Geographic data reading and writing methods for mongodb clusters that store geographic data in a structured manner in geojson format

Abstract

The invention designs a large-scale geographic data storing scheme for a MongoDB cluster, the geographic data are organized in the MongoDB cluster with the GeoJSON format structuring method, and therefore it is made possible that large-scale geographic data can be stored efficiently through a distributed high-speed MongoDB cluster. Aiming at the geographic data storing scheme, the invention discloses a geographic data reading-writing method and a driving program capable of realizing the geographic data reading-writing method. According to the geographic data reading-writing method and the driving program, an OGR class library serves as the design architecture for driving geographic data reading-writing, and MongoDB cluster geographic data sources are read in a structurized mode with the GeoJSON format; an ORG function library is adopted, a bridge is built between the geographic data and the MongoDB cluster through an OGR object established in a memory, so that effective reading-writing of the geographic data of the MongoDB cluster is made possible, and the high-performance geographic analysis algorithm can be applied to the MongoDB database cluster.

Description

technical field [0001] The invention relates to a storage method of geographic data in a MongoDB cluster, in particular to a method for reading and writing geographic data in a MongoDB cluster that stores geographic data in a GeoJSON format structured manner. Background technique [0002] With the continuous development of geographic information technology, technologies such as high-resolution spatial sensors, mobile positioning technology, and radar and laser telemetry are widely popularized and applied, especially the Global Earth Observation System of Systems (GEOSS), national With the implementation of major projects such as the National Information Infrastructure (NII) and the National Spatial Data Infrastructure (NSDI), human beings' ability to comprehensively observe different aspects of the earth and different phenomena has reached an unprecedented level. On the one hand, these advances have enabled massive geographic information to be continuously acquired and updat...

AI Technical Summary

Problems solved by technology

[0006] (1) Performance issues Relational databases are built on the basis of relational models. Multi-table connection queries and strict transaction requirements of relational databases limit the speed of data reading and writing, especially under high concurrency conditions. ACID of transactions Attributes—Atomicity, Consistency, Isolation, and Durability—become a bottleneck affecting read and write performance

[0007] (2) The problem of easy scalability Under the cloud computing architecture, it is difficult for relational databases to expand horizontally

[0008] (3) Problems with database mode The relational mode of relational database has a strict definition. If the business changes, the need to increase or decrease a certain attribute will bring about major changes to the system.

[0011] However, the research and technology of applying NoSQL data to geographic data storage is rare, and geographic data lacks read and write technology for input and output to NoSQL databases, which severely limits the application and implementation of geographic data in the context of big data.

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