NUMA-Aware Spatio-Textual Similarity Join

Spatio-textual similarity join is an operation for finding documents, which are both spatially close and textually relevant. Joins in databases are considered to be the most expensive operation; similarly spatio-textual similarity join is a resource intensive operation. Therefore, it is natural to consider approaches to parallelize this operation. Many modern multi-core systems adopt a NUMA-based memory architecture. NUMA systems entail varying memory access latencies across nodes, which may adversely affect overall query latency. Recent work on spatio-textual similarity join have not addressed the effects of non-uniform access latencies in multi-node NUMA systems. In this paper, we propose a NUMA-aware parallel spatio-textual similarity join algorithm NA-STSJ-WS. It exploits topology-aware work-stealing with adaptive data placement. Experimental evaluation demonstrates that NA-STSJ-WS performs significantly better than existing approaches that are not NUMA-aware, and in the best case we observe 82× speedup over the sequential baseline.