By Amol Deshpande
Adaptive question Processing surveys the basic matters, options, charges, and merits of adaptive question processing. It starts off with a large evaluate of the sector, opting for the scale of adaptive strategies. It then seems to be on the spectrum of techniques on hand to evolve question execution at runtime - basically in a non-streaming context. The emphasis is on simplifying and abstracting the most important recommendations of every method, instead of reproducing the whole information to be had within the papers. The authors determine the strengths and obstacles of the various thoughts, exhibit once they are most valuable, and recommend attainable avenues of destiny examine. Adaptive question Processing serves as a worthwhile reference for college students of databases, delivering an intensive survey of the world. Database researchers will reap the benefits of a extra whole viewpoint, together with a couple of ways which they might not have interested by in the scope in their personal learn.
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Extra resources for Adaptive Query Processing (Foundations and Trends in Databases)
2. Implementation of the Routing Policy: Routing policy refers to the set of rules used by the eddy to choose a routing destination for a tuple among the possible valid destinations. This is the most critical component of an eddy and is directly responsible for the performance benefits of adaptivity. To facilitate clear distinctions between different routing policies, we will use the following unifying framework to describe a routing policy. The routing policy data structures are classified into two parts: — Statistics about the query execution: To aid in making the routing decisions, the eddy monitors certain data and operator characteristics; the specific statistics maintained depend on the routing policy.
Zhu et al.  call this process dynamic plan migration and propose and evaluate several techniques for doing this while minimizing the impact on the query execution. Similarly the STAIR operator  exposes the internal state using carefully designed APIs and allows the query executor to change the internal state, for ensuring correctness or for improving the performance. Corrective query processing  can combine state from different operators that represent the same logical subexpression; conversely, punctuation [42, 83] and other techniques like filter conditions  can be used to perform pruning on state.
Graefe et al. [34, 52] propose dynamic query evaluation plans for this purpose, where special choose-plan operators are used to make decisions about the plans to use based on the runtime information. Some of the choices may not be finalized until after the query has begun executing (as opposed to only at the beginning of execution); this allows the possibility of using the intermediate result sizes as parameters to make the decisions. 2 Choosing an Effective Plan 23 Ioannidis et al.  study parametric optimization for the buffer size parameter, and propose randomized algorithms for generating the parametric plans.
Adaptive Query Processing (Foundations and Trends in Databases) by Amol Deshpande