An overview of data warehousing and OLAP technology
ACM SIGMOD Record
OLAP Query Evaluation in a Database Cluster: A Performance Study on Intra-Query Parallelism
ADBIS '02 Proceedings of the 6th East European Conference on Advances in Databases and Information Systems
Parallel Processing with Autonomous Databases in a Cluster System
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
Features to consider in a data warehousing system
Communications of the ACM - Blueprint for the future of high-performance networking
Parallel SQL execution in Oracle 10g
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Scaling up the preventive replication of autonomous databases in cluster systems
VECPAR'04 Proceedings of the 6th international conference on High Performance Computing for Computational Science
High-Performance Query Processing of a Real-World OLAP Database with ParGRES
High Performance Computing for Computational Science - VECPAR 2008
Parallel OLAP query processing in database clusters with data replication
Distributed and Parallel Databases
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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.