Hyperconverged cloud-edge native database
Release date: April 16, 2024 MatrixOne version: v1.1.3
Compared with the previous v1.1.2, v1.1.3 doesn't introduce any new features but only focuses on bug fixes.
mo_table_size
when the index table name is empty. by @gouhongshen in https://github.com/matrixorigin/matrixone/pull/15403
mo_table_size
by @gouhongshen in https://github.com/matrixorigin/matrixone/pull/15444
Full Changelog: https://github.com/matrixorigin/matrixone/compare/v1.1.2...v1.1.3
Release date: April 02, 2024 MatrixOne version: v1.1.2
Compared with the previous v1.1.1, v1.1.2 doesn't introduce any new features but focuses on some improvements and bug fixes.
system_metrics
db need redirect to sys by @xzxiong in https://github.com/matrixorigin/matrixone/pull/14998
Full Changelog: https://github.com/matrixorigin/matrixone/compare/v1.1.1...v1.1.2
Release date: February 04, 2024 MatrixOne version: v1.1.1
Compared with the previous v1.1.0, v1.1.1 doesn't introduce any new features but focuses on some improvements and bug fixes.
mo_table_size
and metadata_scan
by @gouhongshen in https://github.com/matrixorigin/matrixone/pull/13949
Full Changelog: https://github.com/matrixorigin/matrixone/compare/v1.1.0...v1.1.1
We are excited to announce MatrixOne 1.1.0 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.
These features enable users to quickly build AI applications, such as RAG applications based on large language models (LLMs). Unlike specialized vector databases, MatrixOne is a general database highly compatible with MySQL, enabling users to get started quickly without additional learning barriers. It also integrates structured and vector data processing for building AI applications.
+
,-
,*
,/
.=
, !=
, >
, >=
, <
, <=
.sqrt
,abs
,cast
.summation
,l1_norm
,l2_norm
,vector_dims
,inner_product
,cosine_similarity
.count
.CREATE DYNAMIC TABLE
.CREATE SOURCE
.JSON
or protobuf
format.OOM
error.Full Changelog: https://github.com/matrixorigin/matrixone/compare/v1.0.0...v1.1.0
Release date: December 23, 2023 MatrixOne version: v1.0.2 Compared with the previous v1.0.1, v1.0.2 introduces a few features/improvements but the focus was primarily on bug fixes.
PrefecthDelete
bug: #13590blob
related bug: #13593mo_ctl merge
hung: #13601SHOW SUBSCRIPTIONS;
fails in certain special scenarios: #13656, #13599lcase
, ucase
: #13686Release date: December 16, 2023
MatrixOne version: v1.0.1
Compared with the previous v1.0.0, v1.0.1 introduces a few features but the focus was primarily on improvements and bug fixes.
lockservice
p99 latency performance #12760show processlist
issue #12834blob
type encoding bug #13507lower()
, upper()
, locate()
#13442Release date: November 13, 2023
MatrixOne version: v1.0.0
Compared with the previous v1.0.0-RC2, v1.0.0 doesn't introduce any new features but focuses on some improvements and bug fixes.
Release date: October 24, 2023
MatrixOne version: v1.0.0-RC2
Compared with the previous v1.0.0-RC1, v1.0.0-RC2 doesn't introduce any new features but focuses on some improvements and bug fixes.
delete
operations #11541, #11542, #11882, #11969.on duplicate key
#11632, #11760.mo_ctl
#11680, #12247.partition
related features #11762.runtime filter
#11868.count/min/max
operations #11959.checkpoint
related bugs #12014, #12124, #12172 and etc.alter table/db
related bugs #11429, #11484, #12162 and etc.taskservice
related bugs #11544.show backend servers
incorrect results #11414.insert
related bugs #11495 and etc.We are thrilled to announce the release of MatrixOne 1.0.0-RC1! This milestone marks we have basically achieved the initial design goal of the MatrixOne open-source project. Cheers!
MatrixOne is designed to provide a unified and scalable database management solution for transactional, analytical and streaming workloads and powers a wide range of applications. In general, MatrixOne 1.0.0-RC1 has delivered a cloud-native architecture with separated storage and computing, presenting a fully-functional database with high performance queries and elastic scaling with familiar relational SQL. The OLTP and OLAP performance of MatrixOne has also reached the industry's average level, meanwhile MatrixOne provides an experimental function of Stream table
and Kafka connector
for streaming processing.
CREATE
, ALTER
, and DELETE
databases, tables, views, and partitioned tables.INSERT
, UPDATE
, DELETE
, and data import/export statements.INSERT
, LOAD DATA
, SOURCE
, etc.SELECT INTO
and modump
methods.mo_ctl
.modump
tool.mobr
tool.As the data volume of common IT systems such as OA, ERP, CRM, etc., increases with business growth, traditional single-node databases may not meet their performance requirements. Many companies set up a separate analytical database system to meet the needs of important reporting requirements at month-end or quarter-end, or use sharding techniques to reduce query loads. An operational analytics system is one that allows you to make quick decisions based on the current operational system. MatrixOne can fulfill the requirements of both the business system and the analytical system with a single database, while providing powerful scalability that can seamlessly expand as the business grows.
For typical OLAP (Online Analytical Processing) applications in business, such as dashboards and BI reports, massive data analysis is often required. When the data volume becomes significant, performance bottlenecks may arise, resulting in poor timeliness. MatrixOne offers fast data ingestion, powerful analytical performance and scalability, enabling accelerated processing of complex and large-scale SQL queries, achieving sub-second response times, and improving the agility of enterprise decision-making and analysis.
