This paper proposes an open-source algorithm that performs parallel processing of spatial queries, during which an initial selection of objects to be subjected to spatial relationship tests is done using a spatial index. These data are then further subdivided by the use of the OFFSET and LIMIT clauses into still smaller subgroups, to which spatial relationship tests utilizing complex calculations are assigned, thereby creating multiple processes running in parallel. This algorithm was tested using data from the SQLite/SpatiaLite and PostgreSQL/PostGIS database. In processing spatial relationship queries involving six threads, the algorithm yielded a 3.6X maximum speed-up increase in performance compared to single-thread processing on SQLite/SpatiaLite database and 5.1X maximum speed-up on PostgreSQL/PostGIS database. In single-layer analyses (e.g., area calculation, buffer generation), a 5X speed-up time in query processing was observed.

本文提出了一种执行空间查询并行处理的开源算法，在此期间，使用空间索引完成要进行空间关系测试的对象的初始选择。然后通过使用 OFFSET 和 LIMIT 子句将这些数据进一步细分为更小的子组，将利用复杂计算的空间关系测试分配给这些子组，从而创建多个并行运行的进程。该算法使用来自 SQLite/SpatiaLite 和 PostgreSQL/PostGIS 数据库的数据进行了测试。在处理涉及六个线程的空间关系查询时，与 SQLite/SpatiaLite 数据库上的单线程处理相比，该算法的性能最大提升了 3.6 倍，PostgreSQL/PostGIS 数据库上的最大速度提升了 5.1 倍。在单层分析中（例如，