducing costs.

  • Business Scenarios with Dramatic and Frequent Internet Business Fluctuations

    Internet applications such as games, e-commerce, entertainment, social networking, news, etc., have a massive user base, and the business fluctuates dramatically and frequently. During hot events, these applications often need many computing resources to support business needs. MatrixOne’s fully cloud-native architecture has superior scalability, enabling automatic and rapid scaling with business changes, significantly reducing user operation and maintenance difficulties.

  • Enterprise SaaS Service Business Scenarios

    Enterprise SaaS applications have seen explosive growth in recent years. In developing SaaS applications, they all need to consider their multi-tenant model. Traditional schemes offer two modes of multi-tenant shared database instances and single-tenant exclusive database instances, but they face the dilemma of management cost and isolation. MatrixOne comes with multi-tenant capabilities; tenants are naturally load-isolated and can be independently scaled. At the same time, it provides unified management capabilities, balancing the enterprise’s needs for cost, management simplicity, and isolation, making it an optimal choice for SaaS applications.

Learn More

This section describes the basic introduction of MatrixOne. If you want to learn more detailed information about MatrixOne, see the following content: