Existing studies on parallel spatial join query mostly focus on algorithm process. Few of them pay enough attentions on implementation and application research. After analyzing existing algorithm process, parallel spatial join algorithms are divided into four phases which are parallel candidate tasks generating, assignment, executing and results collection. Each of them is designed and implemented on a parallel RDBMS cluster which is built on a open-source project named ‘PL/Proxy’. In addition, to implement parallel computation, mixed computation migration method is proposed. Spatial extension function is implemented to support the spatial data sets operation on the cluster. Result of experiments using real data sets shows that, the implemented algorithm gains near linear speedup when data declustering scheme is optimal. Moreover, speedup is also gained while significant data skew caused by data declustering exists. The data declustering scheme is replaceable for improving the algorithm performance. A practicable solution for parallel spatial data sets management is provided in this paper.