Optimization for queries with holistic functions

The early grouping technique is a new method for optimizing aggregate queries. It provides more opportunities for the query optimizers to find optimal plans because all possible placements of the GROUP BY operators in the query trees are considered during the optimization process. However to employ this technique. one of the requirements is that the aggregate function in the query must be distributive, Using this property the evaluation of the GROUP BY operator can be started without all the relations are joined (i.e., the entire input are constructed). For queries with the non-distributive (or holistic) aggregate functions, the evaluation of the GROUP BY operators has to wait until the entire input is formed since these aggregate functions cannot be decomposed into sub-aggregate functions and their computations depend on the entire set of the input. As a result, to evaluate queries with the non-distributive aggregate functions, all the relations must be joined before the GROUP BY operator can be evaluated. Though the decem position of the holistic aggregate functions is nor obvious, instead, in this research we computes the auxiliary information, count, on the subsets of the input data, which is later used by the consolidating function as an aid to compute the original holistic aggregate functions. Since the computation of count (using the function COUNT ( ) ) is distributive, this auxiliary information can be computed before all the relations are joined. By extending the ability of the early grouping technique to handle aggregate queries with holistic aggregate functions, we provide the optimizers with mure opportunities to find optimal plans.