Database partitioning in a cluster of processors
ACM Transactions on Database Systems (TODS)
Physical database design for relational databases
ACM Transactions on Database Systems (TODS)
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Measuring the complexity of join enumeration in query optimization
Proceedings of the sixteenth international conference on Very large databases
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Parallel database systems: the future of high performance database systems
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A decomposition-based simulated annealing technique for data clustering
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The COMFORT automatic tuning project
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IBM Systems Journal
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VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
DB2 Advisor: An Optimizer Smart Enough to Recommend its own Indexes
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Physical database design in multiprocessor database systems
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ACM SIGMOD Record
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SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
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IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Automatic physical design tuning: workload as a sequence
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DBA'06 Proceedings of the 24th IASTED international conference on Database and applications
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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DB2 design advisor: integrated automatic physical database design
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Automated design of multidimensional clustering tables for relational databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient use of the query optimizer for automated physical design
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Data & Knowledge Engineering
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EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
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Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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Automated partitioning design in parallel database systems
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Proceedings of the VLDB Endowment
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Physical database design is important for query performance in a shared-nothing parallel database system, in which data is horizontally partitioned among multiple independent nodes. We seek to automate the process of data partitioning. Given a workload of SQL statements, we seek to determine automatically how to partition the base data across multiple nodes to achieve overall optimal (or close to optimal) performance for that workload. Previous attempts use heuristic rules to make those decisions. These approaches fail to consider all of the interdependent aspects of query performance typically modeled by today's sophisticated query optimizers.We present a comprehensive solution to the problem that has been tightly integrated with the optimizer of a commercial shared-nothing parallel database system. Our approach uses the query optimizer itself both to recommend candidate partitions for each table that will benefit each query in the workload, and to evaluate various combinations of these candidates. We compare a rank-based enumeration method with a random-based one. Our experimental results show that the former is more effective.