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Releases: matrixorigin/matrixone

v1.2.3-hotfix-20241101

01 Nov 06:48
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Full Changelog: v1.2.3-hotfix-20240916...v1.2.3-hotfix-20241101

MatrixOne-v2.0.0

01 Nov 04:54
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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

v1.2.3-hotfix-20241016

16 Oct 13:29
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Full Changelog: v1.2.3-hotfix-20241010...v1.2.3-hotfix-20241016

v1.2.3-hotfix-20241010

11 Oct 13:17
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v1.2.4

23 Sep 01:51
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Release Date: September 23, 2024
MatrixOne Version: v1.2.4

This hotfix addresses several critical issues and includes minor improvements aimed at enhancing stability and performance.

What's Changed

Full Changelog: v1.2.3-hotfix-20240916...v1.2.4

v1.2.3-hotfix-20240916

16 Sep 15:56
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Release Date: September 16, 2024
MatrixOne Version: v1.2.3-hotfix-20240916

This hotfix addresses several critical issues and includes minor improvements aimed at enhancing stability and performance.

Bug Fixes

  • Resolved a transaction retrieval issue in GetOrCreateTxnWithMeta.
  • Fixed incorrect value retrieval for unique and secondary keys.
  • Corrected a panic issue in KillRoutineConnections.
  • Fixed a proxy panic when killing a connection.
  • Resolved a bug where sessions could not be transferred after using LOAD DATA LOCAL INFILE.
  • Fixed range checkpoint inconsistencies.

What's Changed

Full Changelog: v1.2.3...v1.2.3-hotfix-20240916

MatrixOne-v1.2.3

11 Sep 07:27
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Release Date: September 11, 2024
MatrixOne Version: v1.2.3

Compared to the previous version, v1.2.3 focuses on bug fixes, performance optimizations, and minor feature enhancements, without introducing major new features.

Improvements

  • Memory Optimization: Enhancements in memory usage, particularly for duplicate checks, SEMI Join, and TableScan operations.
  • Enhanced Logging: Added logs for account restrictions, account suspensions, and additional operations, improving debugging and monitoring capabilities.
  • Performance Enhancements: Optimized handling of LIMIT 0 queries, improved dynamic cache management, and refined query performance for JSON type ordering.

Bug Fixes

This release addresses multiple issues related to memory usage, transaction handling, proxy connection stability, and more. These fixes improve the overall stability and reliability of MatrixOne.

What’s Changed

For a full list of changes and improvements, refer to the complete changelog.

This release is part of our ongoing effort to ensure MatrixOne’s stability, performance, and usability, offering refinements that contribute to a smoother and more efficient database experience.

MatrixOne-v1.2.2

12 Jul 09:38
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Release date: July 12, 2024
MatrixOne version: v1.2.2

Compared with the previous v1.2.1, v1.2.2 doesn't introduce new features but only focuses on bug fixes and minor optimizations.

Improvements

  • Support GROUP BY 1, MAKEDATE
  • Add runtime metrics
  • Support reload auto increment cache
  • Allow loading file info asynchronously for the disk cache
  • Optimize flush and merge

Bugfix

Fixed bugs related to partition state, proxy, MySQL compatibility, lockservice, stats, and merge memory control. Check the What's Changed section for details.

What's Changed

Full Changelog: v1.2.1...v1.2.2

MatrixOne-v1.2.1

30 Jun 06:50
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Release date: June 30, 2024
MatrixOne version: v1.2.1

Compared with the previous v1.2.0, v1.2.1 doesn't introduce new features but only focuses on bug fixes and minor optimizations.

Improvements

  • Memory cache supports multiple memory allocators.
  • Optimize the performance of show accounts.
  • Optimize observability metrics for fileservice.
  • Support incremental backup.
  • Optimize TCP packet estimation.
  • Refactor mologging.
  • Support restoration of system tables.
  • Support manual merge based on zonemap.

Bugfix

Fixed bugs related to snapshot reads, MySQL compatibility, lockservice, runtime filters, and system table upgrades. Check the What's Changed section for details.

What's Changed

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MatrixOne-v1.2.0

20 May 07:21
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We are excited to announce MatrixOne 1.2.0 release!

MatrixOne is a hyper-converged cloud-native database. It is designed to provide a cloud-native, high-performance, highly scalable, MySQL-compatible HTAP database. MatrixOne enables users to handle mixed workloads such as transactions, analytics, time-series, and streaming processing through a one-stop data processing solution.

What's New in v1.2.0?

Snapshot Backup and Restore (Beta)

Database snapshot is an efficient technology for database backup and recovery, providing a read-only static copy of the database at a specific point in time. It assists database administrators and developers in performing various operations while ensuring the consistency and integrity of the data.

  • Support for tenant-level snapshot backup and restore using the mo_br tool and sql statement.
  • Support for uninterrupted database operation during the snapshot creation process.
  • Support for rapidly restoring data to the state at the time of backup, effectively reducing the Recovery Time Objective (RTO).

Incremental Physical Backup(Enterprise Edition)

On the basis of full backup in the mo_backup tool, we support for incremental backup feature , which only backs up the newly changed data, significantly reducing the backup time and storage space requirements.

CTAS

Support for CTAS (Create Table As Select).CTAS is an SQL statement used to quickly create a new table based on existing data. It combines the functionality of table creation with a select query, providing an efficient way to create snapshots of tables, perform data transformations, or build data models for reporting and analysis.

BITMAP Fast Deduplication

BITMAP is a set of built-in functions for handling bitmaps, mainly used for rapid deduplication of large volumes of data.

  • Support for rapid data deduplication using BITMAP functions.
  • Support for determining the bucket number with the BITMAP_BUCKET_NUMBER() function.
  • Support for returning the relative bit position within a bucket using the BITMAP_BIT_POSITION() function.
  • Support for constructing bitmaps with the BITMAP_CONSTRUCT_AGG() function.

Vector Index

  • Support for using vector indexing to accelerate KNN queries.

Other New Features

SQL Statements

  • Support for INSERT IGNORE
  • Support for CREATE TABLE ... LIKE
  • Support for CREATE INDEX ... USING IVFFlat
  • Support for ALTER TABLE ... ALTER REINDEX
  • Support for LOAD DATA ... CHARACTER SET
  • Support for CREATE SNAPSHOT
  • Support for SHOW SNAPSHOTS
  • Support for RESTORE ACCOUNT
  • Support for DROP SNAPSHOT
  • Optimized ALTER PUBLICATION
  • Optimized SHOW PUBLICATIONS
  • Optimized SHOW SUBSCRIPTIONS

Date Type

  • Support for bit

Indexes and Constraints

  • Support for Vector Index

Built-in Functions and Operators

  • Added SYSDATE date function.
  • Added TO_BASE64 and FROM_BASE64 encoding and decoding functions.
  • Added MD5 and SHA1/SHA encryption functions.
  • Added SUBVECTOR function for extracting subvectors.
  • Added SERIAL_EXTRACT function for extracting sub-elements.
  • Added CLUSTER_CENTERS cluster centers function.
  • Support operations between vector and scalar.

System Parameters

  • Added keep_user_target_list_in_result.
  • Added foreign_key_checks.

MySQL Compatibility

  • Refactored the CSV reader and CSV splitting to maintain compatibility with MySQL.

Known Issues

  • Vector Index works only in l2_distance.
  • Only support tenant-level snapshot backup and restore.
  • Snapshot restore works as logical restore, requiring quite a lot of CPU and memory resources.
  • Memory leak occasionally happens and may lead to an OOM error.
  • Occasional system hung under high concurrency workload.

Full Changelog:v1.1.3...v1.2.0