Product Description
This document describes the openGauss database in terms of its product positioning, system architecture, application scenarios, operating environment, technical specifications, basic functions and features, and enterprise-level enhanced features.
Technical Characteristics
Compared with other databases, openGauss features multiple storage modes, NUMA-based kernel structure, and high availability.
Multiple storage modes
- Row-store, supporting frequent service data updates and the fusion engine (Ustore) and the original Astore
- Column-store, supporting service data appending and analysis
NUMA-based kernel structure
- Partitions key data structures to reduce data access conflicts.
- Provides NUMA-based key data structure to reduce the data structure access latency.
- Binds cores to key service threads to prevent inter-core thread drift.
High availability
- Supports multiple deployment modes, such as primary/standby synchronization and primary/standby asynchronization.
- Supports data page cyclic redundancy check (CRC), and automatically restores damaged data pages through the standby node.
High security
Provides fully-encrypted database capabilities and supports security features such as fully-encrypted equality query, access control, encryption authentication, database audit, and dynamic data masking to provide comprehensive end-to-end data security protection.
Software Architecture
openGauss is a standalone system that supports one primary and up to eight standby servers.
Service data is stored on a single physical node, and data access tasks are pushed to service nodes and then executed. The high concurrency of servers enables quick response to data processing. In addition, data can be copied to the standby server through log replication, ensuring high reliability and scalability.
Software Architecture
Figure 1 shows the logical components of openGauss.
Figure 1 openGauss logical architecture
Table 1 Architecture description
Application Scenarios
Transaction applications
Applications need to process highly concurrent online transactions containing a large volume of data, such as e-commerce, finance, O2O, telecom customer relationship management (CRM), and billing.
IoT data
In IoT scenarios, such as industrial monitoring, remote control, smart cities, smart homes, and loV, challenges come from a large number of sensors and monitoring devices, high sampling frequency, additional storage modes, and concurrent operation and analysis.