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@yangj1211 yangj1211 released this 01 Nov 04:54
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v2.0.0
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We are excited to announce the release of MatrixOne v24.2.0.0!

MatrixOne is a hyper-converged, cloud-native database built for modern data demands. Designed to deliver high performance, scalability, and MySQL compatibility, MatrixOne provides a seamless HTAP (Hybrid Transactional/Analytical Processing) experience, allowing users to efficiently handle transactions, analytics, time-series data, and streaming processing in one unified platform.

Highlights of this Release

MatrixOne v24.2.0.0 introduces significant enhancements for enterprise-level high availability, disaster recovery, and expanded AIGC support.
This version features robust improvements to support Generative AI applications, including unstructured data processing, full-text search, optimized vector search, and enhanced MySQL compatibility. Key features include external data access, snapshot backups, point-in-time recovery (PITR), CDC, and disaster recovery through log-based replication for primary-standby clusters. MatrixOne continues to evolve as a leading platform for intelligent, AI-driven data management, providing enterprises with the ideal solution for their data infrastructure needs.

Use Cases

MatrixOne is well-suited for the following application scenarios. We welcome users facing similar challenges to reach out and explore a trial deployment with us.

Generative AI Applications

MatrixOne’s hyper-converged architecture is ideal for Generative AI, offering comprehensive support for multimodal data, real-time data retrieval, and intelligent data processing. In text and image generation scenarios, MatrixOne facilitates rapid response times and high-quality outputs through efficient data management, vector and hybrid search capabilities, data preprocessing with Python UDFs, and GPU-accelerated real-time inference. MatrixOne’s low-latency architecture supports Generative AI workloads like large-scale data storage, online inference, and adaptive feedback, empowering enterprises to drive innovation with speed and efficiency.

Time-Series Data Applications

Modern IoT ecosystems generate massive volumes of real-time data from diverse sources like industrial production lines, smart grids, and autonomous systems. MatrixOne is built to handle such demands with millisecond-level, high-concurrency writes and rapid, scalable data retrieval. MatrixOne’s real-time analytics seamlessly integrates with machine learning models, making it an ideal solution for predictive maintenance, energy optimization, and intelligent monitoring.

Mixed Workload Scenarios

Traditional single-node databases often struggle with the simultaneous processing demands of transactional and analytical workloads in enterprise applications like ERP, CRM, and OA systems. MatrixOne’s support for mixed workloads within a single database enables real-time analytics, continuous reporting, and efficient data-driven decision-making without the need for additional analytical databases or sharding. With MatrixOne’s scalability and high concurrency support, enterprises can confidently meet growing data demands while keeping performance at peak levels.

Enterprise SaaS

With the rise of enterprise SaaS applications, supporting multi-tenancy while ensuring cost-efficiency and data isolation is essential. MatrixOne’s native multi-tenant architecture provides load isolation, independent scaling, and unified management for each tenant. This architecture reduces management overhead, ensures data separation, and improves operational efficiency, making MatrixOne an optimal database choice for SaaS platforms.

Key New Features

Multi-mode Data Management

MatrixOne now supports direct access to external object storage, remote file systems, and local storage through Stage objects, as well as datalink access to files in storage systems. This capability significantly simplifies data pipeline construction for Generative AI applications, reducing development overhead and maintenance costs.

Full-text Indexing for Text and JSON Data

Full-text indexing on JSON and TEXT columns greatly enhances performance in AIoT applications, especially when combined with MatrixOne’s JSON data type, which minimizes data redundancy and boosts efficiency.

Vector Search

Enhanced vector search capabilities now provide rapid, large-scale vector retrieval, a critical feature for Generative AI applications involving large language models (LLMs) and retrieval-augmented generation (RAG).

Snapshot-based Backup and Recovery

Cluster and tenant-level data snapshots capture the database state at specific points in time, ensuring rapid recovery while minimally impacting performance. Snapshots support cross-tenant restoration, bolstering MatrixOne’s disaster recovery.

Primary-standby Log Replication for High Availability

Log replication enables transaction log synchronization between primary and standby databases, supporting high availability and disaster recovery. Standby databases can take over in case of primary database failure, ensuring uninterrupted operations.

Point-in-time Recovery (PITR)

PITR captures all data changes post-snapshot, allowing precise restoration to a historical moment in case of accidental operations or data loss. This approach reduces storage costs, enhances recovery efficiency, and provides flexibility for critical business continuity and compliance.

MatrixOne to MySQL CDC

Change Data Capture (CDC) from MatrixOne to MySQL supports real-time disaster recovery for users transitioning from MySQL, maintaining data continuity.

Table-level Publish-Subscribe

Building on previous database-level publish/subscribe, table-level publish-subscribe in this release enables more granular control over data change synchronization, providing enhanced flexibility for data management.

Additional Updates

SQL Enhancements

  • Added support for rename table, create pitr, drop pitr, alter pitr, restore pitr, show pitrs.
  • Optimized show publications and show subscriptions.
  • Enhanced load data infile command to support user-defined column order.

Data Types

  • Support datalink data type

Indexes and Constraints

  • Support Full-text Index

Functions and Operators

  • Support JSON functions: json_row, jq, try_jq, json_extract_string, json_extract_float64 functions.
  • Enhanced date manipulation for now() function.

Tools

  • mo-backup: Supports PiTR management.
  • mo_cdc: supports CDC task management.

MySQL Compatibility

  • Support Encode()/Decode() function

Quick Start

Community users and enterprise developers can try MatrixOne with the following command:

docker pull matrixorigin/matrixone:2.0.0

For more details, including architectural insights, installation guides, and tutorials, visit our documentation site. Join our discussions or share feedback on GitHub or in our community WeChat group.

Known Issues

  • Standby clusters currently do not support synchronization of data in external tables or stages.
  • Standby clusters support only cold backups and cannot be opened in read-only mode.
  • CDC supports only table-level data synchronization.
  • Snapshot backups support cluster and tenant levels, with restoration possible at the cluster, tenant, database, or table level.
  • Snapshots and PiTR backups cannot recover deleted tenant data.

New Contributors

Full Changelog: v1.2.4...v2.0.0

Contributors

zuyu, Aoang, and Cyberleu
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