We consider the problem of exploiting parallelism to accelerate the performance of spacial access methods and specifically, R-trees [11]. Our goal is to design a server for spatial data, so that to maximize the throughput of range queries. This can be achieved by (a) maximizing parallelism for large range queries, and (b) by engaging as few disks as possible on point queries [22].
We propose a simple hardware architecture consisting of one processor with several disks attached to it. On this architecture, we propose to distribute the nodes of a traditonal R-tree, with cross-disk pointers ("Multiplexed" R-tree). The R-tree code is identical to the one for a single-disk R-tree, with the only addition that we have to decide which disk a newly created R-tree node should be stored in. We propose and examine several criteria to choose a disk for a new node. The most successful one, termed "proximity index" or PI, estimates the similarity of the new node with the other R-tree nodes already on adisk, and chooses the disk with the lowest similarity. Experimental results show that our scheme consistently outperforms all the other heuristics for node-to-disk assignments, achieving up to 55% gains over the Round Robin one. Experiments also indicate that the multiplexed R-tree with PI heuristic gives better response time than the disk-stripping (="Super-node") approach, and imposes lighter load on the I/O sub-system.
The speed up of our method is close to linear speed up, increasing with the size of the queries.
我们考虑了利用并行性来加快空间访问方法(特别是R树)的性能的问题[11]。我们的目标是设计一个用于空间数据的服务器,以便最大程度地提高范围查询的吞吐量。这可以通过(a)最大化大范围查询的并行度,以及(b)通过在点查询中使用尽可能少的磁盘来实现[22]。 P>
我们提出了一种简单的硬件体系结构,该体系结构由一个处理器和多个磁盘组成。在此体系结构上,我们建议使用跨磁盘指针(“ Multiplexed” R-tree)来分布传统R-tree的节点。 R树代码与单磁盘R树的代码相同,唯一的不同是我们必须确定新创建的R树节点应存储在哪个磁盘中。我们提出并研究了几种选择标准用于新节点的磁盘。最成功的称为“邻近索引”或PI,它估计新节点与磁盘上已有的其他R-tree节点的相似度,并选择相似度最低的磁盘。实验结果表明,对于节点到磁盘的分配,我们的方案始终优于所有其他启发式方法,与Round Robin方案相比,可实现高达55%的收益。实验还表明,具有PI启发式功能的多路R树比磁盘剥离(“超级节点”)方法具有更好的响应时间,并且给I / O子系统带来了更轻的负载。 P>
我们的方法的速度接近线性速度,随查询大小的增加而增加。 P>
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