Algorithms And Data Structures

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By Enni S.

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Chu et al. [31, 30] propose least expected cost optimization where the optimizer attempts to find the plan that has the lowest expected cost over the different values the parameters can take, instead of finding the lowest cost plan for the expected values of the parameters. The required probability distributions over the parameters can be computed using histograms or query workload information. This is clearly a more robust optimization goal, assuming only one plan can be chosen and the required probability distributions can be obtained.

34 Foundations of Adaptive Query Processing Fig. 2 Executing a 4-way join query using the MJoin operator. The triangles denote the in-memory hash indexes built on the relations. — s is used to probe into the hash table on T to find matches. Intermediate tuples are constructed by concatenating s and the matches, if any. — If any result tuples were generated during the previous step, they are used to probe into the hash tables on R and U , all in that order. Similarly, when a new R tuple arrives, it is first built into the hash table on R.

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. , before the first pipeline begins) and in some cases at intermediate materialization points. The simplest form uses a set of query execution plans annotated with a set of ranges of parameters of interest; just before query execution commences, the current parameter values are used to find the appropriate plan.

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A 1-(S,T)-edge-connectivity augmentation algorithm by Enni S.


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