The Research Of Vector Spatial Data Storage And Retrieval Based On Hbase

With the development of information technology and spatial information acquisition technology,the global information technology and the extensive application of GIS(geographic information system),the rapid growth of spatial data.Facing the growing mass of spatial data,the traditional spatial data management scheme faces high bottlenecks such as high concurrent reading and writing and scalability.The cloud computing and high scalability of storage capacity and powerful computing power can meet the massive data storage,large data parallel processing,high concurrent search and other needs.In view of the many advantages of cloud computing technology,this paper aims at how to use cloud computing technology to realize the access to massive vector spatial data.This paper focuses on the research and design of the storage model,spatial index construction,data organization scheme,data import,spatial query strategy and attribute SQL query on the cloud platform.The paper works on the following aspects:(1)Vector spatial data cloud storage and retrieval research background and related theoretical analysis.This paper analyzes the background and significance of the study of cloud access in massive spatial data storage,analyzes the current situation of cloud computing at home and abroad and the research status of spatial data access and the shortcomings of current research.Based on the characteristics of Map Reduce parallel computing framework,Vector spatial data parallel processing feasibility,and explore the distributed database HBase and SQL On Hadoop related cloud computing technology to store and manage the mass vector spatial data advantage.(2)Constructs a vector spatial data storage model based on HBase and the management scheme of vector spatial data integrated with No SQL model and relational model.Based on the characteristics of vector spatial data and the HBase data model,the vector spatial data storage model is designed,and the multi-level grid index is designed by quadtree tree subdivision technology.Combining spatial information multi-level grid coding and Hilbert space filling curve Based on the storage rules of HBase database and the structure of data structure of Phoenix operation,the paper designs and designs the management of vector spatial data integrated with No SQL model and relational model,and designs the vector spatial data Program.(3)Designed vector spatial data storage and parallel construction of spatial indexing strategy.Based on the parallel processing feature of Map Reduce,we discuss and design the storage scheme of single vector and parallel processing vector spatial data based on Map Reduce and design the spatial indexing scheme based on Map Reduce.(4)According to the multi-level grid indexing strategy,the spatial query strategy is designed.According to the characteristics of different spatial query operators,multi-level grid index and HBase scanning query data,the spatial query operator optimization strategy,the combined grid coding optimization query strategy and the restricted scanning cluster optimization data filtering strategy are designed and implemented.Spatial query optimization strategy.Finally,a vector spatial data access prototype system based on HBase is designed and implemented.Grid index and multi-level grid index are implemented.Through the comparison experiment of grid index and multi-level grid indexing spatial query efficiency,The validity of the index.Based on the multi-level grid index,the validity of the three spatial query optimization strategies,such as the spatial query operator optimization strategy,the combined grid coding optimization query strategy and the limited scanning column cluster optimization data filtering strategy are verified.