Dynamic Histograms: Capturing Evolving Data Sets
D. Donjerkovic, Yannis Ioannidis, Raghu Ramakrishnan · 2005
In this paper, we introduce dynamic histograms, which are constructed and maintained incrementally. We develop several dynamic histogram construction algorithms and show that they come close to static histograms in quality. Our experimental study covers a wide range of datasets and update patterns, including histogram maintenance in a shared-nothing environment. Building upon the insights offered by the dynamic algorithms, we also propose a new static histogram construction algorithm that is very fast and generates histograms that are close in quality to the highly accurate (but expensive to construct!) V-Optimal histograms. 1 Introduction The cost of executing a relational operator is a function of the sizes of the tuple streams that are input to the operator, which for intermediate operators are in turn determined by selectivities of the previous operators. The more complex a query is, the more important it is to have precise intermediate size estimates. Otherwise, errors in ...