By Amol Deshpande
Adaptive question Processing surveys the basic concerns, recommendations, expenditures, and merits of adaptive question processing. It starts off with a wide assessment of the sector, settling on the scale of adaptive strategies. It then appears on the spectrum of methods on hand to conform question execution at runtime - essentially in a non-streaming context. The emphasis is on simplifying and abstracting the foremost options of every strategy, instead of reproducing the entire info to be had within the papers. The authors establish the strengths and boundaries of the various suggestions, exhibit once they are most beneficial, and recommend attainable avenues of destiny study. Adaptive question Processing serves as a precious reference for college kids of databases, offering a radical survey of the world. Database researchers will take advantage of a extra whole viewpoint, together with a few methods which they won't have eager about in the scope in their personal study.
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Additional info for Adaptive Query Processing (Foundations and Trends in Databases)
In these settings, we would like to immediately react to such changes or adapt the current query plan. Through the remainder of this survey, we focus on such intraquery adaptive query processing techniques that adapt the execution of a single query, for greater throughput, improved response time or more useful incremental results. 3 Foundations of Adaptive Query Processing The goal of adaptive query processing is to find an execution plan and a schedule that are well-suited to runtime conditions; it does this by interleaving query execution with exploration or modification of the plan or scheduling space.
2). The resulting plan is encoded into a routing table, and the next K tuples are routed according to that plan. This delineation between Planning and Actuation results in negligible routing overhead for reasonable batching factors (K = 100) . 2. Routing Policy based on A-Greedy: As observed by Babu et al.
An MJoin has several attractive features over the alternative option of a tree of binary join operators, especially in data stream processing and adaptive query processing. 2 shows an example MJoin operator instantiated for a 4-way join query. MJoins build a hash index on every join attribute of every relation in the query. In the example, three hash indexes (that share the base tuples) are built on the S relation, and one hash table each is built on the other relations. For acyclic query graphs, the total number of hash tables ranges from 2(n − 1) when every join is on a different attribute to n when all joins are on the same attribute.