Physical database design for relational databases
ACM Transactions on Database Systems (TODS)
AutoPart: Automating Schema Design for Large Scientific Databases Using Data Partitioning
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Automatic physical database tuning: a relaxation-based approach
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
COLT: continuous on-line tuning
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
DB2 design advisor: integrated automatic physical database design
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
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
An index selection method without repeated optimizer estimations
Information Sciences: an International Journal
Index interactions in physical design tuning: modeling, analysis, and applications
Proceedings of the VLDB Endowment
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Tuning tools attempt to configure a database to achieve optimal performance for a given workload. Selecting an optimal set of physical structures is computationally hard since it involves searching a vast space of possible configurations. Commercial DBMSs offer tools that can address this problem. The usefulness of such tools, however, is limited by their dependence on greedy heuristics, the need for a-priori (offline) knowledge of the workload, and lack of an optimal materialization schedule to get the best out of suggested design features. Moreover, the open source DBMSs do not provide any automated tuning tools. This demonstration introduces a comprehensive physical designer for the PostgreSQL open source DBMS. The tool suggests design features for both offline and online workloads. It provides close to optimal suggestions for indexes for a given workload by modeling the problem as a combinatorial optimization problem and solving it by sophisticated and mature solvers. It also determines the interaction between indexes to suggest an effective materialization strategy for the selected indexes. The tool is interactive as it allows the database administrator (DBA) to suggest a set of candidate features and shows their benefits and interactions visually. For the demonstration we use large real-world scientific datasets and query workloads.