HBaseSpatial: A Scalable Spatial Data Storage Based on HBase

TLDR

Spatial data volumes are rapidly growing, exposing limitations in traditional DBMS for high insertion rates, complex queries, and terabyte‑scale storage, while key‑value stores can better handle large‑scale operations. This work introduces HBase Spatial, a scalable spatial data storage system built on HBase, aimed at addressing storage and query challenges for large vector datasets. The authors analyze HBase’s distributed storage architecture and design a distributed storage and indexing model tailored for spatial data. Experimental results on large sample sets and benchmark clusters demonstrate that the proposed model outperforms MongoDB and MySQL, significantly improving query speed for big spatial data.

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.