Abstract

Spatial join is a crucial operation in many spatial analysis applications in scientific and geographical information systems. Due to the compute-intensive nature of spatial predicate evaluation, spatial join queries can be slow even with a moderate sized dataset. Efficient parallelization of spatial join is therefore essential to achieve acceptable performance for many spatial applications. Technological trends, including the rising core count and increasingly large main memory, hold great promise in this regard. Previous parallel spatial join approaches tried to partition the dataset so that the number of spatial objects in each partition was as equal as possible. They also focused only on the filter step. However, when the more compute-intensive refinement step is included, significant processing skew may arise due to the uneven size of the objects. This processing skew significantly limits the achievable parallel performance of the spatial join queries, as the longest-running spatial partition determines the overall query execution time. Our solution is SPINOJA, a skew-resistant parallel in-memory spatial join infrastructure. SPINOJA introduces MOD-Quadtree declustering, which partitions the spatial dataset such that the amount of computation demanded by each partition is equalized and the processing skew is minimized. We compare three work metrics used to create the partitions and three load-balancing strategies to assign the partitions to multiple cores. SPINOJA uses an in-memory column-store to store the spatial tables. Our evaluation shows that SPINOJA outperforms in-memory implementations of previous spatial join approaches by a significant margin and a recently proposed in-memory spatial join algorithm by an order of magnitude.

 

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