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Applications ranging from algorithmic trading to scientific data analysisrequire realtime analytics based on views over databases that change at veryhigh rates. Such views have to be kept fresh at low maintenance cost andlatencies. At the same time, these views have to support classical SQL, ratherthan window semantics, to enable applications that combine current with aged orhistorical data. In this paper, we present viewlet transforms, a recursivefinite differencing technique applied to queries. The viewlet transformmaterializes a query and a set of its higher-order deltas as views. These viewssupport each other's incremental maintenance, leading to a reduced overall viewmaintenance cost. The viewlet transform of a query admits efficient evaluation,the elimination of certain expensive query operations, and aggressiveparallelization. We develop viewlet transforms into a workable query executiontechnique, present a heuristic and cost-based optimization framework, andreport on experiments with a prototype dynamic data management system thatcombines viewlet transforms with an optimizing compilation technique. Thesystem supports tens of thousands of complete view refreshes a second for awide range of queries.

DBToaster: Higher-order Delta Processing for Dynamic, Frequently Fresh Views