Performance analysis of parallel query processing algorithms for object-oriented databases

Abstract

Two types of parallel processing and optimization algorithms for processing object-oriented databases are the hybrid-hash pointer-based (HHP) algorithms and multi-wavefront (MWF) algorithms. We analyze these two algorithms and develop analytical formulas to capture their main performance features. We study their performance in three application environments, characterized by large databases having many object classes, each of which, respectively, (1) contains a large number of instances; (2) contains a relatively small number of instances; and (3) is of varying size. A horizontal data partitioning strategy is used in (1). A class-per-node assignment strategy is used in (2). In (3), object classes are partitioned horizontally and assigned to a varying number of processors depending on their different sizes. The MWF algorithm has three distinguishing features which contribute to its better performance: (a) a two-phase processing strategy, (b) vertical partitioning of horizontal segments, and (c) dynamic determination of the collision point in MWF propagations, which results in an optimized query execution plan. If these features are adopted by an HHP algorithm, its performance is comparable with that of the MWF algorithm because the difference in CPU time between them is negligible. The computing environment is a network of workstations having a shared-nothing architecture. The schema and some queries selected from the OO7 benchmark are used in the performance analyses and comparisons. The queries are modified slightly in different data environments in order to reflect the features of diverse database applications.