Parallel processing of spatial batch-queries using \({\text {xBR}}^+\)-trees in solid-state drives

Efficient query processing in spatial databases is of vital importance for numerous modern applications. In most cases, such processing is accomplished by taking advantage of spatial indexes. The \(\text {xBR}^+\)-tree is an index for point data which has been shown to outperform indexes belonging to the R-tree family. On the other hand, Solid-State Drives (SSDs) are secondary storage devices that exhibit higher (especially read) performance than Hard Disk Drives and nowadays are being used in database systems. Regarding query processing, the higher performance of SSDs is maximized when large sequences of queries (batch queries) are executed by exploiting the massive I/O advantages of SSDs. Moreover, nowadays each CPU contains multiple cores which can be utilized to perform calculations in parallel and further improve performance of query processing. In this paper, we present algorithms for processing common spatial (point-location, window and distance-range) batch queries using \(\text {xBR}^+\)-trees in SSDs. Next, we transform these algorithms to additionally take advantage of the multiple CPU cores. Moreover, utilizing small and large datasets, we experimentally study the performance of these new, SSD based, algorithms against processing of batch queries by repeatedly applying existing algorithms for these queries. We further study the performance of the algorithms that utilize parallelism against the ones taking advantage of SSDs only. Our experiments show that the new algorithms taking advantage of SSDs and even further the ones that also utilize multiple cores prevail performance-wise. Nevertheless, we discuss how these new parallel algorithms can be extended to work in a distributed environment, taking advantage of parallelism between machines, while processing data of larger scales.