摘要
近年来,激光点云数据的应用急剧增加,如何对其进行高效存储和快速处理成为当前的一个重要研究方向。点云数据包含着丰富的地理信息,属于空间数据范畴。传统的关系型数据库对海量空间数据的存储和处理相对薄弱,分布式环境下非关系型数据库的应用为此提供了一个新的研究视角。Sharding技术是数据库水平扩展的一种解决方案,在分布式环境下搭建MongoDB的Sharding集群,通过范围分片和哈希分片对大量激光点云数据进行分布式存储、空间查询和MapReduce运算测试,充分体现了分布式下MongoDB在空间数据的存储和处理方面的巨大优势。
In recent years,the application of laser point cloud data has increased drastically. How to efficiently store and fast process the data becomes an important research direction at present. Point cloud data contains a wealth of geographic information,belonging to the spatial data category. Since traditional relational databases are relatively weak in storage and processing of massive spatial data,the application of non-relational databases provides a new perspective of study. Sharding technology is a solution for database level extension. In this paper,Sharding cluster for MongoDB is established under distributed environment,while distributed storage,spatial query and MapReduce operation test for numerous laser point cloud data will be implemented through range-based sharding and Hash-based sharding. The results completely reflect huge advantages of MongoDB under distributed environment in storage and processing for spatial data.
出处
《计算机应用与软件》
2017年第2期71-73,168,共4页
Computer Applications and Software
基金
国家自然科学基金项目(41371434)
