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Received:May 14, 2020 Revised:June 10, 2020
Received:May 14, 2020 Revised:June 10, 2020
中文摘要: 针对海量空间矢量数据分布式存储与计算需求, 研究了基于四叉树格网编码建立要素索引的方法, 设计了HBase预分区优化策略, 提出了一种空间矢量数据分布式存储模型. 基于MapReduce计算框架, 构建了空间数据分布式计算与分析的优化流程. 最后, 针对空间叠加与统计场景, 采用一定规模的业务数据对所提的方法进行测试, 验证了设计方案的可行性和有效性.
Abstract:Research on distributed storage and computing technology of spatial vector data is carried out. The method of quadtree grid coding to establish feature index is studied. HBase pre-partition optimization strategy is designed, a distributed storage model of spatial vector data is proposed. Based on MapReduce computing framework, the process of spatial data distributed computing and analysis is built. For the common application scenario of spatial overlay analysis and statistics, a large-scale data test is carried out. The results show that the scheme is effective.
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基金项目:国家重点研发计划(2018YFD1100103-05); 自然资源部部委计划(1902-033-14)
引用文本：
张嘉,白晓飞,陶超,张小桐.大规模空间矢量数据分布式存储与计算优化.计算机系统应用,2020,29(12):251-256
ZHANG Jia,BAI Xiao-Fei,TAO Chao,ZHANG Xiao-Tong.Storage and Computing Optimization of Large Scale Distributed Spatial Vector Data.COMPUTER SYSTEMS APPLICATIONS,2020,29(12):251-256
张嘉,白晓飞,陶超,张小桐.大规模空间矢量数据分布式存储与计算优化.计算机系统应用,2020,29(12):251-256
ZHANG Jia,BAI Xiao-Fei,TAO Chao,ZHANG Xiao-Tong.Storage and Computing Optimization of Large Scale Distributed Spatial Vector Data.COMPUTER SYSTEMS APPLICATIONS,2020,29(12):251-256
