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Programming and Computer Software

, Volume 27, Issue 6, pp 297–308 | Cite as

Organization of Parallel Query Processing in Multiprocessor Database Machines with Hierarchical Architecture

  • L. B. Sokolinsky
Article

Abstract

The development of database systems with hierarchical hardware architecture is currently a perspective trend in the field of parallel database machines. Hierarchical architectures have been suggested with the aim to combine advantages of shared-nothing architectures and architectures with shared memory and disks. A commonly accepted way of construction of hierarchical systems is to combine shared-memory (shared-everything) clusters in a unique system without shared resources. However, such architectures cannot ensure data accessibility under hardware failures on the processor cluster level, which limits their use in systems with high fault-tolerance requirements. In this paper, an alternative approach to construction of hierarchical systems is suggested. In accordance with this approach, the systems is constructed as an assembly of processor clusters with shared disks, with each cluster being a two-level multiprocessor structure with a standard strongly connected topology of interprocessor connections. A stream model for organization of parallel query processing in systems with the hierarchical architecture suggested is described. This model has been implemented in a prototype parallel database management system Omega designed for Russian multiprocessor computational systems MBC-100/1000. Our experiments show that the total performance of the processor clusters in the Omega system is comparable with that of the processor clusters with shared resources even in the case of great data skew. At the same time, the clusters of the Omega system are capable of ensuring a higher degree of data availability compared to the clusters with shared-memory architectures.

Keywords

Shared Memory Shared Resource Hardware Architecture Database Management System Hierarchical System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© MAIK “Nauka/Interperiodica” 2001

Authors and Affiliations

  • L. B. Sokolinsky
    • 1
  1. 1.Chelyabinsk State UniversityChelyabinskRussia

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