Apuama: Combining Intra-query and Inter-query Parallelism in a Database Cluster

  • Bernardo Miranda
  • Alexandre A. B. Lima
  • Patrick Valduriez
  • Marta Mattoso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4254)


Database clusters provide a cost-effective solutionn for high performance query processing. By using either inter- or intra-query parallelism on replicated data, they can accelerate individual queries and increase throughput. However, there is no database cluster that combines inter- and intra-query parallelism while supporting intensive update transactions. C-JDBC is a successful database cluster that offers inter-query parallelism and controls database replica consistency but cannot accelerate individual heavy-weight queries, typical of OLAP. In this paper, we propose the Apuama Engine, which adds intra-query parallelism to C-JDBC. The result is an open-source package that supports both OLTP and OLAP applications. We validated Apuama on a 32-node cluster running OLAP queries of the TPC-H benchmark on top of PostgreSQL. Our tests show that the Apuama Engine yields super-linear speedup and scale-up in read-only environments. Furthermore, it yields excellent performance under data update operations.


Query Processing Query Execution Query Execution Time Database Cluster Node Processor 
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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  1. 1.
    Akal, F., Böhm, K., Schek, H.-J.: OLAP query evaluation in a database cluster: A performance study on intra-query parallelism. In: Manolopoulos, Y., Návrat, P. (eds.) ADBIS 2002. LNCS, vol. 2435, pp. 218–231. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    Cecchet, E.: C-JDBC: a Middleware Framework for Database Clustering. Proceedings of IEEE Data Engineering Bulletin 27, 19–26 (2004)Google Scholar
  3. 3.
    Chaudhuri, S., Dayal, U.: An Overview of Data Warehousing and OLAP Technology. ACM SIGMOD Record 26, 65–74 (1997)CrossRefGoogle Scholar
  4. 4.
    Coulon, C., Pacitti, E., Valduriez, P.: Scaling up the preventive replication of autonomous databases in cluster systems. In: Daydé, M., Dongarra, J., Hernández, V., Palma, J.M.L.M. (eds.) VECPAR 2004. LNCS, vol. 3402, pp. 170–183. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Cruanes, T., Dageville, B., Ghosh, B.: Parallel SQL Execution in Oracle 10g. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Paris, France, pp. 850–854 (2004)Google Scholar
  6. 6.
    DB2 ICE (2005), (retrieved 11/09/2005)
  7. 7.
    MySQL 5.0 Documentation (2005), (Retrieved 11/09/2005)
  8. 8.
    Gançarski, S., Naacke, H., Pacitti, E., Valduriez, P.: Parallel Processing with Autonomous Databases in a Cluster System. In: Proceedings of International Conference on Cooperative Information Systems (CoopIS), Los Angeles, USA, pp. 410–428 (2002)Google Scholar
  9. 9.
    Gorla, N.: Features to Consider in a Data Warehousing System. Communications of the ACM 46, 111–115 (2003)CrossRefGoogle Scholar
  10. 10.
    HSQL Database Engine(2005), (retrieved 11/09/2005)
  11. 11.
    JDBC (2005), (retrieved 11/09/2005)
  12. 12.
    LGPL (2005), (retrieved on September 11, 2005)
  13. 13.
    Lima, A.A.B.: Intra-Query Parallelism in Database Clusters. COPPE/UFRJ, D.Sc.Thesis, Rio de Janeiro (2004)Google Scholar
  14. 14.
    Lima, A.A.B., Mattoso, M., Valduriez, P.: Adaptive Virtual Partitioning for OLAP Query Processing in a Database Cluster. In: Proceedings of 19h Brazilian Symposium on Databases (SBBD), Brasilia, Brazil, pp. 92–105 (2004)Google Scholar
  15. 15.
    Pape, C.L., Gançarski, S., Valduriez, P.: Refresco: Improving Query Performance Through Freshness Control in a Database Cluster. In: Proceedings of International Conference on Cooperative Information Systems (CoopIS), Agia Napa, Cyprus, pp. 174–193 (2004)Google Scholar
  16. 16.
    PostgreSQL 8.0.1 Documentation (2005), (retrieved 11/09/2005)
  17. 17.
    Paris Project (2005), (retrieved on September 11, 2005)
  18. 18.
    Röhm, U., Böhm, K., Schek, H.-J., Schuldt, H.: FAS - A Freshness-Sensitive Coordination Middleware for a Cluster of OLAP Components. In: Proceedings of the 28th International Conference on Very Large Data Bases (VLDB), Hong Kong, China (2002) 754-765 Google Scholar
  19. 19.
    TPC-H Benchmark (2005), (retrieved on September 11, 2005)

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bernardo Miranda
    • 1
  • Alexandre A. B. Lima
    • 1
    • 3
  • Patrick Valduriez
    • 2
  • Marta Mattoso
    • 1
  1. 1.Computer Science Department, COPPEFederal University of Rio de Janeiro 
  2. 2.Atlas Group, INRIA and LINAUniversity of NantesFrance
  3. 3.School of Engineering and Computer ScienceUniversity of Grande RioBrazil

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