Synonyms
DBC; Middleware for parallel query processing
Definition
A database cluster (DBC) is a parallel database management solution. A DBC uses a standard parallel computer cluster (a cluster of PC nodes) to run a sequential Database Management System (DBMS) instance at each node. A DBC middleware is a software layer between a database application and the DBC. Such middleware is responsible for providing parallel query processing on top of the DBC. It intercepts queries from applications and coordinates distributed and parallel query execution by taking advantage of the DBC. The DBC term comes from an analogy with the term PC cluster, which is a solution for parallel processing by assembling sequential PCs. In a PC cluster there is no need for special hardware to provide parallelism as opposed to parallel machines or supercomputers. In the same way, a DBC takes advantage of off-the-shelf sequential DBMS to run parallel queries. There is no need for special software or hardware as...
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Özsu TM, Valduriez P. Principles of distributed database systems. 3rd ed. New York: Springer; 2011.
Röhm U, Böhm K, Scheck HJ, Schuldt H. FAS – a freshness-sensitive coordination middleware for a cluster of OLAP components. Proceedings of the 28th International Conference on Very Large Data Bases; 2002. p. 754–68.
Akal F, Böhm K, Schek HJ. OLAP query evaluation in a database cluster: a performance study on intra-query parallelism. In: Proceedings of the 6th East-European Conference on Advances in Databases and Information Systems; 2002. p. 218–31.
Cecchet E. C-JDBC: a middleware framework for database clustering. IEEE Data Eng Bull. 2004;27(2):19–26.
Sequoia Project. http://sequoia.continuent.org
Pacitti E, Coulon C, Valduriez P, Özsu MT. Preventive replication in a database cluster. Distrib Parallel Databases. 2005;18(3):223–51.
Mattoso M, et al. ParGRES: a middleware for executing OLAP queries in parallel. COPPE-UFRJ Technical Report, ES-690; 2005.
Pgpool. http://www.pgpool.net.
Lima AAB, Mattoso M, Valduriez P. Adaptive virtual partitioning for OLAP query processing in a database cluster. In: Proceedings of the 14th Brazilian Symposium on Database Systems; 2004. p. 92–105.
Cuzzocrea A, Moussa R. A cloud-based framework for supporting effective and efficient OLAP in big data environments. In: Proceedings of the 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing; 2014. p. 680–684.
Pacitti E, Valduriez P, Mattoso M. Grid data management: open problems and new issues. J Grid Comput. 2007;5(3):273–81.
Kotowski N, Lima AA, Pacitti E, Valduriez P, Mattoso M. Parallel query processing for OLAP in grids. Concurrency Comput Pract Exp. 2008;20(17):2039–48.
Cappello F, Desprez F, Dayde M, et al. Grid’5000: a large scale and highly reconfigurable grid experimental testbed. In: Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing; 2005. p. 99–106.
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Mattoso, M. (2018). Database Clusters. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_1075
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DOI: https://doi.org/10.1007/978-1-4614-8265-9_1075
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