Automated physical design tuning for database systems has recently become an active area of research and development. Existing tuning tools explore the space of feasible solutions by repeatedly optimizing queries in the input workload for several candidate configurations. This general approach, while scalable, often results in tuning sessions waiting for results from the query optimizer over 90 % of the time. In this paper we introduce a novel approach, called Configuration-Parametric Query Optimization, that drastically improves the performance of current tuning tools. By issuing a single optimization call per query, we are able to generate a compact representation of the optimization space that can then produce very efficiently execution plans for the input query under arbitrary configurations. Our experiments show that our technique speedsup query optimization by 30x to over 450x with virtually no loss in quality, and effectively eliminates the optimization bottleneck in existing tuning tools. Our techniques open the door for new, more sophisticated optimization strategies by eliminating the main bottleneck of current tuning tools.

Index tuning, i.e., selecting the indexes appropriate for a workload, is a crucial problem in database system tuning. In this paper, we solve index tuning for large problem instances that are common in practice, e.g., thousands of queries in the workload, thousands of candidate indexes and several hard and soft constraints. Our work is the first to reveal that the index tuning problem has a well structured space of solutions, and this space can be explored efficiently with well known techniques from linear optimization. Experimental results demonstrate that our approach outperforms state-of-theart commercial and research techniques by a significant margin (up to an order of magnitude). 1.

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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 realworld scientific datasets and query workloads.

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contributed articles doi:10.1145/1743546.1743568 Needed are generic, rather than one-off, DBMS solutions automating storage and analysis of data from scientific collaborations.