YMatrix is a hyper-converged database developed by Simdwave based on the classic open-source databases PostgreSQL and Greenplum. In addition to excelling in time-series scenarios, it also supports traditional use cases such as Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP).
It addresses enterprise requirements including high availability, security, high performance, automated operations, visualized installation, and data processing, ensuring reliable deployment for enterprise users.
Its core values are hyper-convergence, high read/write performance, high compression ratio, and high availability.
A community edition of YMatrix is available—your feedback and experience are welcome.
The hyper-converged architecture of YMatrix resolves the issue of data silos in traditional databases, enabling "one database, multiple uses."
YMatrix focuses on performance across all scenarios, including ingestion, storage, querying, analytics, and machine learning.
YMatrix offers 360-degree access security, covering authentication, privilege control, encryption, auditing, and resource management.
In traditional enterprises, massive amounts of data are often scattered across departments, systems, and applications due to organizational strategy, architectural design, or digital transformation efforts. These isolated data silos hinder interoperability and utilization, forming barriers that impede competitive advantage. Beyond technical complexity, they severely impact business operations, management, and growth—making them a critical obstacle in digital transformation.
YMatrix’s hyper-converged architecture has been successfully deployed in production environments such as factory data platforms, corporate group data warehouses, intelligent connected vehicles, and IoT device operations. It significantly reduces technical barriers related to selection, procurement, usage, and maintenance, receiving positive feedback. For example, in smart manufacturing, a single YMatrix instance can collect, store, compute, model, query, and analyze data from ERP (Enterprise Resource Planning), MES (Manufacturing Execution Systems), and equipment systems.
Time-series data forms the foundation of IoT, connected vehicles, industrial internet, and smart cities. Its defining characteristic is real-time processing, placing high demands on database write and storage capabilities. Enterprises must address challenges such as cost-effective performance, secure and rapid scaling to prevent data backlog, and lowering technical barriers to respond quickly to evolving data needs.
YMatrix is optimized for time-series workloads. Thanks to the physical sorting, asynchronous upload, and batch ingestion features of the MARS storage engine, combined with MatrixGate’s high-concurrency, high-performance bulk ingestion, YMatrix exceeds expectations in real-time ingestion, high-speed writes, real-time queries, and transactional integrity.
YMatrix supports graphical scaling—simple operations enable second-level expansion. It also supports smooth, online scaling without service interruption, ensuring business continuity, minimizing downtime losses, and reducing risks.
Typical broad IoT applications include smart campuses, smart homes, intelligent transportation, smart water systems, smart agriculture, and meteorological monitoring. Massive devices generate massive volumes of data, making storage cost (compression ratio) and access efficiency (decompression speed) key factors in building stable data infrastructure. High-speed ingestion and real-time query performance directly affect end-user experience.
Beyond PB-scale cluster capacity, YMatrix features patented Encoding Chain compression technology. Business users can select optimal encoding schemes tailored to each column’s data characteristics, achieving superior cost-effectiveness and saving over 50% in storage costs—turning massive data storage into a manageable burden.
Leveraging MatrixGate’s distributed, high-concurrency, streaming, and batch ingestion capabilities, YMatrix achieves second-level data ingestion when paired with adequate hardware.
With full vectorization (version 5.0 and above), YMatrix achieves SSB benchmark performance 1.24x that of ClickHouse, reaching world-class levels in throughput and latency.
YMatrix is compatible with the PostgreSQL/Greenplum ecosystem, supporting classic OLAP workloads in finance, telecom, government, energy, and manufacturing sectors, including Business Intelligence (BI) and reporting.
These scenarios typically involve non-time-series data processed via Hadoop ecosystems: historical data stored in Hadoop, with Spark used for computing report metrics—a complex pipeline.
With YMatrix, you can unify structured and unstructured data, federated data access, Kafka stream integration via GUI, and hot/cold data separation—all within one platform. This simplifies data consumption workflows while providing automatic failover and recovery mechanisms, resulting in a secure, simple, and easy-to-use solution.
