Abstract
In recent years, the application of laser point cloud data has increased dramatically. How to efficiently store and fast process the data becomes an important research direction at present. Point cloud data contain a wealth of geographic information, belonging to the category of spatial data. Traditional relational databases are relatively weak in massive spatial data storage and processing, while the application of non-relational databases provides a new perspective of study for this fact. Sharding technology is a solution for database level extension. In this thesis, 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 scope sharding and Hash-based sharding, which completely reflects huge advantages of MongoDB under distribution in storage and processing for spatial data.
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