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Parallel OLAP query processing in database clusters with data replication

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Abstract

We consider the problem of improving the performance of OLAP applications in a database cluster (DBC), which is a low cost and effective parallel solution for query processing. Current DBC solutions for OLAP query processing provide for intra-query parallelism only, at the cost of full replication of the database. In this paper, we propose more efficient distributed database design alternatives which combine physical/virtual partitioning with partial replication. We also propose a new load balancing strategy that takes advantage of an adaptive virtual partitioning to redistribute the load to the replicas. Our experimental validation is based on the implementation of our solution on the SmaQSS DBC middleware prototype. Our experimental results using the TPC-H benchmark and a 32-node cluster show very good speedup.

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Authors and Affiliations

  1. COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Camille Furtado & Marta Mattoso

  2. INRIA, Nantes, France

    Patrick Valduriez

  3. School of Science and Technology, Unigranrio University, Rio de Janeiro, Brazil

    Alexandre A. B. Lima

Authors
  1. Alexandre A. B. Lima
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  2. Camille Furtado
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  3. Patrick Valduriez
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  4. Marta Mattoso
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Corresponding author

Correspondence to Alexandre A. B. Lima.

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Communicated by Ladjel Bellatreche.

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Lima, A.A.B., Furtado, C., Valduriez, P. et al. Parallel OLAP query processing in database clusters with data replication. Distrib Parallel Databases 25, 97–123 (2009). https://doi.org/10.1007/s10619-009-7037-8

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