Journal of Geo-information Science ›› 2016, Vol. 18 ›› Issue (2): 151-159.doi: 10.3724/SP.J.1047.2016.00151

• Orginal Article • Previous Articles     Next Articles

Research of the Parallel Spatial Database Proto System Based on MPP Architecture

CHEN Dalun1,2, CHEN Rongguo1,*, XIE Jiong1   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2015-03-12 Revised:2015-05-14 Online:2016-02-10 Published:2016-02-04
  • Contact: CHEN Rongguo

Abstract:

The efficiency for querying complex spatial information resources is an important indicator to evaluate the performance of current spatial databases. Traditional single node relation spatial data management is difficult to meet the demand of high-performance in querying large amounts of spatial data, especially for the complex join query on multi-table. In order to solve this problem, we design and implement a spatial database middleware prototype system. This system takes full advantages of the massive parallel processing (MPP) and shared-nothing architecture. In consideration of the characteristics of spatial data, we design the spatial data parallel import, multi-spatial-tables join strategy, spatial data query optimization and other algorithms and models. This paper firstly introduces the development status of parallel database systems in recent years, and then elaborates its MPP architecture and its organizational model, and the strategy of the join query on multi-spatial-table. Finally, we made some query experiments on massive spatial data and analyzed the results of these inquiries. The experimental results show that this system indicates a good performance (nearly linear speedup) in processing the complex query of massive spatial data. Compared with the tradition single node database, this system can fully improve the efficiency of complex querying for large spatial data, and it is a more efficient solution to solve the complex spatial data queries.

Key words: MPP, spatial database, parallel processing, shared-nothing