Spatial data declustering method considering spatial locality for parallel spatial database

Spatial data declustering is an important data processing method for parallel spatial database especially in shared nothing parallel architecture. Spatial data declustering can achieve parallel dataflow to exploit the I/O bandwidth of multiple parallel nodes by reading and writing them in parallel, which can improve the performance of parallel spatial database evidently. Aiming at the unique spatial objects locality, this paper presents a novel spatial data declustering method, which uses Hilbert space-filling curve to impose a linear ordering on multidimensional spatial objects, and to partition spatial objects logical segments according to this ordering to preserve spatial locality of spatial objects, and then to allocate logical segments to physical parallel nodes based on round-robin rule. Experimental results show that the proposed method can obtain well spatial data declustering results.