Background information

Huaninghuanqiu is a company dedicated to developing a full-stack platform for natural disaster management and risk early warnings based on disaster data and models. The company provides services for public service sectors and industries such as insurance. The company is an industry leader in terms of data and model development as well as the implementation of development results. OpenRIS is the main product of this company that provides a big data platform for disaster and risk management. OpenRIS provides features such as maps, statistical queries, data splitting and downloads, data uploads, and custom online analysis. This platform is provided as a cloud service that integrates disaster data and models. The company faces the following challenges:
  • The spatio-temporal data that is accumulated over a long period of time contains diverse data types and the size of the data is huge. The data includes medium- and high-precision map data for disasters such as earthquakes and other geological disasters, typhoons, floods, high temperatures, low temperatures, rainstorms, snow disasters, forest and grassland fires, forecasting data for global meteorological and marine environments, and data of historical disasters and risks. Traditional relational databases cannot properly process the data of diverse data types. Traditional geographic information system (GIS) servers cannot ensure efficient data storage or management.
  • The data of the company includes a large amount of vector data and high-resolution raster data. Feature statistics and queries on the data of points, lines, and planes consume a large amount of time. This significantly compromises user experience.
  • Traditional GIS software is expensive and cannot be scaled up in an efficient manner. In addition, traditional GIS software cannot be deployed as cloud services.


  • Manage data from diverse data sources in an integrated manner, such as vector data, remote sensing image data, meteorological disaster data, and business data.
  • Move the entire business of the company to Alibaba Cloud. The business is deployed in an architecture that has three layers: the storage layer, the business layer, and the application layer. At the storage layer, the ApsaraDB PolarDB PostgreSQL-compatible edition and the Ganos spatial-temporal database engine are used for data storage. At the business layer, GeoServer is used to publish spatial data services.
  • Directly import various types of raster data to Object Storage Service (OSS). When the data is imported to OSS, the metadata of the data is automatically read. After the data is imported, the corresponding functions can be used to query the relevant attributes.


  • Data from heterogeneous data sources is managed in one database. This reduces costs.
  • The native SQL features provided by Ganos for spatio-temporal data allow developers to perform professional computing. For example, developers can crop, merge, export, queries, and collect high-resolution raster data and can perform combined analysis of vector and raster data. This reduces the workloads of development at the application layer.
  • Spatio-temporal data can be processed in parallel at the table or operation level. This accelerates computing of big data.
  • SQL statements can be executed to implement most features provided by traditional GIS software as the system is deployed as a cloud native service. The performance and storage space can be linearly increased at the same time.


  • The integrated management of domain-specific data from heterogeneous data sources allows the cloud native service to provides a one-stop storage and processing solution.
  • Ganos supports direct access to spatio-temporal data that is stored as files in OSS. This reduces storage costs and ensures performance of data access.
  • Developers and database administrators can execute familiar SQL statements instead of using dedicated GIS software to perform advanced GIS operations and computations. This greatly lowers the development threshold and reduces development costs.
  • Parallel execution can be enabled. This way, you can use multi-core computing resources to accelerate data processing. This reduces the waiting time for requests.
  • API operations for processing raster data are easy to use. These API operations can be called to implement a wide range of features. This eliminates the performance bottleneck of WebGIS in processing raster data.
  • The cloud native architecture of PolarDB ensures that databases are secure and stable. This architecture also allows flexible scaling of nodes to implement high scalability for the entire system.