Data and knowledge in database systems: parallel databases

  • Authors:
  • Shahram Ghandeharizadeh;Frank Sommers

  • Affiliations:
  • Associate Professor of Computer Science, University of Southern California, Los Angeles;CEO, Autospaces, L. L. C., Los Angeles, California

  • Venue:
  • Handbook of data mining and knowledge discovery
  • Year:
  • 2002

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Abstract

Parallel databases based on the shared-nothing architecture are ideally suited for the increasingly demanding data management needs of enterprise decision support systems. In an ideal parallel system, twice as many nodes can perform twice as large a task in the same time, resulting in linear scale-up; or, twice as many nodes can perform the same task twice as quickly, resulting in linear speed-up. Round-robin, hash, range, hybrid, and other declustering techniques ensure that the needed data is available at each node for processing, and thus help approximate the ideal scalability characteristics. Parallelism can be applied to each of the relational operators. For the select operator, interquery parallelism executes several relational queries simultaneously; interoperator parallelism executes several operations within the same query simultaneously; and intraoperator parallelism is employed to each operator within a query.