With the widespread application of sensors and network technologies, various IoT devices generate massive amounts of data, such as manufacturing plant production lines, renewable energy vehicles, city surveillance cameras, and more. The scale of data can easily reach hundreds of terabytes or even petabytes. There is an increasing demand for enterprises to store and utilize this data. Traditional database solutions cannot handle the real-time data ingestion and processing required in such massive and large-scale scenarios. MatrixOne provides powerful capabilities for streaming data ingestion and processing, along with robust scalability to handle any workload and data volume, fully meeting the requirements of this scenario.
For internet-based web applications such as gaming, e-commerce, entertainment, social media, news, etc., where user numbers are large and business fluctuations are frequent and significant, substantial computing resources are often required to support business demands during peak events. MatrixOne, with its fully cloud-native architecture, offers exceptional scalability, automatically scaling up or down in response to changes in the business, thereby greatly reducing the operational complexity for users.
SaaS applications have experienced explosive growth in recent years. In SaaS application development, a multi-tenant model needs to be considered. Traditional solutions often involve either shared database instances for multiple tenants or dedicated database instances for each tenant, facing a trade-off between management costs and isolation. MatrixOne comes with built-in multi-tenancy capabilities, providing natural workload isolation between tenants and independent scalability. It also offers centralized management capabilities, effectively addressing cost, ease of management, and isolation requirements, making it the ideal choice for SaaS applications.
docker pull matrixorigin/matrixone:1.0.0-rc1
To get started with MatrixOne 1.0.0-rc1, please visit our official website at https://docs.matrixorigin.cn/en. You can find detailed documentation, installation instructions, and tutorials to help you explore the features and capabilities of MatrixOne. Additionally, our community forums are available for any questions, discussions, or feedback you may have.
From 0.8 to 1.0, we mainly developed some new functions and improve usability for production-level deployment.
In this version, we have introduced the physical backup feature, allowing users to easily backup and restore databases. Now, you can effortlessly create snapshots of your database and restore to previous states when needed, ensuring data integrity and reliability.
As the final piece of the HSTAP architecture puzzle, we have completed the framework design for stream computing. In this iteration, we have added the ability to create streaming tables and implemented a Kafka connector to meet the streaming data ingestion needs of various time-series scenarios.
Recursive CTE (Common Table Expression) is a feature that allows for repeatedly executing an initial CTE to return subsets of data until the complete result set is obtained. The implementation in this iteration enables users to easily handle hierarchical data and build more complex and flexible queries using recursive queries.
We have further improved MatrixOne's compatibility with MySQL, enabling better support for MySQL table creation statements. Now, users can seamlessly migrate existing MySQL applications to MatrixOne without modifying existing table creation statements.
We have added the capability to migrate sessions seamlessly during distributed instance scaling operations. Users can easily adjust the capacity of MatrixOne without impacting existing sessions and business operations.
We have significantly simplified the startup configuration options for both single-node and distributed versions, allowing users to quickly and easily start the database.
We have optimized the functionality of the mo_ctl maintenance tool for both single-node and distributed setups, providing more powerful and user-friendly maintenance capabilities. Now, you can effortlessly deploy, upgrade, scale, and perform other maintenance operations using the distributed mo_ctl tool (Enterprise Edition).
-Add Replace for DDL -Add column modification for Alter Table -Add Create Stage statement, simplify Data Import/Export process -Add Show Processlist for checking system status -Add enum data type -Add Year data type -Add To_Days/To_Seconds system functions -Improve Group by behavior by alias support
-Secondary Key doesn't improve any performance. -Memory leak occasionally happens and may lead to an OOM error. -DN is a single point of failure for distributed version. -Occasional system hung under high concurrency workload.
We are excited to announce MatrixOne 0.8.0 release. After four months' development, MatrixOne completed its architecture design with Proxy module, which ensures workload and tenant isolation. MatrixOne has also made much improvement with its OLTP and OLAP performance, along with its scalability, stability and user experience. This is also a Beta release, open for developers for testing and feedback. Cheers!
docker pull matrixorigin/matrixone:0.8.0
-OLTP performance: 3-7x improvement, reaching MySQL performance in standalone version. -OLAP performance: SSB and TPCH performance align with Snowflake and Clickhouse. -Scalability: archiving near-linear performance growth with CN horizontal scaling.
-Backward compatibility of data storage format. -Deployment and administration tool for both standalone and distributed version. -Improved MySQL compatibility in case sensitivity, information_schema and DDL statements. -Read committed with pessimistic locking implemented in transaction mode. (Experimental)
-Support window function RANK()
, ROW_NUMBER()
and DENSE_RANK()
-Support the BINARY
type and related functions.
-Support data sharing between tenants and PUBLISH
/SUBSCRIBE
functions.
-Support INSERT...ON DUPLICATE KEY UPDATE
statement.
-Add Sequence
and related statements.
-Support ADD [COLUMN] | DROP [COLUMN]
in the ALTER TABLE
statement.
-Support multi-layer foreign key.
-Support RAND()
built-in function.
-Support setting global variables in configuration files.
-Secure initial MatrixOne account by password replacement.
-Several types of partitioning are supported. (Experimental)
-0.8.0 data format is not compatible with the previous versions.
-Secondary Key doesn't improve any performance.
-Memory leak occasionally happens and may lead to an OOM
error.
-Workload isolation is only supported by JDBC.
-DN is a single point of failure for distributed version.
-Occasional system hung under high concurrency workload.
-Pessimistic transaction has a few fatal bugs remaining.
Full Changelog: https://github.com/matrixorigin/matrixone/compare/v0.7.0...v0.8.0