An experimental analysis of iterated spatial joins in main memory

Abstract

Many modern applications rely on high-performance processing of spatial data. Examples include location-based services, games, virtual worlds, and scientific simulations such as molecular dynamics and behavioral simulations. These applications deal with large numbers of moving objects that continuously sense their environment, and their data access can often be abstracted as a repeated spatial join. Updates to object positions are interspersed with these join operations, and batched for performance. Even for the most demanding scenarios, the data involved in these joins fits comfortably in the main memory of a cluster of machines, and most applications run completely in main memory for performance reasons.Choosing appropriate spatial join algorithms is challenging due to the large number of techniques in the literature. In this paper, we perform an extensive evaluation of repeated spatial join algorithms for distance (range) queries in main memory. Our study is unique in breadth when compared to previous work: We implement, tune, and compare ten distinct algorithms on several workloads drawn from the simulation and spatial indexing literature. We explore the design space of both index nested loops algorithms and specialized join algorithms, as well as the use of moving object indices that can be incrementally maintained. Surprisingly, we find that when queries and updates can be batched, repeatedly re-computing the join result from scratch outperforms using a moving object index in all but the most extreme cases. This suggests that -given the code complexity of index structures for moving objects -specialized join strategies over simple index structures, such as Synchronous Traversal over R-Trees, should be the methods of choice for the above applications.

Spatial joins in main memory

“…The source code of the experimental framework [1] contains the implementations of the ten spatial join techniques considered as well as the workload generator used [7]. Due to the 4-page limit, we show results only for synthetic workloads (based on [2]), but the same performance trends also hold for the simulation workloads.…”

“…In the Gaussian workload, objects are placed around a fixed set of hotspots, and their movements follow a Gaussian-like distribution. For the complete description of the experimental setup, we refer to the original study [7].…”

“…For example, two dimensional coordinates are often encoded as two 4-byte single-precision or integer values [2,3,7,8].…”

“…Sowel et al [7] report on a thorough experimental performance study of ten spatial join techniques in main memory. The techniques are first optimized for in-memory performance and then studied in the same framework.…”

“…The following four static indexes belong to this category: R-Tree [4,6], CR-Tree [5], Linearized KD-Trie [3], and Static Grid [8]. This category reports the best performance results on average [7].…”