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By Ghiani G., Laporte G.

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Such operators dramatically reduce the number of plans considered by the query optimizer, although potentially at the cost of some runtime performance. 2 Parametric Query Optimization An alternative to finding a single robust query plan is to find a small set of plans that are appropriate for different situations. Parametric query optimizers [46, 52, 70] postpone certain planning decisions to runtime, and are especially attractive in scenarios where queries are compiled once and executed repeatedly, possibly with minor parameter changes.

This makes it hard to analyze or reason about the behavior of these systems. As an example, the commonly used “explain” feature (used to inspect the execution plan used by the DBMS for a query) would be nearly impossible to support in such systems. Throughout this survey, along with the adaptivity loop, we will also discuss how to do a retrospective analysis of an adaptive query execution, after it has finished running. 3 Post-mortem Analysis of Adaptive Techniques 41 possible, to express the query execution in terms of traditional query plans or relational algebra expressions.

Plan: Based on the analysis, an adaptive system makes certain decisions about how the system behavior should be changed. Actuate: After the decision is made, the adaptive system executes the decision, by possibly doing extra work to manipulate the system state. In the context of adaptive query processing, the Measure step involves monitoring data characteristics like cardinalities and distributions, and system characteristics like memory utilization and network bandwidth. Analysis is primarily done with respect to performance goals or estimates, although there have been some recent proposals for broader QOS-aware DBMSs that address goals like availability [14, 57, 66, 84, 106].

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