Spatial Data Dynamic Balancing Distribution Method Based on the Minimum Spatial Proximity for Parallel Spatial Database

Spatial data balancing distribution can evidently improve the performance of parallel spatial database in shared nothing parallel architecture. Considering spatial locality and unstructured variable length characteristics of spatial data, this paper proposes a dynamic spatial data balancing distribution method under shared nothing parallel database environment. By using Hilbert ordering code to keep spatial locality relationship between spatial objects, the presented method can fulfill spatial data static balancing distribution status, which depends on hierarchically decomposing Hilbert space-filling curve code to allocate approximately even spatial data volume to parallel nodes in distributed network. Then spatial proximity index is introduced to resolve spatial data unbalancing problem caused by spatial database dynamic updating. Through moving spatial data fragments to goal node which is the minimum spatial proximity with data moving out node, the spatial database redistributing strategy can attain dynamic data balance among all network parallel nodes. Our experimental results show that the proposed method can effectively improve parallel performance deterioration resulted from spatial data unbalancing and achieve spatial data dynamic balancing distribution in parallel spatial database.