YMatrix is a hyper-converged database product developed by 4D Technology based on the classic PostgreSQL/Greenplum open source database. In addition to excelling in time series scenarios, it also supports classic scenarios such as online transaction processing (OLTP) and online analytical processing (OLAP).
It addresses enterprise needs such as high availability, security, high performance, automated operations and maintenance, and visualized installation and data processing, ensuring the successful implementation of enterprise user requirements.
Its core values lie in its hyper-converged architecture, high-performance read/write capabilities, high compression rates, and high availability.
YMatrix also offers a community version; we welcome your feedback and input.
YMatrix's hyper-converged architecture solves the problem of traditional database https://baike.baidu.com/item/信息孤岛?fromModule=lemma_search-box, enabling “one database for multiple uses.”
YMatrix focuses on performance across all scenarios, including write, storage, query, analysis, and machine learning.
YMatrix features a 360-degree security access mechanism, including authentication, permission control, encryption, auditing, and resource control.
In traditional industrial enterprises, massive amounts of data are often scattered across various departments, business systems, and applications due to organizational strategies, architectural configurations, and digital infrastructure development. These data silos are unable to communicate with one another or be effectively utilized, creating numerous information silos. Beyond the technical challenges involved, this situation severely hinders a company's ability to gain a competitive edge in its operations. Data silos severely constrain enterprise management, operations, and development, making them a critical hurdle that must be overcome in digital transformation. Currently, YMatrix's hyper-converged architecture has been successfully applied in real-world production scenarios such as factory data foundations, large corporate group data warehouses, intelligent connected vehicles, and IoT device intelligent operations, significantly lowering technical barriers during enterprise selection, procurement, use, and maintenance, and receiving positive feedback. For example, in smart manufacturing scenarios, a single database can handle the collection, storage, computation, modeling, querying, and analysis of data from enterprise resource planning systems (ERP), manufacturing execution systems (MES), and equipment data.
Time series data is the basic data for the Internet of Things, Internet of Vehicles, Industrial Internet, and Smart Cities. Its core feature is real-time, which requires high database writing and storage capabilities. How to control costs while ensuring performance, how to achieve expansion more safely and quickly to avoid data backlogs, and how to lower the technical threshold to respond more quickly and accurately to new data demands have become issues that enterprises must solve.
YMatrix is optimized for time. Thanks to the MARS series storage engine's physical sorting, different frequency uploads, batch uploads, and MatrixGate's high concurrency and high-performance batch data writing capabilities, YMatrix can exceed expectations in meeting the needs of real-time warehousing, high-speed writing, real-time querying, and transaction assurance in enterprise time series scenarios.
YMatrix supports graphical scaling, with simple operations and instant scaling; it also supports smooth scaling without interrupting business operations, ensuring business security and smoothness, reducing downtime losses, and lowering risks.
Common IoT scenarios include smart campuses, smart homes, smart transportation, smart water management, smart agriculture, and smart meteorology. A large number of devices means a large amount of data to be written, stored, and queried. Storage costs (compression ratio) and access efficiency (decompression efficiency) are decisive factors in the stability of the data infrastructure in this scenario, while high-speed writing and real-time query performance are important indicators of the end-user experience.
In addition to petabyte-scale cluster capacity, YMatrix features patented encoding chain compression technology, enabling business personnel to tailor the most suitable encoding scheme for each data column's characteristics, achieving optimal cost-effectiveness and saving enterprises over 50% in storage costs, making massive data storage no longer a burden.
Thanks to MatrixGate's high-concurrency, distributed, streaming, and batch data writing capabilities, YMatrix can achieve sub-second data ingestion when combined with hardware performance.
Thanks to full vectorization (version 5.0 and above), YMatrix's SSB performance has been tested to be 1.24 times that of Clickhouse, achieving world-class high throughput and low-latency queries.
YMatrix is compatible with the PostgreSQL/Greenplum ecosystem and can support classic OLAP scenarios in industries such as finance, telecommunications, government, energy, and manufacturing, as well as business intelligence (BI) and report analysis.
Such scenarios are more common in non-time series data, using the Hadoop ecosystem to complete data production and consumption: the Hadoop platform stores historical data, and then uses Spark to calculate report indicators, which is a complex process.
Using YMatrix, you can achieve the data consumption required for this scenario in a one-stop manner through features such as integrating structured and unstructured data types, data federation access, graphical access to Kafka data streams, and hot/cold data separation. It also has automatic failover and automatic recovery mechanisms, making it secure, simple, and easy to use.
