| Abstract |
Recently, the trend of developing digital twins for smart cities has driven a need for managing large-scale multidimensional, dynamic, and scene-oriented spatial data. Due to larger data scale and more complex data structure, queries over such data are more complicated and expensive than those on traditional spatial data, which poses challenges to the system efficiency and deployment costs. The existing spatial databases have limited support in both data types and operations. Therefore, a new-generation spatial database with excellent performance and effective deployment costs is needed. This paper presents Ganos, a cloud-native spatial database engine of PolarDB for PostgreSQL that is developed by Alibaba Cloud, to efficiently manage multidimensional, dynamic, and scene-oriented spatial data. Ganos models 3D space and spatio-temporal dynamics as first-class citizens. Also, it natively supports spatial/spatio-temporal data types such as 3DMesh, Trajectory, Raster, PointCloud, etc. Besides, it implements a novel extended-storage mechanism that utilizes cloud-native object storage to reduce storage costs and enable uniform operations on the data in different storages. To facilitate processing “big” queries, Ganos extends PolarDB and provides spatial-oriented multi-level parallelism under the architecture of decoupling compute from storage in cloud-native databases, which achieves elasticity and excellent query performance. We demonstrate Ganos in real-life case studies. The performance of Ganos is evaluated using real datasets, and promising results are obtained. Finally, based on the extensive deployment and application of Ganos, the lessons learned from our customers and the expectations of modern cloud applications for new spatial database features are discussed. © 2022, VLDB Endowment. All rights reserved. |
