Abstract
Recent years, the scale of spatial data is developing more and more huge and its storage has encountered a lot of problems. Traditional DBMS can efficiently handle some big spatial data. However, popular open source relational database systems are overwhelmed by the high insertion rates, querying requirements and terabytes of data that these systems can handle. On the other hand, key-value storage can effectively support large scale operations. To resolve the problems of big vector spatial data's storage and query, we bring forward HBase Spatial, a scalable spatial dada storage based on HBase. At first, we analyze the distributed storage model of HBase. Then, we design a distributed storage and index model. Finally, the advantages of our storage model and index algorithm are proven by experiments with both big sample sets and typical benchmarks on cluster compared with MongoDB and Mysql, which shows that our model can effectively enhance the query speed of big spatial data and provide a good solution for storage.
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