By Amol Deshpande
Adaptive question Processing surveys the basic matters, strategies, bills, and advantages of adaptive question processing. It starts with a large evaluation of the sector, selecting the size of adaptive options. It then seems to be on the spectrum of techniques to be had to evolve question execution at runtime - essentially in a non-streaming context. The emphasis is on simplifying and abstracting the foremost ideas of every strategy, instead of reproducing the whole info on hand within the papers. The authors determine the strengths and boundaries of different concepts, display once they are most beneficial, and recommend attainable avenues of destiny study. Adaptive question Processing serves as a important reference for college students of databases, supplying a radical survey of the realm. Database researchers will make the most of a extra entire perspective, together with a couple of ways which they won't have keen on in the scope in their personal examine.
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Extra resources for Adaptive Query Processing (Foundations and Trends in Databases)
During the query execution, when a new tuple from a relation arrives into the system, it is first built into the hash tables on that relation, and then the hash tables corresponding to the remaining relations are probed in some order to find the matches for the tuple. The order in which the hash tables are probed is called the probing sequence. For example, when a new tuple s ∈ S arrives into the system, the following steps are taken: — s is built into the (three) hash indexes on the relation S.
From the adaptivity perspective, we note the following things. Measurement: The key measurements are the sizes of the materialized tables that get created during the execution. Analysis & Planning: The analysis and planning are done at the beginning of each recursive call (after a call to OVQP). The query processor analyzes the query, and the sizes of the materialized tables and decides the relation to be substituted next. Actuation: Finally, the actuation is done by substituting the values of a tuple as constants in the current query to construct a new (smaller) query.
For example, the original proposal for the eddies technique  does not allow the access methods chosen at the beginning to be changed during execution. Although this restricts the adaptation opportunities somewhat, these techniques can still adapt among a fairly large class of query plans. , the end of a window or the change in a grouping value). , MJoins , SteMs ). SteMs, in fact, use sophisticated duplicate elimination logic that allows them to adapt among a much larger plan space than most other adaptive techniques.
Adaptive Query Processing (Foundations and Trends in Databases) by Amol Deshpande