Integrating a Maximum-Entropy Cardinality Estimator into DB2 UDB

Kutsch, M.; Haas, Peter J.; Markl, Volker; Megiddo, Nimrod; Tran, T. M.

doi:10.1007/11687238_67

Cited by 3 publications

(1 citation statement)

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“…When a checkpoint is triggered, it is an indication to the query processor to switch to a new query plan by re-invoking the optimizer. There are many tricky issues in actuating this switch efficiently: [44,81]. Instead, the optimizer needs to use the maximum entropy principle to consistently estimate cardinalities based on all available information [81].…”

Section: Switching To a New Planmentioning

confidence: 99%

Adaptive Query Processing

Deshpande¹,

Ives²,

Raman³

2007

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As the data management field has diversified to consider settings in which queries are increasingly complex, statistics are less available, or data is stored remotely, there has been an acknowledgment that the traditional optimize-then-execute paradigm is insufficient. This has led to a plethora of new techniques, generally placed under the common banner of adaptive query processing, that focus on using runtime feedback to modify query processing in a way that provides better response time or more efficient CPU utilization. In this survey paper, we identify many of the common issues, themes, and approaches that pervade this work, and the settings in which each piece of work is most appropriate. Our goal with this paper is to be a "value-add" over the existing papers on the material, providing not only a brief overview of each technique, but also a basic framework for understanding the field of adaptive query processing in general. We focus primarily on intra-query adaptivity of long-running, but not full-fledged streaming, queries. We conclude with a discussion of open research problems that are of high importance.

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Section: Switching To a New Planmentioning

confidence: 99%