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Surajit Chaudhuri and Vivek Narasayya. Automating statistics management for query optimizers. In 16th International Conference on Data Engineering, pages Towards self-tuning data placement in parallel database systems. Lohman, and Alan Skelley. An optimizer smart enough to recommend its own indexes.
Statistics for virtual indexes are inferred from existing column statistics. To recommend indexes for a workload, recommend for each query in the workload in sequence and then greedily select a subset of the recommended indexes. Autoadmin 'what-if' index analysis utility.
Configuration includes hypothetic indexes and statistics that allow the optimizer to decide whether such an index should be used. Proposes that sampling be used to collect the statistics. Presents an analysis interface that supports workload analysis and configuration analysis for current and hypothetical configurations.
Search space includes both single and multi-attribute indexes. Index configurations are evaluated by the DBMS optimizer, and several techniques are used to reduce the number of configurations for which optimizer evaluation is required. To generate a set of candidate indexes, this method determines an optimal index configuration independently for each query in the workload.
The initial candidate set is then taken as the union of the indexes in the single-query optimal configurations. Multi-column indexes are handled by first finding an good configuration with single-column indexes, then generating and adding a set of candidate two-column indexes, and then rerunning the optimizer on the new candidate set.
This is repeated to handle indexes with more than two columns. David Lomet and Shahram Ghandeharizadeh, editors.Meet the wonderful members of our Frontier Dental team.
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