OpenGemini is a global open-source cloud-native distributed time series database designed and developed by HUAWEI CLOUD Database Innovation Lab . It provides standalone and distributed versions with excellent read and write performance and efficient data analysis capabilities. Supports mainstream development languages and multi-form deployment (such as cloud, Docker, and physical machine), integrates storage and analysis, and is easy to expand. It is dedicated to efficiently storing and analyzing massive time series data in IoT and O&M monitoring scenarios to further reduce enterprise operation and O&M costs and improve product quality and production efficiency.
Five Core Features
Supports hundreds of millions of time lines and PB-level time series data management, tens of millions of data writes per second, and millisecond-level query response. Compared with InfluxDB, simple query performance is improved by 2 to 5 times, and complex query performance is improved by 60 times.
The MPP architecture is used. It consists of three components: ts-sql, ts-meta, and ts-store. Each component can be independently expanded and can be deployed in a large-scale cluster with more than 100 nodes.
Integrated storage and analysis
The built-in AI data analysis platform provides real-time anomaly detection capabilities for time series data and implements closed-loop management from data storage to data analysis.
Low O&M cost
More than 260 system running monitoring indicators are provided to quickly improve problem solving efficiency. The deployment does not depend on any third-party components or applications, greatly reducing O&M difficulties and costs.
High data compression ratio
Column-based storage is used to provide efficient data compression algorithm. With the same data volume, the storage cost is only 1/20 of that of relational databases and 1/10 of that of NoSQL.
Typical Application Scenarios
Internet of Things (IoT)
|Industrial Internet of Things
|Smart manufacturing, Smart energy, Electricity (water), New energy (photovoltaic and wind power), Smart mines, Agriculture and animal husbandry
|Take smart manufacturing as an example. Equipment management: equipment running status monitoring and predictive maintenance; Quality management: fault prediction and analysis, quality tracing, process optimization, and online quality monitoring; Energy management: energy consumption analysis and power consumption monitoring; Others: BI/Report
|Smart City, Smart Fire Protection, Smart Building, Environmental Monitoring, Smart Logistics, and Smart Campus
|Take smart buildings as an example. Device management: device health check (air conditioners, fans, and elevators), device predictive maintenance, and fault demarcation; Monitoring alarms: exception detection and prediction; Energy management: power consumption monitoring, energy consumption analysis and prediction; Environmental monitoring: air quality monitoring, indoor temperature monitoring
|Consumer Internet of Things
|Smart cars, Smart homes, Smart wearables, Shared economy (sharing bicycles, shared cars, and shared electric vehicles), and smart old-age pension
|Take smart cars as an example. Vehicle management: real-time vehicle status monitoring and vehicle abnormality detection; People-vehicle interaction: query of running positions, track playback, and driving behavior; Product innovation and optimization: driving behavior analysis, vehicle condition analysis
|Stores monitoring metrics, call chains, and logs of IT infrastructure and applications, such as cloud services, containers, microservices, and physical servers. It implements real-time status monitoring, exception detection and prediction, root cause analysis, alarm reporting, and statistics analysis.
|Stores network device and system data, such as uplink and downlink bandwidth, traffic, application API success rate, and access IP address. It implements real-time network performance insight, real-time error alarm, and network attack detection.