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

Abstract

The invention provides a geographic data reading and writing method of a MongoDB cluster of geographic data stored in a GeoJSON-format semi-structured mode. According to the method, a large-scale geographic data storage scheme is designed for the MongoDB cluster. In the MongoDB cluster, the geographic data are organized in the GeoJSON-format semi-structured mode, so it is possible that the distributed high-speed MongoDB cluster efficiently stores the large-scale geographic data. According to the geographic data storage scheme, the geographic data reading and writing method and a drive program which can implement the geographic data reading and writing method are put forward, an OGR class library is used as a design architecture of a geographic data reading and writing driver, and a geographic data source of the MongoDB cluster is read and written in the GeoJSON-format semi-structured mode. According to the method, an OGR function library is adopted, a bridge is established between the geographic data and the MongoDB cluster through an OGR object established in an internal memory, so it is possible that the geographic data of the MongoDB cluster are efficiently read and written, and a high-performance geographical analysis algorithm can be run on 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 semi-structured manner in the GeoJSON format. 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 acquir...

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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