Depth estimation for ranking query optimization

A relational ranking query uses a scoring function to limit the re-sults of a conventional query to a small number of the most relevant answers. The increasing popularity of this query paradigm has led to the introduction of specialized rank join operators that integrate the selection of top tuples with join processing. These operators ac-cess just “enough ” of the input in order to generate just “enough” output and can offer significant speed-ups for query evaluation. The number of input tuples that an operator accesses is called the input depth of the operator, and this is the driving cost factor in rank join processing. This introduces the important problem of depth estima-tion, which is crucial for the costing of rank join operators during query compilation and thus for their integration in optimized phys-ical plans. We introduce an estimation methodology, termed DEEP, for ap-proximating the input depths of rank join operators in a physical execution plan. At the core of DEEP lies a general, principled framework that formalizes depth computation in terms of the joint distribution of scores in the base tables. This framework results in a systematic estimation methodology that takes the characteristics of the data directly into account and thus enables more accurate es-timates. We develop novel estimation algorithms that provide an efficient realization of the formal DEEP framework, and describe their integration on top of the statistics module of an existing query optimizer. We validate the performance of DEEP with an extensive experimental study on data sets of varying characteristics. The re-sults verify the effectiveness of DEEP as an estimation method and demonstrate its advantages over previously proposed techniques. 1